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yiv2001845763yui_3_2_0_31_133211229458158
yiv2001845763yui_3_2_0_34_133211229458160 yiv2001845763yui_3_2_0_39_133211229458162 yiv2001845763yui_3_2_0_46_133211229458164 yiv2001845763yui_3_2_0_47_133211229458166 yiv2001845763yui_3_2_0_48_133211229458168 yiv2001845763yui_3_2_0_49_133211229458170 yiv2001845763yui_3_2_0_52_133211229458172 yiv2001845763yui_3_2_0_53_1332112294581283 yiv2001845763yui_3_2_0_55_133211229458176 yiv2001845763yui_3_2_0_58_133211229458178 yiv2001845763yui_3_2_0_21_133216655506880 yiv2001845763yui_3_2_0_22_133216655506882" style="color:#000;background-color:#fff;font-family:times new roman, new york, times, serif;font-size:10pt;"><div id="yiv2001845763yui_3_2_0_26_133211108998951">*****************************************************************************<br>CBAR 2012: CALL FOR PAPERS<br><span id="yiv2001845763yui_3_2_0_49_1332112294581145" style="color:rgb(0, 0, 0);">SocialCom12 1st International Workshop on </span><span
id="yiv2001845763yui_3_2_0_49_1332112294581146" style="color:rgb(255, 0,
0);font-weight:bold;">CONTEXT BASED AFFECT
RECOGNITION</span><br>http://contextbasedaffectrecog.blogspot.com/ <br><br><br>Submission
Deadline: <span style="color:rgb(255, 0,
0);font-weight:bold;">May 11th,
2012</span><br>*****************************************************************************<br><br>The first workshop on "Context Based Affect Recognition" CBAR12
(http://contextbasedaffectrecog.blogspot.com/) wil be <span id="yiv2001845763yui_3_2_0_24_133211229458196">held in conjunction with the 2012 ASE/IEEE International Conference on Social Computing </span>SocialCom2012 (http://www.asesite.org/conferences/socialcom/2012/).<br><span style="font-size:10.0pt;" lang="EN-US"><b id="yiv2001845763yui_3_2_0_53_1332112294581527" style=""><span id="yiv2001845763yui_3_2_0_53_1332112294581528" style="color:maroon;"></span></b></span><br>-----------------------------<br>Workshop Description<br>-----------------------------<br>
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<div id="yiv2001845763yui_3_2_0_55_1332112294581319" class="yiv2001845763MsoNormal" style="text-align:justify;"><span id="yiv2001845763yui_3_2_0_55_1332112294581318" style="font-size:10.0pt;" lang="EN-US">The past 20 years has witnessed an increasing number
of efforts for automatic recognition of human affect using facial, vocal, body
as well as physiological signals. <span id="yiv2001845763yui_3_2_0_55_1332112294581335" style="color:black;">Several research
areas could benefit from such systems: interactive teaching systems, which
allow teachers to be aware of student stress and inattention; accident
prevention, such as </span>driver fatigue detection; <span id="yiv2001845763yui_3_2_0_55_1332112294581330" style="color:black;">medical
tools for automatic diagnosis and monitoring such as the diagnosis of cognitive
disorder (e.g. depression, anxiety and autism) and pain assessment. </span>However,
despite the significant amount of research on automatic affect recognition, the
current state of the art has not yet achieved the long-term objective of robust
affect recognition, particularly context based affect analysis and
interpretation<span id="yiv2001845763yui_3_2_0_55_1332112294581317" style="color:black;">. Indeed, it is well known that affect
production is accordingly displayed in a particular context, such as the
undergoing task, the other people involved, the identity and natural
expressiveness of the individual. The context tells us which expressions are
more likely to occur and thus can bias the classifier toward the most
likely/relevant classes. Without context, even humans may misunderstand the
observed facial expression. By tackling the issues of context based affect
recognition, i.e. careful study of contextual information and its relevance in
domain-specific applications, its representation, and its effect on the
performance of existing affect recognition methods, we make a step towards
real-world, real-time affect recognition.</span></span><span style="font-size:10.0pt;"></span><span></span><span></span><span></span><span></span></div>
<br>-----------------------------<br>Workshop Objectives<br>-----------------------------<br>
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<div id="yiv2001845763yui_3_2_0_53_1332112294581488" class="yiv2001845763MsoNormal" style="text-align:justify;"><span id="yiv2001845763yui_3_2_0_53_1332112294581487" style="font-size:10.0pt;color:black;" lang="EN-US">Context related affect analysis is still an unexplored area for
