IEEE Transactions on Knowledge and Data Engineering最新文献

Online Dynamic Hybrid Broad Learning System for Real-Time Safety Assessment of Dynamic Systems 用于动态系统实时安全评估的在线动态混合广泛学习系统

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-11-12 DOI: 10.1109/TKDE.2024.3475028

Zeyi Liu;Xiao He

{"title":"Online Dynamic Hybrid Broad Learning System for Real-Time Safety Assessment of Dynamic Systems","authors":"Zeyi Liu;Xiao He","doi":"10.1109/TKDE.2024.3475028","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3475028","url":null,"abstract":"Real-time safety assessment of dynamic systems is of paramount importance in industrial processes since it provides continuous monitoring and evaluation to prevent potential harm to the environment and individuals. However, there are still several challenges to be resolved due to the requirements of time consumption and the non-stationary nature of real-world environments. In this paper, a novel online dynamic hybrid broad learning system, termed ODH-BLS, is proposed to more fully utilize the co-design advantages of active adaptation and passive adaptation. It makes effective use of limited annotations with the proposed sample value function. Simultaneously, anchor points can be dynamically adjusted to accommodate changes of the underlying distribution, thereby leveraging the value of unlabeled samples. An iterative update rule is also derived to ensure adaptation of the assessment model to real-time data at low computational costs. We also provide theoretical analyses to illustrate its practicality. Several experiments regarding the JiaoLong deep-sea manned submersible are carried out. The results demonstrate that the proposed ODH-BLS method achieves a performance improvement of approximately 8% over the baseline method on the benchmark dataset, showing its effectiveness in solving real-time safety assessment tasks for dynamic systems.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"36 12","pages":"8928-8938"},"PeriodicalIF":8.9,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636267","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 Derivative Topic Dissemination Model Based on Representation Learning and Topic Relevance 基于表征学习和主题相关性的衍生主题传播模型

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-10-21 DOI: 10.1109/TKDE.2024.3484496

Qian Li;Yunpeng Xiao;Xinming Zhou;Rong Wang;Sirui Duan;Xiang Yu

{"title":"A Derivative Topic Dissemination Model Based on Representation Learning and Topic Relevance","authors":"Qian Li;Yunpeng Xiao;Xinming Zhou;Rong Wang;Sirui Duan;Xiang Yu","doi":"10.1109/TKDE.2024.3484496","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3484496","url":null,"abstract":"In social networks, topics often demonstrate a “fission” trend, where new topics arise from existing ones. Effectively predicting collective behavioral patterns during the dissemination of derivative topics is crucial for public opinion management. Addressing the symbiotic, antagonistic nature of “native-derived” topics, a derivative topic propagation model based on representation learning, topic relevance is proposed herein. First, considering the transition in user interest levels, cognitive accumulation at different evolutionary stages of native-derivative topics, a user content representation method, namely DTR2vec, is introduced, based on topic-related feature associations, for learning user content features. Then, evolutionary game theory is introduced by recognizing the symbiotic, antagonistic nature of “native-derived” topics during their propagation. Moreover, implicit relationships between users are explored, user influence is quantified for learning user structural features. Finally, considering the graph convolutional network’s ability to process non-euclidean structured data, the proposed model integrates user content, structural features to predict user forwarding behavior. Experimental results indicate that the proposed model not only effectively predicts the dissemination trends of derivative topics but also more authentically reflects the association, game relationships between native, derivative topics during their dissemination.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"36 12","pages":"7468-7482"},"PeriodicalIF":8.9,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645548","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

Iterative Soft Prompt-Tuning for Unsupervised Domain Adaptation 用于无监督领域适应的迭代软提示调整

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-10-21 DOI: 10.1109/TKDE.2024.3483903

Yi Zhu;Shuqin Wang;Jipeng Qiang;Xindong Wu

{"title":"Iterative Soft Prompt-Tuning for Unsupervised Domain Adaptation","authors":"Yi Zhu;Shuqin Wang;Jipeng Qiang;Xindong Wu","doi":"10.1109/TKDE.2024.3483903","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3483903","url":null,"abstract":"Unsupervised domain adaptation aims to facilitate learning tasks in unlabeled target domain with knowledge in the related source domain, which has achieved awesome performance with the pre-trained language models (PLMs). Recently, inspired by GPT, the prompt-tuning model has been widely explored in stimulating rich knowledge in PLMs for language understanding. However, existing prompt-tuning methods still directly applied the model that was learned in the source domain into the target domain to minimize the discrepancy between different domains, e.g., the prompts or the template are trained separately to learn embeddings for transferring to the target domain, which is actually the intuition of end-to-end deep-based approach. In this paper, we propose an Iterative Soft Prompt-Tuning method (ItSPT) for better unsupervised domain adaptation. On the one hand, the prompt-tuning model learned in the source domain is converted into an iterative model to find the true label information in the target domain, the domain adaptation method is then regarded as a few-shot learning task. On the other hand, instead of hand-crafted templates, ItSPT adopts soft prompts for both considering the automatic template generation and classification performance. Experiments on both English and Chinese datasets demonstrate that our method surpasses the performance of SOTA methods.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"36 12","pages":"8580-8592"},"PeriodicalIF":8.9,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645449","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

