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World Congress on Intelligent Control and Automation (WCICA)

作者： Hairong Zhong Xiaojian Liu Shiyao Jin National Lab for Parallel & Distributed Processing National University of Defense Technology Hunan Province China National Lab for Parallel & Distributed Processing National University of Defense Technology Changsha Hunan Province China

ISBN: (纸本)0780382730

Large-scale data dissemination applications, which need large numbers of multicast groups to filter information, were often restricted by the poor support of the relevant network routers. So we proposed hybrid multicasting (HM), which could take the advantage of IP multicasting, and was not limited by the network routers at the same time. Compared with other one-to-many data transfer methods, such as duplicate unicasting, broadcasting and IP multicasting, HM could exploit the capabilities and behavior of the underlying communication network and provided better performance. Basing on the application characteristics, we gave some theorems to guide how to select transfer methods, and presented a new data dissemination allocation algorithm, information Flow Merge with the Hybrid Multicasting First (FM/HMF). Tests showed that FM/HMF could produce better outcome and executes faster than nowadays-prevalent IP multicast group allocation strategies.

关键词： Large-scale systems Application software Broadcasting Filters Multicast algorithms Testing Bandwidth distributed processing Computer science Electronic mail

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Java: From "Hard Coding" to Using an Integrated Development Environment

Java: From "Hard Coding" to Using an Integrated Development ...

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International Conference on (DFMA) distributed Frameworks for Multimedia Applications

作者： Ali R. Mustafa Kattan Rosni Abdullah Rosalina Abdul Salam Parallel & Distributed Processing Laboratory School ofComputer Sciences Universiti Sains Islam Malaysia Penang Malaysia Parallel & Distributed Processing Lab School of Computer Sciences Universiti Sains Malaysia

Being a Java developer the decision of starting to use an integrated development environment (IDE) instead of a set of discrete programming tools, is to be taken sooner or later. This paper identifies the favorite features to opt for, in selecting a suitable IDE, what should be used and by whom with plethora of such IDEs. Though IDEs are meant to facilitate program development and increase productivity, migrating developers might fall into a set of common pitfalls that could hinder productivity. Such pitfalls are indicated and discussed in the view of available IDE features

关键词： Java Productivity Writing Programming profession Education distributed processing Concurrent computing distributed computing Optimized production technology Psychology

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Property-oriented test generation from UML Statecharts 04

Property-oriented test generation from UML Statecharts

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IEEE International Conference on Automated Software Engineering (ASE)

作者： Shuhao Li Ji Wang Zhi-ChangQi National Laboratory for Parallel and Distributed Processing Changsha P.R. China National Lab. for Parallel & Distributed Process. Changsha China

ISBN: (纸本)0769521312

UML Statecharts is an important modeling formalism with hierarchy, concurrency, broadcast-communication mechanisms and data variables. These facilities help the developers to describe the complex behaviors of an object along its lifecycle naturally. However, the ease of modeling is at the expense of testing: the system developed from Statecharts usually has numerous behaviors and is therefore extremely difficult to receive comprehensive and in-depth testing. This work presents an approach to deriving targeted test sequences from UML Statecharts according to tester-specified temporal logic properties. Using this approach, testing efforts can be focused on specific properties of the system and usually only a small portion of the total behaviors will be tested. This method suits well the occasions when the testers are interested in just some specific properties of the system or when they have to focus on its critical properties in case that limited budget is available.

关键词： Unified modeling language Automatic testing Software testing Software engineering

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hi-RF:Incremental Learning Random Forest for Large-Scale Multi-class Data Classification

hi-RF:Incremental Learning Random Forest for Large-Scale Mul...

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2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE2016)

作者： Tingting Xie Changjian Wang Yuxing Peng National Lab for Parallel and Distributed Processing School of ComputerNational University of Defense Technology

ISBN: (纸本)9781510835368

In recent years,dynamically growing data and largescale data classification *** traditional methods struggle to balance the precision and computational burden when data and its number of classes ***,some methods are with weak precision,and the others are *** this paper,we propose an incremental learning method,namely,heterogeneous incremental Nearest Class Mean Random Forest(hi-RF),to handle this *** is a heterogeneous method that either replaces trees or updates trees leaves in the random forest adaptively,to reduce the computational time in comparable performance,when data of new classes ***,to keep the accuracy,one proportion of trees are replaced by new NCM decision trees;to reduce the computational load,the rest trees are updated their leaves probabilities *** of all,outof-bag estimation and out-of-bag boosting are proposed to balance the accuracy and the computational *** experiments were conducted and demonstrated its comparable precision with much less computational time.

关键词： large scale multi-class classification Incremental Learning random forest heterogeneous incremental Nearest Class Mean Random Forest

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BSE-MAML: Model Agnostic Meta-Reinforcement Learning via Bayesian Structured Exploration

BSE-MAML: Model Agnostic Meta-Reinforcement Learning via Bay...

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IEEE International Conference on Services Computing (SCC)

作者： Haonan Wang Yiyun Zhang Dawei Feng Dongsheng Li Feng Huang National Lab for Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (数字)9781728187891

ISBN: (纸本)9781728187907

Deep reinforcement learning (RL) is playing an increasingly important role in web services such as news recommendation, vulnerability detection, and personalized services. Exploration is a key component of RL, which determines whether these RL-based applications could find effective solutions eventually. In this paper, we propose a novel gradient-based fast adaptation approach for model agnostic meta-reinforcement learning via Bayesian structure exploration (BSE-MAML). BSE-MAML could effectively learn exploration strategies from prior experience by updating policy with embedding latent space via a Bayesian mechanism. Coherent stochasticity injected by latent space are more efficient than random noise, and can produce exploration strategies to perform well in novel environment. We have conducted extensive experiments to evaluate BSE-MAML. Experimental results show that BSE-MAML achieves better performance in exploration in realistic environments with sparse rewards, compared to state-of-the-art meta-RL algorithms, RL methods without learning exploration strategies, and task-agnostic exploration approaches.

关键词： Adaptation models Web services Stochastic processes Service computing Probabilistic logic Bayes methods Task analysis

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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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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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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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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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