IEEE Symposium on Computational Intelligence for Human-Like Intelligence (IEEE CIHLI’16)

 

Special Session on: Physiological and Affective Computing for Human Centred Systems

 

December 6-9, 2016, Athens, Greece

 

 

The growth of health, wellbeing, activity monitoring and social computing continues to fuel developments in technologies such as the Internet of Things (IoT), wearable devices, sensors, actuators, mobile communication together with distributed management and information retrieval infrastructures. Through these technology mediums more user centred data can now be captured, monitored, stored and analysed to create ambient personalisation and contextualisation of services tailored to individual needs. To extend these capabilities there is a need to incorporate and utilize physiological information (e.g., as in computer-human interaction, health and fitness monitoring) together with the recognition, interpretation, processing, and modelling of human affective states in order to further enhance applications with human-like intelligence and responsiveness.

 

Practical applications of Affective and Physiological Computing (APC) based systems seek to enhance user context and sensitivity by monitoring, recognising and acting on our emotional states and physiological signals. Integrating these sensing modalities into intelligent and pervasive computing systems raises many new challenges for signal processing and modelling of complex high dimensional data sources such as: body signals (e.g., heart rate, brain waves, skin conductance and respiration) facial features, speech and human kinematics which also can be very noisy/uncertain and subject-dependent.

 

The Physiological and Affective Computing for Human Centred Systems special session is also organised through the IEEE Computational Intelligence Society's Emerging Technologies Task Force on Affective Computing. This special session aims to bring together researchers to discuss how CI techniques can be used to help solve challenging APC problems and conversely, how interpreting and modelling physiological and affect (emotion) data can inspire new approaches in CI and its applications in human centred technologies. 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

·      Architectures for processing emotions and other affective states

·      Automatic emotion recognition & synthesis from physiological signals, facial expressions, body language, speech, or neurocognitive performance

·      Emotion mining from texts, images, or videos

·      Affective interaction with virtual agents and robots

·      Applications of affective computing in interactive learning, affective gaming, personalized robotics, virtual reality, social networking, smart environments, healthcare and behavioural informatics, assistive technology, industrial automation, distributed cognition etc.

 

For paper submissions and formatting guidelines please visit SSCI IEEE 2016 submissions. When submitting a paper please select the track for this special session.

 

 

 

Important Dates

 

Paper submission deadline: 15th August 2016 (Extended)

Author notification: 12th September 2016

Deadline for final manuscript: 10th October 2016

Early registration deadline: 10th October 2016

Conference dates: 6th December – 9th December 2016

 

 

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 received his PhD in Computer Science from the University of Essex in 2006. 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 recently been awarded an advanced fellowship from the Newton International Collaboration Programme for a collaborative project with the Leon Institute of Technology, Leon, Mexico. He has previously been a co-investigator on a Technology Strategy Board funded project on the self-learning car 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 50 papers in peer reviewed international journals, conferences and workshops. He is currently a member of the IEEE Computational Intelligence Society’s (CIS) Emergent Technologies Technical Committee (ETTC), chairs the ‘ETTC Task Force on Affective Computing’ and has been co-organizer of the special session on Computational Intelligence for Physiological and Affective Computing (CIPAC) at the IEEE World Congress on Computational Intelligence (WCCI 2016, 2014) and the special session on Fuzzy Systems for Physiological and Affective Computing (FSPAC) at the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2015, 2009). 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 CIS.

 

 

Dr Rahat Iqbal

School of Computing, Electronics and Maths

Faculty of Engineering, Environment & Computing

Coventry University

Email: r.iqbal AT coventry.ac.uk

http://www.coventry.ac.uk/research/research-directories/researchers/rahat-iqbal/

 

Dr. Rahat Iqbal is a Reader in Human-Centred Technology in the department of computing at Coventry University and has been Director of the Applied Computing Research Centre. A particular focus of his research is to balance technological factors with human aspects so as to explore the implications for better design of computing systems. He has a proven track record of project management and leadership of industrial projects funded by EPSRC, TSB, ERDF and local industries. He was involved in the project management and development of the EU FP7 project CHIL (Computers in Human Interaction Loop) at Technical University of Eindhoven, Netherlands. Recently, he has successfully led a project in collaboration with Jaguar Land Rover on the self-learning car for predicting driver’s behaviour for personalisation of telematics and optimisation of route planning. He has managed more than 10 industrial projects, in intelligent systems, predictive modelling, user behaviour, information retrieval and fault detection.  He has published more than 100 papers in peer-reviewed journals and reputable conferences and workshops. Dr Iqbal is on the programme committee of several international conferences and workshops and organises the annual international workshop on Ubiquitous and Collaborative. He is also a panellist and fellow of the UK Higher Education Academy. Dr Iqbal has also edited several special issues of international journals within the field of information retrieval and user supportive systems.

 

 

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.