Bridging The Gap Between Machine Learning And Affective Computing
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Bridging the Gap between Machine Learning and Affective Computing by Zhen Cui,Abhinav Dhall,Xiaopeng Hong,Yong Li,Wenming Zheng,Yuan Zong Pdf
Affective computing refers to computing that relates to, arises from, or influences emotions, as pioneered by Rosalind Picard in 1995. The goal of affective computing is to bridge the gap between human and machines and ultimately enable robots to communicate with human naturally and emotionally. Recently, the research on affective computing has gained considerable progress with many fields contributing including neuroscience, psychology, education, medicine, behavior, sociology, and computer science. Current research in affective computing mainly focuses on estimating of human emotions through different forms of signals, e.g., face video, EEG, Speech, PET scans or fMRI. Inferring the emotion of humans is difficult, as emotion is a subjective, unconscious experience characterized primarily by psycho-physiological expressions and biological reactions. It is influenced by hormones and neurotransmitters such as dopamine, noradrenaline, serotonin, oxytocin, GABA… etc. The physiology of emotion is closely linked to arousal of the nervous system with various states and strengths relating, apparently, to different particular emotions. To understand “emotion” or “affect” merely by machine learning or big data analysis is not enough, but the understanding and applications from the intrinsic features of emotions from the neuroscience aspect is essential.
Machine Learning Systems for Multimodal Affect Recognition by Markus Kächele Pdf
Markus Kächele offers a detailed view on the different steps in the affective computing pipeline, ranging from corpus design and recording over annotation and feature extraction to post-processing, classification of individual modalities and fusion in the context of ensemble classifiers. He focuses on multimodal recognition of discrete and continuous emotional and medical states. As such, specifically the peculiarities that arise during annotation and processing of continuous signals are highlighted. Furthermore, methods are presented that allow personalization of datasets and adaptation of classifiers to new situations and persons.
Deep Learning Techniques Applied to Affective Computing by Zhen Cui,Wenming Zheng Pdf
Affective computing refers to computing that relates to, arises from, or influences emotions. The goal of affective computing is to bridge the gap between humans and machines and ultimately endow machines with emotional intelligence for improving natural human-machine interaction. In the context of human-robot interaction (HRI), it is hoped that robots can be endowed with human-like capabilities of observation, interpretation, and emotional expression. The research on affective computing has recently achieved extensive progress with many fields contributing including neuroscience, psychology, education, medicine, behavior, sociology, and computer science. Current research in affective computing concentrates on estimating human emotions through different forms of signals such as speech, face, text, EEG, fMRI, and many others. In neuroscience, the neural mechanisms of emotion are explored by combining neuroscience with the psychological study of personality, emotion, and mood. In psychology and philosophy, emotion typically includes a subjective, conscious experience characterized primarily by psychophysiological expressions, biological reactions, and mental states. The multi-disciplinary features of understanding “emotion” result in the fact that inferring the emotion of humans is definitely difficult. As a result, a multi-disciplinary approach is required to facilitate the development of affective computing. One of the challenging problems in affective computing is the affective gap, i.e., the inconsistency between the extracted feature representations and subjective emotions. To bridge the affective gap, various hand-crafted features have been widely employed to characterize subjective emotions. However, these hand-crafted features are usually low-level, and they may hence not be discriminative enough to depict subjective emotions. To address this issue, the recently-emerged deep learning (also called deep neural networks) techniques provide a possible solution. Due to the used multi-layer network structure, deep learning techniques are capable of learning high-level contributing features from a large dataset and have exhibited excellent performance in multiple application domains such as computer vision, signal processing, natural language processing, human-computer interaction, and so on. The goal of this Research Topic is to gather novel contributions on deep learning techniques applied to affective computing across the diverse fields of psychology, machine learning, neuroscience, education, behavior, sociology, and computer science to converge with those active in other research areas, such as speech emotion recognition, facial expression recognition, Electroencephalogram (EEG) based emotion estimation, human physiological signal (heart rate) estimation, affective human-robot interaction, multimodal affective computing, etc. We welcome researchers to contribute their original papers as well as review articles to provide works regarding the neural approach from computation to affective computing systems. This Research Topic aims to bring together research including, but not limited to: • Deep learning architectures and algorithms for affective computing tasks such as emotion recognition from speech, face, text, EEG, fMRI, and many others. • Explainability of deep Learning algorithms for affective computing. • Multi-task learning techniques for emotion, personality and depression detection, etc. • Novel datasets for affective computing • Applications of affective computing in robots, such as emotion-aware human-robot interaction and social robots, etc.
