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International Joint Conference on Neural Networks (IJCNN)

作者： Xin Zhou Yongxue Shan Zixuan Dong Haijiao Liu Xiaodong Wang National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Denfense Technology Changsha China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Temporal Knowledge Graph Completion (TKGC) aims to predict missing parts of quadruples, which is crucial for real-life knowledge graphs. Compared with methods that only use graph neural networks, the emergence of pre-trained model has introduced a trend of simultaneously leveraging text and graph structure information. However, most current methods based on pre-trained models struggle to effectively utilize both text and multi-hop graph structure information concurrently, resulting in insufficient association mining of relations. To address the challenge, we propose a novel model: Temporal Closing Path for Pre-trained Language Model-based TKGC (TCP-PLM). We obtain the temporal closing relation path of the target relation through sampling, and use the relation path as a bridge to simultaneously utilize text and multi-hop graph structure information. Moreover, the relation path serves as a tool for mining associations between relations. At the same time, due to the design of entity-independent relation paths, our model can also handle the inductive setting. Our experiments on three benchmarks, along with extensive analysis, demonstrate that our model not only achieves substantial performance enhancements across four metrics compared to other models but also adeptly handles inductive settings.

关键词： Training Measurement Knowledge engineering Bridges Analytical models Knowledge graphs Transforms

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A malicious code detection method based on statistical analysis

A malicious code detection method based on statistical analy...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Yunlong Wu Chen Chen Huiquan Wang Jie Zhou Xinhai Xu National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha Hunan 410073 China National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha Hunan P. R. China School of Computer National University of Defense Technology Changsha Hunan 410073 China

The malicious code detection based on behaviors has proved effective. But there are high false positives and high false negatives when using this method. Because the behaviors are always out-of-order and redundant. To solve these problems, this paper proposes a detection method based on statistical analysis. Firstly, this method uses association rules to sort out the behaviors, and then we can get the integrated and accurate behavior sequences. Secondly, by using the association algorithm we can pick up the signatures of behavior sequences. In addition, this method can detect the signatures to judge the threat based on statistical analysis. Experimental results indicate that it can reduce both the false positives and the false negatives effectively.

关键词： Mathematical model Statistical analysis Equations Association rules Educational institutions Algorithm design and analysis Estimation

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Multi-agent Fault-tolerant Reinforcement Learning with Noisy Environments

Multi-agent Fault-tolerant Reinforcement Learning with Noisy...

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International Conference on parallel and distributed Systems (ICPADS)

作者： Canhui Luo Xuan Liu Xinning Chen Juan Luo College of Computer Science and Electronic Engineering Hunan University P. R. China The Science and Technology on Parallel and Distributed Processing Laboratory (PDL) P. R. China

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

Multi-agent reinforcement learning system is used to solve the problem that agents achieve specific goals in the interaction with the environment through learning policies. Almost all existing multi-agent reinforcement learning methods assume that the observation of the agents is accurate during the training process. It does not take into account that the observation may be wrong due to the complexity of the actual environment or the existence of dishonest agents, which will make the agent training difficult to succeed. In this paper, considering the limitations of the traditional multi-agent algorithm framework in noisy environments, we propose a multi-agent fault-tolerant reinforcement learning (MAFTRL) algorithm. Our main idea is to establish the agent's own error detection mechanism and design the information communication medium between agents. The error detection mechanism is based on the autoencoder, which calculates the credibility of each agent's observation and effectively reduces the environmental noise. The communication medium based on the attention mechanism can significantly improve the ability of agents to extract effective information. Experimental results show that our approach accurately detects the error observation of the agent, which has good performance and strong robustness in both the traditional reliable environment and the noisy environment. Moreover, MAFTRL significantly outperforms the traditional methods in the noisy environment.

关键词： Training Fault tolerance Working environment noise Fault tolerant systems Reinforcement learning Robustness Noise measurement

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Hybrid Routing: Towards Resilient Routing in Anonymous Communication Networks

Hybrid Routing: Towards Resilient Routing in Anonymous Commu...

