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%%%     date            = "21 May 2015",
%%%     time            = "15:55:05 MDT",
%%%     filename        = "tist.bib",
%%%     address         = "University of Utah
%%%                        Department of Mathematics, 110 LCB
%%%                        155 S 1400 E RM 233
%%%                        Salt Lake City, UT 84112-0090
%%%                        USA",
%%%     telephone       = "+1 801 581 5254",
%%%     FAX             = "+1 801 581 4148",
%%%     URL             = "http://www.math.utah.edu/~beebe",
%%%     checksum        = "58956 13512 72432 691693",
%%%     email           = "beebe at math.utah.edu, beebe at acm.org,
%%%                        beebe at computer.org (Internet)",
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%%%                        Intelligent Systems and Technology (TIST)",
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%%%                        the journal ACM Transactions on Intelligent
%%%                        Systems and Technology (TIST) (CODEN ????,
%%%                        ISSN 2157-6904 (print), 2157-6912
%%%                        (electronic)),  covering all journal issues from
%%%                        2010 -- date.
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%%%
%%%                             2010 (  15)    2012 (  59)    2014 (  30)
%%%                             2011 (  51)    2013 (  95)    2015 (  57)
%%%
%%%                             Article:        307
%%%
%%%                             Total entries:  307
%%%
%%%                        The journal Web page can be found at:
%%%
%%%                            http://www.acm.org/pubs/tist
%%%                            http://portal.acm.org/citation.cfm?id=J1318
%%%
%%%
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%%% ====================================================================
%%% Acknowledgement abbreviations:

@String{ack-nhfb = "Nelson H. F. Beebe,
University of Utah,
Department of Mathematics, 110 LCB,
155 S 1400 E RM 233,
Salt Lake City, UT 84112-0090, USA,
Tel: +1 801 581 5254,
FAX: +1 801 581 4148,
e-mail: \path|beebe@math.utah.edu|,
\path|beebe@acm.org|,
\path|beebe@computer.org| (Internet),
URL: \path|http://www.math.utah.edu/~beebe/|"}

%%% ====================================================================
%%% Journal abbreviations:

@String{j-TIST                 = "ACM Transactions on Intelligent Systems and
Technology (TIST)"}

%%% ====================================================================
%%% Bibliography entries:

@Article{Yang:2010:IAT,
author =       "Qiang Yang",
title =        "Introduction to {ACM TIST}",
journal =      j-TIST,
volume =       "1",
number =       "1",
pages =        "1:1--1:??",
month =        oct,
year =         "2010",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1858948.1858949",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Tue Nov 23 12:18:28 MST 2010",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "1",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Liu:2010:IAT,
author =       "Huan Liu and Dana Nau",
title =        "Introduction to the {ACM TIST} special issue {AI} in
social computing and cultural modeling",
journal =      j-TIST,
volume =       "1",
number =       "1",
pages =        "2:1--2:??",
month =        oct,
year =         "2010",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1858948.1858950",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Tue Nov 23 12:18:28 MST 2010",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "2",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Bainbridge:2010:VWC,
author =       "William Sims Bainbridge",
title =        "Virtual worlds as cultural models",
journal =      j-TIST,
volume =       "1",
number =       "1",
pages =        "3:1--3:??",
month =        oct,
year =         "2010",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1858948.1858951",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Tue Nov 23 12:18:28 MST 2010",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "3",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Feldman:2010:SCR,
author =       "Michal Feldman and Moshe Tennenholtz",
title =        "Structured coalitions in resource selection games",
journal =      j-TIST,
volume =       "1",
number =       "1",
pages =        "4:1--4:??",
month =        oct,
year =         "2010",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1858948.1858952",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Tue Nov 23 12:18:28 MST 2010",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "4",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Wu:2010:OFU,
author =       "Fang Wu and Bernardo A. Huberman",
title =        "Opinion formation under costly expression",
journal =      j-TIST,
volume =       "1",
number =       "1",
pages =        "5:1--5:??",
month =        oct,
year =         "2010",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1858948.1858953",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Tue Nov 23 12:18:28 MST 2010",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "5",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Roos:2010:ESD,
author =       "Patrick Roos and J. Ryan Carr and Dana S. Nau",
title =        "Evolution of state-dependent risk preferences",
journal =      j-TIST,
volume =       "1",
number =       "1",
pages =        "6:1--6:??",
month =        oct,
year =         "2010",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1858948.1858954",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Tue Nov 23 12:18:28 MST 2010",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "6",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Goolsby:2010:SMC,
author =       "Rebecca Goolsby",
title =        "Social media as crisis platform: The future of
community maps\slash crisis maps",
journal =      j-TIST,
volume =       "1",
number =       "1",
pages =        "7:1--7:??",
month =        oct,
year =         "2010",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1858948.1858955",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Tue Nov 23 12:18:28 MST 2010",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "7",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Wang:2010:AIS,
author =       "Meng Wang and Bo Liu and Xian-Sheng Hua",
title =        "Accessible image search for colorblindness",
journal =      j-TIST,
volume =       "1",
number =       "1",
pages =        "8:1--8:??",
month =        oct,
year =         "2010",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1858948.1858956",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Tue Nov 23 12:18:28 MST 2010",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "8",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Chen:2010:PSI,
author =       "Yixin Chen",
title =        "Preface to special issue on applications of automated
planning",
journal =      j-TIST,
volume =       "1",
number =       "2",
pages =        "9:1--9:??",
month =        nov,
year =         "2010",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1869397.1869398",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Wed Jan 26 14:40:50 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "9",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Porteous:2010:API,
author =       "Julie Porteous and Marc Cavazza and Fred Charles",
title =        "Applying planning to interactive storytelling:
Narrative control using state constraints",
journal =      j-TIST,
volume =       "1",
number =       "2",
pages =        "10:1--10:??",
month =        nov,
year =         "2010",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1869397.1869399",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Wed Jan 26 14:40:50 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "10",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Bryce:2010:PIB,
author =       "Daniel Bryce and Michael Verdicchio and Seungchan
Kim",
title =        "Planning interventions in biological networks",
journal =      j-TIST,
volume =       "1",
number =       "2",
pages =        "11:1--11:??",
month =        nov,
year =         "2010",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1869397.1869400",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Wed Jan 26 14:40:50 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "11",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Refanidis:2010:CBA,
author =       "Ioannis Refanidis and Neil Yorke-Smith",
title =        "A constraint-based approach to scheduling an
individual's activities",
journal =      j-TIST,
volume =       "1",
number =       "2",
pages =        "12:1--12:??",
month =        nov,
year =         "2010",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1869397.1869401",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Wed Jan 26 14:40:50 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "12",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

author =       "Abder Rezak Benaskeur and Froduald Kabanza and Eric
Beaudry",
title =        "{CORALS}: a real-time planner for anti-air defense
operations",
journal =      j-TIST,
volume =       "1",
number =       "2",
pages =        "13:1--13:??",
month =        nov,
year =         "2010",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1869397.1869402",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Wed Jan 26 14:40:50 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "13",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

author =       "Kartik Talamadupula and J. Benton and Subbarao
Kambhampati and Paul Schermerhorn and Matthias
Scheutz",
title =        "Planning for human-robot teaming in open worlds",
journal =      j-TIST,
volume =       "1",
number =       "2",
pages =        "14:1--14:??",
month =        nov,
year =         "2010",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1869397.1869403",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Wed Jan 26 14:40:50 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "14",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Cirillo:2010:HAT,
author =       "Marcello Cirillo and Lars Karlsson and Alessandro
Saffiotti",
title =        "Human-aware task planning: An application to mobile
robots",
journal =      j-TIST,
volume =       "1",
number =       "2",
pages =        "15:1--15:??",
month =        nov,
year =         "2010",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1869397.1869404",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Wed Jan 26 14:40:50 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "15",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Zhang:2011:ISI,
author =       "Daqing Zhang and Matthai Philipose and Qiang Yang",
title =        "Introduction to the special issue on intelligent
systems for activity recognition",
journal =      j-TIST,
volume =       "2",
number =       "1",
pages =        "1:1--1:??",
month =        jan,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1889681.1889682",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Wed Jan 26 14:40:51 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "1",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Zheng:2011:LTR,
author =       "Yu Zheng and Xing Xie",
title =        "Learning travel recommendations from user-generated
{GPS} traces",
journal =      j-TIST,
volume =       "2",
number =       "1",
pages =        "2:1--2:??",
month =        jan,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1889681.1889683",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Wed Jan 26 14:40:51 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "2",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Farrahi:2011:DRL,
author =       "Katayoun Farrahi and Daniel Gatica-Perez",
title =        "Discovering routines from large-scale human locations
using probabilistic topic models",
journal =      j-TIST,
volume =       "2",
number =       "1",
pages =        "3:1--3:??",
month =        jan,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1889681.1889684",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Wed Jan 26 14:40:51 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "3",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Hsu:2011:PMC,
author =       "Jane Yung-Jen Hsu and Chia-Chun Lian and Wan-Rong
Jih",
title =        "Probabilistic models for concurrent chatting activity
recognition",
journal =      j-TIST,
volume =       "2",
number =       "1",
pages =        "4:1--4:??",
month =        jan,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1889681.1889685",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Wed Jan 26 14:40:51 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "4",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Zhou:2011:RPA,
author =       "Yue Zhou and Bingbing Ni and Shuicheng Yan and Thomas
S. Huang",
title =        "Recognizing pair-activities by causality analysis",
journal =      j-TIST,
volume =       "2",
number =       "1",
pages =        "5:1--5:??",
month =        jan,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1889681.1889686",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Wed Jan 26 14:40:51 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "5",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Ward:2011:PMA,
author =       "Jamie A. Ward and Paul Lukowicz and Hans W.
Gellersen",
title =        "Performance metrics for activity recognition",
journal =      j-TIST,
volume =       "2",
number =       "1",
pages =        "6:1--6:??",
month =        jan,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1889681.1889687",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Wed Jan 26 14:40:51 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "6",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Wyatt:2011:ICC,
author =       "Danny Wyatt and Tanzeem Choudhury and Jeff Bilmes and
James A. Kitts",
title =        "Inferring colocation and conversation networks from
privacy-sensitive audio with implications for
computational social science",
journal =      j-TIST,
volume =       "2",
number =       "1",
pages =        "7:1--7:??",
month =        jan,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1889681.1889688",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Wed Jan 26 14:40:51 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "7",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Bao:2011:FRC,
author =       "Xinlong Bao and Thomas G. Dietterich",
title =        "{FolderPredictor}: Reducing the cost of reaching the
right folder",
journal =      j-TIST,
volume =       "2",
number =       "1",
pages =        "8:1--8:??",
month =        jan,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1889681.1889689",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Wed Jan 26 14:40:51 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "8",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Ling:2011:ISI,
author =       "Charles X. Ling",
title =        "Introduction to special issue on machine learning for
journal =      j-TIST,
volume =       "2",
number =       "3",
pages =        "18:1--18:??",
month =        apr,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1961189.1961190",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Fri May 13 11:20:03 MDT 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "18",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Dhar:2011:PFM,
author =       "Vasant Dhar",
title =        "Prediction in financial markets: The case for small
disjuncts",
journal =      j-TIST,
volume =       "2",
number =       "3",
pages =        "19:1--19:??",
month =        apr,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1961189.1961191",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Fri May 13 11:20:03 MDT 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "19",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Huang:2011:LBC,
author =       "Szu-Hao Huang and Shang-Hong Lai and Shih-Hsien Tai",
title =        "A learning-based contrarian trading strategy via a
dual-classifier model",
journal =      j-TIST,
volume =       "2",
number =       "3",
pages =        "20:1--20:??",
month =        apr,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1961189.1961192",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Fri May 13 11:20:03 MDT 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "20",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Li:2011:CCD,
author =       "Bin Li and Steven C. H. Hoi and Vivekanand
Gopalkrishnan",
title =        "{CORN}: Correlation-driven nonparametric learning
approach for portfolio selection",
journal =      j-TIST,
volume =       "2",
number =       "3",
pages =        "21:1--21:??",
month =        apr,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1961189.1961193",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Fri May 13 11:20:03 MDT 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "21",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Bonchi:2011:SNA,
author =       "Francesco Bonchi and Carlos Castillo and Aristides
Gionis and Alejandro Jaimes",
title =        "Social Network Analysis and Mining for Business
Applications",
journal =      j-TIST,
volume =       "2",
number =       "3",
pages =        "22:1--22:??",
month =        apr,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1961189.1961194",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Fri May 13 11:20:03 MDT 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "22",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Zhang:2011:HMF,
author =       "Richong Zhang and Thomas Tran",
title =        "A helpfulness modeling framework for electronic
word-of-mouth on consumer opinion platforms",
journal =      j-TIST,
volume =       "2",
number =       "3",
pages =        "23:1--23:??",
month =        apr,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1961189.1961195",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Fri May 13 11:20:03 MDT 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "23",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Ge:2011:MLC,
author =       "Yong Ge and Hui Xiong and Wenjun Zhou and Siming Li
and Ramendra Sahoo",
title =        "Multifocal learning for customer problem analysis",
journal =      j-TIST,
volume =       "2",
number =       "3",
pages =        "24:1--24:??",
month =        apr,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1961189.1961196",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Fri May 13 11:20:03 MDT 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "24",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Hsu:2011:ISI,
author =       "Chun-Nan Hsu",
title =        "Introduction to special issue on large-scale machine
learning",
journal =      j-TIST,
volume =       "2",
number =       "3",
pages =        "25:1--25:??",
month =        apr,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1961189.1961197",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Fri May 13 11:20:03 MDT 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "25",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Liu:2011:PPL,
author =       "Zhiyuan Liu and Yuzhou Zhang and Edward Y. Chang and
Maosong Sun",
title =        "{PLDA+}: Parallel latent {Dirichlet} allocation with
data placement and pipeline processing",
journal =      j-TIST,
volume =       "2",
number =       "3",
pages =        "26:1--26:??",
month =        apr,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1961189.1961198",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Fri May 13 11:20:03 MDT 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "26",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Chang:2011:LLS,
author =       "Chih-Chung Chang and Chih-Jen Lin",
title =        "{LIBSVM}: a library for support vector machines",
journal =      j-TIST,
volume =       "2",
number =       "3",
pages =        "27:1--27:??",
month =        apr,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1961189.1961199",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Fri May 13 11:20:03 MDT 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "LIBSVM is a library for Support Vector Machines
(SVMs). We have been actively developing this package
since the year 2000. The goal is to help users to
easily apply SVM to their applications. LIBSVM has
gained wide popularity in machine learning and many
implementation details of LIBSVM. Issues such as
solving SVM optimization problems theoretical
convergence multiclass classification probability
estimates and parameter selection are discussed in
detail.",
acknowledgement = ack-nhfb,
articleno =    "27",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Gasso:2011:BOL,
author =       "Gilles Gasso and Aristidis Pappaioannou and Marina
Spivak and L{\'e}on Bottou",
title =        "Batch and online learning algorithms for nonconvex
{Neyman--Pearson} classification",
journal =      j-TIST,
volume =       "2",
number =       "3",
pages =        "28:1--28:??",
month =        apr,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1961189.1961200",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Fri May 13 11:20:03 MDT 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "28",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Ma:2011:LRE,
author =       "Hao Ma and Irwin King and Michael R. Lyu",
title =        "Learning to recommend with explicit and implicit
social relations",
journal =      j-TIST,
volume =       "2",
number =       "3",
pages =        "29:1--29:??",
month =        apr,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1961189.1961201",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Fri May 13 11:20:03 MDT 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "29",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Ma:2011:LDM,
author =       "Justin Ma and Lawrence K. Saul and Stefan Savage and
Geoffrey M. Voelker",
title =        "Learning to detect malicious {URLs}",
journal =      j-TIST,
volume =       "2",
number =       "3",
pages =        "30:1--30:??",
month =        apr,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1961189.1961202",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Fri May 13 11:20:03 MDT 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Malicious Web sites are a cornerstone of Internet
criminal activities. The dangers of these sites have
created a demand for safeguards that protect end-users
malicious Web sites from the lexical and host-based
features of their URLs. We show that this problem lends
itself naturally to modern algorithms for online
learning. Online algorithms not only process large
numbers of URLs more efficiently than batch algorithms,
they also adapt more quickly to new features in the
continuously evolving distribution of malicious URLs.
We develop a real-time system for gathering URL
features and pair it with a real-time feed of labeled
URLs from a large Web mail provider. From these
features and labels, we are able to train an online
classifier that detects malicious Web sites with 99\%
accuracy over a balanced dataset.",
acknowledgement = ack-nhfb,
articleno =    "30",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Gomes:2011:ISI,
author =       "Carla Gomes and Qiang Yang",
title =        "Introduction to special issue on computational
sustainability",
journal =      j-TIST,
volume =       "2",
number =       "4",
pages =        "31:1--31:??",
month =        jul,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1989734.1989735",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Fri Jul 22 08:50:59 MDT 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "31",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Krause:2011:SAO,
author =       "Andreas Krause and Carlos Guestrin",
title =        "Submodularity and its applications in optimized
information gathering",
journal =      j-TIST,
volume =       "2",
number =       "4",
pages =        "32:1--32:??",
month =        jul,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1989734.1989736",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Fri Jul 22 08:50:59 MDT 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "32",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Cattafi:2011:SBP,
author =       "Massimiliano Cattafi and Marco Gavanelli and Michela
Milano and Paolo Cagnoli",
title =        "Sustainable biomass power plant location in the
{Italian Emilia-Romagna} region",
journal =      j-TIST,
volume =       "2",
number =       "4",
pages =        "33:1--33:??",
month =        jul,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1989734.1989737",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Fri Jul 22 08:50:59 MDT 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "33",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Patnaik:2011:TDM,
author =       "Debprakash Patnaik and Manish Marwah and Ratnesh K.
Sharma and Naren Ramakrishnan",
title =        "Temporal data mining approaches for sustainable
chiller management in data centers",
journal =      j-TIST,
volume =       "2",
number =       "4",
pages =        "34:1--34:??",
month =        jul,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1989734.1989738",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Fri Jul 22 08:50:59 MDT 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "34",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Ramchurn:2011:ABH,
author =       "Sarvapali D. Ramchurn and Perukrishnen Vytelingum and
Alex Rogers and Nicholas R. Jennings",
title =        "Agent-based homeostatic control for green energy in
the smart grid",
journal =      j-TIST,
volume =       "2",
number =       "4",
pages =        "35:1--35:??",
month =        jul,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1989734.1989739",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Fri Jul 22 08:50:59 MDT 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "35",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Mithal:2011:MGF,
author =       "Varun Mithal and Ashish Garg and Shyam Boriah and
Michael Steinbach and Vipin Kumar and Christopher
Potter and Steven Klooster and Juan Carlos
Castilla-Rubio",
title =        "Monitoring global forest cover using data mining",
journal =      j-TIST,
volume =       "2",
number =       "4",
pages =        "36:1--36:??",
month =        jul,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1989734.1989740",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Fri Jul 22 08:50:59 MDT 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "36",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Li:2011:MMM,
author =       "Zhenhui Li and Jiawei Han and Ming Ji and Lu-An Tang
and Yintao Yu and Bolin Ding and Jae-Gil Lee and Roland
Kays",
title =        "{MoveMine}: Mining moving object data for discovery of
animal movement patterns",
journal =      j-TIST,
volume =       "2",
number =       "4",
pages =        "37:1--37:??",
month =        jul,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1989734.1989741",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Fri Jul 22 08:50:59 MDT 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "37",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Toole:2011:SCC,
author =       "Jameson L. Toole and Nathan Eagle and Joshua B.
Plotkin",
title =        "Spatiotemporal correlations in criminal offense
records",
journal =      j-TIST,
volume =       "2",
number =       "4",
pages =        "38:1--38:??",
month =        jul,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1989734.1989742",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Fri Jul 22 08:50:59 MDT 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "38",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Ding:2011:SCD,
author =       "Wei Ding and Tomasz F. Stepinski and Yang Mu and
Lourenco Bandeira and Ricardo Ricardo and Youxi Wu and
Zhenyu Lu and Tianyu Cao and Xindong Wu",
title =        "Subkilometer crater discovery with boosting and
transfer learning",
journal =      j-TIST,
volume =       "2",
number =       "4",
pages =        "39:1--39:??",
month =        jul,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1989734.1989743",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Fri Jul 22 08:50:59 MDT 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "39",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Berry:2011:PPA,
author =       "Pauline M. Berry and Melinda Gervasio and Bart
Peintner and Neil Yorke-Smith",
title =        "{PTIME}: Personalized assistance for calendaring",
journal =      j-TIST,
volume =       "2",
number =       "4",
pages =        "40:1--40:??",
month =        jul,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1989734.1989744",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Fri Jul 22 08:50:59 MDT 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "40",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Reddy:2011:PSA,
author =       "Sudhakar Y. Reddy and Jeremy D. Frank and Michael J.
Iatauro and Matthew E. Boyce and Elif K{\"u}rkl{\"u}
and Mitchell Ai-Chang and Ari K. J{\'o}nsson",
title =        "Planning solar array operations on the {International
Space Station}",
journal =      j-TIST,
volume =       "2",
number =       "4",
pages =        "41:1--41:??",
month =        jul,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1989734.1989745",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Fri Jul 22 08:50:59 MDT 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "41",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Haigh:2011:RLL,
author =       "Karen Zita Haigh and Fusun Yaman",
title =        "{RECYCLE}: Learning looping workflows from annotated
traces",
journal =      j-TIST,
volume =       "2",
number =       "4",
pages =        "42:1--42:??",
month =        jul,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/1989734.1989746",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
bibdate =      "Fri Jul 22 08:50:59 MDT 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "42",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Guy:2011:I,
author =       "Ido Guy and Li Chen and Michelle X. Zhou",
title =        "Introduction",
journal =      j-TIST,
volume =       "3",
number =       "1",
pages =        "1:1--1:??",
month =        oct,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2036264.2036265",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun Nov 6 07:22:40 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "1",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Lipczak:2011:ETR,
author =       "Marek Lipczak and Evangelos Milios",
title =        "Efficient Tag Recommendation for Real-Life Data",
journal =      j-TIST,
volume =       "3",
number =       "1",
pages =        "2:1--2:??",
month =        oct,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2036264.2036266",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun Nov 6 07:22:40 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "2",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Vasuki:2011:SAR,
author =       "Vishvas Vasuki and Nagarajan Natarajan and Zhengdong
Lu and Berkant Savas and Inderjit Dhillon",
title =        "Scalable Affiliation Recommendation using Auxiliary
Networks",
journal =      j-TIST,
volume =       "3",
number =       "1",
pages =        "3:1--3:??",
month =        oct,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2036264.2036267",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun Nov 6 07:22:40 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "3",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{McNally:2011:CSC,
author =       "Kevin McNally and Michael P. O'Mahony and Maurice
Coyle and Peter Briggs and Barry Smyth",
title =        "A Case Study of Collaboration and Reputation in Social
{Web} Search",
journal =      j-TIST,
volume =       "3",
number =       "1",
pages =        "4:1--4:??",
month =        oct,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2036264.2036268",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun Nov 6 07:22:40 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "4",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Zhao:2011:WDW,
author =       "Shiwan Zhao and Michelle X. Zhou and Xiatian Zhang and
Quan Yuan and Wentao Zheng and Rongyao Fu",
title =        "Who is Doing What and When: Social Map-Based
Recommendation for Content-Centric Social {Web} Sites",
journal =      j-TIST,
volume =       "3",
number =       "1",
pages =        "5:1--5:??",
month =        oct,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2036264.2036269",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun Nov 6 07:22:40 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "5",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Liu:2011:I,
author =       "Huan Liu and Dana Nau",
title =        "Introduction",
journal =      j-TIST,
volume =       "3",
number =       "1",
pages =        "6:1--6:??",
month =        oct,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2036264.2036270",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun Nov 6 07:22:40 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "6",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Shakarian:2011:GGA,
author =       "Paulo Shakarian and V. S. Subrahmanian and Maria Luisa
Sapino",
title =        "{GAPs}: Geospatial Abduction Problems",
journal =      j-TIST,
volume =       "3",
number =       "1",
pages =        "7:1--7:??",
month =        oct,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2036264.2036271",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun Nov 6 07:22:40 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "7",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Gal:2011:AAN,
author =       "Ya'akov Gal and Sarit Kraus and Michele Gelfand and
Hilal Khashan and Elizabeth Salmon",
title =        "An Adaptive Agent for Negotiating with People in
Different Cultures",
journal =      j-TIST,
volume =       "3",
number =       "1",
pages =        "8:1--8:??",
month =        oct,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2036264.2036272",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun Nov 6 07:22:40 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "8",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Vu:2011:FSK,
author =       "Thuc Vu and Yoav Shoham",
title =        "Fair Seeding in Knockout Tournaments",
journal =      j-TIST,
volume =       "3",
number =       "1",
pages =        "9:1--9:??",
month =        oct,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2036264.2036273",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun Nov 6 07:22:40 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "9",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Cioffi-Revilla:2011:GIS,
author =       "Claudio Cioffi-Revilla and J. Daniel Rogers and
Atesmachew Hailegiorgis",
title =        "Geographic Information Systems and Spatial Agent-Based
Model Simulations for Sustainable Development",
journal =      j-TIST,
volume =       "3",
number =       "1",
pages =        "10:1--10:??",
month =        oct,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2036264.2036274",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun Nov 6 07:22:40 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "10",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Jiang:2011:UMS,
author =       "Yingying Jiang and Feng Tian and Xiaolong (Luke) Zhang
and Guozhong Dai and Hongan Wang",
title =        "Understanding, Manipulating and Searching Hand-Drawn
Concept Maps",
journal =      j-TIST,
volume =       "3",
number =       "1",
pages =        "11:1--11:??",
month =        oct,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2036264.2036275",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun Nov 6 07:22:40 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "11",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Wang:2011:IIS,
author =       "Jingdong Wang and Xian-Sheng Hua",
title =        "Interactive Image Search by Color Map",
journal =      j-TIST,
volume =       "3",
number =       "1",
pages =        "12:1--12:??",
month =        oct,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2036264.2036276",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun Nov 6 07:22:40 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "12",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Prettenhofer:2011:CLA,
author =       "Peter Prettenhofer and Benno Stein",
title =        "Cross-Lingual Adaptation Using Structural
Correspondence Learning",
journal =      j-TIST,
volume =       "3",
number =       "1",
pages =        "13:1--13:??",
month =        oct,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2036264.2036277",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun Nov 6 07:22:40 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "13",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Anagnostopoulos:2011:WPS,
author =       "Aris Anagnostopoulos and Andrei Z. Broder and Evgeniy
Gabrilovich and Vanja Josifovski and Lance Riedel",
title =        "{Web} Page Summarization for Just-in-Time Contextual
journal =      j-TIST,
volume =       "3",
number =       "1",
pages =        "14:1--14:??",
month =        oct,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2036264.2036278",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun Nov 6 07:22:40 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "14",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Tang:2011:GPU,
author =       "Lei Tang and Xufei Wang and Huan Liu",
title =        "Group Profiling for Understanding Social Structures",
journal =      j-TIST,
volume =       "3",
number =       "1",
pages =        "15:1--15:??",
month =        oct,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2036264.2036279",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun Nov 6 07:22:40 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "15",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Liu:2011:TWC,
author =       "Zhanyi Liu and Haifeng Wang and Hua Wu and Sheng Li",
title =        "Two-Word Collocation Extraction Using Monolingual Word
Alignment Method",
journal =      j-TIST,
volume =       "3",
number =       "1",
pages =        "16:1--16:??",
month =        oct,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2036264.2036280",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun Nov 6 07:22:40 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "16",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Liao:2011:MCS,
author =       "Zhen Liao and Daxin Jiang and Enhong Chen and Jian Pei
and Huanhuan Cao and Hang Li",
title =        "Mining Concept Sequences from Large-Scale Search Logs
for Context-Aware Query Suggestion",
journal =      j-TIST,
volume =       "3",
number =       "1",
pages =        "17:1--17:??",
month =        oct,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2036264.2036281",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun Nov 6 07:22:40 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "17",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Sukthankar:2011:ARD,
author =       "Gita Sukthankar and Katia Sycara",
title =        "Activity Recognition for Dynamic Multi-Agent Teams",
journal =      j-TIST,
volume =       "3",
number =       "1",
pages =        "18:1--18:??",
month =        oct,
year =         "2011",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2036264.2036282",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun Nov 6 07:22:40 MST 2011",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "18",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Liu:2012:ISS,
author =       "Shixia Liu and Michelle X. Zhou and Giuseppe Carenini
and Huamin Qu",
title =        "Introduction to the Special Section on Intelligent
Visual Interfaces for Text Analysis",
journal =      j-TIST,
volume =       "3",
number =       "2",
pages =        "19:1--19:??",
month =        feb,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2089094.2089095",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Fri Mar 16 15:10:10 MDT 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "19",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Cui:2012:WSU,
author =       "Weiwei Cui and Huamin Qu and Hong Zhou and Wenbin
Zhang and Steve Skiena",
title =        "Watch the Story Unfold with {TextWheel}: Visualization
of Large-Scale News Streams",
journal =      j-TIST,
volume =       "3",
number =       "2",
pages =        "20:1--20:??",
month =        feb,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2089094.2089096",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Fri Mar 16 15:10:10 MDT 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Keyword-based searching and clustering of news
articles have been widely used for news analysis.
However, news articles usually have other attributes
such as source, author, date and time, length, and
sentiment which should be taken into account. In
addition, news articles and keywords have complicated
macro/micro relations, which include relations between
news articles (i.e., macro relation), relations between
keywords (i.e., micro relation), and relations between
news articles and keywords (i.e., macro-micro
relation). These macro/micro relations are time varying
and pose special challenges for news analysis. In this
article we present a visual analytics system for news
streams which can bring multiple attributes of the news
articles and the macro/micro relations between news
streams and keywords into one coherent analytical
context, all the while conveying the dynamic natures of
news streams. We introduce a new visualization
primitive called TextWheel which consists of one or
multiple keyword wheels, a document transportation
belt, and a dynamic system which connects the wheels
and belt. By observing the TextWheel and its content
changes, some interesting patterns can be detected. We
use our system to analyze several news corpora related
to some major companies and the results demonstrate the
high potential of our method.",
acknowledgement = ack-nhfb,
articleno =    "20",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Thai:2012:VAO,
author =       "Vinhtuan Thai and Pierre-Yves Rouille and Siegfried
Handschuh",
title =        "Visual Abstraction and Ordering in Faceted Browsing of
Text Collections",
journal =      j-TIST,
volume =       "3",
number =       "2",
pages =        "21:1--21:??",
month =        feb,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2089094.2089097",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Fri Mar 16 15:10:10 MDT 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Faceted navigation is a technique for the exploration
and discovery of a collection of resources, which can
be of various types including text documents. While
being information-rich resources, documents are usually
not treated as content-bearing items in faceted
browsing interfaces, and yet the required clean
metadata is not always available or matches users'
interest. In addition, the existing linear listing
paradigm for representing result items from the faceted
filtering process makes it difficult for users to
traverse or compare across facet values in different
orders of importance to them. In this context, we
browsing of a collection of documents based on a set of
entities of interest to users. Our proposed approach
involves using a multi-dimensional visualization as an
alternative to the linear listing of focus items. In
this visualization, visual abstraction based on a
combination of a conceptual structure and the
structural equivalence of documents can be
simultaneously used to deal with a large number of
items. Furthermore, the approach also enables visual
ordering based on the importance of facet values to
support prioritized, cross-facet comparisons of focus
items. A user study was conducted and the results
suggest that interfaces using the proposed approach can
support users better in exploratory tasks and were also
well-liked by the participants of the study, with the
hybrid interface combining the multi-dimensional
visualization with the linear listing receiving the
most favorable ratings.",
acknowledgement = ack-nhfb,
articleno =    "21",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Candan:2012:PMV,
author =       "K. Sel{\c{c}}uk Candan and Luigi {Di Caro} and Maria
Luisa Sapino",
title =        "{PhC}: Multiresolution Visualization and Exploration
of Text Corpora with Parallel Hierarchical
Coordinates",
journal =      j-TIST,
volume =       "3",
number =       "2",
pages =        "22:1--22:??",
month =        feb,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2089094.2089098",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Fri Mar 16 15:10:10 MDT 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "The high-dimensional nature of the textual data
complicates the design of visualization tools to
support exploration of large document corpora. In this
article, we first argue that the Parallel Coordinates
(PC) technique, which can map multidimensional vectors
onto a 2D space in such a way that elements with
similar values are represented as similar poly-lines or
curves in the visualization space, can be used to help
users discern patterns in document collections. The
inherent reduction in dimensionality during the mapping
from multidimensional points to 2D lines, however, may
result in visual complications. For instance, the lines
that correspond to clusters of objects that are
separate in the multidimensional space may overlap each
other in the 2D space; the resulting increase in the
number of crossings would make it hard to distinguish
the individual document clusters. Such crossings of
lines and overly dense regions are significant sources
of visual clutter, thus avoiding them may help
that visual clutter can be significantly reduced by
adjusting the resolution of the individual term
coordinates by clustering the corresponding values.
Such reductions in the resolution of the individual
term-coordinates, however, will lead to a certain
degree of information loss and thus the appropriate
resolution for the term-coordinates has to be selected
controlled clutter reduction approach, called Parallel
hierarchical Coordinates (or PhC ), for reducing the
visual clutter in PC-based visualizations of text
corpora. We define visual clutter and information loss
measures and provide extensive evaluations that show
that the proposed PhC provides significant visual gains
(i.e., multiple orders of reductions in visual clutter)
with small information loss during visualization and
exploration of document collections.",
acknowledgement = ack-nhfb,
articleno =    "22",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

author =       "Brynjar Gretarsson and John O'Donovan and Svetlin
Bostandjiev and Tobias H{\"o}llerer and Arthur Asuncion
and David Newman and Padhraic Smyth",
title =        "{TopicNets}: Visual Analysis of Large Text Corpora
with Topic Modeling",
journal =      j-TIST,
volume =       "3",
number =       "2",
pages =        "23:1--23:??",
month =        feb,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2089094.2089099",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Fri Mar 16 15:10:10 MDT 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "We present TopicNets, a Web-based system for visual
and interactive analysis of large sets of documents
using statistical topic models. A range of
visualization types and control mechanisms to support
knowledge discovery are presented. These include
corpus- and document-specific views, iterative topic
modeling, search, and visual filtering. Drill-down
functionality is provided to allow analysts to
visualize individual document sections and their
relations within the global topic space. Analysts can
search across a dataset through a set of expansion
techniques on selected document and topic nodes.
Furthermore, analysts can select relevant subsets of
documents and perform real-time topic modeling on these
subsets to interactively visualize topics at various
levels of granularity, allowing for a better
understanding of the documents. A discussion of the
design and implementation choices for each visual
analysis technique is presented. This is followed by a
discussion of three diverse use cases in which
TopicNets enables fast discovery of information that is
otherwise hard to find. These include a corpus of
50,000 successful NSF grant proposals, 10,000
publications from a large research center, and single
documents including a grant proposal and a PhD
thesis.",
acknowledgement = ack-nhfb,
articleno =    "23",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Zhang:2012:DFE,
author =       "Yi Zhang and Tao Li",
title =        "{DClusterE}: a Framework for Evaluating and
Understanding Document Clustering Using Visualization",
journal =      j-TIST,
volume =       "3",
number =       "2",
pages =        "24:1--24:??",
month =        feb,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2089094.2089100",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Fri Mar 16 15:10:10 MDT 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Over the last decade, document clustering, as one of
the key tasks in information organization and
navigation, has been widely studied. Many algorithms
have been developed for addressing various challenges
in document clustering and for improving clustering
performance. However, relatively few research efforts
have been reported on evaluating and understanding
present DClusterE, a comprehensive and effective
framework for document clustering evaluation and
understanding using information visualization.
DClusterE integrates cluster validation with user
interactions and offers rich visualization tools for
users to examine document clustering results from
multiple perspectives. In particular, through
informative views including force-directed layout view,
matrix view, and cluster view, DClusterE provides not
only different aspects of document
inter/intra-clustering structures, but also the
corresponding relationship between clustering results
and the ground truth. Additionally, DClusterE supports
general user interactions such as zoom in/out,
browsing, and interactive access of the documents at
different levels. Two new techniques are proposed to
implement DClusterE: (1) A novel multiplicative update
algorithm (MUA) for matrix reordering to generate
narrow-banded (or clustered) nonzero patterns from
documents. Combined with coarse seriation, MUA is able
to provide better visualization of the cluster
structures. (2) A Mallows-distance-based algorithm for
establishing the relationship between the clustering
results and the ground truth, which serves as the basis
for coloring schemes. Experiments and user studies are
conducted to demonstrate the effectiveness and
efficiency of DClusterE.",
acknowledgement = ack-nhfb,
articleno =    "24",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Liu:2012:TIT,
author =       "Shixia Liu and Michelle X. Zhou and Shimei Pan and
Yangqiu Song and Weihong Qian and Weijia Cai and
Xiaoxiao Lian",
title =        "{TIARA}: Interactive, Topic-Based Visual Text
Summarization and Analysis",
journal =      j-TIST,
volume =       "3",
number =       "2",
pages =        "25:1--25:??",
month =        feb,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2089094.2089101",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Fri Mar 16 15:10:10 MDT 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "We are building an interactive visual text analysis
tool that aids users in analyzing large collections of
text. Unlike existing work in visual text analytics,
which focuses either on developing sophisticated text
analytic techniques or inventing novel text
visualization metaphors, ours tightly integrates
state-of-the-art text analytics with interactive
visualization to maximize the value of both. In this
article, we present our work from two aspects. We first
introduce an enhanced, LDA-based topic analysis
technique that automatically derives a set of topics to
summarize a collection of documents and their content
evolution over time. To help users understand the
complex summarization results produced by our topic
analysis technique, we then present the design and
development of a time-based visualization of the
results. Furthermore, we provide users with a set of
rich interaction tools that help them further interpret
the visualized results in context and examine the text
collection from multiple perspectives. As a result, our
work offers three unique contributions. First, we
present an enhanced topic modeling technique to provide
users with a time-sensitive and more meaningful text
summary. Second, we develop an effective visual
metaphor to transform abstract and often complex text
summarization results into a comprehensible visual
representation. Third, we offer users flexible visual
interaction tools as alternatives to compensate for the
deficiencies of current text summarization techniques.
We have applied our work to a number of text corpora
and our evaluation shows promise, especially in support
of complex text analyses.",
acknowledgement = ack-nhfb,
articleno =    "25",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Rohrdantz:2012:FBV,
author =       "Christian Rohrdantz and Ming C. Hao and Umeshwar Dayal
and Lars-Erik Haug and Daniel A. Keim",
title =        "Feature-Based Visual Sentiment Analysis of Text
Document Streams",
journal =      j-TIST,
volume =       "3",
number =       "2",
pages =        "26:1--26:??",
month =        feb,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2089094.2089102",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Fri Mar 16 15:10:10 MDT 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
interactive visualizations that are tightly coupled
with the goal to enable users to detect interesting
portions of text document streams. In this scenario the
interestingness is derived from the sentiment, temporal
features for different targets (e.g., persons,
institutions, product attributes, topics, etc.) have.
Contributions are made at different stages of the
visual analytics pipeline, including novel ways to
visualize salient temporal accumulations for further
exploration. Moreover, based on the visualization, an
automatic algorithm aims to detect and preselect
interesting time interval patterns for different
features in order to guide analysts. The main target
group for the suggested methods are business analysts
who want to explore time-stamped customer feedback to
detect critical issues. Finally, application case
studies on two different datasets and scenarios are
conducted and an extensive evaluation is provided for
the presented intelligent visual interface for
feature-based sentiment exploration over time.",
acknowledgement = ack-nhfb,
articleno =    "26",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Sugiyama:2012:ISS,
author =       "Masashi Sugiyama and Qiang Yang",
title =        "Introduction to the Special Section on the {2nd Asia
Conference on Machine Learning (ACML 2010)}",
journal =      j-TIST,
volume =       "3",
number =       "2",
pages =        "27:1--27:??",
month =        feb,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2089094.2089103",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Fri Mar 16 15:10:10 MDT 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "27",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

title =        "Conceptual Imitation Learning in a Human-Robot
journal =      j-TIST,
volume =       "3",
number =       "2",
pages =        "28:1--28:??",
month =        feb,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2089094.2089104",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Fri Mar 16 15:10:10 MDT 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "In general, imitation is imprecisely used to address
different levels of social learning from high-level
knowledge transfer to low-level regeneration of motor
commands. However, true imitation is based on
presents a model for conceptual imitation through
interaction with the teacher to abstract
spatio-temporal demonstrations based on their
functional meaning. Abstraction, concept acquisition,
and self-organization of proto-symbols are performed
through an incremental and gradual learning algorithm.
In this algorithm, Hidden Markov Models (HMMs) are used
to abstract perceptually similar demonstrations.
However, abstract (relational) concepts emerge as a
collection of HMMs irregularly scattered in the
perceptual space but showing the same functionality.
Performance of the proposed algorithm is evaluated in
two experimental scenarios. The first one is a
human-robot interaction task of imitating signs
produced by hand movements. The second one is a
simulated interactive task of imitating whole body
motion patterns of a humanoid model. Experimental
results show efficiency of our model for concept
extraction, proto-symbol emergence, motion pattern
recognition, prediction, and generation.",
acknowledgement = ack-nhfb,
articleno =    "28",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Li:2012:MRC,
author =       "Peipei Li and Xindong Wu and Xuegang Hu",
title =        "Mining Recurring Concept Drifts with Limited Labeled
Streaming Data",
journal =      j-TIST,
volume =       "3",
number =       "2",
pages =        "29:1--29:??",
month =        feb,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2089094.2089105",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Fri Mar 16 15:10:10 MDT 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Tracking recurring concept drifts is a significant
issue for machine learning and data mining that
frequently appears in real-world stream classification
problems. It is a challenge for many streaming
classification algorithms to learn recurring concepts
in a data stream environment with unlabeled data, and
this challenge has received little attention from the
research community. Motivated by this challenge, this
article focuses on the problem of recurring contexts in
streaming environments with limited labeled data. We
propose a semi-supervised classification algorithm for
data streams with REcurring concept Drifts and Limited
LAbeled data, called REDLLA, in which a decision tree
is adopted as the classification model. When growing a
tree, a clustering algorithm based on k -means is
installed to produce concept clusters and unlabeled
data are labeled in the method of majority-class at
leaves. In view of deviations between history and new
concept clusters, potential concept drifts are
distinguished and recurring concepts are maintained.
