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第29届中国数据库学术会议

作者： Wang Yijie 王意洁 Li Xiaoyong 李小勇 Yang Yongtao 杨永滔 Qi Yafei 祁亚斐 Wang Guangdong 王广东 National Key Laboratory for Parallel and Distributed Processing College of Computer National Unive 国防科学技术大学计算机学院并行与分布处理国家重点实验室长沙 410073

当前不确定数据广泛存在于诸如传感器网络、RFID网络、基于位置服务、移动对象管理网上购物和市场监控等各种实际应用中。不确定Skyline查询作为不确定数据管理的一个重要方面，由于其在决策制定、市场分析、环境监控和数据挖掘等方面... 详细信息

当前不确定数据广泛存在于诸如传感器网络、RFID网络、基于位置服务、移动对象管理网上购物和市场监控等各种实际应用中。不确定Skyline查询作为不确定数据管理的一个重要方面，由于其在决策制定、市场分析、环境监控和数据挖掘等方面的重要作用，近年来在数据库和网络计算领域受到广泛关注。首先，概述了各种不确定数据类型上的Skyline查询定义，包括离散、连续概率分布模型以及不完全数据上的Skyline查询定义;其次，分析了不确定Skyline查询的特点，并在此基础上综述了现有的各种不确定数据集上的集中式和分布式Skyline查询方法，重点分析了各种算法的原理和优缺点;再次，介绍了不确定数据流上的Skyline查询定义并综述了各种不确定数据流上的Skyline查询方法;最后，基于最新研究动态指出了未来不确定Skyline查询研究的趋势。

关键词：数据查询不确定数据分布式技术

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基于物理轨迹数据和社会网络的泛化行程推荐

基于物理轨迹数据和社会网络的泛化行程推荐

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第六届中国传感器网络学术会议(CWSN 2012)

作者： MENG Xiang-Xu 孟祥旭 WANG Xiao-Dong 王晓东 ZHOU Xing-Ming 周兴铭 National Key Laboratory of Parallel and Distributed Processing(College of Computer Science National 并行与分布处理国家重点实验室(国防科学技术大学计算机学院) 湖南长沙410073

人类活动行程的制定往往基于宽泛的最初意向，通过综合考虑各种约束条件加以优化而完成。当前，基于位置点名称查找的行程制定方法，不支持用户一次性提交多个具有时序关系的宽泛出行意向，更不能同时为多个地理位置点提供详细的最优驾... 详细信息

人类活动行程的制定往往基于宽泛的最初意向，通过综合考虑各种约束条件加以优化而完成。当前，基于位置点名称查找的行程制定方法，不支持用户一次性提交多个具有时序关系的宽泛出行意向，更不能同时为多个地理位置点提供详细的最优驾车方案。基于位置社交网络信息和车辆历史轨迹数据，探索了支持用户多个模糊意向输入的泛化行程推荐框架，主要工作包括：(1)对泛化的行程推荐问题进行建模;(2)设计并实现了基于分类树的地理位置点(POI)查询策略和算法;(3)提出了基于Voronoi图的GPS轨迹分析模型，并实现了任意两个位置点间最优行驶路径计算方法;(4)联合社会网络和语义交通信息图，基于蚁群算法进行行程的推荐，并实现了原型系统。实验及问卷调查结果表明，推荐结果的用户满意度可达80％。

关键词：行程规划泛化模型优化设计物理轨迹数据社会网络

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arXiv

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arXiv 2024年

作者： Ren, Yingying Li, Qiuli Guo, Yangyang Pedrycz, Witold Xing, Lining Liu, Anfeng Song, Yanjie School of Computer Electronics and Information Guangxi University Nanning China Guangxi Key Laboratory of Multimedia Communications and Network Technology China Key Laboratory of Parallel Distributed and Intelligent Computing Guangxi University China School of Systems Science Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey School of Electronic Engineering Xidian University Xian China School of Electronic Engineering Central South University Changsha China National Engineering Research Center of Maritime Navigation System Dalian Maritime University Dalian China

With the rapid development of the satellite industry, the information transmission network based on communication satellites has gradually become a major and important part of the future satellite ground integration network. However, the low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system and needs to be solved urgently. Effectively planning the task of satellite ground networking by reasonably scheduling resources is crucial for the efficient transmission of task data. In this paper, we hope to provide a task execution scheme that maximizes the profit of the networking task for satellite ground network planning considering feeding mode (SGNPFM). To solve the SGNPFM problem, a mixed-integer planning model with the objective of maximizing the gain of the link-building task is constructed, which considers various constraints of the satellite in the feed-switching mode. Based on the problem characteristics, we propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks. To obtain more high-quality solutions, different similarity evaluation methods are designed to assist the algorithm in intelligently screening individuals. The DSGAalso uses an adaptive crossover strategy based on similarity mechanism, which guides the algorithm to achieve efficient population search. In addition, a task scheduling algorithm considering the feed-switching mode is designed for decoding the algorithm to generate a highquality scheme. The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem. Compared to other algorithms, the proposed algorithm not only obtains higher quality planning schemes but also has faster algorithm convergence speed. The proposed algorithm improves data trans

