Pub Date : 2025-06-04DOI: 10.1109/TCSS.2025.3563733
Yueran Pan;Biyuan Chen;Wenxing Liu;Ming Cheng;Dong Zhang;Hongzhu Deng;Xiaobing Zou;Ming Li
The World Health Organization (WHO) has established the caregiver skill training (CST) program, designed to empower families with children diagnosed with autism spectrum disorder the essential caregiving skills. The joint engagement rating inventory (JERI) protocol evaluates participants’ engagement levels within the CST initiative. Traditionally, rating the expressive language level and use (EXLA) item in JERI relies on retrospective video analysis conducted by qualified professionals, thus incurring substantial labor costs. This study introduces a multimodal behavioral signal-processing framework designed to analyze both child and caregiver behaviors automatically, thereby rating EXLA. Initially, raw audio and video signals are segmented into concise intervals via voice activity detection, speaker diarization and speaker age classification, serving the dual purpose of eliminating nonspeech content and tagging each segment with its respective speaker. Subsequently, we extract an array of audio-visual features, encompassing our proposed interpretable, hand-crafted textual features, end-to-end audio embeddings and end-to-end video embeddings. Finally, these features are fused at the feature level to train a linear regression model aimed at predicting the EXLA scores. Our framework has been evaluated on the largest in-the-wild database currently available under the CST program. Experimental results indicate that the proposed system achieves a Pearson correlation coefficient of 0.768 against the expert ratings, evidencing promising performance comparable to that of human experts.
{"title":"Assessing the Expressive Language Levels of Autistic Children in Home Intervention","authors":"Yueran Pan;Biyuan Chen;Wenxing Liu;Ming Cheng;Dong Zhang;Hongzhu Deng;Xiaobing Zou;Ming Li","doi":"10.1109/TCSS.2025.3563733","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3563733","url":null,"abstract":"The World Health Organization (WHO) has established the caregiver skill training (CST) program, designed to empower families with children diagnosed with autism spectrum disorder the essential caregiving skills. The joint engagement rating inventory (JERI) protocol evaluates participants’ engagement levels within the CST initiative. Traditionally, rating the expressive language level and use (EXLA) item in JERI relies on retrospective video analysis conducted by qualified professionals, thus incurring substantial labor costs. This study introduces a multimodal behavioral signal-processing framework designed to analyze both child and caregiver behaviors automatically, thereby rating EXLA. Initially, raw audio and video signals are segmented into concise intervals via voice activity detection, speaker diarization and speaker age classification, serving the dual purpose of eliminating nonspeech content and tagging each segment with its respective speaker. Subsequently, we extract an array of audio-visual features, encompassing our proposed interpretable, hand-crafted textual features, end-to-end audio embeddings and end-to-end video embeddings. Finally, these features are fused at the feature level to train a linear regression model aimed at predicting the EXLA scores. Our framework has been evaluated on the largest in-the-wild database currently available under the CST program. Experimental results indicate that the proposed system achieves a Pearson correlation coefficient of 0.768 against the expert ratings, evidencing promising performance comparable to that of human experts.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 5","pages":"3647-3659"},"PeriodicalIF":4.5,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145230078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 advent of digital technology, augmented by connected devices, has catalyzed a dramatic increase in multimedia content consumption, facilitating on-the-go access and communication. However, this surge also heightens the risks of unauthorized access, privacy breaches, and cyberattacks. Consequently, ensuring the secure and efficient transmission and storage of multimedia content is of paramount importance. This article presents a robust encryption scheme for secure image transmission, utilizing a novel one-dimensional chaotic map characterized by random and complex dynamics, validated through NIST test and meticulous evaluation. Key matrices are derived from the chaotic map, with the SHA-256 hash of random, nonoverlapping blocks of the input image influencing the initial conditions, thereby ensuring resistance to differential cryptanalysis. The encryption process encompasses a dual shuffling mechanism: an adaptive shuffling guided by the chaotic key, followed by orbital shuffling, which rearranges pixel positions by segmenting the image into distinct orbital patterns. This is complemented by a feedback diffusion technique that ensures each pixel’s encryption is influenced by neighboring values and the keys employed. Extensive evaluation with multimodal images demonstrates the scheme’s versatility, with significant resilience against various cryptographic attacks, as evidenced by thorough assessments. Comparative analysis further highlights the superiority of the proposed scheme over state-of-the-art approaches. These attributes position the proposed scheme as a highly effective solution for contemporary digital security challenges.