Extensive studies on both synthetic and real-world data
confirm the advantages of our REDLLA algorithm over
three state-of-the-art online classification algorithms
of CVFDT, DWCDS, and CDRDT and several known online
semi-supervised algorithms, even in the case with more
than 90\% unlabeled data.",
acknowledgement = ack-nhfb,
articleno =    "29",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Bifet:2012:ERH,
author =       "Albert Bifet and Eibe Frank and Geoff Holmes and
Bernhard Pfahringer",
title =        "Ensembles of Restricted {Hoeffding} Trees",
journal =      j-TIST,
volume =       "3",
number =       "2",
pages =        "30:1--30:??",
month =        feb,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2089094.2089106",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Fri Mar 16 15:10:10 MDT 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "The success of simple methods for classification shows
that is is often not necessary to model complex
attribute interactions to obtain good classification
propose to exploit this phenomenon in the data stream
context by building an ensemble of Hoeffding trees that
are each limited to a small subset of attributes. In
this way, each tree is restricted to model interactions
between attributes in its corresponding subset. Because
it is not known a priori which attribute subsets are
relevant for prediction, we build exhaustive ensembles
that consider all possible attribute subsets of a given
size. As the resulting Hoeffding trees are not all
equally important, we weigh them in a suitable manner
to obtain accurate classifications. This is done by
combining the log-odds of their probability estimates
using sigmoid perceptrons, with one perceptron per
class. We propose a mechanism for setting the
perceptrons' learning rate using the change detection
method for data streams, and also use to reset ensemble
members (i.e., Hoeffding trees) when they no longer
perform well. Our experiments show that the resulting
ensemble classifier outperforms bagging for data
streams in terms of accuracy when both are used in
conjunction with adaptive naive Bayes Hoeffding trees,
at the expense of runtime and memory consumption. We
also show that our stacking method can improve the
performance of a bagged ensemble.",
acknowledgement = ack-nhfb,
articleno =    "30",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Ma:2012:RPC,
author =       "Huadong Ma and Chengbin Zeng and Charles X. Ling",
title =        "A Reliable People Counting System via Multiple
Cameras",
journal =      j-TIST,
volume =       "3",
number =       "2",
pages =        "31:1--31:??",
month =        feb,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2089094.2089107",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Fri Mar 16 15:10:10 MDT 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Reliable and real-time people counting is crucial in
many applications. Most previous works can only count
moving people from a single camera, which cannot count
still people or can fail badly when there is a crowd
build a system for robust and fast people counting
under occlusion through multiple cameras. To improve
the reliability of human detection from a single
camera, we use a dimensionality reduction method on the
multilevel edge and texture features to handle the
large variations in human appearance and poses. To
accelerate the detection speed, we propose a novel
two-stage cascade-of-rejectors method. To handle the
heavy occlusion in crowded scenes, we present a fusion
method with error tolerance to combine human detection
from multiple cameras. To improve the speed and
accuracy of moving people counting, we combine our
multiview fusion detection method with particle
tracking to count the number of people moving in/out
the camera view (border control'). Extensive
experiments and analyses show that our method
outperforms state-of-the-art techniques in single- and
multicamera datasets for both speed and reliability. We
also design a deployed system for fast and reliable
people (still or moving) counting by using multiple
cameras.",
acknowledgement = ack-nhfb,
articleno =    "31",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Kolomvatsos:2012:FLS,
author =       "Kostas Kolomvatsos and Christos Anagnostopoulos and
title =        "A Fuzzy Logic System for Bargaining in Information
Markets",
journal =      j-TIST,
volume =       "3",
number =       "2",
pages =        "32:1--32:??",
month =        feb,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2089094.2089108",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Fri Mar 16 15:10:10 MDT 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Future Web business models involve virtual
environments where entities interact in order to sell
or buy information goods. Such environments are known
as Information Markets (IMs). Intelligent agents are
used in IMs for representing buyers or information
providers (sellers). We focus on the decisions taken by
the buyer in the purchase negotiation process with
sellers. We propose a reasoning mechanism on the offers
(prices of information goods) issued by sellers based
on fuzzy logic. The buyer's knowledge on the
negotiation process is modeled through fuzzy sets. We
propose a fuzzy inference engine dealing with the
decisions that the buyer takes on each stage of the
negotiation process. The outcome of the proposed
reasoning method indicates whether the buyer should
accept or reject the sellers' offers. Our findings are
very promising for the efficiency of automated
transactions undertaken by intelligent agents.",
acknowledgement = ack-nhfb,
articleno =    "32",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Shi:2012:BMA,
author =       "Lixin Shi and Yuhang Zhao and Jie Tang",
title =        "Batch Mode Active Learning for Networked Data",
journal =      j-TIST,
volume =       "3",
number =       "2",
pages =        "33:1--33:??",
month =        feb,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2089094.2089109",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Fri Mar 16 15:10:10 MDT 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "We study a novel problem of batch mode active learning
for networked data. In this problem, data instances are
connected with links and their labels are correlated
with each other, and the goal of batch mode active
learning is to exploit the link-based dependencies and
node-specific content information to actively select a
batch of instances to query the user for learning an
accurate model to label unknown instances in the
network. We present three criteria (i.e., minimum
redundancy, maximum uncertainty, and maximum impact) to
quantify the informativeness of a set of instances, and
formalize the batch mode active learning problem as
selecting a set of instances by maximizing an objective
function which combines both link and content
information. As solving the objective function is
NP-hard, we present an efficient algorithm to optimize
the objective function with a bounded approximation
rate. To scale to real large networks, we develop a
parallel implementation of the algorithm. Experimental
results on both synthetic datasets and real-world
datasets demonstrate the effectiveness and efficiency
of our approach.",
acknowledgement = ack-nhfb,
articleno =    "33",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Shakarian:2012:AGA,
author =       "Paulo Shakarian and John P. Dickerson and V. S.
Subrahmanian",
title =        "Adversarial Geospatial Abduction Problems",
journal =      j-TIST,
volume =       "3",
number =       "2",
pages =        "34:1--34:??",
month =        feb,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2089094.2089110",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Fri Mar 16 15:10:10 MDT 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Geospatial Abduction Problems (GAPs) involve the
inference of a set of locations that best explain' a
given set of locations of observations. For example,
the observations might include locations where a serial
killer committed murders or where insurgents carried
out Improvised Explosive Device (IED) attacks. In both
these cases, we would like to infer a set of locations
that explain the observations, for example, the set of
locations where the serial killer lives/works, and the
set of locations where insurgents locate weapons
caches. However, unlike all past work on abduction,
there is a strong adversarial component to this; an
adversary actively attempts to prevent us from
discovering such locations. We formalize such abduction
problems as a two-player game where both players (an
agent' and an adversary') use a probabilistic model
of their opponent (i.e., a mixed strategy). There is
asymmetry as the adversary can choose both the
locations of the observations and the locations of the
explanation, while the agent (i.e., us) tries to
from the point of view of both players. We define
reward functions axiomatically to capture the
similarity between two sets of explanations (one
corresponding to the locations chosen by the adversary,
one guessed by the agent). Many different reward
functions can satisfy our axioms. We then formalize the
Optimal Adversary Strategy (OAS) problem and the
Maximal Counter-Adversary strategy (MCA) and show that
both are NP-hard, that their associated counting
complexity problems are \#P-hard, and that MCA has no
fully polynomial approximation scheme unless P=NP. We
show that approximation guarantees are possible for MCA
when the reward function satisfies two simple
properties (zero-starting and monotonicity) which many
natural reward functions satisfy. We develop a mixed
integer linear programming algorithm to solve OAS and
two algorithms to (approximately) compute MCA; the
algorithms yield different approximation guarantees and
one algorithm assumes a monotonic reward function. Our
experiments use real data about IED attacks over a
21-month period in Baghdad. We are able to show that
both the MCA algorithms work well in practice; while
MCA-GREEDY-MONO is both highly accurate and slightly
faster than MCA-LS, MCA-LS (to our surprise) always
completely and correctly maximized the expected benefit
to the agent while running in an acceptable time
period.",
acknowledgement = ack-nhfb,
articleno =    "34",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Li:2012:LIS,
author =       "Xueying Li and Huanhuan Cao and Enhong Chen and Jilei
Tian",
title =        "Learning to Infer the Status of Heavy-Duty Sensors for
Energy-Efficient Context-Sensing",
journal =      j-TIST,
volume =       "3",
number =       "2",
pages =        "35:1--35:??",
month =        feb,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2089094.2089111",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Fri Mar 16 15:10:10 MDT 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "With the prevalence of smart mobile devices with
multiple sensors, the commercial application of
intelligent context-aware services becomes more and
more attractive. However, limited by the battery
capacity, the energy efficiency of context-sensing is
the bottleneck for the success of context-aware
applications. Though several previous studies for
energy-efficient context-sensing have been reported,
none of them can be applied to multiple types of
high-energy-consuming sensors. Moreover, applying
machine learning technologies to energy-efficient
we propose to leverage machine learning technologies
for improving the energy efficiency of multiple
high-energy-consuming context sensors by trading off
the sensing accuracy. To be specific, we try to infer
the status of high-energy-consuming sensors according
to the outputs of software-based sensors and the
physical sensors that are necessary to work all the
time for supporting the basic functions of mobile
devices. If the inference indicates the
high-energy-consuming sensor is in a stable status, we
avoid the unnecessary invocation and instead use the
latest invoked value as the estimation. The
experimental results on real datasets show that the
energy efficiency of GPS sensing and audio-level
sensing are significantly improved by the proposed
approach while the sensing accuracy is over 90\%.",
acknowledgement = ack-nhfb,
articleno =    "35",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Zhang:2012:AKR,
author =       "Weinan Zhang and Dingquan Wang and Gui-Rong Xue and
Hongyuan Zha",
title =        "Advertising Keywords Recommendation for Short-Text
{Web} Pages Using {Wikipedia}",
journal =      j-TIST,
volume =       "3",
number =       "2",
pages =        "36:1--36:??",
month =        feb,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2089094.2089112",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Fri Mar 16 15:10:10 MDT 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Advertising keywords recommendation is an
indispensable component for online advertising with the
keywords selected from the target Web pages used for
ranking-based algorithms have been proposed for
recommending advertising keywords. However, for most of
them performance is still lacking, especially when
dealing with short-text target Web pages, that is,
those containing insufficient textual information for
ranking. In some cases, short-text Web pages may not
even contain enough keywords for selection. A natural
alternative is then to recommend relevant keywords not
propose a novel algorithm for advertising keywords
recommendation for short-text Web pages by leveraging
the contents of Wikipedia, a user-contributed online
encyclopedia. Wikipedia contains numerous entities with
related entities on a topic linked to each other. Given
a target Web page, we propose to use a content-biased
PageRank on the Wikipedia graph to rank the related
entities. Furthermore, in order to recommend
two biases, advertising keywords that are both relevant
to a target Web page and valuable for advertising are
recommended. In our experiments, several
state-of-the-art approaches for keyword recommendation
are compared. The experimental results demonstrate that
our proposed approach produces substantial improvement
in the precision of the top 20 recommended keywords on
short-text Web pages over existing approaches.",
acknowledgement = ack-nhfb,
articleno =    "36",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

author =       "Ke Zhou and Jing Bai and Hongyuan Zha and Gui-Rong
Xue",
title =        "Leveraging Auxiliary Data for Learning to Rank",
journal =      j-TIST,
volume =       "3",
number =       "2",
pages =        "37:1--37:??",
month =        feb,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2089094.2089113",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Fri Mar 16 15:10:10 MDT 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "In learning to rank, both the quality and quantity of
the training data have significant impacts on the
performance of the learned ranking functions. However,
in many applications, there are usually not sufficient
labeled training data for the construction of an
accurate ranking model. It is therefore desirable to
leverage existing training data from other tasks when
learning the ranking function for a particular task, an
utilizing a boosting framework with transfer learning.
In particular, we propose to adaptively learn
transferable representations called super-features from
the training data of both the target task and the
auxiliary task. Those super-features and the
coefficients for combining them are learned in an
iterative stage-wise fashion. Unlike previous transfer
learning methods, the super-features can be adaptively
learned by weak learners from the data. Therefore, the
proposed framework is sufficiently flexible to deal
with complicated common structures among different
learning tasks. We evaluate the performance of the
proposed transfer learning method for two datasets from
the Letor collection and one dataset collected from a
commercial search engine, and we also compare our
methods with several existing transfer learning
methods. Our results demonstrate that the proposed
method can enhance the ranking functions of the target
tasks utilizing the training data from the auxiliary
acknowledgement = ack-nhfb,
articleno =    "37",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Peng:2012:MVC,
author =       "Wei Peng and Tong Sun and Shriram Revankar and Tao
Li",
title =        "Mining the Voice of the Customer'' for Business
Prioritization",
journal =      j-TIST,
volume =       "3",
number =       "2",
pages =        "38:1--38:??",
month =        feb,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2089094.2089114",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Fri Mar 16 15:10:10 MDT 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "To gain competitiveness and sustained growth in the
21st century, most businesses are on a mission to
become more customer-centric. In order to succeed in
this endeavor, it is crucial not only to synthesize and
analyze the VOC (the VOice of the Customer) data (i.e.,
the feedbacks or requirements raised by customers), but
also to quickly turn these data into actionable
knowledge. Although there are many technologies being
developed in this complex problem space, most existing
approaches in analyzing customer requests are ad hoc,
time-consuming, error-prone, people-based processes
which hardly scale well as the quantity of customer
information explodes. This often results in the slow
order to mine VOC to extract useful knowledge for the
best product or service quality, we develop a hybrid
framework that integrates domain knowledge with
data-driven approaches to analyze the semi-structured
customer requests. The framework consists of capturing
functional features, discovering the overlap or
correlation among the features, and identifying the
evolving feature trend by using the knowledge
transformation model. In addition, since understanding
the relative importance of the individual customer
request is very critical and has a direct impact on the
effective prioritization in the development process, we
develop a novel semantic enhanced link-based ranking
(SELRank) algorithm for relatively rating/ranking both
customer requests and products. The framework has been
successfully applied on Xerox Office Group Feature
Enhancement Requirements (XOG FER) datasets to analyze
customer requests.",
acknowledgement = ack-nhfb,
articleno =    "38",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Hua:2012:ISS,
author =       "Xian-Sheng Hua and Qi Tian and Alberto {Del Bimbo} and
Ramesh Jain",
title =        "Introduction to the {Special Section on Intelligent
Multimedia Systems and Technology Part II}",
journal =      j-TIST,
volume =       "3",
number =       "3",
pages =        "39:1--39:??",
month =        may,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2168752.2168753",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:23 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "39",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Yang:2012:MRM,
author =       "Yi-Hsuan Yang and Homer H. Chen",
title =        "Machine Recognition of Music Emotion: a Review",
journal =      j-TIST,
volume =       "3",
number =       "3",
pages =        "40:1--40:??",
month =        may,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2168752.2168754",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:23 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "The proliferation of MP3 players and the exploding
amount of digital music content call for novel ways of
music organization and retrieval to meet the
ever-increasing demand for easy and effective
information access. As almost every music piece is
created to convey emotion, music organization and
retrieval by emotion is a reasonable way of accessing
music information. A good deal of effort has been made
in the music information retrieval community to train a
machine to automatically recognize the emotion of a
music signal. A central issue of machine recognition of
music emotion is the conceptualization of emotion and
the associated emotion taxonomy. Different viewpoints
on this issue have led to the proposal of different
ways of emotion annotation, model training, and result
review of the methods that have been proposed for music
emotion recognition. Moreover, as music emotion
recognition is still in its infancy, there are many
open issues. We review the solutions that have been
proposed to address these issues and conclude with
suggestions for further research.",
acknowledgement = ack-nhfb,
articleno =    "40",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Ewerth:2012:RVC,
author =       "Ralph Ewerth and Markus M{\"u}hling and Bernd
Freisleben",
title =        "Robust Video Content Analysis via Transductive
Learning",
journal =      j-TIST,
volume =       "3",
number =       "3",
pages =        "41:1--41:??",
month =        may,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2168752.2168755",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:23 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Reliable video content analysis is an essential
prerequisite for effective video search. An important
current research question is how to develop robust
video content analysis methods that produce
satisfactory results for a large variety of video
sources, distribution platforms, genres, and content.
observation that the appearance of objects and events
is often related to a particular video sequence,
episode, program, or broadcast. This motivates our idea
of considering the content analysis task for a single
video or episode as a transductive setting: the final
classification model must be optimal for the given
video only, and not in general, as expected for
inductive learning. For this purpose, the unlabeled
video test data have to be used in the learning
framework for robust video content analysis based on
feature selection and ensemble classification is
presented. In contrast to related transductive
approaches for video analysis (e.g., for concept
detection), the framework is designed in a general
manner and not only for a single task. The proposed
framework is applied to the following video analysis
tasks: shot boundary detection, face recognition,
semantic video retrieval, and semantic indexing of
computer game sequences. Experimental results for
diverse video analysis tasks and large test sets
demonstrate that the proposed transductive framework
improves the robustness of the underlying
state-of-the-art approaches, whereas transductive
support vector machines do not solve particular tasks
in a satisfactory manner.",
acknowledgement = ack-nhfb,
articleno =    "41",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Suk:2012:VHM,
author =       "Myunghoon Suk and Ashok Ramadass and Yohan Jin and B.
Prabhakaran",
title =        "Video Human Motion Recognition Using a Knowledge-Based
Hybrid Method Based on a Hidden {Markov} Model",
journal =      j-TIST,
volume =       "3",
number =       "3",
pages =        "42:1--42:??",
month =        may,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2168752.2168756",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:23 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Human motion recognition in video data has several
interesting applications in fields such as gaming,
senior/assisted-living environments, and surveillance.
In these scenarios, we may have to consider adding new
motion classes (i.e., new types of human motions to be
recognized), as well as new training data (e.g., for
handling different type of subjects). Hence, both the
accuracy of classification and training time for the
machine learning algorithms become important
we propose a knowledge-based hybrid (KBH) method that
can compute the probabilities for hidden Markov models
(HMMs) associated with different human motion classes.
This computation is facilitated by appropriately mixing
features from two different media types (3D motion
capture and 2D video). We conducted a variety of
experiments comparing the proposed KBH for HMMs and the
of computing the HMM parameter in a noniterative
manner, the KBH method outperforms the Baum-Welch
algorithm both in terms of accuracy as well as in
reduced training time. Moreover, we show in additional
experiments that the KBH method also outperforms the
linear support vector machine (SVM).",
acknowledgement = ack-nhfb,
articleno =    "42",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Zhang:2012:RVT,
author =       "Shengping Zhang and Hongxun Yao and Xin Sun and
Shaohui Liu",
title =        "Robust Visual Tracking Using an Effective Appearance
Model Based on Sparse Coding",
journal =      j-TIST,
volume =       "3",
number =       "3",
pages =        "43:1--43:??",
month =        may,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2168752.2168757",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:23 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Intelligent video surveillance is currently one of the
most active research topics in computer vision,
especially when facing the explosion of video data
captured by a large number of surveillance cameras. As
a key step of an intelligent surveillance system,
robust visual tracking is very challenging for computer
vision. However, it is a basic functionality of the
human visual system (HVS). Psychophysical findings have
shown that the receptive fields of simple cells in the
visual cortex can be characterized as being spatially
localized, oriented, and bandpass, and it forms a
sparse, distributed representation of natural images.
propose an effective appearance model based on sparse
coding and apply it in visual tracking. Specifically,
we consider the responses of general basis functions
extracted by independent component analysis on a large
set of natural image patches as features and model the
appearance of the tracked target as the probability
distribution of these features. In order to make the
tracker more robust to partial occlusion, camouflage
environments, pose changes, and illumination changes,
we further select features that are related to the
target based on an entropy-gain criterion and ignore
those that are not. The target is finally represented
by the probability distribution of those related
features. The target search is performed by minimizing
the Matusita distance between the distributions of the
target model and a candidate using Newton-style
iterations. The experimental results validate that the
proposed method is more robust and effective than three
state-of-the-art methods.",
acknowledgement = ack-nhfb,
articleno =    "43",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Ji:2012:CAS,
author =       "Rongrong Ji and Hongxun Yao and Qi Tian and Pengfei Xu
and Xiaoshuai Sun and Xianming Liu",
title =        "Context-Aware Semi-Local Feature Detector",
journal =      j-TIST,
volume =       "3",
number =       "3",
pages =        "44:1--44:??",
month =        may,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2168752.2168758",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:23 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "How can interest point detectors benefit from
context-aware semi-local detector (CASL) framework to
give a systematic answer with three contributions: (1)
We integrate the context of interest points to
recurrently refine their detections. (2) This
integration boosts interest point detectors from the
traditionally local scale to a semi-local scale to
discover more discriminative salient regions. (3) Such
context-aware structure further enables us to bring
forward category learning (usually in the subsequent
recognition phase) into interest point detection to
locate category-aware, meaningful salient regions. Our
CASL detector consists of two phases. The first phase
accumulates multiscale spatial correlations of local
features into a difference of contextual Gaussians
(DoCG) field. DoCG quantizes detector context to
highlight contextually salient regions at a semi-local
scale, which also reveals visual attentions to a
certain extent. The second phase locates contextual
peaks by mean shift search over the DoCG field, which
subsequently integrates contextual cues into feature
description. This phase enables us to integrate
category learning into mean shift search kernels. This
learning-based CASL mechanism produces more
category-aware features, which substantially benefits
the subsequent visual categorization process. We
conducted experiments in image search, object
characterization, and feature detector repeatability
evaluations, which reported superior discriminability
and comparable repeatability to state-of-the-art
works.",
acknowledgement = ack-nhfb,
articleno =    "44",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Berretti:2012:DFF,
author =       "Stefano Berretti and Alberto {Del Bimbo} and Pietro
Pala",
title =        "Distinguishing Facial Features for Ethnicity-Based
{$3$D} Face Recognition",
journal =      j-TIST,
volume =       "3",
number =       "3",
pages =        "45:1--45:??",
month =        may,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2168752.2168759",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:23 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Among different approaches for 3D face recognition,
solutions based on local facial characteristics are
very promising, mainly because they can manage facial
expression variations by assigning different weights to
different parts of the face. However, so far, a few
works have investigated the individual relevance that
local features play in 3D face recognition with very
simple solutions applied in the practice. In this
article, a local approach to 3D face recognition is
combined with a feature selection model to study the
relative relevance of different regions of the face for
the purpose of discriminating between different
subjects. The proposed solution is experimented using
facial scans of the Face Recognition Grand Challenge
dataset. Results of the experimentation are two-fold:
they quantitatively demonstrate the assumption that
different regions of the face have different relevance
for face discrimination and also show that the
relevance of facial regions changes for different
ethnic groups.",
acknowledgement = ack-nhfb,
articleno =    "45",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Zhang:2012:GAS,
author =       "Ning Zhang and Ling-Yu Duan and Lingfang Li and
Qingming Huang and Jun Du and Wen Gao and Ling Guan",
title =        "A Generic Approach for Systematic Analysis of Sports
Videos",
journal =      j-TIST,
volume =       "3",
number =       "3",
pages =        "46:1--46:??",
month =        may,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2168752.2168760",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:23 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Various innovative and original works have been
applied and proposed in the field of sports video
analysis. However, individual works have focused on
sophisticated methodologies with particular sport types
and there has been a lack of scalable and holistic
solution and presents a systematic and generic approach
which is experimented on a relatively large-scale
sports consortia. The system aims at the event
detection scenario of an input video with an orderly
sequential process. Initially, domain
knowledge-independent local descriptors are extracted
homogeneously from the input video sequence. Then the
video representation is created by adopting a
bag-of-visual-words (BoW) model. The video's genre is
first identified by applying the k-nearest neighbor
(k-NN) classifiers on the initially obtained video
representation, and various dissimilarity measures are
assessed and evaluated analytically. Subsequently, an
unsupervised probabilistic latent semantic analysis
(PLSA)-based approach is employed at the same
histogram-based video representation, characterizing
each frame of video sequence into one of four view
groups, namely closed-up-view, mid-view, long-view, and
outer-field-view. Finally, a hidden conditional random
field (HCRF) structured prediction model is utilized
for interesting event detection. From experimental
results, k-NN classifier using KL-divergence
measurement demonstrates the best accuracy at 82.16\%
for genre categorization. Supervised SVM and
unsupervised PLSA have average classification
accuracies at 82.86\% and 68.13\%, respectively. The
HCRF model achieves 92.31\% accuracy using the
unsupervised PLSA based label input, which is
comparable with the supervised SVM based input at an
accuracy of 93.08\%. In general, such a systematic
approach can be widely applied in processing massive
videos generically.",
acknowledgement = ack-nhfb,
articleno =    "46",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Leung:2012:ISM,
author =       "Clement H. C. Leung and Alice W. S. Chan and Alfredo
Milani and Jiming Liu and Yuanxi Li",
title =        "Intelligent Social Media Indexing and Sharing Using an
journal =      j-TIST,
volume =       "3",
number =       "3",
pages =        "47:1--47:??",
month =        may,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2168752.2168761",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:23 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Effective sharing of diverse social media is often
inhibited by limitations in their search and discovery
mechanisms, which are particularly restrictive for
media that do not lend themselves to automatic
processing or indexing. Here, we present the structure
and mechanism of an adaptive search engine which is
designed to overcome such limitations. The basic
framework of the adaptive search engine is to capture
human judgment in the course of normal usage from user
queries in order to develop semantic indexes which link
search terms to media objects semantics. This approach
is particularly effective for the retrieval of
multimedia objects, such as images, sounds, and videos,
where a direct analysis of the object features does not
allow them to be linked to search terms, for example,
nontextual/icon-based search, deep semantic search, or
when search terms are unknown at the time the media
repository is built. An adaptive search architecture is
presented to enable the index to evolve with respect to
user feedback, while a randomized query-processing
technique guarantees avoiding local minima and allows
the meaningful indexing of new media objects and new
terms. The present adaptive search engine allows for
the efficient community creation and updating of social
media indexes, which is able to instill and propagate
deep knowledge into social media concerning the
advanced search and usage of media resources.
Experiments with various relevance distribution
settings have shown efficient convergence of such
indexes, which enable intelligent search and sharing of
social media resources that are otherwise hard to
discover.",
acknowledgement = ack-nhfb,
articleno =    "47",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Chien:2012:ISS,
author =       "Steve Chien and Amedeo Cesta",
title =        "Introduction to the Special Section on Artificial
Intelligence in Space",
journal =      j-TIST,
volume =       "3",
number =       "3",
pages =        "48:1--48:??",
month =        may,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2168752.2168762",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:23 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "48",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Wagstaff:2012:DLS,
author =       "Kiri L. Wagstaff and Julian Panetta and Adnan Ansar
and Ronald Greeley and Mary Pendleton Hoffer and
Melissa Bunte and Norbert Sch{\"o}rghofer",
title =        "Dynamic Landmarking for Surface Feature Identification
and Change Detection",
journal =      j-TIST,
volume =       "3",
number =       "3",
pages =        "49:1--49:??",
month =        may,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2168752.2168763",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:23 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Given the large volume of images being sent back from
remote spacecraft, there is a need for automated
analysis techniques that can quickly identify
interesting features in those images. Feature
identification in individual images and automated
change detection in multiple images of the same target
are valuable for scientific studies and can inform
subsequent target selection. We introduce a new
approach to orbital image analysis called dynamic
landmarking. It focuses on the identification and
comparison of visually salient features in images. We
have evaluated this approach on images collected by
five Mars orbiters. These evaluations were motivated by
three scientific goals: to study fresh impact craters,
dust devil tracks, and dark slope streaks on Mars. In
the process we also detected a different kind of
surface change that may indicate seasonally exposed
bedforms. These experiences also point the way to how
this approach could be used in an onboard setting to
analyze and prioritize data as it is collected.",
acknowledgement = ack-nhfb,
articleno =    "49",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Estlin:2012:AAS,
author =       "Tara A. Estlin and Benjamin J. Bornstein and Daniel M.
Gaines and Robert C. Anderson and David R. Thompson and
Michael Burl and Rebecca Casta{\~n}o and Michele Judd",
title =        "{AEGIS} Automated Science Targeting for the {MER}
Opportunity Rover",
journal =      j-TIST,
volume =       "3",
number =       "3",
pages =        "50:1--50:??",
month =        may,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2168752.2168764",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:23 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "The Autonomous Exploration for Gathering Increased
Science (AEGIS) system enables automated data
collection by planetary rovers. AEGIS software was
uploaded to the Mars Exploration Rover (MER) mission's
Opportunity rover in December 2009 and has successfully
demonstrated automated onboard targeting based on
scientist-specified objectives. Prior to AEGIS, images
were transmitted from the rover to the operations team
on Earth; scientists manually analyzed the images,
selected geological targets for the rover's
remote-sensing instruments, and then generated a
command sequence to execute the new measurements. AEGIS
represents a significant paradigm shift---by using
onboard data analysis techniques, the AEGIS software
uses scientist input to select high-quality science
targets with no human in the loop. This approach allows
the rover to autonomously select and sequence targeted
observations in an opportunistic fashion, which is
particularly applicable for narrow field-of-view
instruments (such as the MER Mini-TES spectrometer, the
MER Panoramic camera, and the 2011 Mars Science
provides an overview of the AEGIS automated targeting
capability and describes how it is currently being used
onboard the MER mission Opportunity rover.",
acknowledgement = ack-nhfb,
articleno =    "50",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Hayden:2012:UCM,
author =       "David S. Hayden and Steve Chien and David R. Thompson
and Rebecca Casta{\~n}o",
title =        "Using Clustering and Metric Learning to Improve
Science Return of Remote Sensed Imagery",
journal =      j-TIST,
volume =       "3",
number =       "3",
pages =        "51:1--51:??",
month =        may,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2168752.2168765",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:23 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Current and proposed remote space missions, such as
the proposed aerial exploration of Titan by an aerobot,
often can collect more data than can be communicated
back to Earth. Autonomous selective downlink algorithms
can choose informative subsets of data to improve the
science value of these bandwidth-limited transmissions.
This requires statistical descriptors of the data that
reflect very abstract and subtle distinctions in
science content. We propose a metric learning strategy
that teaches algorithms how best to cluster new data
based on training examples supplied by domain
scientists. We demonstrate that clustering informed by
metric learning produces results that more closely
match multiple scientists' labelings of aerial data
than do clusterings based on random or periodic
sampling. A new metric-learning strategy accommodates
training sets produced by multiple scientists with
different and potentially inconsistent mission
objectives. Our methods are fit for current spacecraft
processors (e.g., RAD750) and would further benefit
from more advanced spacecraft processor architectures,
such as OPERA.",
acknowledgement = ack-nhfb,
articleno =    "51",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Hoi:2012:ISS,
author =       "Steven C. H. Hoi and Rong Jin and Jinhui Tang and
Zhi-Hua Zhou",
title =        "Introduction to the Special Section on Distance Metric
Learning in Intelligent Systems",
journal =      j-TIST,
volume =       "3",
number =       "3",
pages =        "52:1--52:??",
month =        may,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2168752.2168766",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:23 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "52",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Zhai:2012:MML,
author =       "Deming Zhai and Hong Chang and Shiguang Shan and Xilin
Chen and Wen Gao",
title =        "Multiview Metric Learning with Global Consistency and
Local Smoothness",
journal =      j-TIST,
volume =       "3",
number =       "3",
pages =        "53:1--53:??",
month =        may,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2168752.2168767",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:23 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "In many real-world applications, the same object may
have different observations (or descriptions) from
multiview observation spaces, which are highly related
but sometimes look different from each other.
Conventional metric-learning methods achieve
satisfactory performance on distance metric computation
of data in a single-view observation space, but fail to
handle well data sampled from multiview observation
spaces, especially those with highly nonlinear
structure. To tackle this problem, we propose a new
method called Multiview Metric Learning with Global
consistency and Local smoothness (MVML-GL) under a
semisupervised learning setting, which jointly
considers global consistency and local smoothness. The
basic idea is to reveal the shared latent feature space
of the multiview observations by embodying global
consistency constraints and preserving local geometric
structures. Specifically, this framework is composed of
two main steps. In the first step, we seek a global
consistent shared latent feature space, which not only
preserves the local geometric structure in each space
but also makes those labeled corresponding instances as
close as possible. In the second step, the explicit
mapping functions between the input spaces and the
shared latent space are learned via regularized locally
linear regression. Furthermore, these two steps both
can be solved by convex optimizations in closed form.
Experimental results with application to manifold
alignment on real-world datasets of pose and facial
expression demonstrate the effectiveness of the
proposed method.",
acknowledgement = ack-nhfb,
articleno =    "53",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Zhang:2012:TML,
author =       "Yu Zhang and Dit-Yan Yeung",
title =        "Transfer Metric Learning with Semi-Supervised
Extension",
journal =      j-TIST,
volume =       "3",
number =       "3",
pages =        "54:1--54:??",
month =        may,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2168752.2168768",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:23 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Distance metric learning plays a very crucial role in
many data mining algorithms because the performance of
an algorithm relies heavily on choosing a good metric.
However, the labeled data available in many
applications is scarce, and hence the metrics learned
a transfer-learning setting in which some related
source tasks with labeled data are available to help
the learning of the target task. We first propose a
convex formulation for multitask metric learning by
covariance matrix. Then we regard transfer learning as
formulation of multitask metric learning to the
transfer-learning setting for our method, called
transfer metric learning (TML). In TML, we learn the
metric and the task covariances between the source
formulation. To solve the convex optimization problem,
we use an alternating method in which each subproblem
has an efficient solution. Moreover, in many
applications, some unlabeled data is also available in
the target task, and so we propose a semi-supervised
extension of TML called STML to further improve the
generalization performance by exploiting the unlabeled
data based on the manifold assumption. Experimental
results on some commonly used transfer-learning
applications demonstrate the effectiveness of our
method.",
acknowledgement = ack-nhfb,
articleno =    "54",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Xu:2012:MLE,
author =       "Jun-Ming Xu and Xiaojin Zhu and Timothy T. Rogers",
title =        "Metric Learning for Estimating Psychological
Similarities",
journal =      j-TIST,
volume =       "3",
number =       "3",
pages =        "55:1--55:??",
month =        may,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2168752.2168769",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:23 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "An important problem in cognitive psychology is to
quantify the perceived similarities between stimuli.
Previous work attempted to address this problem with
multidimensional scaling (MDS) and its variants.
However, there are several shortcomings of the MDS
approaches. We propose Yada, a novel general
metric-learning procedure based on two-alternative
forced-choice behavioral experiments. Our method learns
forward and backward nonlinear mappings between an
objective space in which the stimuli are defined by the
standard feature vector representation and a subjective
space in which the distance between a pair of stimuli
corresponds to their perceived similarity. We conduct
experiments on both synthetic and real human behavioral
datasets to assess the effectiveness of Yada. The
results show that Yada outperforms several standard
embedding and metric-learning algorithms, both in terms
of likelihood and recovery error.",
acknowledgement = ack-nhfb,
articleno =    "55",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Zheng:2012:MTP,
author =       "Yan-Tao Zheng and Zheng-Jun Zha and Tat-Seng Chua",
title =        "Mining Travel Patterns from Geotagged Photos",
journal =      j-TIST,
volume =       "3",
number =       "3",
pages =        "56:1--56:??",
month =        may,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2168752.2168770",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:23 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Recently, the phenomenal advent of photo-sharing
services, such as Flickr and Panoramio, have led to
voluminous community-contributed photos with text tags,
timestamps, and geographic references on the Internet.
The photos, together with their time- and
geo-references, become the digital footprints of photo
takers and implicitly document their spatiotemporal
movements. This study aims to leverage the wealth of
these enriched online photos to analyze people's travel
patterns at the local level of a tour destination.
Specifically, we focus our analysis on two aspects: (1)
tourist movement patterns in relation to the regions of
attractions (RoA), and (2) topological characteristics
of travel routes by different tourists. To do so, we
first build a statistically reliable database of travel
paths from a noisy pool of community-contributed
geotagged photos on the Internet. We then investigate
the tourist traffic flow among different RoAs by
exploiting the Markov chain model. Finally, the
topological characteristics of travel routes are
analyzed by performing a sequence clustering on tour
routes. Testings on four major cities demonstrate
promising results of the proposed system.",
acknowledgement = ack-nhfb,
articleno =    "56",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Rendle:2012:FML,
author =       "Steffen Rendle",
title =        "Factorization Machines with {libFM}",
journal =      j-TIST,
volume =       "3",
number =       "3",
pages =        "57:1--57:??",
month =        may,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2168752.2168771",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:23 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Factorization approaches provide high accuracy in
several important prediction problems, for example,
recommender systems. However, applying factorization
approaches to a new prediction problem is a nontrivial
task and requires a lot of expert knowledge. Typically,
a new model is developed, a learning algorithm is
derived, and the approach has to be implemented.
Factorization machines (FM) are a generic approach
since they can mimic most factorization models just by
feature engineering. This way, factorization machines
combine the generality of feature engineering with the
superiority of factorization models in estimating
interactions between categorical variables of large
domain. libFM is a software implementation for
factorization machines that features stochastic
gradient descent (SGD) and alternating least-squares
(ALS) optimization, as well as Bayesian inference using
summarizes the recent research on factorization
machines both in terms of modeling and learning,
provides extensions for the ALS and MCMC algorithms,
and describes the software tool libFM.",
acknowledgement = ack-nhfb,
articleno =    "57",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Gabrilovich:2012:ISS,
author =       "Evgeniy Gabrilovich and Zhong Su and Jie Tang",
title =        "Introduction to the {Special Section on Computational
Models of Collective Intelligence in the Social Web}",
journal =      j-TIST,
volume =       "3",
number =       "4",
pages =        "58:1--58:??",
month =        sep,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2337542.2337543",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:26 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "58",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Herdagdelen:2012:BGP,
author =       "Ama{\c{c}} Herdagdelen and Marco Baroni",
title =        "Bootstrapping a Game with a Purpose for Commonsense
Collection",
journal =      j-TIST,
volume =       "3",
number =       "4",
pages =        "59:1--59:??",
month =        sep,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2337542.2337544",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:26 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Text mining has been very successful in extracting
huge amounts of commonsense knowledge from data, but
the extracted knowledge tends to be extremely noisy.
Manual construction of knowledge repositories, on the
other hand, tends to produce high-quality data in very
small amounts. We propose an architecture to combine
the best of both worlds: A game with a purpose that
induces humans to clean up data automatically extracted
by text mining. First, a text miner trained on a set of
known commonsense facts harvests many more candidate
facts from corpora. Then, a simple
slot-machine-with-a-purpose game presents these
candidate facts to the players for verification by
playing. As a result, a new dataset of high precision
commonsense knowledge is created. This combined
architecture is able to produce significantly better
commonsense facts than the state-of-the-art text miner
alone. Furthermore, we report that bootstrapping (i.e.,
training the text miner on the output of the game)
improves the subsequent performance of the text
miner.",
acknowledgement = ack-nhfb,
articleno =    "59",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Carmel:2012:FBT,
author =       "David Carmel and Erel Uziel and Ido Guy and Yosi Mass
and Haggai Roitman",
title =        "Folksonomy-Based Term Extraction for Word Cloud
Generation",
journal =      j-TIST,
volume =       "3",
number =       "4",
pages =        "60:1--60:??",
month =        sep,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2337542.2337545",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:26 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "In this work we study the task of term extraction for
word cloud generation in sparsely tagged domains, in
which manual tags are scarce. We present a
folksonomy-based term extraction method, called
tag-boost, which boosts terms that are frequently used
by the public to tag content. Our experiments with
tag-boost based term extraction over different domains
demonstrate tremendous improvement in word cloud
quality, as reflected by the agreement between manual
tags of the testing items and the cloud's terms
extracted from the items' content. Moreover, our
results demonstrate the high robustness of this
approach, as compared to alternative cloud generation
methods that exhibit a high sensitivity to data
sparseness. Additionally, we show that tag-boost can be
effectively applied even in nontagged domains, by using
an external rich folksonomy borrowed from a well-tagged
domain.",
acknowledgement = ack-nhfb,
articleno =    "60",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Wang:2012:IOS,
author =       "Guan Wang and Sihong Xie and Bing Liu and Philip S.
Yu",
title =        "Identify Online Store Review Spammers via Social
Review Graph",
journal =      j-TIST,
volume =       "3",
number =       "4",
pages =        "61:1--61:??",
month =        sep,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2337542.2337546",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:26 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Online shopping reviews provide valuable information
for customers to compare the quality of products, store
services, and many other aspects of future purchases.
However, spammers are joining this community trying to
mislead consumers by writing fake or unfair reviews to
confuse the consumers. Previous attempts have used
reviewers' behaviors such as text similarity and rating
patterns, to detect spammers. These studies are able to
identify certain types of spammers, for instance, those
who post many similar reviews about one target.
However, in reality, there are other kinds of spammers
who can manipulate their behaviors to act just like
normal reviewers, and thus cannot be detected by the
novel concept of review graph to capture the
relationships among all reviewers, reviews and stores
that the reviewers have reviewed as a heterogeneous
graph. We explore how interactions between nodes in
this graph could reveal the cause of spam and propose
an iterative computation model to identify suspicious
reviewers. In the review graph, we have three kinds of
nodes, namely, reviewer, review, and store. We capture
their relationships by introducing three fundamental
concepts, the trustiness of reviewers, the honesty of
reviews, and the reliability of stores, and identifying
their interrelationships: a reviewer is more
trustworthy if the person has written more honesty
reviews; a store is more reliable if it has more
positive reviews from trustworthy reviewers; and a
review is more honest if many other honest reviews
support it. This is the first time such intricate
relationships have been identified for spam detection
and captured in a graph model. We further develop an
effective computation method based on the proposed
graph model. Different from any existing approaches, we
do not use an review text information. Our model is
thus complementary to existing approaches and able to
find more difficult and subtle spamming activities,
which are agreed upon by human judges after they
evaluate our results.",
acknowledgement = ack-nhfb,
articleno =    "61",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Lerman:2012:USM,
author =       "Kristina Lerman and Tad Hogg",
title =        "Using Stochastic Models to Describe and Predict Social
Dynamics of {Web} Users",
journal =      j-TIST,
volume =       "3",
number =       "4",
pages =        "62:1--62:??",
month =        sep,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2337542.2337547",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:26 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "The popularity of content in social media is unequally
distributed, with some items receiving a
disproportionate share of attention from users.
Predicting which newly-submitted items will become
popular is critically important for both the hosts of
social media content and its consumers. Accurate and
timely prediction would enable hosts to maximize
content or ad placement. Prediction would also give
consumers an important tool for filtering the content.
