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IEEE International Conference on Cloud computing Technology and Science (CloudCom)

作者： Jie Cheng Chunming Rong Huijuan Ye Xianghan Zheng Department of Electrical Engineering and Computer Science Risk Management Using Big Real Time Data Department of Computer Science and Electronic Engineering University of Stavanger Stavanger Norway College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China

Adding to societal changes today, are the miscellaneous big data produced in different fields. Coupled with these data is the appearance of risk management. Admittedly, to predict future trend by using these data is conducive to make everything more efficient and easy. Now, no matter companies or individuals, they increasingly focus on identifying risks and managing them before risks. Effective risk management will lead them to deal with potential problems. This thesis focuses on risk management of flight delay area using big real time data. It proposes two different prediction models, one is called General Long Term Departure Prediction Model and the other is named as Improved Real Time Arrival Prediction Model. By studying the main factors lead to flight delay, this thesis takes weather, carrier, National Aviation System, security and previous late aircraft as analysis factors. By utilizing our models can do not only long time but also short term flight delay predictions. The results demonstrate goodness of fit. Besides the theory part, it also presents a practical and beautiful web application for real time flight arrival prediction based on our second model.

关键词： Delays Biological system modeling Meteorology Atmospheric modeling Predictive models Airports Splines (mathematics)

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Personalized Financial News Recommendation Algorithm Based on Ontology

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Procedia Computer Science 2015年 55卷 843-851页

作者： Rui Ren Lingling Zhang Limeng Cui Bo Deng Yong Shi School of Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Research Centre on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 100190 China Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

To deal with the challenge of information overload, in this paper, we propose a financial news recommendation algorithm which help users find the articles that are interesting to read. To settle the ambiguity problem, a new presented OF-IDF method is employed to represent the unstructured text data in the form of key concepts, synonyms and synsets which are all stored in the domain ontology. For users, the recommendation algorithm build the profiles based on their behaviors to detect the genuine interests and predict current interests automatically and in real time by applying the thinking of relevance feedback. Finally, the experiment conducted on a financial news dataset demonstrates that the proposed algorithm significantly outperforms the performance of a traditional recommender.

关键词： news recommendation algorithm ontology relevence feedback OF-IDF

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Parallelized Race Detection Based on GPU Architecture

Parallelized Race Detection Based on GPU Architecture

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10th Annual Conference of Advanced Computer Architecture, ACA 2014

作者： Dai, Zhuofang Zhang, Zheng Wang, Haojun Li, Yi Zhang, Weihua Software School Fudan University Shanghai China Shanghai Key Laboratory of Data Science Fudan University Shanghai China Parallel Processing Institute Fudan University Shanghai 201203 China State Key Laboratory of Mathematic Engineering and Advanced Computing Zhengzhou 450001 China

ISBN: (纸本)9783662444900

In order to harness abundant hardware resources, parallel programming has become a necessity in multicore era. However, parallel programs are prone to concurrency bugs, especially data races. Even worse, current software tools always suffer from both large runtime overheads and poor scalability, while most of hardware supports for race detection are not available in parallel programming. Therefore, it has been a challenge that how to introduce a practical and fast race detection tools. Nowadays, GPUs with massive parallel computation resources have become one of the most popular hardware platforms. Hence, the prevalence of GPU architectures has opened an opportunity of accelerating data race detection. In this paper, we first have a deeply analysis on data race detection algorithms like happens-before and observe that these algorithms have very good computation and data parallelism. Based on the observation, we propose Grace, a software approach that leverages massive parallelism computation units of GPU architectures to accelerate data race detection. Grace deploys detection, the most computation intensive workload, on GPU to fully utilize the computation resource in GPU. Moreover, Grace leverages coarse-grained pipeline parallelism and data parallelism through exploiting the computation resource in multi-core CPUs to further improve performance. Experimental results show that Grace is fast and scalable. It achieves over 80x speedup compared to the sequential version even under a 128-thread configuration. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Graphics processing unit

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On the security of a privacy-preserving key management scheme for location based services in VANETs

On the security of a privacy-preserving key management schem...

