%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{zhang-rr-06-49b,
         author = {Rienks, Rutger and Zhang, Dong and Gatica-Perez, Daniel and Post, Wilfried},
       projects = {Idiap},
          title = {{Detection and Application of Influence Rankings in Small Group Meetings}},
      booktitle = {In the Eighth International Conference on Multimodal Interfaces (ICMI'06)},
           year = {2006},
           note = {IDIAP-RR 06-49},
       crossref = {zhang-rr-06-49},
       abstract = {We address the problem of automatically detecting participant's influence levels in meetings. The impact and social psychological background are discussed. The more influential a participant is, the more he or she influences the outcome of a meeting. Experiments on 40 meetings show that application of statistical (both dynamic and static) models while using simply obtainable features results in a best prediction performance of 70.59\\% when using a static model, a balanced training set, and three discrete classes: high, normal and low. Application of the detected levels are shown in various ways i.e. in a virtual meeting environment as well as in a meeting browser system.},
            pdf = {https://publications.idiap.ch/attachments/reports/2006/rr-06-49.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2006/rr-06-49.ps.gz},
ipdmembership={vision},
}



crossreferenced publications: 
@TECHREPORT{zhang-rr-06-49,
         author = {Rienks, Rutger and Zhang, Dong and Gatica-Perez, Daniel and Post, Wilfried},
       projects = {Idiap},
          title = {{Detection and Application of Influence Rankings in Small Group Meetings}},
           type = {Idiap-RR},
         number = {Idiap-RR-49-2006},
           year = {2006},
    institution = {IDIAP},
        address = {Martigny, Switzerland},
           note = {Published in ``the Eighth International Conference on Multimodal Interfaces (ICMI'06) 2006},
       abstract = {We address the problem of automatically detecting participant's influence levels in meetings. The impact and social psychological background are discussed. The more influential a participant is, the more he or she influences the outcome of a meeting. Experiments on 40 meetings show that application of statistical (both dynamic and static) models while using simply obtainable features results in a best prediction performance of 70.59\\% when using a static model, a balanced training set, and three discrete classes: high, normal and low. Application of the detected levels are shown in various ways i.e. in a virtual meeting environment as well as in a meeting browser system.},
            pdf = {https://publications.idiap.ch/attachments/reports/2006/rr-06-49.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2006/rr-06-49.ps.gz},
ipdinar={2006},
ipdmembership={vision},
language={English},
}