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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Yinong Chen Zhizheng Zhou School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ U.S.A. School of Computer Science and Technology Shandong University of Finance and Economics Shandong China

ISBN: (纸本)9781479941155

Web software development and cloud computing based on Service-Oriented Architecture (SOA) represent the latest parallel distributed computing theories, practices, and technologies. As a distributed software development diagram, SOA is being taught in many computer science programs. We do not suggest using SOA to replace the currently taught Object-Oriented computing paradigm. As SOA is based on Object-Oriented computing, we suggest teaching SOA as the continuation and extension. At Arizona State University, SOA paradigm is incorporated into our computing Science program since 2006. This paper presents the topics of the related courses and the open resources created for the courses, which are available for public accesses, including textbooks, lecture presentation slides, tests and assignments, software tools, and a repository of components, services and applications.

关键词： Educational institutions Service-oriented architecture Robots Programming System-on-chip

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ICEPOLE: High-speed, hardware-oriented authenticated encryption

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8731卷 392-413页

作者： Morawiecki, Pawel Gaj, Kris Homsirikamol, Ekawat Matusiewicz, Krystian Pieprzyk, Josef Rogawski, Marcin Srebrny, Marian Wójcik, Marcin Institute of Computer Science Polish Academy of Sciences Poland Section of Informatics University of Commerce Kielce Poland Department of Computing Macquarie University Australia Electrical Engineering and Computer Science School Queensland University of Technology Brisbane Australia Cryptographic Engineering Research Group George Mason University United States Cryptography and Information Security Group University of Bristol United Kingdom Cadence Design Systems San Jose United States Intel Gdańsk Poland

This paper introduces our dedicated authenticated encryption scheme ICEPOLE. ICEPOLE is a high-speed hardware-oriented scheme, suitable for high-throughput network nodes or generally any environment where specialized hardware (such as FPGAs or ASICs) can be used to provide high data processing rates. ICEPOLE-128 (the primary ICEPOLE variant) is very fast. On the modern FPGA device Virtex 6, a basic iterative architecture of ICEPOLE reaches 41 Gbits/s, which is over 10 times faster than the equivalent implementation of AES-128-GCM. The throughput-to-area ratio is also substantially better when compared to AES-128-GCM. We have carefully examined the security of the algorithm through a range of cryptanalytic techniques and our findings indicate that ICEPOLE offers high security level. © International Association for Cryptologic Research 2014.

关键词： Field programmable gate arrays (FPGA)

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Comparison of genetic algorithm optimization on artificial neural network and support vector machine in intrusion detection system

Comparison of genetic algorithm optimization on artificial n...

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IEEE Conference on Open Systems (ICOS)

作者： Amin Dastanpour Suhaimi Ibrahim Reza Mashinchi Ali Selamat Advanced Informatics School Universiti Teknologi Malaysia Kuala lumpur Faculty of Computing Universiti Technology Malaysia Johor Faculty of Computer Science & IS Universiti Teknologi Malaysia Johor

As the technology trend in the recent years uses the systems with network bases, it is crucial to detect them from threats. In this study, the following methods are applied for detecting the network attacks: support vector machine (SVM) classifier, artificial Neural Networks (ANN), and Genetic Algorithms (GA). The objective of this study is to compare the outcomes of GA with SVM and GA with ANN and then comparing the outcomes of GA with SVM and GA with ANN and other algorithms. Knowledge Discovery and Data Mining (KDD CPU99) data set has been used in this paper for obtaining the results.

关键词： Genetic algorithms Artificial neural networks Support vector machines Intrusion detection Feature extraction Classification algorithms Machine learning algorithms

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Learning automata-based adaptive web services composition

Learning automata-based adaptive web services composition

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

作者： Guoqiang Li Dandan Song Lejian Liao Fuzhen Sun Jianguang Du School of informatics Linyi University Linyi China Language Information Processing & Cloud Computing Applications Beijing Institute of Technology Beijing China Beijing Engineering Research Centre of High Volume Language Information Processing & Cloud Computing Applications School of Computer Science Beijing Institute of Technology Beijing China

ISBN: (纸本)9781479932801

Service-oriented computing is a widely adopted paradigm in real applications. Considering the continuous evolution of services, adaptive service composition has always been a major concern. It is a big challenge to adjust the composition to be optimal in real-time. In this paper, a learning automata-based approach is proposed to attack this problem. It consists of two important components: random environment and a learning automaton. The former can be mapped to the service's execution environment. The latter is responsible for the adaptation achievement using reward and penalty functions, while we take the service composition structures into account to compute the usefulness value of all services. At last, simulation study has shown that our approach is efficient to find the optimal (sub-optimal) composition.

