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International Conference on Emerging eLearning Technologies and Applications (ICETA)

作者： Samuel Tremko Adrián Pekár Ján Juhár Jozef Janitor Department of Computers and Informatics Technical University of Kosice Koå¡ice Slovak Republic Institute of Computer Technology Technical University of Koå¡ice Koå¡ice Slovak Republic

This paper deals with the design, implementation and evaluation of a method for the reduction of IP flow information. The aim of this method is to decrease the utilization of the measuring tools based on the IPFIX protocol. The designed method was implemented in the MyBeem component of the SLAmeter network traffic monitoring tool, which is used for measuring various network traffic characteristics. This method uses the values of the IP flow keys for decreasing the load of the monitoring system by reducing the number of flows sent from the Exporter(s) to the Collector(s). This technique focuses on aggregation by which the number of flows generated by the metering process is controlled. The main contribution of this paper is an IP flow information data reduction technique.

关键词： Monitoring IP networks Databases Electronic learning Protocols Optimization Performance evaluation

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HYRA: An efficient hybrid reporting method for XG-PON upstream resource allocation

HYRA: An efficient hybrid reporting method for XG-PON upstre...

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International Conference on Optical Communication Systems

作者： Panagiotis Sarigiannidis Georgios Papadimitriou Petros Nicopolitidis Emmanouel Varvarigos Konstantinos Yiannopoulos Department of Informatics and Telecommunications Engineering University of Western Macedonia Kozani Greece Department of Informatics Aristotle University of Thessaloniki Thessaloniki Greece Computer Technology Institute and Press “Diophantus” N. Kazantzaki University of Patras Rio Greece Department of Informatics and Telecommunications University of Peloponnese Tripoli Greece

The dynamic bandwidth allocation (DBA) process in the modern passive optical networks (PONs) is crucial since it greatly influences the whole network performance. Recently, the latest new generation PON (NG-PON) standard, known as 10-gigabit-capable passive optical network (XG-PON), standardized by the international telecommunication union telecommunication standardization sector (ITU-T), emerges as one of the most efficient access networking framework to cope with the demanding needs of the fiber to the x (FTTX) paradigm, where x stands for home (FTTH), bulding (FTTB), or curve (FTTC). Motivated by the fact that the ITU-T specifications leave the bandwidth allocation process open for development by both industry and academia, we propose a novel DBA scheme for effectively delivering data in the upstream direction. Our idea is based on a subtle suggestion induced by the XG-PON specifications; each developed DBA method should combine both status reporting (SR) and traffic monitoring (TM) techniques. This means that a XG-PON framework should be cognitive enough in order to be able either to request bandwidth reporting from the connected users or estimate users' bandwidth demands or both. In this article we cover this gap by proposing a robust learning from experience method by utilizing a powerful yet simple tool, the learning automata (LAs). By combining SR and TM methods, the proposed hybrid scheme, called hybrid reporting allocation (HYRA), is capable of taking efficient decisions on deciding when SR or TM method should be employed so as to maximize the efficacy of the bandwidth allocation process. Simulation results reveal the superiority of our scheme in terms of average packet delay offering up to 33% improvement.

关键词： Bandwidth Resource management Passive optical networks Channel allocation Optical network units Standards Monitoring

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Model of Bayesian Tangent Eye Shape for eye capture

Model of Bayesian Tangent Eye Shape for eye capture

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International Conference on Intelligent Systems Design and Applications (ISDA)

作者： Asama Kuder Nsaef Azizah Jaafar Layth Sliman Riza Sulaiman Rahmita Wirza Rahmat Institute of Visual Informatics (IVI) Universiti Kebangsaan Malaysia Bangi Selangor Malaysia EFREI France Faculty of Computer Science and Information Technology University Putra Malaysia Serdang

Iris recognition system captures an image of an individual's eye. In addition, the process of segmentation, normalization and feature extraction is followed by the iris of an eye image in the system. Using the algorithms proposed by J. Daugman, Iris recognition system has significantly improved over the last decade, and it has been used in so many practical applications. However, some difficulties related to Iris position and movement are still to be improved. To overcome these difficulties one can enhance the image acquisition process. Obtaining a method in extracting quality of eye images automatically from the video stream is the main area of interest in this study. Besides, a Bayesian inference solution called Bayesian Tangent Eye Shape Model (BTESM) was suggested depending on estimation of tangent shape. During image acquisition, constraints on the position and motion of the subjects can be decreased owing to this approach. Owing to maximum a posteriori estimation, we can identify similarity transform coefficients as well as the eye shape parameters in BTESM. To apply the maximum a posteriori procedure, tangent Eye shape vector was considered the state of the model which is hidden and expectation maximization depending on searching algorithm was adopted. Hence, after being tested and matched to future studies, the acquisitioned eye image has been proved to be adequate for Iris recognition system.