Predicting the popularity of content in social media is
challenging due to the complex interactions between
content quality and how the social media site
highlights its content. Moreover, most social media
sites selectively present content that has been highly
rated by similar users, whose similarity is indicated
implicitly by their behavior or explicitly by links in
a social network. While these factors make it difficult
to predict popularity a priori, stochastic models of
user behavior on these sites can allow predicting
popularity based on early user reactions to new
content. By incorporating the various mechanisms
through which web sites display content, such models
improve on predictions that are based on simply
extrapolating from the early votes. Specifically, for
one such site, the news aggregator Digg, we show how a
stochastic model distinguishes the effect of the
increased visibility due to the network from how
interested users are in the content. We find a wide
range of interest, distinguishing stories primarily of
interest to users in the network (niche interests'')
from those of more general interest to the user
community. This distinction is useful for predicting a
story's eventual popularity from users' early reactions
to the story.",
acknowledgement = ack-nhfb,
articleno =    "62",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Yin:2012:LCT,
author =       "Zhijun Yin and Liangliang Cao and Quanquan Gu and
Jiawei Han",
title =        "Latent Community Topic Analysis: Integration of
Community Discovery with Topic Modeling",
journal =      j-TIST,
volume =       "3",
number =       "4",
pages =        "63:1--63:??",
month =        sep,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2337542.2337548",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:26 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
topic analysis in text-associated graphs. With the
development of social media, a lot of user-generated
content is available with user networks. Along with
rich information in networks, user graphs can be
extended with text information associated with nodes.
Topic modeling is a classic problem in text mining and
it is interesting to discover the latent topics in
topic modeling methods considering links, we
incorporate community discovery into topic analysis in
text-associated graphs to guarantee the topical
coherence in the communities so that users in the same
community are closely linked to each other and share
common latent topics. We handle topic modeling and
community discovery in the same framework. In our model
we separate the concepts of community and topic, so one
community can correspond to multiple topics and
multiple communities can share the same topic. We
compare different methods and perform extensive
experiments on two real datasets. The results confirm
our hypothesis that topics could help understand
community structure, while community structure could
help model topics.",
acknowledgement = ack-nhfb,
articleno =    "63",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Sizov:2012:LGS,
author =       "Sergej Sizov",
title =        "Latent Geospatial Semantics of Social Media",
journal =      j-TIST,
volume =       "3",
number =       "4",
pages =        "64:1--64:??",
month =        sep,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2337542.2337549",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:26 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Multimodal understanding of shared content is an
important success factor for many Web 2.0 applications
question of geo-spatial awareness in social media
applications. In this context, we introduce an approach
for improved characterization of social media by
combining text features (e.g., tags as a prominent
example of short, unstructured text labels) with
spatial knowledge (e.g., geotags, coordinates of
images, and videos). Our model-based framework GeoFolk
combines these two aspects in order to construct better
algorithms for content management, retrieval, and
sharing. We demonstrate in systematic studies the
benefits of this combination for a broad spectrum of
scenarios related to social media: recommender systems,
automatic content organization and filtering, and event
detection. Furthermore, we establish a simple and
technically sound model that can be seen as a reference
baseline for future research in the field of geotagged
social media.",
acknowledgement = ack-nhfb,
articleno =    "64",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Cortizo:2012:ISS,
author =       "Jos{\'e} Carlos Cortizo and Francisco Carrero and
Iv{\'a}n Cantador and Jos{\'e} Antonio Troyano and
Paolo Rosso",
title =        "Introduction to the Special Section on Search and
Mining User-Generated Content",
journal =      j-TIST,
volume =       "3",
number =       "4",
pages =        "65:1--65:??",
month =        sep,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2337542.2337550",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:26 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "The primary goal of this special section of ACM
Transactions on Intelligent Systems and Technology is
to foster research in the interplay between Social
Media, Data/Opinion Mining and Search, aiming to
reflect the actual developments in technologies that
exploit user-generated content.",
acknowledgement = ack-nhfb,
articleno =    "65",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Paltoglou:2012:TMD,
author =       "Georgios Paltoglou and Mike Thelwall",
title =        "{Twitter}, {MySpace}, {Digg}: Unsupervised Sentiment
Analysis in Social Media",
journal =      j-TIST,
volume =       "3",
number =       "4",
pages =        "66:1--66:??",
month =        sep,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2337542.2337551",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:26 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Sentiment analysis is a growing area of research with
significant applications in both industry and academia.
Most of the proposed solutions are centered around
supervised, machine learning approaches and
the more common informal textual communication on the
Web, such as online discussions, tweets and social
network comments and propose an intuitive, less
domain-specific, unsupervised, lexicon-based approach
that estimates the level of emotional intensity
contained in text in order to make a prediction. Our
approach can be applied to, and is tested in, two
different but complementary contexts: subjectivity
detection and polarity classification. Extensive
experiments were carried on three real-world datasets,
extracted from online social Web sites and annotated by
human evaluators, against state-of-the-art supervised
approaches. The results demonstrate that the proposed
algorithm, even though unsupervised, outperforms
machine learning solutions in the majority of cases,
overall presenting a very robust and reliable solution
for sentiment analysis of informal communication on the
Web.",
acknowledgement = ack-nhfb,
articleno =    "66",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Trivedi:2012:LSB,
author =       "Anusua Trivedi and Piyush Rai and Hal Daum{\'e} and
III and Scott L. Duvall",
title =        "Leveraging Social Bookmarks from Partially Tagged
Corpus for Improved {Web} Page Clustering",
journal =      j-TIST,
volume =       "3",
number =       "4",
pages =        "67:1--67:??",
month =        sep,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2337542.2337552",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:26 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Automatic clustering of Web pages helps a number of
information retrieval tasks, such as improving user
interfaces, collection clustering, introducing
diversity in search results, etc. Typically, Web page
clustering algorithms use only features extracted from
the page-text. However, the advent of
social-bookmarking Web sites, such as StumbleUpon.com
and Delicious.com, has led to a huge amount of
user-generated content such as the social tag
information that is associated with the Web pages. In
extraction approach that leverages the social tag
information to complement the page-contents of a Web
page for extracting beter features, with the goal of
improved clustering performance. In our approach, we
consider page-text and tags as two separate views of
the data, and learn a shared subspace that maximizes
the correlation between the two views. Any clustering
algorithm can then be applied in this subspace. We then
present an extension that allows our approach to be
applicable even if the Web page corpus is only
partially tagged, that is, when the social tags are
present for not all, but only for a small number of Web
pages. We compare our subspace based approach with a
number of baselines that use tag information in various
other ways, and show that the subspace based approach
leads to improved performance on the Web page
clustering task. We also discuss some possible future
work including an active learning extension that can
help in choosing which Web pages to get tags for, if we
only can get the social tags for only a small number of
Web pages.",
acknowledgement = ack-nhfb,
articleno =    "67",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Potthast:2012:IRC,
author =       "Martin Potthast and Benno Stein and Fabian Loose and
Steffen Becker",
title =        "Information Retrieval in the {Commentsphere}",
journal =      j-TIST,
volume =       "3",
number =       "4",
pages =        "68:1--68:??",
month =        sep,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2337542.2337553",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:26 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
related to Web comments. Prerequisite of such a study
and a main contribution of the article is a unifying
survey of the research field. We identify the most
filtering, ranking, and summarization. Within these
comments are utilized and which we designate as
comment-targeting and comment-exploiting. Within the
retrieval targets. Within the second paradigm, the
commented items form the retrieval targets (i.e.,
to improve the retrieval performance for the commented
items). We report on four case studies to demonstrate
the exploration of the commentsphere under information
retrieval aspects: comment filtering, comment ranking,
comment summarization and cross-media retrieval. The
first three studies deal primarily with
comment-targeting retrieval, while the last one deals
with comment-exploiting retrieval. Throughout the
article, connections to information retrieval research
are pointed out.",
acknowledgement = ack-nhfb,
articleno =    "68",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Carmel:2012:RBN,
author =       "David Carmel and Haggai Roitman and Elad Yom-Tov",
title =        "On the Relationship between Novelty and Popularity of
User-Generated Content",
journal =      j-TIST,
volume =       "3",
number =       "4",
pages =        "69:1--69:??",
month =        sep,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2337542.2337554",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:26 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "This work deals with the task of predicting the
popularity of user-generated content. We demonstrate
how the novelty of newly published content plays an
important role in affecting its popularity. More
specifically, we study three dimensions of novelty. The
first one, termed contemporaneous novelty, models the
relative novelty embedded in a new post with respect to
contemporary content that was generated by others. The
second type of novelty, termed self novelty, models the
relative novelty with respect to the user's own
contribution history. The third type of novelty, termed
discussion novelty, relates to the novelty of the
content. We demonstrate the contribution of the new
novelty measures to estimating blog-post popularity by
predicting the number of comments expected for a fresh
post. We further demonstrate how novelty based measures
can be utilized for predicting the citation volume of
acknowledgement = ack-nhfb,
articleno =    "69",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Li:2012:ERQ,
author =       "Xiaonan Li and Chengkai Li and Cong Yu",
title =        "Entity-Relationship Queries over {Wikipedia}",
journal =      j-TIST,
volume =       "3",
number =       "4",
pages =        "70:1--70:??",
month =        sep,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2337542.2337555",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:26 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Wikipedia is the largest user-generated knowledge
base. We propose a structured query mechanism,
entity-relationship query, for searching entities in
the Wikipedia corpus by their properties and
interrelationships. An entity-relationship query
consists of multiple predicates on desired entities.
The semantics of each predicate is specified with
keywords. Entity-relationship query searches entities
directly over text instead of preextracted structured
data stores. This characteristic brings two benefits:
(1) Query semantics can be intuitively expressed by
keywords; (2) It only requires rudimentary entity
annotation, which is simpler than explicitly extracting
and reasoning about complex semantic information before
query-time. We present a ranking framework for general
entity-relationship queries and a position-based
Bounded Cumulative Model (BCM) for accurate ranking of
query answers. We also explore various weighting
schemes for further improving the accuracy of BCM. We
test our ideas on a 2008 version of Wikipedia using a
collection of 45 queries pooled from INEX entity
ranking track and our own crafted queries. Experiments
show that the ranking and weighting schemes are both
effective, particularly on multipredicate queries.",
acknowledgement = ack-nhfb,
articleno =    "70",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Wang:2012:EFW,
author =       "Haofen Wang and Linyun Fu and Wei Jin and Yong Yu",
title =        "{EachWiki}: Facilitating Wiki Authoring by Annotation
Suggestion",
journal =      j-TIST,
volume =       "3",
number =       "4",
pages =        "71:1--71:??",
month =        sep,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2337542.2337556",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:26 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Wikipedia, one of the best-known wikis and the world's
largest free online encyclopedia, has embraced the
power of collaborative editing to harness collective
intelligence. However, using such a wiki to create
high-quality articles is not as easy as people imagine,
given for instance the difficulty of reusing knowledge
already available in Wikipedia. As a result, the heavy
burden of upbuilding and maintaining the ever-growing
online encyclopedia still rests on a small group of
authoring by providing annotation recommendations, thus
lightening the burden of both contributors and
administrators. We leverage the collective wisdom of
the users by exploiting Semantic Web technologies with
Wikipedia data and adopt a unified algorithm to support
link, category, and semantic relation recommendation. A
prototype system named EachWiki is proposed and
evaluated. The experimental results show that it has
achieved considerable improvements in terms of
effectiveness, efficiency and usability. The proposed
approach can also be applied to other wiki-based
collaborative editing systems.",
acknowledgement = ack-nhfb,
articleno =    "71",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Lampos:2012:NES,
author =       "Vasileios Lampos and Nello Cristianini",
title =        "Nowcasting Events from the Social {Web} with
Statistical Learning",
journal =      j-TIST,
volume =       "3",
number =       "4",
pages =        "72:1--72:??",
month =        sep,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2337542.2337557",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:26 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "We present a general methodology for inferring the
occurrence and magnitude of an event or phenomenon by
exploring the rich amount of unstructured textual
information on the social part of the Web. Having
geo-tagged user posts on the microblogging service of
Twitter as our input data, we investigate two case
studies. The first consists of a benchmark problem,
where actual levels of rainfall in a given location and
time are inferred from the content of tweets. The
second one is a real-life task, where we infer regional
Influenza-like Illness rates in the effort of detecting
timely an emerging epidemic disease. Our analysis
builds on a statistical learning framework, which
performs sparse learning via the bootstrapped version
of LASSO to select a consistent subset of textual
features from a large amount of candidates. In both
case studies, selected features indicate close semantic
correlation with the target topics and inference,
conducted by regression, has a significant performance,
especially given the short length --approximately one
year-- of Twitter's data time series.",
acknowledgement = ack-nhfb,
articleno =    "72",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Tang:2012:RUI,
author =       "Xuning Tang and Christopher C. Yang",
title =        "Ranking User Influence in Healthcare Social Media",
journal =      j-TIST,
volume =       "3",
number =       "4",
pages =        "73:1--73:??",
month =        sep,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2337542.2337558",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:26 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Due to the revolutionary development of Web 2.0
technology, individual users have become major
contributors of Web content in online social media. In
light of the growing activities, how to measure a
user's influence to other users in online social media
becomes increasingly important. This research need is
urgent especially in the online healthcare community
since positive influence can be beneficial while
negative influence may cause-negative impact on other
research framework was proposed to study user influence
within the online healthcare community. We proposed a
new approach to incorporate users' reply relationship,
conversation content and response immediacy which
capture both explicit and implicit interaction between
users to identify influential users of online
healthcare community. A weighted social network is
developed to represent the influence between users. We
tested our proposed techniques thoroughly on two
medical support forums. Two algorithms UserRank and
Weighted in-degree are benchmarked with PageRank and
in-degree. Experiment results demonstrated the validity
and effectiveness of our proposed approaches.",
acknowledgement = ack-nhfb,
articleno =    "73",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Strohmaier:2012:EFI,
author =       "Markus Strohmaier and Denis Helic and Dominik Benz and
Christian K{\"o}rner and Roman Kern",
title =        "Evaluation of Folksonomy Induction Algorithms",
journal =      j-TIST,
volume =       "3",
number =       "4",
pages =        "74:1--74:??",
month =        sep,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2337542.2337559",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:26 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Algorithms for constructing hierarchical structures
from user-generated metadata have caught the interest
of the academic community in recent years. In social
tagging systems, the output of these algorithms is
usually referred to as folksonomies (from
folk-generated taxonomies). Evaluation of folksonomies
and folksonomy induction algorithms is a challenging
issue complicated by the lack of golden standards, lack
of comprehensive methods and tools as well as a lack of
research and empirical/simulation studies applying
a broad comparative study of state-of-the-art
folksonomy induction algorithms that we have applied
and evaluated in the context of five social tagging
techniques, we present and adopt a new technique that
can be used to evaluate the usefulness of folksonomies
for navigation. Our work sheds new light on the
properties and characteristics of state-of-the-art
folksonomy induction algorithms and introduces a new
pragmatic approach to folksonomy evaluation, while at
the same time identifying some important limitations
and challenges of folksonomy evaluation. Our results
show that folksonomy induction algorithms specifically
developed to capture intuitions of social tagging
techniques. To the best of our knowledge, this work
represents the largest and most comprehensive
evaluation study of state-of-the-art folksonomy
induction algorithms to date.",
acknowledgement = ack-nhfb,
articleno =    "74",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Zhang:2012:EAL,
author =       "Xiaoqin Shelley Zhang and Bhavesh Shrestha and
Sungwook Yoon and Subbarao Kambhampati and Phillip
DiBona and Jinhong K. Guo and Daniel McFarlane and
Martin O. Hofmann and Kenneth Whitebread and Darren
Scott Appling and Elizabeth T. Whitaker and Ethan B.
Trewhitt and Li Ding and James R. Michaelis and Deborah
L. McGuinness and James A. Hendler and Janardhan Rao
Doppa and Charles Parker and Thomas G. Dietterich and
Anton Rebguns and Diana Spears and Ugur Kuter and Geoff
Levine and Gerald DeJong and Reid L. MacTavish and
and Ashwin Ram and Hala Mostafa and Huzaifa Zafar and
Chongjie Zhang and Daniel Corkill and Victor Lesser and
Zhexuan Song",
title =        "An Ensemble Architecture for Learning Complex
Problem-Solving Techniques from Demonstration",
journal =      j-TIST,
volume =       "3",
number =       "4",
pages =        "75:1--75:??",
month =        sep,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2337542.2337560",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:26 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "We present a novel ensemble architecture for learning
problem-solving techniques from a very small number of
expert solutions and demonstrate its effectiveness in a
complex real-world domain. The key feature of our
Generalized Integrated Learning Architecture'' (GILA)
is a set of heterogeneous independent learning and
reasoning (ILR) components, coordinated by a central
meta-reasoning executive (MRE). The ILRs are weakly
coupled in the sense that all coordination during
learning and performance happens through the MRE. Each
ILR learns independently from a small number of expert
demonstrations of a complex task. During performance,
each ILR proposes partial solutions to subproblems
posed by the MRE, which are then selected from and
pieced together by the MRE to produce a complete
solution. The heterogeneity of the learner-reasoners
allows both learning and problem solving to be more
effective because their abilities and biases are
complementary and synergistic. We describe the
application of this novel learning and problem solving
architecture to the domain of airspace management,
where multiple requests for the use of airspaces need
to be deconflicted, reconciled, and managed
automatically. Formal evaluations show that our system
performs as well as or better than humans after
learning from the same training data. Furthermore, GILA
outperforms any individual ILR run in isolation, thus
demonstrating the power of the ensemble architecture
for learning and problem solving.",
acknowledgement = ack-nhfb,
articleno =    "75",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Wang:2012:LCR,
author =       "Zhenxing Wang and Laiwan Chan",
title =        "Learning Causal Relations in Multivariate Time Series
Data",
journal =      j-TIST,
volume =       "3",
number =       "4",
pages =        "76:1--76:??",
month =        sep,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2337542.2337561",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:26 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Many applications naturally involve time series data
and the vector autoregression (VAR), and the structural
VAR (SVAR) are dominant tools to investigate relations
between variables in time series. In the first part of
this work, we show that the SVAR method is incapable of
identifying contemporaneous causal relations for
Gaussian process. In addition, least squares estimators
become unreliable when the scales of the problems are
large and observations are limited. In the remaining
part, we propose an approach to apply Bayesian network
learning algorithms to identify SVARs from time series
data in order to capture both temporal and
contemporaneous causal relations, and avoid high-order
statistical tests. The difficulty of applying Bayesian
network learning algorithms to time series is that the
sizes of the networks corresponding to time series tend
to be large, and high-order statistical tests are
required by Bayesian network learning algorithms in
this case. To overcome the difficulty, we show that the
search space of conditioning sets d-separating two
vertices should be a subset of the Markov blankets.
Based on this fact, we propose an algorithm enabling us
to learn Bayesian networks locally, and make the
largest order of statistical tests independent of the
scales of the problems. Empirical results show that our
algorithm outperforms existing methods in terms of both
efficiency and accuracy.",
acknowledgement = ack-nhfb,
articleno =    "76",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Mandrake:2012:SSD,
author =       "Lukas Mandrake and Umaa Rebbapragada and Kiri L.
Wagstaff and David Thompson and Steve Chien and Daniel
Tran and Robert T. Pappalardo and Damhnait Gleeson and
Rebecca Casta{\~n}o",
title =        "Surface Sulfur Detection via Remote Sensing and
Onboard Classification",
journal =      j-TIST,
volume =       "3",
number =       "4",
pages =        "77:1--77:??",
month =        sep,
year =         "2012",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2337542.2337562",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Tue Nov 6 18:47:26 MST 2012",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Orbital remote sensing provides a powerful way to
efficiently survey targets such as the Earth and other
planets and moons for features of interest. One such
feature of astrobiological relevance is the presence of
surface sulfur deposits. These deposits have been
observed to be associated with microbial activity at
the Borup Fiord glacial springs in Canada, a location
that may provide an analogue to other icy environments
classifiers for detecting sulfur in remote sensing
observations by the hyperion spectrometer on the EO-1
spacecraft. We determined that a data-driven machine
learning solution was needed because the sulfur could
not be detected by simply matching observations to
sulfur lab spectra. We also evaluated several methods
(manual and automated) for identifying the most
relevant attributes (spectral wavelengths) needed for
successful sulfur detection. Our findings include (1)
the Borup Fiord sulfur deposits were best modeled as
containing two sub-populations: sulfur on ice and
sulfur on rock; (2) as expected, classifiers using
Gaussian kernels outperformed those based on linear
kernels, and should be adopted when onboard
computational constraints permit; and (3) Recursive
Feature Elimination selected sensible and effective
features for use in the computationally constrained
environment onboard EO-1. This study helped guide the
selection of algorithm parameters and configuration for
the classification system currently operational on
EO-1. Finally, we discuss implications for a similar
onboard classification system for a future Europa
orbiter.",
acknowledgement = ack-nhfb,
articleno =    "77",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{King:2013:ISS,
author =       "Irwin King and Wolfgang Nejdl",
title =        "Introduction to the special section on {Twitter} and
microblogging services",
journal =      j-TIST,
volume =       "4",
number =       "1",
pages =        "1:1--1:??",
month =        jan,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2414425.2414426",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "1",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Cheng:2013:CDF,
author =       "Zhiyuan Cheng and James Caverlee and Kyumin Lee",
title =        "A content-driven framework for geolocating microblog
users",
journal =      j-TIST,
volume =       "4",
number =       "1",
pages =        "2:1--2:??",
month =        jan,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2414425.2414427",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Highly dynamic real-time microblog systems have
already published petabytes of real-time human sensor
data in the form of status updates. However, the lack
of user adoption of geo-based features per user or per
post signals that the promise of microblog services as
location-based sensing systems may have only limited
evaluate a probabilistic framework for estimating a
microblog user's location based purely on the content
of the user's posts. Our framework can overcome the
sparsity of geo-enabled features in these services and
bring augmented scope and breadth to emerging
location-based personalized information services. Three
of the key features of the proposed approach are: (i)
its reliance purely on publicly available content; (ii)
a classification component for automatically
identifying words in posts with a strong local
geo-scope; and (iii) a lattice-based neighborhood
smoothing model for refining a user's location
estimate. On average we find that the location
estimates converge quickly, placing 51\% of users
within 100 miles of their actual location.",
acknowledgement = ack-nhfb,
articleno =    "2",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Liu:2013:NER,
author =       "Xiaohua Liu and Furu Wei and Shaodian Zhang and Ming
Zhou",
title =        "Named entity recognition for tweets",
journal =      j-TIST,
volume =       "4",
number =       "1",
pages =        "3:1--3:??",
month =        jan,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2414425.2414428",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Two main challenges of Named Entity Recognition (NER)
for tweets are the insufficient information in a tweet
and the lack of training data. We propose a novel
method consisting of three core elements: (1)
normalization of tweets; (2) combination of a K-Nearest
Neighbors (KNN) classifier with a linear Conditional
Random Fields (CRF) model; and (3) semisupervised
learning framework. The tweet normalization
preprocessing corrects common ill-formed words using a
global linear model. The KNN-based classifier conducts
prelabeling to collect global coarse evidence across
tweets while the CRF model conducts sequential labeling
to capture fine-grained information encoded in a tweet.
The semisupervised learning plus the gazetteers
alleviate the lack of training data. Extensive
experiments show the advantages of our method over the
baselines as well as the effectiveness of
normalization, KNN, and semisupervised learning.",
acknowledgement = ack-nhfb,
articleno =    "3",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Chang:2013:IRR,
author =       "Yi Chang and Anlei Dong and Pranam Kolari and Ruiqiang
Zhang and Yoshiyuki Inagaki and Fernanodo Diaz and
Hongyuan Zha and Yan Liu",
title =        "Improving recency ranking using {Twitter} data",
journal =      j-TIST,
volume =       "4",
number =       "1",
pages =        "4:1--4:??",
month =        jan,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2414425.2414429",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "In Web search and vertical search, recency ranking
refers to retrieving and ranking documents by both
relevance and freshness. As impoverished in-links and
click information is the the biggest challenge for
method to utilize Twitter TinyURL to detect fresh and
high-quality documents, and leverage Twitter data to
generate novel and effective features for ranking. The
empirical experiments demonstrate that the proposed
approach effectively improves a commercial search
engine for both Web search ranking and tweet vertical
ranking.",
acknowledgement = ack-nhfb,
articleno =    "4",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Han:2013:LNS,
author =       "Bo Han and Paul Cook and Timothy Baldwin",
title =        "Lexical normalization for social media text",
journal =      j-TIST,
volume =       "4",
number =       "1",
pages =        "5:1--5:??",
month =        jan,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2414425.2414430",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
real time, but is notoriously noisy, hampering its
out-of-vocabulary words in short text messages and
propose a method for identifying and normalizing
lexical variants. Our method uses a classifier to
detect lexical variants, and generates correction
candidates based on morphophonemic similarity. Both
word similarity and context are then exploited to
select the most probable correction candidate for the
word. The proposed method doesn't require any
annotations, and achieves state-of-the-art performance
over an SMS corpus and a novel dataset based on
acknowledgement = ack-nhfb,
articleno =    "5",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Shen:2013:RUT,
author =       "Keyi Shen and Jianmin Wu and Ya Zhang and Yiping Han
and Xiaokang Yang and Li Song and Xiao Gu",
title =        "Reorder user's tweets",
journal =      j-TIST,
volume =       "4",
number =       "1",
pages =        "6:1--6:??",
month =        jan,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2414425.2414431",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
reversed chronological order, which is not always the
best choice. As Twitter is full of messages of very
different qualities, many informative or relevant
tweets might be flooded or displayed at the bottom
while some nonsense buzzes might be ranked higher. In
this work, we present a supervised learning method for
personalized tweets reordering based on user interests.
User activities on Twitter, in terms of tweeting,
retweeting, and replying, are leveraged to obtain the
training data for reordering models. Through exploring
a rich set of social and personalized features, we
model the relevance of tweets by minimizing the
pairwise loss of relevant and irrelevant tweets. The
tweets are then reordered according to the predicted
relevance scores. Experimental results with real
Twitter user activities demonstrated the effectiveness
of our method. The new method achieved above 30\%
accuracy gain compared with the default ordering in
acknowledgement = ack-nhfb,
articleno =    "6",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Guy:2013:ISS,
author =       "Ido Guy and Li Chen and Michelle X. Zhou",
title =        "Introduction to the special section on social
recommender systems",
journal =      j-TIST,
volume =       "4",
number =       "1",
pages =        "7:1--7:??",
month =        jan,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2414425.2414432",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "7",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Quijano-Sanchez:2013:SFG,
author =       "Lara Quijano-Sanchez and Juan A. Recio-Garcia and
Belen Diaz-Agudo and Guillermo Jimenez-Diaz",
title =        "Social factors in group recommender systems",
journal =      j-TIST,
volume =       "4",
number =       "1",
pages =        "8:1--8:??",
month =        jan,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2414425.2414433",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
group recommender systems and we propose some
improvement based on the study of the different
individual behaviors when carrying out a
decision-making process. Our method includes an
analysis of group personality composition and trust
between each group member to improve the accuracy of
group recommenders. This way we simulate the
argumentation process followed by groups of people when
agreeing on a common activity in a more realistic way.
Moreover, we reflect how they expect the system to
behave in a long term recommendation process. This is
achieved by including a memory of past recommendations
that increases the satisfaction of users whose
preferences have not been taken into account in
previous recommendations.",
acknowledgement = ack-nhfb,
articleno =    "8",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Zhang:2013:GVR,
author =       "Weishi Zhang and Guiguang Ding and Li Chen and
Chunping Li and Chengbo Zhang",
title =        "Generating virtual ratings from {Chinese} reviews to
augment online recommendations",
journal =      j-TIST,
volume =       "4",
number =       "1",
pages =        "9:1--9:??",
month =        jan,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2414425.2414434",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Collaborative filtering (CF) recommenders based on
User-Item rating matrix as explicitly obtained from end
users have recently appeared promising in recommender
systems. However, User-Item rating matrix is not always
available or very sparse in some web applications,
which has critical impact to the application of CF
online recommender system by fusing virtual ratings as
derived from user reviews. Specifically, taking into
account of Chinese reviews' characteristics, we propose
to fuse the self-supervised emotion-integrated
sentiment classification results into CF recommenders,
by which the User-Item Rating Matrix can be inferred by
decomposing item reviews that users gave to the items.
The main advantage of this approach is that it can
extend CF recommenders to some web applications without
user rating information. In the experiments, we have
first identified the self-supervised sentiment
classification's higher precision and recall by
comparing it with traditional classification methods.
Furthermore, the classification results, as behaving as
virtual ratings, were incorporated into both user-based
and item-based CF algorithms. We have also conducted an
experiment to evaluate the proximity between the
virtual and real ratings and clarified the
effectiveness of the virtual ratings. The experimental
results demonstrated the significant impact of virtual
ratings on increasing system's recommendation accuracy
in different data conditions (i.e., conditions with
real ratings and without).",
acknowledgement = ack-nhfb,
articleno =    "9",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Biancalana:2013:ASR,
author =       "Claudio Biancalana and Fabio Gasparetti and Alessandro
Micarelli and Giuseppe Sansonetti",
title =        "An approach to social recommendation for context-aware
mobile services",
journal =      j-TIST,
volume =       "4",
number =       "1",
pages =        "10:1--10:??",
month =        jan,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2414425.2414435",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Nowadays, several location-based services (LBSs) allow
their users to take advantage of information from the
Web about points of interest (POIs) such as cultural
events or restaurants. To the best of our knowledge,
however, none of these provides information taking into
account user preferences, or other elements, in
addition to location, that contribute to define the
context of use. The provided suggestions do not
consider, for example, time, day of week, weather, user
a social recommender system able to identify user
preferences and information needs, thus suggesting
personalized recommendations related to POIs in the
surroundings of the user's current location. The
proposed approach achieves the following goals: (i) to
supply, unlike the current LBSs, a methodology for
identifying user preferences and needs to be used in
the information filtering process; (ii) to exploit the
ever-growing amount of information from social
networking, user reviews, and local search Web sites;
(iii) to establish procedures for defining the context
of use to be employed in the recommendation of POIs
with low effort. The flexibility of the architecture is
such that our approach can be easily extended to any
category of POI. Experimental tests carried out on real
users enabled us to quantify the benefits of the
proposed approach in terms of performance
improvement.",
acknowledgement = ack-nhfb,
articleno =    "10",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Gedikli:2013:IRA,
author =       "Fatih Gedikli and Dietmar Jannach",
title =        "Improving recommendation accuracy based on
item-specific tag preferences",
journal =      j-TIST,
volume =       "4",
number =       "1",
pages =        "11:1--11:??",
month =        jan,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2414425.2414436",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "In recent years, different proposals have been made to
exploit Social Web tagging information to build more
effective recommender systems. The tagging data, for
example, were used to identify similar users or were
recommendable items. Recent research has indicated that
attaching feelings to tags'' is experienced by users
as a valuable means to express which features of an
item they particularly like or dislike. When following
such an approach, users would therefore not only add
tags to an item as in usual Web 2.0 applications, but
also attach a preference ( affect ) to the tag itself,
expressing, for example, whether or not they liked a
certain actor in a given movie. In this work, we show
how this additional preference data can be exploited by
a recommender system to make more accurate predictions.
In contrast to previous work, which also relied on
so-called tag preferences to enhance the predictive
accuracy of recommender systems, we argue that tag
preferences should be considered in the context of an
item. We therefore propose new schemes to infer and
exploit context-specific tag preferences in the
recommendation process. An evaluation on two different
datasets reveals that our approach is capable of
providing more accurate recommendations than previous
tag-based recommender algorithms and recent
tag-agnostic matrix factorization techniques.",
acknowledgement = ack-nhfb,
articleno =    "11",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Chen:2013:MRW,
author =       "Yu-Chih Chen and Yu-Shi Lin and Yu-Chun Shen and
Shou-De Lin",
title =        "A modified random walk framework for handling negative
ratings and generating explanations",
journal =      j-TIST,
volume =       "4",
number =       "1",
pages =        "12:1--12:??",
month =        jan,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2414425.2414437",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "The concept of random walk (RW) has been widely
applied in the design of recommendation systems.
RW-based approaches are effective in handling locality
problem and taking extra information, such as the
relationships between items or users, into
approach has a serious limitation in handling
bidirectional opinions. The propagation of positive and
negative information simultaneously in a graph is
nontrivial using random walk. To address the problem,
model that can handle both positive and negative
comments with the guarantee of convergence.
Furthermore, we argue that a good recommendation system
should provide users not only a list of recommended
items but also reasonable explanations for the
decisions. Therefore, we propose a technique that
generates explanations by backtracking the influential
paths and subgraphs. The results of experiments on the
MovieLens and Netflix datasets show that our model
significantly outperforms state-of-the-art RW-based
algorithms, and is capable of improving the overall
performance in the ensemble with other models.",
acknowledgement = ack-nhfb,
articleno =    "12",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Said:2013:MRC,
author =       "Alan Said and Shlomo Berkovsky and Ernesto W. {De
Luca}",
title =        "Movie recommendation in context",
journal =      j-TIST,
volume =       "4",
number =       "1",
pages =        "13:1--13:??",
month =        jan,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2414425.2414438",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "The challenge and workshop on Context-Aware Movie
Recommendation (CAMRa2010) were conducted jointly in
2010 with the Recommender Systems conference. The
challenge focused on three context-aware recommendation
scenarios: time-based, mood-based, and social
recommendation. The participants were provided with
anonymized datasets from two real-world online movie
recommendation communities and competed against each
other for obtaining the highest accuracy of
recommendations. The datasets contained contextual
features, such as tags, annotation, social
relationsips, and comments, normally not available in
public recommendation datasets. More than 40 teams from
21 countries participated in the challenge. Their
the workshop, which have been extended and revised for
this special section. In this preface we overview the
challenge datasets, tasks, evaluation metrics, and the
obtained outcomes.",
acknowledgement = ack-nhfb,
articleno =    "13",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

author =       "Alejandro Bellog{\'\i}n and Iv{\'a}n Cantador and
Fernando D{\'\i}ez and Pablo Castells and Enrique
Chavarriaga",
title =        "An empirical comparison of social, collaborative
filtering, and hybrid recommenders",
journal =      j-TIST,
volume =       "4",
number =       "1",
pages =        "14:1--14:??",
month =        jan,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2414425.2414439",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "In the Social Web, a number of diverse recommendation
approaches have been proposed to exploit the user
generated contents available in the Web, such as
rating, tagging, and social networking information. In
general, these approaches naturally require the
availability of a wide amount of these user
preferences. This may represent an important limitation
for real applications, and may be somewhat unnoticed in
studies focusing on overall precision, in which a
failure to produce recommendations gets blurred when
averaging the obtained results or, even worse, is just
not accounted for, as users with no recommendations are
typically excluded from the performance calculations.
uncovers and compensates for the incompleteness of
performance evaluations based only on precision. We use
this metric together with precision metrics in an
empirical comparison of several social, collaborative
filtering, and hybrid recommenders. The obtained
results show that a better balance between precision
and coverage can be achieved by combining social-based
filtering (high accuracy, low coverage) and
collaborative filtering (low accuracy, high coverage)
recommendation techniques. We thus explore several
hybrid recommendation approaches to balance this
trade-off. In particular, we compare, on the one hand,
techniques integrating collaborative and social
information into a single model, and on the other,
linear combinations of recommenders. For the last
approach, we also propose a novel strategy to
dynamically adjust the weight of each recommender on a
user-basis, utilizing graph measures as indicators of
the target user's connectedness and relevance in a
social network.",
acknowledgement = ack-nhfb,
articleno =    "14",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Liu:2013:STC,
author =       "Nathan N. Liu and Luheng He and Min Zhao",
title =        "Social temporal collaborative ranking for context
aware movie recommendation",
journal =      j-TIST,
volume =       "4",
number =       "1",
pages =        "15:1--15:??",
month =        jan,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2414425.2414440",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Most existing collaborative filtering models only
consider the use of user feedback (e.g., ratings) and
meta data (e.g., content, demographics). However, in
most real world recommender systems, context
information, such as time and social networks, are also
very important factors that could be considered in
order to produce more accurate recommendations. In this
work, we address several challenges for the context
aware movie recommendation tasks in CAMRa 2010: (1) how
to combine multiple heterogeneous forms of user
feedback? (2) how to cope with dynamic user and item
characteristics? (3) how to capture and utilize social
connections among users? For the first challenge, we
propose a novel ranking based matrix factorization
model to aggregate explicit and implicit user feedback.
For the second challenge, we extend this model to a
sequential matrix factorization model to enable
time-aware parametrization. Finally, we introduce a
network regularization function to constrain user
parameters based on social connections. To the best of
our knowledge, this is the first study that
investigates the collective modeling of social and
temporal dynamics. Experiments on the CAMRa 2010
dataset demonstrated clear improvements over many
baselines.",
acknowledgement = ack-nhfb,
articleno =    "15",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Shi:2013:MCM,
author =       "Yue Shi and Martha Larson and Alan Hanjalic",
title =        "Mining contextual movie similarity with matrix
factorization for context-aware recommendation",
journal =      j-TIST,
volume =       "4",
number =       "1",
pages =        "16:1--16:??",
month =        jan,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2414425.2414441",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Context-aware recommendation seeks to improve
recommendation performance by exploiting various
information sources in addition to the conventional
user-item matrix used by recommender systems. We
propose a novel context-aware movie recommendation
algorithm based on joint matrix factorization (JMF). We
jointly factorize the user-item matrix containing
general movie ratings and other contextual movie
similarity matrices to integrate contextual information
into the recommendation process. The algorithm was
developed within the scope of the mood-aware
recommendation task that was offered by the Moviepilot
mood track of the 2010 context-aware movie
recommendation (CAMRa) challenge. Although the
algorithm could generalize to other types of contextual
information, in this work, we focus on two: movie mood
tags and movie plot keywords. Since the objective in
this challenge track is to recommend movies for a user
given a specified mood, we devise a novel mood-specific
movie similarity measure for this purpose. We enhance
the recommendation based on this measure by also
deploying the second movie similarity measure proposed
keywords. We validate the effectiveness of the proposed
JMF algorithm with respect to the recommendation
performance by carrying out experiments on the
Moviepilot challenge dataset. We demonstrate that
exploiting contextual information in JMF leads to
significant improvement over several state-of-the-art
approaches that generate movie recommendations without
using contextual information. We also demonstrate that
our proposed mood-specific movie similarity is better
suited for the task than the conventional mood-based
movie similarity measures. Finally, we show that the
enhancement provided by the movie similarity capturing
the plot keywords is particularly helpful in improving
the recommendation to those users who are significantly
more active in rating the movies than other users.",
acknowledgement = ack-nhfb,
articleno =    "16",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

author =       "Isamu Okada and Hitoshi Yamamoto",
title =        "Mathematical description and analysis of adaptive risk
choice behavior",
journal =      j-TIST,
volume =       "4",
number =       "1",
pages =        "17:1--17:??",
month =        jan,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2414425.2414442",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Which risk should one choose when facing alternatives
with different levels of risk? We discuss here adaptive
processes in such risk choice behavior by generalizing
the study of Roos et al. [2010]. We deal with an n
-choice game in which every player sequentially chooses
n times of lotteries of which there are two types: a
safe lottery and a risky lottery. We analyze this model
in more detail by elaborating the game. Based on the
results of mathematical analysis, replicator dynamics
analysis, and numerical simulations, we derived some
salient features of risk choice behavior. We show that
all the risk strategies can be divided into two groups:
persistence and nonpersistence. We also proved that the
dynamics with perturbation in which a mutation is
installed is globally asymptotically stable to a unique
equilibrium point for any initial population. The
numerical simulations clarify that the number of
persistent strategies seldom increases regardless of
the increase in n, and suggest that a rarity of
dominant choice strategies is widely observed in many
social contexts. These facts not only go hand-in-hand
with some well-known insights from prospect theory, but
may also provide some theoretical hypotheses for
various fields such as behavioral economics, ecology,
sociology, and consumer behavioral theory.",
acknowledgement = ack-nhfb,
articleno =    "17",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Song:2013:OSM,
author =       "Xuan Song and Huijing Zhao and Jinshi Cui and Xiaowei
Shao and Ryosuke Shibasaki and Hongbin Zha",
title =        "An online system for multiple interacting targets
tracking: Fusion of laser and vision, tracking and
learning",
journal =      j-TIST,
volume =       "4",
number =       "1",
pages =        "18:1--18:??",
month =        jan,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2414425.2414443",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Multitarget tracking becomes significantly more
challenging when the targets are in close proximity or
presents a promising online system to deal with these
problems. The novelty of this system is that laser and
vision are integrated with tracking and online learning
to complement each other in one framework: when the
targets do not interact with each other, the
laser-based independent trackers are employed and the
visual information is extracted simultaneously to train
some classifiers online for possible interacting
targets''. When the targets are in close proximity, the
classifiers learned online are used alongside visual
information to assist in tracking. Therefore, this mode
of cooperation not only deals with various tough
problems encountered in tracking, but also ensures that
the entire process can be completely online and
automatic. Experimental results demonstrate that laser
and vision fully display their respective advantages in
our system, and it is easy for us to obtain a good
trade-off between tracking accuracy and the time-cost
factor.",
acknowledgement = ack-nhfb,
articleno =    "18",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Chopra:2013:ISS,
author =       "Amit K. Chopra and Alexander Artikis and Jamal
Bentahar and Frank Dignum",
title =        "Introduction to the special section on agent
communication",
journal =      j-TIST,
volume =       "4",
number =       "2",
pages =        "19:1--19:??",
month =        mar,
year =         "2013",
CODEN =        "????",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "19",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Chopra:2013:RDA,
author =       "Amit K. Chopra and Alexander Artikis and Jamal
Bentahar and Marco Colombetti and Frank Dignum and
Nicoletta Fornara and Andrew J. I. Jones and Munindar
P. Singh and Pinar Yolum",
title =        "Research directions in agent communication",
journal =      j-TIST,
volume =       "4",
number =       "2",
pages =        "20:1--20:??",
month =        mar,
year =         "2013",
CODEN =        "????",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Increasingly, software engineering involves open
systems consisting of autonomous and heterogeneous
participants or agents who carry out loosely coupled
interactions. Accordingly, understanding and specifying
communications among agents is a key concern. A focus
on ways to formalize meaning distinguishes agent
meaning provides a basis for flexible interactions and
compliance checking. Over the years, a number of
approaches have emerged with some essential and some
irrelevant distinctions drawn among them. As agent
abstractions gain increasing traction in the software
engineering of open systems, it is important to resolve
the irrelevant and highlight the essential
distinctions, so that future research can be focused in
outcome of extensive discussions among agent
communication researchers, aimed at taking stock of the
field and at developing, criticizing, and refining
their positions on specific approaches and future
purposes, including identifying (1) points of broad
consensus; (2) points where substantive differences
remain; and (3) interesting directions of future
work.",
acknowledgement = ack-nhfb,
articleno =    "20",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Gerard:2013:FVP,
author =       "Scott N. Gerard and Munindar P. Singh",
title =        "Formalizing and verifying protocol refinements",
journal =      j-TIST,
volume =       "4",
number =       "2",
pages =        "21:1--21:??",
month =        mar,
year =         "2013",
CODEN =        "????",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "A (business) protocol describes, in high-level terms,
a pattern of communication between two or more
participants, specifically via the creation and
manipulation of the commitments between them. In this
manner, a protocol offers both flexibility and rigor: a
participant may communicate in any way it chooses as
long as it discharges all of its activated commitments.