关键词： Learning automata Quality of service Web services Adaptation models Automata Conferences Real-time systems

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Personal access control system using moving object detection and face recognition

Personal access control system using moving object detection...

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International Conference on High Performance computing & Simulation (HPCS)

作者： Vesna Zeljković Du Zhang Ventzeslav Valev Zhongyu Zhang Shengjie Zhu Junjie Li New York Institute of Technology Old Westbury NY US School of Engineering & Computing Sciences New York Institute of Technology Nanjing Campus USA Associate member of the Institute of Mathematics and Informatics Bulgarian Academy of Sciences Sofia Bulgaria IAPR Fellow School of Computing University of North Florida Florida USA

ISBN: (纸本)9781479951604

Real time automated personal access control system is proposed in order to detect the moving objects, localize, extract and recognize their faces in real image sequence. The described method encompasses two important issues in personal access control system that receives increased attention over years: moving object detection and face recognition. It is tested on personal access controlled area video testing. The efficiency of the described system is illustrated on four real world interior video sequences recorded in indoor/outdoor mixed environment with slight illumination changes.

关键词： Face recognition Face Object detection Feature extraction Access control Lighting Streaming media

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Efficient analysis of node influence based on SIR model over huge complex networks

Efficient analysis of node influence based on SIR model over...

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International Conference on Data Science and Advanced Analytics (DSAA)

作者： Masahiro Kimura Kazumi Saito Kouzou Ohara Hiroshi Motoda Department of Electronics and Informatics Ryukoku University Japan School of Administration and Informatics University of Shizuoka Japan Depatment of Integrted Information Technology Aoyama Gakuin Univerity Japan Institute of Scientific and Industrial Research Osaka University Japan School of Computing and Information Systems University of Tasmania Australia

Node influence is yet another useful concept to quantify how important each node is over a network and can share the same role that other centrality measures have. It can provide new insight into the information diffusion phenomena such as existence of epidemic threshold which the other topology-based centralities cannot do. We focus on information diffusion process based on the SIR model, and address the problem of efficiently estimating the influence degree for all the nodes in the network. The proposed approach is a further improvement over the existing work of the bond percolation process [1], [2] which was demonstrated to be very effective, i.e., three orders of magnitude faster than direct Monte Carlo simulation, in approximately solving the influence maximization problem under a greedy search strategy. We introduce two pruning techniques which improve computational efficiency by an order of magnitude. This is a generic approach for the SIR model setting and can be instantiated to any specific diffusion model. It does not require any approximations or assumptions to the model, e.g., small diffusion probability, shortest path, maximum influence path, etc., that were needed in the existing approaches. We demonstrate its effectiveness by extensive experiments on two large real social networks. Main finding includes that different network structures have different epidemic thresholds and the node influence can identify influential nodes that the other centrality measures cannot.

关键词： Twitter Standards Integrated circuit modeling Approximation methods Educational institutions

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Towards a framework for adaptive resource provisioning in large-scale distributed agent-based simulation

Towards a framework for adaptive resource provisioning in la...

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Lecture Notes in Computer Science

作者： Hanai, Masatoshi Suzumura, Toyotaro Ventresque, Anthony Shudo, Kazuyuki Tokyo Institute of Technology Dept. of Mathematical and Computing Sciences 2-12-1 Ookayama MeguroTokyo152-8552 Japan School of Computer Science and Informatics University College Dublin Ireland Lero The Irish Software Engineering Research Centre Damastown Industrial Estate Mulhuddart Dublin 15 Ireland Smarter Cities Technology Centre IBM Research Damastown Industrial Estate Mulhuddart Dublin 15 Ireland

ISBN: (纸本)9783319143248

Large scale distributed agent-based simulations run on several computing units (e.g., virtual machines in the Cloud, nodes in a supercomputer). Classically, these systems try to (re-)load-balance the nodes as overloaded nodes slow down the *** another challenge in large scale distributed simulations is that the overall load evolves. In this paper we leverage on commodity computing to adapt resource provisioning (number of computing units) to the load during the execution of the simulation. We also propose an asynchronous migration mechanism that migrate workload between computing nodes efficiently when nodes wait for synchronisation barriers to happen. We validate our implementation on a scenario simulating one day of vehicular traffic in Tokyo, running on 2 to 8 machines depending on the demand. Our evaluation shows a 26% reduction in data migration time compared to a naive migration approach between computing units. © Springer International Publishing Switzerland 2014.