关键词： Iris recognition Databases Image recognition Face Irrigation Shape

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A new approach for binary feature selection and combining classifiers

A new approach for binary feature selection and combining cl...

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International Conference on High Performance Computing & Simulation (HPCS)

作者： Asai Asaithambi Ventzeslav Valev Adam Krzyżak Vesna Zeljković Engineering & Construction University of North Florida USA Bulgarian Academy of Sciences Institute of Mathematics and Informatics Sofia Bulgaria Department of Computer Science and Software Engineering Concordia University Canada School of Engineering & Computer Sciences New York Institute of Technology USA

ISBN: (纸本)9781479951604

This paper explores feature selection and combining classifiers when binary features are used. The concept of Non-Reducible Descriptors (NRDs) for binary features is introduced. NRDs are descriptors of patterns that do not contain any redundant information. The underlying mathematical model for the present approach is based on learning Boolean formulas which are used to represent NRDs as conjunctions. Starting with a description of a computational procedure for the construction of all NRDs for a pattern, a two-step solution method is presented for the feature selection problem. The method computes weights of features during the construction of NRDs in the first step. The second step in the method then updates these weights based on repeated occurrences of features in the constructed NRDs. The paper then proceeds to present a new procedure for combining classifiers based on the votes computed for different classifiers. This procedure uses three different approaches for obtaining the single combined classifier, using majority, averaging, and randomized vote.

关键词： Training Pattern recognition Educational institutions Hamming distance Diversity reception Mathematical model Mutual information

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Supporting exploratory search tasks with interactive user modeling

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Proceedings of the Association for Information Science and technology 2014年第1期50卷 1-10页

作者： Tuukka Ruotsalo Kumaripaba Athukorala Dorota Głowacka Ksenia Konyushkova Antti Oulasvirta Samuli Kaipiainen Samuel Kaski Giulio Jacucci Helsinki Institute for Information Technology HIIT Aalto University PO Box 15600 00076 Aalto Finland Helsinki Institute for Information Technology HIIT University of Helsinki Department of Computer Science PL 68 00014 Helsinki Finland Max Planck Institute for Informatics and Saarland University Campus E1 7 66123 Saarbrücken Germany

This paper presents the design and study of interactive user modeling to support exploratory search tasks. Contrary to traditional interactions, such as query based search, query suggestions, or relevance feedback, interactive user modeling allows a user to perceive the state of the user model at all times and provide feedback that directly rewards or penalizes it. The technique allows the user to continuously tune the system's belief about the user's evolving information needs. We demonstrate that such functionality is useful in exploratory search where users need to get accustomed to a body of literature in a domain. We conducted two experiments where scientists carried out exploratory search tasks with our implementation of an interactive user modeling and retrieval system (SciNet) and two baselines: SciNet from which interactive user modeling was excluded and a real-world baseline (Google Scholar). The results show that interactive user modeling can help users to more effectively find relevant, novel and diverse information without compromises in task execution time.

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Fuzzy cloud service selection framework

Fuzzy cloud service selection framework

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IEEE International Conference on Cloud Networking (CLOUDNET)

作者： Masoumeh Tajvidi Rajiv Ranjan Joanna Kolodziej Lizhe Wang Department of Computer Engineering Shahid Beheshti University Tehran Iran CSIRO Computational Informatics Canberra Australia Institute of Computer Science Cracow University of Technology Cracow Poland Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences Beijing China

Cloud computing is the latest computing paradigm that delivers hardware and software resources as virtualization enabled services. Recently, cloud service selection has emerged as an important research problem due to large number of cloud providers and their diverse service configurations, multiple selection criteria, and customer's fuzzy perception of Quality of Service (QoS). In this paper, we propose a novel fuzzy logic framework for cloud service selection based on the individual QoS criteria of customers. In this model, the necessary service configuration and run-time QoS data is collected from reliable sources such as monitoring services, customers' feedbacks, and certified cloud providers' information. The obtained results from conducted case study shows validity and applicability of the proposed framework.

关键词： Quality of service Finance Monitoring Security Reliability Pragmatics Vectors

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Construction of lyapunov functional for networks of coupled delay differential and continuous-time difference equations 19

Construction of lyapunov functional for networks of coupled ...

