IEEE Transactions on Computational Social Systems最新文献_第10页

Review of Advancements in Depression Detection Using Social Media Data 基于社交媒体数据的抑郁症检测进展综述

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-11-11 DOI: 10.1109/TCSS.2024.3448624

Sumit Dalal;Sarika Jain;Mayank Dave

{"title":"Review of Advancements in Depression Detection Using Social Media Data","authors":"Sumit Dalal;Sarika Jain;Mayank Dave","doi":"10.1109/TCSS.2024.3448624","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3448624","url":null,"abstract":"A large population embraced social media to share thoughts, emotions, and daily experiences through text, images, audio, or video posts. This user-generated content (UGC) serves various purposes, including user profiling, sentiment analysis, and disease detection or tracking. Notably, researchers recognized the potential of UGC for assessing mental health due to its unobtrusive and real-time monitoring capabilities. Recent reviews on depression identification from textual UGC using AI models covered tools and techniques but overlooked critical components such as datasets, lexicons, features, and subtasks, which are essential for understanding the progress and tasks undertaken. This survey adopts a systematic approach and formulates five research questions to examine the relevant literature concerning these elements. Additionally, it organizes machine learning and deep learning (ML/DL) training features from textual UGC in a hierarchical manner and maps the literature on depression detection into various subtasks. The review highlights that despite the prevalence studies, datasets are limited in both quantity and size, with many relying on less reliable ground truth collection methods such as self-reported diagnosis statements (SRDS). Furthermore, the review identifies an overemphasis on certain textual features, such as n-grams and affective elements, while others, such as life events, egocentric graphs, and intervention/coping style, remain largely unexplored. It is crucial for practical AI depression detection systems to develop expertise in tasks such as severity, symptom detection, and explainable/interpretable depression analysis to instill confidence and trust among users.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 1","pages":"77-100"},"PeriodicalIF":4.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361493","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}

引用次数: 0

Towards Domain-Specific Cross-Corpus Speech Emotion Recognition Approach 面向特定领域的跨语料库语音情感识别方法

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-11-11 DOI: 10.1109/TCSS.2024.3483964

Yan Zhao;Yuan Zong;Hailun Lian;Cheng Lu;Jingang Shi;Wenming Zheng

{"title":"Towards Domain-Specific Cross-Corpus Speech Emotion Recognition Approach","authors":"Yan Zhao;Yuan Zong;Hailun Lian;Cheng Lu;Jingang Shi;Wenming Zheng","doi":"10.1109/TCSS.2024.3483964","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3483964","url":null,"abstract":"Cross-corpus speech emotion recognition (SER) poses a challenge due to feature distribution mismatch between the training and testing speech samples, potentially degrading the performance of established SER methods. In this article, we tackle this challenge by proposing a novel transfer subspace learning method called acoustic knowledge-guided transfer linear regression (AKTLR). Unlike existing approaches, which often overlook domain-specific knowledge related to SER and simply treat cross-corpus SER as a generic transfer learning task, our AKTLR method is built upon a well-designed acoustic knowledge-guided dual sparsity constraint mechanism. This mechanism emphasizes the potential of minimalistic acoustic parameter feature sets to alleviate classifier over-adaptation, which is empirically validated acoustic knowledge in SER, enabling superior generalization in cross-corpus SER tasks compared to using large feature sets. Through this mechanism, we extend a simple transfer linear regression model to AKTLR. This extension harnesses its full capability to seek emotion-discriminative and corpus-invariant features from established acoustic parameter feature sets used for describing speech signals across two scales: contributive acoustic parameter groups and constituent elements within each contributive group. We evaluate our method through extensive cross-corpus SER experiments on three widely used speech emotion corpora: EmoDB, eNTERFACE, and CASIA. The proposed AKTLR achieves an average UAR of 42.12% across six tasks using the eGeMAPS feature set, outperforming many recent state-of-the-art transfer subspace learning and deep transfer learning methods. This demonstrates the effectiveness and superior performance of our approach. Furthermore, our work provides experimental evidence supporting the feasibility and superiority of incorporating domain-specific knowledge into the transfer learning model to address cross-corpus SER tasks.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 5","pages":"2130-2143"},"PeriodicalIF":4.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145315448","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}

引用次数: 0

A Multigranularity Learning Path Recommendation Framework Based on Knowledge Graph and Improved Ant Colony Optimization Algorithm for E-Learning 基于知识图和改进蚁群优化算法的多粒度学习路径推荐框架

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-11-11 DOI: 10.1109/TCSS.2024.3488373