Principles and Applications of Socio-Cognitive and Affective Computing by Geetha, S.,Renuka, Karthika,Phamila, Asnath Victy,N., Karthikeyan Pdf
Recent advances in socio-cognitive and affective computing require further study as countless benefits and opportunities have emerged from these innovative technologies that may be useful in a number of contexts throughout daily life. In order to ensure these technologies are appropriately utilized across sectors, the challenges and strategies for adoption as well as potential uses must be thoroughly considered. Principles and Applications of Socio-Cognitive and Affective Computing discusses several aspects of affective interactions and concepts in affective computing, the fundamentals of emotions, and emerging research and exciting techniques for bridging the emotional disparity between humans and machines, all within the context of interactions. The book also considers problem and solution guidelines emerging in cognitive computing, thus summarizing the roadmap of current machine computational intelligence techniques for affective computing. Covering a range of topics such as social interaction, robotics, and virtual reality, this reference work is crucial for scientists, engineers, industry professionals, academicians, researchers, scholars, practitioners, instructors, and students.
From Bioinspired Systems and Biomedical Applications to Machine Learning by José Manuel Ferrández Vicente,José Ramón Álvarez-Sánchez,Félix de la Paz López,Javier Toledo Moreo,Hojjat Adeli Pdf
The two volume set LNCS 11486 and 11487 constitutes the proceedings of the International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2019, held in Almería, Spain,, in June 2019. The total of 103 contributions was carefully reviewed and selected from 190 submissions during two rounds of reviewing and improvement. The papers are organized in two volumes, one on understanding the brain function and emotions, addressing topics such as new tools for analyzing neural data, or detection emotional states, or interfacing with physical systems. The second volume deals with bioinspired systems and biomedical applications to machine learning and contains papers related bioinspired programming strategies and all the contributions oriented to the computational solutions to engineering problems in different applications domains, as biomedical systems, or big data solutions.
Applied Affective Computing by Leimin Tian,Sharon Oviatt,Michal Muszynski,Brent Chamberlain,Jennifer Healey,Akane Sano Pdf
Affective computing is a nascent field situated at the intersection of artificial intelligence with social and behavioral science. It studies how human emotions are perceived and expressed, which then informs the design of intelligent agents and systems that can either mimic this behavior to improve their intelligence or incorporate such knowledge to effectively understand and communicate with their human collaborators. Affective computing research has recently seen significant advances and is making a critical transformation from exploratory studies to real-world applications in the emerging research area known as applied affective computing. This book offers readers an overview of the state-of-the-art and emerging themes in affective computing, including a comprehensive review of the existing approaches to affective computing systems and social signal processing. It provides in-depth case studies of applied affective computing in various domains, such as social robotics and mental well-being. It also addresses ethical concerns related to affective computing and how to prevent misuse of the technology in research and applications. Further, this book identifies future directions for the field and summarizes a set of guidelines for developing next-generation affective computing systems that are effective, safe, and human-centered. For researchers and practitioners new to affective computing, this book will serve as an introduction to the field to help them in identifying new research topics or developing novel applications. For more experienced researchers and practitioners, the discussions in this book provide guidance for adopting a human-centered design and development approach to advance affective computing.
Bridging the Gap Between AI, Cognitive Science, and Narratology With Narrative Generation by Ogata, Takashi,Ono, Jumpei Pdf
The use of cognitive science in creating stories, languages, visuals, and characters is known as narrative generation, and it has become a trending area of study. Applying artificial intelligence (AI) techniques to story development has caught the attention of professionals and researchers; however, few studies have inherited techniques used in previous literary methods and related research in social sciences. Implementing previous narratology theories to current narrative generation systems is a research area that remains unexplored. Bridging the Gap Between AI, Cognitive Science, and Narratology With Narrative Generation is a collection of innovative research on the analysis of current practices in narrative generation systems by combining previous theories in narratology and literature with current methods of AI. The book bridges the gap between AI, cognitive science, and narratology with narrative generation in a broad sense, including other content generation, such as a novels, poems, movies, computer games, and advertisements. The book emphasizes that an important method for bridging the gap is based on designing and implementing computer programs using knowledge and methods of narratology and literary theories. In order to present an organic, systematic, and integrated combination of both the fields to develop a new research area, namely post-narratology, this book has an important place in the creation of a new research area and has an impact on both narrative generation studies, including AI and cognitive science, and narrative studies, including narratology and literary theories. It is ideally designed for academicians, researchers, and students, as well as enterprise practitioners, engineers, and creators of diverse content generation fields such as advertising production, computer game creation, comic and manga writing, and movie production.
