The location reasoning of target objects in robot-operated environment is a challenging task. Objects that robots need to interact with are often located at a distance or are contained within containers, making them inaccessible for direct observation by the robot. The uncertainty of the storage location of the target objects and the lack of reasoning ability present considerable challenges. In this article, we propose a method for semantic localization of robot-operated objects based on human common sense and robot experiences. Instead of reasoning the object storage locations solely based on the category of the target object, a probabilistic ontology model is introduced to represent uncertain knowledge in the task of object localization, which combines the expressive power of classical first-order logic and the inference capability of Bayesian inference. The target location is then estimated using the probabilistic ontologies with dynamic integration of human common sense and robot experiences. Experimental results in both simulation and real-world environments demonstrate the effectiveness of the proposed integration of human common sense and robot experiences in the task of semantic localization of robot-operated objects.
{"title":"Location Reasoning of Target Objects Based on Human Common Sense and Robot Experiences","authors":"Yueguang Ge;Yinghao Cai;Shuo Wang;Shaolin Zhang;Tao Lu;Haitao Wang;Junhang Wei","doi":"10.1109/TCDS.2024.3442862","DOIUrl":"10.1109/TCDS.2024.3442862","url":null,"abstract":"The location reasoning of target objects in robot-operated environment is a challenging task. Objects that robots need to interact with are often located at a distance or are contained within containers, making them inaccessible for direct observation by the robot. The uncertainty of the storage location of the target objects and the lack of reasoning ability present considerable challenges. In this article, we propose a method for semantic localization of robot-operated objects based on human common sense and robot experiences. Instead of reasoning the object storage locations solely based on the category of the target object, a probabilistic ontology model is introduced to represent uncertain knowledge in the task of object localization, which combines the expressive power of classical first-order logic and the inference capability of Bayesian inference. The target location is then estimated using the probabilistic ontologies with dynamic integration of human common sense and robot experiences. Experimental results in both simulation and real-world environments demonstrate the effectiveness of the proposed integration of human common sense and robot experiences in the task of semantic localization of robot-operated objects.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 2","pages":"287-302"},"PeriodicalIF":5.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The ability of humanoid robots to exhibit empathetic facial expressions and provide corresponding responses is essential for natural human–robot interaction. To enhance this, we integrate the GPT3.5 model with a facial expression recognition model, creating a multimodal emotion recognition system. Additionally, we address the challenge of realistically mimicking human facial expressions by designing the physical structure of a humanoid robot. Initially, we develop a humanoid robot capable of adjusting the positions of its facial organs and neck through servo displacement to achieve more natural facial expressions. Subsequently, to overcome the current limitation where emotional interaction robots struggle to accurately recognize user emotions, we introduce a coupled generative pretrained transformer (GPT)-based multimodal emotion recognition method that utilizes both text and images, thereby enhancing the robot's emotion recognition accuracy. Finally, we integrate the GPT-3.5 model to generate empathetic responses based on recognized user emotional states and language text, which are then mapped onto the robot to enable empathetic expressions that can achieve a more comfortable human–machine interaction experience. Experimental results on benchmark databases demonstrate that the performance of the coupled GPT-based multimodal emotion recognition method using text and images outperforms other approaches, and it possesses unique empathetic response capabilities relative to alternative methods.