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IEEE International Conference on Communications (ICC)

作者： Yusheng Xia Rongmao Chen Jinshu Su Chen Pan Han Su College of Computer National University of Defense Technology Changsha China National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Anonymous communication networks (ACNs) are intended to protect the metadata during communication. As classic ACNs, onion mix-nets are famous for strong anonymity, in which the source defines a static path and wraps the message multi-times with the public keys of nodes on the path, through which the message is relayed to the destination. However, onion mix-nets lacks in resilience when the static on-path mixes fail. Mix failure easily results in message loss, communication failure, and even specific attacks. Therefore, it is desirable to achieve resilient routing in onion mix-nets, providing persistent routing capability even though node failure. The state-of-theart solutions mainly adopt mix groups and thus need to share secret keys among all the group members which may cause single point of failure. To address this problem, in this work we propose a hybrid routing approach, which embeds the onion mix-net with hop-by-hop routing to increase routing resilience. Furthermore, we propose the threshold hybrid routing to achieve better key management and avoid single point of failure. As for experimental evaluations, we conduct quantitative analysis of the resilience and realize a local T-hybrid routing prototype to test performance. The experimental results show that our proposed routing strategy increases routing resilience effectively, at the expense of acceptable latency.

关键词： Routing Resilience Public key Peer-to-peer computing Encryption

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Who Should Review this Pull-Request: Reviewer Recommendation to Expedite Crowd Collaboration

Who Should Review this Pull-Request: Reviewer Recommendation...

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Asia-Pacific Conference on Software Engineering

作者： Yue Yu Huaimin Wang Gang Yin Charles X. Ling National Laboratory for Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha China Department of Computer Science The University of Western Ontario London Ontario Canada

Github facilitates the pull-request mechanism as an outstanding social coding paradigm by integrating with social media. The review process of pull-requests is a typical crowd sourcing job which needs to solicit opinions of the community. Recommending appropriate reviewers can reduce the time between the submission of a pull-request and the actual review of it. In this paper, we firstly extend the traditional Machine Learning (ML) based approach of bug triaging to reviewer recommendation. Furthermore, we analyze social relations between contributors and reviewers, and propose a novel approach to recommend highly relevant reviewers by mining comment networks (CN) of given projects. Finally, we demonstrate the effectiveness of these two approaches with quantitative evaluations. The results show that CN-based approach achieves a significant improvement over the ML-based approach, and on average it reaches a precision of 78% and 67% for top-1 and top-2 recommendation respectively, and a recall of 77% for top-10 recommendation.

关键词： Communities Rails Training Software Encoding Mathematical model Social network services

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Toward Optimal Deployment of Communication-Intensive Cloud Applications

Toward Optimal Deployment of Communication-Intensive Cloud A...

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IEEE International Conference on Cloud Computing, CLOUD

作者： Pei Fan Ji Wang Zibin Zheng Michael R. Lyu National Laboratory for Parallel & Distributed Processing National University of Defense Technology Changsha China Department of Computer Science & Engineering Chinese University of Hong Kong Hong Kong China

Strongly promoted by the leading industrial companies, cloud computing becomes increasingly popular in re-cent years. The growth rate of cloud computing surpasses even the most optimistic predictions. A cloud application is a large-scale distributed system that consist a lot of distributed cloud nodes. How to make optimal deployment of cloud applications is a challenging research problem. When deploying a cloud application to the cloud environment, cloud node ranking is one of the most important approaches for selecting optimal cloud nodes for the cloud application. Traditional ranking methods usually rank the cloud nodes based on their QoS values, without considering the communication performance between cloud nodes. However, such kind of node relationship is very important for the communication-intensive cloud applications (e.g., Message Passing Interface (MPI) programs), which have a lot of communications between the selected cloud nodes. In this paper, we propose a novel clustering-based method for selecting optimal cloud nodes for deploying communication-intensive applications to the cloud environment. Our method not only takes into account the cloud node qualities, but also the communication performance between different nodes. We deploy several well-known MPI programs on a real-world cloud and compare our method with other methods. The experimental results show the effectiveness of our cluster-based method.

关键词： Time factors Cloud computing Benchmark testing Quality of service Servers Clustering algorithms Databases

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Two-Stage Convolutional Neural Network for Knee Osteoarthritis Diagnosis in X-Rays

Two-Stage Convolutional Neural Network for Knee Osteoarthrit...

