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28th International Conference on software engineering and Knowledge engineering, SEKE 2016

作者： Cheng, Ming Wu, Guoqing Jiang, Min Wan, Hongyan You, Guoan Yuan, Mengting State Key Laboratory of Software Engineering School of Computer Wuhan University Wuhan430072 China Department of Cognitive Science and Technology Fujian Key Laboratory of Brain-Like Intelligent Systems Xiamen University Xiamen361005 China

ISBN: (纸本)189170639X

software defect prediction generally builds models from intra-project data. Lack of training data at the early stage of software testing limits the efficiency of prediction in practice. Thereby researchers proposed cross-project defect prediction using the data from other projects. Most previous efforts assumed the cross-project defect data have the same metrics set which means the metrics used and size of metrics set are same in the data of projects. However, in real scenarios, this assumption may not hold. In addition, software defect datasets have the class imbalance problem increasing the difficulty for the learner to predict defects. In this paper, we advance canonical correlation analysis for deriving a joint feature space for associating crossproject data and propose a novel support vector machine algorithm which incorporates the correlation transfer information into classifier design for cross-project prediction. Moreover, we take different misclassification costs into consideration to make the classification inclining to classify a module as a defective one, alleviating the impact of imbalanced data. Experiments on public heterogeneous datasets from different projects show that our method is more effective, compared to state-of-the-art methods. Copyright © 2016 by KSI Research Inc. and Knowledge systems Institute Graduate School.

关键词： Support vector machines

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Combined Cloud： A Mixture of Voluntary Cloud and Reserved Instance Marketplace

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Journal of computer science & Technology 2016年第6期31卷 1110-1123页

作者： Wei Shen Wan-Chun Dou Fan Wu Shaojie Tang Qiang Ni State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210046 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China Department of Information Systems University of Texas at Dallas Texas TX 75080 U.S.A. School of Computing and Communications Lancaster University Lancaster LA1 4WA U.K.

Voluntary cloud is a new paradigm of cloud computing. It provides an alternative selection along with some well-provisioned clouds. However, for the uncertain time span that participants share their computing resources in voluntary cloud, there are some challenging issues, i.e., fluctuation, under-capacity and low-benefit. In this paper, an architecture is first proposed based on Bittorrent protocol. In this architecture, resources could be reserved or requested from Reserved Instance Marketplace and could be accessed with a lower price in a short circle. Actually, these resources could replenish the inadequate resource pool and relieve the fluctuation and under-capacity issue in voluntary cloud. Then, the fault rate of each node is used to evaluate the uncertainty of its sharing time. By leveraging a linear prediction model, it is enabled by a distribution function which is used for evaluating the computing capacity of the system. Moreover, the cost optimization problem is investigated and a computational method is presented to solve the low-benefit issue in voluntary cloud. At last, the system performance is validated by two sets of simulations. And the experimental results show the effectiveness of our computational method for resource reservation optimization.

关键词： voluntary cloud system architecture resource reservation

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Gene expression imputation across multiple brain regions provides insights into schizophrenia risk (vol 51, pg 659, 2019)

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NATURE GENETICS 2019年第6期51卷 1068-1068页

