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Multi-Owner Keyword Search over Shared Data without Secure Channels in the Cloud

China Communications 2017年第5期14卷 124-133页

作者： Yilun Wu Xicheng Lu Jinshu Su Peixin Chen Xiaofeng Wang Bofeng Zhang College of Computer National University of Defense TechnologyChangsha 410073China National Laboratory for Parallel and Distributed Processing National University of Defense TechnologyCbangsba 410073China

Searchable encryption allows cloud users to outsource the massive encrypted data to the remote cloud and to search over the data without revealing the sensitive information. Many schemes have been proposed to support the keyword search in a public cloud. However,they have some potential limitations. First,most of the existing schemes only consider the scenario with the single data owner. Second,they need secure channels to guarantee the secure transmission of secret keys from the data owner to data users. Third,in some schemes,the data owner should be online to help data users when data users intend to perform the search,which is *** this paper,we propose a novel searchable scheme which supports the multi-owner keyword search without secure channels. More than that,our scheme is a non-interactive solution,in which all the users only need to communicate with the cloud server. Furthermore,the analysis proves that our scheme can guarantee the security even without secure channels. Unlike most existing public key encryption based searchable schemes,we evaluate the performance of our scheme,which shows that our scheme is practical.

关键词： keyword search cloud security secure channels proxy re-encryption

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Providing Virtual Cloud for Special Purposes on Demand in JointCloud Computing Environment

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Journal of Computer Science & Technology 2017年第2期32卷 211-218页

作者： Dong-Gang Cao Member, CCF, IEEE, Bo An Pei-Chang Shi Huai-Min Wang Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of Education Beijing 100871 China National Key Laboratory for Parallel and Distributed Processing National University of Defense and Technology Changsha 410073 China

Cloud computing has been widely adopted by enterprises because of its on-demand and elastic resource usage paradigm. Currently most cloud applications are running on one single cloud. However, more and more applications demand to run across several clouds to satisfy the requirements like best cost efficiency, avoidance of vender lock-in, and geolocation sensitive service. JointCloud computing is a new research initiated by Chinese institutes to address the computing issues concerned with multiple clouds. In JointCloud, users＇ diverse and dynamic requirements on cloud resources axe satisfied by providing users virtual cloud （VC） for special purposes. A virtual cloud for special purposes is in essence a user＇s specific cloud working environment having the customized software stacks, configurations and computing resources readily available. This paper first introduces what is JointCloud computing and then describes the design rationales, motivation examples, mechanisms and enabling technologies of VC in JointCloud.

关键词： cloud computing JointCloud virtual cloud （VC） cloud working environment

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High Performance Graph Analytics with Productivity on Hybrid CPU-GPU Platforms

High Performance Graph Analytics with Productivity on Hybrid...

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2018年第二届高性能编译、计算和通信国际会议

作者： Haoduo Yang Huayou Su Qiang Lan Mei Wen Chunyuan Zhang Department of Computer National University of Defense Technology National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology

In recent years, the rapid-growing scales of graphs have sparked a lot of parallel graph analysis frameworks to leverage the massive hardware resources on CPUs or GPUs. Existing CPU implementations are time-consuming, while GPU implementations are restricted by the memory space and the complexity of programming. In this paper, we present a high performance hybrid CPU-GPU parallel graph analytics framework with good productivity based on GraphMat. We map vertex programs to generalized sparse matrix vector multiplication on GPUs to deliver high performance, and propose a high-level abstraction for developers to implement various graph algorithms with relatively little efforts. Meanwhile, several optimizations have been adopted for reducing the communication cost and leveraging hardware resources, especially the memory hierarchy. We evaluate the proposed framework on three graph primitives(PageRank, BFS and SSSP) with large-scale graphs. The experimental results show that, our implementation achieves an average speedup of 7.0 X than GraphMat on two 6-core Intel Xeon CPUs. It also has the capability to process larger datasets but achieves comparable performance than MapGraph, a state-of-theart GPU-based framework.

