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2017 17th IEEE International Conference on Communication Technology (ICCT 2017)

作者： Qiang Fu Yuling Luo Junxiu Liu Jinjie Bi Senhui Qiu Yi Cao Xuemei Ding Guangxi Key Lab of Multi-Source Information Mining & Security Faculty of Electronic Engineering Guangxi Normal University Guangxi Experiment Centre of Information Science Department of Business Transformation and Sustainable Enterprise Surrey Business SchoolUniversity of Surrey Faculty of Software Fujian Normal University School of Computing and Intelligent Systems Faculty of Computing and Engineering Ulster University

This paper proposes three methods to improve the learning algorithm for spiking neural networks（SNNs）. The aim is to improve learning performance in SNNs where neurons are allowed to fire multiple times. The performance is analyzed based on the convergence rate, the concussion condition in the training period and the error between actual output and desired output. The exclusive-or（XOR） and Wisconsin breast cancer（WBC） classification tasks are employed to validate the proposed optimized methods. Experimental results demonstrate that compared to original learning algorithm, all three methods have less iterations, higher accuracy, and more stable in the training period.

关键词： spiking neural network optimization method learning performance

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Chemical Substance Classification Using Long Short-Term Memory Recurrent Neural Network

Chemical Substance Classification Using Long Short-Term Memo...

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2017 17th IEEE International Conference on Communication Technology (ICCT 2017)

作者： Jinlei Zhang Junxiu Liu Yuling Luo Qiang Fu Jinjie Bi Senhui Qiu Yi Cao Xuemei Ding Guangxi Key Lab of Multi-Source Information Mining & Security Faculty of Electronic Engineering Guangxi Normal University Guangxi Experiment Centre of Information Science Department of Business Transformation and Sustainable Enterprise Surrey Business SchoolUniversity of Surrey Faculty of Software Fujian Normal University School of Computing and Intelligent Systems Faculty of Computing and Engineering Ulster University

This paper proposed a chemical substance detection method using the Long Short-Term Memory of Recurrent Neural Networks(LSTM-RNN). The chemical substance data was collected using a mass spectrometer which is a time-series data. The classification accuracy using the LSTM-RNN classifier is 96.84%, which is higher than 75.07% of the ordinary feed forward neural networks. The experimental results show that the LSTM-RNN can learn the properties of the chemical substance dataset and achieve a high detection accuracy.

关键词： recurrent neural networks chemical substances long short-term memory feed forward neural networks

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Performance comparison between broadcast authentication methods for vehicular networks 16

Performance comparison between broadcast authentication meth...

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4th International Conference on Information and Network Security, ICINS 2016

作者： Grover, Kanika Lim, Alvin Arizona State University School of Computing Informatics and Decision Systems Engineering TempeAZ85287 United States Auburn University Department of Computer Science and Software Engineering AuburnAL36849 United States

ISBN: (纸本)9781450347969

For authenticating time critical broadcast messages, IEEE 1609.2 security standard for Vehicular Ad hoc Networks (VANETs) suggests the use of secure Elliptic Curve Digital Signature Algorithm (ECDSA). Since ECDSA has an expensive verification in terms of time, most commonly suggested alternate algorithms are TESLA and signature amortization. Unfortunately, these algorithms lack immediate authentication and non-repudiation. Therefore, we introduce a probabilistic verification scheme for an ECDSA-based authentication protocol. Using ns2 simulation tools, we compare the performance of all above-mentioned broadcast authentication algorithms. The results show with our proposed scheme, there is an increase in packet processed ratio over that of all the other algorithms. © 2016 ACM.

关键词： Authentication

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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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SmartVM: A Multi-Layer Microservice-Based Platform for Deploying SaaS

SmartVM: A Multi-Layer Microservice-Based Platform for Deplo...

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IEEE International Symposium on Parallel and Distributed Processing with Applications and IEEE International Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Xi Zheng Jiaojiao Jiang Yuqun Zhang Yao Deng Min Fu Tianlei Zheng Xiao Liu Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China School of Software and Electrical Engineering Swinburne University of Technology Melbourne Australia School of Information Technology Deakin University Melbourne Australia Data61 CSIRO Sydney Australia School of Computing and Information Systems The University of Melbourne Melbourne Australia

ISBN: (纸本)9781538637913

With the advent of software-as-a-Service (SaaS), SaaS developers are facing many challenges associated with the multi-tenancy and the dramatically increased number of users. In order to achieve resource-optimized, on-demand dynamic scaling across multiple tenants, and reduce costs, in this paper, a new platform, named SmartVM, is created to enable SaaS developer to create, customize, and deploy SaaS solutions in a multi-tier microservice-based manner. We develop an e-commerce SaaS prototype to evaluate effectiveness and efficiency of SmartVM. The results show that the SmartVM deployments outperforms the conventional monolithic and microservice deployments in smart monitoring, cost reduction, and resource optimization.