automatic affect recognition given the difficulty of modeling this variable and
of its introduction in the classification process. Unconsciously, humans
evaluate situations based on environment and social parameters when recognizing
emotions in social interactions. Contextual information helps us interpret and
respond to social interactions. </span><span style="font-size:10.0pt;"></span><span></span></div>
<div id="yiv2001845763yui_3_2_0_53_1332112294581496" class="yiv2001845763MsoNormal" style="text-align:justify;"><span id="yiv2001845763yui_3_2_0_53_1332112294581495" style="font-size:10.0pt;color:black;" lang="EN-US">The purpose of the workshop is to explore the benefits and drawbacks of
integrating context on affect production, interpretation and recognition. We
wish to investigate what methodologies can be applied to include contextual
information in emotion corpora, how it ought to be represented, what contextual
information are relevant (i.e. is it domain specific or not?), and how it will
improve the performance of existing frameworks for affect recognition. </span><span style="font-size:10.0pt;"></span></div>
<div id="yiv2001845763yui_3_2_0_53_1332112294581504" class="yiv2001845763MsoNormal" style="text-align:justify;"><span id="yiv2001845763yui_3_2_0_53_1332112294581503" style="font-size:10.0pt;color:black;" lang="EN-US">The workshop is relevant in the study of naturalistic social
interactions since contextual information cannot be discounted in doing
automatic analysis of human behavior. Embedding contextual information, such as
culture, provides a different flavor to each interaction, and makes for an
interesting scientific study. Such kinds of analysis lead us to consider
real-world parameters and complexities in affect recognition, especially in
developing human-centric systems. </span><span style="font-size:10.0pt;"></span></div>
<div id="yiv2001845763yui_3_2_0_53_1332112294581512" class="yiv2001845763MsoNormal" style="text-align:justify;"><span id="yiv2001845763yui_3_2_0_53_1332112294581657" style="font-size:10.0pt;color:black;" lang="EN-US">For the workshop we</span><span id="yiv2001845763yui_3_2_0_53_1332112294581511" style="font-size:10.0pt;" lang="EN-US"> invite scientists
working in related areas of affective computing, ambient computing, machine
learning, psychology and cognitive behavior to share their expertise and
achievements in the emerging field of automatic and context based affect
analysis and recognition.<b id="yiv2001845763yui_3_2_0_53_1332112294581520" style=""><span id="yiv2001845763yui_3_2_0_53_1332112294581519" style="color:maroon;"> </span></b></span><span style="font-size:10.0pt;"></span></div>
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<br><span class="yiv2001845763yui_3_2_0_53_1332112294581315" style="" lang="EN-US"><b style=""><span style="color:maroon;"></span></b></span><span class="yiv2001845763yui_3_2_0_53_1332112294581321" style=""></span>
-----------------------------<br>
Workshop Topics<br>
-----------------------------<br>
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--></style><span id="yiv2001845763yui_3_2_0_53_1332112294581543" style="font-size:10.0pt;" lang="EN-US">New and unpublished papers on, but not limited to, the
following topics:</span><span style="font-size:10.0pt;"></span><span id="yiv2001845763yui_3_2_0_53_1332112294581553" style="font-size:10pt;color:black;" lang="EN-US"></span><div id="yiv2001845763yui_3_2_0_53_1332112294581572" class="yiv2001845763MsoNormal" style="margin-left:36.0pt;text-align:justify;"><span id="yiv2001845763yui_3_2_0_53_1332112294581553" style="font-size:10.0pt;color:black;" lang="EN-US"></span><span id="yiv2001845763yui_3_2_0_53_1332112294581577" style="font-size:10.0pt;color:black;" lang="EN-US">·</span><span id="yiv2001845763yui_3_2_0_53_1332112294581561" style="font-size:10.0pt;color:black;" lang="EN-US"> </span><span id="yiv2001845763yui_3_2_0_53_1332112294581590" style="font-size:10.0pt;color:black;" lang="EN-US">Context source detection. </span><span style="font-size:10.0pt;"></span></div>
<div id="yiv2001845763yui_3_2_0_53_1332112294581578" class="yiv2001845763MsoNormal" style="margin-left:36.0pt;text-align:justify;"><span id="yiv2001845763yui_3_2_0_53_1332112294581577" style="font-size:10.0pt;color:black;" lang="EN-US">·</span><span id="yiv2001845763yui_3_2_0_53_1332112294581585" style="font-size:10.0pt;color:black;" lang="EN-US"> </span><span id="yiv2001845763yui_3_2_0_53_1332112294581664" style="font-size:10.0pt;color:black;" lang="EN-US">Context interpretation and analysis.</span><span style="font-size:10.0pt;"></span></div>
