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Asia-Pacific Conference on Software Engineering

作者： Dongyang Hu Tao Wang Junsheng Chang Gang Yin Yang Zhang Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781728119717;9781728119700

Social coding sites like GitHub has enabled developers to easily contribute their comments on multiple issues and switch their discussion between issues, i.e., multi-discussing. Discussing multiple issues simultaneously may enhance the work efficiency of developers. However, multi-discussing also relies on developers' rationally allocating their time and focus, which may bring different influence to the resolution of issues. Therefore, investigating how multi-discussing affects the issue resolution is a meaningful research question which can help developers understand the benefits and limitations when they switch their discussion between issues. In this paper, we present a preliminary study of the impact of multi-discussing on issue resolution in GitHub projects, by using quantitative methods. First, we collect and analyzed data from 631 GitHub projects to explore how multi-discussing affects the average resolution latency of project issues. Further, we develop method for measuring the rate and breadth of a developers' discussion switching behavior, and we use regression modeling to study how discussion-switching affects the single issue resolution latency. We find that multi-discussing is a common behavior of developers in GitHub projects. Also, multi-discussing is associated with shorter average issue resolution latency of project. However, during a single issue resolution, more participants' discussion-switching tend to bring longer issue resolution latency. Our study motivates the need for further research on the multi-discussing.

关键词： Switches Multitasking Collaboration Task analysis Encoding Rails Tools

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Enhancing 2D Representation via Adjacent Views for 3D Shape Retrieval

Enhancing 2D Representation via Adjacent Views for 3D Shape ...

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International Conference on Computer Vision (ICCV)

作者： Cheng Xu Zhaoqun Li Qiang Qiu Biao Leng Jingfei Jiang School of Computer Science & Engineering Beihang University Duke University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beihang University National Laboratory for Parallel and Distributed Processing National University of Defense Technology

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Multi-view shape descriptors obtained from various 2D images are commonly adopted in 3D shape retrieval. One major challenge is that significant shape information are discarded during 2D view rendering through projection. In this paper, we propose a convolutional neural network based method, CenterNet, to enhance each individual 2D view using its neighboring ones. By exploiting cross-view correlations, CenterNet learns how adjacent views can be maximally incorporated for an enhanced 2D representation to effectively describe shapes. We observe that a very small amount of, e.g., six, enhanced 2D views, are already sufficient for a panoramic shape description. Thus, by simply aggregating features from six enhanced 2D views, we arrive at a highly compact yet discriminative shape descriptor. The proposed shape descriptor significantly outperforms state-of-the-art 3D shape retrieval methods on the ModelNet and ShapeNetCore55 benchmarks, and also exhibits robustness against object occlusion.

关键词： Shape Three-dimensional displays Correlation Two dimensional displays Feature extraction Solid modeling Computational modeling

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Character feature learning for named entity recognition

Character feature learning for named entity recognition

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作者： Zeng, Ping Tan, Qingping Zhang, Haoyu Meng, Xiankai Zhang, Zhuo Xu, Jianjun Lei, Yan College of Computer National University of Defense Technology Changsha China National Key Laboratory for Parallel and Distributed Processing Changsha China School of Software Engineering Chongqing University Chongqing China

The deep neural named entity recognition model automatically learns and extracts the features of entities and solves the problem of the traditional model relying heavily on complex feature engineering and obscure professional knowledge. This issue has become a hot topic in recent years. Existing deep neural models only involve simple character learning and extraction methods, which limit their capability. To further explore the performance of deep neural models, we propose two character feature learning models based on convolution neural network and long short-term memory network. These two models consider the local semantic and position features of word characters. Experiments conducted on the CoNLL-2003 dataset show that the proposed models outperform traditional ones and demonstrate excellent performance. © Copyright 2018 The Institute of Electronics Information and Communication Engineers.

关键词： Extraction

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Building a Cloud-Ready Program:A highly scalable Implementation based on Kubernetes

Building a Cloud-Ready Program:A highly scalable Implementat...

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作者： Qiankun Li Gang Yin Tao Wang Yue Yu National Laboratory for Parallel and Distributed Processing National University of Defense Technology

Build system,which can convert source codes into applications,is essential for the development of *** general build systems that relying on single physical or cloud host to run bring problems such as system security,resource shortage,overload,and low availability in the face of massive build *** modularizing and streamlining the steps during a build process,this paper proposes a system that introduces container technology and then builds a large-scale,real-time,and huge-concurrency supported build system based on Kubernetes[1].The system provides a highly scalable and feature-stable cloud architecture that supports huge concurrency with lower resource ***,the system controls programs' behaviors very well to avoid potential security and resource issues and shows excellent performance in concurrency,scalability,security,and load balance even when handling a large number of build tasks.

