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

A Dual Neural Network for Defect Detection With Highly Imbalanced Data in 3-D Printing 三维打印中高度不平衡数据缺陷检测的双神经网络

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-10 DOI: 10.1109/TCSS.2024.3441524

Fang Wang;Gang Xiong;Qihang Fang;Zhen Shen;Di Wang;Xisong Dong;Fei-Yue Wang

{"title":"A Dual Neural Network for Defect Detection With Highly Imbalanced Data in 3-D Printing","authors":"Fang Wang;Gang Xiong;Qihang Fang;Zhen Shen;Di Wang;Xisong Dong;Fei-Yue Wang","doi":"10.1109/TCSS.2024.3441524","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3441524","url":null,"abstract":"Digital light processing (DLP) is a popular additive manufacturing technology that uses light irradiation to fabricate 3-D devices via a projector to achieve laser-sensitive resin curing. However, the performance and reliability of DLP can be affected by internal defects such as printing errors and the accumulation of residual stress. Existing defect detection methods rely on monitoring the printed parts, which leads to resource wastage and struggles to effectively handle imbalanced defect data. In this article, we propose a defect detection method called dual neural network, which involves detecting defects in materials before the printing process to prevent resource wastage and serious consequences. Specifically, to handle the highly imbalanced class distribution problem in online DLP defect detection, dual neural network utilizes a domain learner and balance learner to effectively balance the information of the minority class and learn the generalization knowledge from the imbalanced defect dataset. Experimental results demonstrate the effectiveness of our proposed method, which has also been applied to real-world production equipment successfully.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"8078-8088"},"PeriodicalIF":4.5,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789020","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

Large-Scale Traffic Prediction With Hierarchical Hypergraph Message Passing Networks 基于分层超图消息传递网络的大规模流量预测

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-10 DOI: 10.1109/TCSS.2024.3419008

Jingcheng Wang;Yong Zhang;Yongli Hu;Baocai Yin

{"title":"Large-Scale Traffic Prediction With Hierarchical Hypergraph Message Passing Networks","authors":"Jingcheng Wang;Yong Zhang;Yongli Hu;Baocai Yin","doi":"10.1109/TCSS.2024.3419008","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3419008","url":null,"abstract":"Graph convolutional networks (GCNs) are widely used in social computation such as urban traffic prediction. However, when faced with city-level forecasting challenges, the graph-based deep learning methods struggle to process large-scale multivariate data effectively. To address the challenges of limited scalability, a traffic prediction framework based on a hypergraph message passing network (HMSG) is proposed in this article. The model represents the urban transportation network with hypergraph, where nodes denote transportation hubs and hyperedges represent their relationship at geographical and feature level. Compared with pairwise edges, hyperedges are more scalable and flexible, providing a more descriptive representation of traffic information. The HMSG algorithm updates node and hyperedge features in two steps, facilitating effective and efficient integration of hidden spatial features across layers. The proposed framework is evaluated on large-scale historical datasets and demonstrates its completion of city-scale traffic prediction tasks. The results also show that it matches the accuracy of existing traffic prediction methods on small-scale datasets. This validates the potential of the traffic prediction model based on the HMSG algorithm for intelligent transportation applications.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"7103-7113"},"PeriodicalIF":4.5,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789045","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 Hierarchical Information Compression Approach for Knowledge Discovery From Social Multimedia 面向社会化多媒体知识发现的分层信息压缩方法

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-10 DOI: 10.1109/TCSS.2024.3440997

