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RE-SEGNN:recurrent semantic evidence-aware graph neural network for temporal knowledge graph forecasting

science China(Information sciences) 2025年第2期68卷 103-119页

作者： Wenyu CAI Mengfan LI Xuanhua SHI Yuanxin FAN Quntao ZHU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and Technology

Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.

关键词： knowledge graph reasoning temporal knowledge graph Hawkes process semantic evidence

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When Software Security Meets Large Language Models:A Survey

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 317-334页

作者： Xiaogang Zhu Wei Zhou Qing-Long Han Wanlun Ma Sheng Wen Yang Xiang IEEE the School of Computer and Mathematical Sciences the University of Adelaide the School of Science Computing and Engineering Technologies Swinburne University of Technology

Software security poses substantial risks to our society because software has become part of our life. Numerous techniques have been proposed to resolve or mitigate the impact of software security issues. Among them, software testing and analysis are two of the critical methods, which significantly benefit from the advancements in deep learning technologies. Due to the successful use of deep learning in software security, recently,researchers have explored the potential of using large language models(LLMs) in this area. In this paper, we systematically review the results focusing on LLMs in software security. We analyze the topics of fuzzing, unit test, program repair, bug reproduction, data-driven bug detection, and bug triage. We deconstruct these techniques into several stages and analyze how LLMs can be used in the stages. We also discuss the future directions of using LLMs in software security, including the future directions for the existing use of LLMs and extensions from conventional deep learning research.

关键词： Large language models (LLMs) software analysis software security software testing

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Robust federated contrastive recommender system against targeted model poisoning attack

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science China(Information sciences) 2025年第4期68卷 50-65页

作者： Wei YUAN Chaoqun YANG Liang QU Guanhua YE Quoc Viet Hung NGUYEN Hongzhi YIN School of Electrical Engineering and Computer Science The University of Queensland School of Information and Communication Technology Griffith University Deep Neural Computing Company Limited

Federated recommender systems(FedRecs) have garnered increasing attention recently, thanks to their privacypreserving benefits. However, the decentralized and open characteristics of current FedRecs present at least two ***, the performance of FedRecs is compromised due to highly sparse on-device data for each client. Second, the system's robustness is undermined by the vulnerability to model poisoning attacks launched by malicious users. In this paper, we introduce a novel contrastive learning framework designed to fully leverage the client's sparse data through embedding augmentation, referred to as CL4FedRec. Unlike previous contrastive learning approaches in FedRecs that necessitate clients to share their private parameters, our CL4FedRec aligns with the basic FedRec learning protocol, ensuring compatibility with most existing FedRec implementations. We then evaluate the robustness of FedRecs equipped with CL4FedRec by subjecting it to several state-of-the-art model poisoning attacks. Surprisingly, our observations reveal that contrastive learning tends to exacerbate the vulnerability of FedRecs to these attacks. This is attributed to the enhanced embedding uniformity, making the polluted target item embedding easily proximate to popular items. Based on this insight, we propose an enhanced and robust version of CL4FedRec(rCL4FedRec) by introducing a regularizer to maintain the distance among item embeddings with different popularity levels. Extensive experiments conducted on four commonly used recommendation datasets demonstrate that rCL4FedRec significantly enhances both the model's performance and the robustness of FedRecs.

关键词： federated recommender system contrastive learning model poisoning attack and defense

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Research Progress in Solar Flare Prediction Methods

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Research in Astronomy and Astrophysics 2025年第3期25卷 280-309页

作者： Ke Han Zhen Liu Xian-Yi Zhao Yi-Fei Li De-Quan Zheng Jie Wan School of Computer and Information Engineering Harbin University of Commerce Faculty of Computing Harbin Institute of Technology School of Energy Science and Engineering Harbin Institute of Technology