作者： Huckins, Laura M. Dobbyn, Amanda Ruderfer, Douglas M. Hoffman, Gabriel Wang, Weiqing Pardinas, Antonio F. Rajagopal, Veera M. Als, Thomas D. Nguyen, Hoang T. Girdhar, Kiran Boocock, James Roussos, Panos Fromer, Menachem Kramer, Robin Domenici, Enrico Gamazon, Eric R. Purcell, Shaun Demontis, Ditte Borglum, Anders D. Walters, James T. R. O'Donovan, Michael C. Sullivan, Patrick Owen, Michael J. Devlin, Bernie Sieberts, Solveig K. Cox, Nancy J. Im, Hae Kyung Sklar, Pamela Stahl, Eli A. Pamela Sklar Division of Psychiatric Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Genetics and Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Psychiatry Icahn School of Medicine at Mount Sinai New York NY USA Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai New York NY USA Vanderbilt University Medical Center Nashville TN USA MRC Centre for Neuropsychiatric Genetics and Genomics Cardiff University Cardiff UK Department of Biomedicine Aarhus University Aarhus Denmark The Lundbeck Foundation Initiative for Integrative Psychiatric Research iPSYCH Denmark Center for Integrative Sequencing Aarhus University Aarhus Denmark Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA Human Brain Collection Core National Institute of Mental Health Bethesda MD USA Laboratory of Neurogenomic Biomarkers Centre for Integrative Biology (CIBIO) University of Trento Trento Italy Clare Hall University of Cambridge Cambridge UK University of North Carolina at Chapel Hill Chapel Hill NC USA Karolinska Institutet Stockholm Sweden Department of Psychiatry University of Pittsburgh Pittsburgh PA USA Systems Biology Sage Bionetworks Seattle WA USA Section of Genetic Medicine Department of Medicine University of Chicago Chicago IL USA Integrated Technology Research Laboratories Pharmaceutical Research Division Takeda Pharmaceutical Company Limited Fujisawa Japan Neuropsychiatry Section Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Neuropsychiatric Signaling Program Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Psychiatry JJ Peters Virginia Medical Center Bronx NY USA Department of Neuroscience Icahn School of Medicine at Mount Sinai New York New York NY USA Friedman Brain Institute

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.

关键词： Gene expression Genome-wide association studies Schizophrenia Transcriptomics

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Exploiting Correlation Subspace to Predict Heterogeneous Cross-Project Defects

Exploiting Correlation Subspace to Predict Heterogeneous Cro...

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作者： Cheng, Ming Wu, Guoqing Wan, Hongyan You, Guoan Yuan, Mengting Jiang, Min State Key Laboratory of Software Engineering School of Computer Wuhan University Wuhan Hubei China Department of Cognitive Science and Technology School of Information Science and Engineering Xiamen University Xiamen Fujian China Fujian Key Laboratory of Brain-Like Intelligent Systems School of Information Science and Engineering Xiamen University Xiamen Fujian China

Cross-project defect prediction trains a prediction model using historical data from source projects and applies the model to target projects. Most previous efforts assumed the cross-project data have the same metrics set, which means the metrics used and the size of metrics set are the same. However, this assumption may not hold in practical scenarios. In addition, software defect datasets have the class-imbalance problem which increases the difficulty for the learner to predict defects. In this paper, we advance canonical correlation analysis by deriving a joint feature space for associating cross-project data. We also propose a novel support vector machine algorithm which incorporates the correlation transfer information into classifier design for cross-project prediction. Moreover, we take different misclassification costs into consideration to make the classification inclining to classify a module as a defective one, alleviating the impact of imbalanced data. The experimental results show that our method is more effective compared to state-of-the-art methods. © 2016 World Scientific Publishing Company.

关键词： Support vector machines

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GB-RC4: Effective brute force attacks on RC4 algorithm using GPU 7

GB-RC4: Effective brute force attacks on RC4 algorithm using...

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7th International Green and Sustainable Computing Conference, IGSC 2016

作者： Xue, Pei Li, Tao Dong, Han Liu, Chunbo Ma, Wenjing Pei, Songwen College of Computer and Control Engineering Nankai University Tianjin300071 China Information Security Evaluation Center Civil Aviation University of China Tianjin300300 China Laboratory of Parallel Software and Computational Science State Key Laboratory of Computing Science Institute of Software CAS Beijing China Shanghai Key Lab of Modern Optical Systems Department of Computer Science and Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9781509051175

Encryption algorithms are applied to a variety of fields and the security of encryption algorithms depends heavily on the computational infeasibility of exhaustive key-space search. RC4 algorithm has an extensive application for stream encryption, however, the disadvantages of traditional RC4 serial algorithm are large computational quantity and slow computation speed, which means a great challenge for throughput of computing system. Graphics processors recently have been widely used as accelerators for high throughput computing. In this paper, GPU is used to improve the efficiency and performance of attacking RC4 algorithm. First, we revised RC4 algorithm to make it easier to parallelize on a GPU. Then we take advantage of constant memory and shared memory in GPU to search the key space of RC4 algorithm efficiently. Besides, we solved the bank conflict problem in shared memory to further improve the performance. Our approach was evaluated in the whole key space of 40-bit, and the results show it can complete searching of the whole key space within 12.8 hours. © 2016 IEEE.