Zheng Liu;Yu Weng;Ruiyang Xu;Chaomurilige;Honghao Gao

{"title":"A Hierarchical Information Compression Approach for Knowledge Discovery From Social Multimedia","authors":"Zheng Liu;Yu Weng;Ruiyang Xu;Chaomurilige;Honghao Gao","doi":"10.1109/TCSS.2024.3440997","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3440997","url":null,"abstract":"Knowledge discovery is an ongoing research endeavor aimed at uncovering valuable insights and patterns from large volumes of data in massive social systems (MSSs). Although recent advances in deep learning have made significant progress in knowledge discovery, the “data dimensionality reduction” problem still poses practical challenges. To address this, we have introduced a hierarchical information compression (IC) approach, which emphasizes the elimination of redundant and irrelevant features and the generation of high-quality knowledge representation, aiming to enhance the information density of the knowledge discovery process. Our approach consists of coarse-grained and fine-grained stages for data compression. In the coarse-grained stage, our method employs the key feature distiller based on the Siamese network to effectively identify a substantial number of irrelevant features and latent redundancies within coarse-grained data blocks. Moving on to the fine-grained stage, our model further compresses the internal features of the data, extracting the most crucial knowledge and facilitating data compression by cross-block learning. By implementing these two stages, the approach achieves both inter and innerblock IC while preserving essential knowledge. To validate the performance of our proposed model, we conducted several experiments using WikiSum, a large knowledge corpus based on English Wikipedia in MSSs. The experimental results demonstrate that our model achieved a 2.38% increase on recall-oriented understudy for gisting evaluation (ROUGE)-2 and an improvement of over 7% on the informativeness and conciseness metrics, as evidenced by the improved scores obtained from both automatic and human evaluations. The experimental results prove that our model can effectively select the most pertinent and meaningful content and reduce the redundancy to generate better knowledge representation.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"7754-7765"},"PeriodicalIF":4.5,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777813","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

Knowledge Graph-Based Personalized Multitask Enhanced Recommendation 基于知识图的个性化多任务增强推荐

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-09 DOI: 10.1109/TCSS.2024.3446289

Liangmin Guo;Tingting Liu;Shiming Zhou;Haiyue Tang;Xiaoyao Zheng;Yonglong Luo

{"title":"Knowledge Graph-Based Personalized Multitask Enhanced Recommendation","authors":"Liangmin Guo;Tingting Liu;Shiming Zhou;Haiyue Tang;Xiaoyao Zheng;Yonglong Luo","doi":"10.1109/TCSS.2024.3446289","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3446289","url":null,"abstract":"To address the problem of data sparsity in recommendation systems, various studies have used knowledge graphs as auxiliary information. These studies have employed multitask learning (MTL) to enhance recommendation performance. However, the shared information between tasks is not fully explored when using an MTL strategy for training both recommendation and knowledge graph-related tasks. Moreover, most studies cannot effectively model the knowledge sharing, consequently affecting recommendation performance. In response to these problems, we proposed a novel knowledge graph-based personalized multitask enhanced recommendation model. To explore the shared information between tasks, a relation attention mechanism was proposed to distinguish the relative importance of neighborhood information to the central entity. Additionally, we utilized a lightweight graph convolutional network to more effectively aggregate high-order neighborhood information from the knowledge graph. This approach improves the accuracy of neighborhood feature and ensures that more suitable shared information is obtained. Furthermore, we developed a linear interaction component to model knowledge sharing between recommendation and knowledge graph embedding tasks. This component allows for detailed feature interaction learning between items and entities, enhancing the shared feature representation, generalization capabilities, and overall performance of the recommendation system. The experimental results on three public datasets indicate that our model outperforms other benchmark models in CTR prediction and top-\u0000<inline-formula><tex-math>$boldsymbol{K}$</tex-math></inline-formula>\u0000 recommendation.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"7685-7697"},"PeriodicalIF":4.5,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142761555","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

Fairness-Aware Maximal Cliques Identification in Attributed Social Networks With Concept-Cognitive Learning 基于概念认知学习的归因社会网络公平感最大小集团识别

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-09 DOI: 10.1109/TCSS.2024.3445721

Min Tao;Fei Hao;Ling Wei;Huilai Zhi;Sergei O. Kuznetsov;Geyong Min

{"title":"Fairness-Aware Maximal Cliques Identification in Attributed Social Networks With Concept-Cognitive Learning","authors":"Min Tao;Fei Hao;Ling Wei;Huilai Zhi;Sergei O. Kuznetsov;Geyong Min","doi":"10.1109/TCSS.2024.3445721","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3445721","url":null,"abstract":"Attributed social networks are pervasive in real life and play a crucial role in shaping various aspects of society. These networks not only capture the connections between individuals but also encompass the associated attributes and characteristics. Analyzing and understanding these attributes provide insights into social behaviors, information diffusion patterns, and the formation of influential communities. Consequently, we propose a novel algorithm for detecting fairness-aware maximal cliques in the attributed social networks. We extract the concept lattice of attributed social networks and quantify these concepts using the concept stability and fairness measures defined in this article. By utilizing the proposed fairness-aware distance, we identify fairness-aware maximal cliques within attributed social networks. The effectiveness of the algorithm is then validated using five real-world network datasets. Experimental results fully demonstrate the effectiveness and scalability of our approach in identifying key structures, analyzing attribute networks, and promoting the development of responsible computational systems.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"7373-7385"},"PeriodicalIF":4.5,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789139","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