Protocols thus promise benefits in engineering
software engineering using protocols presupposes a
formalization of protocols and a notion of the
refinement of one protocol by another. Refinement for
protocols is both intuitively obvious (e.g.,
PayViaCheck is clearly a kind of Pay ) and technically
nontrivial (e.g., compared to Pay, PayViaCheck involves
different participants exchanging different messages).
It develops Proton, an analysis tool for protocol
specifications that overlays a model checker to compute
whether one protocol refines another with respect to a
stated mapping. Proton and its underlying theory are
evaluated by formalizing several protocols from the
literature and verifying all and only the expected
refinements.",
acknowledgement = ack-nhfb,
articleno =    "21",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Baldoni:2013:CRS,
author =       "Matteo Baldoni and Cristina Baroglio and Elisa Marengo
and Viviana Patti",
title =        "Constitutive and regulative specifications of
commitment protocols: a decoupled approach",
journal =      j-TIST,
volume =       "4",
number =       "2",
pages =        "22:1--22:??",
month =        mar,
year =         "2013",
CODEN =        "????",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Interaction protocols play a fundamental role in
multiagent systems. In this work, after analyzing the
trends that are emerging not only from research on
multiagent interaction protocols but also from
neighboring fields, like research on workflows and
business processes, we propose a novel definition of
commitment-based interaction protocols, that is
characterized by the decoupling of the constitutive and
the regulative specifications and that explicitly
foresees a representation of the latter based on
constraints among commitments. A clear distinction
between the two representations has many advantages,
mainly residing in a greater openness of multiagent
systems, and an easier reuse of protocols and of action
definitions. A language, named 2CL, for writing
regulative specifications is also given together with a
designer-oriented graphical notation.",
acknowledgement = ack-nhfb,
articleno =    "22",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Falcone:2013:ISS,
author =       "Rino Falcone and Munindar P. Singh",
title =        "Introduction to special section on trust in multiagent
systems",
journal =      j-TIST,
volume =       "4",
number =       "2",
pages =        "23:1--23:??",
month =        mar,
year =         "2013",
CODEN =        "????",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "23",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Zhang:2013:FTM,
author =       "Jie Zhang and Robin Cohen",
title =        "A framework for trust modeling in multiagent
consider varying seller behavior and the limiting of
seller bids",
journal =      j-TIST,
volume =       "4",
number =       "2",
pages =        "24:1--24:??",
month =        mar,
year =         "2013",
CODEN =        "????",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "In this article, we present a framework of use in
electronic marketplaces that allows buying agents to
model the trustworthiness of selling agents in an
effective way, making use of seller ratings provided by
trustworthiness of the advisors is also modeled, using
an approach that combines both personal and public
knowledge and allows the relative weighting to be
adjusted over time. Through a series of experiments
that simulate e-marketplaces, including ones where
sellers may vary their behavior over time, we are able
to demonstrate that our proposed framework delivers
effective seller recommendations to buyers, resulting
in important buyer profit. We also propose limiting
seller bids as a method for promoting seller honesty,
thus facilitating successful selection of sellers by
buyers, and demonstrate the value of this approach
through experimental results. Overall, this research is
focused on the technological aspects of electronic
commerce and specifically on technology that would be
used to manage trust.",
acknowledgement = ack-nhfb,
articleno =    "24",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Erriquez:2013:BUS,
author =       "Elisabetta Erriquez and Wiebe van der Hoek and Michael
Wooldridge",
title =        "Building and using social structures: a case study
using the agent {ART} testbed",
journal =      j-TIST,
volume =       "4",
number =       "2",
pages =        "25:1--25:??",
month =        mar,
year =         "2013",
CODEN =        "????",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
who make decisions in scenarios where trust is
important can benefit from the use of a social
structure, representing the social relationships that
exist between agents. We propose techniques that can be
used by agents to initially build and then
progressively update such a structure in the light of
experience. We describe an implementation of our
techniques in the domain of the Agent ART testbed: we
take two existing agents for this domain (Simplet''
and Connected'') and compare their performance with
versions that use our social structure
(SocialSimplet'' and SocialConnected''). We show
that SocialSimplet and SocialConnected outperform their
counterparts with respect to the quality of the
interactions, the number of rounds won in a
competition, and the total utility gained.",
acknowledgement = ack-nhfb,
articleno =    "25",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Burnett:2013:STB,
author =       "Chris Burnett and Timothy J. Norman and Katia Sycara",
title =        "Stereotypical trust and bias in dynamic multiagent
systems",
journal =      j-TIST,
volume =       "4",
number =       "2",
pages =        "26:1--26:??",
month =        mar,
year =         "2013",
CODEN =        "????",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Large-scale multiagent systems have the potential to
be highly dynamic. Trust and reputation are crucial
concepts in these environments, as it may be necessary
for agents to rely on their peers to perform as
expected, and learn to avoid untrustworthy partners.
However, aspects of highly dynamic systems introduce
issues which make the formation of trust relationships
difficult. For example, they may be short-lived,
precluding agents from gaining the necessary
experiences to make an accurate trust evaluation. This
article describes a new approach, inspired by theories
of human organizational behavior, whereby agents
generalize their experiences with previously
encountered partners as stereotypes, based on the
observable features of those partners and their
behaviors. Subsequently, these stereotypes are applied
when evaluating new and unknown partners. Furthermore,
these stereotypical opinions can be communicated within
the society, resulting in the notion of stereotypical
reputation. We show how this approach can complement
existing state-of-the-art trust models, and enhance the
trustees when direct and reputational information is
lacking or limited. Furthermore, we show how a
stereotyping approach can help agents detect unwanted
biases in the reputational opinions they receive from
others in the society.",
acknowledgement = ack-nhfb,
articleno =    "26",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Falcone:2013:MKR,
author =       "Rino Falcone and Michele Piunti and Matteo Venanzi and
Cristiano Castelfranchi",
title =        "From manifesta to krypta: The relevance of categories
for trusting others",
journal =      j-TIST,
volume =       "4",
number =       "2",
pages =        "27:1--27:??",
month =        mar,
year =         "2013",
CODEN =        "????",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
needed by agents for assessing trust based on inference
and reasoning. We analyze the case in which it is
possible to infer trust towards unknown counterparts by
reasoning on abstract classes or categories of agents
shaped in a concrete application domain. We present a
scenario of interacting agents providing a
computational model implementing different strategies
to assess trust. Assuming a medical domain, categories,
including both competencies and dispositions of
possible trustees, are exploited to infer trust towards
possibly unknown counterparts. The proposed approach
for the cognitive assessment of trust relies on agents'
abilities to analyze heterogeneous information sources
along different dimensions. Trust is inferred based on
specific observable properties (manifesta), namely
features (krypta) regulating agents' behavior and
effectiveness on specific tasks. Simulative experiments
evaluate the performance of trusting agents adopting
different strategies to delegate tasks to possibly
unknown trustees, while experimental results show the
relevance of this kind of cognitive ability in the case
of open multiagent systems.",
acknowledgement = ack-nhfb,
articleno =    "27",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Li:2013:ISS,
author =       "Qing Li and Xiangfeng Luo and Liu Wenyin and Cristina
Conati",
title =        "Introduction to the special section on intelligent
tutoring and coaching systems",
journal =      j-TIST,
volume =       "4",
number =       "2",
pages =        "28:1--28:??",
month =        mar,
year =         "2013",
CODEN =        "????",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "28",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Folsom-Kovarik:2013:TPR,
author =       "Jeremiah T. Folsom-Kovarik and Gita Sukthankar and Sae
Schatz",
title =        "Tractable {POMDP} representations for intelligent
tutoring systems",
journal =      j-TIST,
volume =       "4",
number =       "2",
pages =        "29:1--29:??",
month =        mar,
year =         "2013",
CODEN =        "????",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "With Partially Observable Markov Decision Processes
(POMDPs), Intelligent Tutoring Systems (ITSs) can model
individual learners from limited evidence and plan
ahead despite uncertainty. However, POMDPs need
appropriate representations to become tractable in ITSs
that model many learner features, such as mastery of
individual skills or the presence of specific
representations- state queues and observation chains
POMDPs scale to represent over 100 independent learner
features. A real-world military training problem is
given as one example. A human study ( n = 14) provides
initial validation for the model construction. Finally,
evaluating the experimental representations with
simulated students helps predict their impact on ITS
performance. The compressed representations can model a
wide range of simulated problems with instructional
efficacy equal to lossless representations. With
improved tractability, POMDP ITSs can accommodate more
numerous or more detailed learner states and inputs.",
acknowledgement = ack-nhfb,
articleno =    "29",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Yen:2013:LIS,
author =       "Neil Y. Yen and Timothy K. Shih and Qun Jin",
title =        "{LONET}: an interactive search network for intelligent
lecture path generation",
journal =      j-TIST,
volume =       "4",
number =       "2",
pages =        "30:1--30:??",
month =        mar,
year =         "2013",
CODEN =        "????",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Sharing resources and information on the Internet has
become an important activity for education. In distance
learning, instructors can benefit from resources, also
known as Learning Objects (LOs), to create plenteous
materials for specific learning purposes. Our
repository (called the MINE Registry) has been
developed for storing and sharing learning objects,
around 22,000 in total, in the past few years. To
enhance reusability, one significant concept named
Reusability Tree was implemented to trace the process
of changes. Also, weighting and ranking metrics have
been proposed to enhance the searchability in the
repository. Following the successful implementation,
this study goes further to investigate the
relationships between LOs from a perspective of social
networks. The LONET (Learning Object Network), as an
extension of Reusability Tree, is newly proposed and
constructed to clarify the vague reuse scenario in the
past, and to summarize collaborative intelligence
through past interactive usage experiences. We define a
social structure in our repository based on past usage
experiences from instructors, by proposing a set of
metrics to evaluate the interdependency such as
prerequisites and references. The structure identifies
usage experiences and can be graphed in terms of
implicit and explicit relations among learning objects.
As a practical contribution, an adaptive algorithm is
proposed to mine the social structure in our
repository. The algorithm generates adaptive routes,
based on past usage experiences, by computing possible
interactive input, such as search criteria and feedback
from instructors, and assists them in generating
specific lectures.",
acknowledgement = ack-nhfb,
articleno =    "30",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Ehara:2013:PRS,
author =       "Yo Ehara and Nobuyuki Shimizu and Takashi Ninomiya and
Hiroshi Nakagawa",
title =        "Personalized reading support for second-language {Web}
documents",
journal =      j-TIST,
volume =       "4",
number =       "2",
pages =        "31:1--31:??",
month =        mar,
year =         "2013",
CODEN =        "????",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "A novel intelligent interface eases the browsing of
Web documents written in the second languages of users.
It automatically predicts words unfamiliar to the user
by a collective intelligence method and glosses them
with their meaning in advance. If the prediction
succeeds, the user does not need to consult a
dictionary; even if it fails, the user can correct the
prediction. The correction data are collected and used
to improve the accuracy of further predictions. The
prediction is personalized in that every user's
language ability is estimated by a state-of-the-art
language testing model, which is trained in a practical
response time with only a small sacrifice of prediction
accuracy. The system was evaluated in terms of
simulation results show that this system can reduce the
number of clicks for most readers with insufficient
vocabulary to read documents and can significantly
reduce the remaining number of unfamiliar words after
the prediction and glossing for all users.",
acknowledgement = ack-nhfb,
articleno =    "31",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Wang:2013:RCI,
author =       "Fei-Yue Wang and Pak Kin Wong",
title =        "Research commentary: Intelligent systems and
technology for integrative and predictive medicine: an
{ACP} approach",
journal =      j-TIST,
volume =       "4",
number =       "2",
pages =        "32:1--32:??",
month =        mar,
year =         "2013",
CODEN =        "????",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "One of the principal goals in medicine is to determine
and implement the best treatment for patients through
fastidious estimation of the effects and benefits of
therapeutic procedures. The inherent complexities of
physiological and pathological networks that span
across orders of magnitude in time and length scales,
however, represent fundamental hurdles in determining
effective treatments for patients. Here we argue for a
new approach, called the ACP-based approach, that
combines artificial (societies), computational
(experiments), and parallel (execution) methods in
intelligent systems and technology for integrative and
predictive medicine, or more generally, precision
medicine and smart health management. The advent of
artificial societies that collect the clinically
relevant information in prognostics and therapeutics
provides a promising platform for organizing and
experimenting complex physiological systems toward
integrative medicine. The ability of computational
experiments to analyze distinct, interactive systems
such as the host mechanisms, pathological pathways, and
therapeutic strategies, as well as other factors using
the artificial systems, will enable control and
management through parallel execution of real and
artificial systems concurrently within the integrative
medicine context. The development of this framework in
integrative medicine, fueled by close collaborations
between physicians, engineers, and scientists, will
result in preventive and predictive practices of a
personal, proactive, and precise nature, including
therapeutics, and patient-oriented disease
management.",
acknowledgement = ack-nhfb,
articleno =    "32",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Tabia:2013:PBA,
author =       "Hedi Tabia and Mohamed Daoudi and Jean-Philippe
Vandeborre and Olivier Colot",
title =        "A parts-based approach for automatic {$3$D} shape
categorization using belief functions",
journal =      j-TIST,
volume =       "4",
number =       "2",
pages =        "33:1--33:??",
month =        mar,
year =         "2013",
CODEN =        "????",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Grouping 3D objects into (semantically) meaningful
categories is a challenging and important problem in 3D
mining and shape processing. Here, we present a novel
approach to categorize 3D objects. The method described
and consists of two stages: the training stage, where
3D objects in the same category are processed and a set
of representative parts is constructed, and the
labeling stage, where unknown objects are categorized.
The experimental results obtained on the Tosca-Sumner
and the Shrec07 datasets show that the system
efficiently performs in categorizing 3D models.",
acknowledgement = ack-nhfb,
articleno =    "33",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Wang:2013:LIC,
author =       "Zhengxiang Wang and Yiqun Hu and Liang-Tien Chia",
title =        "Learning image-to-class distance metric for image
classification",
journal =      j-TIST,
volume =       "4",
number =       "2",
pages =        "34:1--34:??",
month =        mar,
year =         "2013",
CODEN =        "????",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Image-To-Class (I2C) distance is a novel distance used
for image classification and has successfully handled
datasets with large intra-class variances. However, it
uses Euclidean distance for measuring the distance
between local features in different classes, which may
not be the optimal distance metric in real image
distance metric learning method to improve the
performance of I2C distance by learning per-class
Mahalanobis metrics in a large margin framework. Our
I2C distance is adaptive to different classes by
combining with the learned metric for each class. These
multiple per-class metrics are learned simultaneously
by forming a convex optimization problem with the
constraints that the I2C distance from each training
image to its belonging class should be less than the
distances to other classes by a large margin. A
subgradient descent method is applied to efficiently
solve this optimization problem. For efficiency and
scalability to large-scale problems, we also show how
to simplify the method to learn a diagonal matrix for
each class. We show in experiments that our learned
Mahalanobis I2C distance can significantly outperform
the original Euclidean I2C distance as well as other
distance metric learning methods in several prevalent
image datasets, and our simplified diagonal matrices
can preserve the performance but significantly speed up
the metric learning procedure for large-scale datasets.
We also show in experiment that our method is able to
correct the class imbalance problem, which usually
leads the NN-based methods toward classes containing
more training images.",
acknowledgement = ack-nhfb,
articleno =    "34",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Song:2013:FOU,
author =       "Xuan Song and Xiaowei Shao and Quanshi Zhang and
Ryosuke Shibasaki and Huijing Zhao and Jinshi Cui and
Hongbin Zha",
title =        "A fully online and unsupervised system for large and
high-density area surveillance: Tracking, semantic
scene learning and abnormality detection",
journal =      j-TIST,
volume =       "4",
number =       "2",
pages =        "35:1--35:??",
month =        mar,
year =         "2013",
CODEN =        "????",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "For reasons of public security, an intelligent
surveillance system that can cover a large, crowded
we propose a novel laser-based system that can
simultaneously perform tracking, semantic scene
learning, and abnormality detection in a fully online
and unsupervised way. Furthermore, these three tasks
cooperate with each other in one framework to improve
their respective performances. The proposed system has
the following key advantages over previous ones: (1) It
can cover quite a large area (more than 60$\times$35m), and simultaneously perform robust tracking,
semantic scene learning, and abnormality detection in a
high-density situation. (2) The overall system can vary
with time, incrementally learn the structure of the
scene, and perform fully online abnormal activity
detection and tracking. This feature makes our system
suitable for real-time applications. (3) The
surveillance tasks are carried out in a fully
unsupervised manner, so that there is no need for
manual labeling and the construction of huge training
datasets. We successfully apply the proposed system to
the JR subway station in Tokyo, and demonstrate that it
can cover an area of 60$\times$35m, robustly track
more than 150 targets at the same time, and
simultaneously perform online semantic scene learning
and abnormality detection with no human intervention.",
acknowledgement = ack-nhfb,
articleno =    "35",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Tran:2013:CPB,
author =       "Vien Tran and Khoi Nguyen and Tran Cao Son and Enrico
Pontelli",
title =        "A conformant planner based on approximation:
{CpA(H)}",
journal =      j-TIST,
volume =       "4",
number =       "2",
pages =        "36:1--36:??",
month =        mar,
year =         "2013",
CODEN =        "????",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Sun May 5 09:06:55 MDT 2013",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "This article describes the planner C pA( H ), the
recipient of the Best Nonobservable Nondeterministic
Planner Award in the Uncertainty Track'' of the 6
$^{th}$ International Planning Competition (IPC), 2008.
The article presents the various techniques that help
CpA( H ) to achieve the level of performance and
scalability exhibited in the competition. The article
also presents experimental results comparing CpA( H )
with state-of-the-art conformant planners.",
acknowledgement = ack-nhfb,
articleno =    "36",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Wang:2013:ISS,
author =       "Haifeng Wang and Bill Dolan and Idan Szpektor and
Shiqi Zhao",
title =        "Introduction to special section on paraphrasing",
journal =      j-TIST,
volume =       "4",
number =       "3",
pages =        "37:1--37:??",
month =        jun,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2483669.2483670",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:09 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "37",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Resnik:2013:UTP,
author =       "Philip Resnik and Olivia Buzek and Yakov Kronrod and
Chang Hu and Alexander J. Quinn and Benjamin B.
Bederson",
title =        "Using targeted paraphrasing and monolingual
crowdsourcing to improve translation",
journal =      j-TIST,
volume =       "4",
number =       "3",
pages =        "38:1--38:??",
month =        jun,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2483669.2483671",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:09 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Targeted paraphrasing is a new approach to the problem
of obtaining cost-effective, reasonable quality
translation, which makes use of simple and inexpensive
human computations by monolingual speakers in
combination with machine translation. The key insight
behind the process is that it is possible to spot
likely translation errors with only monolingual
knowledge of the target language, and it is possible to
generate alternative ways to say the same thing (i.e.,
paraphrases) with only monolingual knowledge of the
source language. Formal evaluation demonstrates that
this approach can yield substantial improvements in
translation quality, and the idea has been integrated
into a broader framework for monolingual collaborative
translation that produces fully accurate, fully fluent
translations for a majority of sentences in a
real-world translation task, with no involvement of
human bilingual speakers.",
acknowledgement = ack-nhfb,
articleno =    "38",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Marton:2013:DPP,
author =       "Yuval Marton",
title =        "Distributional phrasal paraphrase generation for
statistical machine translation",
journal =      j-TIST,
volume =       "4",
number =       "3",
pages =        "39:1--39:??",
month =        jun,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2483669.2483672",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:09 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Paraphrase generation has been shown useful for
various natural language processing tasks, including
statistical machine translation. A commonly used method
for paraphrase generation is pivoting [Callison-Burch
et al. 2006], which benefits from linguistic knowledge
implicit in the sentence alignment of parallel texts,
but has limited applicability due to its reliance on
parallel texts. Distributional paraphrasing [Marton et
al. 2009a] has wider applicability, is more
language-independent, but doesn't benefit from any
linguistic knowledge. Nevertheless, we show that using
distributional paraphrasing can yield greater gains in
translation tasks. We report method improvements
leading to higher gains than previously published, of
almost 2 B leu points, and provide implementation
details, complexity analysis, and further insight into
this method.",
acknowledgement = ack-nhfb,
articleno =    "39",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

author =       "Nitin Madnani and Bonnie J. Dorr",
title =        "Generating targeted paraphrases for improved
translation",
journal =      j-TIST,
volume =       "4",
number =       "3",
pages =        "40:1--40:??",
month =        jun,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2483669.2483673",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:09 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Today's Statistical Machine Translation (SMT) systems
require high-quality human translations for parameter
tuning, in addition to large bitexts for learning the
translation units. This parameter tuning usually
involves generating translations at different points in
the parameter space and obtaining feedback against
human-authored reference translations as to how good
the translations. This feedback then dictates what
point in the parameter space should be explored next.
To measure this feedback, it is generally considered
wise to have multiple (usually 4) reference
translations to avoid unfair penalization of
translation hypotheses which could easily happen given
the large number of ways in which a sentence can be
translated from one language to another. However, this
reliance on multiple reference translations creates a
problem since they are labor intensive and expensive to
obtain. Therefore, most current MT datasets only
contain a single reference. This leads to the problem
of reference sparsity. In our previously published
research, we had proposed the first paraphrase-based
solution to this problem and evaluated its effect on
present extended results for that solution on
additional source languages. More importantly, we
present a novel way to generate targeted''
paraphrases that yields substantially larger gains (up
to 2.7 BLEU points) in translation quality when
compared to our previous solution (up to 1.6 BLEU
points). In addition, we further validate these
improvements by supplementing with human preference
judgments obtained via Amazon Mechanical Turk.",
acknowledgement = ack-nhfb,
articleno =    "40",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Cohn:2013:AAS,
author =       "Trevor Cohn and Mirella Lapata",
title =        "An abstractive approach to sentence compression",
journal =      j-TIST,
volume =       "4",
number =       "3",
pages =        "41:1--41:??",
month =        jun,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2483669.2483674",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:09 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
task. Rather than simply shorten a sentence by deleting
words or constituents, as in previous work, we rewrite
it using additional operations such as substitution,
reordering, and insertion. We present an experimental
study showing that humans can naturally create
abstractive sentences using a variety of rewrite
operations, not just deletion. We next create a new
corpus that is suited to the abstractive compression
task and formulate a discriminative tree-to-tree
transduction model that can account for structural and
lexical mismatches. The model incorporates a grammar
extraction method, uses a language model for coherent
output, and can be easily tuned to a wide range of
compression-specific loss functions.",
acknowledgement = ack-nhfb,
articleno =    "41",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Moon:2013:IBM,
author =       "Taesun Moon and Katrin Erk",
title =        "An inference-based model of word meaning in context as
a paraphrase distribution",
journal =      j-TIST,
volume =       "4",
number =       "3",
pages =        "42:1--42:??",
month =        jun,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2483669.2483675",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:09 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Graded models of word meaning in context characterize
the meaning of individual usages (occurrences) without
reference to dictionary senses. We introduce a novel
approach that frames the task of computing word meaning
in context as a probabilistic inference problem. The
model represents the meaning of a word as a probability
distribution over potential paraphrases, inferred using
an undirected graphical model. Evaluated on
paraphrasing tasks, the model achieves state-of-the-art
performance.",
acknowledgement = ack-nhfb,
articleno =    "42",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Burrows:2013:PAC,
author =       "Steven Burrows and Martin Potthast and Benno Stein",
title =        "Paraphrase acquisition via crowdsourcing and machine
learning",
journal =      j-TIST,
volume =       "4",
number =       "3",
pages =        "43:1--43:??",
month =        jun,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2483669.2483676",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:09 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "To paraphrase means to rewrite content while
preserving the original meaning. Paraphrasing is
important in fields such as text reuse in journalism,
anonymizing work, and improving the quality of
paraphrase acquisition and focuses on two aspects that
are not addressed by current research: (1) acquisition
via crowdsourcing, and (2) acquisition of passage-level
samples. The challenge of the first aspect is automatic
quality assurance; without such a means the
crowdsourcing paradigm is not effective, and without
crowdsourcing the creation of test corpora is
unacceptably expensive for realistic order of
magnitudes. The second aspect addresses the deficit
that most of the previous work in generating and
evaluating paraphrases has been conducted using
sentence-level paraphrases or shorter; these
short-sample analyses are limited in terms of
application to plagiarism detection, for example. We
present the Webis Crowd Paraphrase Corpus 2011
(Webis-CPC-11), which recently formed part of the PAN
2010 international plagiarism detection competition.
This corpus comprises passage-level paraphrases with
4067 positive samples and 3792 negative samples that
failed our criteria, using Amazon's Mechanical Turk for
learned at PAN 2010, and explain in detail the method
used to construct the corpus. The empirical
contributions include machine learning experiments to
explore if passage-level paraphrases can be identified
in a two-class classification problem using paraphrase
similarity features, and we find that a
k-nearest-neighbor classifier can correctly distinguish
between paraphrased and nonparaphrased samples with
0.980 precision at 0.523 recall. This result implies
that just under half of our samples must be discarded
(remaining 0.477 fraction), but our cost analysis shows
that the automation we introduce results in a 18\%
financial saving and over 100 hours of time returned to
the researchers when repeating a similar corpus design.
On the other hand, when building an unrelated corpus
requiring, say, 25\% training data for the automated
component, we show that the financial outcome is cost
neutral, while still returning over 70 hours of time to
the researchers. The work presented here is the first
to join the paraphrasing and plagiarism communities.",
acknowledgement = ack-nhfb,
articleno =    "43",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Bouamor:2013:MPA,
author =       "Houda Bouamor and Aur{\'e}elien Max and Anne Vilnat",
title =        "Multitechnique paraphrase alignment: a contribution to
pinpointing sub-sentential paraphrases",
journal =      j-TIST,
volume =       "4",
number =       "3",
pages =        "44:1--44:??",
month =        jun,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2483669.2483677",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:09 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "This work uses parallel monolingual corpora for a
detailed study of the task of sub-sentential paraphrase
acquisition. We argue that the scarcity of this type of
resource is compensated by the fact that it is the most
suited type for studies on paraphrasing. We propose a
large exploration of this task with experiments on two
languages with five different acquisition techniques,
selected for their complementarity, their combinations,
as well as four monolingual corpus types of varying
comparability. We report, under all conditions, a
significant improvement over all techniques by
validating candidate paraphrases using a maximum
entropy classifier. An important result of our study is
the identification of difficult-to-acquire paraphrase
pairs, which are classified and quantified in a
bilingual typology.",
acknowledgement = ack-nhfb,
articleno =    "44",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Yu:2013:ISS,
author =       "Zhiwen Yu and Daqing Zhang and Nathan Eagle and Diane
Cook",
title =        "Introduction to the special section on intelligent
systems for socially aware computing",
journal =      j-TIST,
volume =       "4",
number =       "3",
pages =        "45:1--45:??",
month =        jun,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2483669.2483678",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:09 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "45",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Schuster:2013:PSC,
author =       "Daniel Schuster and Alberto Rosi and Marco Mamei and
Thomas Springer and Markus Endler and Franco
Zambonelli",
title =        "Pervasive social context: Taxonomy and survey",
journal =      j-TIST,
volume =       "4",
number =       "3",
pages =        "46:1--46:??",
month =        jun,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2483669.2483679",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:09 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "As pervasive computing meets social networks, there is
a fast growing research field called pervasive social
computing. Applications in this area exploit the
richness of information arising out of people using
sensor-equipped pervasive devices in their everyday
life combined with intense use of different social
networking services. We call this set of information
pervasive social context. We provide a taxonomy to
classify pervasive social context along the dimensions
space, time, people, and information source (STiPI) as
well as commenting on the type and reason for creating
such context. A survey of recent research shows the
applicability and usefulness of the taxonomy in
classifying and assessing applications and systems in
the area of pervasive social computing. Finally, we
present some research challenges in this area and
illustrate how they affect the systems being
surveyed.",
acknowledgement = ack-nhfb,
articleno =    "46",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Shi:2013:NLR,
author =       "Yue Shi and Pavel Serdyukov and Alan Hanjalic and
Martha Larson",
title =        "Nontrivial landmark recommendation using geotagged
photos",
journal =      j-TIST,
volume =       "4",
number =       "3",
pages =        "47:1--47:??",
month =        jun,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2483669.2483680",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:09 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Online photo-sharing sites provide a wealth of
information about user behavior and their potential is
increasing as it becomes ever-more common for images to
be associated with location information in the form of
that exploits geotagged images from an online community
for the purpose of personalized landmark
recommendation. Under our formulation of the task,
recommended landmarks should be relevant to user
interests and additionally they should constitute
nontrivial recommendations. In other words,
recommendations of landmarks that are highly popular
and frequently visited and can be easily discovered
through other information sources such as travel guides
should be avoided in favor of recommendations that
relate to users' personal interests. We propose a
collaborative filtering approach to the personalized
landmark recommendation task within a matrix
factorization framework. Our approach, WMF-CR, combines
weighted matrix factorization and category-based
regularization. The integrated weights emphasize the
contribution of nontrivial landmarks in order to focus
the recommendation model specifically on the generation
of nontrivial recommendations. They support the
judicious elimination of trivial landmarks from
valuable for recommendation. Category-based
regularization addresses the sparse data problem, which
is arguably even greater in the case of our landmark
recommendation task than in other recommendation
scenarios due to the limited amount of travel
experience recorded in the online image set of any
given user. We use category information extracted from
Wikipedia in order to provide the system with a method
to generalize the semantics of landmarks and allow the
model to relate them not only on the basis of identity,
but also on the basis of topical commonality. The
proposed approach is computational scalable, that is,
its complexity is linear with the number of observed
preferences in the user-landmark preference matrix and
the number of nonzero similarities in the
category-based landmark similarity matrix. We evaluate
the approach on a large collection of geotagged photos
gathered from Flickr. Our experimental results
demonstrate that WMF-CR outperforms several
state-of-the-art baseline approaches in recommending
that the approach is well suited for addressing data
sparseness and provides particular performance
improvement in the case of users who have limited
travel experience, that is, have visited only few
cities or few landmarks.",
acknowledgement = ack-nhfb,
articleno =    "47",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Wei:2013:EPA,
author =       "Ling-Yin Wei and Wen-Chih Peng and Wang-Chien Lee",
title =        "Exploring pattern-aware travel routes for trajectory
search",
journal =      j-TIST,
volume =       "4",
number =       "3",
pages =        "48:1--48:??",
month =        jun,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2483669.2483681",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:09 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "With the popularity of positioning devices, Web 2.0
technology, and trip sharing services, many users are
willing to log and share their trips on the Web. Thus,
trip planning Web sites are able to provide some new
services by inferring Regions-Of-Interest (ROIs) and
recommending popular travel routes from trip
trajectories. We argue that simply providing some
travel routes consisting of popular ROIs to users is
not sufficient. To tour around a wide geographical
area, for example, a city, some users may prefer a trip
to visit as many ROIs as possible, while others may
like to stop by only a few ROIs for an in-depth visit.
We refer to a trip fitting the former user group as an
in-breadth trip and a trip suitable for the latter user
group as an in-depth trip. Prior studies on trip
planning have focused on mining ROIs and travel routes
without considering these different preferences. In
preference of depth/breadth specified by a user, we
develop a Pattern-Aware Trajectory Search (PATS)
framework to retrieve the top K trajectories passing
through popular ROIs. PATS is novel because the
returned travel trajectories, discovered from travel
patterns hidden in trip trajectories, may represent the
most valuable travel experiences of other travelers
fitting the user's trip preference in terms of depth or
breadth. The PATS framework comprises two components:
travel behavior exploration and trajectory search. The
travel behavior exploration component determines a set
of ROIs along with their attractive scores by
considering not only the popularity of the ROIs but
also the travel sequential relationships among the
ROIs. To capture the travel sequential relationships
among ROIs and to derive their attractive scores, a
user movement graph is constructed. For the trajectory
search component of PATS, we formulate two trajectory
score functions, the depth-trip score function and the
breadth-trip score function, by taking into account the
number of ROIs in a trajectory and their attractive
scores. Accordingly, we propose an algorithm, namely,
Bounded Trajectory Search (BTS), to efficiently
retrieve the top K trajectories based on the two
trajectory scores. The PATS framework is evaluated by
experiments and user studies using a real dataset. The
experimental results demonstrate the effectiveness and
the efficiency of the proposed PATS framework.",
acknowledgement = ack-nhfb,
articleno =    "48",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Yan:2013:STM,
author =       "Zhixian Yan and Dipanjan Chakraborty and Christine
Parent and Stefano Spaccapietra and Karl Aberer",
title =        "Semantic trajectories: Mobility data computation and
annotation",
journal =      j-TIST,
volume =       "4",
number =       "3",
pages =        "49:1--49:??",
month =        jun,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2483669.2483682",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:09 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "With the large-scale adoption of GPS equipped mobile
sensing devices, positional data generated by moving
objects (e.g., vehicles, people, animals) are being
easily collected. Such data are typically modeled as
streams of spatio-temporal (x,y,t) points, called
trajectories. In recent years trajectory management
research has progressed significantly towards efficient
storage and indexing techniques, as well as suitable
knowledge discovery. These works focused on the
geometric aspect of the raw mobility data. We are now
witnessing a growing demand in several application
sectors (e.g., from shipment tracking to geo-social
networks) on understanding the semantic behavior of
moving objects. Semantic behavior refers to the use of
semantic abstractions of the raw mobility data,
including not only geometric patterns but also
knowledge extracted jointly from the mobility data and
the underlying geographic and application domains
in a semantic model and a computation and annotation
platform for developing a semantic approach that
progressively transforms the raw mobility data into
semantic trajectories enriched with segmentations and
annotations. We also analyze a number of experiments we
did with semantic trajectories in different domains.",
acknowledgement = ack-nhfb,
articleno =    "49",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Chin:2013:CPT,
author =       "Alvin Chin and Bin Xu and Hao Wang and Lele Chang and
Hao Wang and Lijun Zhu",
title =        "Connecting people through physical proximity and
physical resources at a conference",
journal =      j-TIST,
volume =       "4",
number =       "3",
pages =        "50:1--50:??",
month =        jun,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2483669.2483683",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:09 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "This work investigates how to bridge the gap between
offline and online behaviors at a conference and how
the physical resources in the conference (the physical
objects used in the conference for gathering attendees
together in engaging an activity such as rooms,
sessions, and papers) can be used to help facilitate
social networking. We build Find and Connect, a system
that integrates offline activities and interactions
captured in real time with online connections in a
conference environment, to provide a list of potential
people one should connect to for forming an ephemeral
social network. We investigate how social connections
can be established and integrated with physical
resources through positioning technology, and the
relationship between physical proximity encounters and
online social connections. Results from our two
datasets of two trials, one at the UIC/ATC 2010
conference and GCJK internal marketing event, show that
social connections that are reciprocal in relationship,
such as friendship and exchanged contacts, have
tighter, denser, and highly clustered networks compared
to unidirectional relationships such as follow. We
discover that there is a positive relationship between
physical proximity encounters and online social
connections before the social connection is made for
friends, but a negative relationship for after the
social connection is made. The first indicates social
selection is strong, and the second indicates social
influence is weak. Even though our dataset is sparse,
nonetheless we believe our work is promising and novel
which is worthy of future research.",
acknowledgement = ack-nhfb,
articleno =    "50",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Yang:2013:ISS,
author =       "Shanchieh Jay Yang and Dana Nau and John Salerno",
title =        "Introduction to the special section on social
computing, behavioral-cultural modeling, and
prediction",
journal =      j-TIST,
volume =       "4",
number =       "3",
pages =        "51:1--51:??",
month =        jun,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2483669.2483684",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:09 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "51",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Hung:2013:OBI,
author =       "Benjamin W. K. Hung and Stephan E. Kolitz and Asuman
Ozdaglar",
title =        "Optimization-based influencing of village social
networks in a counterinsurgency",
journal =      j-TIST,
volume =       "4",
number =       "3",
pages =        "52:1--52:??",
month =        jun,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2483669.2483685",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:09 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
assignment problem in the counterinsurgency in
Afghanistan, the problem of deciding on the people whom
U.S. forces should engage through outreach,
negotiations, meetings, and other interactions in order
to ultimately win the support of the population in
their area of operations. We propose two models: (1)
the Afghan counterinsurgency (COIN) social influence
model, to represent how attitudes of local leaders are
affected by repeated interactions with other local
leaders, insurgents, and counterinsurgents, and (2) the
nonlethal targeting model, a NonLinear Programming
(NLP) optimization formulation that identifies a
strategy for assigning k U.S. agents to produce the
greatest arithmetic mean of the expected long-term
attitude of the population. We demonstrate in an
experiment the merits of the optimization model in
nonlethal targeting, which performs significantly
better than both doctrine-based and random methods of
assignment in a large network.",
acknowledgement = ack-nhfb,
articleno =    "52",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Gintis:2013:MMS,
author =       "Herbert Gintis",
title =        "{Markov} models of social dynamics: Theory and
applications",
journal =      j-TIST,
volume =       "4",
number =       "3",
pages =        "53:1--53:??",
month =        jun,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2483669.2483686",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:09 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
dynamics can be treated rigorously and analytically as
finite Markov processes, and their long-run properties
are then given by an expanded version of the ergodic
theorem for Markov processes. A Markov process model of
a simplified market economy shows the fruitfulness of
this approach.",
acknowledgement = ack-nhfb,
articleno =    "53",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Fridman:2013:UQR,
author =       "Natalie Fridman and Gal A. Kaminka",
title =        "Using qualitative reasoning for social simulation of
crowds",
journal =      j-TIST,
volume =       "4",
number =       "3",
pages =        "54:1--54:??",
month =        jun,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2483669.2483687",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:09 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "The ability to model and reason about the potential
violence level of a demonstration is important to the
police decision making process. Unfortunately, existing
knowledge regarding demonstrations is composed of
partial qualitative descriptions without complete and
describe a first attempt to use qualitative reasoning
techniques to model demonstrations. To our knowledge,
such techniques have never been applied to modeling and
reasoning regarding crowd behaviors, nor in particular
demonstrations. We develop qualitative models
consistent with the partial, qualitative social science
literature, allowing us to model the interactions
between different factors that influence violence in
demonstrations. We then utilize qualitative simulation
to predict the potential eruption of violence, at
various levels, based on a description of the
demographics, environmental settings, and police
responses. We incrementally present and compare three
such qualitative models. The results show that while
two of these models fail to predict the outcomes of
real-world events reported and analyzed in the
literature, one model provides good results. We also
examine whether a popular machine learning algorithm
(decision tree learning) can be used. While the results
show that the decision trees provide improved
predictions, we show that the QR models can be more
sensitive to changes, and can account for what-if
scenarios, in contrast to decision trees. Moreover, we
introduce a novel analysis algorithm that analyzes the
QR simulations, to automatically determine the factors
that are most important in influencing the outcome in
specific real-world demonstrations. We show that the
algorithm identifies factors that correspond to
experts' analysis of these events.",
acknowledgement = ack-nhfb,
articleno =    "54",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Saito:2013:DCI,
author =       "Kazumi Saito and Masahiro Kimura and Kouzou Ohara and
Hiroshi Motoda",
title =        "Detecting changes in information diffusion patterns
over social networks",
journal =      j-TIST,
volume =       "4",
number =       "3",
pages =        "55:1--55:??",
month =        jun,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2483669.2483688",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:09 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "We addressed the problem of detecting the change in
behavior of information diffusion over a social network
which is caused by an unknown external situation change
using a small amount of observation data in a
retrospective setting. The unknown change is assumed
effectively reflected in changes in the parameter
values in the probabilistic information diffusion
model, and the problem is reduced to detecting where in
time and how long this change persisted and how big
this change is. We solved this problem by searching the
change pattern that maximizes the likelihood of
generating the observed information diffusion
sequences, and in doing so we devised a very efficient
general iterative search algorithm using the derivative
of the likelihood which avoids parameter value
optimization during each search step. This is in
contrast to the naive learning algorithm in that it has
to iteratively update the pattern boundaries, each
requiring the parameter value optimization and thus is
very inefficient. We tested this algorithm for two
instances of the probabilistic information diffusion
model which has different characteristics. One is of
information push style and the other is of information
pull style. We chose Asynchronous Independent Cascade
(AsIC) model as the former and Value-weighted Voter
(VwV) model as the latter. The AsIC is the model for
general information diffusion with binary states and
the parameter to detect its change is diffusion
probability and the VwV is the model for opinion
formation with multiple states and the parameter to
detect its change is opinion value. The results tested
on these two models using four real-world network
structures confirm that the algorithm is robust enough
and can efficiently identify the correct change pattern
of the parameter values. Comparison with the naive
method that finds the best combination of change
boundaries by an exhaustive search through a set of
randomly selected boundary candidates shows that the
proposed algorithm far outperforms the native method
both in terms of accuracy and computation time.",
acknowledgement = ack-nhfb,
articleno =    "55",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Marathe:2013:AFN,
author =       "Achla Marathe and Zhengzheng Pan and Andrea Apolloni",
title =        "Analysis of friendship network and its role in
explaining obesity",
journal =      j-TIST,
volume =       "4",
number =       "3",
pages =        "56:1--56:??",
month =        jun,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2483669.2483689",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:09 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "We employ Add Health data to show that friendship
networks, constructed from mutual friendship
nominations, are important in building weight
perception, setting weight goals, and measuring social
study the relationship between individuals' perceived
weight status, actual weight status, weight status
relative to friends' weight status, and weight goals.
