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arXiv 2022年

作者： Jelescu, Ileana O. Grussu, Francesco Ianus, Andrada Hansen, Brian Barrett, Rachel L.C. Aggarwal, Manisha Michielse, Stijn Nasrallah, Fatima Syeda, Warda Wang, Nian Veraart, Jelle Roebroeck, Alard Bagdasarian, Andrew F. Eichner, Cornelius Sepehrband, Farshid Zimmermann, Jan Soustelle, Lucas Bowman, Christien Tendler, Benjamin C. Hertanu, Andreea Jeurissen, Ben Verhoye, Marleen Frydman, Lucio de Looij, Yohan van Hike, David Dunn, Jeff F. Miller, Karla Landman, Bennett A. Shemesh, Noam Anderson, Adam McKinnon, Emilie Farquharson, Shawna Acqua, Flavio Dell Pierpaoli, Carlo Drobnjak, Ivana Leemans, Alexander Harkins, Kevin D. Descoteaux, Maxime Xu, Duan Huang, Hao Santin, Mathieu D. Grant, Samuel C. Obenaus, Andre Kim, Gene S. Wu, Dan Le Bihan, Denis Blackband, Stephen J. Ciobanu, Luisa Fieremans, Els Bai, Ruiliang Leergaard, Trygve B. Zhang, Jiangyang Dyrby, Tim B. Johnson, G. Allan Cohen-Adad, Julien Budde, Matthew D. Schilling, Kurt G. Department of Radiology Lausanne University Hospital University of Lausanne Lausanne Switzerland CIBM Center for Biomedical Imaging Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland Radiomics Group Vall d’Hebron Institute of Oncology Vall d’Hebron Barcelona Hospital Campus Barcelona Spain Queen Square MS Centre Queen Square Institute of Neurology Faculty of Brain Sciences University College London London United Kingdom Champalimaud Research Champalimaud Foundation Lisbon Portugal Center of Functionally Integrative Neuroscience Aarhus University Aarhus Denmark Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom NatBrainLab Department of Forensics and Neurodevelopmental Sciences Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine BaltimoreMD United States Maastricht University Medical Center Maastricht Netherlands The Queensland Brain Institute The University of Queensland Queensland Australia Melbourne Neuropsychiatry Centre The University of Melbourne ParkvilleVIC Australia Department of Radiology and Imaging Sciences Indiana University IN United States Stark Neurosciences Research Institute Indiana University School of Medicine IN United States Center for Biomedical Imaging NYU Grossman School of Medicine New YorkNY United States Faculty of psychology and Neuroscience Maastricht University Maastricht Netherlands Department of Chemical & Biomedical Engineering FAMU-FSU College of Engineering Florida State University TallahasseeFL United States Center for Interdisciplinary Magnetic Resonance National HIgh Magnetic Field Laboratory TallahasseeFL United States Department of Neuropsychology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany USC Stevens Neuroimaging and Informatics Instit

Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected recommendations and guidelines from the diffusion community, on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We then give guidelines for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including pre-processing, model-fitting, and tractography. Finally, we provide an online resource which lists publicly available preclinical dMRI datasets and software packages, to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. While we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal herein is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge. © 2022, CC BY-NC-ND.

关键词： Invertebrates

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Scalable tag recommendation for software information sites 24

Scalable tag recommendation for software information sites

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24th IEEE International Conference on software Analysis, Evolution, and Reengineering, SANER 2017

作者： Zhou, Pingyi Liu, Jin Yang, Zijiang Zhou, Guangyou State Key Lab. of Software Engineering Computer School Wuhan University China Department of Computer Science Western Michigan University Kalamazoo United States School of Computer Central China Normal University Wuhan China

ISBN: (纸本)9781509055012

software developers can search, share and learn development experience, solutions, bug fixes and open source projects in software information sites such as StackOverflow and Freecode. Many software information sites rely on tags to classify their contents, i.e. software objects, in order to improve the performance and accuracy of various operations on the sites. The quality of tags thus has a significant impact on the usefulness of these sites. High quality tags are expected to be concise and can describe the most important features of the software objects. © 2017 IEEE.

关键词： Open source software

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A Test Language for Avionics System

A Test Language for Avionics System

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2017 IEEE 8th International Conference on software engineering and Service science

作者： Yanfang Liu Jianghua Lv Wei Wang Tao Li Shilong Ma Department of Computer Science and Engineering Beihang University State Key Laboratory of Software Development Environment System Engineering Research Institute of China State Shipbuilding Corporation

In this paper, we proposed an automated test framework, logical test device which makes complex avionics systems transparent and jump machines which ensure data security of SUT(System Under Test). Based on this test framework, we mainly proposed a universal test language for avionics systems. The key feature of our test language is to introduce device type data and device collaboration operations which support automatic collaboration between logical test device and jump machines, meanwhile make test language more universal. This test language has been applied to the actual avionics system testing in China Academy of Electronics and Information Technology. Relative to avionics system testing before automation, our test language changes work pattern of avionics system testing and improves the test efficiency.

