Artificial intelligence technology has revolutionized every industry and trade in recent years. However, its own development is encountering bottlenecks that it is unable to implement empathy with human emotions. So a...Artificial intelligence technology has revolutionized every industry and trade in recent years. However, its own development is encountering bottlenecks that it is unable to implement empathy with human emotions. So affective computing is getting more attention from researchers. In this paper, we propose an amygdala-inspired affective computing framework to realize the recognition of all kinds of human personalized emotions. Similar to the amygdala, the instantaneous emergency emotion is first computed more quickly in a low-redundancy convolutional neural network compressed by pruning and weight sharing with hashing trick. Then, the real-time process emotion is identified more accurately by the memory level neural networks, which is good at handling time-related signals. Finally, the intracranial emotion is recognized in personalized hidden Markov models. We demonstrate on Facial Expression of Emotion Dataset and the recognition accuracy of external emotions(including the emergency emotion and the process emotion) reached 85.72%. And the experimental results proved that the personalized affective model can generate desired intracranial emotions as expected.展开更多
As the frontier of intelligent computing technology,affective computing has been used in border inspection,case investigation,crime assessment,public opinion management,traffic management and other scenarios of public...As the frontier of intelligent computing technology,affective computing has been used in border inspection,case investigation,crime assessment,public opinion management,traffic management and other scenarios of public governance.However,there are still public risks associated with its failure to meet the basic requirements of modern public governance,and these risks are rooted in its technical characteristics.The technical characteristics of turning emotions into signals can give rise to such problems as degrading the right to informed consent,de-governance,and undermining human dignity when applied in public governance,and consequently can lead to social rights anxiety.Additionally,the affective modeling characteristics of affective computing tend to incur the rights risks of insufficient algorithm accuracy,algorithmic discrimination,and algorithmic black boxes.To avoid these risks,it is necessary to adopt the dynamic consent model as the premise for applying affective computing in public governance,and to regulate the auxiliary application of affective computing in public governance in a hierarchical manner,to achieve a balance between the application of affective computing technology and the protection of citizens'rights and the maintenance of public ethics.展开更多
This research is framed within the affective computing, which explains the importance of emotions in human cognition (decision making, perception, interaction and human intelligence). Applying this approach to a pedag...This research is framed within the affective computing, which explains the importance of emotions in human cognition (decision making, perception, interaction and human intelligence). Applying this approach to a pedagogical agent is an essential part to enhance the effectiveness of the teaching-learning process of an intelligent learning system. This work focuses on the design of the inference engine that will give life to the interface, where the latter is represented by a pedagogical agent. The inference engine is based on an affective-motivational model. This model is implemented by using artificial intelligence technique called fuzzy cognitive maps.展开更多
A personalized emotion space is proposed to bridge the ＂affective gap＂ in video affective content understanding. In order to unify the discrete and dimensional emotion model, fuzzy C-mean （FCM） clustering algorith...A personalized emotion space is proposed to bridge the ＂affective gap＂ in video affective content understanding. In order to unify the discrete and dimensional emotion model, fuzzy C-mean （FCM） clustering algorithm is adopted to divide the emotion space. Gaussian mixture model （GMM） is used to determine the membership functions of typical affective subspaces. At every step of modeling the space, the inputs rely completely on the affective experiences recorded by the audiences. The advantages of the improved V-A （Velance-Arousal） emotion model are the per- sonalization, the ability to define typical affective state areas in the V-A emotion space, and the convenience to explicitly express the intensity of each affective state. The experimental results validate the model and show it can be used as a personalized emotion space for video affective content representation.展开更多
Traditional industrial process control activities relevant to multi-objective optimization problems,such as proportional integral derivative(PID)parameter tuning and operational optimizations,always demand for process...Traditional industrial process control activities relevant to multi-objective optimization problems,such as proportional integral derivative(PID)parameter tuning and operational optimizations,always demand for process knowledge and human operators’experiences during human-computer interactions.However,the impact of human operators’preferences on human-computer interactions has been rarely highlighted ever since.In response to this problem,a novel multilayer cognitive affective computing model based on human personalities and pleasure-arousal-dominance(PAD)emotional space states is established in this paper.Therein,affective preferences are employed to update the affective computing model during human-machine interactions.Accordingly,we propose affective parameters mining strategies based on genetic algorithms(GAs),which are responsible for gradually grasping human operators’operational preferences in the process control activities.Two routine process control tasks,including PID controller tuning for coupling loops and operational optimization for batch beer fermenter processes,are carried out to illustrate the effectiveness of the contributions,leading to the satisfactory results.展开更多