关键词： Profitability

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不确定数据流上的并行Skyline查询算法

不确定数据流上的并行Skyline查询算法

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第29届中国数据库学术会议

作者： WANG Guangdong 王广东 WANG Yijie 王意洁 LI Xiaoyong 李小勇 WANG Yuan 王媛 National Key Laboratory for Parallel and Distributed Processing College of Computer Science Nation 国防科技大学计算机学院并行与分布处理国家重点实验室长沙410073

不确定数据流上的Skyline查询技术逐步引起研究者的关注，传统的集中式流处理算法难以满足海量数据的查询需求，并且云计算所提供的海量计算资源和有效的存储管理模式，为研究并行Skyline查询技术提供了充足的条件。基于上述事实，提出... 详细信息

不确定数据流上的Skyline查询技术逐步引起研究者的关注，传统的集中式流处理算法难以满足海量数据的查询需求，并且云计算所提供的海量计算资源和有效的存储管理模式，为研究并行Skyline查询技术提供了充足的条件。基于上述事实，提出了一种不确定数据流上的并行Skyline查询算法(PSUDS)。该算法通过交叉划分滑动窗口的方式，将集中式流查询转化为并行处理，以并行执行的方式来解决集中式算法处理性能不足的问题。大量实验结果表明，该算法具有较好的并行可扩展性。

关键词：不确定数据流云计算并行处理查询算法性能测试

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Journal of Information Science and Engineering: Editorial notice

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Journal of Information Science and Engineering 2005年第5期21卷 i-ii页

作者： Lee, Der-Tsai Amato, Nancy M. Chang, Shih-Fu Chen, Homer H. Chen, Tsuhan Hsu, Tsan-Sheng Hwang, Jenq-Neng Kuo, Sy-Yen Kuo, Tei-Wei Li, Chung-Sheng Tokuyama, Takeshi Wing, Jeannette Wu, Tzong-Chen Yen, John Computer Science at Texas A and M University United States Parasol Laboratory United States IEEE Transactions on Parallel and Distributed Systems Computing Research Association's Committee on the Status of Women in Computing Research CRA-W's Distributed Mentor Program United States Department of Electrical Engineering Columbia University United States Lab. United States College of Electrical Engineering and Computer Science National Taiwan University Taiwan IEEE Transactions on Circuits and Systems for Video Technology IEEE Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA United States ACM ACM SIGACT IEEE Computer Society IICM Austria Research and Development of the Department Multimedia Signal Processing Technical Committee IEEE Signal Processing Society United States IEEE Transactions on Circuits and systems for Video Technology College of Electrical Engineering and Computer Science National Taiwan Ocean University Keelung Taiwan Department of Electrical Engineering National Taiwan University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan IEEE Technical Committee on Real-Time Systems Computer Science Division IBM T.J. Watson Research Center United States IBM Research Division Graduate School of Information Sciences Tohoku University Japan ACM IPSJ Mathematical Society of Japan Japan Department of Computer Science Computer Science Department Carnegie Mellon University United States National Academies of Science's Computer Science and Telecommunications Board United States Microsoft's Trustworthy Academic Advisory Board Intel Research Pittsburgh's Advisory Board United States Dartmouth's Institute for Security Technology Studies Advisory Committee Canada Sloan Research Fellowships Program Committee United States ACM Taiwan China Information Sciences and Technology Pennsylvania State University United States Laboratory for Intelligent Agents Penn State's School of Info

No abstract available

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Corrigendum to “A distributed Relation Detection Approach in the Internet of Things”

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Mobile Information Systems 2019年第1期2019卷

作者： Weiping Zhu Hongliang Lu Xiaohui Cui Jiannong Cao International School of Software Wuhan University Wuhan *** Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha *** Department of Computing Hong Kong Polytechnic University Kowloon Hong Kongpolyu.edu.hk