Yaqian Zheng;Deliang Wang;Yaping Xu;Yanyan Li

{"title":"A Multigranularity Learning Path Recommendation Framework Based on Knowledge Graph and Improved Ant Colony Optimization Algorithm for E-Learning","authors":"Yaqian Zheng;Deliang Wang;Yaping Xu;Yanyan Li","doi":"10.1109/TCSS.2024.3488373","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3488373","url":null,"abstract":"In e-learning, extracting suitable learning objects (LOs) from a vast resource pool and organizing them into high-quality learning paths is crucial for helping e-learners achieve their goals. Numerous approaches have been proposed to recommend optimal learning paths for e-learners. However, it is essential to emphasize that e-learning systems typically consist of a wide range of LOs with varying levels of granularity, ranging from fine-grained to coarse-grained. Unfortunately, current research has not adequately considered the underlying granularity structure of LOs when optimizing learning paths. Existing methods primarily focus on organizing LOs at a single granularity level, limiting their applicability in real-world e-learning systems. To address the limitations, we propose a multigranularity learning path recommendation (MGLPR) framework that aims to flexibly and effectively integrate the diverse granularity levels of LOs into high-quality learning paths. In this framework, a two-layer [knowledge point (KP) and LO layers] model is developed to formulate the MGLPR problem as a constrained optimization problem and an improved ant colony optimization algorithm (IACO) is introduced to solve it to identify optimal learning paths for e-learners. To evaluate the effectiveness of the proposed IACO, we conducted extensive computational experiments using 30 simulation datasets with varying problem sizes and complexities. The results demonstrate that our proposed IACO achieves superior performance and robustness compared with other competitors. Additionally, an empirical study was conducted to investigate the efficacy of the proposed approach in an authentic learning context, with results indicating that the proposed method outperforms the traditional self-organized ones.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"586-607"},"PeriodicalIF":4.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783381","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}

引用次数: 0

Ugan: Uncertainty-Guided Graph Augmentation Network for EEG Emotion Recognition 乌干达：用于脑电图情绪识别的不确定性引导图增强网络

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-11-11 DOI: 10.1109/TCSS.2024.3488201

Bianna Chen;C. L. Philip Chen;Tong Zhang

{"title":"Ugan: Uncertainty-Guided Graph Augmentation Network for EEG Emotion Recognition","authors":"Bianna Chen;C. L. Philip Chen;Tong Zhang","doi":"10.1109/TCSS.2024.3488201","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3488201","url":null,"abstract":"The underlying time-variant and subject-specific brain dynamics lead to statistical uncertainty in electroencephalogram (EEG) representations and connectivities under diverse individual biases. Current works primarily augment statisticallike EEG data based on deterministic modes without comprehensively considering uncertain statistical discrepancies in representations and connectivities. This results in insufficient domain diversity to cover more domain variations for a generalized model independent of individuals. This article proposes an uncertainty-guided graph augmentation network (Ugan) to generalize EEG emotion recognition across subjects by comprehensively mimicking and constraining the uncertain statistical shifts across individuals. Specifically, an uncertainty-guided graph augmentation module is employed to augment both connectivities and features of EEG graph by manipulating domain statistical characteristics. With the original and augmented EEG graph covering diverse domain variations, the model can mimic the uncertain domain shifts to achieve better generalizability against potential subject variability. To extract discriminative characteristics and preserve emotional semantics after augmentation, a graph coteaching learning module is designed to facilitate coteaching knowledge learning between the original and augmented views. Moreover, a coteaching regularization module is developed to constrain semantic domain invariance and consistency, thereby rendering the model invariant to uncertain statistical shifts. Extensive experiments on three public EEG emotion datasets, i.e., Shanghai Jiao Tong University emotion EEG dataset (SEED), SEED-IV, and SEED-V, validate the superior generalizability of Ugan compared to the state-of-the-art methods.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"695-707"},"PeriodicalIF":4.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783311","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}

引用次数: 0

Toward Knowledge Integration With Large Language Model for End-to-End Aspect-Based Sentiment Analysis in Social Multimedia 基于大语言模型的端到端社交多媒体情感分析知识集成研究

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-11-11 DOI: 10.1109/TCSS.2024.3484460