Solar flares are one of the strongest outbursts of solar activity,posing a serious threat to Earth’s critical infrastructure,such as communications,navigation,power,and ***,it is essential to accurately predict solar flares in order to ensure the safety of human ***,the research focuses on two directions:first,identifying predictors with more physical information and higher prediction accuracy,and second,building flare prediction models that can effectively handle complex observational *** terms of flare observability and predictability,this paper analyses multiple dimensions of solar flare observability and evaluates the potential of observational parameters in *** flare prediction models,the paper focuses on data-driven models and physical models,with an emphasis on the advantages of deep learning techniques in dealing with complex and high-dimensional *** reviewing existing traditional machine learning,deep learning,and fusion methods,the key roles of these techniques in improving prediction accuracy and efficiency are *** prevailing challenges,this study discusses the main challenges currently faced in solar flare prediction,such as the complexity of flare samples,the multimodality of observational data,and the interpretability of *** conclusion summarizes these findings and proposes future research directions and potential technology advancement.

关键词： (Sun:) sunspots magnetohydrodynamics (MHD) Sun: activity Sun: flares Sun: magnetic fields

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PerFedKG: two-stage information-loop federated knowledge graph for personalized privacy-preserving recommendation systems

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science China(Information sciences) 2025年第4期68卷 98-99页

作者： Fan WANG Xuyun ZHANG Weiming LIU Li LI Yuwen LIU Zhongyuan ZHANG Guanfeng LIU Shengye PANG Xiaolong XU Lianyong QI College of Computer Science and Technology Zhejiang University Department of Computing Macquarie University College of Computer Science and Technology China University of Petroleum (East China) School of Software Nanjing University of Information Science and Technology

Knowledge graphs(KGs) effectively mitigate data sparsity in recommendation systems(RSs) by providing valuable auxiliary information [1]. However, traditional centralized KG-based RSs increase the risk of user privacy *** learning(FL) enhances RS's privacy by enabling model training on decentralized data [2]. Although integrating KG and FL can address both data sparsity and privacy issues in RSs [3], several challenges persist. CH1,Each client's local model relies on a consistent global model from the server, limiting personalized deployment to endusers.

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Efficient Distributed Sparse Relative Similarity Learning

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ACM Transactions on Knowledge Discovery from Data 2025年第3期19卷 1-27页

作者： Yao, Dezhong Li, Sanmu Wang, Zhiwei Zhao, Peilin Wu, Gang Yu, Chen Jin, Hai School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology Tencent AI Lab Shenzhen China National Super Computing Center in Zhengzhou Zhengzhou University Zhengzhou China

Learning a good similarity measure for large-scale high-dimensional data is a crucial task in machine learning applications, yet it poses a significant challenge. Distributed minibatch Stochastic Gradient Descent (SGD) serves as an efficient optimization method in large-scale distributed training, allowing linear speedup in proportion to the number of workers. However, communication efficiency in distributed SGD requires a sufficiently large minibatch size, presenting two distinct challenges. Firstly, a large minibatch size leads to high memory usage and computational complexity during parallel training of high-dimensional models. Second, a larger batch size of data reduces the convergence rate. To overcome these challenges, we propose an Efficient Distributed Sparse Relative Similarity Learning (EDSRSL) framework. This framework integrates two strategies: local minibatch SGD and sparse relative similarity learning. By effectively reducing the number of updates through synchronous delay while maintaining a large batch size, we address the issue of high computational cost. Additionally, we incorporate sparse model learning into the training process, significantly reducing computational cost. This article also provides theoretical proof that the convergence rate does not decrease significantly with increasing batch size. Various experiments on six high-dimensional real-world datasets demonstrate the efficacy and efficiency of the proposed algorithms, with a communication cost reduction of up to 90.89% and a maximum wall time speedup of 5.66× compared to the baseline methods. © 2025 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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Federated learning for Healthcare 5.0: a comprehensive survey, taxonomy, challenges, and solutions

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Soft computing 2025年第2期29卷 673-700页