<div id="yiv2001845763yui_3_2_0_53_1332112294581606" class="yiv2001845763MsoNormal" style="margin-left:36.0pt;text-align:justify;"><span style="font-size:10.0pt;color:black;" lang="EN-US">·</span><span id="yiv2001845763yui_3_2_0_53_1332112294581605" style="font-size:10.0pt;color:black;" lang="EN-US"> </span><span id="yiv2001845763yui_3_2_0_53_1332112294581669" style="font-size:10.0pt;" lang="EN-US">Context based affect production </span><span style="font-size:10.0pt;"></span></div>
<div id="yiv2001845763yui_3_2_0_53_1332112294581614" class="yiv2001845763MsoNormal" style="margin-left:36.0pt;text-align:justify;"><span style="font-size:10.0pt;color:black;" lang="EN-US">·</span><span id="yiv2001845763yui_3_2_0_53_1332112294581613" style="font-size:10.0pt;color:black;" lang="EN-US"> </span><span id="yiv2001845763yui_3_2_0_53_1332112294581674" style="font-size:10.0pt;color:black;" lang="EN-US">Context based facial affect recognition</span><span style="font-size:10.0pt;"></span></div>
<div id="yiv2001845763yui_3_2_0_53_1332112294581622" class="yiv2001845763MsoNormal" style="margin-left:36.0pt;text-align:justify;"><span style="font-size:10.0pt;color:black;" lang="EN-US">·</span><span id="yiv2001845763yui_3_2_0_53_1332112294581621" style="font-size:10.0pt;color:black;" lang="EN-US"> </span><span id="yiv2001845763yui_3_2_0_53_1332112294581679" style="font-size:10.0pt;color:black;" lang="EN-US">Context based vocal affect recognition</span><span style="font-size:10.0pt;"></span></div>
<div id="yiv2001845763yui_3_2_0_53_1332112294581630" class="yiv2001845763MsoNormal" style="margin-left:36.0pt;text-align:justify;"><span style="font-size:10.0pt;color:black;" lang="EN-US">·</span><span id="yiv2001845763yui_3_2_0_53_1332112294581629" style="font-size:10.0pt;color:black;" lang="EN-US"> </span><span id="yiv2001845763yui_3_2_0_53_1332112294581684" style="font-size:10.0pt;color:black;" lang="EN-US">Context based gesture affect recognition</span><span style="font-size:10.0pt;"></span></div>
<div id="yiv2001845763yui_3_2_0_53_1332112294581638" class="yiv2001845763MsoNormal" style="margin-left:36.0pt;text-align:justify;"><span style="font-size:10.0pt;" lang="EN-US">·</span><span id="yiv2001845763yui_3_2_0_53_1332112294581637" style="font-size:10.0pt;" lang="EN-US"> </span><span id="yiv2001845763yui_3_2_0_53_1332112294581689" style="font-size:10.0pt;color:black;" lang="EN-US">Context based multimodal fusion.</span><span style="font-size:10.0pt;" lang="EN-US"> </span><span style="font-size:10.0pt;"></span></div>
<div id="yiv2001845763yui_3_2_0_53_1332112294581646" class="yiv2001845763MsoNormal" style="margin-left:36.0pt;text-align:justify;"><span style="font-size:10.0pt;" lang="EN-US">·</span><span id="yiv2001845763yui_3_2_0_53_1332112294581645" style="font-size:10.0pt;" lang="EN-US"> </span><span id="yiv2001845763yui_3_2_0_53_1332112294581694" style="font-size:10.0pt;" lang="EN-US">Applications (Context related affect applications).</span><span style="font-size:10.0pt;"></span></div>
<br><br>For details concerning the workshop program, paper submission guidelines, etc. please visit our workshop website at: http://contextbasedaffectrecog.blogspot.com/ <br><br>Best regards,<br>Zakia Hammal<br><br><span id="yiv2001845763yui_3_2_0_24_133211229458178"></span><span></span><span></span><span></span><span id="yiv2001845763yui_3_2_0_34_1332112294581103"></span><span id="yiv2001845763yui_3_2_0_39_1332112294581141"></span><span id="yiv2001845763yui_3_2_0_46_1332112294581129"></span><span id="yiv2001845763yui_3_2_0_47_1332112294581201"></span><span></span><span id="yiv2001845763yui_3_2_0_49_1332112294581135"></span><span></span><span id="yiv2001845763yui_3_2_0_53_1332112294581695"></span><span id="yiv2001845763yui_3_2_0_53_1332112294581698"></span></div><div id="yiv2001845763yui_3_2_0_26_1332111089989226">Zakia Hammal,
PhD<br id="yiv2001845763yui_3_2_0_26_133211108998956">The Robotics
Institute, Carnegie Mellon University<br id="yiv2001845763yui_3_2_0_26_133211108998961">http://www.ri.cmu.edu/<br id="yiv2001845763yui_3_2_0_26_133211108998966"><br id="yiv2001845763yui_3_2_0_26_133211108998969"></div><div id="yiv2001845763yui_3_2_0_26_133211108998972">Human-Machine
Interaction<br>Facial Expression Recognition<br>Visual Perception<br>http://www.pitt.edu/~emotion/ZakiaHammal.html<br><br> </div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div> </div> </div></div></div></div><br><br> </div> </div> </div></div></div></div><br><br> </div> </div> </div></div></body></html>