{"title":"Multimodal Emotion Fusion Mechanism and Empathetic Responses in Companion Robots","authors":"Xiaofeng Liu;Qincheng Lv;Jie Li;Siyang Song;Angelo Cangelosi","doi":"10.1109/TCDS.2024.3442203","DOIUrl":"10.1109/TCDS.2024.3442203","url":null,"abstract":"The ability of humanoid robots to exhibit empathetic facial expressions and provide corresponding responses is essential for natural human–robot interaction. To enhance this, we integrate the GPT3.5 model with a facial expression recognition model, creating a multimodal emotion recognition system. Additionally, we address the challenge of realistically mimicking human facial expressions by designing the physical structure of a humanoid robot. Initially, we develop a humanoid robot capable of adjusting the positions of its facial organs and neck through servo displacement to achieve more natural facial expressions. Subsequently, to overcome the current limitation where emotional interaction robots struggle to accurately recognize user emotions, we introduce a coupled generative pretrained transformer (GPT)-based multimodal emotion recognition method that utilizes both text and images, thereby enhancing the robot's emotion recognition accuracy. Finally, we integrate the GPT-3.5 model to generate empathetic responses based on recognized user emotional states and language text, which are then mapped onto the robot to enable empathetic expressions that can achieve a more comfortable human–machine interaction experience. Experimental results on benchmark databases demonstrate that the performance of the coupled GPT-based multimodal emotion recognition method using text and images outperforms other approaches, and it possesses unique empathetic response capabilities relative to alternative methods.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 2","pages":"271-286"},"PeriodicalIF":5.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-13DOI: 10.1109/TCDS.2024.3442172
Jing Luo;Chaoyi Zhang;Chao Zeng;Yiming Jiang;Chenguang Yang
In physical human–robot interaction (pHRI), the interaction profiles, such as impedance and interaction force are greatly influenced by the operator's muscle activities, impedance and interaction force between the robot and the operator. Actually, parameters of interaction profiles are easy to be measured, such as position, velocity, acceleration, and muscle activities. However, the impedance cannot be directly measured. In some areas, it is difficult to capture the force information, especially where the force sensor is hard to be attached on the robots. In this sense, it is worth developing a feasible and simple solution to recognize the impedance parameters by exploring the potential relationship among the above mentioned interaction profiles. To this end, a framework of impedance recognition based on different time-based weight membership functions with broad learning system (TWMF-BLS) is developed for stable/unstable pHRI. Specifically, a linear weight membership function and a nonlinear weight membership function are proposed for stable and unstable pHRI by using the hybrid features for estimating the interaction force. And then the human arm impedance can be estimated without a biological model or a robot's model. Experimental results have demonstrated the feasibility and effectiveness of the proposed approach.
{"title":"An Impedance Recognition Framework Based on Electromyogram for Physical Human–Robot Interaction","authors":"Jing Luo;Chaoyi Zhang;Chao Zeng;Yiming Jiang;Chenguang Yang","doi":"10.1109/TCDS.2024.3442172","DOIUrl":"10.1109/TCDS.2024.3442172","url":null,"abstract":"In physical human–robot interaction (pHRI), the interaction profiles, such as impedance and interaction force are greatly influenced by the operator's muscle activities, impedance and interaction force between the robot and the operator. Actually, parameters of interaction profiles are easy to be measured, such as position, velocity, acceleration, and muscle activities. However, the impedance cannot be directly measured. In some areas, it is difficult to capture the force information, especially where the force sensor is hard to be attached on the robots. In this sense, it is worth developing a feasible and simple solution to recognize the impedance parameters by exploring the potential relationship among the above mentioned interaction profiles. To this end, a framework of impedance recognition based on different time-based weight membership functions with broad learning system (TWMF-BLS) is developed for stable/unstable pHRI. Specifically, a linear weight membership function and a nonlinear weight membership function are proposed for stable and unstable pHRI by using the hybrid features for estimating the interaction force. And then the human arm impedance can be estimated without a biological model or a robot's model. Experimental results have demonstrated the feasibility and effectiveness of the proposed approach.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 1","pages":"205-218"},"PeriodicalIF":5.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-12DOI: 10.1109/TCDS.2024.3436255
{"title":"IEEE Transactions on Cognitive and Developmental Systems Information for Authors","authors":"","doi":"10.1109/TCDS.2024.3436255","DOIUrl":"https://doi.org/10.1109/TCDS.2024.3436255","url":null,"abstract":"","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"16 4","pages":"C4-C4"},"PeriodicalIF":5.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10633870","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141973491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-12DOI: 10.1109/TCDS.2024.3436251
