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December 2018

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Call For Chapters | Neural and Machine Learning for Emotion and Empathy Recognition: Experiences from the OMG-Challenges by Springer
by Pablo Barros 27 Dec '18

27 Dec '18

Dear Colleagues, We would like to invite you to contribute a chapter for the upcoming volume entitled “Neural and Machine Learning for Emotion and Empathy Recognition: Experiences from the OMG-Challenges” to be published by the Springer Series on Competitions in Machine Learning. Our book will be available by fall 2019. Website: https://easychair.org/cfp/OMGBook2019 Short description of the volume: Emotional expression perception and categorization are extremely popular in the affective computing community. However, the inclusion of emotions in the decision-making process of an agent is not considered in most of the research in this field. To treat emotion expressions as the final goal, although necessary, reduces the usability of such solutions in more complex scenarios. To create a general affective model to be used as a modulator for learning different cognitive tasks, such as modeling intrinsic motivation, creativity, dialog processing, grounded learning, and human-level communication, instantaneous emotion perception cannot be the pivotal focus. This book aims to present recent contributions for multimodal emotion recognition and empathy prediction which take into consideration the long-term development of affective concepts. On this regard, we provide access to two datasets: the OMG-Emotion Behavior Recognition and OMG-Empathy Prediction datasets. These datasets were designed, collected and formalized to be used on the OMG-Emotion Recognition Challenge and the OMG-Empathy Prediction challenge, respectively. All the participants of our challenges are invited to submit their contribution to our book. We also invite interested authors to use our datasets on the development of inspiring and innovative research on affective computing. By formatting these solutions and editing this book, we hope to inspire further research in affective and cognitive computing over longer timescales. TOPICS OF INTEREST The topics of interest for this call for chapters include, but are not limited to: - New theories and findings on continuous emotion recognition - Multi- and Cross-modal emotion perception and interpretation - Novel neural network models for affective processing - Lifelong affect analysis, perception, and interpretation - New neuroscientific and psychological findings on continuous emotion representation - Embodied artificial agents for empathy and emotion appraisal - Machine learning for affect-driven interventions - Socially intelligent human-robot interaction - Personalized systems for human affect recognition - New theories and findings on empathy modeling - Multimodal processing of empathetic and social signals - Novel neural network models for empathy understanding - Lifelong models for empathetic interactions - Empathetic Human-Robot-Interaction Scenarios - New neuroscientific and psychological findings on empathy representation - Multi-agent communication for empathetic interactions - Empathy as a decision-making modulator - Personalized systems for empathy prediction Each contributed chapter is expected to present a novel research study, a comparative study, or a survey of the literature. We also expect that each contributed chapter approach somehow at least one of our datasets: the OMG-Emotion and the OMG-Empathy. SUBMISSIONS All submissions should be done via EasyChair: https://easychair.org/cfp/OMGBook2019 Original artwork and a signed copyright release form will be required for all accepted chapters. For author instructions, please visit: https://www.springer.com/us/authors-editors/book-authors-editors/resources-… ACCESS TO THE DATASETS - OMG-EMOTION - https://www2.informatik.uni-hamburg.de/wtm/omgchallenges/omg_emotion.html - OMG-EMPATHY - https://www2.informatik.uni-hamburg.de/wtm/omgchallenges/omg_empathy.html To have access to the datasets, please send an e-mail to: barros(a)informatik.uni-hamburg.de IMPORTANT DATES: - Submission of abstracts: 08th of February 2019 - Notification of initial editorial decisions: 15th of February 2019 - Submissions of full-length chapters: 29th of March 2019 - Notification of final editorial decisions 17th of May 2019 - Submission of revised chapters: 07th of June, 2019 -- Best regards, *Pablo Barros* *http://www.pablobarros.net <http://www.pablobarros.net>*

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CFP: TAC | Special Issue on Automated Perception of Human Affect from Longitudinal Behavioral Data
by Pablo Barros 11 Dec '18

