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Determinants of pull-based development in the context of continuous integration

science China(Information sciences) 2016年第8期59卷 53-66页

作者： Yue YU Gang YIN Tao WANG Cheng YANG Huaimin WANG College of Computer National University of Defense Technology National Laboratory for Parallel and Distributed Processing

The pull-based development model, widely used in distributed software teams on open source communities, can efficiently gather the wisdom from crowds. Instead of sharing access to a central repository,contributors create a fork, update it locally, and request to have their changes merged back, i.e., submit a pull-request. On the one hand, this model lowers the barrier to entry for potential contributors since anyone can submit pull-requests to any repository, but on the other hand it also increases the burden on integrators, who are responsible for assessing the proposed patches and integrating the suitable changes into the central repository. The role of integrators in pull-based development is crucial. They must not only ensure that pull-requests should meet the project’s quality standards before being accepted, but also finish the evaluations in a timely manner. To keep up with the volume of incoming pull-requests, continuous integration(CI) is widely adopted to automatically build and test every pull-request at the time of submission. CI provides extra evidences relating to the quality of pull-requests, which would help integrators to make final decision(i.e., accept or reject). In this paper, we present a quantitative study that tries to discover which factors affect the process of pull-based development model, including acceptance and latency in the context of CI. Using regression modeling on data extracted from a sample of Git Hub projects deploying the Travis-CI service, we find that the evaluation process is a complex issue, requiring many independent variables to explain adequately. In particular, CI is a dominant factor for the process, which not only has a great influence on the evaluation process per se, but also changes the effects of some traditional predictors.

关键词： pull-request continuous integration Git Hub distributed software development empirical analysis

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A systematic review of structured sparse learning

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Frontiers of Information technology & Electronic Engineering 2017年第4期18卷 445-463页

作者： Lin-bo QIAO Bo-feng ZHANG Jin-shu SU Xi-cheng LU 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

High-dimensional data arising from diverse scientific research fields and industrial development have led to increased interest in sparse learning due to model parsimony and computational advantage. With the assumption of sparsity, many computational problems can be handled efficiently in practice. Structured sparse learning encodes the structural information of the variables and has been quite successful in numerous research fields. With various types of structures discovered, sorts of structured regularizations have been proposed. These regularizations have greatly improved the efficacy of sparse learning algorithms through the use of specific structural information. In this article, we present a systematic review of structured sparse learning including ideas, formulations, algorithms, and applications. We present these algorithms in the unified framework of minimizing the sum of loss and penalty functions, summarize publicly accessible software implementations, and compare the computational complexity of typical optimization methods to solve structured sparse learning problems. In experiments, we present applications in unsupervised learning, for structured signal recovery and hierarchical image reconstruction, and in supervised learning in the context of a novel graph-guided logistic regression.

关键词： Sparse learning Structured sparse learning Structured regularization

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引用

International Joint Conference on Neural Networks

作者： Hao Yu Zhaoning Zhang Zijie Zhu Wang Xiong Gen Zhang Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China College of Meteorology and Oceanology National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China

Similarity of nominal data plays fundamental roles in numerous fields of both machine learning and data mining. Unlike the similarity of numerical data, that of nominal data is much more difficult to describe, and few efforts have been done for it. Although existing nominal similarity measures can reveal a part of data properties, they suffer from low accuracy due to ignoring value relationships or integrating multi-view relationships inappropriately. In this paper, we propose a novel hierarchical measure for nominal data similarity (HNS). The HNS leverages the intrinsic data characteristics by considering low-level information both within and between attributes, and hierarchically seizes the value distributions, attribute interactions and attribute to object contributions. Meanwhile, it aggregates multi-view relationships trough a bottom to top framework, remaining consistency as well as complementary details. We theoretically analyzed this measure, and experiments on six UCI data sets demonstrate that the HNS outperforms the state-of-the-art nominal similarity measures in term of target alignment and clustering accuracy.

关键词： Sea measurements Frequency measurement Task analysis Medical services distributed databases Machine learning

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Predicting potential gene ontology from cellular response data 17

Predicting potential gene ontology from cellular response da...