关键词： Containers Monitoring Business software as a service Australia Scalability Measurement

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Achieve sustainable ultra-dense heterogeneous networks for 5G

arXiv

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arXiv 2017年

作者： An, Jianping Yang, Kai Wu, Jinsong Ye, Neng Guo, Song Liao, Zhifang School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Fractional Signals and Systems Beijing Department of Electrical Engineering Universidad de Chile Santiago Chile Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong School of Software Central South University Changsha China

Due to the exponentially increased demands of mobile data traffic, e.g., a 1000-fold increase in traffic demand from 4G to 5G, network densification is considered as a key mechanism in the evolution of cellular networks, and ultra-dense heterogeneous network (UDHN) is a promising technique to meet the requirements of explosive data traffic in 5G networks. In the UDHN, base station is brought closer and closer to users through densely deploying small cells, which would result in extremely high spectral efficiency and energy efficiency. In this article, we first present a potential network architecture for the UDHN, and then propose a generalized orthogonal/non-orthogonal random access scheme to improve the network efficiency while reducing the signaling overhead. Simulation results demonstrate the effectiveness of the proposed scheme. Finally, we present some of the key challenges of the UDHN. Copyright © 2017, The Authors. All rights reserved.

关键词： Heterogeneous networks

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Hand gesture recognition based on sEMG signals using Support Vector Machines

Hand gesture recognition based on sEMG signals using Support...

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IEEE International Conference on Consumer Electronics - Berlin (ICCE-Berlin)

作者： Gonzalo Pomboza-Junez Juan Holgado Terriza Department of Languages and Systems National University of Chimborazo Riobamba Ecuador Department of Software Engineering University of Granada Granada Spain

This paper demonstrates the application of electromyography (EMG) signals for controlling home devices. To achieve this we have used an armband called MYO® that has an array of eight sEMG sensors around the forearm. We have studied 15 different hand gestures to create a dictionary of gesture control. We have achieved gesture recognition using Support Vector Machines (SVMs) as a classification method. We tested different types of kernels (radial, polynomial and sigmoid) to achieve the optimum conditions for gesture learning and recognition as well as an accurate determination of these movements. Furthermore, to show the effectiveness and applicability of the results, a Gesture Control System has been implemented as an embedded system. The system also enable Bluetooth communication with the armband, send gesture control commands to household devices using iR protocol.

关键词： Electromyography Sensors Kernel Support vector machines High definition video Protocols Bluetooth

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Datawarehouse design for educational data mining

Datawarehouse design for educational data mining

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International Conference on Information Technology Based Higher Education and Training, ITHET

作者： Oswaldo Moscoso-Zea Andres-Sampedro Sergio Luján-Mora Faculty of Engineering Equinoctial Technological University Quito Ecuador Department of Software and Computing Systems University of Alicante Alicante Spain

ISBN: (纸本)9781509007790

Business intelligence (BI) builds upon a set of tools and applications that enable the analysis of vast amounts of information (Big Data). Educational institutions handle large volumes of Big Data every year. There is a strong need for the use of BI in these institutions to improve their processes and support decision making. The core technology in a BI project is a datawarehouse (DW). This paper describes the design considerations for the implementation of the DW in an educational scenario. The DW will be used in a knowledge discovery process to handle the information for the analysis of key performance indicators using educational data mining (EDM) techniques. The DW along with an enterprise architecture (EA) repository are the key technological assets of a knowledge management framework (KMF). This framework was designed to put order in the creation, capture, transfer and digitalization of knowledge. This guide and the framework are two of the outcomes of a research project in a private university. Furthermore, a case study suggests how to choose the best methodology in higher institutions. In the case study the steps for the DW design are presented. This study can be useful for academics and practitioners that plan to design a DW to analyze information using EDM techniques.

