IEEE Transactions on Computational Social Systems最新文献_第3页

Unraveling the Deception of Web3 Phishing Scams: Dynamic Multiperspective Cascade Graph Approach for Ethereum Phishing Detection 揭露Web3网络钓鱼诈骗的欺骗：动态多角度级联图方法用于以太坊网络钓鱼检测

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-12-31 DOI: 10.1109/TCSS.2024.3516144

Lejun Zhang;Xucan Zhang;Siyi Xiao;Zexin Li;Shen Su;Jing Qiu;Zhihong Tian

{"title":"Unraveling the Deception of Web3 Phishing Scams: Dynamic Multiperspective Cascade Graph Approach for Ethereum Phishing Detection","authors":"Lejun Zhang;Xucan Zhang;Siyi Xiao;Zexin Li;Shen Su;Jing Qiu;Zhihong Tian","doi":"10.1109/TCSS.2024.3516144","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3516144","url":null,"abstract":"Ethereum, as one of the most active cryptocurrency trading platforms, has garnered significant academic interest due to its transparent and accessible transaction data. In recent years, phishing scams have emerged as a serious criminal activity on Ethereum. Although most studies model Ethereum account transactions as networks and analyze them using traditional machine learning or network representation learning techniques, these approaches often rely solely on the latest static transaction records or use manually designed features while neglecting transaction histories, thus failing to fully capture the dynamic interactions and potential trading patterns between accounts. This article introduces an innovative multiperspective cascaded dynamic graph neural network model named DMPCG, which extracts phishing transaction data from authoritative databases like blockchain explorers to construct transaction network graphs. The model elevates the analysis from the microscopic features of nodes to the macroscopic dynamics of the entire network, integrating the attributes of static snapshot graphs with the evolution of dynamic trading networks, significantly enhancing the accuracy of phishing detection. Experimental results demonstrate that the DMPCG method achieves an impressive precision of 92.6% and an F1-score of 90.9%, outperforming existing baseline models and traditional subgraph sampling techniques.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"498-510"},"PeriodicalIF":4.5,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783261","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

Portfolio Selection by Maximizing Various Risk-Adjusted Return Ratios via Convex Reformulations 通过凸重构最大化各种风险调整回报率的投资组合选择

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-12-27 DOI: 10.1109/TCSS.2024.3507927

Jun Wang;Fangyu Zhang;Wei Zhang

引用次数: 0

CMAAN: Cross-Modal Aggregation Attention Network for Next POI Recommendation CMAAN：下一个POI推荐的跨模态聚合关注网络

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-12-27 DOI: 10.1109/TCSS.2024.3513947

Zhuang Zhuang;Lingbo Liu;Heng Qi;Yanming Shen;Baocai Yin

{"title":"CMAAN: Cross-Modal Aggregation Attention Network for Next POI Recommendation","authors":"Zhuang Zhuang;Lingbo Liu;Heng Qi;Yanming Shen;Baocai Yin","doi":"10.1109/TCSS.2024.3513947","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3513947","url":null,"abstract":"Next point-of-interest (POI) recommendation is to explore the historical check-in sequence information in location-based social networks (LBSNs) to recommend the next location that he/she might be interested in. However, most previous methods used only limited information of unimodal data (i.e., check-in sequences), while some recent methods have attempted to explore multimodal data (e.g., textual content) but lacked sufficient interactions between geographic behavior patterns and content behavior patterns. In this work, we argue that users usually consider geographical trajectories and textual content interdependently to determine the next location to visit. To this end, we propose a novel cross-modal aggregation attention network (CMAAN), which interactively learns multiview representations from POI sequence and content sequence for predicting the next POI. Our approach models inter-modal interaction correlations, intra-modal sequence correlations, and intra-modal semantic correlations simultaneously to fully discover contextual potential relations along the trajectories. Specifically, the intra-modal semantic correlations are able to capture the variable location functionalities under different contextual relationships of cross-modal interaction information. Moreover, we apply the aggregation attention to adaptively aggregate multiview representations which represent the comprehensive hidden state of the next POI. Extensive experiments on two large-scale datasets clearly demonstrate that our CMAAN achieves state-of-the-art performance.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"1025-1037"},"PeriodicalIF":4.5,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178873","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