关键词： Graphics processing unit

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Network slicing for service-oriented networks under resource constraints

arXiv

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arXiv 2017年

作者： Zhang, Nan Liu, Ya-Feng Farmanbar, Hamid Chang, Tsung-Hui Hong, Mingyi Luo, Zhi-Quan School of Mathematical Sciences Peking University State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Huawei Canada Research Center Ottawa Canada Department of Electrical and Computer Engineering University of Minnesota United States Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen China

To support multiple on-demand services over fixed communication networks, network operators must allow flexible customization and fast provision of their network resources. One effective approach to this end is network virtualization, whereby each service is mapped to a virtual subnetwork providing dedicated on-demand support to network users. In practice, each service consists of a prespecified sequence of functions, called a service function chain (SFC), while each service function in a SFC can only be provided by some given network nodes. Thus, to support a given service, we must select network function nodes according to the SFC and determine the routing strategy through the function nodes in a specified order. A crucial network slicing problem that needs to be addressed is how to optimally localize the service functions in a physical network as specified by the SFCs, subject to link and node capacity constraints. In this paper, we formulate the network slicing problem as a mixed binary linear program and establish its strong NP-hardness. Furthermore, we propose efficient penalty successive upper bound minimization (PSUM) and PSUM-R(ounding) algorithms, and two heuristic algorithms to solve the problem. Simulation results are shown to demonstrate the effectiveness of the proposed algorithms. Copyright © 2017, The Authors. All rights reserved.

关键词： Network function virtualization

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DASHMM accelerated adaptive fast multipole poisson-boltzmann solver on distributed memory architecture

arXiv

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arXiv 2017年

作者： Zhang, B. DeBuhr, J. Niedzielski, D. Mayolo, S. Lu, B. Sterling, T. Center for Research in Extreme Scale Technologies School of Informatics Computing and Engineering Indiana University BloomingtonIN47404 United States Department of Physics Rensselaer Polytechnic Institute TroyNY12180 United States Department of Computer Science Tennessee Technological University CookevilleTN38505 United States State Key Laboratory of Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190

We present an updated version of the AFMPB package for fast calculation of molecular solvation-free energy. The main feature of the new version is the successful adoption of the DASHMM library, which enables AFMPB to operate on distributed memory computers. As a result, the new version can easily handle larger molecules or situations with higher accuracy requirements. To demonstrate the updated code, we applied the new version to a dengue virus system with more than one million atoms and a mesh with approximately 20 million triangles, and were able to reduce the time-to-solution from 10 hours reported in the previous release on a shared memory computer to less than 30 seconds on a Cray XC30 cluster using 12, 288 cores. Copyright © 2017, The Authors. All rights reserved.

关键词： Boundary element method

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Support Vector Regression for Predicting the Enhancement Duration of software Projects

Support Vector Regression for Predicting the Enhancement Dur...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Cuauhtemoc Lopez-Martin Shadi Banitaan Andres Garcia-Floriano Cornelio Yanez-Marquez Department of Information Systems Universidad de Guadalajara México Department of Mathematics Computer Science and Software Engineering University of Detroit Mercy Detroit MI USA Unidad de Estudios Superiores Tepotzotlan Universidad Mexiquense del Bicentenario México State México Laboratorio de Computo Inteligente Centro de Investigacion en Computacion México City México

software engineering (SE) has been defined as the application of a systematic, disciplined, quantifiable approach to the development, operation, and maintenance of software. Enhancement is a type of software maintenance. SE involves software planning (SP), and SP includes prediction. In this study, we propose the application of two types of support vector regression (SVR) termed ε-SVR and ν-SVR to predict the duration of the software enhancement. A SVR is a type of support vector machine, which is a machine learning technique. Two data sets of software projects were used for training and testing the ε-SVR and ν-SVR. The prediction accuracy of the SVRs was compared to that of a statistical regression. Based on statistical tests, results showed that a ε-SVR with linear kernel was statistically better than that of a statistical regression model when software projects were enhanced on Mid Range platform and coded in programming languages of third generation.