Is Sharing Neighbor Generator in Federated Graph Learning Safe? 联盟图学习中共享邻居生成器安全吗？

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-10-18 DOI: 10.1109/TKDE.2024.3482448

Liuyi Yao;Zhen Wang;Yuexiang Xie;Yaliang Li;Weirui Kuang;Daoyuan Chen;Bolin Ding

{"title":"Is Sharing Neighbor Generator in Federated Graph Learning Safe?","authors":"Liuyi Yao;Zhen Wang;Yuexiang Xie;Yaliang Li;Weirui Kuang;Daoyuan Chen;Bolin Ding","doi":"10.1109/TKDE.2024.3482448","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3482448","url":null,"abstract":"Nowadays, as privacy concerns continue to rise, federated graph learning (FGL) which generalizes the classic federated learning to graph data has attracted increasing attention. However, while the focus has been on designing collaborative learning algorithms, the potential risks of privacy leakage through the sharing of necessary graph-related information in FGL, such as node embeddings and neighbor generators, have been largely neglected. In this paper, we verify the potential risks of privacy leakage in FGL, and provide insights about the cautions in FGL algorithm design. Specifically, we propose a novel privacy attack algorithm named Privacy Attack on federated Graph learning (PAG) towards reconstructing participants’ private node attributes and the linkage relationships. The participant performing the PAG attack is able to reconstruct the node attributes of the victim by matching the received gradients of the generator, and then train a link prediction model based on its local sub-graph to inductively infer the linkages connected to these reconstructed nodes. We theoretically and empirically demonstrate that under PAG attack, directly sharing the neighbor generators makes the FGL vulnerable to the data reconstruction attack. Furthermore, an investigation into the key factors that can hinder the success of the PAG attack provides insights into corresponding defense strategies and inspires future research into privacy-preserving FGL.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"36 12","pages":"8568-8579"},"PeriodicalIF":8.9,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645572","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

L-ASCRA: A Linearithmic Time Approximate Spectral Clustering Algorithm Using Topologically-Preserved Representatives L-ASCRA：使用拓扑保留代表的线性算术时间近似谱聚类算法

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-10-18 DOI: 10.1109/TKDE.2024.3483572

Abdul Atif Khan;Mohammad Maksood Akhter;Rashmi Maheshwari;Sraban Kumar Mohanty

{"title":"L-ASCRA: A Linearithmic Time Approximate Spectral Clustering Algorithm Using Topologically-Preserved Representatives","authors":"Abdul Atif Khan;Mohammad Maksood Akhter;Rashmi Maheshwari;Sraban Kumar Mohanty","doi":"10.1109/TKDE.2024.3483572","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3483572","url":null,"abstract":"Approximate spectral clustering (ASC) algorithms work on the representative points of the data for discovering intrinsic groups. The existing ASC methods identify fewer representatives as compared to the number of data points to reduce the cubic computational overhead of the spectral clustering technique. However, identifying such representative points without any domain knowledge to capture the shapes and topology of the clusters remains a challenge. This work proposes an ASC method that suitably computes enough well-scattered representatives to efficiently capture the topology of the data, making the ASC faster without the requirement of tuning any external parameters. The proposed ASC algorithm first applies two-level partitioning using both boundary points and centroids-based partitioning to identify quality representatives in less time. In the next step, we calculate the proximity between the neighboring representatives using \u0000<inline-formula><tex-math>$k$</tex-math></inline-formula>\u0000-rounds of minimum spanning tree (MST) by considering the distribution of edge weights in each round to find \u0000<inline-formula><tex-math>$k$</tex-math></inline-formula>\u0000. The proposed method effectively utilizes the number of representatives in a way that the overall computational time is bounded by \u0000<inline-formula><tex-math>$O(Nlg N)$</tex-math></inline-formula>\u0000. The experimental results suggest that the proposed ASC method outperforms the competing ASC methods in terms of both running time and clustering quality.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"36 12","pages":"8643-8654"},"PeriodicalIF":8.9,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645546","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

Human-AI Interaction: Human Behavior Routineness Shapes AI Performance 人与人工智能的交互：人类行为的常规性决定了人工智能的性能

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-10-14 DOI: 10.1109/TKDE.2024.3480317

Tianao Sun;Kai Zhao;Meng Chen

引用次数: 0

When Quantum Computing Meets Database: A Hybrid Sampling Framework for Approximate Query Processing 当量子计算遇上数据库：近似查询处理的混合采样框架

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-10-14 DOI: 10.1109/TKDE.2024.3480278