Interpretable Multimodal Tucker Fusion Model With Information Filtering for Multimodal Sentiment Analysis 基于信息过滤的可解释多模态Tucker融合模型用于多模态情感分析

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-12-24 DOI: 10.1109/TCSS.2024.3459929

Xin Nie;Laurence T. Yang;Zhe Li;Xianjun Deng;Fulan Fan;Zecan Yang

{"title":"Interpretable Multimodal Tucker Fusion Model With Information Filtering for Multimodal Sentiment Analysis","authors":"Xin Nie;Laurence T. Yang;Zhe Li;Xianjun Deng;Fulan Fan;Zecan Yang","doi":"10.1109/TCSS.2024.3459929","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3459929","url":null,"abstract":"Multimodal sentiment analysis (MSA) integrates multiple sources of sentiment information for processing and has demonstrated superior performance compared to single-modal sentiment analysis, making it widely applicable in domains such as human–computer interaction and public opinion supervision. However, current MSA models heavily rely on black-box deep learning (DL) methods, which lack interpretability. Additionally, effectively integrating multimodal data, reducing noise and redundancy, as well as bridging the semantic gap between heterogeneous data remain challenging issues in multimodal DL. To address these challenges, we propose an interpretable multimodal Tucker fusion model with information filtering (IMTFMIF). We are the first to utilize the multimodal Tucker fusion model for MSA tasks. This approach maps multimodal data into a unified tensor space for fusion, effectively reducing modal heterogeneity and eliminating redundant information while maintaining interpretability. Furthermore, mutual information is employed to filter out task-irrelevant information and explain the association between input and output from an information flow perspective. We propose a novel approach to enhance the comprehension of multimodal data and optimize model performance in MSA tasks. Finally, extensive experiments conducted on three public multimodal datasets demonstrate that our proposed IMTFMIF achieves competitive performance compared to state-of-the-art methods.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"1351-1364"},"PeriodicalIF":4.5,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144184369","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

Exploring Risk Sharing in Stochastic Exchange Networks 随机交换网络风险分担研究

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-12-24 DOI: 10.1109/TCSS.2024.3508803

Arnaud Z. Dragicevic

引用次数: 0

Multibranch Attentive Transformer With Joint Temporal and Social Correlations for Traffic Agents Trajectory Prediction 具有联合时间和社会关联的多支路关注变压器交通agent轨迹预测

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-12-24 DOI: 10.1109/TCSS.2024.3517656

Xiaobo Chen;Yuwen Liang;Junyu Wang;Qiaolin Ye;Yingfeng Cai

{"title":"Multibranch Attentive Transformer With Joint Temporal and Social Correlations for Traffic Agents Trajectory Prediction","authors":"Xiaobo Chen;Yuwen Liang;Junyu Wang;Qiaolin Ye;Yingfeng Cai","doi":"10.1109/TCSS.2024.3517656","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3517656","url":null,"abstract":"Accurately predicting the future trajectories of traffic agents is paramount for autonomous unmanned systems, such as self-driving cars and mobile robotics. Extracting abundant temporal and social features from trajectory data and integrating the resulting features effectively pose great challenges for predictive models. To address these issues, this article proposes a novel multibranch attentive transformer (MBAT) trajectory prediction network for traffic agents. Specifically, to explore and reveal diverse correlations of agents, we propose a decoupled temporal and spatial feature learning module with multibranch to extract temporal, spatial, as well as spatiotemporal features. Such design ensures each branch can be specifically tailored for different types of correlations, thus enhancing the flexibility and representation ability of features. Besides, we put forward an attentive transformer architecture that simultaneously models the complex correlations possibly occurring in historical and future timesteps. Moreover, the temporal, spatial, and spatiotemporal features can be effectively integrated based on different types of attention mechanisms. Empirical results demonstrate that our model achieves outstanding performance on public ETH, UCY, SDD, and INTERACTION datasets. Detailed ablation studies are conducted to verify the effectiveness of the model components.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"525-538"},"PeriodicalIF":4.5,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783268","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