关键词： software Support vector machines Data models Predictive models Maintenance engineering Training Testing

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A system-of-systems architecture-driven modeling method for combat system effectiveness simulation 2

A system-of-systems architecture-driven modeling method for ...

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2nd Annual IEEE International Symposium on systems engineering, ISSE 2016

作者： Li, Xiaobo Wang, Weiping Shu, Zhe Zhu, Ning He, Hua Liao, Tianjun College of Information Systems and Management National University of Defense Technology Changsha Hunan410073 China Department of Mathematics and Computer Science University of Antwerp Antwerp2020 Belgium State Key Laboratory of Complex System Simulation Beijing Institute of Systems Engineering Beijing China

ISBN: (纸本)9781509007936

Under the background of system-of-systems (SoS) counterwork, it is more apparent that the simulation modeling of the equipment should be domain-specific, formal, automatic and composable. Current equipment effectiveness simulation modeling approaches are mainly based on the methods and techniques from the modeling and simulation (M&S) community and software engineering community, and they are not capable to satisfy these four requirements systematically. Thus the systems engineering architecture model and domain knowledge should be used to support effectiveness simulation modeling more effectively. In this research, we propose an architecture-driven modeling method which conforms to the principle of architecture-driven development, uses ontology techniques to build equipment SoS architecture model and sub-domain ontology. This method performs architecture driven simulation modeling to realize the transformation from architecture models to simulation model frameworks;employs ontological metamodeling to design domain-specific modeling languages (DSML) based on the comprehensive usage of architecture models, sub-domain ontologies and M&S formalisms;integrates domain-specific simulation models from various domains using the model framework, and supports the composable development of simulation applications. © 2016 IEEE.

关键词： System of systems

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Radiative Cooling of the Thermally Isolated System in KAGRA Gravitational Wave Telescope

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Journal of Physics: Conference Series 2021年第1期1857卷

