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Simulation analysis of heavy-ion-induced single-event response for nanoscale bulk-Si FinFETs and conventional planar devices

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Science China(Technological Sciences) 2017年第3期60卷 459-466页

作者： YU JunTing CHEN ShuMing CHEN JianJun HUANG PengCheng College of Computer National University of Defense Technology Changsha 410073 China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China

FinFET technologies are becoming the mainstream process as technology scales *** on 28-nm bulk-Si FinFETs and

关键词： charge collection bipolar amplification reversed bipolar effect single-event effect(SEE) single-event transient(SET) FinFET planar device

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Instance-Specific Algorithm Selection via Multi-Output Learning

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Tsinghua Science and Technology 2017年第2期22卷 210-217页

作者： Kai Chen Yong Dou Qi Lv Zhengfa Liang the National Laboratory for Parallel and Distributed Processing National University of Defense TechnologyChangsha 410037China the College of Computer National University of Defense TechnologyChangsha 410037China

Instance-specific algorithm selection technologies have been successfully used in many research fields,such as constraint satisfaction and planning. Researchers have been increasingly trying to model the potential relations between different candidate algorithms for the algorithm selection. In this study, we propose an instancespecific algorithm selection method based on multi-output learning, which can manage these relations more *** kinds of multi-output learning methods are used to predict the performances of the candidate algorithms：（1）multi-output regressor stacking;（2） multi-output extremely randomized trees; and（3） hybrid single-output and multioutput trees. The experimental results obtained using 11 SAT datasets and 5 Max SAT datasets indicate that our proposed methods can obtain a better performance over the state-of-the-art algorithm selection methods.

关键词： algorithm selection multi-output learning extremely randomized trees performance prediction constraint satisfaction

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TEMPORAL PYRAMID RELATION NETWORK FOR VIDEO-BASED GESTURE RECOGNITION

TEMPORAL PYRAMID RELATION NETWORK FOR VIDEO-BASED GESTURE RE...

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IEEE International Conference on Image processing

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

Gesture recognition in video is an important application of computer vision. However, there are few works talked about the temporal order or relation of the frames in video, which is important for model gestures. In this paper, we propose Temporal Pyramid Relation Network (TPRN) which can model the temporal relation of video frames effectively and efficiently. First, we use Temporal Pyramid Pooling (TPP) layer to get temporal feature sequences of multiple scale pyramids. Then, a Temporal Relation Network (TRN) is stacked on the feature sequence of each scale respectively to model the temporal relations of video frames at multiple scales. At last, representations of all scales are aggregated to get the final prediction. TPRN can take video clips of various length as input and is scalable for video length. We evaluate TPRN on a recently released very large video-based gesture recognition dataset - 20BN-Jester dataset v1, and TPRN achieves competitive performance.

关键词： Feature extraction Gesture recognition Solid modeling Hidden Markov models Three-dimensional displays Computational modeling Training

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Density-Attentive Head Detector for Crowd Counting

Density-Attentive Head Detector for Crowd Counting

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International Conference on Data Science and Business Analytics (ICDSBA)

作者： Ziyu Zhao Xingyu Shen Long Lan Xiaoyao Yin Xiang Zhang Yonggang Che Zhigang Luo College of Computer National University of Defense Technology Changsha China Institute for Quantum Information & State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (数字)9781728146447

ISBN: (纸本)9781728146454

In crowd counting, regression-based method shows better performance in extreme density scenes by introduces a density map. However, the regression-based method fails to locate the positions of each head, which significantly restricts its applications. Detection-based method counts each head with their accurate locations but works only in middle or low-level density scenes. In this paper, a joint learning method, named Density-attentive Head Detector (DAHD) is developed to overcome their respective shortcomings via a multi-task training procedure. Specifically, to guarantee the sensitivity of the detector to small or partially occluded heads, we carefully equip the detector with a density map which learned from regression module. Moreover, a novel Dilated Feature Pyramid Network (DFPN) is introduced to our method to enlarge the receptive field of convolutional kernel, bringing confirmative additional benefits to identify small heads. Experiments on the popular ShanghaiTech and Mall datasets confirm the improved performance of DAHD compared with the current detection-based approaches, and a comparable performance to regression-based approaches in term of counting.

关键词： Detectors Feature extraction Object detection Faces Kernel Training Task analysis

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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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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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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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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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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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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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