Anomaly Detection on Attributed Networks via Multiview and Multiscale Contrastive Learning 基于多视角和多尺度对比学习的属性网络异常检测

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-12-17 DOI: 10.1109/TCSS.2024.3514148

Shuxin Qin;Yongcan Luo;Jing Zhu;Gaofeng Tao;Jingya Zheng;Zhongjun Ma

{"title":"Anomaly Detection on Attributed Networks via Multiview and Multiscale Contrastive Learning","authors":"Shuxin Qin;Yongcan Luo;Jing Zhu;Gaofeng Tao;Jingya Zheng;Zhongjun Ma","doi":"10.1109/TCSS.2024.3514148","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3514148","url":null,"abstract":"Detecting abnormal nodes from attributed networks plays an important role in various applications, including cybersecurity, finance, and social networks. Most existing methods focus on learning different scales of graphs or using augmented data to improve the quality of feature representation. However, the performance is limited due to two critical problems. First, the high sensitivity of attributed networks makes it uncontrollable and uncertain to use conventional methods for data augmentation, leading to limited improvement in representation and generalization capabilities. Second, under the unsupervised paradigm, anomalous nodes mixed in the training data may interfere with the learning of normal patterns and weaken the discrimination ability. In this work, we propose a novel multiview and multiscale contrastive learning framework to address these two issues. Specifically, a network augmentation method based on parameter perturbation is introduced to generate augmented views for both node–node and node–subgraph level contrast branches. Then, cross-view graph contrastive learning is employed to improve the representation without the need for augmented data. We also provide a cycle training strategy where normal samples detected in the former step are collected for an additional training step. In this way, the ability to learn normal patterns is enhanced. Extensive experiments on six benchmark datasets demonstrate that our method outperforms the existing state-of-the-art baselines.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"1038-1051"},"PeriodicalIF":4.5,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178886","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

Optimized Consensus Group Selection Focused on Node Transmission Delay in Sharding Blockchains 基于节点传输延迟的分片区块链共识组优化选择

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-12-17 DOI: 10.1109/TCSS.2024.3514186

Liping Tao;Yang Lu;Yuqi Fan;Chee Wei Tan;Zhen Wei

{"title":"Optimized Consensus Group Selection Focused on Node Transmission Delay in Sharding Blockchains","authors":"Liping Tao;Yang Lu;Yuqi Fan;Chee Wei Tan;Zhen Wei","doi":"10.1109/TCSS.2024.3514186","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3514186","url":null,"abstract":"Sharding presents an enticing path toward improving blockchain scalability. However, the consensus mechanism within individual shards faces mounting security challenges due to the restricted number of consensus nodes and the reliance on conventional, unchanging nodes for consensus. Common strategies to enhance shard consensus security often involve increasing the number of consensus nodes per shard. While effective in bolstering security, this approach also leads to a notable rise in consensus delay within each shard, potentially offsetting the scalability advantages of sharding. Hence, it becomes imperative to strategically select nodes to form dedicated consensus groups for each shard. These groups should not only enhance shard consensus security but also do so without exacerbating consensus delay. In this article, we propose a novel consensus group selection based on transmission delay between nodes (CGSTD) to address this challenge, with the goal of minimizing the overall consensus delay across the system. CGSTD intelligently selects nodes from various shards to form distinct consensus groups for each shard, thereby enhancing shard security while maintaining optimal system-wide consensus efficiency. We conduct a rigorous theoretical analysis to evaluate the security properties of CGSTD and derive approximation ratios under various operational scenarios. Simulation results validate the superior performance of CGSTD compared to baseline algorithms, showcasing reductions in total consensus delay, mitigated increases in shard-specific delay, optimized block storage utilization per node, and streamlined participation of nodes in consensus groups.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"1052-1067"},"PeriodicalIF":4.5,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178936","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 Exploring Fairness in Visual Transformer Based Natural and GAN Image Detection Systems 探索基于视觉变压器的自然和GAN图像检测系统的公平性

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-12-16 DOI: 10.1109/TCSS.2024.3509340

Manjary P. Gangan;Anoop Kadan;Lajish V. L.