{"title":"A Novel Chaotic Map and Its Application to Secure Transmission of Multimodal Images","authors":"Parkala Vishnu Bharadwaj Bayari;Yashmita Sangwan;Gaurav Bhatnagar;Chiranjoy Chattopadhyay","doi":"10.1109/TCSS.2025.3568467","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3568467","url":null,"abstract":"The advent of digital technology, augmented by connected devices, has catalyzed a dramatic increase in multimedia content consumption, facilitating on-the-go access and communication. However, this surge also heightens the risks of unauthorized access, privacy breaches, and cyberattacks. Consequently, ensuring the secure and efficient transmission and storage of multimedia content is of paramount importance. This article presents a robust encryption scheme for secure image transmission, utilizing a novel one-dimensional chaotic map characterized by random and complex dynamics, validated through NIST test and meticulous evaluation. Key matrices are derived from the chaotic map, with the SHA-256 hash of random, nonoverlapping blocks of the input image influencing the initial conditions, thereby ensuring resistance to differential cryptanalysis. The encryption process encompasses a dual shuffling mechanism: an adaptive shuffling guided by the chaotic key, followed by orbital shuffling, which rearranges pixel positions by segmenting the image into distinct orbital patterns. This is complemented by a feedback diffusion technique that ensures each pixel’s encryption is influenced by neighboring values and the keys employed. Extensive evaluation with multimodal images demonstrates the scheme’s versatility, with significant resilience against various cryptographic attacks, as evidenced by thorough assessments. Comparative analysis further highlights the superiority of the proposed scheme over state-of-the-art approaches. These attributes position the proposed scheme as a highly effective solution for contemporary digital security challenges.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 5","pages":"3765-3777"},"PeriodicalIF":4.5,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145230015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-06-03DOI: 10.1109/TCSS.2025.3561921
Xuan Luo;Bin Liang;Qianlong Wang;Jing Li;Erik Cambria;Xiaojun Zhang;Yulan He;Min Yang;Ruifeng Xu
Sexism has become a pressing issue, driven by the rapid-spreading influence of societal norms, media portrayals, and online platforms that perpetuate and amplify gender biases. Curbing sexism has emerged as a critical challenge globally. Being capable of recognizing sexist statements and behaviors is of particular importance since it is the first step in mind change. This survey provides an extensive overview of recent advancements in sexism detection. We present details of the various resources used in this field and methodologies applied to the task, covering different languages, modalities, models, and approaches. Moreover, we examine the specific challenges these models encounter in accurately identifying and classifying sexism. Additionally, we highlight areas that require further research and propose potential new directions for future exploration in the domain of sexism detection. Through this comprehensive exploration, we strive to contribute to the advancement of interdisciplinary research, fostering a collective effort to combat sexism in its multifaceted manifestations.
