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}
Pub Date : 2025-03-30DOI: 10.1109/TCSS.2025.3567776
Yingying She;Fang Liu;Baorong Yang;Bin Hu
{"title":"Converging Real and Virtual: Embodied Intelligence-Driven Immersive VR Biofeedback for Brain Health Modulation","authors":"Yingying She;Fang Liu;Baorong Yang;Bin Hu","doi":"10.1109/TCSS.2025.3567776","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3567776","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"938-946"},"PeriodicalIF":4.5,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11018521","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178874","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 : 2025-03-30DOI: 10.1109/TCSS.2025.3567690
{"title":"IEEE Transactions on Computational Social Systems Publication Information","authors":"","doi":"10.1109/TCSS.2025.3567690","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3567690","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"C2-C2"},"PeriodicalIF":4.5,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11018522","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178883","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 : 2025-03-30DOI: 10.1109/TCSS.2025.3567692
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/TCSS.2025.3567692","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3567692","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"C3-C3"},"PeriodicalIF":4.5,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11018523","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144185939","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 : 2025-03-30DOI: 10.1109/TCSS.2025.3567694
{"title":"IEEE Transactions on Computational Social Systems Information for Authors","authors":"","doi":"10.1109/TCSS.2025.3567694","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3567694","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"C4-C4"},"PeriodicalIF":4.5,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11018520","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144185801","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 : 2025-03-21DOI: 10.1109/TCSS.2025.3548057
Yu Mao;Shaojie Lin;Zilong Lin;Yaojin Lin
Few-shot learning (FSL) aims to train a model using a minimal number of samples and subsequently apply this model to recognize unseen classes. Recently, metric-based methods mainly focus on exploring the relationship between the support set and the query set through attention mechanism in solving FSL problems. However, these methods typically employ unidirectional computation when calculating the attention relationship between support and query. This unidirectional approach not only limits the depth and breadth of knowledge acquisition but may also lead to mismatched patches between support and query, thereby affecting the overall performance of the model. In this article, we propose a bidirectional patch-aware attention network for few-shot learning (BPAN) to address this issue. First, we extract subimages via grid cropping and feed them into the learned feature extractor to obtain patch features. Moreover, self-attention is used to assign different weights to patch features and reconstruct them. Then, PFCAM is proposed to mutually explore the patch feature relationship between the support set and the support set, further reconstruct the patch features, and aggregate multiple patch features of each image into one feature through a learnable parameter matrix for the purpose of prediction. Finally, the template for each class is constructed to extend the results of PFCAM to the few-shot classification scenario. Experiments on three benchmark datasets show that BPAN achieves superior performance.
{"title":"Bidirectional Patch-Aware Attention Network for Few-Shot Learning","authors":"Yu Mao;Shaojie Lin;Zilong Lin;Yaojin Lin","doi":"10.1109/TCSS.2025.3548057","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3548057","url":null,"abstract":"Few-shot learning (FSL) aims to train a model using a minimal number of samples and subsequently apply this model to recognize unseen classes. Recently, metric-based methods mainly focus on exploring the relationship between the support set and the query set through attention mechanism in solving FSL problems. However, these methods typically employ unidirectional computation when calculating the attention relationship between support and query. This unidirectional approach not only limits the depth and breadth of knowledge acquisition but may also lead to mismatched patches between support and query, thereby affecting the overall performance of the model. In this article, we propose a bidirectional patch-aware attention network for few-shot learning (BPAN) to address this issue. First, we extract subimages via grid cropping and feed them into the learned feature extractor to obtain patch features. Moreover, self-attention is used to assign different weights to patch features and reconstruct them. Then, PFCAM is proposed to mutually explore the patch feature relationship between the support set and the support set, further reconstruct the patch features, and aggregate multiple patch features of each image into one feature through a learnable parameter matrix for the purpose of prediction. Finally, the template for each class is constructed to extend the results of PFCAM to the few-shot classification scenario. Experiments on three benchmark datasets show that BPAN achieves superior performance.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 5","pages":"3698-3708"},"PeriodicalIF":4.5,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145230073","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 : 2025-03-07DOI: 10.1109/TCSS.2025.3544263
Aakash Singh;Deepawali Sharma;Vivek Kumar Singh
The massive expansion of social media and the rapid growth in multimedia content on it has resulted in a growing interest in visual content analysis and classification. There are now a good number of studies that focus on identifying hateful and offensive content in social media posts. The social media content is often analyzed through automated algorithmic approaches, with respect to being unsuitable or harmful for different groups such as women and children. There is, however, a noticeable gap in the exploration of positive content, particularly in the case of multimodal content such as GIFs. Therefore, the present work attempted to address this gap by introducing a high-quality annotated dataset of animated GIFs. The dataset provides for two subtasks: 1) subtask 1 involves binary classification, determining whether a GIF provides emotional support; and 2) subtask 2 involves multiclass classification, wherein the GIFs are categorized into three different emotional support categories. The data annotation quality is assessed using Fleiss' kappa. Various unimodal models, utilizing text-only and image-only approaches, are implemented. Additionally, an effective multimodal approach is proposed that combines visual and textual information for detecting emotional support in animated GIFs. Both sequence and frame-level visual features are extracted from animated GIFs and utilized for classification tasks. The proposed multimodal long-term spatiotemporal model employs a weighted late fusion technique. The results obtained show that the proposed multimodal model outperformed the implemented unimodal models for both subtasks. The proposed LTST model achieved a weighted F1-score of 0.8304 and 0.7180 for subtask 1 and subtask 2, respectively. The experimental work and analysis confirm the suitability of the dataset and proposed algorithmic model for the task.
{"title":"EmoGif: A Multimodal Approach to Detect Emotional Support in Animated GIFs","authors":"Aakash Singh;Deepawali Sharma;Vivek Kumar Singh","doi":"10.1109/TCSS.2025.3544263","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3544263","url":null,"abstract":"The massive expansion of social media and the rapid growth in multimedia content on it has resulted in a growing interest in visual content analysis and classification. There are now a good number of studies that focus on identifying hateful and offensive content in social media posts. The social media content is often analyzed through automated algorithmic approaches, with respect to being unsuitable or harmful for different groups such as women and children. There is, however, a noticeable gap in the exploration of positive content, particularly in the case of multimodal content such as GIFs. Therefore, the present work attempted to address this gap by introducing a high-quality annotated dataset of animated GIFs. The dataset provides for two subtasks: 1) subtask 1 involves binary classification, determining whether a GIF provides emotional support; and 2) subtask 2 involves multiclass classification, wherein the GIFs are categorized into three different emotional support categories. The data annotation quality is assessed using Fleiss' kappa. Various unimodal models, utilizing text-only and image-only approaches, are implemented. Additionally, an effective multimodal approach is proposed that combines visual and textual information for detecting emotional support in animated GIFs. Both sequence and frame-level visual features are extracted from animated GIFs and utilized for classification tasks. The proposed multimodal long-term spatiotemporal model employs a weighted late fusion technique. The results obtained show that the proposed multimodal model outperformed the implemented unimodal models for both subtasks. The proposed LTST model achieved a weighted F1-score of 0.8304 and 0.7180 for subtask 1 and subtask 2, respectively. The experimental work and analysis confirm the suitability of the dataset and proposed algorithmic model for the task.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 5","pages":"3791-3803"},"PeriodicalIF":4.5,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145230024","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}
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