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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Shuhao Li Ji Wang Xin Wang Zhi-Chang Qi National Lab. for Parallel & Distributed Process. Changsha China

This paper presents a new approach to generating configuration-oriented executable symbolic test sequences from extended finite state machine (EFSM) models. The information about the values of the context variables and the domain intervals of the input parameters are exploited to guide the derivation of the test sequences. Meanwhile, the transition guards along the test sequences are continually used to reduce the domain intervals of the input parameters. Experiments indicate that this method significantly reduces the EFSM state space to be explored and the number of non-executable symbolic test sequences to be generated. Since parameterized input events are allowed to occur in EFSM cycles, this method is suitable for testing the open reactive systems that interact with the environments via parameterized input events.

关键词： Genetic algorithms Automatic testing Software testing Computer science Annealing Law Legal factors Costs Evolution (biology) Software quality

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TeeSwap: Private Data Exchange using Smart Contract and Trusted Execution Environment 23

TeeSwap: Private Data Exchange using Smart Contract and Trus...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Chen, Peng Shi, Peichang Xu, Jie Fu, Xiang Li, Linhui Zhong, Tao Xiang, Liangliang Kong, Jinzhu College of Computer National University of Defense Technology National Key Laboratory of Parallel and Distributed Processing Changsha410073 China Zhejiang Lab Hangzhou311100 China Kylin Software Co. Ltd Changsha410073 China

ISBN: (纸本)9781665494571

With importance of data value is approved, data-sharing will create more and greater value has become consensus. However, data exchange has to use a trusted third party(TTP) as an intermediary in an untrusted network environment, which may lead to leakage of data privacy. In this paper, we design a system named TeeSwap, which uses smart contract to complete data exchange, and at the same time uses cryptographic algorithms and trusted execution environment (TEE) to ensure privacy in process of data transmission, verification and contract execution. Meanwhile, TeeSwap can ensure the credibility, fairness, integrity and privacy while data are shared in our system. In the end, we implement contracts about data market and data exchange. When confirming the data transaction, data market contract can automatically generate and deploy exchange contract, which can complete the data exchange authoritatively. © 2021 IEEE.

关键词： Data privacy Smart contracts Data communication Cryptography

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A mobile recommendation system based on Logistic Regression and Gradient Boosting Decision Trees

A mobile recommendation system based on Logistic Regression ...

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International Joint Conference on Neural Networks

作者： Yaozheng WANG Dawei FENG Dongsheng LI Xinyuan CHEN Yunxiang ZHAO Xin NIU National Lab for Parallel and Distributed Processing School of Computer Science National University of Defense Technology

ISBN: (纸本)9781509006212

Real-life behaviors shown by the mobile users typically exhibit plenty noises, making it hard to construct an effective recommendation engine. In this paper, we present a fused model based on the LR algorithm and the GBDT algorithm to recommend vertical industry commodities in a mobile setting. A set of specifically designed methods are proposed to deal with the data preprocessing and feature extraction problem for the mobile recommendation scenario. The proposed method is evaluated on a large scale real-world dataset provided by the Alibaba mobile shopping department. Result on the F1 score has seen an improvement of 2%-36% compared with the baseline.

关键词： Mobile communication Feature extraction Predictive models Prediction algorithms Logistics Data models Computational modeling

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Modelling with relational calculus of object and component systems - RCOS

Modelling with relational calculus of object and component s...

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作者： Chen, Zhenbang Hannousse, Abdel Hakim Van Hung, Dang Knoll, Istvan Li, Xiaoshan Liu, Zhiming Liu, Yang Nan, Qu Okika, Joseph C. Ravn, Anders P. Stolz, Volker Yang, Lu Zhan, Naijun International Institute for Software Technology United Nations University China Faculty of Science and Technology University of Macau Department of Computer Science Aalborg University Denmark Lab. of Computer Science Institute of Software CAS China National Laboratory for Parallel and Distributed Processing China

ISBN: (纸本)3540852883

This chapter presents a formalization of functional and behavioural requirements, and a refinement of requirements to a design for CoCoME using the Relational Calculus of Object and Component Systems (rCOS). We give a model of requirements based on an abstraction of the use cases described in Chapter 3.2. Then the refinement calculus of rCOS is used to derive design models corresponding to the top level designs of Chapter 3.4. We demonstrate how rCOS supports modelling different views and their relationships of the system and the separation of concerns in the development. © 2008 Springer-Verlag Berlin Heidelberg.

关键词： Calculations

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Temporal Inconsistency-Based Intrinsic Reward for Multi-Agent Reinforcement Learning

Temporal Inconsistency-Based Intrinsic Reward for Multi-Agen...

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International Joint Conference on Neural Networks (IJCNN)

作者： Shaoqi Sun Kele Xu National Key Lab of Parallel and Distributed Processing National University of Defense Technology Changsha China

Multi-agent reinforcement learning (MARL) has shown promising results in many challenging sequential decision-making tasks. Recently, deep neural networks have dominated this field. However, the policy networks of agent's may fall into local optimum during the training phase, which severely constrains the performance of exploration. To address this issue, we propose a novel MARL learning framework named PSAM, which contains a new temporal inconsistency-based intrinsic reward and a diversity control strategy. Specifically, we save the parameters of the deep models along the optimization path of the agent's policy network, which can be denoted as snapshots. Through measuring the difference between snapshots, we can employ the difference as an intrinsic reward. Moreover, we propose a diversity control strategy to improve the performance further. Finally, to verify the effectiveness of the proposed method, we conduct extensive experiments in several widely used MARL environments. The results show that in many environments, PSAM can not only achieve state-of-the-art performance and prevent the policy network from getting stuck in local minima but also accelerate the agent's learning of the policy. It is worth noting that the proposed regularizer can be used using a plug-and-play manner without introducing any additional hyper-parameters and training costs.