{"title":"A Literature Survey on Multimodal and Multilingual Sexism Detection","authors":"Xuan Luo;Bin Liang;Qianlong Wang;Jing Li;Erik Cambria;Xiaojun Zhang;Yulan He;Min Yang;Ruifeng Xu","doi":"10.1109/TCSS.2025.3561921","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3561921","url":null,"abstract":"Sexism has become a pressing issue, driven by the rapid-spreading influence of societal norms, media portrayals, and online platforms that perpetuate and amplify gender biases. Curbing sexism has emerged as a critical challenge globally. Being capable of recognizing sexist statements and behaviors is of particular importance since it is the first step in mind change. This survey provides an extensive overview of recent advancements in sexism detection. We present details of the various resources used in this field and methodologies applied to the task, covering different languages, modalities, models, and approaches. Moreover, we examine the specific challenges these models encounter in accurately identifying and classifying sexism. Additionally, we highlight areas that require further research and propose potential new directions for future exploration in the domain of sexism detection. Through this comprehensive exploration, we strive to contribute to the advancement of interdisciplinary research, fostering a collective effort to combat sexism in its multifaceted manifestations.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 5","pages":"3709-3727"},"PeriodicalIF":4.5,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145230029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-04-08DOI: 10.1109/TCSS.2025.3555419
Zijian Long;Haopeng Wang;Haiwei Dong;Abdulmotaleb El Saddik
The social metaverse is a growing digital ecosystem that blends virtual and physical worlds. It allows users to interact socially, work, shop, and enjoy entertainment. However, privacy remains a major challenge, as immersive interactions require continuous collection of biometric and behavioral data. At the same time, ensuring high-quality, low-latency streaming is difficult due to the demands of real-time interaction, immersive rendering, and bandwidth optimization. To address these issues, we propose adaptive social metaverse streaming (ASMS), a novel streaming system based on federated multiagent proximal policy optimization (F-MAPPO). ASMS leverages F-MAPPO, which integrates federated learning (FL) and deep reinforcement learning (DRL) to dynamically adjust streaming bit rates while preserving user privacy. Experimental results show that ASMS improves user experience by at least 14% compared to existing streaming methods across various network conditions. Therefore, ASMS enhances the social metaverse experience by providing seamless and immersive streaming, even in dynamic and resource-constrained networks, while ensuring that sensitive user data remain on local devices.
{"title":"Adaptive Social Metaverse Streaming Based on Federated Multiagent Deep Reinforcement Learning","authors":"Zijian Long;Haopeng Wang;Haiwei Dong;Abdulmotaleb El Saddik","doi":"10.1109/TCSS.2025.3555419","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3555419","url":null,"abstract":"The social metaverse is a growing digital ecosystem that blends virtual and physical worlds. It allows users to interact socially, work, shop, and enjoy entertainment. However, privacy remains a major challenge, as immersive interactions require continuous collection of biometric and behavioral data. At the same time, ensuring high-quality, low-latency streaming is difficult due to the demands of real-time interaction, immersive rendering, and bandwidth optimization. To address these issues, we propose adaptive social metaverse streaming (ASMS), a novel streaming system based on federated multiagent proximal policy optimization (F-MAPPO). ASMS leverages F-MAPPO, which integrates federated learning (FL) and deep reinforcement learning (DRL) to dynamically adjust streaming bit rates while preserving user privacy. Experimental results show that ASMS improves user experience by at least 14% compared to existing streaming methods across various network conditions. Therefore, ASMS enhances the social metaverse experience by providing seamless and immersive streaming, even in dynamic and resource-constrained networks, while ensuring that sensitive user data remain on local devices.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 5","pages":"3804-3815"},"PeriodicalIF":4.5,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145230077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sentiment analysis of social media platforms is crucial for extracting actionable insights from unstructured textual data. However, modern sentiment analysis models using deep learning lack explainability, acting as black box and limiting trust. This study focuses on improving the explainability of sentiment analysis models of social media platforms by leveraging explainable artificial intelligence (XAI). We propose a novel explainable sentiment analysis (XSA) framework incorporating intrinsic and posthoc XAI methods, i.e., local interpretable model-agnostic explanations (LIME) and counterfactual explanations. Specifically, to solve the problem of lack of local fidelity and stability in interpretations caused by the LIME random perturbation sampling method, a new model-independent interpretation method is proposed, which uses the isometric mapping virtual sample generation method based on manifold learning instead of LIMEs random perturbation sampling method to generate samples. Additionally, a generative link tree is presented to create counterfactual explanations that maintain strong data fidelity, which constructs counterfactual narratives by leveraging examples from the training data, employing a divide-and-conquer strategy combined with local greedy. Experiments conducted on social media datasets from Twitter, YouTube comments, Yelp, and Amazon demonstrate XSAs ability to provide local aspect-level explanations while maintaining sentiment analysis performance. Analyses reveal improved model explainability and enhanced user trust, demonstrating XAIs potential in sentiment analysis of social media platforms. The proposed XSA framework provides a valuable direction for developing transparent and trustworthy sentiment analysis models for social media platforms.