关键词： Supercomputers

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New analysis of ηπ tensor resonances measured at the COMPASS experiment

arXiv

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arXiv 2017年

作者： Jackura, A. Fernández-Ramírez, C. Mikhasenko, M. Pilloni, A. Mathieu, V. Nys, J. Pauk, V. Szczepaniak, A.P. Fox, G. Aghasyan, M. Akhunzyanov, R. Alexeev, M.G. Alexeev, G.D. Amoroso, A. Andrieux, V. Anfimov, N.V. Anosov, V. Antoshkin, A. Augsten, K. Augustyniak, W. Austregesilo, A. Azevedo, C.D.R. Badelek, B. Balestra, F. Ball, M. Barth, J. Beck, R. Bedfer, Y. Bernhard, J. Bicker, K. Bielert, E.R. Birsa, R. Bodlak, M. Bordalo, P. Bradamante, F. Bressan, A. Büchele, M. Burtsev, V.E. Chang, W.-C. Chatterjee, C. Chiosso, M. Choi, I. Chumakov, A.G. Chung, S.-U. Cicuttin, A. Crespo, M.L. Dalla Torre, S. Dasgupta, S.S. Dasgupta, S. Denisov, O.Yu. Dhara, L. Donskov, S.V. Doshita, N. Dreisbach, Ch. Dusaev, R.R. Dziewiecki, M. Efremov, A. Eversheim, P.D. Ferrero, A. Finger, M. Finger, M. Fischer, H. Franco, C. du Fresne von Hohenesche, N. Friedrich, J.M. Frolov, V. Fuchey, E. Gautheron, F. Gavrichtchouk, O.P. Gerassimov, S. Giarra, J. Giordano, F. Gnesi, I. Gorzellik, M. Grasso, A. Grosse Perdekamp, M. Grube, B. Grussenmeyer, T. Guskov, A. Hahne, D. Hamar, G. von Harrach, D. Heinsius, F.H. Heitz, R. Herrmann, F. Horikawa, N. d'Hose, N. Hsieh, C.-Y. Huber, S. Ishimoto, S. Ivanov, A. Ivanshin, Yu. Iwata, T. Jary, V. Joosten, R. Jörg, P. Kabuß, E. Kerbizi, A. Ketzer, B. Khaustov, G.V. Khokhlov, Yu.A. Kisselev, Yu. Klein, F. Koivuniemi, J.H. Kolosov, V.N. Kondo, K. Königsmann, K. Konorov, I. Konstantinov, V.F. Kotzinian, A.M. Kouznetsov, O.M. Kral, Z. Krämer, M. Kremser, P. Krinner, F. Kroumchtein, Z.V. Kulinich, Y. Kunne, F. Kurek, K. Kurjata, R.P. Kuznetsov, I.I. Kveton, A. Lednev, A.A. Levchenko, E.A. Levillain, M. Levorato, S. Lian, Y.-S. Lichtenstadt, J. Longo, R. Lyubovitskij, V.E. Maggiora, A. Magnon, A. Makins, N. Makke, N. Mallot, G.K. Mamon, S.A. Marianski, B. Martin, A. Marzec, J. Matoušek, J. Matsuda, H. Matsuda, T. Meshcheryakov, G.V. Meyer, M. Meyer, W. Mikhailov, Yu.V. Mikhasenko, M. Mitrofanov, E. Mitrofanov, N. Miyachi, Y. University of Aveiro Dept. of Physics Aveiro3810-193 Portugal Physics Dept. Indiana University BloomingtonIN47405 United States Center for Exploration of Energy and Matter Indiana University BloomingtonIN47403 United States School of Informatics and Computing Indiana University BloomingtonIN47405 United States Universität Bochum Institut für Experimentalphysik Bochum44780 Germany Universität Bonn Helmholtz-Institut für Strahlen- und Kernphysik Bonn53115 Germany Universität Bonn Physikalisches Institut Bonn53115 Germany Institute of Scientific Instruments AS CR Brno61264 Czech Republic Matrivani Institute of Experimental Research & Education Calcutta700 030 India Joint Institute for Nuclear Research Dubna Moscow region141980 Russia Dept. of Physics and Astronomy Ghent University Ghent9000 Belgium Universität Freiburg Physikalisches Institut Freiburg79104 Germany CERN Geneva 231211 Switzerland Technical University in Liberec Liberec46117 Czech Republic LIP Lisbon1000-149 Portugal Universität Mainz Institut für Kernphysik Mainz55099 Germany Instituto de Ciencias Nucleares Universidad Nacional Autónoma de México Ciudad de México04510 Mexico University of Miyazaki Miyazaki889-2192 Japan Lebedev Physical Institute Moscow119991 Russia Technische Universität München Physik Dept. Garching85748 Germany Theory Center Thomas Jefferson National Accelerator Facility Newport NewsVA23606 United States Nagoya University Nagoya464 Japan Charles University in Prague Faculty of Mathematics and Physics Prague18000 Czech Republic Czech Technical University in Prague Prague16636 Czech Republic State Scientific Center Institute for High Energy Physics of National Research Center 'Kurchatov Institute' Protvino142281 Russia IRFU CEA Université Paris-Saclay Gif-sur-Yvette91191 France Academia Sinica Institute of Physics Taipei11529 Taiwan Tel Aviv University School of Physics and Astronomy Tel Aviv69978 Israel University of Trieste