Zhiyuan Ma;Meiqi Pan;Yunfeng Hou;Ling Yang;Wei Wang

{"title":"Toward Knowledge Integration With Large Language Model for End-to-End Aspect-Based Sentiment Analysis in Social Multimedia","authors":"Zhiyuan Ma;Meiqi Pan;Yunfeng Hou;Ling Yang;Wei Wang","doi":"10.1109/TCSS.2024.3484460","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3484460","url":null,"abstract":"Aspect-based sentiment analysis (ABSA) aims to identify specific sentiment elements in social multimedia content. To address aspect extraction and sentiment prediction together, recent studies have utilized a sequence tagging approach, mainly leveraging pretrained language models (PLMs) with specific architecture and auxiliary subtasks. However, these approaches often overlook task-related knowledge and struggle to scale across different domains. With advances in large language models (LLMs), there is a rising trend in constructing generative ABSA models. Nevertheless, these techniques tend to emphasize specific frameworks and overlook comprehensive knowledge representation. To address these challenges while leveraging the advantages of LLM and PLM-based methods, we propose a hybrid knowledge integration framework (HFABGKI). It employs a parameter-efficient fine-tuning technique, allowing for plug-and-play integration with existing LLMs. To bridge the LLM and PLM-based models, HF-ABGKI incorporates a global label semantic representation for potential aspect tokens, in which a simplified gating mechanism is proposed to filter useful information. Experimental results from six public social multimedia datasets demonstrate that our approach can accurately extract aspect terms and predict their sentiment polarity, achieving state-of-the-art performance compared to existing ABSA methods.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 5","pages":"3844-3857"},"PeriodicalIF":4.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145230075","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}

引用次数: 0

AE-AMT: Attribute-Enhanced Affective Music Generation With Compound Word Representation AE-AMT：复合词表示的属性增强情感音乐生成

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-11-11 DOI: 10.1109/TCSS.2024.3486536

Weiyi Yao;C. L. Philip Chen;Zongyan Zhang;Tong Zhang

{"title":"AE-AMT: Attribute-Enhanced Affective Music Generation With Compound Word Representation","authors":"Weiyi Yao;C. L. Philip Chen;Zongyan Zhang;Tong Zhang","doi":"10.1109/TCSS.2024.3486536","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3486536","url":null,"abstract":"Affective music generation is a challenge for symbolic music generation. Existing methods face the problem that the perceived emotion of the generated music is not evident because music datasets containing emotional labels are relatively small in quantity and scale. To address this issue, an attribute-enhanced affective music transformer (AE-AMT) model is proposed to generate perceived affective music with attribute enhancement. In addition, a multiquantile-based attribute discretization (MQAD) strategy is designed, enabling the model to generate intensity-controllable affective music pieces. Furthermore, A replication-expanded compound representation of the control signals (RECR) method is designed for control signals to improve the controllability of the model. In objective experiments, the AE-AMT model demonstrated a 29.25% and 19.5% improvement in overall emotion accuracy, along with a 30% and 32% improvement in arousal accuracy on the datasets EMOPIA and VGMIDI. These improvements are achieved without significant difference in objective music quality, while also providing ample novelty and diversity compared to the current state-of-the-art approach. Moreover, subjective experiments revealed that the AE-AMT model outperformed comparison models, especially in low valence and arousal based on the Wilcoxon signed ranks test. Additionally, the soft variant model of AE-AMT exhibited a significant advantage in valence, low arousal, and overall music quality. These experiments showcase the AE-AMT model's ability to significantly enhance arousal performance and strike a balance between emotional intensity and musical quality through adaptable strategies.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"890-904"},"PeriodicalIF":4.5,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769493","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}

引用次数: 0

A Classification-Based Product Selection Method Based on Online Reviews on Multifaceted Attributes 基于多属性在线评论的分类产品选择方法

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-11-08 DOI: 10.1109/TCSS.2024.3485009

Xingli Wu;Huchang Liao;Benjamin Lev;Weiping Ding

{"title":"A Classification-Based Product Selection Method Based on Online Reviews on Multifaceted Attributes","authors":"Xingli Wu;Huchang Liao;Benjamin Lev;Weiping Ding","doi":"10.1109/TCSS.2024.3485009","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3485009","url":null,"abstract":"While the development of e-commerce brings convenience to consumers, a large quantity of products and information increase the difficulty of making purchase decisions. This study constructs a classification-based product selection method driven by online reviews to assist consumers in making purchase decisions. First, the multifaceted attribute evaluations of products are extracted from textual reviews that contain more abundant and useful information than those provided by vendors. The evaluations are modeled by probabilistic linguistic term sets such that sentiment words in texts are described at different frequencies. Then, a classification-based product selection method is developed to rank products considering multifaceted attributes in which alternative products are divided into the acceptance class, rejection class, and uncertainty class through a classification strategy. Each class of products is compared based on the performance scores calculated by a probabilistic linguistic aggregation operator. A case study of selecting laptops based on real data from Amazon.com is given to illustrate the method. Comparative analysis with existing ranking methods shows the advantages of the proposed method in matching consumers’ risk aversion behavior and preserving uncertain information.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 1","pages":"11-24"},"PeriodicalIF":4.5,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361490","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}