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6th International Symposium on Foundations and Practice of Security, FPS 2013

作者： Liu, Bao Zhang, Lei Domingo-Ferrer, Josep Shanghai Key Laboratory of Trustworthy Computing Software Engineering Institute East China Normal University Shanghai China UNESCO Chr. in Data Privacy Department of Computer Engineering and Mathematics Universitat Rovira i Virgili Tarragona Spain

ISBN: (纸本)9783319053011

Location based services (LBSs) are promising value-added services in vehicular ad hoc networks (VANETs), which can yield substantial economic profits. To extensively deploy LBSs in VANETs, it is essential to establish an efficient privacy-preserving key management scheme. In this paper, we point out a privacy weakness in a recent key management scheme based on group signatures for LBSs in VANETs;then we propose a secure and privacy-enhanced version. In our scheme, roadside units (RSUs) act as group managers. Vehicles are distributed into groups maintained by these RSUs. If a vehicle's member key is compromised, one just needs to update the group public key corresponding to its group manager. With this method, the member revocation and privacy leakage problems in schemes based on group signatures are solved effectively. As a result, a vehicle may enjoy LBSs efficiently without surrendering its privacy. © 2014 Springer International Publishing Switzerland.

关键词： Location based services

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Does web news media have opinions? Evidence from real estate market prediction 18

Does web news media have opinions? Evidence from real estate...

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18th Pacific Asia Conference on Information Systems, PACIS 2014

作者： Sun, Daoyuan Zhang, Ce Xu, Wei Zuo, Meiyun Zhou, Junjie Du, Yudie Department of Information Systems School of Computing National University of Singapore Singapore Singapore School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China School of Information Beijing China School of E-Commerce and Logistics Management Henan University of Economics and Law Zhengzhou China

In this paper, we propose a novel method for real estate price prediction using web new media sentiments by incorporating human searching behaivor on the web. By combining online daily news' sentiments and Google search engine query data, we construct a web news content and online search behavior-based integrated model for real estate prediction. Besides these factors, real estate price time series data are also considered into the model in order to improve the forecasting performance. Furthermore, we make a comparison between the integrated model and the baseline model without search engine query data. Experimental results indicate that the integrated model outperforms the non-integrated model, which suggests that online user searching behavior is of great value in enhancing the prediction performance. These findings imply that the proposed integrated model is effective and feasible for real estate market prediction.

关键词： Search engines

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Evolving Commitments for Self-Adaptive Socio-technical Systems

Evolving Commitments for Self-Adaptive Socio-technical Syste...

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IEEE International Conference on engineering of Complex Computer Systems

作者： Xin Peng Yi Xie Yijun Yu John Mylopoulos Wenyun Zhao Shanghai Key Laboratory of Data Science Fudan University China Department of Computing The Open University UK Department of Information Engineering and Computer Science University of Trento Italy

Socio-technical systems (STSs) consist of human, hardware and software agents that work in tandem to fulfill stakeholder requirements. A specification for an STS consists of a set of (social) commitments among participating agents that serve as a contract among them. However, by their very nature, STSs are open, dynamic and continuously evolving along with their environments. To ensure that such systems continue to satisfy their requirements, the agents that comprise an STS must continuously adapt their behaviors to take into account risks and opportunities that arise at runtime. This paper presents a decision-theoretic self-adaptation framework that proposes candidate adaptation strategies for participating agents. These strategies are implementable by reconfiguration of plans or even changes in contractual commitments among agents. The adaptation procedure involves negotiating commitment changes and possible compensations to/from creditor agents. To evaluate the proposal, the paper also presents the results of an experimental study with a simulated STS, which confirms that success rates for achieving stakeholder goals and overall trade off utility can be improved significantly by incorporating evolving commitments to support STS adaptation.

关键词： Adaptation models Switches Motorcycles Estimation Proposals Lifting equipment Protocols

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Clonepedia: Summarizing Code Clones by Common Syntactic Context for Software Maintenance

Clonepedia: Summarizing Code Clones by Common Syntactic Cont...