作者： Amin, Muhammad Sadiq Ahmad, Shabir Loh, Woong-Kee School of Electrical and Computer Science National University of Science and Technology Islamabad Pakistan School of Computing Gachon University Seongnam13120 Korea Republic of

The advent of Healthcare 5.0 heralds a groundbreaking revolution in digital healthcare, superseding the achievements of its predecessor, Healthcare 4.0. Integrating cutting-edge technologies such as the Internet of Medical Things (IoMT), smart wearables, and the extraordinary capabilities of Artificial Intelligence (AI), Healthcare 5.0 envisions a unified framework that grants seamless access to health records, fosters interconnectedness among individuals, resources, and institutions, and empowers intelligent responses to medical concerns. However, the realization of Healthcare 5.0 faces a significant challenge in the form of high-speed data transmission using smart devices. Conventional AI approaches relying on centralized data processing raise compelling concerns surrounding information privacy and scalability within the Healthcare 5.0 context. Amidst this backdrop, federated learning emerges as a beacon of hope, offering a decentralized AI paradigm that facilitates on-device machine learning without compromising end-user privacy through centralized data export. Safeguarding data integrity, federated learning holds the key to unlocking the full potential of Healthcare 5.0. In this pioneering study, we conduct an extensive survey, exploring the transformative implications of federated learning within the realm of Healthcare 5.0. By shedding light on recent advancements tailored to this paradigm, we delve into the fundamental concepts of resource-awareness, privacy preservation, incentivization, and personalization, all within the framework of federated learning. Moreover, we meticulously scrutinize key parameters including security, sparsification, quantization, robustness, scalability, and privacy, providing an authentic evaluation of the current progress in federated learning for Healthcare 5.0. This comprehensive survey serves as an indispensable cornerstone for the evolution of Healthcare 5.0, offering invaluable insights into its unique requirements and untapp

关键词： Electronic health record

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Detecting the symptoms of COVID-19 during pandemic environment using smart spectacle thermal images and deep capsule networks

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Multimedia Tools and Applications 2025年第8期84卷 4475-4501页

作者： Dwarakanath, B. Pandimurugan, V. Mohandas, R. Sambath, M. Baiju, B.V. Chinnasamy, A. SRM Institute of Science and Technology Ramapuram Chennai India Networking and Communications School of Computing SRMIST kattankulathur Chennai India Computational Intelligence School of Computing SRMIST kattankulathur Chennai India Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Data Science and Business Systems School of Computing SRMIST kattankulathur Chennai India

The pandemic creates a more complicated providence of medical assistance and diagnosis procedures. In the world, Covid-19, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS Cov-2), and plague are widely known pandemic disease desperations. Due to the recent COVID-19 pandemic tragedies, various medical diagnosis models and intelligent computing solutions are proposed for medical applications. In this era of computer-based medical environment, conventional clinical solutions are surpassed by many Machine Learning and Deep Learning-based COVID-19 diagnosis models. Anyhow, many existing models are developing lab-based diagnosis environments. Notably, the Gated Recurrent Unit-based Respiratory Data Analysis (GRU-RE), Intelligent Unmanned Aerial Vehicle-based Covid Data Analysis (Thermal Images) (I-UVAC), and Convolutional Neural Network-based computer Tomography Image Analysis (CNN-CT) are enriched with lightweight image data analysis techniques for obtaining mass pandemic data at real-time conditions. However, the existing models directly deal with bulk images (thermal data and respiratory data) to diagnose the symptoms of COVID-19. Against these works, the proposed spectacle thermal image data analysis model creates an easy and effective way of disease diagnosis deployment strategies. Particularly, the mass detection of disease symptoms needs a more lightweight equipment setup. In this proposed model, each patient's thermal data is collected via the spectacles of medical staff, and the data are analyzed with the help of a complex set of capsule network functions. Comparatively, the conventional capsule network functions are enriched in this proposed model using adequate sampling and data reduction solutions. In this way, the proposed model works effectively for mass thermal data diagnosis applications. In the experimental platform, the proposed and existing models are analyzed in various dimensions (metrics). The comparative results obtained in the experiments just