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International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2021

作者： Wang, Kang Niu, Xin Dou, Yong Yang, Di Xie, Dongxing Yang, Tuo National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha410073 China Department of Orthopaedics Xiangya Hospital Central South University Changsha410008 China Department of Health Management Center Xiangya Hospital Central South University Changsha410008 China

ISBN: (纸本)9789811638794

Knee osteoarthritis (OA) is a common musculoskeletal illness. To solve the problem that inaccurate knee joint localization and inadequate knee OA features extracted from plain radiographs affect the accuracy of knee OA diagnosis in X-rays, we propose a novel Two-Stage Convolutional Neural Network (TS-CNN) method, consisting of the KneeDetnet and the KLnet. The KneeDetnet with two small multi-task convolutional neural networks is proposed to locate knee joints, improving the accuracy of knee joint localization. Then KLnet is designed to assess knee OA, where a shared Siamese network via ResNet is used to extract more discriminative deep learning features that are fused with gender information for obtaining richer features. Our method is evaluated on public OAI and MOST datasets. The highest detection accuracy of knee joints can reach 99.93% and 99.02% on two datasets, respectively. The KLnet algorithm achieves 78.85% and 68.20% prediction accuracy on the OAI and MOST datasets, respectively. Experimental results show that our method outperforms the existing workhorse. The proposed approach may become a potentially useful tool for assisting physicians. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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NR-MPI: A Non-stop and Fault Resilient MPI

NR-MPI: A Non-stop and Fault Resilient MPI

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International Conference on parallel and distributed Systems (ICPADS)

作者： Guang Suo Yutong Lu Xiangke Liao Min Xie Hongjia Cao State Key Laboratory of High Performance Computing National University of Defense Technology Changsha Hunan Province China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha Hunan Province China

Fault resilience has became a major issue for HPC systems, in particular in the perspective of future E-scale systems, which will consist of millions of CPU cores and other components. Fault tolerant MPI was proposed to offer support of software level fault tolerance approaches. However, the widely used MPI implementations, such as MPICH and Mvapich2, provide limited support for fault tolerance. This paper proposes NR-MPI, a Non-stop and Fault Resilient MPI. NR-MPI implements the semantics of FT-MPI based on MPICH. Specifically, this paper focuses on failure detection in MPI library, online failure recovery of communicators for multiple failures, friendly programming interface extending for NR-MPI. Furthermore, to support failure recovery of applications, NR-MPI implements data backup and restore interfaces based on double in-memory checkpoint/restart. We conduct experiments with NPB benchmarks on TH-1A supercomputer. Experimental results show that NR-MPI based fault tolerant programs can recover from failures online without restarting, and the overhead is small even for applications with tens of thousands of cores.

关键词： Fault tolerance Fault tolerant systems Libraries Resource management Context Programming Semantics

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A compact analytical model for multi-island single electron transistors

A compact analytical model for multi-island single electron ...

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International Conference on ASIC

作者： Yaqing Chi Bingcai Sui Liang Fang Hailiang Zhou Haiqing Zhong He Sun National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Hunan China National University of Defense Technology Changsha Hunan CN

Multi-island single electron transistor (SET) has become a promising candidate for the kernel device of the logic circuit in the near future. A novel compact analytical model for multi-island SET is proposed in terms of current. The new approach is based on the orthodox theory of single electron tunnelling and steady-state master equation. The model is accurate and fast compared with SIMON, and suitable for the ASIC design of multi-island SET circuit simulation.

关键词： Analytical models Single electron transistors

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A distributed approach to consistent order delivery of concurrent events in asynchronous DVEs

ICETC 2010 - 2010 2nd International Conference on Education ...

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ICETC 2010 - 2010 2nd International Conference on Education technology and Computer 2010年 4卷 V495-V498页

作者： Zhou, Hangjun Zhang, Wei Peng, Yuxing Li, Sikun Key Laboratory of Science and Technology for National Defence of Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha Hunan 410073 China Department of Information Management Hunan College of Finance and Economics Changsha Hunan 410205 China

ISBN: (纸本)9781424463688

In large-scale asynchronous distributed virtual environments(DVEs), one of the difficult problems is to deliver the concurrent events in a consistent order at each node. Generally, the previous consistency control approaches can be classified into two categories: causal order and time stamped order. However, causal order approaches can merely preserve the cause-effect relation of events and time stamped order approaches seem intrinsically complex to be used in serverless large-scale asynchronous DVEs. In this paper, we proposed a novel distributed algorithm to identify the concurrent events and preserve the consistent order delivery of them at different nodes. Simulation studies are also carried out to compare the performance of this algorithm with that of the previous ones. The results show that the new algorithm can effectively deliver the concurrent events in consistent order at each node and is more efficient than the previous algorithms in large-scale asynchronous DVEs. © 2010 IEEE.

关键词： Virtual reality

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