关键词： Data mining Organizations Decision making Loading software

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NWChem: Past, present, and future

arXiv

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arXiv 2020年

作者： Aprà, E. Bylaska, E.J. de Jong, W.A. Govind, N. Kowalski, K. Straatsma, T.P. Valiev, M. van Dam, H.J.J. Alexeev, Y. Anchell, J. Anisimov, V. Aquino, F.W. Atta-Fynn, R. Autschbach, J. Bauman, N.P. Becca, J.C. Bernholdt, D.E. Bhaskaran-Nair, K. Bogatko, S. Borowski, P. Boschen, J. Brabec, J. Bruner, A. Cauët, E. Chen, Y. Chuev, G.N. Cramer, C.J. Daily, J. Deegan, M.J.O. Dunning, T.H. Dupuis, M. Dyall, K.G. Fann, G.I. Fischer, S.A. Fonari, A. Früchtl, H. Gagliardi, L. Garza, J. Gawande, N. Ghosh, S. Glaesemann, K. Götz, A.W. Hammond, J. Helms, V. Hermes, E.D. Hirao, K. Hirata, S. Jacquelin, M. Jensen, L. Johnson, B.G. Jónsson, H. Kendall, R.A. Klemm, M. Kobayashi, R. Konkov, V. Krishnamoorthy, S. Krishnan, M. Lin, Z. Lins, R.D. Littlefield, R.J. Logsdail, A.J. Lopata, K. Ma, W. Marenich, A.V. Martin del Campo, J. Mejia-Rodriguez, D. Moore, J.E. Mullin, J.M. Nakajima, T. Nascimento, D.R. Nichols, J.A. Nichols, P.J. Nieplocha, J. Otero-de-la-Roza, A. Palmer, B. Panyala, A. Pirojsirikul, T. Peng, B. Peverati, R. Pittner, J. Pollack, L. Richard, R.M. Sadayappan, P. Schatz, G.C. Shelton, W.A. Silverstein, D.W. Smith, D.M.A. Soares, T.A. Song, D. Swart, M. Taylor, H.L. Thomas, G.S. Tipparaju, V. Truhlar, D.G. Tsemekhman, K. van Voorhis, T. Vázquez-Mayagoitia, Á. Verma, P. Villa, O. Vishnu, A. Vogiatzis, K.D. Wang, D. Weare, J.H. Williamson, M.J. Windus, T.L. Woliński, K. Wong, A.T. Wu, Q. Yang, C. Yu, Q. Zacharias, M. Zhang, Z. Zhao, Y. Harrison, R.J. Pacific Northwest National Laboratory RichlandWA99352 United States Lawrence Berkeley National Laboratory BerkeleyCA94720 United States National Center for Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Brookhaven National Laboratory UptonNY11973 United States Argonne Leadership Computing Facility Argonne National Laboratory ArgonneIL60439 United States Intel Corporation Santa ClaraCA95054 United States QSimulate CambridgeMA02139 United States Department of Physics University of Texas at Arlington ArlingtonTX76019 United States Department of Chemistry University at Buffalo State University of New York BuffaloNY14260 United States Department of Chemistry Pennsylvania State University University ParkPA16802 United States Oak Ridge National Laboratory Oak RidgeTN37831 United States Washington University St. LouisMO63130 United States 4G Clinical WellesleyMA02481 United States Faculty of Chemistry Maria Curie-Sklodowska University in Lublin Lublin20-031 Poland Department of Chemistry Iowa State University AmesIA50011 United States J. Heyrovský Institute of Physical Chemistry Academy of Sciences of the Czech Republic Prague 818223 Czech Republic Department of Chemistry and Physics University of Tennessee at Martin MartinTN38238 United States Université libre de Bruxelles BrusselsB-1050 Belgium Facebook Menlo ParkCA94025 United States Institute of Theoretical and Experimental Biophysics Russian Academy of Science Pushchino Moscow142290 Russia Department of Chemistry Chemical Theory Center Supercomputing Institute University of Minnesota MinneapolisMN55455 United States SKAO Jodrell Bank Observatory MacclesfieldSK11 9DL United Kingdom Department of Chemistry University of Washington SeattleWA98195 United States Dirac Solutions PortlandOR97229 United States Chemistry Division U. S. Naval Research Laboratory WashingtonDC20375 United States School of Chemistry and Biochemis

Specialized computational chemistry packages have permanently reshaped the landscape of chemical and materials science by providing tools to support and guide experimental efforts and for the prediction of atomistic and electronic properties. In this regard, electronic structure packages have played a special role by using first-principle-driven methodologies to model complex chemical and materials processes. Over the last few decades, the rapid development of computing technologies and the tremendous increase in computational power have offered a unique chance to study complex transformations using sophisticated and predictive many-body techniques that describe correlated behavior of electrons in molecular and condensed phase systems at different levels of theory. In enabling these simulations, novel parallel algorithms have been able to take advantage of computational resources to address the polynomial scaling of electronic structure methods. In this paper, we briefly review the NWChem computational chemistry suite, including its history, design principles, parallel tools, current capabilities, outreach and outlook. Copyright © 2020, The Authors. All rights reserved.