This analysis helps us understand how individual weight
perceptions might be formed, what these perceptions do
to the weight goals, and how friends' relative weight
affects weight perception and weight goals. Combining
this information with individuals' friendship network
helps determine the influence of social relationships
on weight-related variables. Multinomial logistic
regression results indicate that relative status is
indeed a significant predictor of perceived status, and
perceived status is a significant predictor of weight
goals. We also address the issue of causality between
actual weight status and social marginalization (as
measured by the number of friends) and show that
obesity precedes social marginalization in time rather
than the other way around. This lends credence to the
hypothesis that obesity leads to social marginalization
not vice versa. Attributes of the friendship network
can provide new insights into effective interventions
for combating obesity since adolescent friendships
provide an important social context for weight-related
behaviors.",
acknowledgement = ack-nhfb,
articleno =    "56",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Jiang:2013:MSB,
author =       "Daxin Jiang and Jian Pei and Hang Li",
title =        "Mining search and browse logs for {Web} search: a
survey",
journal =      j-TIST,
volume =       "4",
number =       "4",
pages =        "57:1--57:??",
month =        sep,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2508037.2508038",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:13 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Huge amounts of search log data have been accumulated
at Web search engines. Currently, a popular Web search
engine may receive billions of queries and collect
terabytes of records about user search behavior daily.
Beside search log data, huge amounts of browse log data
have also been collected through client-side browser
plugins. Such massive amounts of search and browse log
data provide great opportunities for mining the wisdom
of crowds and improving Web search. At the same time,
designing effective and efficient methods to clean,
process, and model log data also presents great
challenges. In this survey, we focus on mining search
introduction to search and browse log data and an
overview of frequently-used data summarizations in log
mining. We then elaborate how log mining applications
enhance the five major components of a search engine,
namely, query understanding, document understanding,
document ranking, user understanding, and monitoring
and feedback. For each aspect, we survey the major
methods.",
acknowledgement = ack-nhfb,
articleno =    "57",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Li:2013:SAM,
author =       "Xi Li and Weiming Hu and Chunhua Shen and Zhongfei
Zhang and Anthony Dick and Anton {Van Den Hengel}",
title =        "A survey of appearance models in visual object
tracking",
journal =      j-TIST,
volume =       "4",
number =       "4",
pages =        "58:1--58:??",
month =        sep,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2508037.2508039",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:13 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Visual object tracking is a significant computer
vision task which can be applied to many domains, such
as visual surveillance, human computer interaction, and
video compression. Despite extensive research on this
topic, it still suffers from difficulties in handling
complex object appearance changes caused by factors
such as illumination variation, partial occlusion,
shape deformation, and camera motion. Therefore,
effective modeling of the 2D appearance of tracked
objects is a key issue for the success of a visual
tracker. In the literature, researchers have proposed a
variety of 2D appearance models. To help readers
swiftly learn the recent advances in 2D appearance
models for visual object tracking, we contribute this
survey, which provides a detailed review of the
existing 2D appearance models. In particular, this
survey takes a module-based architecture that enables
readers to easily grasp the key points of visual object
tracking. In this survey, we first decompose the
problem of appearance modeling into two different
processing stages: visual representation and
statistical modeling. Then, different 2D appearance
models are categorized and discussed with respect to
their composition modules. Finally, we address several
issues of interest as well as the remaining challenges
for future research on this topic. The contributions of
this survey are fourfold. First, we review the
literature of visual representations according to their
feature-construction mechanisms (i.e., local and
global). Second, the existing statistical modeling
schemes for tracking-by-detection are reviewed
according to their model-construction mechanisms:
generative, discriminative, and hybrid
generative-discriminative. Third, each type of visual
representations or statistical modeling techniques is
analyzed and discussed from a theoretical or practical
viewpoint. Fourth, the existing benchmark resources
(e.g., source codes and video datasets) are examined in
this survey.",
acknowledgement = ack-nhfb,
articleno =    "58",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Cena:2013:PSA,
author =       "Federica Cena and Antonina Dattolo and Pasquale Lops
and Julita Vassileva",
title =        "Perspectives in {Semantic Adaptive Social Web}",
journal =      j-TIST,
volume =       "4",
number =       "4",
pages =        "59:1--59:??",
month =        sep,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2501603",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:13 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "The Social Web is now a successful reality with its
quickly growing number of users and applications. Also
the Semantic Web, which started with the objective of
describing Web resources in a machine-processable way,
is now outgrowing the research labs and is being
massively exploited in many websites, incorporating
high-quality user-generated content and semantic
annotations. The primary goal of this special section
is to showcase some recent research at the intersection
of the Social Web and the Semantic Web that explores
the benefits that adaptation and personalization have
to offer in the Web of the future, the so-called Social
Adaptive Semantic Web. We have selected two articles
out of fourteen submissions based on the quality of the
articles and we present the main lessons learned from
the overall analysis of these submissions.",
acknowledgement = ack-nhfb,
articleno =    "59",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Biancalana:2013:SSQ,
author =       "Claudio Biancalana and Fabio Gasparetti and Alessandro
Micarelli and Giuseppe Sansonetti",
title =        "Social semantic query expansion",
journal =      j-TIST,
volume =       "4",
number =       "4",
pages =        "60:1--60:??",
month =        sep,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2508037.2508041",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:13 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Weak semantic techniques rely on the integration of
Semantic Web techniques with social annotations and aim
propose a novel weak semantic technique for query
expansion. Traditional query expansion techniques are
based on the computation of two-dimensional
co-occurrence matrices. Our approach proposes the use
of three-dimensional matrices, where the added
dimension is represented by semantic classes (i.e.,
categories comprising all the terms that share a
semantic property) related to the folksonomy extracted
from social bookmarking services, such as delicious and
StumbleUpon. The results of an indepth experimental
evaluation performed on both artificial datasets and
real users show that our approach outperforms
traditional techniques, such as relevance feedback and
personalized PageRank, so confirming the validity and
usefulness of the categorization of the user needs and
preferences in semantic classes. We also present the
results of a questionnaire aimed to know the users
opinion regarding the system. As one drawback of
several query expansion techniques is their high
computational costs, we also provide a complexity
analysis of our system, in order to show its capability
of operating in real time.",
acknowledgement = ack-nhfb,
articleno =    "60",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Chen:2013:WMS,
author =       "Chao Chen and Qiusha Zhu and Lin Lin and Mei-Ling
Shyu",
title =        "{Web} media semantic concept retrieval via tag removal
and model fusion",
journal =      j-TIST,
volume =       "4",
number =       "4",
pages =        "61:1--61:??",
month =        sep,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2508037.2508042",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:13 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Multimedia data on social websites contain rich
semantics and are often accompanied with user-defined
tags. To enhance Web media semantic concept retrieval,
the fusion of tag-based and content-based models can be
novel semantic concept retrieval framework that
incorporates tag removal and model fusion is proposed
to tackle such a challenge. Tags with useful
information can facilitate media search, but they are
often imprecise, which makes it important to apply
noisy tag removal (by deleting uncorrelated tags) to
improve the performance of semantic concept retrieval.
Therefore, a multiple correspondence analysis
(MCA)-based tag removal algorithm is proposed, which
utilizes MCA's ability to capture the relationships
among nominal features and identify representative and
discriminative tags holding strong correlations with
the target semantic concepts. To further improve the
retrieval performance, a novel model fusion method is
also proposed to combine ranking scores from both
tag-based and content-based models, where the
adjustment of ranking scores, the reliability of
models, and the correlations between the intervals
divided on the ranking scores and the semantic concepts
are all considered. Comparative results with extensive
experiments on the NUS-WIDE-LITE as well as the
NUS-WIDE-270K benchmark datasets with 81 semantic
concepts show that the proposed framework outperforms
baseline results and the other comparison methods with
each component being evaluated separately.",
acknowledgement = ack-nhfb,
articleno =    "61",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Reddy:2013:ISS,
author =       "Chandan K. Reddy and Cristopher C. Yang",
title =        "Introduction to the special section on intelligent
systems for health informatics",
journal =      j-TIST,
volume =       "4",
number =       "4",
pages =        "62:1--62:??",
month =        sep,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2508037.2508043",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:13 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "62",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Batal:2013:TPM,
and Milos Hauskrecht",
title =        "A temporal pattern mining approach for classifying
electronic health record data",
journal =      j-TIST,
volume =       "4",
number =       "4",
pages =        "63:1--63:??",
month =        sep,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2508037.2508044",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:13 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "We study the problem of learning classification models
from complex multivariate temporal data encountered in
electronic health record systems. The challenge is to
define a good set of features that are able to
represent well the temporal aspect of the data. Our
method relies on temporal abstractions and temporal
pattern mining to extract the classification features.
Temporal pattern mining usually returns a large number
of temporal patterns, most of which may be irrelevant
present the Minimal Predictive Temporal Patterns
framework to generate a small set of predictive and
nonspurious patterns. We apply our approach to the
real-world clinical task of predicting patients who are
at risk of developing heparin-induced thrombocytopenia.
The results demonstrate the benefit of our approach in
efficiently learning accurate classifiers, which is a
key step for developing intelligent clinical monitoring
systems.",
acknowledgement = ack-nhfb,
articleno =    "63",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Rashidi:2013:CMM,
author =       "Parisa Rashidi and Diane J. Cook",
title =        "{COM}: a method for mining and monitoring human
activity patterns in home-based health monitoring
systems",
journal =      j-TIST,
volume =       "4",
number =       "4",
pages =        "64:1--64:??",
month =        sep,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2508037.2508045",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:13 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "The increasing aging population in the coming decades
will result in many complications for society and in
particular for the healthcare system due to the
shortage of healthcare professionals and healthcare
facilities. To remedy this problem, researchers have
pursued developing remote monitoring systems and
assisted living technologies by utilizing recent
advances in sensor and networking technology, as well
as in the data mining and machine learning fields. In
for discovering and monitoring patterns of daily
activities. Discovering and tracking patterns of daily
activities can provide unprecedented opportunities for
health monitoring and assisted living applications,
especially for older adults and individuals with mental
disabilities. Previous approaches usually rely on
preselected activities or labeled data to track and
fully automated approach by discovering natural
activity patterns and their variations in real-life
data. We will show how our activity discovery component
can be integrated with an activity recognition
component to track and monitor various daily activity
patterns. We also provide an activity visualization
component to allow caregivers to visually observe and
examine the activity patterns using a user-friendly
interface. We validate our algorithms using real-life
data obtained from two apartments during a three-month
period.",
acknowledgement = ack-nhfb,
articleno =    "64",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Wolf:2013:DUR,
author =       "Hannes Wolf and Klaus Herrmann and Kurt Rothermel",
title =        "Dealing with uncertainty: Robust workflow navigation
in the healthcare domain",
journal =      j-TIST,
volume =       "4",
number =       "4",
pages =        "65:1--65:??",
month =        sep,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2508037.2508046",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:13 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Processes in the healthcare domain are characterized
by coarsely predefined recurring procedures that are
flexibly adapted by the personnel to suite-specific
situations. In this setting, a workflow management
system that gives guidance and documents the
personnel's actions can lead to a higher quality of
care, fewer mistakes, and higher efficiency. However,
most existing workflow management systems enforce rigid
inflexible workflows and rely on direct manual input.
Both are inadequate for healthcare processes. In
particular, direct manual input is not possible in most
cases since (1) it would distract the personnel even in
critical situations and (2) it would violate
fundamental hygiene principles by requiring disinfected
doctors and nurses to touch input devices. The solution
could be activity recognition systems that use sensor
data (e.g., audio and acceleration data) to infer the
current activities by the personnel and provide input
to a workflow (e.g., informing it that a certain
activity is finished now). However, state-of-the-art
activity recognition technologies have difficulties in
providing reliable information. We describe a
comprehensive framework tailored for flexible
human-centric healthcare processes that improves the
reliability of activity recognition data. We present a
set of mechanisms that exploit the application
knowledge encoded in workflows in order to reduce the
uncertainty of this data, thus enabling unobtrusive
robust healthcare workflows. We evaluate our work based
on a real-world case study and show that the robustness
of unobtrusive healthcare workflows can be increased to
an absolute value of up to 91\% (compared to only 12\%
with a classical workflow system). This is a major
breakthrough that paves the way towards future
IT-enabled healthcare systems.",
acknowledgement = ack-nhfb,
articleno =    "65",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Park:2013:CPC,
author =       "Yubin Park and Joydeep Ghosh",
title =        "{CUDIA}: Probabilistic cross-level imputation using
individual auxiliary information",
journal =      j-TIST,
volume =       "4",
number =       "4",
pages =        "66:1--66:??",
month =        sep,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2508037.2508047",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:13 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "In healthcare-related studies, individual patient or
hospital data are not often publicly available due to
privacy restrictions, legal issues, or reporting norms.
However, such measures may be provided at a higher or
more aggregated level, such as state-level,
county-level summaries or averages over health zones,
such as hospital referral regions (HRR) or hospital
service areas (HSA). Such levels constitute partitions
over the underlying individual level data, which may
not match the groupings that would have been obtained
if one clustered the data based on individual-level
attributes. Moreover, treating aggregated values as
representatives for the individuals can result in the
ecological fallacy. How can one run data mining
procedures on such data where different variables are
available at different levels of aggregation or
utilization of variably aggregated datasets, which are
possibly assembled from different sources. We propose a
novel cross-level imputation technique that models the
generative process of such datasets using a Bayesian
directed graphical model. The imputation is based on
the underlying data distribution and is shown to be
unbiased. This imputation can be further utilized in a
subsequent predictive modeling, yielding improved
accuracies. The experimental results using a simulated
dataset and the Behavioral Risk Factor Surveillance
System (BRFSS) dataset are provided to illustrate the
generality and capabilities of the proposed
framework.",
acknowledgement = ack-nhfb,
articleno =    "66",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Hoens:2013:RMR,
author =       "T. Ryan Hoens and Marina Blanton and Aaron Steele and
Nitesh V. Chawla",
title =        "Reliable medical recommendation systems with patient
privacy",
journal =      j-TIST,
volume =       "4",
number =       "4",
pages =        "67:1--67:??",
month =        sep,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2508037.2508048",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:13 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "One of the concerns patients have when confronted with
a medical condition is which physician to trust. Any
recommendation system that seeks to answer this
question must ensure that any sensitive medical
information collected by the system is properly
concerns in a privacy-friendly framework and present
two architectures that realize it: the Secure
Processing Architecture (SPA) and the Anonymous
Contributions Architecture (ACA). In SPA, patients
submit their ratings in a protected form without
revealing any information about their data and the
computation of recommendations proceeds over the
protected data using secure multiparty computation
techniques. In ACA, patients submit their ratings in
the clear, but no link between a submission and patient
data can be made. We discuss various aspects of both
architectures, including techniques for ensuring
reliability of computed recommendations and system
performance, and provide their comparison.",
acknowledgement = ack-nhfb,
articleno =    "67",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Khan:2013:VOM,
author =       "Atif Khan and John A. Doucette and Robin Cohen",
title =        "Validation of an ontological medical decision support
system for patient treatment using a repository of
patient data: Insights into the value of machine
learning",
journal =      j-TIST,
volume =       "4",
number =       "4",
pages =        "68:1--68:??",
month =        sep,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2508037.2508049",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:13 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
medical decision support system, and argue for its
value over purely probabilistic approaches that reason
about patients for time-critical decision scenarios. We
then progress to present Holmes, a Hybrid Ontological
and Learning MEdical System which supports decision
making about patient treatment. This system is
introduced in order to cope with the case of missing
data. We demonstrate its effectiveness by operating on
an extensive set of real-world patient health data from
the CDC, applied to the decision-making scenario of
administering sleeping pills. In particular, we clarify
how the combination of semantic, ontological
representations, and probabilistic reasoning together
enable the proposal of effective patient treatments.
Our focus is thus on presenting an approach for
interpreting medical data in the context of real-time
decision making. This constitutes a comprehensive
framework for the design of medical recommendation
systems for potential use by medical professionals and
patients both, with the end result being personalized
patient treatment. We conclude with a discussion of the
value of our particular approach for such diverse
considerations as coping with misinformation provided
by patients, performing effectively in time-critical
environments where real-time decisions are necessary,
and potential applications facilitating patient
information gathering.",
acknowledgement = ack-nhfb,
articleno =    "68",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Lee:2013:CPR,
author =       "Suk Jin Lee and Yuichi Motai and Elisabeth Weiss and
Shumei S. Sun",
title =        "Customized prediction of respiratory motion with
clustering from multiple patient interaction",
journal =      j-TIST,
volume =       "4",
number =       "4",
pages =        "69:1--69:??",
month =        sep,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2508037.2508050",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:13 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Information processing of radiotherapy systems has
become an important research area for sophisticated
delivery of radiotherapy in the thorax and upper
abdomen is compromised by respiratory motion during
treatment. Accurate prediction of the respiratory
motion would be beneficial for improving tumor
targeting. However, a wide variety of breathing
patterns can make it difficult to predict the breathing
motion with explicit models. We proposed a respiratory
motion predictor, that is, customized prediction with
multiple patient interactions using neural network
(CNN). For the preprocedure of prediction for
individual patient, we construct the clustering based
on breathing patterns of multiple patients using the
feature selection metrics that are composed of a
variety of breathing features. In the intraprocedure,
the proposed CNN used neural networks (NN) for a part
of the prediction and the extended Kalman filter (EKF)
for a part of the correction. The prediction accuracy
of the proposed method was investigated with a variety
of prediction time horizons using normalized root mean
squared error (NRMSE) values in comparison with the
alternate recurrent neural network (RNN). We have also
evaluated the prediction accuracy using the marginal
value that can be used as the reference value to judge
how many signals lie outside the confidence level. The
experimental results showed that the proposed CNN can
outperform RNN with respect to the prediction accuracy
with an improvement of 50\%.",
acknowledgement = ack-nhfb,
articleno =    "69",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Baralis:2013:EPH,
author =       "Elena Baralis and Tania Cerquitelli and Silvia
Chiusano and Vincenzo D'elia and Riccardo Molinari and
Davide Susta",
title =        "Early prediction of the highest workload in
incremental cardiopulmonary tests",
journal =      j-TIST,
volume =       "4",
number =       "4",
pages =        "70:1--70:??",
month =        sep,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2508037.2508051",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:13 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Incremental tests are widely used in cardiopulmonary
exercise testing, both in the clinical domain and in
sport sciences. The highest workload (denoted
W$_{peak}$ ) reached in the test is key information for
assessing the individual body response to the test and
for analyzing possible cardiac failures and planning
rehabilitation, and training sessions. Being physically
very demanding, incremental tests can significantly
increase the body stress on monitored individuals and
presents a new approach to cardiopulmonary testing that
addresses these drawbacks. During the test, our
approach analyzes the individual body response to the
exercise and predicts the W$_{peak}$ value that will be
reached in the test and an evaluation of its accuracy.
When the accuracy of the prediction becomes
satisfactory, the test can be prematurely stopped, thus
avoiding its entire execution. To predict W$_{peak}$,
we introduce a new index, the CardioPulmonary
Efficiency Index (CPE), summarizing the cardiopulmonary
response of the individual to the test. Our approach
analyzes the CPE trend during the test, together with
the characteristics of the individual, and predicts
W$_{peak}$. A K-nearest-neighbor-based classifier and
an ANN-based classier are exploited for the prediction.
The experimental evaluation showed that the W$_{peak}$
value can be predicted with a limited error from the
first steps of the test.",
acknowledgement = ack-nhfb,
articleno =    "70",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Lee:2013:SFI,
author =       "Yugyung Lee and Saranya Krishnamoorthy and Deendayal
Dinakarpandian",
title =        "A semantic framework for intelligent matchmaking for
clinical trial eligibility criteria",
journal =      j-TIST,
volume =       "4",
number =       "4",
pages =        "71:1--71:??",
month =        sep,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2508037.2508052",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:13 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "An integral step in the discovery of new treatments
for medical conditions is the matching of potential
subjects with appropriate clinical trials. Eligibility
criteria for clinical trials are typically specified as
inclusion and exclusion criteria for each study in
freetext form. While this is sufficient for a human to
guide a recruitment interview, it cannot be reliably
and computationally construed to identify potential
subjects. Standardization of the representation of
eligibility criteria can enhance the efficiency and
semantic framework that facilitates intelligent
matchmaking by identifying a minimal set of eligibility
criteria with maximal coverage of clinical trials. In
contrast to existing top-down manual standardization
efforts, a bottom-up data driven approach is presented
to find a canonical nonredundant representation of an
arbitrary collection of clinical trial criteria. The
methodology has been validated with a corpus of 709
clinical trials related to Generalized Anxiety Disorder
containing 2,760 inclusion and 4,871 exclusion
eligibility criteria. This corpus is well represented
by a relatively small number of 126 inclusion clusters
and 175 exclusion clusters, each of which corresponds
to a semantically distinct criterion. Internal and
external validation measures provide an objective
evaluation of the method. An eligibility criteria
ontology has been constructed based on the clustering.
The resulting model has been incorporated into the
development of the MindTrial clinical trial recruiting
system. The prototype for clinical trial recruitment
illustrates the effectiveness of the methodology in
characterizing clinical trials and subjects and
accurate matching between them.",
acknowledgement = ack-nhfb,
articleno =    "71",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Bi:2013:MLA,
author =       "Jinbo Bi and Jiangwen Sun and Yu Wu and Howard Tennen
and Stephen Armeli",
title =        "A machine learning approach to college drinking
prediction and risk factor identification",
journal =      j-TIST,
volume =       "4",
number =       "4",
pages =        "72:1--72:??",
month =        sep,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2508037.2508053",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:13 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Alcohol misuse is one of the most serious public
the United States. National statistics shows that
nearly 90\% of alcohol consumed by youth under 21 years
of age involves binge drinking and 44\% of college
students engage in high-risk drinking activities.
Conventional alcohol intervention programs, which aim
at installing either an alcohol reduction norm or
prohibition against underage drinking, have yielded
little progress in controlling college binge drinking
over the years. Existing alcohol studies are deductive
where data are collected to investigate a
psychological/behavioral hypothesis, and statistical
analysis is applied to the data to confirm the
hypothesis. Due to this confirmatory manner of
analysis, the resulting statistical models are
cohort-specific and typically fail to replicate on a
learning approaches for a secondary analysis of
longitudinal data collected in college alcohol studies
sponsored by the National Institute on Alcohol Abuse
and Alcoholism. Our approach aims to discover
knowledge, from multiwave cohort-sequential daily data,
which may or may not align with the original hypothesis
but quantifies predictive models with higher likelihood
to generalize to new samples. We first propose a
so-called temporally-correlated support vector machine
to construct a classifier as a function of daily moods,
stress, and drinking expectancies to distinguish days
with nighttime binge drinking from days without for
individual students. We then propose a combination of
cluster analysis and feature selection, where cluster
analysis is used to identify drinking patterns based on
averaged daily drinking behavior and feature selection
is used to identify risk factors associated with each
pattern. We evaluate our methods on two cohorts of 530
total college students recruited during the Spring and
Fall semesters, respectively. Cross validation on these
two cohorts and further on 100 random partitions of the
total students demonstrate that our methods improve the
model generalizability in comparison with traditional
multilevel logistic regression. The discovered risk
factors and the interaction of these factors delineated
in our models can set a potential basis and offer
insights to a new design of more effective college
alcohol interventions.",
acknowledgement = ack-nhfb,
articleno =    "72",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Subbu:2013:LMF,
author =       "Kalyan Pathapati Subbu and Brandon Gozick and Ram
Dantu",
title =        "{LocateMe}: Magnetic-fields-based indoor localization
using smartphones",
journal =      j-TIST,
volume =       "4",
number =       "4",
pages =        "73:1--73:??",
month =        sep,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2508037.2508054",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:13 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Fine-grained localization is extremely important to
accurately locate a user indoors. Although innovative
solutions have already been proposed, there is no
solution that is universally accepted, easily
implemented, user centric, and, most importantly, works
in the absence of GSM coverage or WiFi availability.
The advent of sensor rich smartphones has paved a way
to develop a solution that can cater to these
requirements. By employing a smartphone's built-in
magnetic field sensor, magnetic signatures were
collected inside buildings. These signatures displayed
a uniqueness in their patterns due to the presence of
different kinds of pillars, doors, elevators, etc.,
that consist of ferromagnetic materials like steel or
iron. We theoretically analyze the cause of this
uniqueness and then present an indoor localization
solution by classifying signatures based on their
patterns. However, to account for user walking speed
variations so as to provide an application usable to a
variety of users, we follow a dynamic
time-warping-based approach that is known to work on
similar signals irrespective of their variations in the
time axis. Our approach resulted in localization
distances of approximately 2m--6m with accuracies
between 80--100\% implying that it is sufficient to
walk short distances across hallways to be located by
the smartphone. The implementation of the application
on different smartphones yielded response times of less
than five secs, thereby validating the feasibility of
our approach and making it a viable solution.",
acknowledgement = ack-nhfb,
articleno =    "73",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Chen:2013:RWM,
author =       "Bin Chen and Jian Su and Chew Lim Tan",
title =        "Random walks down the mention graphs for event
coreference resolution",
journal =      j-TIST,
volume =       "4",
number =       "4",
pages =        "74:1--74:??",
month =        sep,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2508037.2508055",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:13 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Event coreference is an important task in event
extraction and other natural language processing tasks.
Despite its importance, it was merely discussed in
coreference resolution system dedicated to various
sophisticated event coreference phenomena. First, seven
resolvers are utilized to resolve different event and
object coreference mention pairs with a new instance
selection strategy and new linguistic features. Second,
a global solution-a modified random walk
partitioning-is employed for the chain formation. Being
the first attempt to apply the random walk model for
coreference resolution, the revised model utilizes a
sampling method, termination criterion, and stopping
probability to greatly improve the effectiveness of
random walk model for event coreference resolution.
Last but not least, the new model facilitates a
convenient way to incorporate sophisticated linguistic
constraints and preferences, the related object mention
graph, as well as pronoun coreference information not
used in previous studies for effective chain formation.
In total, these techniques impose more than 20\%
F-score improvement over the baseline system.",
acknowledgement = ack-nhfb,
articleno =    "74",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Editors:2013:ISS,
author =       "Editors:",
title =        "Introduction to special section on intelligent mobile
knowledge discovery and management systems",
journal =      j-TIST,
volume =       "5",
number =       "1",
pages =        "1:1--1:??",
month =        dec,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2542182.2542183",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:16 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "1",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Ying:2013:MGT,
author =       "Josh Jia-Ching Ying and Wang-Chien Lee and Vincent S.
Tseng",
title =        "Mining geographic-temporal-semantic patterns in
trajectories for location prediction",
journal =      j-TIST,
volume =       "5",
number =       "1",
pages =        "2:1--2:??",
month =        dec,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2542182.2542184",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:16 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "In recent years, research on location predictions by
mining trajectories of users has attracted a lot of
attention. Existing studies on this topic mostly treat
such predictions as just a type of location
recommendation, that is, they predict the next location
of a user using location recommenders. However, an user
usually visits somewhere for reasons other than
mining-based location prediction approach called
Geographic-Temporal-Semantic-based Location Prediction
(GTS-LP), which takes into account a user's
geographic-triggered intentions, temporal-triggered
intentions, and semantic-triggered intentions, to
estimate the probability of the user in visiting a
location. The core idea underlying our proposal is the
discovery of trajectory patterns of users, namely GTS
patterns, to capture frequent movements triggered by
the three kinds of intentions. To achieve this goal, we
define a new trajectory pattern to capture the key
properties of the behaviors that are motivated by the
three kinds of intentions from trajectories of users.
In our GTS-LP approach, we propose a series of novel
matching strategies to calculate the similarity between
the current movement of a user and discovered GTS
patterns based on various moving intentions. On the
basis of similitude, we make an online prediction as to
the location the user intends to visit. To the best of
our knowledge, this is the first work on location
prediction based on trajectory pattern mining that
explores the geographic, temporal, and semantic
properties simultaneously. By means of a comprehensive
evaluation using various real trajectory datasets, we
show that our proposed GTS-LP approach delivers
excellent performance and significantly outperforms
existing state-of-the-art location prediction
methods.",
acknowledgement = ack-nhfb,
articleno =    "2",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Tang:2013:FTC,
author =       "Lu-An Tang and Yu Zheng and Jing Yuan and Jiawei Han
and Alice Leung and Wen-Chih Peng and Thomas {La
Porta}",
title =        "A framework of traveling companion discovery on
trajectory data streams",
journal =      j-TIST,
volume =       "5",
number =       "1",
pages =        "3:1--3:??",
month =        dec,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2542182.2542185",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:16 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
volumes of spatio-temporal data collected in the form
of trajectory data streams. In this study, we
investigate the problem of discovering object groups
that travel together (i.e., traveling companions ) from
trajectory data streams. Such technique has broad
applications in the areas of scientific study,
transportation management, and military surveillance.
To discover traveling companions, the monitoring system
should cluster the objects of each snapshot and
intersect the clustering results to retrieve
moving-together objects. Since both clustering and
intersection steps involve high computational overhead,
the key issue of companion discovery is to improve the
efficiency of algorithms. We propose the models of
closed companion candidates and smart intersection to
accelerate data processing. A data structure termed
traveling buddy is designed to facilitate scalable and
flexible companion discovery from trajectory streams.
The traveling buddies are microgroups of objects that
are tightly bound together. By only storing the object
relationships rather than their spatial coordinates,
the buddies can be dynamically maintained along the
trajectory stream with low cost. Based on traveling
buddies, the system can discover companions without
accessing the object details. In addition, we extend
the proposed framework to discover companions on more
complicated scenarios with spatial and temporal
constraints, such as on the road network and
battlefield. The proposed methods are evaluated with
extensive experiments on both real and synthetic
datasets. Experimental results show that our proposed
buddy-based approach is an order of magnitude faster
than the baselines and achieves higher accuracy in
companion discovery.",
acknowledgement = ack-nhfb,
articleno =    "3",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Doo:2013:MTF,
author =       "Myungcheol Doo and Ling Liu",
title =        "{Mondrian} tree: a fast index for spatial alarm
processing",
journal =      j-TIST,
volume =       "5",
number =       "1",
pages =        "4:1--4:??",
month =        dec,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2542182.2542186",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:16 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "With ubiquitous wireless connectivity and
technological advances in mobile devices, we witness
the growing demands and increasing market shares of
mobile intelligent systems and technologies for
real-time decision making and location-based knowledge
discovery. Spatial alarms are considered as one of the
fundamental capabilities for intelligent mobile
location-based systems. Like time-based alarms that
remind us the arrival of a future time point, spatial
alarms remind us the arrival of a future spatial point.
Existing approaches for scaling spatial alarm
processing are focused on computing Alarm-Free Regions
(A fr) and Alarm-Free Period (Afp) such that mobile
objects traveling within an Afr can safely hibernate
the alarm evaluation process for the computed Afp, to
save battery power, until approaching the nearest alarm
of interest. A key technical challenge in scaling
spatial alarm processing is to efficiently compute Afr
and Afp such that mobile objects traveling within an
Afr can safely hibernate the alarm evaluation process
during the computed Afp, while maintaining high
computation of Afr is expensive and may not scale well
for dense populations of mobile objects. Instead, we
propose to maintain an index for both spatial alarms
and empty regions (Afr) such that for a given mobile
user's location, we can find relevant spatial alarms
and whether it is in an alarm-free region more
efficiently. We also show that conventional spatial
indexing methods, such as R-tree family, k -d tree,
Quadtree, and Grid, are by design not well suited to
index empty regions. We present Mondrian Tree --- a
region partitioning tree for indexing both spatial
alarms and alarm-free regions. We first introduce the
Mondrian Tree indexing algorithms, including index
construction, search, and maintenance. Then we describe
a suite of Mondrian Tree optimizations to further
enhance the performance of spatial alarm processing.
Our experimental evaluation shows that the Mondrian
Tree index, as an intelligent technology for mobile
systems, outperforms traditional index methods, such as
R-tree, Quadtree, and k -d tree, for spatial alarm
processing.",
acknowledgement = ack-nhfb,
articleno =    "4",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Bonchi:2013:ISI,
author =       "Francesco Bonchi and Wray Buntine and Ricard
Gavald{\'a} and Shengbo Guo",
title =        "Introduction to the special issue on {Social Web}
mining",
journal =      j-TIST,
volume =       "5",
number =       "1",
pages =        "5:1--5:??",
month =        dec,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2542182.2542187",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:16 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
articleno =    "5",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{He:2013:DJS,
author =       "Yulan He and Chenghua Lin and Wei Gao and Kam-Fai
Wong",
title =        "Dynamic joint sentiment-topic model",
journal =      j-TIST,
volume =       "5",
number =       "1",
pages =        "6:1--6:??",
month =        dec,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2542182.2542188",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:16 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Social media data are produced continuously by a large
and uncontrolled number of users. The dynamic nature of
such data requires the sentiment and topic analysis
model to be also dynamically updated, capturing the
most recent language use of sentiments and topics in
text. We propose a dynamic Joint Sentiment-Topic model
(dJST) which allows the detection and tracking of views
of current and recurrent interests and shifts in topic
and sentiment. Both topic and sentiment dynamics are
captured by assuming that the current
sentiment-topic-specific word distributions are
generated according to the word distributions at
previous epochs. We study three different ways of
accounting for such dependency information: (1) sliding
window where the current sentiment-topic word
distributions are dependent on the previous
sentiment-topic-specific word distributions in the last
S epochs; (2) skip model where history sentiment topic
word distributions are considered by skipping some
epochs in between; and (3) multiscale model where
previous long- and short- timescale distributions are
taken into consideration. We derive efficient online
inference procedures to sequentially update the model
with newly arrived data and show the effectiveness of
our proposed model on the Mozilla add-on reviews
crawled between 2007 and 2011.",
acknowledgement = ack-nhfb,
articleno =    "6",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Cataldi:2013:PET,
author =       "Mario Cataldi and Luigi {Di Caro} and Claudio
Schifanella",
title =        "Personalized emerging topic detection based on a term
aging model",
journal =      j-TIST,
volume =       "5",
number =       "1",
pages =        "7:1--7:??",
month =        dec,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2542182.2542189",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:16 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Twitter is a popular microblogging service that acts
as a ground-level information news flashes portal where
people with different background, age, and social
condition provide information about what is happening
in front of their eyes. This characteristic makes
Twitter probably the fastest information service in the
Twitter and propose a novel, user-aware topic detection
technique that permits to retrieve, in real time, the
most emerging topics of discussion expressed by the
community within the interests of specific users.
First, we analyze the topology of Twitter looking at
how the information spreads over the network, taking
into account the authority/influence of each active
user. Then, we make use of a novel term aging model to
compute the burstiness of each term, and provide a
graph-based method to retrieve the minimal set of terms
that can represent the corresponding topic. Finally,
since any user can have topic preferences inferable
from the shared content, we leverage such knowledge to
highlight the most emerging topics within her foci of
interest. As evaluation we then provide several
experiments together with a user study proving the
validity and reliability of the proposed approach.",
acknowledgement = ack-nhfb,
articleno =    "7",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Arias:2013:FTD,
author =       "Marta Arias and Argimiro Arratia and Ramon Xuriguera",
title =        "Forecasting with {Twitter} data",
journal =      j-TIST,
volume =       "5",
number =       "1",
pages =        "8:1--8:??",
month =        dec,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2542182.2542190",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:16 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "The dramatic rise in the use of social network
the availability of vast and growing user-contributed
repositories of data. Exploiting this data by
extracting useful information from it has become a
great challenge in data mining and knowledge discovery.
A recently popular way of extracting useful information
from social network platforms is to build indicators,
often in the form of a time series, of general public
mood by means of sentiment analysis. Such indicators
have been shown to correlate with a diverse variety of
and set out to assess, in a rigorous manner, whether a
public sentiment indicator extracted from daily Twitter
messages can indeed improve the forecasting of social,
economic, or commercial indicators. To this end we have
collected and processed a large amount of Twitter posts
from March 2011 to the present date for two very
different domains: stock market and movie box office
revenue. For each of these domains, we build and
evaluate forecasting models for several target time
series both using and ignoring the Twitter-related
data. If Twitter does help, then this should be
reflected in the fact that the predictions of models
that use Twitter-related data are better than the
models that do not use this data. By systematically
varying the models that we use and their parameters,
together with other tuning factors such as lag or the
way in which we build our Twitter sentiment index, we
obtain a large dataset that allows us to test our
hypothesis under different experimental conditions.
Using a novel decision-tree-based technique that we
call summary tree we are able to mine this large
dataset and obtain automatically those configurations
that lead to an improvement in the prediction power of
our forecasting models. As a general result, we have
data when forecasting trends in volatility indices,
while linear ones fail systematically when forecasting
any kind of financial time series. In the case of
predicting box office revenue trend, it is support
vector machines that make best use of Twitter data. In
addition, we conduct statistical tests to determine the
relation between our Twitter time series and the
different target time series.",
acknowledgement = ack-nhfb,
articleno =    "8",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Lee:2013:CES,
author =       "Kyumin Lee and James Caverlee and Zhiyuan Cheng and
Daniel Z. Sui",
title =        "Campaign extraction from social media",
journal =      j-TIST,
volume =       "5",
number =       "1",
pages =        "9:1--9:??",
month =        dec,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2542182.2542191",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:16 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "In this manuscript, we study the problem of detecting
coordinated free text campaigns in large-scale social
media. These campaigns-ranging from coordinated spam
messages to promotional and advertising campaigns to
political astro-turfing-are growing in significance and
reach with the commensurate rise in massive-scale
social systems. Specifically, we propose and evaluate a
content-driven framework for effectively linking free
text posts with common talking points'' and
extracting campaigns from large-scale social media.
Three of the salient features of the campaign
extraction framework are: (i) first, we investigate
graph mining techniques for isolating coherent
campaigns from large message-based graphs; (ii) second,
we conduct a comprehensive comparative study of
text-based message correlation in message and user
levels; and (iii) finally, we analyze temporal
behaviors of various campaign types. Through an
experimental study over millions of Twitter messages we
identify five major types of campaigns-namely Spam,
Promotion, Template, News, and Celebrity campaigns-and
we show how these campaigns may be extracted with high
precision and recall.",
acknowledgement = ack-nhfb,
articleno =    "9",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Fire:2013:CEL,
author =       "Michael Fire and Lena Tenenboim-Chekina and Rami Puzis
and Ofrit Lesser and Lior Rokach and Yuval Elovici",
title =        "Computationally efficient link prediction in a variety
of social networks",
journal =      j-TIST,
volume =       "5",
number =       "1",
pages =        "10:1--10:??",
month =        dec,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2542182.2542192",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:16 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Online social networking sites have become
increasingly popular over the last few years. As a
result, new interdisciplinary research directions have
emerged in which social network analysis methods are
applied to networks containing hundreds of millions of
users. Unfortunately, links between individuals may be
missing either due to an imperfect acquirement process
or because they are not yet reflected in the online
network (i.e., friends in the real world did not form a
virtual connection). The primary bottleneck in link
prediction techniques is extracting the structural
features required for classifying links. In this
article, we propose a set of simple, easy-to-compute
structural features that can be analyzed to identify
missing links. We show that by using simple structural
features, a machine learning classifier can
successfully identify missing links, even when applied
to a predicament of classifying links between
individuals with at least one common friend. We also
present a method for calculating the amount of data
needed in order to build more accurate classifiers. The
new Friends measure and Same community features we
developed are shown to be good predictors for missing
links. An evaluation experiment was performed on ten
large social networks datasets: Academia.edu, DBLP,
provide social network site operators with the
capability of helping users to find known, offline
contacts and to discover new friends online. They may
also be used for exposing hidden links in online social
networks.",
acknowledgement = ack-nhfb,
articleno =    "10",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Cruz:2013:CDV,
author =       "Juan David Cruz and C{\'e}cile Bothorel and
Fran{\c{c}}ois Poulet",
title =        "Community detection and visualization in social
networks: Integrating structural and semantic
information",
journal =      j-TIST,
volume =       "5",
number =       "1",
pages =        "11:1--11:??",
month =        dec,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2542182.2542193",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:16 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Due to the explosion of social networking and the
information sharing among their users, the interest in
analyzing social networks has increased over the recent
years. Two general interests in this kind of studies
are community detection and visualization. In the first
case, most of the classic algorithms for community
detection use only the structural information to
identify groups, that is, how clusters are formed
according to the topology of the relationships.
However, these methods do not take into account any
semantic information which could guide the clustering
process, and which may add elements to conduct further
analyses. In the second case most of the layout
algorithms for clustered graphs have been designed to
differentiate the groups within the graph, but they are
not designed to analyze the interactions between such
groups. Identifying these interactions gives an insight
into the way different communities exchange messages or
information, and allows the social network researcher
to identify key actors, roles, and paths from one
model to use, in a conjoint way, the semantic
information from the social network and its structural
information to, first, find structurally and
semantically related groups of nodes, and second, a
layout algorithm for clustered graphs which divides the
nodes into two types, one for nodes with edges
connecting other communities and another with nodes
connecting nodes only within their own community. With
this division the visualization tool focuses on the
connections between groups facilitating deep studies of
augmented social networks.",
acknowledgement = ack-nhfb,
articleno =    "11",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Cagliero:2013:PTR,
author =       "Luca Cagliero and Alessandro Fiori and Luigi
Grimaudo",
title =        "Personalized tag recommendation based on generalized
rules",
journal =      j-TIST,
volume =       "5",
number =       "1",
pages =        "12:1--12:??",
month =        dec,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2542182.2542194",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:16 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Tag recommendation is focused on recommending useful
tags to a user who is annotating a Web resource. A
relevant research issue is the recommendation of
additional tags to partially annotated resources, which
may be based on either personalized or collective
knowledge. However, since the annotation process is
usually not driven by any controlled vocabulary, the
collections of user-specific and collective annotations
are often very sparse. Indeed, the discovery of the
most significant associations among tags becomes a
personalized tag recommendation system that discovers
and exploits generalized association rules, that is,
tag correlations holding at different abstraction
levels, to identify additional pertinent tags to
suggest. The use of generalized rules relevantly
improves the effectiveness of traditional rule-based
systems in coping with sparse tag collections, because:
(i) correlations hidden at the level of individual tags
may be anyhow figured out at higher abstraction levels
and (ii) low-level tag associations discovered from
collective data may be exploited to specialize
high-level associations discovered in the user-specific
context. The effectiveness of the proposed system has
been validated against other personalized approaches on
popular photo-sharing system Flickr.",
acknowledgement = ack-nhfb,
articleno =    "12",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Elahi:2013:ALS,
author =       "Mehdi Elahi and Francesco Ricci and Neil Rubens",
title =        "Active learning strategies for rating elicitation in
collaborative filtering: a system-wide perspective",
journal =      j-TIST,
volume =       "5",
number =       "1",
pages =        "13:1--13:??",
month =        dec,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2542182.2542195",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:16 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "The accuracy of collaborative-filtering recommender
systems largely depends on three factors: the quality
of the rating prediction algorithm, and the quantity
and quality of available ratings. While research in the
field of recommender systems often concentrates on
improving prediction algorithms, even the best
algorithms will fail if they are fed poor-quality data
during training, that is, garbage in, garbage out.