Attention mechanism has been a successful method for multimodal affective analysis in recent years. Despite the advances, several significant challenges remain in fusing language and its nonverbal context information....Attention mechanism has been a successful method for multimodal affective analysis in recent years. Despite the advances, several significant challenges remain in fusing language and its nonverbal context information. One is to generate sparse attention coefficients associated with acoustic and visual modalities, which helps locate critical emotional se-mantics. The other is fusing complementary cross‐modal representation to construct optimal salient feature combinations of multiple modalities. A Conditional Transformer Fusion Network is proposed to handle these problems. Firstly, the authors equip the transformer module with CNN layers to enhance the detection of subtle signal patterns in nonverbal sequences. Secondly, sentiment words are utilised as context conditions to guide the computation of cross‐modal attention. As a result, the located nonverbal fea-tures are not only salient but also complementary to sentiment words directly. Experi-mental results show that the authors’ method achieves state‐of‐the‐art performance on several multimodal affective analysis datasets.展开更多
The emerging field of affective computing focuses on enhancing computers’ability to understand and appropriately respond to people’s affective states in human-computer interactions,and has revealed significant poten...The emerging field of affective computing focuses on enhancing computers’ability to understand and appropriately respond to people’s affective states in human-computer interactions,and has revealed significant potential for a wide spectrum of applications.Recently,the electroencephalography(EEG)based affective computing has gained increasing interest for its good balance between mechanistic exploration and real-world practical application.The present work reviewed ten theoretical and operational challenges for the existing affective computing researches from an interdisciplinary perspective of information technology,psychology,and neuroscience.On the theoretical side,we suggest that researchers should be well aware of the limitations of the commonly used emotion models,and be cautious about the widely accepted assumptions on EEG-emotion relationships as well as the transferability of findings based on different research paradigms.On the practical side,we propose several operational recommendations for the challenges about data collection,feature extraction,model implementation,online system design,as well as the potential ethical issues.The present review is expected to contribute to an improved understanding of EEG-based affective computing and promote further applications.展开更多
Computer scientists and psychologists are collaborating to work out an intelligent computer system that is able to perceive human’s emotions. The affective computing is a newly developed technology that allows comput...Computer scientists and psychologists are collaborating to work out an intelligent computer system that is able to perceive human’s emotions. The affective computing is a newly developed technology that allows computers to receive human’s emotional signals, interpret their emotional states by analyzing their biological signals and affect human’s emotions through various ways. This paper mainly discusses current achievements on Affective Computing, including several well- known methods to detect facial expressions,three different emotional models that are widely accepted in affective computing, three recent inventions based on affective computing in which affective wearable computers are emphasized, main obstacles during the development of affective computing and different ideas from scientists about affective computing.展开更多
基金supported by National Key R&D Program of China, No. 2018YFB1003905Natural Science Foundation of China, No.61873026the Fundamental Research Funds for the Central Universities, No.FRFTP-18-008A3
文摘Artificial intelligence technology has revolutionized every industry and trade in recent years. However, its own development is encountering bottlenecks that it is unable to implement empathy with human emotions. So affective computing is getting more attention from researchers. In this paper, we propose an amygdala-inspired affective computing framework to realize the recognition of all kinds of human personalized emotions. Similar to the amygdala, the instantaneous emergency emotion is first computed more quickly in a low-redundancy convolutional neural network compressed by pruning and weight sharing with hashing trick. Then, the real-time process emotion is identified more accurately by the memory level neural networks, which is good at handling time-related signals. Finally, the intracranial emotion is recognized in personalized hidden Markov models. We demonstrate on Facial Expression of Emotion Dataset and the recognition accuracy of external emotions(including the emergency emotion and the process emotion) reached 85.72%. And the experimental results proved that the personalized affective model can generate desired intracranial emotions as expected.
基金a phased achievement of the 2020 Youth Fund Project of the Ministry of Education in Humanities and Social Sciences of China,titled“Legislative Research on Collaborative Dispute Resolution Mechanisms for Medical Disputes in the Guangdong-Hong Kong-Macao Greater Bay Area”(Project Number 20YJC820023)。
文摘As the frontier of intelligent computing technology,affective computing has been used in border inspection,case investigation,crime assessment,public opinion management,traffic management and other scenarios of public governance.However,there are still public risks associated with its failure to meet the basic requirements of modern public governance,and these risks are rooted in its technical characteristics.The technical characteristics of turning emotions into signals can give rise to such problems as degrading the right to informed consent,de-governance,and undermining human dignity when applied in public governance,and consequently can lead to social rights anxiety.Additionally,the affective modeling characteristics of affective computing tend to incur the rights risks of insufficient algorithm accuracy,algorithmic discrimination,and algorithmic black boxes.To avoid these risks,it is necessary to adopt the dynamic consent model as the premise for applying affective computing in public governance,and to regulate the auxiliary application of affective computing in public governance in a hierarchical manner,to achieve a balance between the application of affective computing technology and the protection of citizens'rights and the maintenance of public ethics.
