%%% -*-BibTeX-*-
%%% ====================================================================
%%%  BibTeX-file{
%%%     author          = "Nelson H. F. Beebe",
%%%     version         = "1.10",
%%%     date            = "18 July 2014",
%%%     time            = "14:15:56 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        = "31223 9982 52629 503947",
%%%     email           = "beebe at math.utah.edu, beebe at acm.org,
%%%                        beebe at computer.org (Internet)",
%%%     codetable       = "ISO/ASCII",
%%%     keywords        = "bibliography; BibTeX; ACM Transactions on
%%%                        Intelligent Systems and Technology (TIST)",
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%%%     supported       = "yes",
%%%     docstring       = "This is a COMPLETE BibTeX bibliography for
%%%                        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.
%%%
%%%                        At version 1.10, the COMPLETE journal
%%%                        coverage looked like this:
%%%
%%%                             2010 (  15)    2012 (  59)    2014 (  18)
%%%                             2011 (  51)    2013 (  95)
%%%
%%%                             Article:        238
%%%
%%%                             Total entries:  238
%%%
%%%                        The journal Web page can be found at:
%%%
%%%                            http://www.acm.org/pubs/tist
%%%                            http://portal.acm.org/citation.cfm?id=J1318
%%%
%%%                        The journal table of contents page is at:
%%%
%%%                            http://www.acm.org/pubs/contents/journals/tist/
%%%
%%%                        The initial draft was extracted from the
%%%                        journal Web site.
%%%
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%%%                        with credit, so article abstracts, keywords,
%%%                        and subject classifications have been
%%%                        included in this bibliography wherever
%%%                        available.  Article reviews have been
%%%                        omitted, until their copyright status has
%%%                        been clarified.
%%%
%%%                        URL keys in the bibliography point to
%%%                        World Wide Web locations of additional
%%%                        information about the entry.
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%%%
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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",
}

@Article{Benaskeur:2010:CRT,
  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",
}

@Article{Talamadupula:2010:PHR,
  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
                 business applications",
  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
                 other areas. In this article, we present all
                 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
                 from visiting them. This article explores how to detect
                 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 Advertising",
  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
                 report in this article a visual support toward faceted
                 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
                 interpret the visualization. In this article, we note
                 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
                 carefully. Thus, in this article we propose a
                 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",
}

@Article{Gretarsson:2012:TVA,
  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
                 document clustering results. In this article, we
                 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",
  abstract =     "This article describes automatic methods and
                 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
                 density, and context coherence that comments about
                 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",
}

@Article{Hajimirsadeghi:2012:CIL,
  author =       "Hossein Hajimirsadeghi and Majid Nili Ahmadabadi and
                 Babak Nadjar Araabi and Hadi Moradi",
  title =        "Conceptual Imitation Learning in a Human-Robot
                 Interaction Paradigm",
  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
                 abstraction and conceptualization. This article
                 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
                 accuracy on practical problems. In this article, we
                 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
                 (i.e., heavy occlusion occurs). In this article, we
                 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
                 Stathes Hadjiefthymiades",
  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
                 discover these. In this article, we study the problem
                 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
                 context-sensing is underexplored too. In this article,
                 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
                 contextual advertising or sponsored search. Several
                 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
                 present in the target Web pages. In this article, we
                 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
                 high-quality advertising keywords, we also add an
                 advertisement-biased factor into our model. With these
                 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",
}

@Article{Zhou:2012:LAD,
  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
                 important problem which we tackle in this article
                 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
                 tasks.",
  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
                 response to customer requests. In this article, in
                 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
                 visualization. This article provides a comprehensive
                 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.
                 The work presented in this article exploits the
                 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
                 process. In this article, a transductive 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
                 performance parameters in these cases. In this article,
                 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
                 traditional Baum-Welch algorithms. With the advantage
                 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.
                 In this article, motivated by these findings, we
                 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
                 contextual cues? In this articles, we introduce a
                 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
                 frameworks in this field. This article proposes a
                 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
                 Adaptive Indexing Search Engine",
  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
                 Laboratory (MSL) ChemCam spectrometer). This article
                 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
                 are often unsatisfactory. In this article, we consider
                 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
                 modeling the task relationships in the form of a task
                 covariance matrix. Then we regard transfer learning as
                 a special case of multitask learning and adapt the
                 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
                 tasks and the target task under a unified convex
                 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
                 Markov Chain Monto Carlo (MCMC). This article
                 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
                 available techniques. In this article, we propose a
                 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
                 revenue through differential pricing for access to
                 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",
  abstract =     "This article studies the problem of latent community
                 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
                 text-associated graphs. Different from traditional
                 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
                 and platforms. This article addresses the fundamental
                 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
                 review-oriented datasets. In this article, we focus on
                 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
                 this article, we present a subspace based feature
                 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",
  abstract =     "This article studies information retrieval tasks
                 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
                 important retrieval tasks related to comments, namely
                 filtering, ranking, and summarization. Within these
                 tasks, we distinguish two paradigms according to which
                 comments are utilized and which we designate as
                 comment-targeting and comment-exploiting. Within the
                 first paradigm, the comments themselves form the
                 retrieval targets. Within the second paradigm, the
                 commented items form the retrieval targets (i.e.,
                 comments are used as an additional information source
                 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
                 comments associated by readers with respect to the post
                 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
                 academic papers.",
  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
                 people. In this article, we aim at facilitating wiki
                 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
                 users of the same community. In this article, a
                 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
                 these methods. In this article, we report results from
                 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
                 systems. In addition to adopting semantic evaluation
                 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
                 systems outperform traditional hierarchical clustering
                 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
                 Prasad Tadepalli and Weng-Keen Wong and Derek Green and
                 Anton Rebguns and Diana Spears and Ugur Kuter and Geoff
                 Levine and Gerald DeJong and Reid L. MacTavish and
                 Santiago Onta{\~n}{\'o}n and Jainarayan Radhakrishnan
                 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
                 such as Europa. This article evaluates automated
                 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
                 reach and impact. Thus, in this article, we propose and
                 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
                 recency ranking, we advocate the use of Twitter data to
                 address the challenge in this article. We propose a
                 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",
  abstract =     "Twitter provides access to large volumes of data in
                 real time, but is notoriously noisy, hampering its
                 utility for NLP. In this article, we target
                 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
                 Twitter.",
  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",
  abstract =     "Twitter displays the tweets a user received in a
                 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
                 Twitter based on time.",
  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",
  abstract =     "In this article we review the existing techniques in
                 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
                 recommenders. In this article we aim to enhance the
                 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
                 activity or means of transport. This article describes
                 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
                 viewed as additional information about the
                 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
                 consideration. However, the traditional RW-based
                 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,
                 this article presents a novel and efficient RW-based
                 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
                 participation was summarized by 10 papers published by
                 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",
}