11 Dec '18

2nd CALL FOR PAPERS IEEE Transactions on Affective Computing Special Issue on Automated Perception of Human Affect from Longitudinal Behavioral Data Website: https://www2.informatik.uni-hamburg.de/wtm/omgchallenges/tacSpecialIssue201… I. Aim and Scope Research trends within artificial intelligence and cognitive sciences are still heavily based on computational models that attempt to imitate human perception in various behavior categorization tasks. However, most of the research in the field focuses on instantaneous categorization and interpretation of human affect, such as the inference of six basic emotions from face images, and/or affective dimensions (valence-arousal), stress and engagement from multi-modal (e.g., video, audio, and autonomic physiology) data. This diverges from the developmental aspect of emotional behavior perception and learning, where human behavior and expressions of affect evolve and change over time. Moreover, these changes are present not only in the temporal domain but also within different populations and more importantly, within each individual. This calls for a new perspective when designing computational models for analysis and interpretation of human affective behaviors: the computational models that can timely and efficiently adapt to different contexts and individuals over time, and also incorporate existing neurophysiological and psychological findings (prior knowledge). Thus, the long-term goal is to create life-long personalized learning and inference systems for analysis and perception of human affective behaviors. Such systems would benefit from long-term contextual information (including demographic and social aspects) as well as individual characteristics. This, in turn, would allow building intelligent agents (such as mobile and robot technologies) capable of adapting their behavior in a continuous and on-line manner to the target contexts and individuals. This special issue aims at contributions from computational neuroscience and psychology, artificial intelligence, machine learning, and affective computing, challenging and expanding current research on interpretation and estimation of human affective behavior from longitudinal behavioral data, i.e., single or multiple modalities captured over extended periods of time allowing efficient profiling of target behaviors and their inference in terms of affect and other socio-cognitive dimensions. We invite contributions focusing on both the theoretical and modeling perspective, as well as applications ranging from human-human, human-computer and human-robot interactions. II. Potential Topics Given computational models, the capability to perceive and understand emotion behavior is an important and popular research topic. That is why recent special issues on the IEEE Journal on Transactions on Affective Computing covered topics from emotion behavior analysis “in-the-wild” to personality analysis. However, most of the research published by these specific calls treat emotion behavior as an instantaneous event, relating mostly to emotion recognition, and thus neglect the development of complex emotion behavior models. Our special issue will foster the development of the field by focusing excellent research on emotion models for long-term behavior analysis. The topics of interest for this special issue include, but are not limited to: - New theories and findings on continuous emotion recognition - Multi- and Cross-modal emotion perception and interpretation - Lifelong affect analysis, perception, and interpretation - Novel neural network models for affective processing - New neuroscientific and psychological findings on continuous emotion representation - Embodied artificial agents for empathy and emotion appraisal - Machine learning for affect-driven interventions - Socially intelligent human-robot interaction - Personalized systems for human affect recognition III. Submission Prospective authors are invited to submit their manuscripts electronically, adhering to the IEEE Transactions on Affective Computing guidelines ( https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5165369) Please submit your papers through the online system ( https://mc.manuscriptcentral.com/taffc-cs) and be sure to select the special issue: Special Issue/Section on Automated Perception of Human Affect from Longitudinal Behavioral Data. IV. IMPORTANT DATES: Submissions Deadline: 15th of January 2019 Deadline for reviews and response to authors: 06th of April 2019 Camera-ready deadline: 05th of August 2019 V. Guest Editors Pablo Barros, University of Hamburg, Germany Stefan Wermter, University of Hamburg, Germany Ognjen (Oggi) Rudovic, Massachusetts Institute of Technology, United States of America Hatice Gunes, University of Cambridge, United Kingdom -- Dr. Pablo Barros Postdoctoral Research Associate - Crossmodal Learning Project (CML) Knowledge Technology Department of Informatics University of Hamburg Vogt-Koelln-Str. 30 22527 Hamburg, Germany Phone: +49 40 42883 2535 Fax: +49 40 42883 2515 barros at informatik.uni-hamburg.dehttp://www.pablobarros.nethttps://www.inf.uni-hamburg.de/en/inst/ab/wtm/people/barros.htmlhttps://www.inf.uni-hamburg.de/en/inst/ab/wtm/

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