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arXiv 2018年

作者： Qin, Zheng Zhang, Zhaoning Zhang, Shiqing Yu, Hao Peng, Yuxing Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China

Compact neural networks are inclined to exploit "sparsely-connected" convolutions such as depthwise convolution and group convolution for employment in mobile applications. Compared with standard "fully-connected" convolutions, these convolutions are more computationally economical. However, "sparsely-connected" convolutions block the inter-group information exchange, which induces severe performance degradation. To address this issue, we present two novel operations named merging and evolution to leverage the inter-group information. Our key idea is encoding the inter-group information with a narrow feature map, then combining the generated features with the original network for better representation. Taking advantage of the proposed operations, we then introduce the Merging-and-Evolution (ME) module, an architectural unit specifically designed for compact networks. Finally, we propose a family of compact neural networks called MENet based on ME modules. Extensive experiments on ILSVRC 2012 dataset and PASCAL VOC 2007 dataset demonstrate that MENet consistently outperforms other state-of-the-art compact networks under different computational budgets. For instance, under the computational budget of 140 MFLOPs, MENet surpasses ShuffleNet by 1% and MobileNet by 1.95% on ILSVRC 2012 top-1 accuracy, while by 2.3% and 4.1% on PASCAL VOC 2007 mAP, respectively. Copyright © 2018, The Authors. All rights reserved.

关键词： Convolution

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FD-Mobilenet: Improved mobilenet with a fast downsampling strategy

arXiv

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arXiv 2018年

作者： Qin, Zheng Zhang, Zhaoning Chen, Xiaotao Peng, Yuxing Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China

We present Fast-Downsampling MobileNet (FD-MobileNet), an efficient and accurate network for very limited computational budgets (e.g., 10-140 MFLOPs). Our key idea is applying a fast downsampling strategy to MobileNet framework. In FD-MobileNet, we perform 32× downsampling within 12 layers, only half the layers in the original MobileNet. This design brings three advantages: (i) It remarkably reduces the computational cost. (ii) It increases the information capacity and achieves significant performance improvements. (iii) It is engineering-friendly and provides fast actual inference speed. Experiments on ILSVRC 2012 and PASCAL VOC 2007 datasets demonstrate that FD-MobileNet consistently outperforms MobileNet and achieves comparable results with ShuffleNet under different computational budgets, for instance, surpassing MobileNet by 5.5% on the ILSVRC 2012 top-1 accuracy and 3.6% on the VOC 2007 mAP under a complexity of 12 MFLOPs. On an ARM-based device, FD-MobileNet achieves 1.11× inference speedup over MobileNet and 1.82× over ShuffleNet under the same complexity. Copyright © 2018, The Authors. All rights reserved.

关键词： Signal sampling

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

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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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Deep Learning-based Cooperative Trail Following for Multi-Robot System

Deep Learning-based Cooperative Trail Following for Multi-Ro...

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International Joint Conference on Neural Networks

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

Following trails in the wild is an essential capability of out-door autonomous mobile robots. Recently, deep learning-based approaches have made great advancements in this field. However, the existing research only focuses on the trail following with a single robot. In contrast, many robotic tasks in the reality, such as search and patrolling, are conducted by a group of robots. While these robots are grouped to move in the wild, they can cooperate to significantly promote the trail following accuracy, for example, by sharing images of different view angles or real-time decision fusion. This paper proposes such an approach named DL-Cooper that enables multi-robot vision-based trail following based on deep learning algorithms. It allows each robot to make a decision respectively with deep neural network and then fusion the decisions on the collective level with the support of back-end cloud computing infrastructure. It also takes Quality of Service (QoS) assurance, a very essential property of robotic software, into consideration. By limiting the condition to fusion decisions, the time latency can be minimally sacrificed. Experiments on the real-world dataset show that our approach has significantly improved the accuracy of the single-robot system.

关键词： Task analysis Robot kinematics Machine learning Multi-robot systems Quality of service Cloud computing

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Design of an Indoor Exploration and Multi-Objective Navigation System 37

Design of an Indoor Exploration and Multi-Objective Navigati...

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37th Chinese Control Conference, CCC 2018

作者： Zheng, Guoxian Zhang, Lei Zhang, Huaxi Yulin Ding, Bo State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China MIS INSSET Universite de Picardie Jules Verne Amiens80039 France National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha410008 China

ISBN: (纸本)9789881563941

In this paper, we propose an indoor robot autonomous navigation system. The robot firstly explores in an unknown environment, and then navigates autonomously by using the explored map. The robot is equipped a 2D laser scanner as the main sensor. The laser scanner is used for path planning and frontier-based exploration. A 2D global occupancy map is built for path planning, frontier-based exploration and multi-objective autonomous navigation. Laser scans are transmitted into Simultaneous Localization and Mapping (SLAM) process in the exploration phase. In indoor environment, the exploration efficiency is improved by merging a heuristic algorithm. By using multi-threading technology and a 3D perception approach proposed in this paper, the robot equipped with a low-cost RGBD sensor can detect all kinds of obstacles to achieve highly reliable navigation in complicated 3D environment. Meanwhile, we develop a multi-objective navigation application to make human-robot interaction more convenient and satisfy multi-task deployment. Our approaches are demonstrated by experimental results. © 2018 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Motion planning

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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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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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DSDR: Dynamic Semantic Discard Reader for Open-Domain Question Answering

DSDR: Dynamic Semantic Discard Reader for Open-Domain Questi...

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International Joint Conference on Neural Networks

作者： Xu Wang Zhen Huang Yiming Zhang Ludan Tan Yang Liu Science and Technology on Parallel and Distributed Laboratory College of Computer National University of Defense Technology Changsha China

Open-domain question answering (QA) is an important problem in natural language processing. A QA system usually consists of an information retrieve (IR) model for finding the relevant passages that may have answers and a read comprehension (RC) model for generating answers from the selected passages. The state-of-the-art RC model uses all the passages for feature extraction when matching semantic information of text pairs. In this paper, however, we argue that the texts of passages often have different importance for specific questions and some even provide negative information that might affect the correctness of final prediction. To address this problem, we design dynamic semantic discard reader (DSDR), a deep model that tries to drop negative information irrelevant to the questions. The result of our experiment shows that DSDR outperforms existing methods in the exact-match and F1 scores on open-domain QA datasets.

关键词： Semantics Feature extraction Neural networks Encoding TV Task analysis Numerical models

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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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