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<p>Dear colleagues,</p>
<p>This special session addresses the processing of all types of
data related to understanding of human behaviour, emotion, and
their reasons, such as current or past context. Understanding
human behaviour and context may be beneficial for many services
both online and in physical spaces. For example detecting lack of
skills, confusion or other negative states may help to adapt
online learning programmes, to detect a bottleneck in the
production line, to recognise poor workplace culture etc., or
maybe to detect a dangerous spot on a road before any accident
happens there. Detection of unusual behaviour may help to improve
security of travellers and safety of dementia sufferers and
visually/audio impaired individuals, for example, to help them
stay away from potentially dangerous strangers, e.g., drunk people
or football fans forming in a big crowd.</p>
<p>In the context of multimedia retrieval, understanding human
behaviour and emotions could help not only for multimedia
indexing, but also to derive implicit (i.e., other than
intentionally reported) human feedback regarding multimedia news,
videos, advertisements, navigators, hotels, shopping items etc.
and improve multimedia retrieval. </p>
<p>Humans are good at understanding other humans, their emotions and
reasons. For example, when looking at people engaged in different
activities (sport, driving, working on a computer, working on a
construction site, using public transport etc.), a human observer
can understand whether a person is engaged in the task or
distracted, stopped the recommended video because the video was
not interesting, or because the person quickly found what he
needed in the beginning of the video. After observing another
human for some time, humans can also learn the observed
individuals’ tastes, skills and personality traits.</p>
<p>Hence the interest of this session is, how to improve AI
understanding of the same aspects? The topics include (but are not
limited to) the following:</p>
<ul>
<li>Use of various sensors for monitoring and understanding human
behaviour, emotion / mental state / cognition, and context:
video, audio, infrared, wearables, virtual (e.g., mobile device
usage, computer usage) sensors etc.</li>
<li>Methods for information fusion, including information from
various heterogeneous sources.</li>
<li>Methods to learn human traits and preferences from long term
observations.</li>
<li>Methods to detect human implicit feedback from past and
current observations.</li>
<li>Methods to assess task performance: skills, emotions,
confusion, engagement in the task and/or context. </li>
<li>Methods to detect potential security and safety threats and
risks.</li>
<li>Methods to adapt behavioural and emotional models to different
end users and contexts without collecting a lot of labels from
each user and/or for each context: transfer learning,
semi-supervised learning, anomaly detection, one-shot learning
etc.</li>
<li>How to collect data for training AI methods from various
sources, e.g., internet, open data, field pilots etc. </li>
<li>Use of behavioural or emotional data to model humans and adapt
services either online or in physical spaces. </li>
<li>Ethics and privacy issues in modelling human emotions,
behaviour, context and reasons.</li>
</ul>
<p><strong>Organisers of this special session are:</strong></p>
<ul>
<li>Elena Vildjiounaite, Johanna Kallio, Sari Järvinen, Satu-Marja
Mäkela, and Sari Järvinen, <br>
VTT Technical Research Center of Finland, Finland.</li>
<li>Benjamin Allaert, IMT-Nord_Europe, France.</li>
<li>Ioan Marious Bilasco, University of Lille, France.</li>
<li>Franziska Schmalfuss, IAV GmbH, Germany. </li>
</ul>
<p>Please direct correspondence to <a
href="mailto:uhber@cbmi2024.org" class="moz-txt-link-freetext">uhber@cbmi2024.org</a><br>
<br>
<b>Paper submission:</b> 6 pages + 1 page of references<br>
<b>Deadline:</b> 22 march 2024<br>
<a class="moz-txt-link-freetext"
href="https://cbmi2024.org/?page_id=94#submissions">https://cbmi2024.org/?page_id=94#submissions</a><br>
<br>
Best regards</p>
<p><br>
</p>
--<br>
Ioan Marius BILASCO<br>
<tt><a class="moz-txt-link-freetext"
href="https://pro.univ-lille.fr/marius-bilasco/">https://pro.univ-lille.fr/marius-bilasco/</a></tt>
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