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.
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.
This book explores China’s urban development, examining the history and culture of Chinese cities and providing a cultural background to the rapid urban development of contemporary China. It offers a new perspective on Chinese urban history, showcasing the traditional culture which underpins the emergence of the modern city and highlighting how traditional Chinese philosophical thought is reflected in the culture of urban planning and architecture in China, notably examining such issues as ‘the integration of man and nature’, yin and yang, bagua, and the Wu Xing.
This book considers urban development in China, highlighting links between China’s history and civilization and the rapid evolution of its urban forms. It explores the early days of urban dwelling in China, progressing to an analysis of residential environments in the industrial age. It also examines China’s modern and postmodern architecture, considered as derivative or lacking spiritual meaning or personality, and showcases how China's traditional culture underpins the emergence of China’s modern cities. Focusing on the notion of “courtyard spirit” in China, it offers a study of the urban public squares central to Chinese society, and examines the disruption of the traditional Square model and the rise and growth of new architectural models.
Mou Zongsan (1909–1995), one of the representatives of Modern Confucianism, belongs to the most important Chinese philosophers of the twentieth century. From a more traditional Confucian perspective, this book makes a critical analysis on Mou’s "moral metaphysics," mainly his thoughts about Confucian ethos. The author observes that Mou simplifies Confucian ethos rooted in various and specific environments, making them equal to modern ethics, which is a subversion of the ethical order of life advocated by traditional Confucianism. The author believes, also, that Mou has twisted Confucian ethos systematically by introducing Kant’s concept of autonomy into the interpretation of Confucian thoughts. Scholars and students in Chinese philosophy, especially those in Confucian studies, will be attracted by this book. Also, it will appeal to readers interested in comparative philosophy.
This title critically examines Mou Zongsan’s philosophical system of moral metaphysics on the level of metaphysics and history philosophy, which combines Confucianism and Kantianism philosophy. Mou Zongsan (1909–1995) is one of the representatives of Modern Confucianism and an important Chinese philosopher of the twentieth century. The two-volume set looks into the problems in the moral metaphysics by Mou and his systematic subversion of Confucianism on three levels: ethics, metaphysics and historical philosophy. In this second volume the author critiques Mou’s philosophical development of Confucianism on the latter two levels. The first part analyzes Mou’s view on conscience as ontology and his interpretation of the heavenly principles in Confucianism, arguing that his theory in fact abolishes Confucian cosmology based on modern scientific concepts and speaks for modern humanity. The second part focuses on Mou’s remolding of historical philosophy based on the concept of freedom of Kant, Hegel, and modern Western philosophy, then assesses his ideological distortions of historical and political concepts in the Confucian tradition. The title will appeal to scholars, students and philosophers interested in Chinese philosophy, Confucian ethics, Neo-Confucianism, and Comparative Philosophy.
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