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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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A survey on cross-user federated recommendation

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science China(Information sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

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Efficiency-optimized 6G:A virtual network resource orchestration strategy by enhanced particle swarm optimization

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Digital Communications and Networks 2024年第5期10卷 1221-1233页

作者： Sai Zou Junrui Wu Haisheng Yu Wenyong Wang Lisheng Huang Wei Ni Yan Liu College of Big Data and Information Engineering Guizhou UniversityGuiyangChina Prospective Technology Lab of the China Future Internet Engineering Center BeijingChina College of Computer Science and Engineering University of Electronic Science and TechnologyChengduChina International Institute for Next Generation Internet Macao University of Science and TechnologyMacaoChina Data61 Business Unit CSIROAustralia

The future Sixth-Generation (6G) wireless systems are expected to encounter emerging services with diverserequirements. In this paper, 6G network resource orchestration is optimized to support customized networkslicing of services, and place network functions generated by heterogeneous devices into available *** is a combinatorial optimization problem that is solved by developing a Particle Swarm Optimization (PSO)based scheduling strategy with enhanced inertia weight, particle variation, and nonlinear learning factor, therebybalancing the local and global solutions and improving the convergence speed to globally near-optimal *** show that the method improves the convergence speed and the utilization of network resourcescompared with other variants of PSO.

关键词： Virtualization Network function orchestration Network resource virtualized orchestration (NRVO) Particle swarm optimization(PSO)

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Precise control of page cache for containers

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Frontiers of Computer science 2024年第2期18卷 17-30页

作者： Kun WANG Song WU Shengbang LI Zhuo HUANG Hao FAN Chen YU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Container-based virtualization isbecoming increasingly popular in cloud computing due to its efficiency and *** isolation is a fundamental property of *** works have indicated weak resource isolation could cause significant performance degradation for containerized applications and enhanced resource ***,current studies have almost not discussed the isolation problems of page cache which is a key resource for *** leverage memory cgroup to control page cache ***,existing policy introduces two major problems in a container-based ***,containers can utilize more memory than limited by their cgroup,effectively breaking memory ***,the Os kernel has to evict page cache to make space for newly-arrived memory requests,slowing down containerized *** paper performs an empirical study of these problems and demonstrates the performance impacts on containerized *** we propose pCache(precise control of page cache)to address the problems by dividing page cache into private and shared and controlling both kinds of page cache separately and *** do so,pCache leverages two new technologies:fair account(f-account)and evict on demand(EoD).F-account splits the shared page cache charging based on per-container share to prevent containers from using memory for free,enhancing memory *** EoD reduces unnecessary page cache evictions to avoid the performance *** evaluation results demonstrate that our system can effectively enhance memory isolation for containers and achieve substantial performance improvement over the original page cache management policy.

关键词： page cache memory cgroup container isolation cloud computing

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Resource investment for DDoS attack resistant SDN: a practical assessment

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science China(Information sciences) 2023年第7期66卷 112-129页

作者： Bin YUAN Fan ZHANG Jun WAN Huan ZHAO Shui YU Deqing ZOU Qiangsheng HUA Hai JIN School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Shenzhen Huazhong University of Science and Technology Research Institute School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab School of Computer Science University of Technology Sydney

Software-defined networks(SDNs) present a novel network architecture that is widely used in various datacenters. However, SDNs also suffer from many types of security threats, among which a distributed denial of service(DDoS) attack, which aims to drain the resources of SDN switches and controllers,is one of the most common. Once the switch or controller is damaged, the network services can be *** defense schemes against DDoS attacks have been proposed from the perspective of attack detection;however, such defense schemes are known to suffer from a time consuming and unpromising accuracy, which could result in an unavailable network service before specific countermeasures are taken. To address this issue through a systematic investigation, we propose an elaborate resource-management mechanism against DDoS attacks in an SDN. Specifically, by considering the SDN topology, we leverage the M/M/c queuing model to measure the resistance of an SDN to DDoS attacks. Network administrators can therefore invest a reasonable number of resources into SDN switches and SDN controllers to defend against DDoS attacks while guaranteeing the quality of service(QoS). Comprehensive analyses and empirical data-based experiments demonstrate the effectiveness of the proposed approach.