关键词： Computational chemistry

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Author Correction: Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

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Scientific data 2021年第1期8卷 242页

作者： Julien Cohen-Adad Eva Alonso-Ortiz Mihael Abramovic Carina Arneitz Nicole Atcheson Laura Barlow Robert L Barry Markus Barth Marco Battiston Christian Büchel Matthew Budde Virginie Callot Anna J E Combes Benjamin De Leener Maxime Descoteaux Paulo Loureiro de Sousa Marek Dostál Julien Doyon Adam Dvorak Falk Eippert Karla R Epperson Kevin S Epperson Patrick Freund Jürgen Finsterbusch Alexandru Foias Michela Fratini Issei Fukunaga Claudia A M Gandini Wheeler-Kingshott Giancarlo Germani Guillaume Gilbert Federico Giove Charley Gros Francesco Grussu Akifumi Hagiwara Pierre-Gilles Henry Tomáš Horák Masaaki Hori James Joers Kouhei Kamiya Haleh Karbasforoushan Miloš Keřkovský Ali Khatibi Joo-Won Kim Nawal Kinany Hagen H Kitzler Shannon Kolind Yazhuo Kong Petr Kudlička Paul Kuntke Nyoman D Kurniawan Slawomir Kusmia René Labounek Maria Marcella Laganà Cornelia Laule Christine S Law Christophe Lenglet Tobias Leutritz Yaou Liu Sara Llufriu Sean Mackey Eloy Martinez-Heras Loan Mattera Igor Nestrasil Kristin P O'Grady Nico Papinutto Daniel Papp Deborah Pareto Todd B Parrish Anna Pichiecchio Ferran Prados Àlex Rovira Marc J Ruitenberg Rebecca S Samson Giovanni Savini Maryam Seif Alan C Seifert Alex K Smith Seth A Smith Zachary A Smith Elisabeth Solana Y Suzuki George Tackley Alexandra Tinnermann Jan Valošek Dimitri Van De Ville Marios C Yiannakas Kenneth A Weber Ii Nikolaus Weiskopf Richard G Wise Patrik O Wyss Junqian Xu NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. jcohen@polymtl.ca. Functional Neuroimaging Unit CRIUGM Université de Montréal Montreal QC Canada. jcohen@polymtl.ca. Mila - Quebec AI Institute Montreal QC Canada. jcohen@polymtl.ca. NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. Department of Radiology Swiss Paraplegic Centre Nottwil Switzerland. Centre for Advanced Imaging The University of Queensland Brisbane Australia. Department of Radiology University of British Columbia Vancouver BC Canada. Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Charlestown MA USA. Department of Radiology Harvard Medical School Boston MA USA. Harvard-Massachusetts Institute of Technology Health Sciences & Technology Cambridge MA USA. School of Information Technology and Electrical Engineering The University of Queensland Brisbane Australia. NMR Research Unit Queen Square MS Centre UCL Queen Square Institute of Neurology Faculty of Brain Sciences University College London London UK. Department of Systems Neuroscience University Medical Center Hamburg-Eppendorf Hamburg Germany. Department of Neurosurgery Medical College of Wisconsin Milwaukee WI USA. Aix-Marseille Univ CNRS CRMBM Marseille France. APHM Hopital Universitaire Timone CEMEREM Marseille France. Vanderbilt University Institute of Imaging Science Vanderbilt University Medical Center Nashville TN USA. Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada. CHU Sainte-Justine Research Centre Montreal QC Canada. Centre de Recherche CHUS CIMS Sherbrooke Canada. Sherbrooke Connectivity Imaging Lab (SCIL) Computer Science department Université de Sherbrooke Sherbrooke Canada. Université de Strasbourg CNRS ICube Strasbourg France. UHB - University Hospital Brno and Masaryk University Department of Radiology and Nuclear

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