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5th International Conference on Bioinformatics and Computational Biology, ICBCB 2017

作者： Hong, Hao Yin, Xiaoyao Li, Fei Guan, Naiyang Bo, Xiaochen Luo, Zhigang Department of Chemistry and Biology National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China Department of Biotechnology Beijing Institute of Radiation Medicine Beijing China

ISBN: (纸本)9781450348270

Ontologies have proven to be useful for capturing and organizing knowledge as a hierarchical set of terms and their relationships. However, curating gene ontology data by hand requires specialized knowledge of certain field, which is inefficient. Thus inferring gene ontology from the exponentially increased biological data is getting hot. Based on the Library of Integrated Network-Based Cellular Signatures (LINCS) data we came up with the hypothesis that genes participate in analogous biological processes might affect cells accordantly. By assessing cellular response after genes were knock out we built a similarity matrix with the Gene Set Enrichment Analysis (GSEA) and clustered the genes with affinity propagation algorithm. Next we mapped the cluster result to gene ontology biological process data for annotation and enrichment analysis, which confirmed our hypothesis and made it possible to predict biological processes for unannotated genes from cellular response data after genes are knock out for the first time. We further validated the rationality from the gene ontology molecular function data. © 2017 ACM.

关键词： Gene Ontology

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Asynchronous Bundle Method for Large-Scale Regularized Risk Minimization

Asynchronous Bundle Method for Large-Scale Regularized Risk ...

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International Joint Conference on Neural Networks

作者： Menglong Lu Dawei Feng Linbo Qiao Dawen Ding 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 CMC AS2 South CSC Asiainfo Nanchang China

Bundle method for regularized risk minimization (BMRM) is a variant of Cutting Plane Method (CPM). It performs efficiently in solving a convex minimization problem, which is a core part in a plethora of machine learning applications. Nonetheless, while exposed to the challenge of large-scale learning, the synchronous parallel implementation of BMRM easily encounters the straggler problem due to the diversity among heterogeneous working nodes' capability and unevenness in the inherent data distribution. In this paper, we propose a novel asynchronous distributed BMRM implementation, which employs an asynchronous computing window to fully explore the fast nodes' computational capabilities while reserving the good convergence of the BMRM. Extensive experiments show that the asynchronous BMRM algorithm has significant improvement of performance over its synchronous counterpart, and owns the ability to solve large-scale problems efficiently.

关键词： Estimation Linear programming Machine learning Risk management Convergence Servers Minimization

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Design of an Indoor Exploration and Multi-Objective Navigation System

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

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Chinese Control Conference (CCC)

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

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.

关键词： Navigation Three-dimensional displays Robot sensing systems Lasers Path planning Two dimensional displays

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Exploiting contention and congestion aware switch allocation in network-on-chips 17

Exploiting contention and congestion aware switch allocation...

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50th ACM Turing Conference - China, ACM TUR-C 2017

作者： Li, Cunlu Dongy, Dezun Liao, Xiangke National Laboratory for Parallel and Distributed Processing Collaborative Innovation Center of High Performance Computing College of Computer National University of Defense Technology Changsha Hunan410073 China

ISBN: (纸本)9781450348737

Network-on-chip system plays an important role to improve the performance of chip multiprocessor systems. As the complexity of the network increases, congestion problem has become the major performance bottleneck and seriously influence the performance of NoCs. Prior works have focused on designing effective routing algorithm based on collecting congestion and contention information to load balance the traffic. However, most prior works do not consider balancing the traffic load during switch allocation. Due to the lack of congestion information in switch allocation stage, switch allocator performs allocation only based on packet requests and thus aggravates the congestion in the ports of switch. In this paper, we propose to add the congestion and contention information into the switch allocation process and design an efficient on-chip switching strategy which utilizes the contention and congestion information to load balance the traffic and achieve efficient switch allocation. To further enhance the performance of our design, we carefully design our switch allocation strategy to balance the trade-off between traffic load balance and the matching efficiency in switch allocation. We evaluate our design under synthetic traffic and trace of parsec benchmarks. Our evaluations show this mechanism achieves optimal latency compared to best previous switch allocation strategies. © 2017 ACM.