关键词： SDN security DDoS attacks resource management queueing theory QoS

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Answering reachability queries with ordered label constraints over labeled graphs

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Frontiers of Computer science 2024年第1期18卷 105-117页

作者： Daoliang HE Pingpeng YUAN 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&TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Reachability query plays a vital role in many graph analysis *** researches proposed many methods to efficiently answer reachability queries between vertex *** many real graphs are labeled graph,it highly demands label-Constrained Reachability(LCR)query inwhich constraint includes a set of labels besides vertex *** researches proposed several methods for answering some LCR queries which require appearance of some labels specified in constraints in the *** that constraint may be a label set,query constraint may be ordered labels,namely OLCR(Ordered-label-Constrained Reachability)queries which retrieve paths matching a sequence of ***,no solutions are available for ***,we propose DHL,a novel bloom filter based indexing technique for answering OLCR *** can be used to check reachability between vertex *** the answers are not no,then constrained DFS is ***,we employ DHL followed by performing constrained DFS to answer OLCR *** show that DHL has a bounded false positive rate,and it's powerful in saving indexing time and *** experiments on 10 real-life graphs and 12 synthetic graphs demonstrate that DHL achieves about 4.8-22.5 times smaller index space and 4.6-114 times less index construction time than two state-of-art techniques for LCR queries,while achieving comparable query response *** results also show that our algorithm can answer OLCR queries effectively.

关键词： graph ordered-label-constrained reachability partial index bloom filter query processing

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Robust zero-watermarking algorithm based on discrete wavelet transform and daisy descriptors for encrypted medical image

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CAAI Transactions on Intelligence Technology 2024年第1期9卷 40-53页

作者： Yiyi Yuan Jingbing Li Jing Liu Uzair Aslam Bhatti Zilong Liu Yen-wei Chen School of Information and Communication Engineering Hainan UniversityHaikouChina Research Center for Healthcare Data Science Zhejiang LabHangzhouChina Graduate School of Information Science and Engineering Ritsumeikan University ShigaJapan

In the intricate network environment,the secure transmission of medical images faces challenges such as information leakage and malicious tampering,significantly impacting the accuracy of disease diagnoses by medical *** address this problem,the authors propose a robust feature watermarking algorithm for encrypted medical images based on multi-stage discrete wavelet transform(DWT),Daisy descriptor,and discrete cosine transform(DCT).The algorithm initially encrypts the original medical image through DWT-DCT and Logistic ***,a 3-stage DWT transformation is applied to the encrypted medical image,with the centre point of the LL3 sub-band within its low-frequency component serving as the sampling *** Daisy descriptor matrix for this point is then ***,a DCT transformation is performed on the Daisy descriptor matrix,and the low-frequency portion is processed using the perceptual hashing algorithm to generate a 32-bit binary feature vector for the medical *** scheme utilises cryptographic knowledge and zero-watermarking technique to embed watermarks without modifying medical images and can extract the watermark from test images without the original image,which meets the basic re-quirements of medical image *** embedding and extraction of water-marks are accomplished in a mere 0.160 and 0.411s,respectively,with minimal computational *** results demonstrate the robustness of the algorithm against both conventional attacks and geometric attacks,with a notable performance in resisting rotation attacks.

关键词： daisy descriptor DCT DWT encryption domain medical image zero-watermarking

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Identification of domain-specific euphemistic tweets using clustering

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International Journal of Information Technology (Singapore) 2024年第1期16卷 21-31页

作者： Devi, Maibam Debina Saharia, Navanath Data Engineering Lab Department of Computer Science & Engineering IIIT Senapati Manipur Imphal 795002 India