We present a new amplitude analysis of the ηπ D-wave in π− p → ηπ− p measured by COMPASS. Employing an analytical model based on the principles of the relativistic S-matrix, we find two resonances that can be identified with the a2(1320) and the excited a02(1700), and perform a comprehensive analysis of their pole positions. For the mass and width of the a2 we find M = (1307 ±1 ± 6) MeV and Γ = (112±1±8) MeV, and for the excited state a02 we obtain M = (1720±10±60) MeV and Γ = (280 ±10 ±70) MeV, respectively. Copyright © 2017, The Authors. All rights reserved.

关键词： Excited states

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Concept Drift Awareness in Twitter Streams

Concept Drift Awareness in Twitter Streams

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International Conference on Machine Learning and Applications (ICMLA)

作者： Joana Costa Catarina Silva Mário Antunes Bernardete Ribeiro Center for Informatics and Systems University of Coimbra Portugal School of Technology and Management Polytechnic Institute of Leiria Portugal Center for Research in Advanced Computing Systems INESC-TEC University of Porto Portugal

Learning in non-stationary environments is not an easy task and requires a distinctive approach. The learning model must not only have the ability to continuously learn, but also the ability to acquired new concepts and forget the old ones. Additionally, given the significant importance that social networks gained as information networks, there is an ever-growing interest in the extraction of complex information used for trend detection, promoting services or market sensing. This dynamic nature tends to limit the performance of traditional static learning models and dynamic learning strategies must be put forward. In this paper we present a learning strategy to learn with drift in the occurrence of concepts in Twitter. We propose three different models: a time-window model, an ensemble-based model and an incremental model. Since little is known about the types of drift that can occur in Twitter, we simulate different types of drift by artificially time stamping real Twitter messages in order to evaluate and validate our strategy. Results are so far encouraging regarding learning in the presence of drift, along with classifying messages in Twitter streams.

关键词： Twitter Adaptation models Time-frequency analysis Event detection Context Vectors

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Dimensionality Reduction Assisted Tensor Clustering

Dimensionality Reduction Assisted Tensor Clustering

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International Joint Conference on Neural Networks

作者： Yanfeng Sun Junbin Gao Xia Hong Yi Guo Chris J. Harris Beijing Municipal Key Lab of Multimedia and Intelligent Software Technology Beijing University of Technology School of Computing and Mathematics Charles Sturt University School of Systems Engineering University of Reading CSIRO Mathematics Informatics and Statistics Electronics and Computer Science University of Southampton

ISBN: (纸本)9781479914821

This paper is concerned with tensor clustering with the assistance of dimensionality reduction approaches. A class of formulation for tensor clustering is introduced based on tensor Tucker decomposition models. In this formulation, an extra tensor mode is formed by a collection of tensors of the same dimensions and then used to assist a Tucker decomposition in order to achieve data dimensionality reduction. We design two types of clustering models for the tensors: PCA Tensor Clustering model and Non-negative Tensor Clustering model, by utilizing different regularizations. The tensor clustering can thus be solved by the optimization method based on the alternative coordinate scheme. Interestingly, our experiments show that the proposed models yield comparable or even better performance compared to most recent clustering algorithms based on matrix factorization.

关键词： Tensor Tucker Decomposition Tensor Clustering Matrix Factorization Tensor PCA

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