Special Session on Computational Intelligence for Physiological
and Affective Computing (CIPAC)
Affective
Computing (AC) is “computing that relates to, arises from, or deliberately influences
emotions,” as initially coined by Professor R. Picard (Media Lab, MIT). It has
been gaining popularity rapidly in the last decade because it has great
potential in the next generation of human-computer interfaces. One goal of
affective computing is to design a computer system that responds in a rational
and strategic fashion to real-time changes in user affect (e.g., happiness,
sadness, etc.), cognition (e.g., frustration, boredom, etc.) and motivation, as
represented by speech, facial expressions, gestures, physiological signals,
neurocognitive performance, etc. Physiological Computing (PC) relates to
computation that incorporates physiological signals in order to produce useful
outputs (e.g., in computer-human interaction). It mainly differs from AC in the
sense that its foremost focus is not the modeling of
affect but rather the utilization of physiological information generally.
Practical
applications of AC and PC based systems seek to achieve a positive impact on
our everyday lives by monitoring, recognising and acting on our emotional
states and physiological signals. Integrating these sensing modalities into
intelligent and pervasive computing systems will reveal a far richer picture of
how our fleeting emotional responses, changing moods, feelings and sensations,
such as pain, touch, tastes and smells, are a reaction to or influence how we
implicitly or explicitly interact with the environment and increasingly the
connected computing artifacts within.
The
integration and use of AC and PC raise many new challenges for signal
processing, machine learning and computational intelligence. Fuzzy Logic
Systems in particular provide a highly promising avenue for addressing some of
the fundamental research challenges in AC/PC where most data sources such as:
body signals (e.g., heart rate, brain waves, skin conductance and respiration)
facial features, speech and human kinematics are very noisy/uncertain and
subject-dependent. Clearly however, other key areas of Computational
Intelligence (CI) research, such as evolutionary learning algorithms and neural
network based classifiers provide essential tools to address the significant
challenge of AC/PC.
The
Computational Intelligence for Physiological and Affective Computing special
session is also organised through the IEEE Computational Intelligence Society's
Emerging Technologies Task
Force on Affective Computing. The session aims to bring
together researchers from the three areas of CI to discuss how CI techniques
can be used individually or in combination to help solve challenging AC/PC
problems, and conversely, how physiological and affect (emotion) and its modeling can inspire new approaches in CI and its
applications. Topics of interest for this special session include but are not
limited to:
·
Models of emotion and physiological information
·
Classifiers for
physiological information
·
Applications based
on/around physiological information
·
Fuzzy set and system
based architectures for processing emotions and other affective states
·
Automatic emotion
recognition & synthesis from physiological signals, facial expressions,
body language, speech, or neurocognitive performance
·
Multimodal sensor
fusion for emotion recognition
·
Emotion mining from
texts, images, or videos
·
Affective interaction
with virtual agents and robots based on fuzzy systems
·
Physiological and
emotion driven control
·
Applications of
affective computing in interactive learning, affective gaming, personalized
robotics, virtual reality, social networking, smart environments, healthcare
and behavioral informatics, etc.
