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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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Knowledge Graph Completion Method of Combining Structural Information with Semantic Information

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Chinese Journal of Electronics 2024年第6期33卷 1412-1420页

作者： Binhao HU Jianpeng ZHANG Hongchang CHEN School of Cyber Science and Engineering Zhengzhou University National Digital Switching System Engineering & Technological R&D Center (NDSC)

With the development of knowledge graphs, a series of applications based on knowledge graphs have emerged. The incompleteness of knowledge graphs makes the effect of the downstream applications affected by the quality of the knowledge graphs. To improve the quality of knowledge graphs, translation-based graph embeddings such as TransE, learn structural information by representing triples as low-dimensional dense vectors. However, it is difficult to generalize to the unseen entities that are not observed during training but appear during testing. Other methods use the powerful representational ability of pre-trained language models to learn entity descriptions and contextual representation of triples. Although they are robust to incompleteness, they need to calculate the score of all candidate entities for each triple during inference. We consider combining two models to enhance the robustness of unseen entities by semantic information, and prevent combined explosion by reducing inference overhead through structured information. We use a pre-training language model to code triples and learn the semantic information within them, and use a hyperbolic space-based distance model to learn structural information, then integrate the two types of information together. We evaluate our model by performing link prediction experiments on standard datasets. The experimental results show that our model achieves better performances than state-of-the-art methods on two standard datasets.

关键词： Training Semantics Knowledge graphs Predictive models Vectors Robustness Explosions Standards Testing Context modeling

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Spiking Reinforcement Learning Enhanced by Bioinspired Event Source of Multi-dendrite Spiking Neuron and Dynamic Thresholds

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 618-629页

作者： Xingyue Liang Qiaoyun Wu Yun Zhou Chunyu Tan Hongfu Yin Changyin Sun the School of Artificial Intelligence Anhui University Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education IEEE the Institute of Artificial Intelligence Hefei Comprehensive National Science Center the School of Automation Southeast University the Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education

Deep reinforcement learning(DRL) achieves success through the representational capabilities of deep neural networks(DNNs). Compared to DNNs, spiking neural networks(SNNs),known for their binary spike information processing, exhibit more biological characteristics. However, the challenge of using SNNs to simulate more biologically characteristic neuronal dynamics to optimize decision-making tasks remains, directly related to the information integration and transmission in *** by the advanced computational power of dendrites in biological neurons, we propose a multi-dendrite spiking neuron(MDSN) model based on Multi-compartment spiking neurons(MCN), expanding dendrite types from two to multiple and deriving the analytical solution of somatic membrane *** apply the MDSN to deep distributional reinforcement learning to enhance its performance in executing complex decisionmaking tasks. The proposed model can effectively and adaptively integrate and transmit meaningful information from different sources. Our model uses a bioinspired event-enhanced dendrite structure to emphasize features. Meanwhile, by utilizing dynamic membrane potential thresholds, it adaptively maintains the homeostasis of MDSN. Extensive experiments on Atari games show that the proposed model outperforms some state-of-the-art spiking distributional RL models by a significant margin.

关键词： Deep reinforcement learning multi-compartment spiking neurons spiking neural network

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Fault Diagnosis for Railway Point Machines Using VMD Multi-Scale Permutation Entropy and ReliefF Based on Vibration Signals

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Chinese Journal of Electronics 2025年第1期34卷 204-211页

作者： Yongkui Sun Yuan Cao Peng Li Shuai Su National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University School of Automation and Intelligence Beijing Jiaotong University Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University

The railway point machine plays an important part in railway systems. It is closely related to the safe operation of trains. Considering the advantages of vibration signals on anti-interference, this paper develops a novel vibration signal-based diagnosis approach for railway point machines. First, variational mode decomposition(VMD)is adopted for data preprocessing, which is verified more effective than empirical mode decomposition. Next, multiscale permutation entropy is extracted to characterize the fault features from multiple scales. Then ReliefF is utilized for feature selection, which can greatly decrease the feature dimension and improve the diagnosis accuracy. By experiment comparisons, the proposed approach performs best on diagnosis for railway point machines. The diagnosis accuracies on reverse-normal and normal-reverse processes are respectively 100% and 98.29%.