关键词： Avionics system test framework test language automated testing device collaboration

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A privacy-aware two-factor authentication protocol based on elliptic curve cryptography for wireless sensor networks

A privacy-aware two-factor authentication protocol based on ...

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作者： Jiang, Qi Kumar, Neeraj Ma, Jianfeng Shen, Jian He, Debiao Chilamkurti, Naveen School of Cyber Engineering Xidian University Xi'an China Co-innovation Center for Information Supply and Assurance Technology Anhui University Hefei China Department of Computer Science and Engineering Thapar University Patiala India School of Computer and Software Nanjing University of Information Science and Technology Nanjing China State Key Lab of Software Engineering Computer School Wuhan University Wuhan China Department of Computer Systems and Computer Science La Trobe University Melbourne Australia

As one of the enabling components of Internet of things (IoT), wireless sensor networks (WSNs) have found applications in a wide range of fields, in which outside users need to directly interact with sensors to obtain sensed data. However, WSNs are vulnerable to various attacks over wireless links, such as eavesdropping and tampering. How to ensure that sensitive or critical information is only available to legal users becomes a challenging issue. Two-factor authentication combining password and smart card perfectly matches this requirement because of its practicality. However, a dozen of two-factor authentication protocols have been suggested in recent years. Owing to the challenging mission of reconciling efficiency and security requirements, it is still challenging to propose a privacy-aware two-factor protocol that is capable of providing various security features while maintaining acceptable efficiency. In this paper, we put forward a privacy-aware two-factor authentication protocol based on elliptic curve cryptography for WSNs. Our new protocol accomplishes various security features necessary for real-life application environments while maintaining acceptable efficiency. We prove that the new protocol fulfills mutual authentication in the Burrows–Abadi–Needham logic. Additionally, by way of informal security analysis, we show that the new protocol can withstand a variety of attacks and provide desirable security features. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

关键词： Wireless sensor networks

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Random asynchronous iterations in distributed coordination algorithms

arXiv

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arXiv 2018年

作者： Chen, Yao Xia, Weiguo Cao, Ming Lü, Jinhu Department of Computer Science Southwestern University of Finance and Economics Chengdu611130 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China Faculty of Science and Engineering ENTEG University of Groningen Groningen9747 AG Netherlands School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

Distributed coordination algorithms (DCA) carry out information processing processes among a group of networked agents without centralized information fusion. Though it is well known that DCA characterized by an SIA (stochastic, indecomposable, aperiodic) matrix generate consensus asymptotically via synchronous iterations, the dynamics of DCA with asynchronous iterations have not been studied extensively, especially when viewed as stochastic processes. This paper aims to show that for any given irreducible stochastic matrix, even non-SIA, the corresponding DCA lead to consensus successfully via random asynchronous iterations under a wide range of conditions on the transition probability. Particularly, the transition probability is neither required to be independent and identically distributed, nor characterized by a Markov chain. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

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Framework for Social Relationship Recommendation

Framework for Social Relationship Recommendation

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2017 2nd International Conference on Control, Automation and Artificial Intelligence(CAAI2017)

作者： Jia Chen Tongge Xu Zhang Xiong The State Key Laboratory of Software Development Environment Beihang University Research Institute of Beihang University in Shenzhen Lab 673 School of Computer Science and Engineering Beihang University

ISBN: (纸本)9781510845541

Find out the potential favorite data is the essence of recommendation techniques, which facilitates the recommendation techniques become a vital issue in the big data researches and applications. The traditional recommendation research and application centers on the commercial items and resources. We apply the recommendation to social relationships in social networks. We design a framework which can analyze and recommend various types of relationships. The framework has four modules and three work modes. Furthermore, we design a hybrid recommendation approach in the framework for the balancing between accuracy and efficiency. The framework can recommend the social relationships with high accuracy and low cost.

关键词： big data framework recommendation social relationships

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How to reduce software development cost with personnel assignment optimization: Exemplary improvement on the Hungarian Algorithm 17

How to reduce software development cost with personnel assig...