As a cross-disciplinary and CI applications oriented special session, accepted and presented papers will be published under one of the three conference proceedings (Fuzzy-IEEE, IJCNN or IEEE CEC) that are most appropriate for the presented research. For paper submissions please visit WCCI IEEE 2016 submissions
Important
Dates
Paper submission deadline: 31st
January 2016 Author notification: 15th March
2016 Deadline for final manuscript: 15th
April 2016 Early registration deadline: 15th
April 2016 |
Program Committee
Egon L. van den Broek,
University of Twente, The Netherlands
Hani Hagras, University
of Essex, UK
Marie-Jeanne Lesot, LIP6-UPMC, France
Chin-Teng
Lin, National Chiao Tung University, Taiwan
Jiann-Shing Shieh, Yuan Ze University, Taiwan
Shrikanth Narayanan, University of
Southern California, USA
Ana Paiva,
Technical University of Lisbon, Portugal
Rahat Iqbal, Coventry
University, UK
Dr. Vernon Lawhern, US Army Research Lab
Mei Si, Rensselaer
Polytechnic Institute, USA
Carlo Strapparava, Fondazione Bruno
Kessler, Italy
Shangfei Wang, University of
Science and Technology of China, China
Dongrui Wu, GE Global
Research, USA
Georgios Yannakakis, IT University of
Copenhagen, Denmark
Slawomir Zadrozny,
Polish academy of science, Poland
Michel Valstar, University of Nottingham, UK
Christopher Peters, KTH
Royal Institute of Technology, Sweden
Palaniappan Ramaswamy,
University of Kent, UK
Ahmed Kattan, Um Al-Qura University,
Saudi Arabia
Brent Lance, US Army
Research Lab
Victor Zamudio, Instituto Tecnologico de Leon,
Mexico
Vasile Palade, Coventry
University, UK
Li-Wei Ko, National Chiao
Tung University, Taiwan
Nicolas Sabouret, University Paris-Sud,
France
Maria Rifqi, University Panthéon-Assas,
France
Organisers
Dr Faiyaz
Doctor
School of Computing,
Electronics and Maths
Faculty of Engineering,
Environment & Computing
Coventry University
Email: faiyaz.doctor AT coventry.ac.uk
http://www.coventry.ac.uk/research/research-directories/researchers/faiyaz-doctor/
Dr. Faiyaz Doctor is a
Senior Lecturer in the Faculty of Engineering and Computing at Coventry
University. He has previously worked jointly in industry and academia to
develop novel artificial intelligence solutions for addressing real world
problems related to smart environments, energy optimization, predictive
analytics and decision support. His work
has resulted in high profile innovation awards (Best KTP Regional Finalist
2011, Load Stafford Award for Innovation) and an international patent on
improved approaches for data analysis and decision-making using hybrid
neuro-fuzzy and type-2 fuzzy systems: WO/2009/141631. He has previously been a
co-investigator on a Technology Strategy Board funded project on developing
driver prediction models using artificial intelligence approaches in
collaboration with Jaguar Land Rover Ltd. His research interests are in the
area of computational intelligence with an emphasis on fuzzy logic, type-2
fuzzy logic and hybrid systems where his research has been applied to ambient
intelligence, pervasive and affective computing, industrial automation and
biomedical systems. Dr. Doctor has published over 40
papers in peer reviewed international journals, conferences and workshops. He
currently chairs the IEEE Computational Intelligence Society’s Emergent
Technologies ‘Affective Computing’ Task Force and has been co-organizer of the
special session on Fuzzy Systems for Physiological and Affective Computing
(FSPAC) at the 2015 IEEE International Conference on Fuzzy Systems and the
special session on Computational Intelligence for Physiological and Affective
Computing (CIPAC) and co-chair at the special session on Brain and
Physiological Computation for Affective Computing at the 2014 IEEE World
Congress on Computational Intelligence. He has been a guest editor for the
Journal of Ambient Intelligence and Smart Environments (JAISE), Thematic Issue
on Affect Aware Ubiquitous Computing and has served as co-organiser of the
International Workshop on Applications of Affective Computing in Intelligent
Environments (ACIE 2013, 2014) in conjunction with the International Conference
on Intelligent Environments. He is also and member of the IEEE and IEEE
Computational Intelligence Society.
Dr
Christian Wagner
Horizon Digital Economy
Institute & Intelligent Modeling and Analysis
Group
School of Computer
Science
University of
Nottingham, UK
Email: christian.wagner AT nottingham.ac.uk
http://ima.ac.uk/wagner
Christian Wagner is an Associate Professor in Computer Science
at the University of Nottingham, UK. He received his
PhD in Computer Science from the University of Essex in 2009 after which he was
involved both in the management and scientific work of the EU FP7 project
ATRACO, joining the University of Nottingham in 2011. His main research
interests are centred on uncertainty handling, approximate reasoning (reasoning
in the face of uncertainty, lack of knowledge and vagueness), decision support
and data-driven decision making using computational intelligence techniques.