@Article{Bellogin:2013:ECS,
  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.
                 In this article, we propose a coverage metric that
                 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
                 in this article that takes into account the movie plot
                 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",
}

@Article{Okada:2013:MDA,
  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
                 frequently interact with each other. This article
                 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
                 communication from traditional distributed computing:
                 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
                 the most productive directions. This article is an
                 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
                 challenges. This article serves some important
                 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
                 cross-organizational business processes. However,
                 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).
                 This article formalizes protocols and their refinement.
                 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
                 electronic marketplaces with buying advisors to
                 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
                 other buying agents known as advisors. The
                 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",
  abstract =     "This article investigates the conjecture that agents
                 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
                 confidence in the evaluations that can be made about
                 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",
  abstract =     "In this article we consider the special abilities
                 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
                 explicitly readable signals indicating internal
                 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
                 misconceptions. This article describes two POMDP
                 representations- state queues and observation chains
                 -that take advantage of ITS task properties and let
                 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
                 prediction accuracy and reading simulation. The reading
                 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
                 rational combinatorial treatments, adaptive
                 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
                 in this article, is a belief-function-based approach
                 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
                 classification problems. In this article, we propose a
                 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
                 public area has become an urgent need. In this article,
                 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",
}

@Article{Madnani:2013:GTP,
  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
                 Chinese--English translation. In this article, we first
                 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",
  abstract =     "In this article we generalize the sentence compression
                 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
                 customer-written reviews. This article contributes to
                 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
                 crowdsourcing. In this article, we review the lessons
                 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
                 geotags. In this article, we propose a novel approach
                 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
                 consideration without also discarding information
                 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
                 nontrivial landmarks. Additionally, they demonstrate
                 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
                 this article, given a spatial range and a user
                 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
                 information. The core contribution of this article lies
                 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",
  abstract =     "This article considers the nonlethal targeting
                 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",
  abstract =     "This article shows how agent-based models of social
                 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
                 precise numerical information. In this article we
                 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
                 marginalization among adolescents and young adults. We
                 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
                 and browse log data for Web search. We start with an
                 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
                 tasks, fundamental principles, and state-of-the-art
                 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
                 to embrace the strengths of both. In this article, we
                 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
                 used, though it is very challenging. In this article, a
                 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,
  author =       "Iyad Batal and Hamed Valizadegan and Gregory F. Cooper
                 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
                 to the classification task. To address this problem, we
                 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
                 this article, we report on our fully automated approach
                 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
                 monitor daily activities. In this article, we present a
                 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
                 granularity? In this article, we seek a better
                 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
                 secured. In this article, we codify these privacy
                 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",
  abstract =     "In this article, we begin by presenting OMeD, a
                 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
                 radiation treatment methodology. Geometrically precise
                 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
                 may cause cardiopulmonary overload. This article
                 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
                 accuracy of this process. This article presents a
                 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
                 health problems facing adolescents and young adults in
                 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
                 different sample. This article presents two machine
                 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
                 previous studies. In this article, we present a global
                 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
                 interestingness. In this article, we propose a novel
                 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",
  abstract =     "The advance of mobile technologies leads to huge
                 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
                 accuracy. In this article we argue that on-demand
                 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
                 world. In this article, we recognize this role of
                 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
                 platforms such as Facebook or Twitter has resulted in
                 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
                 phenomena. In this article we follow this line of work
                 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
                 seen that nonlinear models do take advantage of Twitter
                 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,
                 Facebook, Flickr, Flixster, Google+, Gowalla,
                 TheMarker, Twitter, and YouTube. Our methods can
                 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
                 community to another. This article presents a novel
                 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
                 challenging task. This article presents a novel
                 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
                 real-life and benchmark collections retrieved from the
                 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
                 acquisition). In this article we show that an elicited
                 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
                 problems. In this article, we propose a parallel online
                 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",
  abstract =     "This article addresses the problem of finding suitable
                 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
                 business constraints, internal/external regulations and
                 desired best practices. In this work, we present
                 Mobucon EC, a novel monitoring framework that tracks
                 streams of events and continuously determines the state
                 of business constraints. In Mobucon EC, business
                 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
                 planning problems. In this article, we consider the
                 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
                 user types. In this article, we present a novel
                 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
                 TREC-5 ad-hoc topics showed obvious advantage of our
                 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
                 difficult to achieve high accuracy. In this article,
                 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
                 interactions among system administrators. Not
                 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{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",
}

%%% Check for missing articles 37--48 in volume 5: not in either issue 2 or 3 on 18 July 2014