文摘This research is framed within the affective computing, which explains the importance of emotions in human cognition (decision making, perception, interaction and human intelligence). Applying this approach to a pedagogical agent is an essential part to enhance the effectiveness of the teaching-learning process of an intelligent learning system. This work focuses on the design of the inference engine that will give life to the interface, where the latter is represented by a pedagogical agent. The inference engine is based on an affective-motivational model. This model is implemented by using artificial intelligence technique called fuzzy cognitive maps.
基金Supported by the National Natural Science Foundation of China (60703049)the "Chenguang" Foundation for Young Scientists (200850731353)the National Postdoctoral Foundation of China (20060400847)
文摘A personalized emotion space is proposed to bridge the ＂affective gap＂ in video affective content understanding. In order to unify the discrete and dimensional emotion model, fuzzy C-mean （FCM） clustering algorithm is adopted to divide the emotion space. Gaussian mixture model （GMM） is used to determine the membership functions of typical affective subspaces. At every step of modeling the space, the inputs rely completely on the affective experiences recorded by the audiences. The advantages of the improved V-A （Velance-Arousal） emotion model are the per- sonalization, the ability to define typical affective state areas in the V-A emotion space, and the convenience to explicitly express the intensity of each affective state. The experimental results validate the model and show it can be used as a personalized emotion space for video affective content representation.
基金the National Natural Science Foundation of China(No.61603023)。
文摘Traditional industrial process control activities relevant to multi-objective optimization problems,such as proportional integral derivative(PID)parameter tuning and operational optimizations,always demand for process knowledge and human operators’experiences during human-computer interactions.However,the impact of human operators’preferences on human-computer interactions has been rarely highlighted ever since.In response to this problem,a novel multilayer cognitive affective computing model based on human personalities and pleasure-arousal-dominance(PAD)emotional space states is established in this paper.Therein,affective preferences are employed to update the affective computing model during human-machine interactions.Accordingly,we propose affective parameters mining strategies based on genetic algorithms(GAs),which are responsible for gradually grasping human operators’operational preferences in the process control activities.Two routine process control tasks,including PID controller tuning for coupling loops and operational optimization for batch beer fermenter processes,are carried out to illustrate the effectiveness of the contributions,leading to the satisfactory results.
基金National Key Research and Development Plan of China, Grant/Award Number: 2021YFB3600503National Natural Science Foundation of China, Grant/Award Numbers: 62276065, U21A20472。
文摘Attention mechanism has been a successful method for multimodal affective analysis in recent years. Despite the advances, several significant challenges remain in fusing language and its nonverbal context information. One is to generate sparse attention coefficients associated with acoustic and visual modalities, which helps locate critical emotional se-mantics. The other is fusing complementary cross‐modal representation to construct optimal salient feature combinations of multiple modalities. A Conditional Transformer Fusion Network is proposed to handle these problems. Firstly, the authors equip the transformer module with CNN layers to enhance the detection of subtle signal patterns in nonverbal sequences. Secondly, sentiment words are utilised as context conditions to guide the computation of cross‐modal attention. As a result, the located nonverbal fea-tures are not only salient but also complementary to sentiment words directly. Experi-mental results show that the authors’ method achieves state‐of‐the‐art performance on several multimodal affective analysis datasets.
基金supported by National Science Foundation of China under Grant U1736220MOE(Ministry of Education China)Project of Humanities and Social Sciences(17YJA190017)+1 种基金National Social Science Foundation of China under Grant 17ZDA323National Key Research and Development Plan under Grant 2016YFB1001200.
文摘The emerging field of affective computing focuses on enhancing computers’ability to understand and appropriately respond to people’s affective states in human-computer interactions,and has revealed significant potential for a wide spectrum of applications.Recently,the electroencephalography(EEG)based affective computing has gained increasing interest for its good balance between mechanistic exploration and real-world practical application.The present work reviewed ten theoretical and operational challenges for the existing affective computing researches from an interdisciplinary perspective of information technology,psychology,and neuroscience.On the theoretical side,we suggest that researchers should be well aware of the limitations of the commonly used emotion models,and be cautious about the widely accepted assumptions on EEG-emotion relationships as well as the transferability of findings based on different research paradigms.On the practical side,we propose several operational recommendations for the challenges about data collection,feature extraction,model implementation,online system design,as well as the potential ethical issues.The present review is expected to contribute to an improved understanding of EEG-based affective computing and promote further applications.
文摘Computer scientists and psychologists are collaborating to work out an intelligent computer system that is able to perceive human’s emotions. The affective computing is a newly developed technology that allows computers to receive human’s emotional signals, interpret their emotional states by analyzing their biological signals and affect human’s emotions through various ways. This paper mainly discusses current achievements on Affective Computing, including several well- known methods to detect facial expressions,three different emotional models that are widely accepted in affective computing, three recent inventions based on affective computing in which affective wearable computers are emphasized, main obstacles during the development of affective computing and different ideas from scientists about affective computing.