{"title":"Explaining Sentiments: Improving Explainability in Sentiment Analysis Using Local Interpretable Model-Agnostic Explanations and Counterfactual Explanations","authors":"Xin Wang;Jianhui Lyu;J. Dinesh Peter;Byung-Gyu Kim;B.D. Parameshachari;Keqin Li;Wei Wei","doi":"10.1109/TCSS.2025.3531718","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3531718","url":null,"abstract":"Sentiment analysis of social media platforms is crucial for extracting actionable insights from unstructured textual data. However, modern sentiment analysis models using deep learning lack explainability, acting as black box and limiting trust. This study focuses on improving the explainability of sentiment analysis models of social media platforms by leveraging explainable artificial intelligence (XAI). We propose a novel explainable sentiment analysis (XSA) framework incorporating intrinsic and posthoc XAI methods, i.e., local interpretable model-agnostic explanations (LIME) and counterfactual explanations. Specifically, to solve the problem of lack of local fidelity and stability in interpretations caused by the LIME random perturbation sampling method, a new model-independent interpretation method is proposed, which uses the isometric mapping virtual sample generation method based on manifold learning instead of LIMEs random perturbation sampling method to generate samples. Additionally, a generative link tree is presented to create counterfactual explanations that maintain strong data fidelity, which constructs counterfactual narratives by leveraging examples from the training data, employing a divide-and-conquer strategy combined with local greedy. Experiments conducted on social media datasets from Twitter, YouTube comments, Yelp, and Amazon demonstrate XSAs ability to provide local aspect-level explanations while maintaining sentiment analysis performance. Analyses reveal improved model explainability and enhanced user trust, demonstrating XAIs potential in sentiment analysis of social media platforms. The proposed XSA framework provides a valuable direction for developing transparent and trustworthy sentiment analysis models for social media platforms.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"1390-1403"},"PeriodicalIF":4.5,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144186005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-04-04DOI: 10.1109/TCSS.2025.3548862
Xiaohong Guan;Xiaobing Li;Björn W. Schuller;Xinran Zhang
{"title":"Guest Editorial: Special Issue on Music Intelligence and Social Computation","authors":"Xiaohong Guan;Xiaobing Li;Björn W. Schuller;Xinran Zhang","doi":"10.1109/TCSS.2025.3548862","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3548862","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"847-850"},"PeriodicalIF":4.5,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10949083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-04-03DOI: 10.1109/TCSS.2025.3548750
{"title":"IEEE Transactions on Computational Social Systems Information for Authors","authors":"","doi":"10.1109/TCSS.2025.3548750","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3548750","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"C4-C4"},"PeriodicalIF":4.5,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10948562","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-04-03DOI: 10.1109/TCSS.2025.3548863
Yanan Zhang;Chen Xu;Kexin Zhu;Yu Ma;Kang Wang;Haoran Gao;Jian Shen;Bin Hu
{"title":"New Paradigm for Intelligent Mental Health: A Synergistic Framework Integrating Large Language Models and Virtual Standardized Patients","authors":"Yanan Zhang;Chen Xu;Kexin Zhu;Yu Ma;Kang Wang;Haoran Gao;Jian Shen;Bin Hu","doi":"10.1109/TCSS.2025.3548863","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3548863","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"464-472"},"PeriodicalIF":4.5,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10948541","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-04-03DOI: 10.1109/TCSS.2025.3548746
{"title":"IEEE Transactions on Computational Social Systems Publication Information","authors":"","doi":"10.1109/TCSS.2025.3548746","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3548746","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"C2-C2"},"PeriodicalIF":4.5,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10948563","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 : 2025-04-03DOI: 10.1109/TCSS.2025.3548748
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/TCSS.2025.3548748","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3548748","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"C3-C3"},"PeriodicalIF":4.5,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10948565","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}
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