关键词： Build system Container Kubernetes Concurrent build

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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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Foundational Competencies and Responsibilities of a Research Software Engineer

arXiv

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arXiv 2023年

作者： Goth, Florian Alves, Renato Braun, Matthias Castro, Leyla Jael Chourdakis, Gerasimos Christ, Simon Cohen, Jeremy Druskat, Stephan Erxleben, Fredo Grad, Jean-Noël Hagdorn, Magnus Hodges, Toby Juckeland, Guido Kempf, Dominic Lamprecht, Anna-Lena Linxweiler, Jan Löffler, Frank Martone, Michele Schwarzmeier, Moritz Seibold, Heidi Thiele, Jan Philipp Waldow, Harald von Wittke, Samantha Institute for Theoretical Physics and Astrophysics University of Würzburg Germany European Molecular Biology Laboratory Heidelberg Germany Cluster of Excellence IntCDC University of Stuttgart Germany ZB MED Information Centre for Life Sciences Cologne Germany School of Computation Information and Technology Technical University of Munich Garching Germany Institute for Parallel and Distributed Systems University of Stuttgart Stuttgart Germany Leibniz University Hannover Department of Cell Biology and Biophysics Computational Biology Germany Imperial College London London United Kingdom Institute for Software Technology Berlin Germany Humboldt-Universität zu Berlin Department of Computer Science Berlin Germany Helmholtz-Zentrum Dresden-Rossendorf Germany Institute for Computational Physics University of Stuttgart Germany Geschäftsbereich IT Charité Universitätsmedizin Berlin Germany The Carpentries United States Heidelberg University Scientific Software Center Germany Institute of Computer Science University of Potsdam Germany Technische Universität Braunschweig Germany Michael Stifel Center Jena Friedrich Schiller University Jena Germany Leibniz Supercomputing Centre Garching Germany Mathematical Modeling and Analysis TU Darmstadt Germany IGDORE Munich Germany Weierstrass Institute Berlin Germany Leibniz University Hannover Institute of Applied Mathematics Scientific Computing Hannover Germany Johann Heinrich von Thünen Institute Centre for Information Management Germany

The term Research Software Engineer, or RSE, emerged a little over 10 years ago as a way to represent individuals working in the research community but focusing on software development. The term has been widely adopted and there are a number of high-level definitions of what an RSE is. However, the roles of RSEs vary depending on the institutional context they work in. At one end of the spectrum, RSE roles may look similar to a traditional research role. At the other extreme, they resemble that of a software engineer in industry. Most RSE roles inhabit the space between these two extremes. Therefore, providing a straightforward, comprehensive definition of what an RSE does and what experience, skills and competencies are required to become one is challenging. In this community paper we define the broad notion of what an RSE is, explore the different types of work they undertake, and define a list of foundational competencies as well as values that outline the general profile of an RSE. Further research and training can build upon this foundation of skills and focus on various aspects in greater detail. We expect that graduates and practitioners will have a larger and more diverse set of skills than outlined here. On this basis, we elaborate on the progression of these skills along different dimensions. We look at specific types of RSE roles, propose recommendations for organisations, and give examples of future specialisations. An appendix details how existing curricula fit into this framework. © 2023, CC BY.

关键词： Computer aided software engineering

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JCDTA: The Data Trading Archtecture Design in JointCloud Computing 24

JCDTA: The Data Trading Archtecture Design in JointCloud Com...

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24th IEEE International Conference on parallel and distributed Systems, ICPADS 2018

作者： Yue, Xikun Wang, Huaimin Liu, Wei Li, Wei Shi, Peichang Ouyang, Xue Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China Troops No.75835 PLA China China

ISBN: (纸本)9781538673089

JointCloud computing is a new generation cloud computing model based on collaboration among Cloud Service Providers, making resources from multiple clouds deeply integrated., and supporting customize cloud service. To achieve the data confirmation Right when doing data trading to prevent resell is one of the most important challenges faced by such JointCloud environment. In this paper, we propose the JointCloud Computing Data Trading Architecture (JCDTA), an optimized data trading architecture for cross-stakeholder. Firstly, the announced mechanism is used to extract the data resources summary and the owner information into blockchain. Secondly, the untampering of the blockchain is used to record the data resources information, such as data resources statement information, data resources transaction records and data resources operation records. Finally, the supervision mechanism confirms the declaration of the data sources while the encryption technology ensures the privacy of the data resources. JCDTA guarantees the security and the reliability of data trading in the JointCloud environment and ensures the value invariance of data resources. © 2018 IEEE.

关键词： Blockchain

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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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