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21st International Conference on Evaluation and Assessment in software engineering, EASE 2017

作者： Wang, Chong Luo, Zhong Lin, Luxin Daneva, Maya State Key Lab. of Software Engineering Computer School Wuhan University Wuhan430072 China University of Twente Enschede7522HN Netherlands Science and Research Department Naval University of Engineering Wuhan430033 China Computer School Wuhan University Wuhan430072 China

ISBN: (纸本)9781450348041

Reasonable reduction and controlling of software cost is always a challenge for software companies. To estimate software development cost more precisely, current research effort is focused on improving the measurement of software size or complexity by combining or adjusting key cost drivers, such as function points and other observable project context factors. However, personnel factors are seldom investigated or treated in depth as a way to reduce the estimated software development cost. On the premise that a software project is decomposed in a number of tasks, and that predetermined developers are available as resources for it, this paper intends to optimize the allocation of available personnel for lower development cost. In this research, we consider the problem of allocating competent developers to suitable tasks as an unbalanced personnel assignment problem, and improve the traditional Hungarian Algorithm by applying three strategies to find optimal personnel allocation solutions for diverse requirements. Moreover, the performance of our improved algorithms is evaluated and compared through a series of experiments on simulation datasets to identify and validate the measurement indicators and influence factors of their performance. © 2017 ACM.

关键词： Cost estimating

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App store analysis: Using regression model for app downloads prediction 2nd

App store analysis: Using regression model for app downloads...

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2nd International Conference on Young computer Scientists, Engineers and Educators, ICYCSEE 2016

作者： Wang, Shanshan Wu, Wenjun Zhou, Xuan State Key Laboratory of Software Development Environment Department of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9789811020520

App store provides rich information for software vendors and customers to understand the market of mobile applications. However, app store analysis don’t consider some vital factors such as version number, app description and app name currently. In this paper we propose an approach that App Store Analysis can be used to predict app downloads. We use data mining to extract app name and description and app rank information etc. from the Wandoujia App Store and AppCha App Store. We use questionnaire and sentiment analysis to quantify some app nonnumeric information. We revealed strong correlations app name score, app rank, app rating with app downloads by Spearman’s rank correlation analysis respectively. Finally, we establish a multiple nonlinear regression model which app downloads defined as dependent variable and three relevant attributes defined as independent variable. On average, 59.28% of apps in Wandoujia App Store and 66.68% of apps in AppCha App Store can be predicted accurately within threshold which error rate is 25%. One can observe the more detailed classification of app store, the more accurate for regression modeling to predict app downloads. Our approach can help app developers to notice and optimize the vital factors which influence app downloads. © Springer science+Business Media Singapore 2016.

关键词： Application programs

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BioTIME 2.0: Expanding and Improving a Database of Biodiversity Time Series

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Global Ecology and Biogeography 2025年第5期34卷