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International Conference on Software Maintenance (ICSM)

作者： Yun Lin Zhenchang Xing Xin Peng Yang Liu Jun Sun Wenyun Zhao Jinsong Dong Shanghai Key Laboratory of Data Science Fudan University Shanghai China School of Computer Engineering Nanyang Technological University Singapore Singapore University of Technology and Design Singapore School of Computing National University of Singapore Singapore

Code clones have to be made explicit and be managed in software maintenance. Researchers have developed many clone detection tools to detect and analyze code clones in software systems. These tools report code clones as similar code fragments in source files. However, clone-related maintenance tasks (e.g., refactorings) often involve a group of code clones appearing in larger syntactic context (e.g., code clones in sibling classes or code clones calling similar methods). Given a list of low-level code-fragment clones, developers have to manually summarize from bottom up low-level code clones that are relevant to the syntactic context of a maintenance task. In this paper, we present a clone summarization technique to summarize code clones with respect to their common syntactic context. The clone summarization allows developers to locate and maintain code clones in a top-down manner by type hierarchy and usage dependencies. We have implemented our approach in the Clonepedia tool and conducted a user study on JHotDraw with 16 developers. Our results show that Clonepedia users can better locate and refactor code clones, compared with developers using the Clone Detective tool.

关键词： Cloning Syntactics Abstracts Context Ontologies Production facilities Maintenance engineering

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Appearance-order-based schema matching

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Journal of computing Science and engineering 2014年第2期8卷 94-106页

作者： Ding, Guohui Cao, keyan Wang, Guoren Han, Dong Department of Computer Science Shenyang Aerospace University Shenyang China Key Laboratory of Medical Image Computing Ministry of Education Northeastern University Shenyang China College of Information Science and Engineering Northeastern University Shenyang China National Marine Data and Information Service Tianjin China

Schema matching is widely used in many applications, such as data integration, ontology merging, data warehouse and dataspaces. In this paper, we propose a novel matching technique that is based on the order of attributes appearing in the schema structure of query results. The appearance order embodies the extent of the importance of an attribute for the user examining the query results. The core idea of our approach is to collect statistics about the appearance order of attributes from the query logs, to find correspondences between attributes in the schemas to be matched. As a first step, we employ a matrix to structure the statistics around the appearance order of attributes. Then, two scoring functions are considered to measure the similarity of the collected statistics. Finally, a traditional algorithm is employed to find the mapping with the highest score. Furthermore, our approach can be seen as a complementary member to the family of the existing matchers, and can also be combined with them to obtain more accurate results. We validate our approach with an experimental study, the results of which demonstrate that our approach is effective, and has good performance. © 2014. The Korean Institute of Information Scientists and Engineers.

关键词： data integration

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Design of a high-performance full-system instrumentation

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Dongbei Daxue Xuebao/Journal of Northeastern University 2014年 35卷 129-134页

作者： Min, Qing-Hao Zhang, Zheng Wang, Hao-Jun Zhang, Wei-Hua Software School Fudan University Shanghai201203 China Shanghai Key Laboratory of Data Science Fudan University Shanghai201203 China Parallel Processing Institute Fudan University Shanghai201203 China State Key Laboratory of Mathematic Engineering and Advanced Computing PLA Information Engineering University Zhengzhou450001 China

Instrumentation is an important mechanism to collect run-time information of applications for further analysis like bottleneck analysis, hotspot analysis and even bug detection and revision. Currently there are lots of analyzers using instrumentation to collect their source data. However, most of these tools lack of full-system support and thus become inadequate for analyzing multi-core programs as they cannot provide full-system information like scheduling, system calls and inter-process communication, etc. Meanwhile, full-system instrumentation needs to collect OS and hardware information, resulting to performance degradation. In this paper, a high-performance full-system instrumentation method was designed for collecting full-system information as well as application information. The proposed method was implemented inside the QEMU full-system emulator, and 40 MIPS performance on average was achieved after optimization, which was appropriated for normal program analysis. ©, 2014, Northeastern University. All right reserved.

关键词： Full-system High-performance Instrumentation Multi-core

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