引用次数: 0

Effective Analysis of Machine and Deep Learning Methods for Diagnosing Mental Health Using Social Media Conversations 使用社交媒体对话诊断心理健康的机器和深度学习方法的有效分析

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-11-08 DOI: 10.1109/TCSS.2024.3487168

Yashwanth Kasanneni;Achyut Duggal;R. Sathyaraj;S. P. Raja

{"title":"Effective Analysis of Machine and Deep Learning Methods for Diagnosing Mental Health Using Social Media Conversations","authors":"Yashwanth Kasanneni;Achyut Duggal;R. Sathyaraj;S. P. Raja","doi":"10.1109/TCSS.2024.3487168","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3487168","url":null,"abstract":"The increasing incidence of mental health issues demands innovative diagnostic methods, especially within digital communication. Traditional assessments are challenged by the sheer volume of data and the nuanced language found on social media and other text-based platforms. This study seeks to apply machine learning (ML) to interpret these digital narratives and identify patterns that signal mental health conditions. We apply natural language processing (NLP) techniques to analyze sentiments and emotional cues across datasets from social media and other text-based communication. Using ML, deep learning, and transfer learning models such as bidirectional encoder representations (BERTs), robustly optimized BERT approach (RoBERTa), distilled BERT (DistilBERT), and generalized autoregressive pretraining for language understanding (XLNet), we assess their ability to detect early signs of mental health concerns. The results show that BERT, RoBERTa, and XLNet consistently achieve over 95% accuracy, highlighting their strong contextual understanding and effectiveness in this application. The significance of this research lies in its potential to revolutionize mental health diagnostics by providing a scalable, data-driven approach to early detection. By harnessing the power of advanced NLP models, this study offers a pathway to more timely and accurate identification of individuals in need of mental health support, thereby contributing to better outcomes in public health.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 1","pages":"274-294"},"PeriodicalIF":4.5,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361060","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}

引用次数: 0

Influence of External Information on Large Language Models Mirrors Social Cognitive Patterns 外部信息对大型语言模型的影响反映了社会认知模式

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-11-06 DOI: 10.1109/TCSS.2024.3476030

Ning Bian;Hongyu Lin;Peilin Liu;Yaojie Lu;Chunkang Zhang;Ben He;Xianpei Han;Le Sun

{"title":"Influence of External Information on Large Language Models Mirrors Social Cognitive Patterns","authors":"Ning Bian;Hongyu Lin;Peilin Liu;Yaojie Lu;Chunkang Zhang;Ben He;Xianpei Han;Le Sun","doi":"10.1109/TCSS.2024.3476030","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3476030","url":null,"abstract":"Social cognitive theory explains how people learn and acquire knowledge through observing others. Recent years have witnessed the rapid development of large language models (LLMs), which suggests their potential significance as agents in the society. LLMs, as AI agents, can observe external information, which shapes their cognition and behaviors. However, the extent to which external information influences LLMs’ cognition and behaviors remains unclear. This study investigates how external statements and opinions influence LLMs’ thoughts and behaviors from a social cognitive perspective. Three experiments were conducted to explore the effects of external information on LLMs’ memories, opinions, and social media behavioral decisions. Sociocognitive factors, including source authority, social identity, and social role, were analyzed to investigate their moderating effects. Results showed that external information can significantly shape LLMs’ memories, opinions, and behaviors, with these changes mirroring human social cognitive patterns such as authority bias, in-group bias, emotional positivity, and emotion contagion. This underscores the challenges in developing safe and unbiased LLMs, and emphasizes the importance of understanding the susceptibility of LLMs to external influences.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"1115-1131"},"PeriodicalIF":4.5,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178958","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}

引用次数: 0

Model-Based and Data-Driven Stochastic Hybrid Control for Rumor Propagation in Dual-Layer Network 基于模型和数据驱动的双层网络谣言传播随机混合控制

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-11-06 DOI: 10.1109/TCSS.2024.3469972

Xiaojing Zhong;Chaolong Luo;Feiqi Deng;Guiyun Liu;Chunlei Li;Zhipei Hu

引用次数: 0