{"title":"Toward Exploring Fairness in Visual Transformer Based Natural and GAN Image Detection Systems","authors":"Manjary P. Gangan;Anoop Kadan;Lajish V. L.","doi":"10.1109/TCSS.2024.3509340","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3509340","url":null,"abstract":"Image forensics research has recently witnessed a lot of advancements toward developing computational models capable of accurately detecting natural images captured by cameras and generative adversarial network (GAN) generated images. However, it is also important to ensure whether these computational models are fair enough and do not produce biased outcomes that could eventually harm certain societal groups or cause serious security threats. Exploring fairness in image forensic algorithms is an initial step toward mitigating these biases. This study explores bias in visual transformer based image forensic algorithms that classify natural and GAN images, since visual transformers are recently being widely used in image classification based tasks, including in the area of image forensics. The proposed study procures bias evaluation corpora to analyze bias in gender, racial, affective, and intersectional domains using a wide set of individual and pairwise bias evaluation measures. Since the robustness of the algorithms against image compression is an important factor to be considered in forensic tasks, this study also analyzes the impact of image compression on model bias. Hence, to study the impact of image compression on model bias, a two-phase evaluation setting is followed, where the experiments are carried out in uncompressed and compressed evaluation settings. The study could identify bias existences in the visual transformer based models distinguishing natural and GAN images, and also observes that image compression impacts model biases, predominantly amplifying the presence of biases in class GAN predictions.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"1068-1079"},"PeriodicalIF":4.5,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178881","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

Designing Energy-Aware Scheduling and Task Allocation Algorithms for Online Reinforcement Learning Applications in Cloud Environments 云环境下在线强化学习应用的能量感知调度和任务分配算法设计

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-12-16 DOI: 10.1109/TCSS.2024.3508089

Harshal Janjani;Tanmay Agarwal;M. P. Gopinath;Vimoh Sharma;S. P. Raja

{"title":"Designing Energy-Aware Scheduling and Task Allocation Algorithms for Online Reinforcement Learning Applications in Cloud Environments","authors":"Harshal Janjani;Tanmay Agarwal;M. P. Gopinath;Vimoh Sharma;S. P. Raja","doi":"10.1109/TCSS.2024.3508089","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3508089","url":null,"abstract":"With the rapid proliferation of machine learning applications in cloud computing environments, addressing crucial challenges concerning energy efficiency becomes pressing, including addressing the high power consumption of such workloads. In this regard, this work focuses much on the development of an energy-aware scheduling and task assignment algorithm that, while optimizing energy consumption, maintains required performance standards in deploying machine-learning applications in cloud environments. It therefore, pivots on leveraging online reinforcement learning to deduce an optimal planning and allocation strategy. This proposed algorithm leverages the capability of RL in making sequential decisions with the aim of achieving maximum cumulative rewards. The algorithm design and its implementation are examined in detail, considering the nature of workloads and how the computational resources are utilized. The algorithm’s performance is analyzed by looking into different performance metrics that assess the success of the model. All the results indicate that energy-aware scheduling combined with task assignment algorithms are bound to reduce energy consumption by a great margin while meeting the required performance for large-scale workloads. These results hold much promise for the improvement of sustainable cloud computing infrastructures and consequently, to energy-efficient machine learning. The future research directions involve enhancing the proposed algorithm’s generalization capabilities and addressing challenges related to scalability and convergence.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"1218-1232"},"PeriodicalIF":4.5,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178926","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