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
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
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
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
Nowadays,AC electronic loads with energy recovery are widely used in the testing of uninterruptible power supplies and power supply equipment.To tackle the problems of control difficulty,strategy complexity,and poor d...Nowadays,AC electronic loads with energy recovery are widely used in the testing of uninterruptible power supplies and power supply equipment.To tackle the problems of control difficulty,strategy complexity,and poor dynamic performance of AC electronic load with energy recovery of the conventional control strategy,a control strategy of AC electronic load with energy recovery based on Finite Control Set Model Predictive Control(FCSMPC)is developed.To further reduce the computation burden of the FCS-MPC,a simplified FCS-MPC with transforming the predicted variables and using sector to select expected state is proposed.Through simplified model and equivalent approximation analysis,the transfer function of the system is obtained,and the stability and robustness of the system are analyzed.The performance of the simplified FCS-MPC is compared with space vector control(SVPWM)and conventional FCS-MPC.The results show that the FCS-MPC method performs better dynamic response and this advantage is more obvious when simulating high power loads.The simplified FCS-MPC shows similar control performance to conventional FCS-MPC at less computation burden.The control performance of the system also shows better simulation results.展开更多
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.
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.
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.
文摘Nowadays,AC electronic loads with energy recovery are widely used in the testing of uninterruptible power supplies and power supply equipment.To tackle the problems of control difficulty,strategy complexity,and poor dynamic performance of AC electronic load with energy recovery of the conventional control strategy,a control strategy of AC electronic load with energy recovery based on Finite Control Set Model Predictive Control(FCSMPC)is developed.To further reduce the computation burden of the FCS-MPC,a simplified FCS-MPC with transforming the predicted variables and using sector to select expected state is proposed.Through simplified model and equivalent approximation analysis,the transfer function of the system is obtained,and the stability and robustness of the system are analyzed.The performance of the simplified FCS-MPC is compared with space vector control(SVPWM)and conventional FCS-MPC.The results show that the FCS-MPC method performs better dynamic response and this advantage is more obvious when simulating high power loads.The simplified FCS-MPC shows similar control performance to conventional FCS-MPC at less computation burden.The control performance of the system also shows better simulation results.