Comparative Analysis of Digital Consciousness and Human Consciousness: Bridging the Divide in AI Discourse by Lathabhavan, Remya,Mishra, Nidhi Pdf
In today's digital age, the rapid advancement of AI and digital technologies has led to the emergence of digital consciousness, blurring the lines between human and machine thinking. At the same time, these technologies offer unprecedented convenience and efficiency but pose significant challenges. Individuals are increasingly facing issues such as stress, anxiety, and technology addiction, impacting their overall well-being and decision-making processes. The dichotomy between digital consciousness and human consciousness raises critical questions about how we can navigate these challenges in a rapidly evolving technological landscape. To address these pressing concerns, Comparative Analysis of Digital Consciousness and Human Consciousness: Bridging the Divide in AI Discourse offers a comprehensive exploration of the impacts of digital consciousness on human well-being and decision-making. This book delves into the paradoxes and challenges posed by the coexistence of digital and human consciousness, providing insights from psychological perspectives, practitioner experiences, and academic research. By offering a nuanced understanding of these concepts, we aim to equip readers with the knowledge and tools needed to manage the implications of digital consciousness in their personal and professional lives.
Understanding the Brain Function and Emotions by José Manuel Ferrández Vicente,José Ramón Álvarez-Sánchez,Félix de la Paz López,Javier Toledo Moreo,Hojjat Adeli Pdf
The two volume set LNCS 11486 and 11487 constitutes the proceedings of the International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2019, held in Almería, Spain,, in June 2019. The total of 103 contributions was carefully reviewed and selected from 190 submissions during two rounds of reviewing and improvement. The papers are organized in two volumes, one on understanding the brain function and emotions, addressing topics such as new tools for analyzing neural data, or detection emotional states, or interfacing with physical systems. The second volume deals with bioinspired systems and biomedical applications to machine learning and contains papers related bioinspired programming strategies and all the contributions oriented to the computational solutions to engineering problems in different applications domains, as biomedical systems, or big data solutions.
Using Machine Learning to Detect Emotions and Predict Human Psychology by Rai, Mritunjay,Pandey, Jay Kumar Pdf
In the realm of analyzing human emotions through Artificial Intelligence (AI), a myriad of challenges persist. From the intricate nuances of emotional subtleties to the broader concerns of ethical considerations, privacy implications, and the ongoing battle against bias, AI faces a complex landscape when venturing into the understanding of human emotions. These challenges underscore the intricate balance required to navigate the human psyche with accuracy. The book, Using Machine Learning to Detect Emotions and Predict Human Psychology, serves as a guide for innovative solutions in the field of emotion detection through AI. It explores facial expression analysis, where AI decodes real-time emotions through subtle cues such as eyebrow movements and micro-expressions. In speech and voice analysis, the book unveils how AI processes vocal nuances to discern emotions, considering elements like tone, pitch, and language intricacies. Additionally, the power of text analysis is of great importance, revealing how AI extracts emotional tones from diverse textual communications. By weaving these systems together, the book offers a holistic solution to the challenges faced by AI in understanding the complex landscape of human emotions.
Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications by José Manuel Ferrández Vicente,José Ramón Álvarez-Sánchez,Félix de la Paz López,Hojjat Adeli Pdf
The two volume set LNCS 13258 and 13259 constitutes the proceedings of the International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2022, held in Puerto de la Cruz, Tenerife, Spain in May – June 2022. The total of 121 contributions was carefully reviewed and selected from 203 submissions. The papers are organized in two volumes, with the following topical sub-headings: Part I: Machine Learning in Neuroscience; Neuromotor and Cognitive Disorders; Affective Analysis; Health Applications, Part II: Affective Computing in Ambient Intelligence; Bioinspired Computing Approaches; Machine Learning in Computer Vision and Robot; Deep Learning; Artificial Intelligence Applications.
The Oxford Handbook of Voice Perception by Sascha Frühholz,Pascal Belin Pdf
Speech perception has been the focus of innumerable studies over the past decades. While our abilities to recognize individuals by their voice state plays a central role in our everyday social interactions, limited scientific attention has been devoted to the perceptual and cerebral mechanisms underlying nonverbal information processing in voices. The Oxford Handbook of Voice Perception takes a comprehensive look at this emerging field and presents a selection of current research in voice perception. The forty chapters summarise the most exciting research from across several disciplines covering acoustical, clinical, evolutionary, cognitive, and computational perspectives. In particular, this handbook offers an invaluable window into the development and evolution of the 'vocal brain', and considers in detail the voice processing abilities of non-human animals or human infants. By providing a full and unique perspective on the recent developments in this burgeoning area of study, this text is an important and interdisciplinary resource for students, researchers, and scientific journalists interested in voice perception.