Active learning aims to remedy this problem by focusing
on obtaining better-quality data that more aptly
reflects a user's preferences. However, traditional
evaluation of active learning strategies has two major
flaws, which have significant negative ramifications on
accurately evaluating the system's performance
(prediction error, precision, and quantity of elicited
ratings). (1) Performance has been evaluated for each
user independently (ignoring system-wide improvements).
(2) Active learning strategies have been evaluated in
isolation from unsolicited user ratings (natural
rating has effects across the system, so a typical
user-centric evaluation which ignores any changes of
rating prediction of other users also ignores these
cumulative effects, which may be more influential on
the performance of the system as a whole (system
centric). We propose a new evaluation methodology and
use it to evaluate some novel and state-of-the-art
rating elicitation strategies. We found that the
system-wide effectiveness of a rating elicitation
strategy depends on the stage of the rating elicitation
process, and on the evaluation measures (MAE, NDCG, and
Precision). In particular, we show that using some
common user-centric strategies may actually degrade the
overall performance of a system. Finally, we show that
the performance of many common active learning
strategies changes significantly when evaluated
concurrently with the natural acquisition of ratings in
recommender systems.",
acknowledgement = ack-nhfb,
articleno =    "13",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{deMeo:2013:AUB,
author =       "Pasquale de Meo and Emilio Ferrara and Fabian Abel and
Lora Aroyo and Geert-Jan Houben",
title =        "Analyzing user behavior across social sharing
environments",
journal =      j-TIST,
volume =       "5",
number =       "1",
pages =        "14:1--14:??",
month =        dec,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2535526",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:16 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "In this work we present an in-depth analysis of the
user behaviors on different Social Sharing systems. We
consider three popular platforms, Flickr, Delicious and
StumbleUpon, and, by combining techniques from social
network analysis with techniques from semantic
analysis, we characterize the tagging behavior as well
as the tendency to create friendship relationships of
the users of these platforms. The aim of our
investigation is to see if (and how) the features and
goals of a given Social Sharing system reflect on the
behavior of its users and, moreover, if there exists a
correlation between the social and tagging behavior of
the users. We report our findings in terms of the
characteristics of user profiles according to three
different dimensions: (i) intensity of user activities,
(ii) tag-based characteristics of user profiles, and
(iii) semantic characteristics of user profiles.",
acknowledgement = ack-nhfb,
articleno =    "14",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Shi:2013:ACL,
author =       "Ziqiang Shi and Jiqing Han and Tieran Zheng",
title =        "Audio classification with low-rank matrix
representation features",
journal =      j-TIST,
volume =       "5",
number =       "1",
pages =        "15:1--15:??",
month =        dec,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2542182.2542197",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:16 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "In this article, a novel framework based on trace norm
minimization for audio classification is proposed. In
this framework, both the feature extraction and
classification are obtained by solving corresponding
convex optimization problem with trace norm
regularization. For feature extraction, robust
principle component analysis (robust PCA) via
minimization a combination of the nuclear norm and the
l$_1$ -norm is used to extract low-rank matrix features
which are robust to white noise and gross corruption
for audio signal. These low-rank matrix features are
fed to a linear classifier where the weight and bias
are learned by solving similar trace norm constrained
problems. For this linear classifier, most methods find
the parameters, that is the weight matrix and bias in
batch-mode, which makes it inefficient for large scale
framework using accelerated proximal gradient method.
This framework has advantages in processing speed and
memory cost. In addition, as a result of the
regularization formulation of matrix classification,
the Lipschitz constant was given explicitly, and hence
the step size estimation of the general proximal
gradient method was omitted, and this part of computing
burden is saved in our approach. Extensive experiments
on real data sets for laugh/non-laugh and
applause/non-applause classification indicate that this
novel framework is effective and noise robust.",
acknowledgement = ack-nhfb,
articleno =    "15",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Osman:2013:TMA,
author =       "Nardine Osman and Carles Sierra and Fiona Mcneill and
Juan Pane and John Debenham",
title =        "Trust and matching algorithms for selecting suitable
agents",
journal =      j-TIST,
volume =       "5",
number =       "1",
pages =        "16:1--16:??",
month =        dec,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2542182.2542198",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:16 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
agents to collaborate with for a given interaction in
distributed open systems, such as multiagent and P2P
systems. The agent in question is given the chance to
describe its confidence in its own capabilities.
However, since agents may be malicious, misinformed,
suffer from miscommunication, and so on, one also needs
to calculate how much trusted is that agent. This
article proposes a novel trust model that calculates
the expectation about an agent's future performance in
a given context by assessing both the agent's
willingness and capability through the semantic
comparison of the current context in question with the
agent's performance in past similar experiences. The
proposed mechanism for assessing trust may be applied
to any real world application where past commitments
are recorded and observations are made that assess
these commitments, and the model can then calculate
one's trust in another with respect to a future
commitment by assessing the other's past performance.",
acknowledgement = ack-nhfb,
articleno =    "16",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Montali:2013:MBC,
author =       "Marco Montali and Fabrizio M. Maggi and Federico
Chesani and Paola Mello and Wil M. P. van der Aalst",
title =        "Monitoring business constraints with the event
calculus",
journal =      j-TIST,
volume =       "5",
number =       "1",
pages =        "17:1--17:??",
month =        dec,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2542182.2542199",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:16 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Today, large business processes are composed of
smaller, autonomous, interconnected subsystems,
achieving modularity and robustness. Quite often, these
large processes comprise software components as well as
human actors, they face highly dynamic environments and
their subsystems are updated and evolve independently
of each other. Due to their dynamic nature and
complexity, it might be difficult, if not impossible,
to ensure at design-time that such systems will always
exhibit the desired/expected behaviors. This, in turn,
triggers the need for runtime verification and
monitoring facilities. These are needed to check
whether the actual behavior complies with expected
desired best practices. In this work, we present
Mobucon EC, a novel monitoring framework that tracks
streams of events and continuously determines the state
constraints are defined using the declarative language
Declare. For the purpose of this work, Declare has been
suitably extended to support quantitative time
constraints and non-atomic, durative activities. The
logic-based language Event Calculus (EC) has been
adopted to provide a formal specification and semantics
to Declare constraints, while a light-weight, logic
programming-based EC tool supports dynamically
reasoning about partial, evolving execution traces. To
demonstrate the applicability of our approach, we
describe a case study about maritime safety and
security and provide a synthetic benchmark to evaluate
its scalability.",
acknowledgement = ack-nhfb,
articleno =    "17",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Lu:2013:SBA,
author =       "Qiang Lu and Ruoyun Huang and Yixin Chen and You Xu
and Weixiong Zhang and Guoliang Chen",
title =        "A {SAT-based} approach to cost-sensitive temporally
expressive planning",
journal =      j-TIST,
volume =       "5",
number =       "1",
pages =        "18:1--18:??",
month =        dec,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2542182.2542200",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:16 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Complex features, such as temporal dependencies and
numerical cost constraints, are hallmarks of real-world
challenging problem of cost-sensitive temporally
expressive (CSTE) planning, which requires concurrency
of durative actions and optimization of action costs.
We first propose a scheme to translate a CSTE planning
problem to a minimum cost (MinCost) satisfiability
(SAT) problem and to integrate with a relaxed parallel
planning semantics for handling true temporal
expressiveness. Our scheme finds solution plans that
optimize temporal makespan, and also minimize total
action costs at the optimal makespan. We propose two
approaches for solving MinCost SAT. The first is based
on a transformation of a MinCost SAT problem to a
weighted partial Max-SAT (WPMax-SAT), and the second,
called BB-CDCL, is an integration of the
branch-and-bound technique and the conflict driven
clause learning (CDCL) method. We also develop a CSTE
customized variable branching scheme for BB-CDCL which
can significantly improve the search efficiency. Our
experiments on the existing CSTE benchmark domains show
that our planner compares favorably to the
state-of-the-art temporally expressive planners in both
efficiency and quality.",
acknowledgement = ack-nhfb,
articleno =    "18",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Shieh:2013:RTS,
author =       "Jyh-Ren Shieh and Ching-Yung Lin and Shun-Xuan Wang
and Ja-Ling Wu",
title =        "Relational term-suggestion graphs incorporating
multipartite concept and expertise networks",
journal =      j-TIST,
volume =       "5",
number =       "1",
pages =        "19:1--19:??",
month =        dec,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2542182.2542201",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:16 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Term suggestions recommend query terms to a user based
on his initial query. Suggesting adequate terms is a
challenging issue. Most existing commercial search
engines suggest search terms based on the frequency of
prior used terms that match the leading alphabets the
mechanism to construct semantic term-relation graphs to
suggest relevant search terms in the semantic level. We
built term-relation graphs based on multipartite
networks of existing social media, especially from
Wikipedia. The multipartite linkage networks of
contributor-term, term-category, and term-term are
extracted from Wikipedia to eventually form term
relation graphs. For fusing these multipartite linkage
networks, we propose to incorporate the
contributor-category networks to model the expertise of
the contributors. Based on our experiments, this step
has demonstrated clear enhancement on the accuracy of
the inferred relatedness of the term-semantic graphs.
Experiments on keyword-expanded search based on 200
algorithms over existing approaches.",
acknowledgement = ack-nhfb,
articleno =    "19",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Chen:2013:EEM,
author =       "Tianshi Chen and Yunji Chen and Qi Guo and Zhi-Hua
Zhou and Ling Li and Zhiwei Xu",
title =        "Effective and efficient microprocessor design space
exploration using unlabeled design configurations",
journal =      j-TIST,
volume =       "5",
number =       "1",
pages =        "20:1--20:??",
month =        dec,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2542182.2542202",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:16 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Ever-increasing design complexity and advances of
technology impose great challenges on the design of
modern microprocessors. One such challenge is to
determine promising microprocessor configurations to
meet specific design constraints, which is called
Design Space Exploration (DSE). In the computer
architecture community, supervised learning techniques
have been applied to DSE to build regression models for
predicting the qualities of design configurations. For
supervised learning, however, considerable simulation
costs are required for attaining the labeled design
configurations. Given limited resources, it is
inspired by recent advances in semisupervised learning
and active learning, we propose the COAL approach which
can exploit unlabeled design configurations to
significantly improve the models. Empirical study
demonstrates that COAL significantly outperforms a
state-of-the-art DSE technique by reducing mean squared
error by 35\% to 95\%, and thus, promising
architectures can be attained more efficiently.",
acknowledgement = ack-nhfb,
articleno =    "20",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Singh:2013:NBG,
author =       "Munindar P. Singh",
title =        "Norms as a basis for governing sociotechnical
systems",
journal =      j-TIST,
volume =       "5",
number =       "1",
pages =        "21:1--21:??",
month =        dec,
year =         "2013",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2542182.2542203",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu Mar 13 07:29:16 MDT 2014",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "We understand a sociotechnical system as a
multistakeholder cyber-physical system. We introduce
governance as the administration of such a system by
the stakeholders themselves. In this regard, governance
is a peer-to-peer notion and contrasts with traditional
management, which is a top-down hierarchical notion.
Traditionally, there is no computational support for
governance and it is achieved through out-of-band
surprisingly, traditional approaches simply do not
scale up to large sociotechnical systems. We develop an
approach for governance based on a computational
representation of norms in organizations. Our approach
is motivated by the Ocean Observatory Initiative, a
thirty-year \$400 million project, which supports a variety of resources dealing with monitoring and studying the world's oceans. These resources include autonomous underwater vehicles, ocean gliders, buoys, and other instrumentation as well as more traditional computational resources. Our approach has the benefit of directly reflecting stakeholder needs and assuring stakeholders of the correctness of the resulting governance decisions while yielding adaptive resource allocation in the face of changes in both stakeholder needs and physical circumstances.", acknowledgement = ack-nhfb, articleno = "21", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{He:2014:ISI, author = "Qi He and Juanzi Li and Rong Yan and John Yen and Haizheng Zhang", title = "Introduction to the {Special Issue on Linking Social Granularity and Functions}", journal = j-TIST, volume = "5", number = "2", pages = "22:1--22:??", month = apr, year = "2014", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2594452", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Apr 24 16:09:50 MDT 2014", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, articleno = "22", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wang:2014:IUI, author = "Jinpeng Wang and Wayne Xin Zhao and Yulan He and Xiaoming Li", title = "Infer User Interests via Link Structure Regularization", journal = j-TIST, volume = "5", number = "2", pages = "23:1--23:??", month = apr, year = "2014", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2499380", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Apr 24 16:09:50 MDT 2014", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Learning user interests from online social networks helps to better understand user behaviors and provides useful guidance to design user-centric applications. Apart from analyzing users' online content, it is also important to consider users' social connections in the social Web. Graph regularization methods have been widely used in various text mining tasks, which can leverage the graph structure information extracted from data. Previously, graph regularization methods operate under the cluster assumption that nearby nodes are more similar and nodes on the same structure (typically referred to as a cluster or a manifold) are likely to be similar. We argue that learning user interests from complex, sparse, and dynamic social networks should be based on the link structure assumption under which node similarities are evaluated based on the local link structures instead of explicit links between two nodes. We propose a regularization framework based on the relation bipartite graph, which can be constructed from any type of relations. Using Twitter as our case study, we evaluate our proposed framework from social networks built from retweet relations. Both quantitative and qualitative experiments show that our proposed method outperforms a few competitive baselines in learning user interests over a set of predefined topics. It also gives superior results compared to the baselines on retweet prediction and topical authority identification.", acknowledgement = ack-nhfb, articleno = "23", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Javari:2014:CBC, author = "Amin Javari and Mahdi Jalili", title = "Cluster-Based Collaborative Filtering for Sign Prediction in Social Networks with Positive and Negative Links", journal = j-TIST, volume = "5", number = "2", pages = "24:1--24:??", month = apr, year = "2014", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2501977", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Apr 24 16:09:50 MDT 2014", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Social network analysis and mining get ever-increasingly important in recent years, which is mainly due to the availability of large datasets and advances in computing systems. A class of social networks is those with positive and negative links. In such networks, a positive link indicates friendship (or trust), whereas links with a negative sign correspond to enmity (or distrust). Predicting the sign of the links in these networks is an important issue and has many applications, such as friendship recommendation and identifying malicious nodes in the network. In this manuscript, we proposed a new method for sign prediction in networks with positive and negative links. Our algorithm is based first on clustering the network into a number of clusters and then applying a collaborative filtering algorithm. The clusters are such that the number of intra-cluster negative links and inter-cluster positive links are minimal, that is, the clusters are socially balanced as much as possible (a signed graph is socially balanced if it can be divided into clusters with all positive links inside the clusters and all negative links between them). We then used similarity between the clusters (based on the links between them) in a collaborative filtering algorithm. Our experiments on a number of real datasets showed that the proposed method outperformed previous methods, including those based on social balance and status theories and one based on a machine learning framework (logistic regression in this work).", acknowledgement = ack-nhfb, articleno = "24", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Chen:2014:CCB, author = "Yi-Cheng Chen and Wen-Yuan Zhu and Wen-Chih Peng and Wang-Chien Lee and Suh-Yin Lee", title = "{CIM}: Community-Based Influence Maximization in Social Networks", journal = j-TIST, volume = "5", number = "2", pages = "25:1--25:??", month = apr, year = "2014", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2532549", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Apr 24 16:09:50 MDT 2014", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Given a social graph, the problem of influence maximization is to determine a set of nodes that maximizes the spread of influences. While some recent research has studied the problem of influence maximization, these works are generally too time consuming for practical use in a large-scale social network. In this article, we develop a new framework, community-based influence maximization (CIM), to tackle the influence maximization problem with an emphasis on the time efficiency issue. Our proposed framework, CIM, comprises three phases: (i) community detection, (ii) candidate generation, and (iii) seed selection. Specifically, phase (i) discovers the community structure of the network; phase (ii) uses the information of communities to narrow down the possible seed candidates; and phase (iii) finalizes the seed nodes from the candidate set. By exploiting the properties of the community structures, we are able to avoid overlapped information and thus efficiently select the number of seeds to maximize information spreads. The experimental results on both synthetic and real datasets show that the proposed CIM algorithm significantly outperforms the state-of-the-art algorithms in terms of efficiency and scalability, with almost no compromise of effectiveness.", acknowledgement = ack-nhfb, articleno = "25", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Yang:2014:SOG, author = "Jaewon Yang and Jure Leskovec", title = "Structure and Overlaps of Ground-Truth Communities in Networks", journal = j-TIST, volume = "5", number = "2", pages = "26:1--26:??", month = apr, year = "2014", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2594454", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Apr 24 16:09:50 MDT 2014", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "One of the main organizing principles in real-world networks is that of network communities, where sets of nodes organize into densely linked clusters. Even though detection of such communities is of great interest, understanding the structure communities in large networks remains relatively limited. In particular, due to the unavailability of labeled ground-truth data, it was traditionally very hard to develop accurate models of network community structure. Here we use six large social, collaboration, and information networks where nodes explicitly state their ground-truth community memberships. For example, nodes in social networks join into explicitly defined interest based groups, and we use such groups as explicitly labeled ground-truth communities. We use such ground-truth communities to study their structural signatures by analyzing how ground-truth communities emerge in networks and how they overlap. We observe some surprising phenomena. First, ground-truth communities contain high-degree hub nodes that reside in community overlaps and link to most of the members of the community. Second, the overlaps of communities are more densely connected than the non-overlapping parts of communities. We show that this in contrast to the conventional wisdom that community overlaps are more sparsely connected than the non-overlapping parts themselves. We then show that many existing models of network communities do not capture dense community overlaps. This in turn means that most present models and community detection methods confuse overlaps as separate communities. In contrast, we present the community-affiliation graph model (AGM), a conceptual model of network community structure. We demonstrate that AGM reliably captures the overall structure of networks as well as the overlapping and hierarchical nature of network communities.", acknowledgement = ack-nhfb, articleno = "26", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Gong:2014:JLP, author = "Neil Zhenqiang Gong and Ameet Talwalkar and Lester Mackey and Ling Huang and Eui Chul Richard Shin and Emil Stefanov and Elaine (Runting) Shi and Dawn Song", title = "Joint Link Prediction and Attribute Inference Using a Social-Attribute Network", journal = j-TIST, volume = "5", number = "2", pages = "27:1--27:??", month = apr, year = "2014", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2594455", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Apr 24 16:09:50 MDT 2014", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The effects of social influence and homophily suggest that both network structure and node-attribute information should inform the tasks of link prediction and node-attribute inference. Recently, Yin et al. [2010a, 2010b] proposed an attribute-augmented social network model, which we call Social-Attribute Network (SAN), to integrate network structure and node attributes to perform both link prediction and attribute inference. They focused on generalizing the random walk with a restart algorithm to the SAN framework and showed improved performance. In this article, we extend the SAN framework with several leading supervised and unsupervised link-prediction algorithms and demonstrate performance improvement for each algorithm on both link prediction and attribute inference. Moreover, we make the novel observation that attribute inference can help inform link prediction, that is, link-prediction accuracy is further improved by first inferring missing attributes. We comprehensively evaluate these algorithms and compare them with other existing algorithms using a novel, large-scale Google+ dataset, which we make publicly available (http://www.cs.berkeley.edu/~stevgong/gplus.html).", acknowledgement = ack-nhfb, articleno = "27", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Pool:2014:DDC, author = "Simon Pool and Francesco Bonchi and Matthijs van Leeuwen", title = "Description-Driven Community Detection", journal = j-TIST, volume = "5", number = "2", pages = "28:1--28:??", month = apr, year = "2014", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2517088", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Apr 24 16:09:50 MDT 2014", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Traditional approaches to community detection, as studied by physicists, sociologists, and more recently computer scientists, aim at simply partitioning the social network graph. However, with the advent of online social networking sites, richer data has become available: beyond the link information, each user in the network is annotated with additional information, for example, demographics, shopping behavior, or interests. In this context, it is therefore important to develop mining methods which can take advantage of all available information. In the case of community detection, this means finding good communities (a set of nodes cohesive in the social graph) which are associated with good descriptions in terms of user information (node attributes). Having good descriptions associated to our models make them understandable by domain experts and thus more useful in real-world applications. Another requirement dictated by real-world applications, is to develop methods that can use, when available, any domain-specific background knowledge. In the case of community detection the background knowledge could be a vague description of the communities sought in a specific application, or some prototypical nodes (e.g., good customers in the past), that represent what the analyst is looking for (a community of similar users). Towards this goal, in this article, we define and study the problem of finding a diverse set of cohesive communities with concise descriptions. We propose an effective algorithm that alternates between two phases: a hill-climbing phase producing (possibly overlapping) communities, and a description induction phase which uses techniques from supervised pattern set mining. Our framework has the nice feature of being able to build well-described cohesive communities starting from any given description or seed set of nodes, which makes it very flexible and easily applicable in real-world applications. Our experimental evaluation confirms that the proposed method discovers cohesive communities with concise descriptions in realistic and large online social networks such as Delicious, Flickr, and LastFM.", acknowledgement = ack-nhfb, articleno = "28", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Li:2014:LPH, author = "Nan Li and William Cushing and Subbarao Kambhampati and Sungwook Yoon", title = "Learning Probabilistic Hierarchical Task Networks as Probabilistic Context-Free Grammars to Capture User Preferences", journal = j-TIST, volume = "5", number = "2", pages = "29:1--29:??", month = apr, year = "2014", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2589481", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Apr 24 16:09:50 MDT 2014", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "We introduce an algorithm to automatically learn probabilistic hierarchical task networks (pHTNs) that capture a user's preferences on plans by observing only the user's behavior. HTNs are a common choice of representation for a variety of purposes in planning, including work on learning in planning. Our contributions are twofold. First, in contrast with prior work, which employs HTNs to represent domain physics or search control knowledge, we use HTNs to model user preferences. Second, while most prior work on HTN learning requires additional information (e.g., annotated traces or tasks) to assist the learning process, our system only takes plan traces as input. Initially, we will assume that users carry out preferred plans more frequently, and thus the observed distribution of plans is an accurate representation of user preference. We then generalize to the situation where feasibility constraints frequently prevent the execution of preferred plans. Taking the prevalent perspective of viewing HTNs as grammars over primitive actions, we adapt an expectation-maximization (EM) technique from the discipline of probabilistic grammar induction to acquire probabilistic context-free grammars (pCFG) that capture the distribution on plans. To account for the difference between the distributions of possible and preferred plans, we subsequently modify this core EM technique by rescaling its input. We empirically demonstrate that the proposed approaches are able to learn HTNs representing user preferences better than the inside-outside algorithm. Furthermore, when feasibility constraints are obfuscated, the algorithm with rescaled input performs better than the algorithm with the original input.", acknowledgement = ack-nhfb, articleno = "29", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Reches:2014:FEC, author = "Shulamit Reches and Meir Kalech and Philip Hendrix", title = "A Framework for Effectively Choosing between Alternative Candidate Partners", journal = j-TIST, volume = "5", number = "2", pages = "30:1--30:??", month = apr, year = "2014", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2589482", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Apr 24 16:09:50 MDT 2014", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Many multi-agent settings require that agents identify appropriate partners or teammates with whom to work on tasks. When selecting potential partners, agents may benefit from obtaining information about the alternatives, for instance, through gossip (i.e., by consulting others) or reputation systems. When information is uncertain and associated with cost, deciding on the amount of information needed is a hard optimization problem. This article defines a statistical model, the Information-Acquisition Source Utility model (IASU), by which agents, operating in an uncertain world, can determine (1) which information sources they should request for information, and (2) the amount of information to collect about potential partners from each source. To maximize the expected gain from the choice, IASU computes the utility of choosing a partner by estimating the benefit of additional information. The article presents empirical studies through a simulation domain as well as a real-world domain of restaurants. We compare the IASU model to other relevant models and show that the use of the IASU model significantly increases agents' overall utility.", acknowledgement = ack-nhfb, articleno = "30", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Heath:2014:CST, author = "Derrall Heath and David Norton and Dan Ventura", title = "Conveying Semantics through Visual Metaphor", journal = j-TIST, volume = "5", number = "2", pages = "31:1--31:??", month = apr, year = "2014", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2589483", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Apr 24 16:09:50 MDT 2014", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In the field of visual art, metaphor is a way to communicate meaning to the viewer. We present a computational system for communicating visual metaphor that can identify adjectives for describing an image based on a low-level visual feature representation of the image. We show that the system can use this visual-linguistic association to render source images that convey the meaning of adjectives in a way consistent with human understanding. Our conclusions are based on a detailed analysis of how the system's artifacts cluster, how these clusters correspond to the semantic relationships of adjectives as documented in WordNet, and how these clusters correspond to human opinion.", acknowledgement = ack-nhfb, articleno = "31", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Lian:2014:MCH, author = "Defu Lian and Xing Xie", title = "Mining Check-In History for Personalized Location Naming", journal = j-TIST, volume = "5", number = "2", pages = "32:1--32:??", month = apr, year = "2014", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2490890", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Apr 24 16:09:50 MDT 2014", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Many innovative location-based services have been established to offer users greater convenience in their everyday lives. These services usually cannot map user's physical locations into semantic names automatically. The semantic names of locations provide important context for mobile recommendations and advertisements. In this article, we proposed a novel location naming approach which can automatically provide semantic names for users given their locations and time. In particular, when a user opens a GPS device and submits a query with her physical location and time, she will be returned the most appropriate semantic name. In our approach, we drew an analogy between location naming and local search, and designed a local search framework to propose a spatiotemporal and user preference (STUP) model for location naming. STUP combined three components, user preference (UP), spatial preference (SP), and temporal preference (TP), by leveraging learning-to-rank techniques. We evaluated STUP on 466,190 check-ins of 5,805 users from Shanghai and 135,052 check-ins of 1,361 users from Beijing. The results showed that SP was most effective among three components and that UP can provide personalized semantic names, and thus it was a necessity for location naming. Although TP was not as discriminative as the others, it can still be beneficial when integrated with SP and UP. Finally, according to the experimental results, STUP outperformed the proposed baselines and returned accurate semantic names for 23.6\% and 26.6\% of the testing queries from Beijing and Shanghai, respectively.", acknowledgement = ack-nhfb, articleno = "32", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Bian:2014:EUP, author = "Jiang Bian and Bo Long and Lihong Li and Taesup Moon and Anlei Dong and Yi Chang", title = "Exploiting User Preference for Online Learning in {Web} Content Optimization Systems", journal = j-TIST, volume = "5", number = "2", pages = "33:1--33:??", month = apr, year = "2014", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2493259", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Apr 24 16:09:50 MDT 2014", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Web portal services have become an important medium to deliver digital content (e.g. news, advertisements, etc.) to Web users in a timely fashion. To attract more users to various content modules on the Web portal, it is necessary to design a recommender system that can effectively achieve Web portal content optimization by automatically estimating content item attractiveness and relevance to user interests. The state-of-the-art online learning methodology adapts dedicated pointwise models to independently estimate the attractiveness score for each candidate content item. Although such pointwise models can be easily adapted for online recommendation, there still remain a few critical problems. First, this pointwise methodology fails to use invaluable user preferences between content items. Moreover, the performance of pointwise models decreases drastically when facing the problem of sparse learning samples. To address these problems, we propose exploring a new dynamic pairwise learning methodology for Web portal content optimization in which we exploit dynamic user preferences extracted based on users' actions on portal services to compute the attractiveness scores of content items. In this article, we introduce two specific pairwise learning algorithms, a straightforward graph-based algorithm and a formalized Bayesian modeling one. Experiments on large-scale data from a commercial Web portal demonstrate the significant improvement of pairwise methodologies over the baseline pointwise models. Further analysis illustrates that our new pairwise learning approaches can benefit personalized recommendation more than pointwise models, since the data sparsity is more critical for personalized content optimization.", acknowledgement = ack-nhfb, articleno = "33", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Hossain:2014:AFS, author = "M. Shahriar Hossain and Manish Marwah and Amip Shah and Layne T. Watson and Naren Ramakrishnan", title = "{AutoLCA}: a Framework for Sustainable Redesign and Assessment of Products", journal = j-TIST, volume = "5", number = "2", pages = "34:1--34:??", month = apr, year = "2014", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2505270", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Apr 24 16:09:50 MDT 2014", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "With increasing public consciousness regarding sustainability, companies are ever more eager to introduce eco-friendly products and services. Assessing environmental footprints and designing sustainable products are challenging tasks since they require analysis of each component of a product through their life cycle. To achieve sustainable design of products, companies need to evaluate the environmental impact of their system, identify the major contributors to the footprint, and select the design alternative with the lowest environmental footprint. In this article, we formulate sustainable design as a series of clustering and classification problems, and propose a framework called AutoLCA that simplifies the effort of estimating the environmental footprint of a product bill of materials by more than an order of magnitude over current methods, which are mostly labor intensive. We apply AutoLCA to real data from a large computer manufacturer. We conduct a case study on bill of materials of four different products, perform a hotspot'' assessment analysis to identify major contributors to carbon footprint, and determine design alternatives that can reduce the carbon footprint from 1\% to 36\%.", acknowledgement = ack-nhfb, articleno = "34", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Shi:2014:MLC, author = "Chuan Shi and Xiangnan Kong and Di Fu and Philip S. Yu and Bin Wu", title = "Multi-Label Classification Based on Multi-Objective Optimization", journal = j-TIST, volume = "5", number = "2", pages = "35:1--35:??", month = apr, year = "2014", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2505272", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Apr 24 16:09:50 MDT 2014", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Multi-label classification refers to the task of predicting potentially multiple labels for a given instance. Conventional multi-label classification approaches focus on single objective setting, where the learning algorithm optimizes over a single performance criterion (e.g., Ranking Loss ) or a heuristic function. The basic assumption is that the optimization over one single objective can improve the overall performance of multi-label classification and meet the requirements of various applications. However, in many real applications, an optimal multi-label classifier may need to consider the trade-offs among multiple inconsistent objectives, such as minimizing Hamming Loss while maximizing Micro F1. In this article, we study the problem of multi-objective multi-label classification and propose a novel solution (called Moml) to optimize over multiple objectives simultaneously. Note that optimization objectives may be inconsistent, even conflicting, thus one cannot identify a single solution that is optimal on all objectives. Our Moml algorithm finds a set of non-dominated solutions which are optimal according to different trade-offs among multiple objectives. So users can flexibly construct various predictive models from the solution set, which provides more meaningful classification results in different application scenarios. Empirical studies on real-world tasks demonstrate that the Moml can effectively boost the overall performance of multi-label classification by optimizing over multiple objectives simultaneously.", acknowledgement = ack-nhfb, articleno = "35", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Tang:2014:DSM, author = "Xuning Tang and Christopher C. Yang", title = "Detecting Social Media Hidden Communities Using Dynamic Stochastic Blockmodel with Temporal {Dirichlet} Process", journal = j-TIST, volume = "5", number = "2", pages = "36:1--36:??", month = apr, year = "2014", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2517085", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu Apr 24 16:09:50 MDT 2014", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Detecting evolving hidden communities within dynamic social networks has attracted significant attention recently due to its broad applications in e-commerce, online social media, security intelligence, public health, and other areas. Many community network detection techniques employ a two-stage approach to identify and detect evolutionary relationships between communities of two adjacent time epochs. These techniques often identify communities with high temporal variation, since the two-stage approach detects communities of each epoch independently without considering the continuity of communities across two time epochs. Other techniques require identification of a predefined number of hidden communities which is not realistic in many applications. To overcome these limitations, we propose the Dynamic Stochastic Blockmodel with Temporal Dirichlet Process, which enables the detection of hidden communities and tracks their evolution simultaneously from a network stream. The number of hidden communities is automatically determined by a temporal Dirichlet process without human intervention. We tested our proposed technique on three different testbeds with results identifying a high performance level when compared to the baseline algorithm.", acknowledgement = ack-nhfb, articleno = "36", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zheng:2014:ISS, author = "Yu Zheng and Licia Capra and Ouri Wolfson and Hai Yang", title = "Introduction to the Special Section on Urban Computing", journal = j-TIST, volume = "5", number = "3", pages = "37:1--37:??", month = sep, year = "2014", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2642650", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:08 MST 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, articleno = "37", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zheng:2014:UCC, author = "Yu Zheng and Licia Capra and Ouri Wolfson and Hai Yang", title = "Urban Computing: Concepts, Methodologies, and Applications", journal = j-TIST, volume = "5", number = "3", pages = "38:1--38:??", month = sep, year = "2014", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2629592", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:08 MST 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Urbanization's rapid progress has modernized many people's lives but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities (e.g., traffic flow, human mobility, and geographical data). Urban computing connects urban sensing, data management, data analytics, and service providing into a recurrent process for an unobtrusive and continuous improvement of people's lives, city operation systems, and the environment. Urban computing is an interdisciplinary field where computer sciences meet conventional city-related fields, like transportation, civil engineering, environment, economy, ecology, and sociology in the context of urban spaces. This article first introduces the concept of urban computing, discussing its general framework and key challenges from the perspective of computer sciences. Second, we classify the applications of urban computing into seven categories, consisting of urban planning, transportation, the environment, energy, social, economy, and public safety and security, presenting representative scenarios in each category. Third, we summarize the typical technologies that are needed in urban computing into four folds, which are about urban sensing, urban data management, knowledge fusion across heterogeneous data, and urban data visualization. Finally, we give an outlook on the future of urban computing, suggesting a few research topics that are somehow missing in the community.", acknowledgement = ack-nhfb, articleno = "38", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Etienne:2014:MBC, author = "C{\^o}me Etienne and Oukhellou Latifa", title = "Model-Based Count Series Clustering for Bike Sharing System Usage Mining: a Case Study with the {V{\'e}lib'} System of {Paris}", journal = j-TIST, volume = "5", number = "3", pages = "39:1--39:??", month = sep, year = "2014", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2560188", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:08 MST 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Today, more and more bicycle sharing systems (BSSs) are being introduced in big cities. These transportation systems generate sizable transportation data, the mining of which can reveal the underlying urban phenomenon linked to city dynamics. This article presents a statistical model to automatically analyze the trip data of a bike sharing system. The proposed solution partitions (i.e., clusters) the stations according to their usage profiles. To do so, count series describing the stations's usage through departure/arrival counts per hour throughout the day are built and analyzed. The model for processing these count series is based on Poisson mixtures and introduces a station scaling factor that handles the differences between the stations's global usage. Differences between weekday and weekend usage are also taken into account. This model identifies the latent factors that shape the geography of trips, and the results may thus offer insights into the relationships between station neighborhood type (its amenities, its demographics, etc.) and the generated mobility patterns. In other words, the proposed method brings to light the different functions in different areas that induce specific patterns in BSS data. These potentials are demonstrated through an in-depth analysis of the results obtained on the Paris V{\'e}lib' large-scale bike sharing system.", acknowledgement = ack-nhfb, articleno = "39", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ying:2014:MUC, author = "Josh Jia-Ching Ying and Wen-Ning Kuo and Vincent S. Tseng and Eric Hsueh-Chan Lu", title = "Mining User Check-In Behavior with a Random Walk for Urban Point-of-Interest Recommendations", journal = j-TIST, volume = "5", number = "3", pages = "40:1--40:??", month = sep, year = "2014", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2523068", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:08 MST 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In recent years, research into the mining of user check-in behavior for point-of-interest (POI) recommendations has attracted a lot of attention. Existing studies on this topic mainly treat such recommendations in a traditional manner-that is, they treat POIs as items and check-ins as ratings. However, users usually visit a place for reasons other than to simply say that they have visited. In this article, we propose an approach referred to as Urban POI-Walk (UPOI-Walk), which takes into account a user's social-triggered intentions (SI), preference-triggered intentions (PreI), and popularity-triggered intentions (PopI), to estimate the probability of a user checking-in to a POI. The core idea of UPOI-Walk involves building a HITS-based random walk on the normalized check-in network, thus supporting the prediction of POI properties related to each user's preferences. To achieve this goal, we define several user--POI graphs to capture the key properties of the check-in behavior motivated by user intentions. In our UPOI-Walk approach, we propose a new kind of random walk model-Dynamic HITS-based Random Walk-which comprehensively considers the relevance between POIs and users from different aspects. On the basis of similitude, we make an online recommendation as to the POI the user intends to visit. To the best of our knowledge, this is the first work on urban POI recommendations that considers user check-in behavior motivated by SI, PreI, and PopI in location-based social network data. Through comprehensive experimental evaluations on two real datasets, the proposed UPOI-Walk is shown to deliver excellent performance.", acknowledgement = ack-nhfb, articleno = "40", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Mcardle:2014:UDF, author = "Gavin Mcardle and Eoghan Furey and Aonghus Lawlor and Alexei Pozdnoukhov", title = "Using Digital Footprints for a City-Scale Traffic Simulation", journal = j-TIST, volume = "5", number = "3", pages = "41:1--41:??", month = sep, year = "2014", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2517028", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:08 MST 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "This article introduces a microsimulation of urban traffic flows within a large-scale scenario implemented for the Greater Dublin region in Ireland. Traditionally, the data available for traffic simulations come from a population census and dedicated road surveys that only partly cover shopping, leisure, or recreational trips. To account for the latter, the presented traffic modeling framework exploits the digital footprints of city inhabitants on services such as Twitter and Foursquare. We enriched the model with findings from our previous studies on geographical layout of communities in a country-wide mobile phone network to account for socially related journeys. These datasets were used to calibrate a variant of a radiation model of spatial choice, which we introduced in order to drive individuals' decisions on trip destinations within an assigned daily activity plan. We observed that given the distribution of population, the workplace locations, a comprehensive set of urban facilities, and a list of typical activity sequences of city dwellers collected within a national travel survey, the developed microsimulation reproduces not only the journey statistics such as peak travel periods but also the traffic volumes at main road segments with surprising accuracy.", acknowledgement = ack-nhfb, articleno = "41", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Momtazpour:2014:CSI, author = "Marjan Momtazpour and Patrick Butler and Naren Ramakrishnan and M. Shahriar Hossain and Mohammad C. Bozchalui and Ratnesh Sharma", title = "Charging and Storage Infrastructure Design for Electric Vehicles", journal = j-TIST, volume = "5", number = "3", pages = "42:1--42:??", month = sep, year = "2014", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2513567", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:08 MST 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Ushered by recent developments in various areas of science and technology, modern energy systems are going to be an inevitable part of our societies. Smart grids are one of these modern systems that have attracted many research activities in recent years. Before utilizing the next generation of smart grids, we should have a comprehensive understanding of the interdependent energy networks and processes. Next-generation energy systems networks cannot be effectively designed, analyzed, and controlled in isolation from the social, economic, sensing, and control contexts in which they operate. In this article, we present a novel framework to support charging and storage infrastructure design for electric vehicles. We develop coordinated clustering techniques to work with network models of urban environments to aid in placement of charging stations for an electrical vehicle deployment scenario. Furthermore, we evaluate the network before and after the deployment of charging stations, to recommend the installation of appropriate storage units to overcome the extra load imposed on the network by the charging stations. We demonstrate the multiple factors that can be simultaneously leveraged in our framework to achieve practical urban deployment. Our ultimate goal is to help realize sustainable energy system management in urban electrical infrastructure by modeling and analyzing networks of interactions between electric systems and urban populations.", acknowledgement = ack-nhfb, articleno = "42", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Tan:2014:OOT, author = "Chang Tan and Qi Liu and Enhong Chen and Hui Xiong and Xiang Wu", title = "Object-Oriented Travel Package Recommendation", journal = j-TIST, volume = "5", number = "3", pages = "43:1--43:??", month = sep, year = "2014", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2542665", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:08 MST 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Providing better travel services for tourists is one of the important applications in urban computing. Though many recommender systems have been developed for enhancing the quality of travel service, most of them lack a systematic and open framework to dynamically incorporate multiple types of additional context information existing in the tourism domain, such as the travel area, season, and price of travel packages. To that end, in this article, we propose an open framework, the Objected-Oriented Recommender System (ORS), for the developers performing personalized travel package recommendations to tourists. This framework has the ability to import all the available additional context information to the travel package recommendation process in a cost-effective way. Specifically, the different types of additional information are extracted and uniformly represented as feature--value pairs. Then, we define the Object, which is the collection of the feature--value pairs. We propose two models that can be used in the ORS framework for extracting the implicit relationships among Objects. The Objected-Oriented Topic Model (OTM) can extract the topics conditioned on the intrinsic feature--value pairs of the Objects. The Objected-Oriented Bayesian Network (OBN) can effectively infer the cotravel probability of two tourists by calculating the co-occurrence time of feature--value pairs belonging to different kinds of Objects. Based on the relationships mined by OTM or OBN, the recommendation list is generated by the collaborative filtering method. Finally, we evaluate these two models and the ORS framework on real-world travel package data, and the experimental results show that the ORS framework is more flexible in terms of incorporating additional context information, and thus leads to better performances for travel package recommendations. Meanwhile, for feature selection in ORS, we define the feature information entropy, and the experimental results demonstrate that using features with lower entropies usually leads to better recommendation results.", acknowledgement = ack-nhfb, articleno = "43", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Gurung:2014:TIP, author = "Sashi Gurung and Dan Lin and Wei Jiang and Ali Hurson and Rui Zhang", title = "Traffic Information Publication with Privacy Preservation", journal = j-TIST, volume = "5", number = "3", pages = "44:1--44:??", month = sep, year = "2014", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2542666", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:08 MST 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "We are experiencing the expanding use of location-based services such as AT\&T's TeleNav GPS Navigator and Intel's Thing Finder. Existing location-based services have collected a large amount of location data, which has great potential for statistical usage in applications like traffic flow analysis, infrastructure planning, and advertisement dissemination. The key challenge is how to wisely use the data without violating each user's location privacy concerns. In this article, we first identify a new privacy problem, namely, the inference-route problem, and then present our anonymization algorithms for privacy-preserving trajectory publishing. The experimental results have demonstrated that our approach outperforms the latest related work in terms of both efficiency and effectiveness.", acknowledgement = ack-nhfb, articleno = "44", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Hsieh:2014:MRT, author = "Hsun-Ping Hsieh and Cheng-Te Li and Shou-De Lin", title = "Measuring and Recommending Time-Sensitive Routes from Location-Based Data", journal = j-TIST, volume = "5", number = "3", pages = "45:1--45:??", month = sep, year = "2014", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2542668", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:08 MST 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Location-based services allow users to perform geospatial recording actions, which facilitates the mining of the moving activities of human beings. This article proposes to recommend time-sensitive trip routes consisting of a sequence of locations with associated timestamps based on knowledge extracted from large-scale timestamped location sequence data (e.g., check-ins and GPS traces). We argue that a good route should consider (a) the popularity of places, (b) the visiting order of places, (c) the proper visiting time of each place, and (d) the proper transit time from one place to another. By devising a statistical model, we integrate these four factors into a route goodness function that aims to measure the quality of a route. Equipped with the route goodness, we recommend time-sensitive routes for two scenarios. The first is about constructing the route based on the user-specified source location with the starting time. The second is about composing the route between the specified source location and the destination location given a starting time. To handle these queries, we propose a search method, Guidance Search, which consists of a novel heuristic satisfaction function that guides the search toward the destination location and a backward checking mechanism to boost the effectiveness of the constructed route. Experiments on the Gowalla check-in datasets demonstrate the effectiveness of our model on detecting real routes and performing cloze test of routes, comparing with other baseline methods. We also develop a system TripRouter as a real-time demo platform.", acknowledgement = ack-nhfb, articleno = "45", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Joseph:2014:CIB, author = "Kenneth Joseph and Kathleen M. Carley and Jason I. Hong", title = "Check-ins in {Blau Space''}: Applying {Blau}'s Macrosociological Theory to Foursquare Check-ins from New {York} City", journal = j-TIST, volume = "5", number = "3", pages = "46:1--46:??", month = sep, year = "2014", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2566617", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:08 MST 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Peter Blau was one of the first to define a latent social space and utilize it to provide concrete hypotheses. Blau defines social structure via social parameters'' (constraints). Actors that are closer together (more homogeneous) in this social parameter space are more likely to interact. One of Blau's most important hypotheses resulting from this work was that the consolidation of parameters could lead to isolated social groups. For example, the consolidation of race and income might lead to segregation. In the present work, we use Foursquare data from New York City to explore evidence of homogeneity along certain social parameters and consolidation that breeds social isolation in communities of locations checked in to by similar users. More specifically, we first test the extent to which communities detected via Latent Dirichlet Allocation are homogeneous across a set of four social constraints-racial homophily, income homophily, personal interest homophily and physical space. Using a bootstrapping approach, we find that 14 (of 20) communities are statistically, and all but one qualitatively, homogeneous along one of these social constraints, showing the relevance of Blau's latent space model in venue communities determined via user check-in behavior. We then consider the extent to which communities with consolidated parameters, those homogeneous on more than one parameter, represent socially isolated populations. We find communities homogeneous on multiple parameters, including a homosexual community and a hipster'' community, that show support for Blau's hypothesis that consolidation breeds social isolation. We consider these results in the context of mediated communication, in particular in the context of self-representation on social media.", acknowledgement = ack-nhfb, articleno = "46", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Mahmud:2014:HLI, author = "Jalal Mahmud and Jeffrey Nichols and Clemens Drews", title = "Home Location Identification of {Twitter} Users", journal = j-TIST, volume = "5", number = "3", pages = "47:1--47:??", month = sep, year = "2014", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2528548", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:08 MST 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "We present a new algorithm for inferring the home location of Twitter users at different granularities, including city, state, time zone, or geographic region, using the content of users' tweets and their tweeting behavior. Unlike existing approaches, our algorithm uses an ensemble of statistical and heuristic classifiers to predict locations and makes use of a geographic gazetteer dictionary to identify place-name entities. We find that a hierarchical classification approach, where time zone, state, or geographic region is predicted first and city is predicted next, can improve prediction accuracy. We have also analyzed movement variations of Twitter users, built a classifier to predict whether a user was travelling in a certain period of time, and use that to further improve the location detection accuracy. Experimental evidence suggests that our algorithm works well in practice and outperforms the best existing algorithms for predicting the home location of Twitter users.", acknowledgement = ack-nhfb, articleno = "47", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Neviarouskaya:2014:IIT, author = "Alena Neviarouskaya and Masaki Aono and Helmut Prendinger and Mitsuru Ishizuka", title = "Intelligent Interface for Textual Attitude Analysis", journal = j-TIST, volume = "5", number = "3", pages = "48:1--48:??", month = sep, year = "2014", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2535912", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:08 MST 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "This article describes a novel intelligent interface for attitude sensing in text driven by a robust computational tool for the analysis of fine-grained attitudes (emotions, judgments, and appreciations) expressed in text. The module responsible for textual attitude analysis was developed using a compositional linguistic approach based on the attitude-conveying lexicon, the analysis of syntactic and dependency relations between words in a sentence, the compositionality principle applied at various grammatical levels, the rules elaborated for semantically distinct verb classes, and a method considering the hierarchy of concepts. The performance of this module was evaluated on sentences from personal stories about life experiences. The developed web-based interface supports recognition of nine emotions, positive and negative judgments, and positive and negative appreciations conveyed in text. It allows users to adjust parameters, to enable or disable various functionality components of the algorithm, and to select the format of text annotation and attitude statistics visualization.", acknowledgement = ack-nhfb, articleno = "48", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Song:2014:UGF, author = "Yicheng Song and Yongdong Zhang and Juan Cao and Jinhui Tang and Xingyu Gao and Jintao Li", title = "A Unified Geolocation Framework for {Web} Videos", journal = j-TIST, volume = "5", number = "3", pages = "49:1--49:??", month = jul, year = "2014", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2533989", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Jul 18 14:11:13 MDT 2014", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In this article, we propose a unified geolocation framework to automatically determine where on the earth a web video was shot. We analyze different social, visual, and textual relationships from a real-world dataset and find four relationships with apparent geography clues that can be used for web video geolocation. Then, the geolocation process is formulated as an optimization problem that simultaneously takes the social, visual, and textual relationships into consideration. The optimization problem is solved by an iterative procedure, which can be interpreted as a propagation of the geography information among the web video social network. Extensive experiments on a real-world dataset clearly demonstrate the effectiveness of our proposed framework, with the geolocation accuracy higher than state-of-the-art approaches.", acknowledgement = ack-nhfb, articleno = "49", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhao:2014:PRL, author = "Yi-Liang Zhao and Liqiang Nie and Xiangyu Wang and Tat-Seng Chua", title = "Personalized Recommendations of Locally Interesting Venues to Tourists via Cross-Region Community Matching", journal = j-TIST, volume = "5", number = "3", pages = "50:1--50:??", month = jul, year = "2014", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2532439", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Jul 18 14:11:13 MDT 2014", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "You are in a new city. You are not familiar with the places and neighborhoods. You want to know all about the exciting sights, food outlets, and cultural venues that the locals frequent, in particular those that suit your personal interests. Even though there exist many mapping, local search, and travel assistance sites, they mostly provide popular and famous listings such as Statue of Liberty and Eiffel Tower, which are well-known places but may not suit your personal needs or interests. Therefore, there is a gap between what tourists want and what dominant tourism resources are providing. In this work, we seek to provide a solution to bridge this gap by exploiting the rich user-generated location contents in location-based social networks in order to offer tourists the most relevant and personalized local venue recommendations. In particular, we first propose a novel Bayesian approach to extract the social dimensions of people at different geographical regions to capture their latent local interests. We next mine the local interest communities in each geographical region. We then represent each local community using aggregated behaviors of community members. Finally, we correlate communities across different regions and generate venue recommendations to tourists via cross-region community matching. We have sampled a representative subset of check-ins from Foursquare and experimentally verified the effectiveness of our proposed approaches.", acknowledgement = ack-nhfb, articleno = "50", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Wang:2014:VNF, author = "Shuaiqiang Wang and Jiankai Sun and Byron J. Gao and Jun Ma", title = "{VSRank}: a Novel Framework for Ranking-Based Collaborative Filtering", journal = j-TIST, volume = "5", number = "3", pages = "51:1--51:??", month = jul, year = "2014", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2542048", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Jul 18 14:11:13 MDT 2014", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Collaborative filtering (CF) is an effective technique addressing the information overload problem. CF approaches generally fall into two categories: rating based and ranking based. The former makes recommendations based on historical rating scores of items and the latter based on their rankings. Ranking-based CF has demonstrated advantages in recommendation accuracy, being able to capture the preference similarity between users even if their rating scores differ significantly. In this study, we propose VSRank, a novel framework that seeks accuracy improvement of ranking-based CF through adaptation of the vector space model. In VSRank, we consider each user as a document and his or her pairwise relative preferences as terms. We then use a novel degree-specialty weighting scheme resembling TF-IDF to weight the terms. Extensive experiments on benchmarks in comparison with the state-of-the-art approaches demonstrate the promise of our approach.", acknowledgement = ack-nhfb, articleno = "51", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Castells:2015:ISI, author = "Pablo Castells and Jun Wang and Rub{\'e}n Lara and Dell Zhang", title = "Introduction to the Special Issue on Diversity and Discovery in Recommender Systems", journal = j-TIST, volume = "5", number = "4", pages = "52:1--52:??", month = jan, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2668113", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, articleno = "52", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ribeiro:2015:MPE, author = "Marco Tulio Ribeiro and Nivio Ziviani and Edleno {Silva De Moura} and Itamar Hata and Anisio Lacerda and Adriano Veloso", title = "Multiobjective {Pareto}-Efficient Approaches for Recommender Systems", journal = j-TIST, volume = "5", number = "4", pages = "53:1--53:??", month = jan, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2629350", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Recommender systems are quickly becoming ubiquitous in applications such as e-commerce, social media channels, and content providers, among others, acting as an enabling mechanism designed to overcome the information overload problem by improving browsing and consumption experience. A typical task in many recommender systems is to output a ranked list of items, so that items placed higher in the rank are more likely to be interesting to the users. Interestingness measures include how accurate, novel, and diverse are the suggested items, and the objective is usually to produce ranked lists optimizing one of these measures. Suggesting items that are simultaneously accurate, novel, and diverse is much more challenging, since this may lead to a conflicting-objective problem, in which the attempt to improve a measure further may result in worsening other measures. In this article, we propose new approaches for multiobjective recommender systems based on the concept of Pareto efficiency-a state achieved when the system is devised in the most efficient manner in the sense that there is no way to improve one of the objectives without making any other objective worse off. Given that existing multiobjective recommendation algorithms differ in their level of accuracy, diversity, and novelty, we exploit the Pareto-efficiency concept in two distinct manners: (i) the aggregation of ranked lists produced by existing algorithms into a single one, which we call Pareto-efficient ranking, and (ii) the weighted combination of existing algorithms resulting in a hybrid one, which we call Pareto-efficient hybridization. Our evaluation involves two real application scenarios: music recommendation with implicit feedback (i.e., Last.fm) and movie recommendation with explicit feedback (i.e., MovieLens). We show that the proposed Pareto-efficient approaches are effective in suggesting items that are likely to be simultaneously accurate, diverse, and novel. We discuss scenarios where the system achieves high levels of diversity and novelty without compromising its accuracy. Further, comparison against multiobjective baselines reveals improvements in terms of accuracy (from 10.4\% to 10.9\%), novelty (from 5.7\% to 7.5\%), and diversity (from 1.6\% to 4.2\%).", acknowledgement = ack-nhfb, articleno = "53", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Adamopoulos:2015:URS, author = "Panagiotis Adamopoulos and Alexander Tuzhilin", title = "On Unexpectedness in Recommender Systems: Or How to Better Expect the Unexpected", journal = j-TIST, volume = "5", number = "4", pages = "54:1--54:??", month = jan, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2559952", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Although the broad social and business success of recommender systems has been achieved across several domains, there is still a long way to go in terms of user satisfaction. One of the key dimensions for significant improvement is the concept of unexpectedness. In this article, we propose a method to improve user satisfaction by generating unexpected recommendations based on the utility theory of economics. In particular, we propose a new concept of unexpectedness as recommending to users those items that depart from what they would expect from the system --- the consideration set of each user. We define and formalize the concept of unexpectedness and discuss how it differs from the related notions of novelty, serendipity, and diversity. In addition, we suggest several mechanisms for specifying the users' expectations and propose specific performance metrics to measure the unexpectedness of recommendation lists. We also take into consideration the quality of recommendations using certain utility functions and present an algorithm for providing users with unexpected recommendations of high quality that are hard to discover but fairly match their interests. Finally, we conduct several experiments on real-world'' datasets and compare our recommendation results with other methods. The proposed approach outperforms these baseline methods in terms of unexpectedness and other important metrics, such as coverage, aggregate diversity and dispersion, while avoiding any accuracy loss.", acknowledgement = ack-nhfb, articleno = "54", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Kucuktunc:2015:DCR, author = "Onur K{\"u}{\c{c}}{\"u}ktun{\c{c}} and Erik Saule and Kamer Kaya and {\"U}mit V. {\c{C}}ataly{\"u}rek", title = "Diversifying Citation Recommendations", journal = j-TIST, volume = "5", number = "4", pages = "55:1--55:??", month = jan, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2668106", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Literature search is one of the most important steps of academic research. With more than 100,000 papers published each year just in computer science, performing a complete literature search becomes a Herculean task. Some of the existing approaches and tools for literature search cannot compete with the characteristics of today's literature, and they suffer from ambiguity and homonymy. Techniques based on citation information are more robust to the mentioned issues. Thus, we recently built a Web service called the advisor, which provides personalized recommendations to researchers based on their papers of interest. Since most recommendation methods may return redundant results, diversifying the results of the search process is necessary to increase the amount of information that one can reach via an automated search. This article targets the problem of result diversification in citation-based bibliographic search, assuming that the citation graph itself is the only information available and no categories or intents are known. The contribution of this work is threefold. We survey various random walk--based diversification methods and enhance them with the direction awareness property to allow users to reach either old, foundational (possibly well-cited and well-known) research papers or recent (most likely less-known) ones. Next, we propose a set of novel algorithms based on vertex selection and query refinement. A set of experiments with various evaluation criteria shows that the proposed$ \gamma $-RLM algorithm performs better than the existing approaches and is suitable for real-time bibliographic search in practice.", acknowledgement = ack-nhfb, articleno = "55", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Javari:2015:ANR, author = "Amin Javari and Mahdi Jalili", title = "Accurate and Novel Recommendations: an Algorithm Based on Popularity Forecasting", journal = j-TIST, volume = "5", number = "4", pages = "56:1--56:??", month = jan, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2668107", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Recommender systems are in the center of network science, and they are becoming increasingly important in individual businesses for providing efficient, personalized services and products to users. Previous research in the field of recommendation systems focused on improving the precision of the system through designing more accurate recommendation lists. Recently, the community has been paying attention to diversity and novelty of recommendation lists as key characteristics of modern recommender systems. In many cases, novelty and precision do not go hand in hand, and the accuracy--novelty dilemma is one of the challenging problems in recommender systems, which needs efforts in making a trade-off between them. In this work, we propose an algorithm for providing novel and accurate recommendation to users. We consider the standard definition of accuracy and an effective self-information--based measure to assess novelty of the recommendation list. The proposed algorithm is based on item popularity, which is defined as the number of votes received in a certain time interval. Wavelet transform is used for analyzing popularity time series and forecasting their trend in future timesteps. We introduce two filtering algorithms based on the information extracted from analyzing popularity time series of the items. The popularity-based filtering algorithm gives a higher chance to items that are predicted to be popular in future timesteps. The other algorithm, denoted as a novelty and population-based filtering algorithm, is to move toward items with low popularity in past timesteps that are predicted to become popular in the future. The introduced filters can be applied as adds-on to any recommendation algorithm. In this article, we use the proposed algorithms to improve the performance of classic recommenders, including item-based collaborative filtering and Markov-based recommender systems. The experiments show that the algorithms could significantly improve both the accuracy and effective novelty of the classic recommenders.", acknowledgement = ack-nhfb, articleno = "56", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Shen:2015:ISI, author = "Dou Shen and Deepak Agarwal", title = "Introduction to the Special Issue on Online Advertising", journal = j-TIST, volume = "5", number = "4", pages = "57:1--57:??", month = jan, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2668123", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, articleno = "57", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhu:2015:MMU, author = "Hengshu Zhu and Enhong Chen and Hui Xiong and Kuifei Yu and Huanhuan Cao and Jilei Tian", title = "Mining Mobile User Preferences for Personalized Context-Aware Recommendation", journal = j-TIST, volume = "5", number = "4", pages = "58:1--58:??", month = jan, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2532515", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Recent advances in mobile devices and their sensing capabilities have enabled the collection of rich contextual information and mobile device usage records through the device logs. These context-rich logs open a venue for mining the personal preferences of mobile users under varying contexts and thus enabling the development of personalized context-aware recommendation and other related services, such as mobile online advertising. In this article, we illustrate how to extract personal context-aware preferences from the context-rich device logs, or context logs for short, and exploit these identified preferences for building personalized context-aware recommender systems. A critical challenge along this line is that the context log of each individual user may not contain sufficient data for mining his or her context-aware preferences. Therefore, we propose to first learn common context-aware preferences from the context logs of many users. Then, the preference of each user can be represented as a distribution of these common context-aware preferences. Specifically, we develop two approaches for mining common context-aware preferences based on two different assumptions, namely, context-independent and context-dependent assumptions, which can fit into different application scenarios. Finally, extensive experiments on a real-world dataset show that both approaches are effective and outperform baselines with respect to mining personal context-aware preferences for mobile users.", acknowledgement = ack-nhfb, articleno = "58", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ashkan:2015:LQA, author = "Azin Ashkan and Charles L. A. Clarke", title = "Location- and Query-Aware Modeling of Browsing and Click Behavior in Sponsored Search", journal = j-TIST, volume = "5", number = "4", pages = "59:1--59:??", month = jan, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2534398", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "An online advertisement's clickthrough rate provides a fundamental measure of its quality, which is widely used in ad selection strategies. Unfortunately, ads placed in contexts where they are rarely viewed-or where users are unlikely to be interested in commercial results-may receive few clicks regardless of their quality. In this article, we model the variability of a user's browsing behavior for the purpose of click analysis and prediction in sponsored search. Our model incorporates several important contextual factors that influence ad clickthrough rates, including the user's query and ad placement on search engine result pages. We formally model these factors with respect to the list of ads displayed on a result page, the probability that the user will initiate browsing of this list, and the persistence of the user in browsing the list. We incorporate these factors into existing click models by augmenting them with appropriate query and location biases. Using expectation maximization, we learn the parameters of these augmented models from click signals recorded in the logs of a commercial search engine. To evaluate the performance of the models and to compare them with state-of-the-art performance, we apply standard evaluation metrics, including log-likelihood and perplexity. Our evaluation results indicate that, through the incorporation of query and location biases, significant improvements can be achieved in predicting browsing and click behavior in sponsored search. In addition, we explore the extent to which these biases actually reflect varying behavioral patterns. Our observations confirm that correlations exist between the biases and user search behavior.", acknowledgement = ack-nhfb, articleno = "59", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Qin:2015:SSA, author = "Tao Qin and Wei Chen and Tie-Yan Liu", title = "Sponsored Search Auctions: Recent Advances and Future Directions", journal = j-TIST, volume = "5", number = "4", pages = "60:1--60:??", month = jan, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2668108", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Sponsored search has been proven to be a successful business model, and sponsored search auctions have become a hot research direction. There have been many exciting advances in this field, especially in recent years, while at the same time, there are also many open problems waiting for us to resolve. In this article, we provide a comprehensive review of sponsored search auctions in hopes of helping both industry practitioners and academic researchers to become familiar with this field, to know the state of the art, and to identify future research topics. Specifically, we organize the article into two parts. In the first part, we review research works on sponsored search auctions with basic settings, where fully rational advertisers without budget constraints, preknown click-through rates (CTRs) without interdependence, and exact match between queries and keywords are assumed. Under these assumptions, we first introduce the generalized second price (GSP) auction, which is the most popularly used auction mechanism in the industry. Then we give the definitions of several well-studied equilibria and review the latest results on GSP's efficiency and revenue in these equilibria. In the second part, we introduce some advanced topics on sponsored search auctions. In these advanced topics, one or more assumptions made in the basic settings are relaxed. For example, the CTR of an ad could be unknown and dependent on other ads; keywords could be broadly matched to queries before auctions are executed; and advertisers are not necessarily fully rational, could have budget constraints, and may prefer rich bidding languages. Given that the research on these advanced topics is still immature, in each section of the second part, we provide our opinions on how to make further advances, in addition to describing what has been done by researchers in the corresponding direction.", acknowledgement = ack-nhfb, articleno = "60", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Chapelle:2015:SSR, author = "Olivier Chapelle and Eren Manavoglu and Romer Rosales", title = "Simple and Scalable Response Prediction for Display Advertising", journal = j-TIST, volume = "5", number = "4", pages = "61:1--61:??", month = jan, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2532128", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Clickthrough and conversation rates estimation are two core predictions tasks in display advertising. We present in this article a machine learning framework based on logistic regression that is specifically designed to tackle the specifics of display advertising. The resulting system has the following characteristics: It is easy to implement and deploy, it is highly scalable (we have trained it on terabytes of data), and it provides models with state-of-the-art accuracy.", acknowledgement = ack-nhfb, articleno = "61", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Balakrishnan:2015:RTB, author = "Raju Balakrishnan and Rushi P. Bhatt", title = "Real-Time Bid Optimization for Group-Buying Ads", journal = j-TIST, volume = "5", number = "4", pages = "62:1--62:??", month = jan, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2532441", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Group-buying ads seeking a minimum number of customers before the deal expiry are increasingly used by daily-deal providers. Unlike traditional web ads, the advertiser's profits for group-buying ads depend on the time to expiry and additional customers needed to satisfy the minimum group size. Since both these quantities are time-dependent, optimal bid amounts to maximize profits change with every impression. Consequently, traditional static bidding strategies are far from optimal. Instead, bid values need to be optimized in real-time to maximize expected bidder profits. This online optimization of deal profits is made possible by the advent of ad exchanges offering real-time (spot) bidding. To this end, we propose a real-time bidding strategy for group-buying deals based on the online optimization of bid values. We derive the expected bidder profit of deals as a function of the bid amounts and dynamically vary the bids to maximize profits. Furthermore, to satisfy time constraints of the online bidding, we present methods of minimizing computation timings. Subsequently, we derive the real-time ad selection, admissibility, and real-time bidding of the traditional ads as the special cases of the proposed method. We evaluate the proposed bidding, selection, and admission strategies on a multimillion click stream of 935 ads. The proposed real-time bidding, selection, and admissibility show significant profit increases over the existing strategies. Further experiments illustrate the robustness of the bidding and acceptable computation timings.", acknowledgement = ack-nhfb, articleno = "62", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Liu:2015:IAC, author = "Qingzhong Liu and Zhongxue Chen", title = "Improved Approaches with Calibrated Neighboring Joint Density to Steganalysis and Seam-Carved Forgery Detection in {JPEG} Images", journal = j-TIST, volume = "5", number = "4", pages = "63:1--63:??", month = jan, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2560365", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/cryptography2010.bib; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Steganalysis and forgery detection in image forensics are generally investigated separately. We have designed a method targeting the detection of both steganography and seam-carved forgery in JPEG images. We analyze the neighboring joint density of the DCT coefficients and reveal the difference between the untouched image and the modified version. In realistic detection, the untouched image and the modified version may not be obtained at the same time, and different JPEG images may have different neighboring joint density features. By exploring the self-calibration under different shift recompressions, we propose calibrated neighboring joint density-based approaches with a simple feature set to distinguish steganograms and tampered images from untouched ones. Our study shows that this approach has multiple promising applications in image forensics. Compared to the state-of-the-art steganalysis detectors, our approach delivers better or comparable detection performances with a much smaller feature set while detecting several JPEG-based steganographic systems including DCT-embedding-based adaptive steganography and Yet Another Steganographic Scheme (YASS). Our approach is also effective in detecting seam-carved forgery in JPEG images. By integrating calibrated neighboring density with spatial domain rich models that were originally designed for steganalysis, the hybrid approach obtains the best detection accuracy to discriminate seam-carved forgery from an untouched image. Our study also offers a promising manner to explore steganalysis and forgery detection together.", acknowledgement = ack-nhfb, articleno = "63", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Azaria:2015:SID, author = "Amos Azaria and Zinovi Rabinovich and Claudia V. Goldman and Sarit Kraus", title = "Strategic Information Disclosure to People with Multiple Alternatives", journal = j-TIST, volume = "5", number = "4", pages = "64:1--64:??", month = jan, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2558397", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In this article, we study automated agents that are designed to encourage humans to take some actions over others by strategically disclosing key pieces of information. To this end, we utilize the framework of persuasion games-a branch of game theory that deals with asymmetric interactions where one player (Sender) possesses more information about the world, but it is only the other player (Receiver) who can take an action. In particular, we use an extended persuasion model, where the Sender's information is imperfect and the Receiver has more than two alternative actions available. We design a computational algorithm that, from the Sender's standpoint, calculates the optimal information disclosure rule. The algorithm is parameterized by the Receiver's decision model (i.e., what choice he will make based on the information disclosed by the Sender) and can be retuned accordingly. We then provide an extensive experimental study of the algorithm's performance in interactions with human Receivers. First, we consider a fully rational (in the Bayesian sense) Receiver decision model and experimentally show the efficacy of the resulting Sender's solution in a routing domain. Despite the discrepancy in the Sender's and the Receiver's utilities from each of the Receiver's choices, our Sender agent successfully persuaded human Receivers to select an option more beneficial for the agent. Dropping the Receiver's rationality assumption, we introduce a machine learning procedure that generates a more realistic human Receiver model. We then show its significant benefit to the Sender solution by repeating our routing experiment. To complete our study, we introduce a second (supply--demand) experimental domain and, by contrasting it with the routing domain, obtain general guidelines for a Sender on how to construct a Receiver model.", acknowledgement = ack-nhfb, articleno = "64", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Liu:2015:SPA, author = "Si Liu and Qiang Chen and Shuicheng Yan and Changsheng Xu and Hanqing Lu", title = "{Snap \& Play}: Auto-Generated Personalized Find-the-Difference Game", journal = j-TIST, volume = "5", number = "4", pages = "65:1--65:??", month = jan, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2668109", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In this article, by taking a popular game, the Find-the-Difference (FiDi) game, as a concrete example, we explore how state-of-the-art image processing techniques can assist in developing a personalized, automatic, and dynamic game. Unlike the traditional FiDi game, where image pairs (source image and target image) with five different patches are manually produced by professional game developers, the proposed Personalized FiDi (P-FiDi) electronic game can be played in a fully automatic Snap \& Play mode. Snap means that players first take photos with their digital cameras. The newly captured photos are used as source images and fed into the P-FiDi system to autogenerate the counterpart target images for users to play. Four steps are adopted to autogenerate target images: enhancing the visual quality of source images, extracting some changeable patches from the source image, selecting the most suitable combination of changeable patches and difference styles for the image, and generating the differences on the target image with state-of-the-art image processing techniques. In addition, the P-FiDi game can be easily redesigned for the im-game advertising. Extensive experiments show that the P-FiDi electronic game is satisfying in terms of player experience, seamless advertisement, and technical feasibility.", acknowledgement = ack-nhfb, articleno = "65", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Reches:2015:CCU, author = "Shulamit Reches and Meir Kalech", title = "Choosing a Candidate Using Efficient Allocation of Biased Information", journal = j-TIST, volume = "5", number = "4", pages = "66:1--66:??", month = jan, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2558327", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "This article deals with a decision-making problem concerning an agent who wants to choose a partner from multiple candidates for long-term collaboration. To choose the best partner, the agent can rely on prior information he knows about the candidates. However, to improve his decision, he can request additional information from information sources. Nonetheless, acquiring information from external information sources about candidates may be biased due to different personalities of the agent searching for a partner and the information source. In addition, information may be costly. Considering the bias and the cost of the information sources, the optimization problem addressed in this article is threefold: (1) determining the necessary amount of additional information, (2) selecting information sources from which to request the information, and (3) choosing the candidates on whom to request the additional information. We propose a heuristic to solve this optimization problem. The results of experiments on simulated and real-world domains demonstrate the efficiency of our algorithm.", acknowledgement = ack-nhfb, articleno = "66", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhuang:2015:CDS, author = "Jinfeng Zhuang and Tao Mei and Steven C. H. Hoi and Xian-Sheng Hua and Yongdong Zhang", title = "Community Discovery from Social Media by Low-Rank Matrix Recovery", journal = j-TIST, volume = "5", number = "4", pages = "67:1--67:??", month = jan, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2668110", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The pervasive usage and reach of social media have attracted a surge of attention in the multimedia research community. Community discovery from social media has therefore become an important yet challenging issue. However, due to the subjective generating process, the explicitly observed communities (e.g., group-user and user-user relationship) are often noisy and incomplete in nature. This paper presents a novel approach to discovering communities from social media, including the group membership and user friend structure, by exploring a low-rank matrix recovery technique. In particular, we take Flickr as one exemplary social media platform. We first model the observed indicator matrix of the Flickr community as a summation of a low-rank true matrix and a sparse error matrix. We then formulate an optimization problem by regularizing the true matrix to coincide with the available rich context and content (i.e., photos and their associated tags). An iterative algorithm is developed to recover the true community indicator matrix. The proposed approach leads to a variety of social applications, including community visualization, interest group refinement, friend suggestion, and influential user identification. The evaluations on a large-scale testbed, consisting of 4,919 Flickr users, 1,467 interest groups, and over five million photos, show that our approach opens a new yet effective perspective to solve social network problems with sparse learning technique. Despite being focused on Flickr, our technique can be applied in any other social media community.", acknowledgement = ack-nhfb, articleno = "67", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Yang:2015:IPI, author = "Yiyang Yang and Zhiguo Gong and Leong Hou U.", title = "Identifying Points of Interest Using Heterogeneous Features", journal = j-TIST, volume = "5", number = "4", pages = "68:1--68:??", month = jan, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2668111", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Wed Feb 11 12:29:09 MST 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Deducing trip-related information from web-scale datasets has received large amounts of attention recently. Identifying points of interest (POIs) in geo-tagged photos is one of these problems. The problem can be viewed as a standard clustering problem of partitioning two-dimensional objects. In this work, we study spectral clustering, which is the first attempt for the identification of POIs. However, there is no unified approach to assigning the subjective clustering parameters, and these parameters vary immensely in different metropolitans and locations. To address this issue, we study a self-tuning technique that can properly determine the parameters for the clustering needed. Besides geographical information, web photos inherently store other rich information. Such heterogeneous information can be used to enhance the identification accuracy. Thereby, we study a novel refinement framework that is based on the tightness and cohesion degree of the additional information. We thoroughly demonstrate our findings by web-scale datasets collected from Flickr.", acknowledgement = ack-nhfb, articleno = "68", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ji:2015:WLM, author = "Rongrong Ji and Yue Gao and Wei Liu and Xing Xie and Qi Tian and Xuelong Li", title = "When Location Meets Social Multimedia: a Survey on Vision-Based Recognition and Mining for Geo-Social Multimedia Analytics", journal = j-TIST, volume = "6", number = "1", pages = "1:1--1:??", month = mar, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2597181", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 27 18:08:08 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Coming with the popularity of multimedia sharing platforms such as Facebook and Flickr, recent years have witnessed an explosive growth of geographical tags on social multimedia content. This trend enables a wide variety of emerging applications, for example, mobile location search, landmark recognition, scene reconstruction, and touristic recommendation, which range from purely research prototype to commercial systems. In this article, we give a comprehensive survey on these applications, covering recent advances in recognition and mining of geographical-aware social multimedia. We review related work in the past decade regarding to location recognition, scene summarization, tourism suggestion, 3D building modeling, mobile visual search and city navigation. At the end, we further discuss potential challenges, future topics, as well as open issues related to geo-social multimedia computing, recognition, mining, and analytics.", acknowledgement = ack-nhfb, articleno = "1", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Chin:2015:FPS, author = "Wei-Sheng Chin and Yong Zhuang and Yu-Chin Juan and Chih-Jen Lin", title = "A Fast Parallel Stochastic Gradient Method for Matrix Factorization in Shared Memory Systems", journal = j-TIST, volume = "6", number = "1", pages = "2:1--2:??", month = mar, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2668133", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 27 18:08:08 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Matrix factorization is known to be an effective method for recommender systems that are given only the ratings from users to items. Currently, stochastic gradient (SG) method is one of the most popular algorithms for matrix factorization. However, as a sequential approach, SG is difficult to be parallelized for handling web-scale problems. In this article, we develop a fast parallel SG method, FPSG, for shared memory systems. By dramatically reducing the cache-miss rate and carefully addressing the load balance of threads, FPSG is more efficient than state-of-the-art parallel algorithms for matrix factorization.", acknowledgement = ack-nhfb, articleno = "2", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Feuz:2015:TLA, author = "Kyle D. Feuz and Diane J. Cook", title = "Transfer Learning across Feature-Rich Heterogeneous Feature Spaces via {Feature-Space Remapping (FSR)}", journal = j-TIST, volume = "6", number = "1", pages = "3:1--3:??", month = mar, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2629528", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 27 18:08:08 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Transfer learning aims to improve performance on a target task by utilizing previous knowledge learned from source tasks. In this paper we introduce a novel heterogeneous transfer learning technique, Feature-Space Remapping (FSR), which transfers knowledge between domains with different feature spaces. This is accomplished without requiring typical feature-feature, feature instance, or instance-instance co-occurrence data. Instead we relate features in different feature-spaces through the construction of metafeatures. We show how these techniques can utilize multiple source datasets to construct an ensemble learner which further improves performance. We apply FSR to an activity recognition problem and a document classification problem. The ensemble technique is able to outperform all other baselines and even performs better than a classifier trained using a large amount of labeled data in the target domain. These problems are especially difficult because, in addition to having different feature-spaces, the marginal probability distributions and the class labels are also different. This work extends the state of the art in transfer learning by considering large transfer across dramatically different spaces.", acknowledgement = ack-nhfb, articleno = "3", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Patel:2015:DSI, author = "Dhaval Patel", title = "On Discovery of Spatiotemporal Influence-Based Moving Clusters", journal = j-TIST, volume = "6", number = "1", pages = "4:1--4:??", month = mar, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2631926", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 27 18:08:08 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "A moving object cluster is a set of objects that move close to each other for a long time interval. Existing works have utilized object trajectories to discover moving object clusters efficiently. In this article, we define a spatiotemporal influence-based moving cluster that captures spatiotemporal influence spread over a set of spatial objects. A spatiotemporal influence-based moving cluster is a sequence of spatial clusters, where each cluster is a set of nearby objects, such that each object in a cluster influences at least one object in the next immediate cluster and is also influenced by an object from the immediate preceding cluster. Real-life examples of spatiotemporal influence-based moving clusters include diffusion of infectious diseases and spread of innovative ideas. We study the discovery of spatiotemporal influence-based moving clusters in a database of spatiotemporal events. While the search space for discovering all spatiotemporal influence-based moving clusters is prohibitively huge, we design a method, STIMer, to efficiently retrieve the maximal answer. The algorithm STIMer adopts a top-down recursive refinement method to generate the maximal spatiotemporal influence-based moving clusters directly. Empirical studies on the real data as well as large synthetic data demonstrate the effectiveness and efficiency of our method.", acknowledgement = ack-nhfb, articleno = "4", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Sepehri-Rad:2015:ICW, author = "Hoda Sepehri-Rad and Denilson Barbosa", title = "Identifying Controversial {Wikipedia} Articles Using Editor Collaboration Networks", journal = j-TIST, volume = "6", number = "1", pages = "5:1--5:??", month = mar, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2630075", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 27 18:08:08 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Wikipedia is probably the most commonly used knowledge reference nowadays, and the high quality of its articles is widely acknowledged. Nevertheless, disagreement among editors often causes some articles to become controversial over time. These articles span thousands of popular topics, including religion, history, and politics, to name a few, and are manually tagged as controversial by the editors, which is clearly suboptimal. Moreover, disagreement, bias, and conflict are expressed quite differently in Wikipedia compared to other social media, rendering previous approaches ineffective. On the other hand, the social process of editing Wikipedia is partially captured in the edit history of the articles, opening the door for novel approaches. This article describes a novel controversy model that builds on the interaction history of the editors and not only predicts controversy but also sheds light on the process that leads to controversy. The model considers the collaboration history of pairs of editors to predict their attitude toward one another. This is done in a supervised way, where the votes of Wikipedia administrator elections are used as labels indicating agreement (i.e., support vote) or disagreement (i.e., oppose vote). From each article, a collaboration network is built, capturing the pairwise attitude among editors, allowing the accurate detection of controversy. Extensive experimental results establish the superiority of this approach compared to previous work and very competitive baselines on a wide range of settings.", acknowledgement = ack-nhfb, articleno = "5", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Changuel:2015:RSU, author = "Sahar Changuel and Nicolas Labroche and Bernadette Bouchon-Meunier", title = "Resources Sequencing Using Automatic Prerequisite--Outcome Annotation", journal = j-TIST, volume = "6", number = "1", pages = "6:1--6:??", month = mar, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2505349", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 27 18:08:08 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The objective of any tutoring system is to provide resources to learners that are adapted to their current state of knowledge. With the availability of a large variety of online content and the disjunctive nature of results provided by traditional search engines, it becomes crucial to provide learners with adapted learning paths that propose a sequence of resources that match their learning objectives. In an ideal case, the sequence of documents provided to the learner should be such that each new document relies on concepts that have been already defined in previous documents. Thus, the problem of determining an effective learning path from a corpus of web documents depends on the accurate identification of outcome and prerequisite concepts in these documents and on their ordering according to this information. Until now, only a few works have been proposed to distinguish between prerequisite and outcome concepts, and to the best of our knowledge, no method has been introduced so far to benefit from this information to produce a meaningful learning path. To this aim, this article first describes a concept annotation method that relies on machine-learning techniques to predict the class of each concept-prerequisite or outcome-on the basis of contextual and local features. Then, this categorization is exploited to produce an automatic resource sequencing on the basis of different representations and scoring functions that transcribe the precedence relation between learning resources. Experiments conducted on a real dataset built from online resources show that our concept annotation approach outperforms the baseline method and that the learning paths automatically generated are consistent with the ground truth provided by the author of the online content.", acknowledgement = ack-nhfb, articleno = "6", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Ghosh:2015:MTD, author = "Siddhartha Ghosh and Steve Reece and Alex Rogers and Stephen Roberts and Areej Malibari and Nicholas R. Jennings", title = "Modeling the Thermal Dynamics of Buildings: a Latent-Force- Model-Based Approach", journal = j-TIST, volume = "6", number = "1", pages = "7:1--7:??", month = mar, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2629674", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 27 18:08:08 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Minimizing the energy consumed by heating, ventilation, and air conditioning (HVAC) systems of residential buildings without impacting occupants' comfort has been highlighted as an important artificial intelligence (AI) challenge. Typically, approaches that seek to address this challenge use a model that captures the thermal dynamics within a building, also referred to as a thermal model. Among thermal models, gray-box models are a popular choice for modeling the thermal dynamics of buildings. They combine knowledge of the physical structure of a building with various data-driven inputs and are accurate estimators of the state (internal temperature). However, existing gray-box models require a detailed specification of all the physical elements that can affect the thermal dynamics of a building a priori. This limits their applicability, particularly in residential buildings, where additional dynamics can be induced by human activities such as cooking, which contributes additional heat, or opening of windows, which leads to additional leakage of heat. Since the incidence of these additional dynamics is rarely known, their combined effects cannot readily be accommodated within existing models. To overcome this limitation and improve the general applicability of gray-box models, we introduce a novel model, which we refer to as a latent force thermal model of the thermal dynamics of a building, or LFM-TM. Our model is derived from an existing gray-box thermal model, which is augmented with an extra term referred to as the learned residual. This term is capable of modeling the effect of any a priori unknown additional dynamic, which, if not captured, appears as a structure in a thermal model's residual (the error induced by the model). More importantly, the learned residual can also capture the effects of physical elements such as a building's envelope or the lags in a heating system, leading to a significant reduction in complexity compared to existing models. To evaluate the performance of LFM-TM, we apply it to two independent data sources. The first is an established dataset, referred to as the FlexHouse data, which was previously used for evaluating the efficacy of existing gray-box models [Bacher and Madsen 2011]. The second dataset consists of heating data logged within homes located on the University of Southampton campus, which were specifically instrumented to collect data for our thermal modeling experiments. On both datasets, we show that LFM-TM outperforms existing models in its ability to accurately fit the observed data, generate accurate day-ahead internal temperature predictions, and explain a large amount of the variability in the future observations. This, along with the fact that we also use a corresponding efficient sequential inference scheme for LFM-TM, makes it an ideal candidate for model-based predictive control, where having accurate online predictions of internal temperatures is essential for high-quality solutions.", acknowledgement = ack-nhfb, articleno = "7", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2015:SPL, author = "Zhao Zhang and Cheng-Lin Liu and Ming-Bo Zhao", title = "A Sparse Projection and Low-Rank Recovery Framework for Handwriting Representation and Salient Stroke Feature Extraction", journal = j-TIST, volume = "6", number = "1", pages = "9:1--9:??", month = mar, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2601408", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 27 18:08:08 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "In this article, we consider the problem of simultaneous low-rank recovery and sparse projection. More specifically, a new Robust Principal Component Analysis (RPCA)-based framework called Sparse Projection and Low-Rank Recovery (SPLRR) is proposed for handwriting representation and salient stroke feature extraction. In addition to achieving a low-rank component encoding principal features and identify errors or missing values from a given data matrix as RPCA, SPLRR also learns a similarity-preserving sparse projection for extracting salient stroke features and embedding new inputs for classification. These properties make SPLRR applicable for handwriting recognition and stroke correction and enable online computation. A cosine-similarity-style regularization term is incorporated into the SPLRR formulation for encoding the similarities of local handwriting features. The sparse projection and low-rank recovery