关键词： COVID-19

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FlexIM: Efficient and Verifiable Index Management in Blockchain

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IEEE Transactions on Knowledge and Data Engineering 2025年第6期37卷 3399-3412页

作者： Li, Binhong Lin, Licheng Zhang, Shijie Xu, Jianliang Xiao, Jiang Li, Bo Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Laboratory The Cluster and Grid Computing Laboratory School of Computer Science and Technology Wuhan430074 China Hong Kong Baptist University School of Computer Science Hong Kong Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong999077 Hong Kong

Blockchain-based query with its traceability and data provenance has become increasingly popular and widely adopted in numerous applications. Yet existing index-based query approaches are only efficient under static blockchain query workloads where the query attribute or type must be fixed. It turns out to be particularly challenging to construct an efficient index for dynamic workloads due to prohibitively long construction time and excessive storage consumption. In this paper, we present FlexIM, the first efficient and verifiable index management system for blockchain dynamic queries. The key innovation in FlexIM is to uncover the inherent characteristics of blockchain, i.e., data distribution and block access frequency, and then to optimally choose the index by utilizing reinforcement learning technique under varying workloads. In addition, we enhance and facilitate verifiability with low storage overhead by leveraging Root Merkle Tree (RMT) and Bloom Filter Merkle Tree (BMT). Our comprehensive evaluations demonstrate that FlexIM outperforms the state-of-the-art blockchain query mechanism, vChain+, by achieving a 26.5% speedup while consuming 94.2% less storage, on average, over real-world Bitcoin datasets. © 1989-2012 IEEE.

关键词： Structured Query Language

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Optimizing Image Retrieval in Cloud Servers with TN-AGW: A Secure and Efficient Approach

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Journal of The Institution of Engineers (India): Series B 2025年第2期106卷 459-473页

作者： Ponnuviji, N.P. Nirmala, G. Kokila, M. L. Sworna Priyadharshini, S. Indra Department of Computer Science and Engineering RMK College of Engineering and Technology Puduvoyal Thiruvallur India Department of Computer Science and Engineering RMD Engineering College Chennai India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nādu Kattankulathur Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

The increasing use of cloud-based image storage and retrieval systems has made ensuring security and efficiency crucial. The security enhancement of image retrieval and image archival in cloud computing has received considerable attention in transmitting data and ensuring data confidentiality among cloud servers and users. Various traditional image retrieval techniques regarding security have developed in recent years but they do not apply to large-scale environments. This paper introduces a new approach called Triple network-based adaptive grey wolf (TN-AGW) to address these challenges. The TN-AGW framework combines the adaptability of the Grey Wolf Optimization (GWO) algorithm with the resilience of Triple Network (TN) to enhance image retrieval in cloud servers while maintaining robust security measures. By using adaptive mechanisms, TN-AGW dynamically adjusts its parameters to improve the efficiency of image retrieval processes, reducing latency and utilization of resources. However, the image retrieval process is efficiently performed by a triple network and the parameters employed in the network are optimized by Adaptive Grey Wolf (AGW) optimization. Imputation of missing values, Min–Max normalization, and Z-score standardization processes are used to preprocess the images. The image extraction process is undertaken by a modified convolutional neural network (MCNN) approach. Moreover, input images are taken from datasets such as the Landsat 8 dataset and the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset is employed for image retrieval. Further, the performance such as accuracy, precision, recall, specificity, F1-score, and false alarm rate (FAR) is evaluated, the value of accuracy reaches 98.1%, the precision of 97.2%, recall of 96.1%, and specificity of 917.2% respectively. Also, the convergence speed is enhanced in this TN-AGW approach. Therefore, the proposed TN-AGW approach achieves greater efficiency in image retrieving than other existing

关键词： Extraction

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