IEEE Transactions on Computational Social Systems最新文献_第2页

MusicAOG: An Energy-Based Model for Learning and Sampling a Hierarchical Representation of Symbolic Music MusicAOG：一个基于能量的学习和采样符号音乐分层表示模型

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2025-02-27 DOI: 10.1109/TCSS.2024.3521445

Yikai Qian;Tianle Wang;Jishang Chen;Peiyang Yu;Duo Xu;Xin Jin;Feng Yu;Song-Chun Zhu

{"title":"MusicAOG: An Energy-Based Model for Learning and Sampling a Hierarchical Representation of Symbolic Music","authors":"Yikai Qian;Tianle Wang;Jishang Chen;Peiyang Yu;Duo Xu;Xin Jin;Feng Yu;Song-Chun Zhu","doi":"10.1109/TCSS.2024.3521445","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3521445","url":null,"abstract":"In addressing the challenge of interpretability and generalizability of artificial music intelligence, this article introduces a novel symbolic representation that amalgamates both explicit and implicit musical information across diverse traditions and granularities. Utilizing a hierarchical and-or graph representation, the model employs nodes and edges to encapsulate a broad spectrum of musical elements, including structures, textures, rhythms, and harmonies. This hierarchical approach expands the representability across various scales of music. This representation serves as the foundation for an energy-based model, uniquely tailored to learn musical concepts through a flexible algorithm framework relying on the minimax entropy principle. Utilizing an adapted Metropolis–Hastings sampling technique, the model enables fine-grained control over music generation. Through a comprehensive empirical evaluation, this novel approach demonstrates significant improvements in interpretability and controllability compared to existing methodologies. This study marks a substantial contribution to the fields of music analysis, composition, and computational musicology.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"873-889"},"PeriodicalIF":4.5,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777722","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

Explainable Dual-Branch Combination Network With Key Words Embedding and Position Attention for Sentimental Analytics of Social Media Short Comments 基于关键词嵌入和位置关注的可解释双分支组合网络社交媒体短评论情感分析

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2025-02-06 DOI: 10.1109/TCSS.2025.3532984

Zixuan Wang;Pan Wang;Lianyong Qi;Zhixin Sun;Xiaokang Zhou

{"title":"Explainable Dual-Branch Combination Network With Key Words Embedding and Position Attention for Sentimental Analytics of Social Media Short Comments","authors":"Zixuan Wang;Pan Wang;Lianyong Qi;Zhixin Sun;Xiaokang Zhou","doi":"10.1109/TCSS.2025.3532984","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3532984","url":null,"abstract":"Social media platforms such as Weibo and TikTok have become more influential than traditional media. Sentiment in social media comments reflects users’ attitudes and impacts society, making sentiment analysis (SA) crucial. AI driven models, especially deep-learning models, have achieved excellent results in SA tasks. However, most existing models are not interpretable enough. First, deep learning models have numerous parameters, and their transparency is insufficient. People cannot easily understand how the models extract features from input data and make sentiment judgments. Second, most models lack intuitive explanations. They cannot clearly indicate which words or phrases are key for emotion prediction. Moreover, extracting sentiment factors from comments is challenging because a comment often contains multiple sentiment characteristics. To address these issues, we propose a dual-branch combination network (DCN) for SA of social media short comments, achieving both word-level and sentence-level interpretability. The network includes a key word feature extraction network (KWFEN) and a key word order feature extraction network (KWOFEN). KWFEN uses popular emotional words and SHAP for word-level interpretability. KWOFEN employs position embedding and an attention layer to visualize attention weights for sentence-level interpretability. We validated our method on the public dataset weibo2018 and TSATC. The results show that our method effectively extracts positive and negative sentiment factors, establishing a clear mapping between model inputs and outputs, demonstrating good interpretability performance.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"1376-1389"},"PeriodicalIF":4.5,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144185941","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 Maximization in Sentiment Propagation With Multisearch Particle Swarm Optimization Algorithm 基于多搜索粒子群优化算法的情感传播影响最大化

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2025-02-05 DOI: 10.1109/TCSS.2025.3528890

Qiang He;Xin Yan;Alireza Jolfaei;Amr Tolba;Keping Yu;Yu-Kai Fu;Yuliang Cai

{"title":"Influence Maximization in Sentiment Propagation With Multisearch Particle Swarm Optimization Algorithm","authors":"Qiang He;Xin Yan;Alireza Jolfaei;Amr Tolba;Keping Yu;Yu-Kai Fu;Yuliang Cai","doi":"10.1109/TCSS.2025.3528890","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3528890","url":null,"abstract":"Sentiment propagation plays a crucial role in the continuous emergence of social public opinion and network group events. By analyzing the maximum Influence of sentiment propagation, we can gain a better understanding of how network group events arise and evolve. Influence maximization (IM) is a critical fundamental issue in the field of informatics, whose purpose is to identify the collection of individuals and maximize the specific information's influence in real-world social networks, and the sentiments expressed by nodes with the greatest influence can significantly impact the emotions of the entire group. The IM issue has been established to be an NP-hard (nondeterministic polynomial) challenge. Although some methods based on the greedy framework can achieve ideal results, they bring unacceptable computational overhead, while the performance of other methods is unsatisfactory. In this article, we explicate the IM problem and design a local influence evaluation function as the objective function of the IM to estimate the influence spread in the cascade diffusion models. We redefine particle parameters, update rules for IM problems, and introduce learning automata to realize multiple search modes. Then, we propose a multisearch particle Swarm optimization algorithm (MSPSO) to optimize the objective function. This algorithm incorporates a heuristic-based initialization strategy and a local search scheme to expedite MSPSO convergence. Experimental results on five real-world social network datasets consistently demonstrate MSPSO's superior efficiency and performance compared with baseline algorithms.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"1365-1375"},"PeriodicalIF":4.5,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144185940","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