are calculated from a convex minimization problem that can be efficiently solved in polynomial time. Besides, the supervised extension of SPLRR is also elaborated. The effectiveness of our SPLRR is examined by extensive handwritten digital repairing, stroke correction, and recognition based on benchmark problems. Compared with other related techniques, SPLRR delivers strong generalization capability and state-of-the-art performance for handwriting representation and recognition.", acknowledgement = ack-nhfb, articleno = "9", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Stapleton:2015:CST, author = "Gem Stapleton and Beryl Plimmer and Aidan Delaney and Peter Rodgers", title = "Combining Sketching and Traditional Diagram Editing Tools", journal = j-TIST, volume = "6", number = "1", pages = "10:1--10:??", month = mar, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2631925", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Fri Mar 27 18:08:08 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The least cognitively demanding way to create a diagram is to draw it with a pen. Yet there is also a need for more formal visualizations, that is, diagrams created using both traditional keyboard and mouse interaction. Our objective is to allow the creation of diagrams using traditional and stylus-based input. Having two diagram creation interfaces requires that changes to a diagram should be automatically rendered in the other visualization. Because sketches are imprecise, there is always the possibility that conversion between visualizations results in a lack of syntactic consistency between the two visualizations. We propose methods for converting diagrams between forms, checking them for equivalence, and rectifying inconsistencies. As a result of our theoretical contributions, we present an intelligent software system allowing users to create and edit diagrams in sketch or formal mode. Our proof-of-concept tool supports diagrams with connected and spatial syntactic elements. Two user studies show that this approach is viable and participants found the software easy to use. We conclude that supporting such diagram creation is now possible in practice.", acknowledgement = ack-nhfb, articleno = "10", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Hong:2015:VUR, author = "Richang Hong and Shuicheng Yan and Zhengyou Zhang", title = "Visual Understanding with {RGB-D} Sensors: an Introduction to the Special Issue", journal = j-TIST, volume = "6", number = "2", pages = "11:1--11:??", month = apr, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2732265", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, articleno = "11", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Chen:2015:KDR, author = "Chongyu Chen and Jianfei Cai and Jianmin Zheng and Tat Jen Cham and Guangming Shi", title = "{Kinect} Depth Recovery Using a Color-Guided, Region-Adaptive, and Depth-Selective Framework", journal = j-TIST, volume = "6", number = "2", pages = "12:1--12:??", month = apr, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2700475", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Considering that the existing depth recovery approaches have different limitations when applied to Kinect depth data, in this article, we propose to integrate their effective features including adaptive support region selection, reliable depth selection, and color guidance together under an optimization framework for Kinect depth recovery. In particular, we formulate our depth recovery as an energy minimization problem, which solves the depth hole filling and denoising simultaneously. The energy function consists of a fidelity term and a regularization term, which are designed according to the Kinect characteristics. Our framework inherits and improves the idea of guided filtering by incorporating structure information and prior knowledge of the Kinect noise model. Through analyzing the solution to the optimization framework, we also derive a local filtering version that provides an efficient and effective way of improving the existing filtering techniques. Quantitative evaluations on our developed synthesized dataset and experiments on real Kinect data show that the proposed method achieves superior performance in terms of recovery accuracy and visual quality.", acknowledgement = ack-nhfb, articleno = "12", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Figueroa:2015:CAT, author = "Nadia Figueroa and Haiwei Dong and Abdulmotaleb {El Saddik}", title = "A Combined Approach Toward Consistent Reconstructions of Indoor Spaces Based on {$6$D RGB-D} Odometry and {KinectFusion}", journal = j-TIST, volume = "6", number = "2", pages = "14:1--14:??", month = apr, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2629673", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "We propose a 6D RGB-D odometry approach that finds the relative camera pose between consecutive RGB-D frames by keypoint extraction and feature matching both on the RGB and depth image planes. Furthermore, we feed the estimated pose to the highly accurate KinectFusion algorithm, which uses a fast ICP (Iterative Closest Point) to fine-tune the frame-to-frame relative pose and fuse the depth data into a global implicit surface. We evaluate our method on a publicly available RGB-D SLAM benchmark dataset by Sturm et al. The experimental results show that our proposed reconstruction method solely based on visual odometry and KinectFusion outperforms the state-of-the-art RGB-D SLAM system accuracy. Moreover, our algorithm outputs a ready-to-use polygon mesh (highly suitable for creating 3D virtual worlds) without any postprocessing steps.", acknowledgement = ack-nhfb, articleno = "14", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zha:2015:RMF, author = "Zheng-Jun Zha and Yang Yang and Jinhui Tang and Meng Wang and Tat-Seng Chua", title = "Robust Multiview Feature Learning for {RGB-D} Image Understanding", journal = j-TIST, volume = "6", number = "2", pages = "15:1--15:??", month = apr, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2735521", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The availability of massive RGB-depth (RGB-D) images poses a compelling need for effective RGB-D content understanding techniques. RGB-D images provide synchronized information from multiple views (e.g., color and depth) of real-world objects and scenes. This work proposes learning compact and discriminative features from the multiple views of RGB-D content toward effective feature representation for RGB-D image understanding. In particular, a robust multiview feature learning approach is developed, which exploits the intrinsic relations among multiple views. The feature learning in multiple views is jointly optimized in an integrated formulation. The joint optimization essentially exploits the intrinsic relations among the views, leading to effective features and making the learning process robust to noises. The feature learning function is formulated as a robust nonnegative graph embedding function over multiple graphs in various views. The graphs characterize the local geometric and discriminating structure of the multiview data. The joint sparsity in$l_1$-norm graph embedding and$l_{21}-norm data factorization further enhances the robustness of feature learning. We derive an efficient computational solution for the proposed approach and provide rigorous theoretical proof with regard to its convergence. We apply the proposed approach to two RGB-D image understanding tasks: RGB-D object classification and RGB-D scene categorization. We conduct extensive experiments on two real-world RGB-D image datasets. The experimental results have demonstrated the effectiveness of the proposed approach.", acknowledgement = ack-nhfb, articleno = "15", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2015:RDI, author = "Quanshi Zhang and Xuan Song and Xiaowei Shao and Huijing Zhao and Ryosuke Shibasaki", title = "From {RGB-D} Images to {RGB} Images: Single Labeling for Mining Visual Models", journal = j-TIST, volume = "6", number = "2", pages = "16:1--16:??", month = apr, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2629701", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Mining object-level knowledge, that is, building a comprehensive category model base, from a large set of cluttered scenes presents a considerable challenge to the field of artificial intelligence. How to initiate model learning with the least human supervision (i.e., manual labeling) and how to encode the structural knowledge are two elements of this challenge, as they largely determine the scalability and applicability of any solution. In this article, we propose a model-learning method that starts from a single-labeled object for each category, and mines further model knowledge from a number of informally captured, cluttered scenes. However, in these scenes, target objects are relatively small and have large variations in texture, scale, and rotation. Thus, to reduce the model bias normally associated with less supervised learning methods, we use the robust 3D shape in RGB-D images to guide our model learning, then apply the properly trained category models to both object detection and recognition in more conventional RGB images. In addition to model training for their own categories, the knowledge extracted from the RGB-D images can also be transferred to guide model learning for a new category, in which only RGB images without depth information in the new category are provided for training. Preliminary testing shows that the proposed method performs as well as fully supervised learning methods.", acknowledgement = ack-nhfb, articleno = "16", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Huang:2015:ARM, author = "Meiyu Huang and Yiqiang Chen and Wen Ji and Chunyan Miao", title = "Accurate and Robust Moving-Object Segmentation for Telepresence Systems", journal = j-TIST, volume = "6", number = "2", pages = "17:1--17:??", month = apr, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2629480", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Moving-object segmentation is the key issue of Telepresence systems. With monocular camera--based segmentation methods, desirable segmentation results are hard to obtain in challenging scenes with ambiguous color, illumination changes, and shadows. Approaches based on depth sensors often cause holes inside the object and missegmentations on the object boundary due to inaccurate and unstable estimation of depth data. This work proposes an adaptive multi-cue decision fusion method based on Kinect (which integrates a depth sensor with an RGB camera). First, the algorithm obtains an initial foreground mask based on the depth cue. Second, the algorithm introduces a postprocessing framework to refine the segmentation results, which consists of two main steps: (1) automatically adjusting the weight of two weak decisions to identify foreground holes based on the color and contrast cue separately; and (2) refining the object boundary by integrating the motion probability weighted temporal prior, color likelihood, and smoothness constraint. The extensive experiments we conducted demonstrate that our method can segment moving objects accurately and robustly in various situations in real time.", acknowledgement = ack-nhfb, articleno = "17", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhu:2015:FMF, author = "Yu Zhu and Wenbin Chen and Guodong Guo", title = "Fusing Multiple Features for Depth-Based Action Recognition", journal = j-TIST, volume = "6", number = "2", pages = "18:1--18:??", month = apr, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2629483", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Human action recognition is a very active research topic in computer vision and pattern recognition. Recently, it has shown a great potential for human action recognition using the three-dimensional (3D) depth data captured by the emerging RGB-D sensors. Several features and/or algorithms have been proposed for depth-based action recognition. A question is raised: Can we find some complementary features and combine them to improve the accuracy significantly for depth-based action recognition? To address the question and have a better understanding of the problem, we study the fusion of different features for depth-based action recognition. Although data fusion has shown great success in other areas, it has not been well studied yet on 3D action recognition. Some issues need to be addressed, for example, whether the fusion is helpful or not for depth-based action recognition, and how to do the fusion properly. In this article, we study different fusion schemes comprehensively, using diverse features for action characterization in depth videos. Two different levels of fusion schemes are investigated, that is, feature level and decision level. Various methods are explored at each fusion level. Four different features are considered to characterize the depth action patterns from different aspects. The experiments are conducted on four challenging depth action databases, in order to evaluate and find the best fusion methods generally. Our experimental results show that the four different features investigated in the article can complement each other, and appropriate fusion methods can improve the recognition accuracies significantly over each individual feature. More importantly, our fusion-based action recognition outperforms the state-of-the-art approaches on these challenging databases.", acknowledgement = ack-nhfb, articleno = "18", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Spurlock:2015:EGD, author = "Scott Spurlock and Richard Souvenir", title = "An Evaluation of Gamesourced Data for Human Pose Estimation", journal = j-TIST, volume = "6", number = "2", pages = "19:1--19:??", month = apr, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2629465", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Gamesourcing has emerged as an approach for rapidly acquiring labeled data for learning-based, computer vision recognition algorithms. In this article, we present an approach for using RGB-D sensors to acquire annotated training data for human pose estimation from 2D images. Unlike other gamesourcing approaches, our method does not require a specific game, but runs alongside any gesture-based game using RGB-D sensors. The automatically generated datasets resulting from this approach contain joint estimates within a few pixel units of manually labeled data, and a gamesourced dataset created using a relatively small number of players, games, and locations performs as well as large-scale, manually annotated datasets when used as training data with recent learning-based human pose estimation methods for 2D images.", acknowledgement = ack-nhfb, articleno = "19", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Sun:2015:LSV, author = "Chao Sun and Tianzhu Zhang and Changsheng Xu", title = "Latent Support Vector Machine Modeling for Sign Language Recognition with {Kinect}", journal = j-TIST, volume = "6", number = "2", pages = "20:1--20:??", month = apr, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2629481", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Vision-based sign language recognition has attracted more and more interest from researchers in the computer vision field. In this article, we propose a novel algorithm to model and recognize sign language performed in front of a Microsoft Kinect sensor. Under the assumption that some frames are expected to be both discriminative and representative in a sign language video, we first assign a binary latent variable to each frame in training videos for indicating its discriminative capability, then develop a latent support vector machine model to classify the signs, as well as localize the discriminative and representative frames in each video. In addition, we utilize the depth map together with the color image captured by the Kinect sensor to obtain a more effective and accurate feature to enhance the recognition accuracy. To evaluate our approach, we conducted experiments on both word-level sign language and sentence-level sign language. An American Sign Language dataset including approximately 2,000 word-level sign language phrases and 2,000 sentence-level sign language phrases was collected using the Kinect sensor, and each phrase contains color, depth, and skeleton information. Experiments on our dataset demonstrate the effectiveness of the proposed method for sign language recognition.", acknowledgement = ack-nhfb, articleno = "20", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Tang:2015:RTH, author = "Ao Tang and Ke Lu and Yufei Wang and Jie Huang and Houqiang Li", title = "A Real-Time Hand Posture Recognition System Using Deep Neural Networks", journal = j-TIST, volume = "6", number = "2", pages = "21:1--21:??", month = apr, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2735952", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Hand posture recognition (HPR) is quite a challenging task, due to both the difficulty in detecting and tracking hands with normal cameras and the limitations of traditional manually selected features. In this article, we propose a two-stage HPR system for Sign Language Recognition using a Kinect sensor. In the first stage, we propose an effective algorithm to implement hand detection and tracking. The algorithm incorporates both color and depth information, without specific requirements on uniform-colored or stable background. It can handle the situations in which hands are very close to other parts of the body or hands are not the nearest objects to the camera and allows for occlusion of hands caused by faces or other hands. In the second stage, we apply deep neural networks (DNNs) to automatically learn features from hand posture images that are insensitive to movement, scaling, and rotation. Experiments verify that the proposed system works quickly and accurately and achieves a recognition accuracy as high as 98.12\%.", acknowledgement = ack-nhfb, articleno = "21", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2015:RTS, author = "Liyan Zhang and Fan Liu and Jinhui Tang", title = "Real-Time System for Driver Fatigue Detection by {RGB-D} Camera", journal = j-TIST, volume = "6", number = "2", pages = "22:1--22:??", month = apr, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2629482", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Drowsy driving is one of the major causes of fatal traffic accidents. In this article, we propose a real-time system that utilizes RGB-D cameras to automatically detect driver fatigue and generate alerts to drivers. By introducing RGB-D cameras, the depth data can be obtained, which provides extra evidence to benefit the task of head detection and head pose estimation. In this system, two important visual cues (head pose and eye state) for driver fatigue detection are extracted and leveraged simultaneously. We first present a real-time 3D head pose estimation method by leveraging RGB and depth data. Then we introduce a novel method to predict eye states employing the WLBP feature, which is a powerful local image descriptor that is robust to noise and illumination variations. Finally, we integrate the results from both head pose and eye states to generate the overall conclusion. The combination and collaboration of the two types of visual cues can reduce the uncertainties and resolve the ambiguity that a single cue may induce. The experiments were performed using an inside-car environment during the day and night, and they fully demonstrate the effectiveness and robustness of our system as well as the proposed methods of predicting head pose and eye states.", acknowledgement = ack-nhfb, articleno = "22", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Kyan:2015:ABD, author = "Matthew Kyan and Guoyu Sun and Haiyan Li and Ling Zhong and Paisarn Muneesawang and Nan Dong and Bruce Elder and Ling Guan", title = "An Approach to Ballet Dance Training through {MS Kinect} and Visualization in a {CAVE} Virtual Reality Environment", journal = j-TIST, volume = "6", number = "2", pages = "23:1--23:??", month = apr, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2735951", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "This article proposes a novel framework for the real-time capture, assessment, and visualization of ballet dance movements as performed by a student in an instructional, virtual reality (VR) setting. The acquisition of human movement data is facilitated by skeletal joint tracking captured using the popular Microsoft (MS) Kinect camera system, while instruction and performance evaluation are provided in the form of 3D visualizations and feedback through a CAVE virtual environment, in which the student is fully immersed. The proposed framework is based on the unsupervised parsing of ballet dance movement into a structured posture space using the spherical self-organizing map (SSOM). A unique feature descriptor is proposed to more appropriately reflect the subtleties of ballet dance movements, which are represented as gesture trajectories through posture space on the SSOM. This recognition subsystem is used to identify the category of movement the student is attempting when prompted (by a virtual instructor) to perform a particular dance sequence. The dance sequence is then segmented and cross-referenced against a library of gestural components performed by the teacher. This facilitates alignment and score-based assessment of individual movements within the context of the dance sequence. An immersive interface enables the student to review his or her performance from a number of vantage points, each providing a unique perspective and spatial context suggestive of how the student might make improvements in training. An evaluation of the recognition and virtual feedback systems is presented.", acknowledgement = ack-nhfb, articleno = "23", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Shi:2015:ESC, author = "Miaojing Shi and Xinghai Sun and Dacheng Tao and Chao Xu and George Baciu and Hong Liu", title = "Exploring Spatial Correlation for Visual Object Retrieval", journal = j-TIST, volume = "6", number = "2", pages = "24:1--24:??", month = apr, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2641576", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Bag-of-visual-words (BOVW)-based image representation has received intense attention in recent years and has improved content-based image retrieval (CBIR) significantly. BOVW does not consider the spatial correlation between visual words in natural images and thus biases the generated visual words toward noise when the corresponding visual features are not stable. This article outlines the construction of a visual word co-occurrence matrix by exploring visual word co-occurrence extracted from small affine-invariant regions in a large collection of natural images. Based on this co-occurrence matrix, we first present a novel high-order predictor to accelerate the generation of spatially correlated visual words and a penalty tree (PTree) to continue generating the words after the prediction. Subsequently, we propose two methods of co-occurrence weighting similarity measure for image ranking: Co-Cosine and Co-TFIDF. These two new schemes down-weight the contributions of the words that are less discriminative because of frequent co-occurrences with other words. We conduct experiments on Oxford and Paris Building datasets, in which the ImageNet dataset is used to implement a large-scale evaluation. Cross-dataset evaluations between the Oxford and Paris datasets and Oxford and Holidays datasets are also provided. Thorough experimental results suggest that our method outperforms the state of the art without adding much additional cost to the BOVW model.", acknowledgement = ack-nhfb, articleno = "24", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Doherty:2015:PMT, author = "Jonathan Doherty and Kevin Curran and Paul McKevitt", title = "Pattern Matching Techniques for Replacing Missing Sections of Audio Streamed across Wireless Networks", journal = j-TIST, volume = "6", number = "2", pages = "25:1--25:??", month = apr, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2663358", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Streaming media on the Internet can be unreliable. Services such as audio-on-demand drastically increase the loads on networks; therefore, new, robust, and highly efficient coding algorithms are necessary. One method overlooked to date, which can work alongside existing audio compression schemes, is that which takes into account the semantics and natural repetition of music. Similarity detection within polyphonic audio has presented problematic challenges within the field of music information retrieval. One approach to deal with bursty errors is to use self-similarity to replace missing segments. Many existing systems exist based on packet loss and replacement on a network level, but none attempt repairs of large dropouts of 5 seconds or more. Music exhibits standard structures that can be used as a forward error correction (FEC) mechanism. FEC is an area that addresses the issue of packet loss with the onus of repair placed as much as possible on the listener's device. We have developed a server--client-based framework (SoFI) for automatic detection and replacement of large packet losses on wireless networks when receiving time-dependent streamed audio. Whenever dropouts occur, SoFI swaps audio presented to the listener between a live stream and previous sections of the audio stored locally. Objective and subjective evaluations of SoFI where subjects were presented with other simulated approaches to audio repair together with simulations of replacements including varying lengths of time in the repair give positive results.", acknowledgement = ack-nhfb, articleno = "25", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Hai:2015:ABU, author = "Zhen Hai and Kuiyu Chang and Gao Cong and Christopher C. Yang", title = "An Association-Based Unified Framework for Mining Features and Opinion Words", journal = j-TIST, volume = "6", number = "2", pages = "26:1--26:??", month = apr, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2663359", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Mining features and opinion words is essential for fine-grained opinion analysis of customer reviews. It is observed that semantic dependencies naturally exist between features and opinion words, even among features or opinion words themselves. In this article, we employ a corpus statistics association measure to quantify the pairwise word dependencies and propose a generalized association-based unified framework to identify features, including explicit and implicit features, and opinion words from reviews. We first extract explicit features and opinion words via an association-based bootstrapping method (ABOOT). ABOOT starts with a small list of annotated feature seeds and then iteratively recognizes a large number of domain-specific features and opinion words by discovering the corpus statistics association between each pair of words on a given review domain. Two instances of this ABOOT method are evaluated based on two particular association models, likelihood ratio tests (LRTs) and latent semantic analysis (LSA). Next, we introduce a natural extension to identify implicit features by employing the recognized known semantic correlations between features and opinion words. Experimental results illustrate the benefits of the proposed association-based methods for identifying features and opinion words versus benchmark methods.", acknowledgement = ack-nhfb, articleno = "26", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Huang:2015:HMC, author = "Shanshan Huang and Jun Ma and Peizhe Cheng and Shuaiqiang Wang", title = "A {Hybrid Multigroup CoClustering} Recommendation Framework Based on Information Fusion", journal = j-TIST, volume = "6", number = "2", pages = "27:1--27:??", month = apr, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2700465", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Collaborative Filtering (CF) is one of the most successful algorithms in recommender systems. However, it suffers from data sparsity and scalability problems. Although many clustering techniques have been incorporated to alleviate these two problems, most of them fail to achieve further significant improvement in recommendation accuracy. First of all, most of them assume each user or item belongs to a single cluster. Since usually users can hold multiple interests and items may belong to multiple categories, it is more reasonable to assume that users and items can join multiple clusters (groups), where each cluster is a subset of like-minded users and items they prefer. Furthermore, most of the clustering-based CF models only utilize historical rating information in the clustering procedure but ignore other data resources in recommender systems such as the social connections of users and the correlations between items. In this article, we propose HMCoC, a Hybrid Multigroup CoClustering recommendation framework, which can cluster users and items into multiple groups simultaneously with different information resources. In our framework, we first integrate information of user--item rating records, user social networks, and item features extracted from the DBpedia knowledge base. We then use an optimization method to mine meaningful user--item groups with all the information. Finally, we apply the conventional CF method in each cluster to make predictions. By merging the predictions from each cluster, we generate the top-n recommendations to the target users for return. Extensive experimental results demonstrate the superior performance of our approach in top-n recommendation in terms of MAP, NDCG, and F1 compared with other clustering-based CF models.", acknowledgement = ack-nhfb, articleno = "27", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Fire:2015:DMO, author = "Michael Fire and Yuval Elovici", title = "Data Mining of Online Genealogy Datasets for Revealing Lifespan Patterns in Human Population", journal = j-TIST, volume = "6", number = "2", pages = "28:1--28:??", month = apr, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2700464", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Tue Apr 21 11:29:25 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Online genealogy datasets contain extensive information about millions of people and their past and present family connections. This vast amount of data can help identify various patterns in the human population. In this study, we present methods and algorithms that can assist in identifying variations in lifespan distributions of the human population in the past centuries, in detecting social and genetic features that correlate with the human lifespan, and in constructing predictive models of human lifespan based on various features that can easily be extracted from genealogy datasets. We have evaluated the presented methods and algorithms on a large online genealogy dataset with over a million profiles and over 9 million connections, all of which were collected from the WikiTree website. Our findings indicate that significant but small positive correlations exist between the parents' lifespan and their children's lifespan. Additionally, we found slightly higher and significant correlations between the lifespans of spouses. We also discovered a very small positive and significant correlation between longevity and reproductive success in males, and a small and significant negative correlation between longevity and reproductive success in females. Moreover, our predictive models presented results with a Mean Absolute Error as low as 13.18 in predicting the lifespans of individuals who outlived the age of 10, and our classification models presented better than random classification results in predicting which people who outlive the age of 50 will also outlive the age of 80. We believe that this study will be the first of many studies to utilize the wealth of data on human populations, existing in online genealogy datasets, to better understand factors that influence the human lifespan. Understanding these factors can assist scientists in providing solutions for successful aging.", acknowledgement = ack-nhfb, articleno = "28", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zheng:2015:TDM, author = "Yu Zheng", title = "Trajectory Data Mining: an Overview", journal = j-TIST, volume = "6", number = "3", pages = "29:1--29:??", month = may, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2743025", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu May 21 15:49:31 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles, and animals. Many techniques have been proposed for processing, managing, and mining trajectory data in the past decade, fostering a broad range of applications. In this article, we conduct a systematic survey on the major research into trajectory data mining, providing a panorama of the field as well as the scope of its research topics. Following a road map from the derivation of trajectory data, to trajectory data preprocessing, to trajectory data management, and to a variety of mining tasks (such as trajectory pattern mining, outlier detection, and trajectory classification), the survey explores the connections, correlations, and differences among these existing techniques. This survey also introduces the methods that transform trajectories into other data formats, such as graphs, matrices, and tensors, to which more data mining and machine learning techniques can be applied. Finally, some public trajectory datasets are presented. This survey can help shape the field of trajectory data mining, providing a quick understanding of this field to the community.", acknowledgement = ack-nhfb, articleno = "29", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Bouguessa:2015:IAO, author = "Mohamed Bouguessa and Lotfi Ben Romdhane", title = "Identifying Authorities in Online Communities", journal = j-TIST, volume = "6", number = "3", pages = "30:1--30:??", month = may, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2700481", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu May 21 15:49:31 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Several approaches have been proposed for the problem of identifying authoritative actors in online communities. However, the majority of existing methods suffer from one or more of the following limitations: (1) There is a lack of an automatic mechanism to formally discriminate between authoritative and nonauthoritative users. In fact, a common approach to authoritative user identification is to provide a ranked list of users expecting authorities to come first. A major problem of such an approach is the question of where to stop reading the ranked list of users. How many users should be chosen as authoritative? (2) Supervised learning approaches for authoritative user identification suffer from their dependency on the training data. The problem here is that labeled samples are more difficult, expensive, and time consuming to obtain than unlabeled ones. (3) Several approaches rely on some user parameters to estimate an authority score. Detection accuracy of authoritative users can be seriously affected if incorrect values are used. In this article, we propose a parameterless mixture model-based approach that is capable of addressing the three aforementioned issues in a single framework. In our approach, we first represent each user with a feature vector composed of information related to its social behavior and activity in an online community. Next, we propose a statistical framework, based on the multivariate beta mixtures, in order to model the estimated set of feature vectors. The probability density function is therefore estimated and the beta component that corresponds to the most authoritative users is identified. The suitability of the proposed approach is illustrated on real data extracted from the Stack Exchange question-answering network and Twitter.", acknowledgement = ack-nhfb, articleno = "30", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Lee:2015:WWR, author = "Kyumin Lee and Jalal Mahmud and Jilin Chen and Michelle Zhou and Jeffrey Nichols", title = "Who Will Retweet This? {Detecting} Strangers from {Twitter} to Retweet Information", journal = j-TIST, volume = "6", number = "3", pages = "31:1--31:??", month = may, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2700466", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu May 21 15:49:31 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "There has been much effort on studying how social media sites, such as Twitter, help propagate information in different situations, including spreading alerts and SOS messages in an emergency. However, existing work has not addressed how to actively identify and engage the right strangers at the right time on social media to help effectively propagate intended information within a desired time frame. To address this problem, we have developed three models: (1) a feature-based model that leverages people's exhibited social behavior, including the content of their tweets and social interactions, to characterize their willingness and readiness to propagate information on Twitter via the act of retweeting; (2) a wait-time model based on a user's previous retweeting wait times to predict his or her next retweeting time when asked; and (3) a subset selection model that automatically selects a subset of people from a set of available people using probabilities predicted by the feature-based model and maximizes retweeting rate. Based on these three models, we build a recommender system that predicts the likelihood of a stranger to retweet information when asked, within a specific time window, and recommends the top-N qualified strangers to engage with. Our experiments, including live studies in the real world, demonstrate the effectiveness of our work.", acknowledgement = ack-nhfb, articleno = "31", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Hirschprung:2015:SDD, author = "Ron Hirschprung and Eran Toch and Oded Maimon", title = "Simplifying Data Disclosure Configurations in a Cloud Computing Environment", journal = j-TIST, volume = "6", number = "3", pages = "32:1--32:??", month = may, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2700472", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu May 21 15:49:31 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Cloud computing offers a compelling vision of computation, enabling an unprecedented level of data distribution and sharing. Beyond improving the computing infrastructure, cloud computing enables a higher level of interoperability between information systems, simplifying tasks such as sharing documents between coworkers or enabling collaboration between an organization and its suppliers. While these abilities may result in significant benefits to users and organizations, they also present privacy challenges due to unwanted exposure of sensitive information. As information-sharing processes in cloud computing are complex and domain specific, configuring these processes can be an overwhelming and burdensome task for users. This article investigates the feasibility of configuring sharing processes through a small and representative set of canonical configuration options. For this purpose, we present a generic method, named SCON-UP (Simplified CON-figuration of User Preferences). SCON-UP simplifies configuration interfaces by using a clustering algorithm that analyzes a massive set of sharing preferences and condenses them into a small number of discrete disclosure levels. Thus, the user is provided with a usable configuration model while guaranteeing adequate privacy control. We describe the algorithm and empirically evaluate our model using data collected in two user studies (n = 121 and n = 352). Our results show that when provided with three canonical configuration options, on average, 82\% of the population can be covered by at least one option. We exemplify the feasibility of discretizing sharing levels and discuss the tradeoff between coverage and simplicity in discrete configuration options.", acknowledgement = ack-nhfb, articleno = "32", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Elbadrawy:2015:USF, author = "Asmaa Elbadrawy and George Karypis", title = "User-Specific Feature-Based Similarity Models for Top-n$Recommendation of New Items", journal = j-TIST, volume = "6", number = "3", pages = "33:1--33:??", month = may, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2700495", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu May 21 15:49:31 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Recommending new items for suitable users is an important yet challenging problem due to the lack of preference history for the new items. Noncollaborative user modeling techniques that rely on the item features can be used to recommend new items. However, they only use the past preferences of each user to provide recommendations for that user. They do not utilize information from the past preferences of other users, which can potentially be ignoring useful information. More recent factor models transfer knowledge across users using their preference information in order to provide more accurate recommendations. These methods learn a low-rank approximation for the preference matrix, which can lead to loss of information. Moreover, they might not be able to learn useful patterns given very sparse datasets. In this work, we present {{\sc UFSM}, a method for top-$n$recommendation of new items given binary user preferences. {\sc UFSM} learns {{\bf U}ser}-specific {\bf F}eature}-based item-{\bf S}imilarity {\bf M}odels, and its strength lies in combining two points: (1) exploiting preference information across all users to learn multiple global item similarity functions and (2) learning user-specific weights that determine the contribution of each global similarity function in generating recommendations for each user. {\sc UFSM} can be considered as a sparse high-dimensional factor model where the previous preferences of each user are incorporated within his or her latent representation. This way, {\sc UFSM} combines the merits of item similarity models that capture local relations among items and factor models that learn global preference patterns. A comprehensive set of experiments was conduced to compare {\sc UFSM} against state-of-the-art collaborative factor models and noncollaborative user modeling techniques. Results show that {\sc UFSM} outperforms other techniques in terms of recommendation quality. {\sc UFSM} manages to yield better recommendations even with very sparse datasets. Results also show that {\sc UFSM} can efficiently handle high-dimensional as well as low-dimensional item feature spaces.", acknowledgement = ack-nhfb, articleno = "33", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2015:TGO, author = "Mingjin Zhang and Huibo Wang and Yun Lu and Tao Li and Yudong Guang and Chang Liu and Erik Edrosa and Hongtai Li and Naphtali Rishe", title = "{TerraFly GeoCloud}: an Online Spatial Data Analysis and Visualization System", journal = j-TIST, volume = "6", number = "3", pages = "34:1--34:??", month = may, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2700494", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu May 21 15:49:31 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "With the exponential growth of the usage of web map services, geo-data analysis has become more and more popular. This article develops an online spatial data analysis and visualization system, TerraFly GeoCloud, which helps end-users visualize and analyze spatial data and share the analysis results. Built on the TerraFly Geo spatial database, TerraFly GeoCloud is an extra layer running upon the TerraFly map and can efficiently support many different visualization functions and spatial data analysis models. Furthermore, users can create unique URLs to visualize and share the analysis results. TerraFly GeoCloud also enables the MapQL technology to customize map visualization using SQL-like statements. The system is available at http://terrafly.fiu.edu/GeoCloud/.", acknowledgement = ack-nhfb, articleno = "34", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Chen:2015:SCP, author = "Yi-Cheng Chen and Wen-Chih Peng and Jiun-Long Huang and Wang-Chien Lee", title = "Significant Correlation Pattern Mining in Smart Homes", journal = j-TIST, volume = "6", number = "3", pages = "35:1--35:??", month = may, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2700484", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu May 21 15:49:31 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Owing to the great advent of sensor technology, the usage data of appliances in a house can be logged and collected easily today. However, it is a challenge for the residents to visualize how these appliances are used. Thus, mining algorithms are much needed to discover appliance usage patterns. Most previous studies on usage pattern discovery are mainly focused on analyzing the patterns of single appliance rather than mining the usage correlation among appliances. In this article, a novel algorithm, namely Correlation Pattern Miner (CoPMiner), is developed to capture the usage patterns and correlations among appliances probabilistically. CoPMiner also employs four pruning techniques and a statistical model to reduce the search space and filter out insignificant patterns, respectively. Furthermore, the proposed algorithm is applied on a real-world dataset to show the practicability of correlation pattern mining.", acknowledgement = ack-nhfb, articleno = "35", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Guo:2015:ISI, author = "Bin Guo and Alvin Chin and Zhiwen Yu and Runhe Huang and Daqing Zhang", title = "An Introduction to the Special Issue on Participatory Sensing and Crowd Intelligence", journal = j-TIST, volume = "6", number = "3", pages = "36:1--36:??", month = may, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2745712", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu May 21 15:49:31 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", acknowledgement = ack-nhfb, articleno = "36", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Zhang:2015:SPU, author = "Fuzheng Zhang and Nicholas Jing Yuan and David Wilkie and Yu Zheng and Xing Xie", title = "Sensing the Pulse of Urban Refueling Behavior: a Perspective from Taxi Mobility", journal = j-TIST, volume = "6", number = "3", pages = "37:1--37:??", month = may, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2644828", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu May 21 15:49:31 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Urban transportation is an important factor in energy consumption and pollution, and is of increasing concern due to its complexity and economic significance. Its importance will only increase as urbanization continues around the world. In this article, we explore drivers' refueling behavior in urban areas. Compared to questionnaire-based methods of the past, we propose a complete data-driven system that pushes towards real-time sensing of individual refueling behavior and citywide petrol consumption. Our system provides the following: detection of individual refueling events (REs) from which refueling preference can be analyzed; estimates of gas station wait times from which recommendations can be made; an indication of overall fuel demand from which macroscale economic decisions can be made, and a spatial, temporal, and economic view of urban refueling characteristics. For individual behavior, we use reported trajectories from a fleet of GPS-equipped taxicabs to detect gas station visits. For time spent estimates, to solve the sparsity issue along time and stations, we propose context-aware tensor factorization (CATF), a factorization model that considers a variety of contextual factors (e.g., price, brand, and weather condition) that affect consumers' refueling decision. For fuel demand estimates, we apply a queue model to calculate the overall visits based on the time spent inside the station. We evaluated our system on large-scale and real-world datasets, which contain 4-month trajectories of 32,476 taxicabs, 689 gas stations, and the self-reported refueling details of 8,326 online users. The results show that our system can determine REs with an accuracy of more than 90\%, estimate time spent with less than 2 minutes of error, and measure overall visits in the same order of magnitude with the records in the field study.", acknowledgement = ack-nhfb, articleno = "37", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Tangmunarunkit:2015:OGE, author = "H. Tangmunarunkit and C. K. Hsieh and B. Longstaff and S. Nolen and J. Jenkins and C. Ketcham and J. Selsky and F. Alquaddoomi and D. George and J. Kang and Z. Khalapyan and J. Ooms and N. Ramanathan and D. Estrin", title = "{Ohmage}: a General and Extensible End-to-End Participatory Sensing Platform", journal = j-TIST, volume = "6", number = "3", pages = "38:1--38:??", month = may, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2717318", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu May 21 15:49:31 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Participatory sensing (PS) is a distributed data collection and analysis approach where individuals, acting alone or in groups, use their personal mobile devices to systematically explore interesting aspects of their lives and communities [Burke et al. 2006]. These mobile devices can be used to capture diverse spatiotemporal data through both intermittent self-report and continuous recording from on-board sensors and applications. Ohmage (http://ohmage.org) is a modular and extensible open-source, mobile to Web PS platform that records, stores, analyzes, and visualizes data from both prompted self-report and continuous data streams. These data streams are authorable and can dynamically be deployed in diverse settings. Feedback from hundreds of behavioral and technology researchers, focus group participants, and end users has been integrated into ohmage through an iterative participatory design process. Ohmage has been used as an enabling platform in more than 20 independent projects in many disciplines. We summarize the PS requirements, challenges and key design objectives learned through our design process, and ohmage system architecture to achieve those objectives. The flexibility, modularity, and extensibility of ohmage in supporting diverse deployment settings are presented through three distinct case studies in education, health, and clinical research.", acknowledgement = ack-nhfb, articleno = "38", fjournal = "ACM Transactions on Intelligent Systems and Technology (TIST)", journal-URL = "http://portal.acm.org/citation.cfm?id=J1318", } @Article{Xiong:2015:EEE, author = "Haoyi Xiong and Daqing Zhang and Leye Wang and J. Paul Gibson and Jie Zhu", title = "{EEMC}: Enabling Energy-Efficient Mobile Crowdsensing with Anonymous Participants", journal = j-TIST, volume = "6", number = "3", pages = "39:1--39:??", month = may, year = "2015", CODEN = "????", DOI = "http://dx.doi.org/10.1145/2644827", ISSN = "2157-6904 (print), 2157-6912 (electronic)", ISSN-L = "2157-6904", bibdate = "Thu May 21 15:49:31 MDT 2015", bibsource = "http://portal.acm.org/; http://www.math.utah.edu/pub/tex/bib/tist.bib", abstract = "Mobile Crowdsensing (MCS) requires users to be motivated to participate. However, concerns regarding energy consumption and privacy-among other things-may compromise their willingness to join such a crowd. Our preliminary observations and analysis of common MCS applications have shown that the data transfer in MCS applications may incur significant energy consumption due to the 3G connection setup. However, if data are transferred in parallel with a traditional phone call, then such transfer can be done almost for free'': with only an insignificant additional amount of energy required to piggy-back the data-usually incoming task assignments and outgoing sensor results-on top of the call. Here, we present an {$<$i$>$Energy}-Efficient Mobile {Crowdsensing$<$}/{i$>$} (EEMC) framework where task assignments and sensing results are transferred in parallel with phone calls. The main objective, and the principal contribution of this article, is an MCS task assignment scheme that guarantees that a minimum number of anonymous participants return sensor results within a specified time frame, while also minimizing the waste of energy due to redundant task assignments and considering privacy concerns of participants. Evaluations with a large-scale real-world phone call dataset show that our proposed {$<$i$>$EEMC$<$}/{i$>\$}
framework outperforms the baseline approaches, and it
can reduce overall energy consumption in data transfer
by 54--66\% when compared to the 3G-based solution.",
acknowledgement = ack-nhfb,
articleno =    "39",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Zhang:2015:CSS,
author =       "Wangsheng Zhang and Guande Qi and Gang Pan and Hua Lu
and Shijian Li and Zhaohui Wu",
title =        "City-Scale Social Event Detection and Evaluation with
Taxi Traces",
journal =      j-TIST,
volume =       "6",
number =       "3",
pages =        "40:1--40:??",
month =        may,
year =         "2015",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2700478",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu May 21 15:49:31 MDT 2015",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "A social event is an occurrence that involves lots of
people and is accompanied by an obvious rise in human
flow. Analysis of social events has real-world
importance because events bring about impacts on many
aspects of city life. Traditionally, detection and
impact measurement of social events rely on social
investigation, which involves considerable human
effort. Recently, by analyzing messages in social
networks, researchers can also detect and evaluate
country-scale events. Nevertheless, the analysis of
city-scale events has not been explored. In this
article, we use human flow dynamics, which reflect the
social activeness of a region, to detect social events
and measure their impacts. We first extract human flow
dynamics from taxi traces. Second, we propose a method
that can not only discover the happening time and venue
of events from abnormal social activeness, but also
measure the scale of events through changes in such
activeness. Third, we extract traffic congestion
information from traces and use its change during
social events to measure their impact. The results of
experiments validate the effectiveness of both the
event detection and impact measurement methods.",
acknowledgement = ack-nhfb,
articleno =    "40",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Sang:2015:ASC,
author =       "Jitao Sang and Tao Mei and Changsheng Xu",
title =        "Activity Sensor: Check-In Usage Mining for Local
Recommendation",
journal =      j-TIST,
volume =       "6",
number =       "3",
pages =        "41:1--41:??",
month =        may,
year =         "2015",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2700468",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu May 21 15:49:31 MDT 2015",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "While on the go, people are using their phones as a
personal concierge discovering what is around and
deciding what to do. Mobile phone has become a
recommendation terminal customized for
individuals-capable of recommending activities and
simplifying the accomplishment of related tasks. In
data, with summarized statistics identifying the local
recommendation challenges of huge solution space,
sparse available data, and complicated user intent, and
discovered observations to motivate the hierarchical,
contextual, and sequential solution. We present a
point-of-interest (POI) category-transition--based
approach, with a goal of estimating the visiting
probability of a series of successive POIs conditioned
on current user context and sensor context. A mobile
local recommendation demo application is deployed. The
objective and subjective evaluations validate the
effectiveness in providing mobile users both accurate
recommendation and favorable user experience.",
acknowledgement = ack-nhfb,
articleno =    "41",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}

@Article{Zhang:2015:EDQ,
author =       "Bo Zhang and Zheng Song and Chi Harold Liu and Jian Ma
and Wendong Wang",
title =        "An Event-Driven {QoI}-Aware Participatory Sensing
Framework with Energy and Budget Constraints",
journal =      j-TIST,
volume =       "6",
number =       "3",
pages =        "42:1--42:??",
month =        may,
year =         "2015",
CODEN =        "????",
DOI =          "http://dx.doi.org/10.1145/2630074",
ISSN =         "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L =       "2157-6904",
bibdate =      "Thu May 21 15:49:31 MDT 2015",
bibsource =    "http://portal.acm.org/;
http://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract =     "Participatory sensing systems can be used for
concurrent event monitoring applications, like noise
levels, fire, and pollutant concentrations. However,
they are facing new challenges as to how to accurately
detect the exact boundaries of these events, and
further, to select the most appropriate participants to
collect the sensing data. On the one hand,
participants' handheld smart devices are constrained
with different energy conditions and sensing
capabilities, and they move around with uncontrollable
mobility patterns in their daily life. On the other
hand, these sensing tasks are within time-varying
quality-of-information (QoI) requirements and budget to
afford the users' incentive expectations. Toward this
participatory sensing framework with energy and budget
constraints. The main method of this framework is event
boundary detection. For the former, a two-step
heuristic solution is proposed where the coarse-grained
detection step finds its approximation and the
fine-grained detection step identifies the exact
location. Participants are selected by explicitly
considering their mobility pattern, required QoI of
multiple tasks, and users' incentive requirements,
under the constraint of an aggregated task budget.
Extensive experimental results, based on a real trace
in Beijing, show the effectiveness and robustness of
our approach, while comparing with existing schemes.",
acknowledgement = ack-nhfb,
articleno =    "42",
fjournal =     "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL =  "http://portal.acm.org/citation.cfm?id=J1318",
}