作者： T. Akutsu M. Ando K. Arai Y. Arai S. Araki A. Araya N. Aritomi Y. Aso S.-W. Bae Y.-B. Bae L. Baiotti R. Bajpai M. A. Barton K. Cannon E. Capocasa M.-L. Chan C.-S. Chen K.-H. Chen Y.-R. Chen H.-Y. Chu Y.-K. Chu S. Eguchi Y. Enomoto R. Flaminio Y. Fujii M. Fukunaga M. Fukushima G.-G. Ge A. Hagiwara S. Haino K. Hasegawa H. Hayakawa K. Hayama Y. Himemoto Y. Hiranuma N. Hirata E. Hirose Z. Hong B.-H. Hsieh G.-Z. Huang P.-W. Huang Y.-J. Huang B. Ikenoue S. Imam K. Inayoshi Y. Inoue K. Ioka Y. Itoh K. Izumi K. Jung P. Jung T. Kajita M. Kamiizumi N. Kanda G.-W. Kang K. Kawaguchi N. Kawai T. Kawasaki C. Kim J. Kim W. Kim Y.-M. Kim N. Kimura N. Kita H. Kitazawa Y. Kojima K. Kokeyama K. Komori A. K. H. Kong K. Kotake C. Kozakai R. Kozu R. Kumar J. Kume C.-M. Kuo H.-S. Kuo S. Kuroyanagi K. Kusayanagi K. Kwak H.-K. Lee H.-W. Lee R.-K. Lee M. Leonardi C.-Y. Lin F.-L. Lin L. C.-C. Lin G.-C. Liu L.-W. Luo M. Marchio Y. Michimura N. Mio O. Miyakawa A. Miyamoto Y. Miyazaki K. Miyo S. Miyoki S. Morisaki Y. Moriwaki K. Nagano S. Nagano K. Nakamura H. Nakano M. Nakano R. Nakashima T. Narikawa R. Negishi W.-T. Ni A. Nishizawa Y. Obuchi W. Ogaki J. J. Oh S.-H. Oh M. Ohashi N. Ohishi M. Ohkawa K. Okutomi K. Oohara C.-P. Ooi S. Oshino K.-C. Pan H.-F. Pang J. Park F. E. Peiia Arellano I. Pinto N. Sago S. Saito Y. Saito K. Sakai Y. Sakai Y. Sakuno S. Sato T. Sato T. Sawada T. Sekiguchi Y. Sekiguchi S. Shibagaki R. Shimizu T. Shimoda K. Shimode H. Shinkai T. Shishido A. Shoda K. Somiya E. J. Son H. Sotani R. Sugimoto T. Suzuki H. Tagoshi H. Takahashi R. Takahashi A. Takamori S. Takano H. Takeda M. Takeda H. Tanaka K. Tanaka T. Tanaka S. Tanioka E. N. Tapia San Martin S. Telada T. Tomaru Y. Tomigami T. Tomura F. Travasso L. Trozzo T. T. L. Tsang K. Tsubono S. Tsuchida T. Tsuzuki D. Tuyenbayev N. Uchikata T. Uchiyama A. Ueda T. Uehara K. Ueno G. Ueshima F. Uraguchi T. Ushiba M. H. P. M. van Putten H. Vocca J. Wang C.-M. Wu H.-C. Wu S.-R. Wu W.-R. Xu T. Yamada K. Yamamoto T. Yamamoto K. Yokogawa J. Yokoyama T. Yokozawa T. Yoshioka H. Yuzurihar National Astronomical Observatory of Japan (NAOJ) 2-21-1 Osawa Mitaka-City Tokyo 181-8588 Japan Research Center for the Early Universe (RESCEU) The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Department of Physics The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo 113-0033 Japan Institute for Cosmic Ray Research (ICRR) The University of Tokyo 5-1-5 Kashiwa-no-Ha Kashiwa-City Chiba 277-8582Japan Accelerator Laboratory High Energy Accelerator Research Organization (KEK) 1-1 OhoTsukuba-City Ibaraki 305-0801 Japan Earthquake Research Institute The University of Tokyo 1-1-1Yayoi Bunkyo-ku Tokyo 113-0032Japan Kamioka Branch National Astronomical Observatory of Japan (NAOJ) 238 Higashi-Mozumi Kamioka-cho Hida-City Gifu Pref. Japan The Graduate University for Advanced Studies (SOKENDAI) 2-21-1 Osawa Mitaka-City Tokyo 181-8588 Japan Korea Institute of Science and Technology Information (KISTI) 245 Daehak-ro Yuseong-gu Daejeon 34141 South Korea National Institute for Mathematical Sciences 70 Yuseong-daero 1689 beon-gil Yuseong-gu Daejeon 34047 South Korea Graduate School of Science Osaka University 1-1 Machikaneyama-cho Toyonaka-City Osaka560-0043 Japan School of High Energy Accelerator Science The Graduate University for Advanced Studies (SOKENDAI) 1-1 Oho Tsukuba-City Ibaraki 305-0801 Japan Institute for Gravitational Research University of Glasgow Kelvin Building G12 8QQ UK Department of Applied Physics Fukuoka University Fukuoka Jonan Nanakuma 814-0180 Japan Department of Physics Tamkang university No.151 Yingzhuan Rd. Danshui Dist. New Taipei-City 25137 Taiwan Department of Physics the Center for High Energy and High Field Physics National Central University No. 300 Zhongda Road Zhongyi District Taiyuan-City 320 Taiwan Institute of Astronomy National Tsing Hua University No. 101 Section 2 Kuang-Fu Road Hsinchu 30013 Taiwan Department of Physics National Tsing Hua University No. 101 Section 2

Radiative cooling of the thermally isolated system is investigated in KAGRA gravitational wave telescope. KAGRA is a laser interferometer-based detector and main mirrors constituting optical cavities are cool down to 20K to reduce noises caused by the thermal fluctuation. The mirror is suspended with the multi-stage pendulum to isolate any vibration. Therefore, this mirror suspension system has few heat links to reduce vibration injection. Thus, this system is mainly cooled down with thermal radiation. In order to understand the process of radiative cooling of the mirror, we analyzed cooling curve based on mass and specific heat. As a result, it was newly found that a cryogenic part called 'cryogenic duct-shield' seems to have large contribution in the beginning of the mirror cooling. This finding will help to design new cooling system for the next generation cryogenic gravitational wave detector.

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