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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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QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge

arXiv

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arXiv 2024年

作者： Li, Hongwei Bran Navarro, Fernando Ezhov, Ivan Bayat, Amirhossein Das, Dhritiman Kofler, Florian Shit, Suprosanna Waldmannstetter, Diana Paetzold, Johannes C. Hu, Xiaobin Wiestler, Benedikt Zimmer, Lucas Amiranashvili, Tamaz Prabhakar, Chinmay Berger, Christoph Weidner, Jonas Alonso-Basanta, Michelle Rashid, Arif Baid, Ujjwal Adel, Wesam Alis, Deniz Baheti, Bhakti Bai, Yingbin Bhat, Ishaan Cetindag, Sabri Can Chen, Wenting Cheng, Li Dutande, Prasad Dular, Lara Elattar, Mustafa A. Feng, Ming Gao, Shengbo Huisman, Henkjan Hu, Weifeng Innani, Shubham Ji, Wei Karimi, Davood Kuijf, Hugo J. Kwak, Jin Tae Le, Hoang Long Li, Xiang Lin, Huiyan Liu, Tongliang Ma, Jun Ma, Kai Ma, Ting Oksuz, Ilkay Holland, Robbie Oliveira, Arlindo L. Pal, Jimut Bahan Pei, Xuan Qiao, Maoying Saha, Anindo Selvan, Raghavendra Shen, Linlin Silva, Joao Lourenco Spiclin, Ziga Talbar, Sanjay Wang, Dadong Wang, Wei Wang, Xiong Wang, Yin Xi, Ruiling Xu, Kele Yang, Yanwu Yergin, Mert Yu, Shuang Zeng, Lingxi Zhang, YingLin Zhao, Jiachen Zheng, Yefeng Zukovec, Martin Do, Richard Becker, Anton Simpson, Amber Konukoglu, Ender Jakab, Andras Bakas, Spyridon Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School United States Department of Quantitative Biomedicine University of Zurich Switzerland University Children’s Hospital Zurich University of Zurich Switzerland Department of Radioncology and Radiation Theraphy Klinikum rechts der Isar Technical University of Munich Germany Department of Information Technology and Electrical Engineering ETH-Zurich Switzerland Department of Radiology Memorial Sloan Kettering Cancer Center New York City United States Department of Biomedical and Molecular Sciences Queen’s University Canada TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany McGovern Institute Massachusetts Institute of Technology United States Institute for Diagnostic and Interventional Radiology Unveristy Zurich Hospital Switzerland BioMedIA Imperial College London United Kingdom Department of Radiation Oncology University of Pennsylvania PA United States University of Pennsylvania PA United States Department of Radiation Oncology Winship Cancer Institute of Emory University Georgia United States Nile University Cairo Egypt Department of Medical Sciences Acibadem University Istanbul Turkey Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Trustworthy Machine Learning Lab University of Sydney Australia Image Sciences Institute University Medical Center Utrecht Netherlands Computer Engineering Department Istanbul Technical University Istanbul Turkey School of Computer Science Shenzhen University Shenzhen China University of Alberta United States University of Ljubljana Faculty of Electrical Engineering Ljubljana Slovenia Tongji University Shanghai China OPPO Research Institute Shanghai China School of Biological and Medical Engineering Beihang University Beijing China Harvard Medical School Boston

Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a significant challenge in achieving consistent and reliable image segmentation. This variability not only reflects the inherent complexity and subjective nature of medical image interpretation but also directly impacts the development and evaluation of automated segmentation algorithms. Accurately modeling and quantifying this variability is essential for enhancing the robustness and clinical applicability of these algorithms. We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in Biomedical Image Quantification Challenge (QUBIQ), which was organized in conjunction with International Conferences on Medical Image Computing and computer-Assisted Intervention (MICCAI) 2020 and 2021. The challenge focuses on the uncertainty quantification of medical image segmentation which considers the omnipresence of inter-rater variability in imaging datasets. The large collection of images with multi-rater annotations features various modalities such as MRI and CT;various organs such as the brain, prostate, kidney, and pancreas;and different image dimensions 2D-vs-3D. A total of 24 teams submitted different solutions to the problem, combining various baseline models, Bayesian neural networks, and ensemble model techniques. The obtained results indicate the importance of the ensemble models, as well as the need for further research to develop efficient 3D methods for uncertainty quantification methods in 3D segmentation tasks. © 2024, CC BY-NC-ND.

关键词： Image segmentation

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The Hi-GAL compact source catalogue. I : The physical properties of the clumps in the inner Galaxy (−71.0◦ 67.0◦)