{"title":"IEEE Transactions on Cognitive and Developmental Systems Publication Information","authors":"","doi":"10.1109/TCDS.2024.3436251","DOIUrl":"https://doi.org/10.1109/TCDS.2024.3436251","url":null,"abstract":"","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"16 4","pages":"C2-C2"},"PeriodicalIF":5.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10633810","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141973531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-12DOI: 10.1109/TCDS.2024.3436253
{"title":"IEEE Computational Intelligence Society Information","authors":"","doi":"10.1109/TCDS.2024.3436253","DOIUrl":"https://doi.org/10.1109/TCDS.2024.3436253","url":null,"abstract":"","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"16 4","pages":"C3-C3"},"PeriodicalIF":5.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10633812","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141973521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-12DOI: 10.1109/TCDS.2024.3441865
Lu Dong;Pinle Ding;Xin Yuan;Andi Xu;Jie Gui
This article investigates the service path problem of multi-unmanned aerial vehicle (multi-UAV) providing communication services to multiuser in urban environments with limited endurance. Our goal is to learn an optimal multi-UAV centralized control policy that will enable UAVs to find the illumination areas in urban environments through curiosity-driven exploration and harvest energy to continue providing communication services to users. First, we propose a reinforcement learning (RL)-based multi-UAV centralized control strategy to maximize the accumulated communication service score. In the proposed framework, curiosity can act as an internal incentive signal, allowing UAVs to explore the environment without any prior knowledge. Second, a two-phase exploring protocol is proposed for practical implementation. Compared to the baseline method, our proposed method can achieve a significantly higher accumulated communication service score in the exploitation-intensive phase. The results demonstrate that the proposed method can obtain accurate service paths over the baseline method and handle the exploration-exploitation tradeoff well.
{"title":"Reinforcement-Learning-Based Multi-Unmanned Aerial Vehicle Optimal Control for Communication Services With Limited Endurance","authors":"Lu Dong;Pinle Ding;Xin Yuan;Andi Xu;Jie Gui","doi":"10.1109/TCDS.2024.3441865","DOIUrl":"10.1109/TCDS.2024.3441865","url":null,"abstract":"This article investigates the service path problem of multi-unmanned aerial vehicle (multi-UAV) providing communication services to multiuser in urban environments with limited endurance. Our goal is to learn an optimal multi-UAV centralized control policy that will enable UAVs to find the illumination areas in urban environments through curiosity-driven exploration and harvest energy to continue providing communication services to users. First, we propose a reinforcement learning (RL)-based multi-UAV centralized control strategy to maximize the accumulated communication service score. In the proposed framework, curiosity can act as an internal incentive signal, allowing UAVs to explore the environment without any prior knowledge. Second, a two-phase exploring protocol is proposed for practical implementation. Compared to the baseline method, our proposed method can achieve a significantly higher accumulated communication service score in the exploitation-intensive phase. The results demonstrate that the proposed method can obtain accurate service paths over the baseline method and handle the exploration-exploitation tradeoff well.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 1","pages":"219-231"},"PeriodicalIF":5.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Since sudden and recurrent epileptic seizures seriously affect people's lives, computer-aided automatic seizure detection is crucial for precise diagnosis and prompt treatment. A novel seizure detection algorithm named channel selection-based temporal convolutional network (CS-TCN) was proposed in this article. First, electroencephalogram (EEG) recordings were segmented into 2-s intervals and features were extracted from both the time and frequency domains. Then, the expanded fisher score channel selection method was employed to select channels that contribute the most to seizure detection. Finally, the features from selected EEG channels were fed into the TCN to capture inherent temporal dependencies of EEG signals and detect seizure events. Children Hospital Boston and Massachusetts Institute of Technology (CHB-MIT) and Siena datasets were used to verify the detection performance of the CS-TCN algorithm, achieving sensitivities of 98.56% and 98.88%, and specificities of 99.80% and 99.88% in samplewise analysis, respectively. In eventwise analysis, the algorithm achieved sensitivities of 97.57% and 95.00%, with delays of 6.91 and 18.62 s, and FDR/h of 0.11 and 0.39, respectively. These results surpassed state-of-the-art few-channel algorithms for both datasets. CS-TCN algorithm offers excellent performance while simplifying model complexity and computational requirements, thus showcasing its potential for facilitating seizure detection in home environments.