关键词： parallel Computing Graph Analytics Hybrid CPU-GPU

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Adaptive Linearized Alternating Direction Method of Multipliers for Non-Convex Compositely Regularized Optimization Problems

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Tsinghua Science and Technology 2017年第3期22卷 328-341页

作者： Linbo Qiao Bofeng Zhang Xicheng Lu Jinshu Su College of Computer National University of Defense Technologyand National Laboratory for Parallel and Distributed ProcessingNational University of Defense TechnologyChangsha 410073China College of Computer National University of Defense TechnologyChangsha 410073China

We consider a wide range of non-convex regularized minimization problems, where the non-convex regularization term is composite with a linear function engaged in sparse learning. Recent theoretical investigations have demonstrated their superiority over their convex counterparts. The computational challenge lies in the fact that the proximal mapping associated with non-convex regularization is not easily obtained due to the imposed linear composition. Fortunately, the problem structure allows one to introduce an auxiliary variable and reformulate it as an optimization problem with linear constraints, which can be solved using the Linearized Alternating Direction Method of Multipliers （LADMM）. Despite the success of LADMM in practice, it remains unknown whether LADMM is convergent in solving such non-convex compositely regularized optimizations. In this research, we first present a detailed convergence analysis of the LADMM algorithm for solving a non-convex compositely regularized optimization problem with a large class of non-convex penalties. Furthermore, we propose an Adaptive LADMM （AdaLADMM） algorithm with a line-search criterion. Experimental results on different genres of datasets validate the efficacy of the proposed algorithm.

关键词： adaptive linearized alternating direction method of multipliers non-convex compositely regularizedoptimization cappled-ll regularized logistic regression

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Correction to: A compressed matrix sequence method for solving normal equations of bundle adjustment

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Machine Vision and Applications 2021年第4期32卷 1-1页

作者： Peng, Jiaxin Liu, Jie Wei, Hua Laboratory of Software Engineering for Complex Systems School of Computer Science National University of Defense Technology Changsha China Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Changsha China Algorithm Lab Changsha Morale Network Technology Co. Ltd. Changsha China Water and Land Resources Research Center of the Middle Reaches of Yangtze River School of Resources Environment Science and Engineering Hubei University of Science and Technology Xianning China

A correction to this paper has been published: https://***/10.1007/s00138-021-01222-5

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Kraken: A continuous incremental data acquisition system for GitHub and git repositories 7

Kraken: A continuous incremental data acquisition system for...

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2017 7th International Workshop on Computer Science and Engineering, WCSE 2017

作者： Zeng, Lingbin Yin, Gang Wang, Tao Yu, Yue Fan, Qiang Li, Zhi-Xing Yu, Jie Wang, H.M. National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789811136719

With the quick development of open source software, quantity of software is produced in the open source community (OSC) [1]. Lots of researches are launched to study the internal regular patterns of OSC [2], [3]. GitHub is one of the most famous open source community which owns thousands software projects. As a result, there are massive and abundant data of software development activities in GitHub. With the purpose to offer an accuracy and efficient dataset of GitHub, this paper proposes Kraken which is a continuous incremental data acquisition system for GitHub. Kraken contains three main modules which are independent with each other. Kraken gets the data of GitHub from two ways: git repositories and rest API. The final result shows that Kraken could extract the commits information of git repositories and get pull requests(PRs) and issues through rest API. The commits information contains the detail development history of software and the feedbacks and wisdom of software engineers are showed through PRs and issues.

关键词： Open source software

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DEEP IMAGE CLUSTERING USING CONVOLUTIONAL AUTOENCODER EMBEDDING WITH INCEPTION-LIKE BLOCK

DEEP IMAGE CLUSTERING USING CONVOLUTIONAL AUTOENCODER EMBEDD...

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IEEE International Conference on Image processing

作者： Qiang Wang Jiaqing Xu Rongchun Li Peng Qiao Ke Yang Shijie Li Yong Dou National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China School of Computer National University of Defense Technology Changsha China

Image clustering is one of the challenging tasks in machine learning, and has been extensively used in various applications. Recently, various deep clustering methods has been proposed. These methods take a two-stage approach, feature learning and clustering, sequentially or jointly. We observe that these works usually focus on the combination of reconstruction loss and clustering loss, relatively little work has focused on improving the learning representation of the neural network for clustering. In this paper, we propose a deep convolutional embedded clustering algorithm with inception-like block (DCECI). Specifically, an inception-like block with different type of convolution filters are introduced in the symmetric deep convolutional network to preserve the local structure of convolution layers. We simultaneously minimize the reconstruction loss of the convolutional autoencoders with inception-like block and the clustering loss. Experimental results on multiple image datasets exhibit the promising performance of our proposed algorithm compared with other competitive methods.