Social media platforms (SMPs) are frequently utilised as a readily accessible and comprehensive medium for expressing personal opinions nowdays. The use of euphemism, a linguistic strategy in which the underlying feeling of expressive content is veiled by the use of mild language, has been a longtime practise in the realm of SMPs for the purpose of reducing harshness or to discuss sensitive topics [1]. The identification of masked contents [2] in euphemism is challenging due to their inherent nature. This study presents a proposed identification mechanism aimed at detecting domain-specific euphemisms through the utilisation of clustering techniques. The pattern categorization feature is created utilising domain-specific lexical features combined with frequency-based features. In order to identify the most suitable match, the hybrid feature extraction algorithms incorporate uni-gram and bi-gram features dependent on frequency based feature, in conjunction with a lexicon. The objective of the dimension reduction phase is to address the issue of sparsity and to identify the most significant words for each sample in order to classify them into different domains using centroid and density-based clustering techniques. The DBSCAN algorithm is employed with an epsilon value of 2.5 and a minimum number of points set to 6, resulting in the identification of 7 distinct clusters. To calculate the optimal value for k in the K-means algorithm, the Silhouette score is utilised. The clusters that were obtained are examined by manual means. We compare our model to FLUTE dataset with epsilon value of 0.2, minpoints of 5 for DBSCAN, and obtain validation score of 0.55. The DBSCAN clustering algorithm generates distinct clusters that extend beyond the scope of the inquiry domain. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： DBSCAN Domain-specific euphemistic text Euphemism K-means Silhouette score

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A survey on dynamic graph processing on GPUs: concepts, terminologies and systems

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Frontiers of Computer science 2024年第4期18卷 1-23页

作者： Hongru GAO Xiaofei LIAO Zhiyuan SHAO Kexin LI Jiajie CHEN Hai JIN National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China

Graphs that are used to model real-world entities with vertices and relationships among entities with edges,have proven to be a powerful tool for describing real-world problems in *** most real-world scenarios,entities and their relationships are subject to constant *** that record such changes are called dynamic *** recent years,the widespread application scenarios of dynamic graphs have stimulated extensive research on dynamic graph processing systems that continuously ingest graph updates and produce up-to-date graph analytics *** the scale of dynamic graphs becomes larger,higher performance requirements are demanded to dynamic graph processing *** the massive parallel processing power and high memory bandwidth,GPUs become mainstream vehicles to accelerate dynamic graph processing ***-based dynamic graph processing systems mainly address two challenges:maintaining the graph data when updates occur(i.e.,graph updating)and producing analytics results in time(i.e.,graph computing).In this paper,we survey GPU-based dynamic graph processing systems and review their methods on addressing both graph updating and graph *** comprehensively discuss existing dynamic graph processing systems on GPUs,we first introduce the terminologies of dynamic graph processing and then develop a taxonomy to describe the methods employed for graph updating and graph *** addition,we discuss the challenges and future research directions of dynamic graph processing on GPUs.

关键词： dynamic graphs graph processing graph algorithms GPUs

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ARCHER:a ReRAM-based accelerator for compressed recommendation systems

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Frontiers of Computer science 2024年第5期18卷 147-160页

作者： Xinyang SHEN Xiaofei LIAO Long ZHENG Yu HUANG Dan CHEN Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabClusters and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Modern recommendation systems are widely used in modern data *** random and sparse embedding lookup operations are the main performance bottleneck for processing recommendation systems on traditional platforms as they induce abundant data movements between computing units and ***-based processing-in-memory(PIM)can resolve this problem by processing embedding vectors where they are ***,the embedding table can easily exceed the capacity limit of a monolithic ReRAM-based PIM chip,which induces off-chip accesses that may offset the PIM ***,we deploy the decomposed model on-chip and leverage the high computing efficiency of ReRAM to compensate for the decompression performance *** this paper,we propose ARCHER,a ReRAM-based PIM architecture that implements fully yon-chip recommendations under resource ***,we make a full analysis of the computation pattern and access pattern on the decomposed *** on the computation pattern,we unify the operations of each layer of the decomposed model in multiply-and-accumulate *** on the access observation,we propose a hierarchical mapping schema and a specialized hardware design to maximize resource *** the unified computation and mapping strategy,we can coordinatethe inter-processing elements *** evaluation shows that ARCHER outperforms the state-of-the-art GPU-based DLRM system,the state-of-the-art near-memory processing recommendation system RecNMP,and the ReRAM-based recommendation accelerator REREC by 15.79×,2.21×,and 1.21× in terms of performance and 56.06×,6.45×,and 1.71× in terms of energy savings,respectively.

关键词： recommendation system ReRAM processing-in-memory embedding layer

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