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作者： Chen, Xinhai Xie, Peizhen Chi, Lihua Liu, Jie Gong, Chunye Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China Institute of Advanced Science and Technology Hunan Institute of Traffic Engineering Hengyang China

Sparse matrix-vector multiplication (SpMV) is an essential kernel in sparse linear algebra and has been studied extensively on all modern processor and accelerator architectures. Compressed Sparse Row (CSR) is a frequently used format for sparse matrices storage. However, CSR-based SpMV has poor performance on processors with vector units. In order to take full advantage of SIMD acceleration technology in SpMV, we proposed a new matrix storage format called CSR-SIMD. The new storage format compresses the non-zero elements into many variable-length data fragments with consecutive memory access addresses. Thus, the data locality of sparse matrix A and dense vector x expands and the floating-point operations for each fragment can be completely calculated by vectorized implementation on wide SIMD units. Our experimental results indicate that CSR-SIMD has better storage efficiency and low-overhead for format conversion. Besides, the new format achieves high scalability on wide SIMD units. In comparison with the CSR-based and BCSR-based SpMV, CSR-SIMD obtains better performance on FT1500A, Intel Xeon, and Intel Xeon Phi. © 2018 John Wiley & Sons, Ltd.

关键词： Storage efficiency

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Understanding User Interests Acquisition in Personalized Online Course Recommendation

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Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint Conference on Web and Big Data, APWeb-WAIM 2018

作者： Li, Xiao Wang, Ting Wang, Huaimin Tang, Jintao Information Center National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China National Key Laboratory of Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (纸本)9783030012977

MOOCs have attracted a large number of learners with different education background all over the world. Despite its increasing popularity, MOOCs still suffer from the problem of high drop-out rate. One important reason may be due to the difficulty in understanding learning demand and user interests. To helper users find the most suitable courses, personalized course recommendation technology has become a hot research topic in e-learning and data mining community. One of the keys to the success of personalized course recommendation is a good user modeling method. Previous works in course recommendation often focus on developing user modeling methodology which learns latent user interests from historic learning data. Recently, interactive course recommendation has become more and more popular. In this paradigm, recommender systems can directly query user interests through survey tables or questionnaires and thus the learned interests may be more accurate. In this paper, we study the user interest acquisition problem based on the interactive course recommendation framework (ICRF). Under this framework, we systematically discuss different settings on querying user interests. To reduce performance-cost score, we propose the ICRF user interest acquisition algorithm that combines representative sampling and interest propagation algorithm to acquire user interests in a cost-effective way. With extensive experiments on real-world MOOC course enrollment datasets, we empirically demonstrate that our selective acquisition strategy is very effective and it can reduce the performance-cost score by 30.25% compared to the traditional aggressive acquisition strategies. © 2018, Springer Nature Switzerland AG.

关键词： Surveys

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A novel hybrid architecture for high speed regular expression matching 1

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1st International Symposium on Mobile Internet Security, MobiSec 2016

作者： Xu, Chengcheng Zhao, Baokang Chen, Shuhui Su, Jinshu College of Computer National University of Defense Technology No. 137 Yanwachi Street ChangshaHunan410073 China National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha410073 China

ISBN: (数字)9789811078507

ISBN: (纸本)9789811078491

Mobile devices play an important role in our everyday lives, but they also bring great security threats. Deep packet inspection (DPI) is one of the most efficient methods to detect the malicious information hidden in the mobile traffic, and regular expression matching is widely used in DPI for its powerful expressive ability. However, with the increasing complexity of regular expressions, traditional solutions cannot meet the requirements of both storage and high performance. In this paper, we propose a novel hybrid matching architecture and two-stage memory architecture for the state of the art hybrid FA to solve this problem. Experiment results confirm that our architecture is scalable to complex rule sets, and the matching performance outperforms state of the art memory centric solution by up to 15x. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Memory architecture

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Exploiting a depth context model in visual tracking with correlation filter

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Frontiers of Information Technology & Electronic Engineering 2017年第5期18卷 667-679页

作者： Zhao-yun CHEN Lei LUO Da-fei HUANG Mei WEN Chun-yuan ZHANG College of Computer National University of Defense TechnologyChangsha 410073China National Key Laboratory of Parallel and Distributed Processing Changsha 410073China

Recently correlation filter based trackers have attracted considerable attention for their high computational efficiency. However, they cannot handle occlusion and scale variation well enough. This paper aims at preventing the tracker from failure in these two situations by integrating the depth information into a correlation filter based tracker. By using RGB-D data, we construct a depth context model to reveal the spatial correlation between the target and its surrounding regions. Furthermore, we adopt a region growing method to make our tracker robust to occlusion and scale variation. Additional optimizations such as a model updating scheme are applied to improve the performance for longer video sequences. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed tracker performs favourably against state-of-the-art algorithms.