关键词： Convolutional codes Convolution Clustering algorithms Image reconstruction Decoding Clustering methods Task analysis

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An empirical study of reviewer recommendation in pull-based development model 17

An empirical study of reviewer recommendation in pull-based ...

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9th Asia-Pacific Symposium on Internetware, Internetware 2017

作者： Yang, Cheng Zhang, Xunhui Zeng, Lingbin Fan, Qiang Yin, Gang Wang, Huaimin National Laboratory for Parallel and Distributed Processing College of Computer Changsha Hunan410073 China

ISBN: (纸本)1595930361

Code review is an important process to reduce code defects and improve software quality. However, in social coding communities using the pull-based model, everyone can submit code changes, which increases the required code review efforts. Therefore, there is a great need of knowing the process of code review and analyzing the pre-existing reviewer recommendation algorithms. In this paper, we do an empirical study about the PRs and their reviewers in Rails project. Moreover, we reproduce a popular and effective IR-based code reviewer recommendation algorithm and validate it on our dataset which contains 16,049 *** find that the inactive reviewers are very important to code reviewing process, however, the pre-existing method's recommendation result strongly depends on the activeness of reviewers. © 2017 Association for Computing Machinery.

关键词： Codes (symbols)

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HPGA: A High-Performance Graph Analytics Framework on the GPU

HPGA: A High-Performance Graph Analytics Framework on the GP...

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International Conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Haoduo Yang Huayou Su Mei Wen Chunyuan Zhang Department 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: (纸本)9781538657393;9781538657386

In recent years, the rapidly growing use of graphs has sparked parallel graph analytics frameworks for leveraging the massive hardware resources, specifically graphics processing units (GPUs). However, the issues of the unpredictable control flows, memory divergence, and the complexity of programming have restricted high-level GPU graph libraries. In this work, we present HPGA, a high performance parallel graph analytics framework targeting the GPU. HPGA implements an abstraction which maps vertex programs to generalized sparse matrix operations on GPUs for delivering high performance. HPGA incorporates high-performance GPU computing primitives and optimization strategies with a high-level programming model. We evaluate the performance of HPGA for three graph primitives (BFS, SSSP, PageRank) with large-scale datasets. The experimental results show that HPGA matches or even exceeds the performance of MapGraph and nvGRAPH, two state-of-the-art GPU graph libraries.

关键词： Graphics processing units Sparse matrices Programming Computational modeling Arrays Optimization

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Learning from internet: Handling uncertainty in robotic environment modeling 17

Learning from internet: Handling uncertainty in robotic envi...

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9th Asia-Pacific Symposium on Internetware, Internetware 2017

作者： Li, Yiying Wang, Huaimin Ding, Bo Che, Huimin National Key Laboratory of Parallel and Distributed Processing College of Computer National University of Defense Technology ChangSha China

ISBN: (纸本)1595930361

Uncertainty is a great challenge for environment perception of autonomous robots. For instance, while building semantic maps (i.e., maps with semantic labels such as object names), the robot may encounter unexpected objects of which it has no knowledge. It will lead to inevitable failures in traditional environment modeling software. The abundant knowledge being accumulated on the Internet has the potential to assist robots to handle such kind of uncertainty. However, existing researches have not touched this issue yet. This paper proposes a cloud-based semantic mapping engine named SemaCloud, which can not only augment robot's environment modeling capability by the rich cloud resources but also cope with uncertainty by utilizing the Internet knowledge on necessary. It adopts a state-of-art Deep Neural Network (DNN) for real-time and accurate recognition of pre-trained objects. If an object is beyond the knowledge of this DNN, a special mechanism named QoS-aware cloud phase transition is triggered to seek help from existing recognition services on the Internet. By a set of carefullydesigned algorithms, it can maximize benefits and minimize the negative impacts on the Quality of Service (QoS) properties of robotic applications, which is essential to many robot scenarios. The experiments on both open datasets and real robots show that our work can handle uncertainty successfully in robotic semantic mapping without sacrificing critical real-time constraints. © 2017 Association for Computing Machinery.

关键词： Deep neural networks

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