{"title":"Channel-Selection-Based Temporal Convolutional Network for Patient-Specific Epileptic Seizure Detection","authors":"Guangming Wang;Xiyuan Lei;Wen Li;Won Hee Lee;Lianchi Huang;Jialin Zhu;Shanshan Jia;Dong Wang;Yang Zheng;Hua Zhang;Badong Chen;Gang Wang","doi":"10.1109/TCDS.2024.3433551","DOIUrl":"10.1109/TCDS.2024.3433551","url":null,"abstract":"Since sudden and recurrent epileptic seizures seriously affect people's lives, computer-aided automatic seizure detection is crucial for precise diagnosis and prompt treatment. A novel seizure detection algorithm named channel selection-based temporal convolutional network (CS-TCN) was proposed in this article. First, electroencephalogram (EEG) recordings were segmented into 2-s intervals and features were extracted from both the time and frequency domains. Then, the expanded fisher score channel selection method was employed to select channels that contribute the most to seizure detection. Finally, the features from selected EEG channels were fed into the TCN to capture inherent temporal dependencies of EEG signals and detect seizure events. Children Hospital Boston and Massachusetts Institute of Technology (CHB-MIT) and Siena datasets were used to verify the detection performance of the CS-TCN algorithm, achieving sensitivities of 98.56% and 98.88%, and specificities of 99.80% and 99.88% in samplewise analysis, respectively. In eventwise analysis, the algorithm achieved sensitivities of 97.57% and 95.00%, with delays of 6.91 and 18.62 s, and FDR/h of 0.11 and 0.39, respectively. These results surpassed state-of-the-art few-channel algorithms for both datasets. CS-TCN algorithm offers excellent performance while simplifying model complexity and computational requirements, thus showcasing its potential for facilitating seizure detection in home environments.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 1","pages":"179-188"},"PeriodicalIF":5.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141784744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Emotion recognition based on electroencephalography (EEG) has significant advantages in terms of reliability and accuracy. However, individual differences in EEG limit the ability of sentiment classifiers to generalize across subjects. Furthermore, due to the nonstationarity of EEG, subject signals can vary with time, an important challenge for temporal emotion recognition. Several emotion recognition methods have been developed that consider the alignment of conditional distributions, but do not balance the weights of conditional and marginal distributions. In this article, we propose a novel approach to generalize emotion recognition models across individuals and time, i.e., global and local associative domain adaptation (GLADA). The proposed method consists of three parts: 1) deep neural networks are used to extract deep features from emotional EEG data; 2) considering that marginal and conditional distributions between domains can contribute to adaptation differently, a method that combines coarse-grained adversarial adaptation and fine-grained adversarial adaptation is used to narrow the domain distance of the joint distribution in the EEG data between subjects (i.e., reduce intersubject variability), and the weights of the marginal and conditional distributions are automatically balanced using dynamic balancing factors; and 3) domain adaptation is used to accelerate model convergence. Using GLADA, subject-independent EEG emotion recognition is improved by reducing the influence of the subject’s personal information on EEG emotion. Experimental results demonstrate that the GLADA model effectively addresses the domain transfer problem, resulting in improved performance across multiple EEG emotion recognition tasks.