IEEE Transactions on Computational Social Systems Publication Information IEEE计算社会系统汇刊信息

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2025-01-28 DOI: 10.1109/TCSS.2025.3531587

引用次数: 0

IEEE Systems, Man, and Cybernetics Society Information IEEE系统、人与控制论学会信息

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2025-01-28 DOI: 10.1109/TCSS.2025.3531589

引用次数: 0

IEEE Transactions on Computational Social Systems Information for Authors IEEE计算社会系统信息汇刊

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2025-01-28 DOI: 10.1109/TCSS.2025.3531591

引用次数: 0

Creating Healthier Living Environments: The Role of Soundscapes in Promoting Mental Health and Well-Being 创造更健康的生活环境：声景在促进心理健康和幸福中的作用

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2025-01-28 DOI: 10.1109/TCSS.2025.3530618

Ziwen Sun;Jian Kang;Kun Qian;Björn W. Schuller;Bin Hu

引用次数: 0

Understanding Inciting Speech as New Malice 理解煽动性言论是一种新的恶意

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2025-01-27 DOI: 10.1109/TCSS.2024.3504357

Vaibhav Garg;Ganning Xu;Munindar P. Singh

{"title":"Understanding Inciting Speech as New Malice","authors":"Vaibhav Garg;Ganning Xu;Munindar P. Singh","doi":"10.1109/TCSS.2024.3504357","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3504357","url":null,"abstract":"Inciting speech seeks to instill hostility or anger in readers or motivate them to take action against a target group. Whereas hate speech in social media has garnered much attention, inciting speech has not been well studied in domains such as religion. We address two aspects of religious incitement: 1) what rhetorical strategies are used in it?; and 2) do the same strategies apply across disparate social contexts and targets? We identify inciting speech against Muslims but demonstrate the generality of the construct vis à vis other targets. We adopt existing datasets of Islamophobic WhatsApp posts and hateful and offensive posts (Twitter and Gab) against other targets. Our methods include: 1) qualitative analysis revealing rhetorical strategies; and 2) an iterative process to label the data, yielding a tool to detect incitement. Incitement applies three rhetorical strategies focused, respectively, on the target group's identity, their imputed misdeeds, and an exhortation to act against them. These strategies carry distinct textual signatures. Our tool (with additional verification) reveals that inciting sentences appear in non-Islamophobic posts and in other contexts (e.g., posts against certain gender identities), indicating the generality of incitement as a concept. Incitement reflects a wide swath of malicious speech omitted from traditional analyses. Understanding and identifying incitement can facilitate online moderation and thus concomitantly reduce harm in real life.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"947-956"},"PeriodicalIF":4.5,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144179157","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 Novel Graph-Based Approach to Identify Opinion Leaders in Twitter 一种基于图表的Twitter意见领袖识别方法

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2025-01-13 DOI: 10.1109/TCSS.2024.3455415

Marco Furini;Luca Mariotti;Riccardo Martoglia;Manuela Montangero

引用次数: 0

Socially Enhanced Defense in Energy-Transportation Systems 能源运输系统的社会增强防御

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2025-01-09 DOI: 10.1109/TCSS.2024.3517140

Alexis Pengfei Zhao;Shuangqi Li;Yunqi Wang;Mohannad Alhazmi

{"title":"Socially Enhanced Defense in Energy-Transportation Systems","authors":"Alexis Pengfei Zhao;Shuangqi Li;Yunqi Wang;Mohannad Alhazmi","doi":"10.1109/TCSS.2024.3517140","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3517140","url":null,"abstract":"The ever-increasing entwinement of information and communication technology (ICT) infrastructure with the proliferation of electric vehicles (EVs) has resulted in a congruent coalescence of energy and transportation networks. However, the surfeit of data communication and processing capabilities inherent in these systems also poses a potential peril to cyber security. Hence, a bifurcated logistics operation and cyberattack defense strategy have been propounded for green integrated power-transportation networks (IPTN) with renewable penetration. This strategy leverages the potential of social participation from EVs to amplify the defense operation. The bifurcation comprises of a preclusive stage aimed at fortifying and preserving resource allocation within IPTN and a defensive stage aimed at mitigating the deleterious impacts of cyberattacks through rapid response measures. Conventional measures such as load shedding and operation adjustments are augmented by an innovative defense involvement incentive, designed to elicit additional support from EV users. A mean-risk distributionally robust optimization methodology predicated on Kullback–Leibler divergence is posited to address the limitations in data availability in simulating cyberattack consequences. Empirical investigations through case studies in an urbane IPTN are conducted to evaluate the adverse impacts of cyberattacks and examine countermeasures aimed at mitigating their effects to the greatest extent possible.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"563-572"},"PeriodicalIF":4.5,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783264","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