关键词： Network-on-chip

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Collaborative deep learning across multiple data centers

arXiv

引用

arXiv 2018年

作者： Xu, Kele Mi, Haibo Feng, Dawei Wang, Huaimin Chen, Chuan Zheng, Zibin Lan, Xu National Key Laboratory of Parallel and Distributed Processing Changsha China College of Computer National University of Defense Technology Changsha China School of Data and Computer Science Sun Yat-Sen University Guangzhou China Queen Mary University of London London United Kingdom

Valuable training data is often owned by independent organizations and located in multiple data centers. Most deep learning approaches require to centralize the multi-datacenter data for performance purpose. In practice, however, it is often infeasible to transfer all data to a centralized data center due to not only bandwidth limitation but also the constraints of privacy regulations. Model averaging is a conventional choice for data parallelized training, but its ineffectiveness is claimed by previous studies as deep neural networks are often non-convex. In this paper, we argue that model averaging can be effective in the decentralized environment by using two strategies, namely, the cyclical learning rate and the increased number of epochs for local model training. With the two strategies, we show that model averaging can provide competitive performance in the decentralized mode compared to the data-centralized one. In a practical environment with multiple data centers, we conduct extensive experiments using state-of-the-art deep network architectures on different types of data. Results demonstrate the effectiveness and robustness of the proposed method. Copyright © 2018, The Authors. All rights reserved.

关键词： Network architecture

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Loss rank mining: A general hard example mining method for real-time Detectors

arXiv

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arXiv 2018年

作者： Yu, Hao Zhang, Zhaoning Qin, Zheng Wu, Hao Li, Dongsheng Zhao, Jun Lu, Xicheng Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China College of Electronic and Engineering National University of Defense Technology Changsha China College of Meteorology and Oceanology National University of Defense Technology Changsha China

Modern object detectors usually suffer from low accuracy issues, as foregrounds always drown in tons of backgrounds and become hard examples during training. Compared with those proposal-based ones, real-time detectors are in far more serious trouble since they renounce the use of regionproposing stage which is used to filter a majority of backgrounds for achieving real-time rates. Though foregrounds as hard examples are in urgent need of being mined from tons of backgrounds, a considerable number of state-of-the-art realtime detectors, like YOLO series, have yet to profit from existing hard example mining methods, as using these methods need detectors fit series of prerequisites. In this paper, we propose a general hard example mining method named Loss Rank Mining (LRM) to fill the gap. LRM is a general method for real-time detectors, as it utilizes the final feature map which exists in all real-time detectors to mine hard examples. By using LRM, some elements representing easy examples in final feature map are filtered and detectors are forced to concentrate on hard examples during training. Extensive experiments validate the effectiveness of our method. With our method, the improvements of YOLOv2 detector on auto-driving related dataset KITTI and more general dataset PASCAL VOC are over 5% and 2% mAP, respectively. In addition, LRM is the first hard example mining strategy which could fit YOLOv2 perfectly and make it better applied in series of real scenarios where both real-time rates and accurate detection are strongly demanded. Copyright © 2018, The Authors. All rights reserved.

关键词： Mining

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Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors

Loss Rank Mining: A General Hard Example Mining Method for R...

引用

International Joint Conference on Neural Networks

作者： Hao Yu Zhaoning Zhang Zheng Qin Hao Wu Dongsheng Li Jun Zhao Xicheng Lu Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China College of Electronic and Engineering National University of Defense Technology Changsha China College of Meteorology and Oceanology National University of Defense Technology Changsha China

Modern object detectors usually suffer from low accuracy issues, as foregrounds always drown in tons of back-grounds and become hard examples during training. Compared with those proposal-based ones, real-time detectors are in far more serious trouble since they renounce the use of region-proposing stage which is used to filter a majority of back-grounds for achieving real-time rates. Though foregrounds as hard examples are in urgent need of being mined from tons of backgrounds, a considerable number of state-of-the-art real-time detectors, like YOLO series, have yet to profit from existing hard example mining methods, as using these methods need detectors fit series of prerequisites. In this paper, we propose a general hard example mining method named Loss Rank Mining (LRM) to fill the gap. LRM is a general method for real-time detectors, as it utilizes the final feature map which exists in all real-time detectors to mine hard examples. By using LRM, some elements representing easy examples in final feature map are filtered and detectors are forced to concentrate on hard examples during training. Extensive experiments validate the effectiveness of our method. With our method, the improvements of YOLOv2 detector on auto-driving related dataset KITTI and more general dataset PASCAL VOC are over 5% and 2% mAP, respectively. In addition, LRM is the first hard example mining strategy which could fit YOLOv2 perfectly and make it better applied in series of real scenarios where both real-time rates and accurate detection are strongly demanded.

关键词： Detectors Real-time systems Feature extraction Training Object detection Pipelines Task analysis

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