Sai Wu;Meng Shi;Dongxiang Zhang;Junbo Zhao;Gongsheng Yuan;Gang Chen

{"title":"When Quantum Computing Meets Database: A Hybrid Sampling Framework for Approximate Query Processing","authors":"Sai Wu;Meng Shi;Dongxiang Zhang;Junbo Zhao;Gongsheng Yuan;Gang Chen","doi":"10.1109/TKDE.2024.3480278","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3480278","url":null,"abstract":"Quantum computing represents a next-generation technology in data processing, promising to transcend the limitations of traditional computation. In this paper, we undertake an early exploration of the potential integration of quantum computing with database query optimization. We introduce a pioneering hybrid classical-quantum algorithm for sampling-based approximate query processing (AQP). The core concept of the algorithm revolves around identifying rare groups, which often follow a long-tail distribution, and applying distinct sampling methodologies to normal and rare groups. By leveraging the quantum capabilities of the diffusion gate and QRAM, the algorithm defines a novel quantum sampling approach that iteratively amplifies the signals of these infrequent groups. The algorithm operates without the need for preprocessing or prior knowledge of workloads or data. It utilizes the power of quadratic acceleration to achieve well-balanced sampling across various data categories. Experimental results demonstrate that in the context of AQP, the new sampling scheme provides higher accuracy at the same sampling cost. Additionally, the benefits of quantum computing become more pronounced as query selectivity increases.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"36 12","pages":"9532-9546"},"PeriodicalIF":8.9,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636337","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

Debiased Pairwise Learning for Implicit Collaborative Filtering 隐式协作过滤的有偏差配对学习

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-10-14 DOI: 10.1109/TKDE.2024.3479240

Bin Liu;Qin Luo;Bang Wang

引用次数: 0

Accurate and Scalable Graph Convolutional Networks for Recommendation Based on Subgraph Propagation 基于子图传播的用于推荐的精确且可扩展的图卷积网络

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-10-11 DOI: 10.1109/TKDE.2024.3467333

Xueqi Li;Guoqing Xiao;Yuedan Chen;Kenli Li;Gao Cong

{"title":"Accurate and Scalable Graph Convolutional Networks for Recommendation Based on Subgraph Propagation","authors":"Xueqi Li;Guoqing Xiao;Yuedan Chen;Kenli Li;Gao Cong","doi":"10.1109/TKDE.2024.3467333","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3467333","url":null,"abstract":"In recommendation systems, Graph Convolutional Networks (GCNs) often suffer from significant computational and memory cost when propagating features across the entire user-item graph. While various sampling strategies have been introduced to reduce the cost, the challenge of neighbor explosion persists, primarily due to the iterative nature of neighbor aggregation. This work focuses on exploring subgraph propagation for scalable recommendation by addressing two primary challenges: \u0000<italic>efficient and effective subgraph construction\u0000 and \u0000<italic>subgraph sparsity\u0000. To address these challenges, we propose a novel \u0000<underline>GCN\u0000 model for recommendation based on \u0000<underline>Sub\u0000graph propagation, called SubGCN. One key component of SubGCN is BiPPR, a technique that fuses both source- and target-based Personalized PageRank (PPR) approximations, to overcome the challenge of \u0000<italic>efficient and effective subgraph construction\u0000. Furthermore, we propose a source-target contrastive learning scheme to mitigate the impact of \u0000<italic>subgraph sparsity\u0000 for SubGCN. We conduct extensive experiments on two large and two medium-sized datasets to evaluate the scalability, efficiency, and effectiveness of SubGCN. On medium-sized datasets, compared to full-graph GCNs, SubGCN achieves competitive accuracy while using only 23.79% training time on Gowalla and 16.3% on Yelp2018. On large datasets, where full-graph GCNs ran out of the GPU memory, our proposed SubGCN outperforms widely used sampling strategies in terms of training efficiency and recommendation accuracy.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"36 12","pages":"7556-7568"},"PeriodicalIF":8.9,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645549","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 Survey on Time-Series Pre-Trained Models 时间序列预训练模型调查

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-10-07 DOI: 10.1109/TKDE.2024.3475809

Qianli Ma;Zhen Liu;Zhenjing Zheng;Ziyang Huang;Siying Zhu;Zhongzhong Yu;James T. Kwok

{"title":"A Survey on Time-Series Pre-Trained Models","authors":"Qianli Ma;Zhen Liu;Zhenjing Zheng;Ziyang Huang;Siying Zhu;Zhongzhong Yu;James T. Kwok","doi":"10.1109/TKDE.2024.3475809","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3475809","url":null,"abstract":"Time-Series Mining (TSM) is an important research area since it shows great potential in practical applications. Deep learning models that rely on massive labeled data have been utilized for TSM successfully. However, constructing a large-scale well-labeled dataset is difficult due to data annotation costs. Recently, pre-trained models have gradually attracted attention in the time series domain due to their remarkable performance in computer vision and natural language processing. In this survey, we provide a comprehensive review of Time-Series Pre-Trained Models (TS-PTMs), aiming to guide the understanding, applying, and studying TS-PTMs. Specifically, we first briefly introduce the typical deep learning models employed in TSM. Then, we give an overview of TS-PTMs according to the pre-training techniques. The main categories we explore include supervised, unsupervised, and self-supervised TS-PTMs. Further, extensive experiments involving 27 methods, 434 datasets, and 679 transfer learning scenarios are conducted to analyze the advantages and disadvantages of transfer learning strategies, Transformer-based models, and representative TS-PTMs. Finally, we point out some potential directions of TS-PTMs for future work.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"36 12","pages":"7536-7555"},"PeriodicalIF":8.9,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142645397","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