作者： Maria Dornelas Laura H. Antão Amanda E. Bates Viviana Brambilla Jonathan M. Chase Cher F. Y. Chow Ada Fontrodona-Eslava Anne E. Magurran Inês S. Martins Faye Moyes Alban Sagouis Samuel Adu-Acheampong Daniel Acquah-Lamptey Dušan Adam Penelope A. Ajani Aitor Albaina Pablo Almaraz Jeongseop An Roger Sigismund Anderson Madelaine Jean Robertson Anderson Alexsander Z. Antunes Ivan Arismendi Linda Armbrecht Pedro Aros-Mardones Sreejith Kalpuzha Ashtamoorthy Narayanan Ayyappan Gal Badihi Joseph J. Bailey Andrew H. Baird Mark Edward Baird Sreekumar Vadakkethil Balakrishnan José António L. Barão-Nóbrega Adi Barash Miguel Barbosa Jos Barlow Claus Bässler Matthieu Beaumont Natalie Beenaerts Tiago Octavio Begot Wallace Beiroz Ricardo Beldade David M. Bell Alecia Bellgrove Jonathan Belmaker Lisandro Benedetti-Cecchi Cassandra E. Benkwitt Pamela Medina-van Berkum Brandon T. Bestelmeyer Matthew G. Betts Maxwell Kelvin Billah Anne D. Bjorkman Magdalena Błażewicz Christopher P. Bloch Shane A. Blowes Antonio Bode Juliano A. Bogoni Thomas Bolger Timothy C. Bonebrake Erik Bonsdorff Roberta Bottarin Luke N. Brokensha Rob W. Brooker Andrew J. Brooks Helge Bruelheide Thiago Almeida Bueno Claire Laguionie Mariana Lopes Campagnoli James Cant Erica Pellegrini Caramaschi Alexandre Caron Tadhg Carroll Tancredi Caruso Juan Carvajal-Quintero Giuseppe Castaldelli Edward Castañeda-Moya Pedro V. Castilho Sonia Zanini Cechin Shahar Chaikin Uchangi Manjunatha Chandrashekara Tory J. Chase Chaolun Allen Chen Jorge José Cherem Sei-Woong Choi Erica M. Christensen Alexander V. Christianini Jackson Wing Four Chu Peter Coad Carl Van Colen Lise Comte Elisabeth J. Cooper J. Hans C. Cornelissen Eddy Cosson Unai Cotano Luc Crevecoeur Shannan Kyle Crow Graeme S. Cumming Vanessa S. Daga Gabriella Damasceno Gergana N. Daskalova Claire H. Davies Robert A. Davis Frank P. Day Sussy De-La-Zerda Amy Elizabeth Deacon Indradatta de Castro-Arrazola Steven Degraer Kharran Deonarinesingh Juan C. Diaz-Ricaurte Christopher R. Dickman Tara Dirilgen Ciaran John Dolan J. Emmet Centre for Biological Diversity School of Biology University of St Andrews Fife UK MARE—Centro de Ciências do Mar e do Ambiente Faculdade de Ciências Universidade de Lisboa Lisboa Portugal Research Centre for Ecological Change Organismal and Evolutionary Biology Research Programme Faculty of Biological and Environmental Sciences University of Helsinki Helsinki Finland Department of Biology University of Victoria Victoria British Columbia Canada German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig Leipzig Germany Department of Computer Science Martin Luther University Halle-Wittenberg Halle (Saale) Germany Leverhulme Centre for Anthropocene Biodiversity Department of Biology University of York York UK Department of Animal Biology and Conservation Science University of Ghana Accra Ghana Philipps Universität Marburg Marburg Germany Department of Forest Ecology Silva Tarouca Research Institute Brno Czech Republic School of Life Sciences University of Technology Sydney Sydney New South Wales Australia Zoology and Animal Cell Biology Department University of the Basque Country (UPV/EHU) Leioa Spain Grupo de Oceanografía de Ecosistemas Instituto de Ciencias Marinas de Andalucía CSIC Campus Universitario de Puerto Real Puerto Real Spain National Institute of Ecology Seocheon Republic of Korea African Regional Postgraduate Programme in Insect Science University of Ghana Accra Ghana Département de Biologie Université de Sherbrooke Sherbrooke Quebec Canada Núcleo de Conservação da Biodiversidade Instituto de Pesquisas Ambientais Secretaria de Meio Ambiente Infraestrutura e Logística do Estado de São Paulo São Paulo Brazil Department of Fisheries Wildlife and Conservation Sciences Oregon State University Corvallis Oregon USA Institute for Marine and Antarctic Studies Ecology & Biodiversity Centre University of Tasmania Hobart Tasmania Australia Graduate Program in Oceanography Department of Oceanography Faculty of Natural Scienc

Motivation Here, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database. Main Types of Variables Included The database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and Grain Sampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and Grain The earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample-level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of Measurement The database includes any eukaryotic taxa, with a combined total of 56,400 taxa. software Format csv and. SQL.

关键词： assemblage biodiversity community database ecoinformatics species taxa time

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Dynamic Shortest Path Monitoring in Spatial Networks

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Journal of computer science & Technology 2016年第4期31卷 637-648页

作者： Shuo Shang Lisi Chen Zhe-Wei Wei Dan-Huai Guo Ji-Rong Wen State Key Laboratory of Software Development Environment Beijing 100191 China Department of Computer Science China University of Petroleum Beijing 102249 China School of Computer Engineering Nanyang Technological University Singapore 639798 Singapore Beijing Key Laboratory of Big-Data Management and Analysis Methods Renmin University of China Beijing 100080 China Computer Network Information Center Chinese Academy of Sciences Beijing 100190 China

With the increasing availability of real-time traffic information, dynamic spatial networks are pervasive nowa- days and path planning in dynamic spatial networks becomes an important issue. In this light, we propose and investigate a novel problem of dynamically monitoring shortest paths in spatial networks （DSPM query）. When a traveler aims to a des- tination, his/her shortest path to the destination may change due to two reasons： 1） the travel costs of some edges have been updated and 2） the traveler deviates from the pre-planned path. Our target is to accelerate the shortest path computing in dynamic spatial networks, and we believe that this study may be useful in many mobile applications, such as route planning and recommendation, car navigation and tracking, and location-based services in general. This problem is challenging due to two reasons： 1） how to maintain and reuse the existing computation results to accelerate the following computations, and 2） how to prune the search space effectively. To overcome these challenges, filter-and-refinement paradigm is adopted. We maintain an expansion tree and define a pair of upper and lower bounds to prune the search space. A series of optimization techniques are developed to accelerate the shortest path computing. The performance of the developed methods is studied in extensive experiments based on real spatial data.

关键词： shortest path dynamic spatial network spatial database location-based service

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