Semantic Communication for VR Music Live Streaming With Rate Splitting 具有速率分裂的VR音乐直播的语义通信

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-05 DOI: 10.1109/TCSS.2024.3443176

Jiaqi Zou;Lvxin Xu;Songlin Sun

引用次数: 0

DGCN-TES: Dynamic GCN-Based Multitask Model With Temporal Event Sharing for Rumor Detection 基于时间事件共享的动态gcn多任务模型谣言检测

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-05 DOI: 10.1109/TCSS.2024.3443275

Shuzhen Wan;Guanghao Yang;Fangmin Dong;Mengyuan Wang

{"title":"DGCN-TES: Dynamic GCN-Based Multitask Model With Temporal Event Sharing for Rumor Detection","authors":"Shuzhen Wan;Guanghao Yang;Fangmin Dong;Mengyuan Wang","doi":"10.1109/TCSS.2024.3443275","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3443275","url":null,"abstract":"The rumor detection task aims to identify unofficial and unconfirmed information that is spreading on social media. At any given moment, different users express their opinions, focusing on some propagation events, and the posts they make gradually form a social network that expands as it grows. Over time, nodes and edges form a dynamic graph that presents different states at different moments. However, most existing research focuses more on the text content, social context, propagation mode, etc., and they ignore the factors from many aspects and do not consider the dynamic relationships implied in the propagation development of social media. To analyze these dynamic properties, this article proposes a dynamic network-based multitask rumor detection method called dynamic GCN-based multitask model with temporal event sharing for rumor detection (DGCN-TES). This method can effectively capture the dynamic patterns of relationships in propagation events and change them over time to detect rumors. It is mainly divided into three modules: 1) dynamic-graph convolutional network (GCN) module, which uses dynamic graph neural network to construct the propagation graph of rumor events at different times, which can better capture the dynamic spatial features that change over time; 2) content-long short-term memory (LSTM), which uses the LSTM network as a benchmark model and has been improved to better capture time-series text features over time and for multitask shared interactions; and 3) temporal event sharing layer is the sharing layer, which uses time step as the basic unit of sharing, and realizes the sharing interaction between dynamic structural features and temporal text features between the first two modules. We tested the method on two real-world rumor detection datasets PHEME and WEIBO, and the final results show that the method improved F1-score by more than 2.63% and 3.91% compared to the other best baselines baseline.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"7658-7670"},"PeriodicalIF":4.5,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142761453","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

Sustainable COVID-19 Policy Responses With Urban Mobility Network Epidemic Models 基于城市交通网络流行病模型的可持续COVID-19政策应对

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-05 DOI: 10.1109/TCSS.2024.3418622

Yanggang Cheng;Shibo He;Cunqi Shao;Chao Li;Jiming Chen

{"title":"Sustainable COVID-19 Policy Responses With Urban Mobility Network Epidemic Models","authors":"Yanggang Cheng;Shibo He;Cunqi Shao;Chao Li;Jiming Chen","doi":"10.1109/TCSS.2024.3418622","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3418622","url":null,"abstract":"The COVID-19 pandemic has challenged countries worldwide to strike a balance between implementing epidemic control measures and maintaining economic activity. In response, many countries have adopted sustainable, precise, region-specific, and multilevel prevention and control measures. To apply these measures more effectively and purposefully, it is imperative to quantify their impact on the transmission of COVID-19 within urban areas. Here, we propose a dynamic metapopulation susceptible-exposed-infectious-removed (SEIR) model that incorporates the urban mobility network to simulate the spread of COVID-19 in Beijing and investigate the effects of precise intervention measures. Our proposed model accurately fits the real epidemic trajectory, even with the significant changes in human mobility patterns before and after the epidemic. Additionally, it can also serve as a useful policy evaluation tool by simulating the impact of perturbations in mobility networks on epidemic transmission dynamics. Based on this tool, our results demonstrate that point-of-interest capacity limitation measures can significantly reduce the number of infections with only a minor loss of urban mobility. Furthermore, we show that community dynamic management measures can effectively control and mitigate COVID-19 spread while enabling the normal operation of most economic and social activities. By quantifying the impact of precise intervention measures on new infections and mobility losses, our model enables a cost-benefit analysis of these measures, thus informing targeted and sustainable policy responses to COVID-19.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"7086-7102"},"PeriodicalIF":4.5,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789150","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