arXiv

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arXiv 2017年

作者： Elia, Davide Molinari, S. Schisano, E. Pestalozzi, M. Pezzuto, S. Merello, M. Noriega-Crespo, A. Moore, T.J.T. Russeil, D. Mottram, J.C. Paladini, R. Strafella, F. Benedettini, M. Bernard, J.P. Di Giorgio, A. Eden, D.J. Fukui, Y. Plume, R. Bally, J. Martin, P.G. Ragan, S.E. Jaffa, S.E. Motte, F. Olmi, L. Schneider, N. Testi, L. Wyrowski, F. Zavagno, A. Calzoletti, L. Faustini, F. Natoli, P. Palmerim, P. Piacentini, F. Piazzo, L. Pilbratt, G.L. Polychroni, D. Baldeschi, A. Beltrán, M.T. Billot, N. Cambrésy, L. Cesaroni, R. García-Lario, P. Hoare, M.G. Huang, M. Joncas, G. Liu, S.J. Maiolo, B.M.T. Marsh, K.A. Maruccia, Y. Mège, P. Peretto, N. Rygl, K.L.J. Schilke, P. Thompson, M.A. Traficante, A. Umana, G. Veneziani, M. Ward-Thompson, D. Whitworth, A.P. Arab, H. Bandieramonte, M. Becciani, U. Brescia, M. Buemi, C. Bufano, F. Butora, R. Cavuoti, S. Costa, A. Fiorellino, E. Hajnal, A. Hayakawa, T. Kacsuk, P. Leto, P. Li Causi, G. Marchili, N. Martinavarro-Armengol, S. Mercurio, A. Molinaro, M. Riccio, G. Sano, H. Sciacca, E. Tachihara, K. Torii, K. Trigilio, C. Vitello, F. Yamamoto, H. INAF-IAPS via del Fosso del Cavaliere 100 Roma00133 Italy Space Telescope Science Institute 3700 San Martin Dr. BaltimoreMD21218 United States Astrophysics Research Institute Liverpool John Moores University Liverpool Science Park Ic2 146 Brownlow Hill LiverpoolL3 5RF United Kingdom Aix Marseille Univ. CNRS LAM Laboratoire d’Astrophysique de Marseille Marseille France Max-Planck Institute for Astronomy Königstuhl 17 HeidelbergD-69117 Germany Infrared Processing Analysis Center California Institute of Technology 770 South Wilson Ave. PasadenaCA91125 United States Dipartimento di Matematica e Fisica Università del Salento Lecce73100 Italy CNRS IRAP 9 Av. colonel Roche BP 44346 Toulouse cedex 4F-31028 France Université de Toulouse UPS-OMP IRAP Toulouse cedex 4F-31028 France Department of Physics Nagoya University Chikusa-ku Nagoya Aichi464-8601 Japan Department of Physics & Astronomy University of Calgary ABT2N 1N4 Canada Center for Astrophysics and Space Astronomy University of Colorado BoulderCO80309 United States Canadian Institute for Theoretical Astrophysics University of Toronto McLennan Physical Laboratories 60 St. George Street TorontoON Canada School of Physics and Astronomy University of Leeds LeedsLS2 9JT United Kingdom School of Physics and Astronomy Cardiff University Cardiff WalesCF24 3AA United Kingdom IPAG University Grenoble Alpes Grenoble38000 France AIM Paris-Saclay CEA/IRFU - CNRS INSU - Univ. Paris Diderot Service d’Astrophysique CEA-Saclay Gif-surYvette Cedex91191 France INAF Osservatorio Astrofisico di Arcetri Largo E. Fermi 5 Firenze50125 Italy I. Physik. Institut University of Cologne Zülpicher Strasse Köln50937 Germany European Southern Observatory Karl Schwarzschild str. 2 Garching85748 Germany Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 Bonn53121 Germany ASI Science Data centre 00044 Frascati Roma Italy ESA/ESAC PO Box 78 Villanueva de la Cañada Madrid28691 Spain

Hi-GAL is a large-scale survey of the Galactic plane, performed with Herschel in five infrared continuum bands between 70 and 500 µm. We present a band-merged catalogue of spatially matched sources and their properties derived from fits to the spectral energy distributions (SEDs) and heliocentric distances, based on the photometric catalogs presented in Molinari et al. (2016a), covering the portion of Galactic plane −71.0◦ 67.0◦. The band-merged catalogue contains 100922 sources with a regular SED, 24584 of which show a 70 µm counterpart and are thus considered proto-stellar, while the remainder are considered starless. Thanks to this huge number of sources, we are able to carry out a preliminary analysis of early stages of star formation, identifying the conditions that characterise different evolutionary phases on a statistically significant basis. We calculate surface densities to investigate the gravitational stability of clumps and their potential to form massive stars. We also explore evolutionary status metrics such as the dust temperature, luminosity and bolometric temperature, finding that these are higher in proto-stellar sources compared to pre-stellar ones. The surface density of sources follows an increasing trend as they evolve from pre-stellar to proto-stellar, but then it is found to decrease again in the majority of the most evolved clumps. Finally, we study the physical parameters of sources with respect to Galactic longitude and the association with spiral arms, finding only minor or no differences between the average evolutionary status of sources in the fourth and first Galactic quadrants, or between "on-arm" and "inter-arm" positions. Copyright © 2017, The Authors. All rights reserved.

关键词： Galaxies

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