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

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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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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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 environmental sounds with multi-scale convolutional neural network

arXiv

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arXiv 2018年

作者： Zhu, Boqing Wang, Changjian Liu, Feng Lei, Jin Lu, Zengquan Peng, Yuxing Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology College of Meteorology and Oceanology National University of Defense Technology Changsha China

Deep learning has dramatically improved the performance of sounds recognition. However, learning acoustic models directly from the raw waveform is still challenging. Current waveform-based models generally use time-domain convolutional layers to extract features. The features extracted by single size filters are insufficient for building discriminative representation of audios. In this paper, we propose multi-scale convolution operation, which can get better audio representation by improving the frequency resolution and learning filters cross all frequency area. For leveraging the waveform-based features and spectrogram-based features in a single model, we introduce twophase method to fuse the different features. Finally, we propose a novel end-to-end network called WaveMsNet based on the multi-scale convolution operation and two-phase method. On the environmental sounds classification datasets ESC-10 and ESC-50, the classification accuracies of our WaveMsNet achieve 93.75% and 79.10% respectively, which improve significantly from the previous methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Convolution

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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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Detecting duplicate pull-requests in GitHub 17

Detecting duplicate pull-requests in GitHub

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9th Asia-Pacific Symposium on Internetware, Internetware 2017

作者： Li, Zhixing Yin, Gang Yu, Yue Wang, Tao Wang, Huaimin National Laboratory for Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha410073 China

ISBN: (纸本)1595930361

The widespread use of pull-requests boosts the development and evolution for many open source software projects. However, due to the parallel and uncoordinated nature of development process in GitHub, duplicate pull-requests may be submitted by different contributors to solve the same problem. Duplicate pull-requests increase the maintenance cost of GitHub, result in the waste of time spent on the redundant effort of code review, and even frustrate developers' willing to offer continuous contribution. In this paper, we investigate using text information to automatically detect duplicate pull-requests in GitHub. For a new-arriving pull-request, we compare the textual similarity between it and other existing pull-requests, and then return a candidate list of the most similar *** evaluate our approach on three popular projects hosted in GitHub, namely Rails, Elasticsearch and ***. The evaluation shows that about 55.3% - 71.0% of the duplicates can be found when we use the combination of title similarity and description similarity. © 2017 Association for Computing Machinery.

关键词： Open source software

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Towards a multi-array architecture for accelerating large-scale matrix multiplication on FPGAs

arXiv

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arXiv 2018年

作者： Shen, Junzhong Qiao, Yuran Huang, You Wen, Mei Zhang, Chunyuan College of Computer National University of Defense Technology Changsha410073 China National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha410073 China

Large-scale floating-point matrix multiplication is a fundamental kernel in many scientific and engineering applications. Most existing work only focus on accelerating matrix multiplication on FPGA by adopting a linear systolic array. This paper towards the extension of this architecture by proposing a scalable and highly configurable multi-array architecture. In addition, we propose a work-stealing scheme to ensure the equality in the workload partition among multiple linear arrays. Furthermore, an analytical model is developed to determine the optimal design parameters. Experiments on a real-life convolutional neural network (CNN) show that we can obtain the optimal extension of the linear array architecture. Copyright © 2018, The Authors. All rights reserved.

关键词： Field programmable gate arrays (FPGA)

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Deep Discriminative Clustering Network

Deep Discriminative Clustering Network

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International Joint Conference on Neural Networks

作者： Xuying Shao Keshi Ge Huayou Su Lei Luo Baoyun Peng Dongsheng Li Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China

Deep clustering aims to cluster unlabeled data by embedding them into a subspace based on deep model. The key challenge of deep clustering is to learn discriminative representations for input data with high dimensions. In this paper, we present a deep discriminative clustering network for clustering the real-world images. We use a convolutional auto-encoder stacked with a softmax layer to predict clustering assignments. To learn a discriminative representations, the proposed approach adds discriminative loss as embedded regularization with relative entropy minimization. With the discriminative loss, the network can not only produce clustering assignments, but also learn discriminative features by reducing intra-cluster distance and increasing inter-cluster distance. We evaluate the proposed method on three datasets: MNIST-full, YTF and FRGC-v2.0. We outperform state-of-the-art results on MNIST-full and FRGC-v2.0 and achieve competitive result on YTF. The source code has been made publicly available at .

关键词： Machine learning Clustering algorithms Image reconstruction Linear programming Clustering methods Neural networks Task analysis

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