关键词： Vibrations Fault diagnosis Accuracy Data preprocessing Switches Feature extraction Rail transportation Entropy Maintenance Monitoring

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Secure Speculation via Speculative Secret Flow Tracking

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Journal of Computer science & Technology 2023年第2期38卷 422-438页

作者：崔宏伟杨春程旭 School of Computer Science Engineering Research Center of Microprocessor and System Peking UniversityBeijing 100871China

Speculative execution attacks can leak arbitrary program data under malicious speculation,presenting a severe security *** on two key observations,this paper presents a software-transparent defense mechanism called speculative secret flow tracking(SSFT),which is capable of defending against all cache-based speculative execution attacks with a low performance ***,we observe that the attacker must use array or pointer variables in the victim code to access arbitrary memory ***,we propose a strict definition of secret data to reduce the amount of data to be ***,if the load is not data-dependent and control-dependent on secrets,its speculative execution will not leak any ***,this paper introduces the concept of speculative secret flow to analyze how secret data are obtained and propagated during speculative *** tracking speculative secret flow in hardware,SSFT can identify all unsafe speculative loads(USLs)that are dependent on ***,SSFT exploits three different methods to constrain USLs’speculative execution and prevent them from leaking secrets into the cache and translation lookaside buffer(TLB)*** paper evaluates the performance of SSFT on the SPEC CPU 2006 workloads,and the results show that SSFT is effective and its performance overhead is very *** defend against all speculative execution attack variants,SSFT only incurs an average slowdown of 4.5%(Delay USL-L1Miss)or 3.8%(Invisible USLs)compared to a non-secure *** analysis also shows that SSFT maintains a low hardware overhead.

关键词： cache side channel attack hardware speculative execution attack security

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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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Asynchronous distributed algorithm for constrained optimization and its application

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science China Technological sciences 2025年第6期68卷 93-104页

作者： Ting WANG Zhongmei LI Rong NIE Wenli DU Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Engineering Research Center of Process System Engineering Ministry of Education East China University of Science and Technology

This study focuses on the distributed convex optimization problem with local boundary constraints and multiple inequality constraints, specifically considering scenarios involving communication delays and inconsistent updates between *** tackle the problem with guaranteed constraint satisfaction, a distributed asynchronous optimization algorithm is proposed based on the parameter projection method. Moreover, an asynchronous gradient tracking mechanism is employed to accelerate convergence. In the convergence analysis, an augmented synchronous system with virtual nodes is adopted to transform the delayed optimization problem into a problem without delays. Based on the generalized small gain theory, the proposed algorithm is proved to achieve a geometric convergence rate. Finally, numerical simulations and industrial experiments verify the effectiveness of the proposed algorithm.

关键词： distributed optimization inequality constraints communication delay gradient tracking geometric convergence

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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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A second-order knowledge filter transfer learning algorithm for modeling nonlinear systems

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science China Technological sciences 2025年第6期68卷 105-120页

作者： Honggui HAN Mengmeng LI Xiaolong WU Hongyan YANG Junfei QIAO School of Information Science and Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education

Transfer learning algorithms can transform prior knowledge into linearization knowledge to model nonlinear systems. However, the linearization knowledge-based models tend to diverge in the process of knowledge linearization due to the neglected information of higher-order terms. To overcome this problem, a second-order knowledge filter transfer learning algorithm(SOFTLA) is developed for modeling nonlinear systems. First, a knowledge transformation strategy is introduced to transform the linearization source knowledge into comprehensive knowledge containing first-order and second-order *** with the original knowledge, the transformed source knowledge with second-order term can prevent information loss during the knowledge linearization. Second, a knowledge filter algorithm is proposed to eliminate the useless information in the source knowledge. Subsequently, a suitable filter gain is designed to reduce the cumulative error in knowledge updating process. Third, a model adaptation mechanism is designed to enable effective knowledge transfer by updating the structure and parameters of the target model simultaneously. Subsequently, the adaptability of the source knowledge is enhanced to facilitate learning tasks in the target domain. Finally, a benchmark problem and several practical industrial applications are presented to validate the superiority of SOFTLA. The experimental discussions illustrate that SOFTLA can obtain obvious advantages over contrastive methods.

关键词： transfer learning knowledge transformation strategy knowledge filter algorithm model adaptation mechanism

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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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