关键词： Visual tracking Depth context model Correlation filter Region growing

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VISUAL CONFUSION LABEL TREE FOR IMAGE CLASSIFICATION

VISUAL CONFUSION LABEL TREE FOR IMAGE CLASSIFICATION

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IEEE International Conference on Multimedia and Expo

作者： Yuntao Liu Yong Dou Ruochun Jin Rongchun Li National University of Defense Technology National Laboratory for Parallel and Distributed Processing Changsha China

Convolution neural network models are widely used in image classification tasks. However, the running time of such models is so long that it is not the conforming to the strict real-time requirement of mobile devices. In order to optimize models and meet the requirement mentioned above, we propose a method that replaces the fully-connected layers of convolution neural network models with a tree classifier. Specifically, we construct a Visual Confusion Label Tree based on the output of the convolution neural network models, and use a multi-kernel SVM plus classifier with hierarchical constraints to train the tree classifier. Focusing on those confusion subsets instead of the entire set of categories makes the tree classifier more discriminative and the replacement of the fully-connected layers reduces the original running time. Experiments show that our tree classifier obtains a significant improvement over the state-of-the-art tree classifier by 4.3% and 2.4% in terms of top-1 accuracy on CIFAR-100 and ImageNet datasets respectively. Additionally, our method achieves 124× and 115× speedup ratio compared with fully-connected layers on AlexNet and VGG16 without accuracy decline.

关键词： Image classification Convolution neural network Confusion graph Label tree

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Cloud-Based Framework for Scalable and Real-Time Multi-Robot SLAM

Cloud-Based Framework for Scalable and Real-Time Multi-Robot...

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IEEE International Conference on Web Services (ICWS)

作者： Pengfei Zhang Huaimin Wang Bo Ding Suning Shang National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

In the past decade, multi-robot simultaneous localization and mapping (SLAM) has been widely studied. However, the problem of collaborative SLAM with a large number of robots, such as dozens of robots, is far from being well solved. The challenges stem from not only the computation complexity in large-scale map merging but also the inefficiency to enable the parallel computing in this process, which is indispensable for us to make avail of the frontier of computing technology such as powerful cloud infrastructure. To effectively address these challenges, especially the latter one, we propose a scalable and real-time multi-robot visual SLAM framework based on the cloud robotic paradigm. The prominent feature of our framework is that it can distribute the SLAM process to multiple computing hosts in a cluster, which enables map building in parallel. To eliminate the bottleneck from data sharing between different sub-tasks, we also introduce diversified messaging pattern for various messaging scenarios, as well as the consistency policies for map data. The evaluations on the prototype of our framework, have shown that our method can do support as many as 256 robot entities simultaneously, without any compromising on the precision of poses estimation and map building.

关键词： Simultaneous localization and mapping Cloud computing Robot kinematics Task analysis Instruction sets Feature extraction

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Software Ranker: A New Comprehensive Software Ranking Approach

Software Ranker: A New Comprehensive Software Ranking Approa...

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IEEE International Conference on Software Engineering and Service Sciences (ICSESS)

作者： Yarong Zeng Xunhui Zhang Yaozong Li Tao Wang Gang Yin Huaimin Wang National Laboratory for Parallel and Distributed Processing National University of Defence Technology Changsha China

ISBN: (纸本)9781538665664;9781538665657

With the popularity of the open source, the open source community has accumulated a large number of open source project. While these massive projects provide developers with rich reusable resource, they also bring difficulties for users to choose the appropriate software. Therefore, it is very meaningful to rank the software and tell users which is better. In this paper, we propose a novel approach that ranks software based on a global perspective different from traditional software evaluation and ranking methods. We evaluate the software from four dimensions, namely community popularity, development activity, software health and team health. Each dimension contains some metrics. We demonstrate the effectiveness of our method through comparative experiments. This method has been integrated into to the OSSEAN platform to form a software leaderboard.

关键词： Mathematical model Open source software Tools Software quality Software measurement

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A Research on Area Coverage Algorithm for Robotics

A Research on Area Coverage Algorithm for Robotics

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IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE)

作者： Yaru Kang Dianxi Shi National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (纸本)9781538674178

Area coverage path planning is a special path planning method, which requires the robot to go through every point except obstacles in workspace. It has been used in many fields, such as lawn mowing, snow removal, search and rescue task, pesticide spraying, demining robots, cleaning robots and so on. This paper presents the achievements of the last two decades on area coverage. We propose a new Classification based on method of Choset. We analyze and summarize the recently research results by the new classification method and list the advantages and disadvantages. Finally, we give a summary of the classification methods and propose future research directions.

关键词： Path planning Task analysis Robot sensing systems Classification algorithms Approximation algorithms

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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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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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