One Person One Vote: Achieving Temporal Dynamic and Byzantine-Resilient Digital Community 一人一票：实现时间动态和拜占庭弹性数字社区

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-05 DOI: 10.1109/TCSS.2024.3440990

Ping Zhao;Yaqiong Mu;Guanglin Zhang

{"title":"One Person One Vote: Achieving Temporal Dynamic and Byzantine-Resilient Digital Community","authors":"Ping Zhao;Yaqiong Mu;Guanglin Zhang","doi":"10.1109/TCSS.2024.3440990","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3440990","url":null,"abstract":"Digital communities are dynamically developed with users admitted in as digital identities, and process their affairs via egalitarian decision processes, namely one person one vote. However, the digital democracy in these digital communities is threatened by Byzantines therein. Most existing works focused on Byzantine detection, but we are interested in growing Byzantine-resilient community rather than whitelisting. Several works concerning developing a Byzantine-resilient digital community are vulnerable to the collapse of these selected digital identities or impractical binarized trust relations among digital identities. To this end, we propose two practical schemes based on edge links and attributes that can achieve temporal dynamic and Byzantine-resilient digital communities, providing digital democracy. Specifically, we first propose the mixed sampling of links and attributes in digital community to output node-edge sequences. Then, we further design the skip gram-based quantification of trust relationships using the node-edge sequences. Thereafter, based on the quantified trust relationships, we propose vertex-based and edge-based strategies that prove the constraints when dynamically developing a Byzantine-resilient digital community. The key advantage is that our work can be applied to any graph containing both digital identity nodes and attribute nodes, rather than the graphs with one kind node and the fully connected graphs. Last, we conduct experiments on four real-world datasets, and the extensive results indicate the superior performance of our work, compared to four existing works. This work can be applied to social networks, online shopping platforms, etc., and keep digital democracy therein.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"7742-7753"},"PeriodicalIF":4.5,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777668","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

Region-Farm Crop Planning Through Double Deep Q-Learning Toward Sustainable Agriculture 通过双深度q学习实现可持续农业的区域作物规划

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-04 DOI: 10.1109/TCSS.2024.3441543

Xiujuan Wang;Yulin Xu;Haoyu Wang;Mengzhen Kang;Jing Hua;Fei-Yue Wang

{"title":"Region-Farm Crop Planning Through Double Deep Q-Learning Toward Sustainable Agriculture","authors":"Xiujuan Wang;Yulin Xu;Haoyu Wang;Mengzhen Kang;Jing Hua;Fei-Yue Wang","doi":"10.1109/TCSS.2024.3441543","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3441543","url":null,"abstract":"Global food market faces escalating risks and uncertainties, bringing great challenges in balancing a country's food supply and demand. Therefore, it is of great significance to carry out crop planning and reasonably divide the planting area of each crop to ensure the national food security. However, the existing planting planning methods have the problems of inaccurate crop price prediction and poor flexibility, and challenges remain on how to motivate farmers. With the rapid development of science and technology, agricultural crop planning techniques have made great progress. This study focuses on agricultural planting planning, exploring both planting area planning based on predicted crop prices and a crop allocation model within a multifarmer context. The regional planting goals are decomposed into specific allocations for individual farmers and plots, addressing objectives including maximizing farmer profits and expanding soybean cultivation for national self-sufficiency. The work employs the long short-term memory (LSTM) model to predict the prices of soybean, wheat, and maize. First, linear programming model is applied to plan planting areas of crops, incorporating constraints to encourage sustainable agricultural practices. Second, a multifarmer crop allocation model, utilizing the double deep Q network (DDQN) algorithm, is developed to enhance the fairness among farmers and assure rotational benefits. Experimental validation confirms the effectiveness of the proposed algorithms, providing valuable decision support for agricultural planning with economic and ecological sustainability.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 6","pages":"7608-7617"},"PeriodicalIF":4.5,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142761452","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