Recent applications of his research have focused in particular on decision
support in environmental and infrastructure planning & management contexts
as well as cyber-security. He has published more than 60 peer-reviewed articles
in international journals and conferences, two of which recently won best paper
awards (Outstanding IEEE Transactions on Fuzzy Systems paper 2013 (for a paper
in 2010) and a best paper award for a Fuzz-IEEE 2012 conference paper), and
several book chapters. Dr Wagner is currently active PI and Co-I on a number of
research projects, with overall funding as PI of £1 million and funding as Co-I
of 2 million. He is an Associate Editor of the IEEE Transactions on Fuzzy
Systems journal (IF: 6.3) and is actively involved in the academic community
through for example the organization of special sessions at premiere IEEE
conference such as the World Congress on Computational Intelligence 2014 and
the IEEE Conference on System, Man and Cybernetics 2015. He has developed and
been involved in the creation of multiple open source software frameworks,
making cutting edge research accessible both to peer researchers as well as to
different (multidisciplinary - beyond computer science) research and
practitioner communities, including R and Java based toolkits for type-2 fuzzy
systems in use in more than ten countries.
Dr Dongrui
Wu
DataNova, NY, USA
Email: drwu09 AT
gmail.com
https://sites.google.com/site/drwu09/
Dongrui Wu received a PhD in Electrical Engineering from the
University of Southern California (USC) in 2009. He
was a Research Associate in the USC Institute for Creative Technologies and
Signal Analysis & Interpretation Laboratory (SAIL) 2009-2010, and a Lead
Research Engineer in the Machine Learning Lab of GE Global Research 2010-2015.
Now he is starting his own business. Dr. Wu's research
interests include affective computing, brain-computer interaction,
computational intelligence, intelligent control, machine learning,
optimization, and text analysis. He has over 80 publications, including a book
"Perceptual Computing." He received IEEE Computational Intelligence
Society (CIS) Outstanding PhD Dissertation Award in 2012, IEEE Transactions on
Fuzzy Systems Outstanding Paper Award in 2014, and NAFIPS Early Career Award in
2014. He was a selected participant of the 1st Heidelberg Abel/Fields/Turing
Laureate Forum in 2013, NAE 2015 German-American Frontiers of Engineering, and
the 13th Annual National Academies Keck Futures Initiative (NAKFI) conference
in 2015. Dr. Wu had worked on a broad range of
projects from GE Capital, Healthcare, Transportation, Power and Water, and Oil
& Gas. Two of his projects won the prestigious CIO 100 Awards in 2012 (TrueSense for GE Water) and 2014 (Fleet Optimizer for GE
Capital), respectively. Additionally, he received 10 Above and Beyond Awards
for outstanding performance. Dr. Wu is an Associate
Editor of IEEE Transactions on Fuzzy Systems, IEEE Transactions on
Human-Machine Systems, and PeerJ Computer Science. He
was the lead Guest Editor of the IEEE Computational Intelligence Magazine
Special Issue on Computational Intelligence and Affective Computing, and former
Chair of the IEEE CIS Affective Computing Task Force. He is a Senior Member of
IEEE, and an Executive Committee member of the Association for the Advancement
of Affective Computing (AAAC).
Dr Marie-Jeanne
Lesot
Université Pierre et Marie Curie
Laboratoire d'Informatique
de Paris 6, LIP6
Email: Marie-Jeanne.Lesot AT lip6.fr
http://webia.lip6.fr/~lesot/
Marie-Jeanne Lesot obtained her PhD in
2005 from the University Pierre et Marie Curie in Paris.
Since 2006 she has been an associate professor in the department of Computer
Science Lab of Paris 6 (LIP6) and a member of the Learning and Fuzzy
Intelligent systems (LFI) group. Her research interests focus on fuzzy machine
learning with an objective of data interpretation and semantics integration
and, in particular, to model and manage subjective information; they include
similarity measures, fuzzy clustering, linguistic summaries, affective
computing and information scoring. She has published 43 international journal
and conference papers and co-edited a book on scalable fuzzy algorithms for
data management and analysis. She has also participated in various joint
projects with companies, on topics such as fraud detection, dynamic monitoring
of information propagation, emotional design and emotion mining among others.
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