{"title":"GLADA: Global and Local Associative Domain Adaptation for EEG-Based Emotion Recognition","authors":"Tianxu Pan;Nuo Su;Jun Shan;Yang Tang;Guoqiang Zhong;Tianzi Jiang;Nianming Zuo","doi":"10.1109/TCDS.2024.3432752","DOIUrl":"10.1109/TCDS.2024.3432752","url":null,"abstract":"Emotion recognition based on electroencephalography (EEG) has significant advantages in terms of reliability and accuracy. However, individual differences in EEG limit the ability of sentiment classifiers to generalize across subjects. Furthermore, due to the nonstationarity of EEG, subject signals can vary with time, an important challenge for temporal emotion recognition. Several emotion recognition methods have been developed that consider the alignment of conditional distributions, but do not balance the weights of conditional and marginal distributions. In this article, we propose a novel approach to generalize emotion recognition models across individuals and time, i.e., global and local associative domain adaptation (GLADA). The proposed method consists of three parts: 1) deep neural networks are used to extract deep features from emotional EEG data; 2) considering that marginal and conditional distributions between domains can contribute to adaptation differently, a method that combines coarse-grained adversarial adaptation and fine-grained adversarial adaptation is used to narrow the domain distance of the joint distribution in the EEG data between subjects (i.e., reduce intersubject variability), and the weights of the marginal and conditional distributions are automatically balanced using dynamic balancing factors; and 3) domain adaptation is used to accelerate model convergence. Using GLADA, subject-independent EEG emotion recognition is improved by reducing the influence of the subject’s personal information on EEG emotion. Experimental results demonstrate that the GLADA model effectively addresses the domain transfer problem, resulting in improved performance across multiple EEG emotion recognition tasks.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 1","pages":"167-178"},"PeriodicalIF":5.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141784822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Given that emotional content spreads more widely than rational content in social networks, as well as the complexity of user cognition and the interaction of derivative topics, this article proposes a derivative topic dissemination model that integrates multidimensional cognition and game theory. First, regarding the issue of user emotional reactions in mining topics. In this article, we quantify the affective influence among users by considering user behaviors as continuous conversations through conversation-level sentiment analysis and the proximity centrality of social networks. Second, considering that user behavior is influenced by multidimensional cognition, this article proposes a method based on S(Sensibility) R(Rationality) 2vec to simulate the dialectical relationship between sensibility and rationality in the user decision-making process. Finally, considering the cooperative and competitive relationship among derived topics, this article uses evolutionary game theory to analyze the topic life cycle and quantify its impact on user behavior by time discretization method. Accordingly, we propose a CG-back-propagation (BP) model incorporating a BP neural network to efficiently simulate the nonlinear relationship of user behavior. Experiments show that the model can not only effectively tap the influence of multidimensional cognition on users’ retweeting behavior, but also effectively perceive the propagation dynamics of derived topics.
{"title":"A Derivative Topic Propagation Model Based on Multidimensional Cognition and Game Theory","authors":"Qian Li;Long Gao;Wenyi Xi;Tun Li;Rong Wang;Junwei Ge;Yunpeng Xiao","doi":"10.1109/TCDS.2024.3432337","DOIUrl":"10.1109/TCDS.2024.3432337","url":null,"abstract":"Given that emotional content spreads more widely than rational content in social networks, as well as the complexity of user cognition and the interaction of derivative topics, this article proposes a derivative topic dissemination model that integrates multidimensional cognition and game theory. First, regarding the issue of user emotional reactions in mining topics. In this article, we quantify the affective influence among users by considering user behaviors as continuous conversations through conversation-level sentiment analysis and the proximity centrality of social networks. Second, considering that user behavior is influenced by multidimensional cognition, this article proposes a method based on S(Sensibility) R(Rationality) 2vec to simulate the dialectical relationship between sensibility and rationality in the user decision-making process. Finally, considering the cooperative and competitive relationship among derived topics, this article uses evolutionary game theory to analyze the topic life cycle and quantify its impact on user behavior by time discretization method. Accordingly, we propose a CG-back-propagation (BP) model incorporating a BP neural network to efficiently simulate the nonlinear relationship of user behavior. Experiments show that the model can not only effectively tap the influence of multidimensional cognition on users’ retweeting behavior, but also effectively perceive the propagation dynamics of derived topics.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"17 1","pages":"189-204"},"PeriodicalIF":5.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141784748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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