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A routing protocol for urban vehicular ad hoc networks to support non-safety applications

Digital Communications and Networks 2018年第3期4卷 189-199页

作者： S.K. Bhoi P.M. Khilar M. Singh R.R. Sahoo R.R. Swain Department of Computer Science and Engineering ParMa Maharaja Engineering College (Govt.) Berharnpur 761003 India Parallel and Distributed Computing Lob Department of Compeer Science and Engineering National Institute of Technology Rourkela 769008 India Department of Computer Science and Engineering VIT University Bhopal 466114 India

The Urban Vehicular Ad Hoc Network （UVANET） provides non-safety applications like media sharing, internet service, file transfer, gaming, and so on. Routing plays an important role in providing better services to the users in UVANET. In this paper, a novel routing protocol is proposed for UVANET to support non-safety applications. We consider a non-safety application wherein the drivers and passengers of different parking lots can play multiplayer games with each other. To play the games smoothly, the game data should reach the destination in a minimum time. Simulation results show that when the vehicle density in the city area is high, the proposed protocol meets the end-to-end delay tolerance of 100 ms. Finally, an experiment is performed to validate the proposed routing protocol by running a simple puzzle game in a UVANET prototype designed in an indoor laboratory environment and in an outdoor environment.

关键词： UVANET Non-safety applications Routing Gaming Slow-paced games

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Time-invariant degree growth in preferential attachment network models

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Physical Review E 2020年第2期101卷 022309-022309页

作者： Jun Sun (孙骏) Matúš Medo Steffen Staab Institute for Web Science and Technologies Universität Koblenz–Landau 56070 Koblenz Germany Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu 610054 People's Republic of China Department of Radiation Oncology Inselspital Bern University Hospital and University of Bern 3010 Bern Switzerland and Department of Physics University of Fribourg 1700 Fribourg Switzerland Institute for Parallel and Distributed Systems Universität Stuttgart 70569 Stuttgart Germany and Web and Internet Science Research Group University of Southampton Southampton SO17 1BJ United Kingdom

Preferential attachment drives the evolution of many complex networks. Its analytical studies mostly consider the simplest case of a network that grows uniformly in time despite the accelerating growth of many real networks. Motivated by the observation that the average degree growth of nodes is time invariant in empirical network data, we study the degree dynamics in the relevant class of network models where preferential attachment is combined with heterogeneous node fitness and aging. We propose an analytical framework based on the time invariance of the studied systems and show that it is self-consistent only for two special network growth forms: the uniform and the exponential network growth. Conversely, the breaking of such time invariance explains the winner-takes-all effect in some model settings, revealing the connection between the Bose-Einstein condensation in the Bianconi-Barabási model and similar gelation in superlinear preferential attachment. Aging is necessary to reproduce realistic node degree growth curves and can prevent the winner-takes-all effect under weak conditions. Our results are verified by extensive numerical simulations.

关键词： Degree distributions Network formation & growth Preferential attachment Real world networks Barabasi-Albert model Fitness models Preferential attachment models

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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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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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Correction to: A compressed matrix sequence method for solving normal equations of bundle adjustment

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Machine Vision and Applications 2021年第4期32卷 1-1页

作者： Peng, Jiaxin Liu, Jie Wei, Hua Laboratory of Software Engineering for Complex Systems School of Computer Science National University of Defense Technology Changsha China Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Changsha China Algorithm Lab Changsha Morale Network Technology Co. Ltd. Changsha China Water and Land Resources Research Center of the Middle Reaches of Yangtze River School of Resources Environment Science and Engineering Hubei University of Science and Technology Xianning China

A correction to this paper has been published: https://***/10.1007/s00138-021-01222-5

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Using Document Embedding Techniques for Similar Bug Reports Recommendation

Using Document Embedding Techniques for Similar Bug Reports ...

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

作者： Ming Chen Dongyang Hu Tao Wang Jun Long Gang Yin Yue Yu Yang Zhang Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781538665664;9781538665657

In the software development, it is common to find that several bug reports are related to many common code files, i.e., similar bugs. Similar bug recommendation is a meaningful task that could assist developers in bug triaging and fixing. If developers can focus on fewer code files, they can fix similar bugs in less time with higher quality. Therefore, building a similar bug recommendation system is a meaningful task that can improve development efficiency. As the state of the art, Yang et al.'s work presented an approach that combines TF-IDF method with Word Embedding model and achieved a good result. To further improve the performance of their approach, in this paper, we propose a novel approach using Document Embedding model. In our preliminary evaluation, we conduct the experiment on the datasets of J dt, Birt and Eclipse platform. And the results show that our approach outperforms Yang et ale `s, with 2.67-9.50% of improvement.

关键词： Computer bugs Task analysis Predictive models Measurement Context modeling Training Software

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Asia-Pacific Conference on Software Engineering

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

ISBN: (纸本)9781728119717;9781728119700

In the software development, it is not uncommon to find that several bug reports are related to many common code files, i.e., similar bugs. Similar bug recommendation is a meaningful task which can assist developers in bug triaging and fixing. As the state of the art, Yang et al.'s work presented an approach that combines TF-IDF method with word embedding model and achieved a good result. To further improve the performance of their approach, in this paper, we propose a novel approach using Document Embedding model. In our preliminary evaluation, we conduct the experiment on 13,090 bug reports from the Eclipse platform and the results show that our approach outperforms Yang et al.'s, with 7.89-8.96% of improvement.

关键词： Computer bugs Software engineering Task analysis Measurement Software Information retrieval Natural language processing

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Fisher Discriminant Analysis Random Forest for Online Class Incremental Learning

Fisher Discriminant Analysis Random Forest for Online Class ...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Wang Xiong Yijie Wang Li Cheng Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781728111421;9781728111414

Class incremental learning needs to deal with a dynamic environment where data class appears incrementally, it is a challenge to learn new knowledge while preserving what has already been learned. On the other hand, due to the limited storage of the online scenario, algorithm is usually obstructed to frequently scan or simply store all historical data, it is another challenge to reduce the historical data storage for algorithm. Few existing work have addressed above challenges simultaneously. In this paper, we propose Fisher Discriminant Analysis Random Forest (FDARF), which consists of two parts, GHS (Generate Hierarchical Split) and RRS (Random Reform Subtree), that cooperatively operate. GHS combines FDA (Fisher Discriminant Analysis) with tree hierarchy to learn a hierarchical split of data space that provides strong ability for classification. The statistics in leaves (i.e. historical data) can be described by covariance matrix and further optimized by matrix sketching algorithm to reduce storage; for every tree initialized by GHS, RRS randomly reforms certain state subtree, which creates diversity that can be ensemble for ensuring effectiveness of class incremental learning. Extensive experiments on diverse datasets validate that FDARF can well adapt to the online class incremental learning.

关键词： Vegetation Training Covariance matrices Memory Forestry Learning systems

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Multi-Discussing across Issues in GitHub: A Preliminary Study

Multi-Discussing across Issues in GitHub: A Preliminary Stud...

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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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