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A mesh quality discrimination method based on convolutional neural network

A mesh quality discrimination method based on convolutional ...

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2020 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2020

作者： Xu, Zhenpeng Chen, Xinhai Chi, Lihua Liu, Jie Gong, Chunye Sci. and Technol. on Parallel and Distributed Proc. Lab. Lab. of Software Eng. for Complex Systems National University of Defense Technology Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9781728170046

With the rapid development of highperformance computing, computational fluid dynamics (CFD) has become an important part of hydrodynamics and aerodynamics. Mesh quality is the key factor that affects the accuracy and efficiency of CFD numerical calculation. However, the current the process of mesh quality discrimination is very time-consuming. The manpower time needed for this process takes up a large proportion in the whole numerical calculation process. A large number of artificial intelligence algorithms have been put forward to replace the human to efficiently complete all kinds of tedious tasks. In this paper, we propose a convolutional neural network (CNN) based mesh quality discrimination method, MeshNet. MeshNet uses residual neural network structure to learn mesh features and automatically judge the mesh quality. The experimental results show that the proposed network can greatly save lab.r time cost and achieve an accuracy of 94.41% for mesh quality discrimination. © 2020 IEEE.

关键词： Convolutional neural networks

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Low-latency last-level cache structure based on grouped cores in Chip Multi-Processor

Low-latency last-level cache structure based on grouped core...

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IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Jinbo Xu Weixia Xu Kefei Wang Zhengbin Pang School of Computer National University of Defense Technology Changsha China National Lab. for Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (纸本)9781479975761

Last-Level Cache (LLC) plays an important role in Chip Multi-Processor (CMP). The objective of this work is to optimize the structure and management strategy of LLC. Based on 8-core CMP, a LLC structure based on grouped cores is proposed, where 8 cores are divided into 4 groups. All LLC resources are classified into three types, which are fixed private cache, dynamic private cache and dynamic shared cache. The layout of the LLC structure and the corresponding dynamic partitioning strategy are designed to achieve low access latency and high efficiency. Experimental results on full-system simulator suggest that the proposed structure and method are able to reduce the access latency by 2% to 12% compared with previous works, such as tiled structure, cache-centered structure and core-centered structure. Consequently, performance measured by IPC is improved up to 7%. The contribution of this paper is useful for CMP performance, and applies to not only 8-core CMP but also all small-scale CMPs.

关键词： Dynamic scheduling Multicore processing Educational institutions Organizations Delays Semiconductor device measurement Research and development

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Graph pooling via coarsened graph infomax

arXiv

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arXiv 2021年

作者： Pang, Yunsheng Zhao, Yunxiang Li, Dongsheng The University of Melbourne National Lab for Parallel and Distributed Processing College of Computer National University of Defense Technology

Graph pooling that summaries the information in a large graph into a compact form is essential in hierarchical graph representation learning. Existing graph pooling methods either suffer from high computational complexity or cannot capture the global dependencies between graphs before and after pooling. To address the problems of existing graph pooling methods, we propose Coarsened Graph Infomax Pooling (CGIPool) that maximizes the mutual information between the input and the coarsened graph of each pooling layer to preserve graph-level dependencies. To achieve mutual information neural maximization, we apply contrastive learning and propose a self-attention-based algorithm for learning positive and negative samples. Extensive experimental results on seven datasets illustrate the superiority of CGIPool comparing to the state-of-the-art methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Graph neural networks

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An Improved Independent Set Ordering Algorithm for Solving Large-scale Sparse Linear Systems

An Improved Independent Set Ordering Algorithm for Solving L...

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2010 Second International Conference on Intelligent Human-Machine Systems and Cybernetics(第二届智能人机系统与控制论国际学术会议 IHMSC 2010)

作者： Lu Yao Wei Cao Zongzhe Li Yongxian Wang Zhenghua Wang National Key Lab for Parallel and Distributed Processing National Univ.of Defense TechnologyChangshaChina

The independent set ordering algorithm is a heuristic algorithm based on finding maximal independent sets of vertices in the matrix adjacency graph, which is commonly used for parallel matrix factorization. However, Disadvantages appear when it is applied to large-scale sparse linear systems. In this paper, we propose an improved algorithm by finding an optimal size of independent set in each elimination step rather than find a maximal independent set, which is proved to be effective by both theoretical analysis and parallel implementation.

关键词： independent set parallel LU decomposition large-scale sparse linear systems matrix ordering

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Shuffle-Efficient distributed Locality Sensitive Hashing on Spark

Shuffle-Efficient Distributed Locality Sensitive Hashing on ...

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IEEE Conference on Computer Communications Workshops

作者： Wanxin Zhang Dongsheng Li Ying Xu Yiming Zhang National Lab for Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha Hunan China

ISBN: (纸本)9781467399562

Locality Sensitive Hashing (LSH) is an important indexing technique for approximate similarity search in high-dimensional spaces. An obvious limitation of LSH approaches is the lack of capability and scalab.lity to deal with massive data. This paper proposes a distributed variant of LSH called Spark-LSH, which is implemented on Apache Spark, a well-known distributed computing framework. We design a shuffle-efficient indexing scheme for the Spark-LSH, which can reduce the data shuffle and improve the network efficiency when constructing the hash table indices. Furthermore, we propose a location-aware querying scheme to improve the query performance. Experiments show that the Spark-LSH scheme can reduce the network shuffle overhead remarkably and accelerate the query significantly.

关键词： Locality Sensitive Hashing Spark Shuffle

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