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19th IFAC World Congress on International Federation of Automatic Control, IFAC 2014

作者： Ito, Hiroshi Mazenc, Frédéric Pepe, Pierdomenico Department of Systems Design and Informatics Kyushu Institute of Technology Iizuka Fukuoka820-8502 Japan EPIINRIA DISCO L2S-CNRS-Supélec 3 rue Joliot Curie Gif-sur-Yvette91192 France Department of Information Engineering Computer Science and Mathematics University of L'Aquila L'Aquila67100 Italy

ISBN: (纸本)9783902823625

To address various types of delays including the neutral-type arising in dynamical networks, this paper deals with coupled delay differential and continuous-time difference equations and develops stability and robustness criteria. Subsystems described by differential equations are not required to be input-to-state stable. No assumptions on network topology are made. To tackle networks in such a general formulation, this paper explicitly constructs Lyapunov-type functionals establishing stability and robustness of the overall networks. The construction requires only simple characterizations of subsystems in terms of inequalities with Lyapunov functions and instantaneous norms. © IFAC.

关键词： Continuous time systems

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FlowPro: A flow propagation method for single community detection

FlowPro: A flow propagation method for single community dete...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Costas Panagiotakis Harris Papadakis Paraskevi Fragopoulou Department of Business Administration Technological Educational Institute of Crete Agios Nikolaos Greece Departmentof Informatics Engineering Technological Educational Institute of Crete Heraklion Greece Research and Technology-Hellas Institute of Computer Science Heraklion Crete Greece

In this paper, we propose a flow propagation algorithm (FlowPro) that finds the community of a node in a complex network. The novelty of the proposed approach is the fact that FlowPro is local and it does not require the knowledge of the entire graph as most of the existing methods from literature. This makes possible the application of FlowPro in extremely large graphs or in cases where the entire graph is unknown like in most social networks. In addition, it simply compute the community of exactly one node, that is the major issue in most social network based applications. Experimental results and comparisons with other methods from the literature are presented for a variety of benchmark graphs with known community structure, derived by varying a number of graph parameters and real dataset graphs.

关键词： Communities Benchmark testing Image edge detection Accuracy Partitioning algorithms Bridges Conferences

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Proceedings of the 15th Annual UT-KBRIN Bioinformatics Summit 2016 : Cadiz, KY, USA. 8-10 April 2016

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BMC bioinformatics 2016年第SUPPL 10期17卷 297页

作者： Eric C. Rouchka Julia H. Chariker Benjamin J. Harrison Juw Won Park Xueyuan Cao Stanley Pounds Susana Raimondi James Downing Raul Ribeiro Jeffery Rubnitz Jatinder Lamba Bernie J. Daigle Deborah Burgess Stephanie Gehrlich John C. Carmen Nicholas Johnson Chandrakanth Emani Kalpani De Silva Michael P. Heaton Theodore S. Kalbfleisch Teeradache Viangteeravat Rahul Mudunuri Oluwaseun Ajayi Fatih Şen Eunice Y. Huang Mohammad Mohebbi Luaire Florian Douglas J. Jackson John F. Naber AKM Sabbir Sally R. Ellingson Yuping Lu Charles A Phillips Michael A. Langston Rahul K. Sevakula Raghuveer Thirukovalluru Nishchal K. Verma Yan Cui Mohammed Sayed Jing Wang Qi Liu Yu Shyr Xiaofei Zhang Naresh Prodduturi Gavin R. Oliver Diane Grill Jie Na Jeanette Eckel-Passow Eric W. Klee Michael M. Goodin Mark Farman Harrison Inocencio Chanyong Jang Jerzy W. Jaromczyk Neil Moore Kelly Sovacool Leon Dent Mike Izban Sammed Mandape Shruti Sakhare Siddharth Pratap Dana Marshall M Scotty DePriest James N. MacLeod Hanady Adam Ethan Blandford Joel Campbell Joshua Castlen Brittany Dixon Ginger Gilbert Aaron Hall Philip Kreisle Jessica Lasher Bethany Oakes Allison Speer Maximilian Valentine Naga Satya V. Rao Nagisetty Rony Jose Robert Rooney David Hains Department of Computer Engineering and Computer Science University of Louisville Duthie Center for Engineering Louisville USA Kentucky Biomedical Research Infrastructure (KBRIN) Bioinformatics Core Louisville USA Department of Psychological and Brain Sciences University of Louisville Louisville USA Department of Anatomical Sciences and Neurobiology University of Louisville Louisville USA Department of Biostatistics St. Jude Children’s Research Hospital Memphis USA Department of Pathology St. Jude Children’s Research Hospital Memphis USA Department of Oncology St. Jude Children’s Research Hospital Memphis USA Department of Pharmacotherapy and Translational Research College of Pharmacy University of Florida Gainesville USA Department of Biological Sciences University of Memphis Memphis USA Department of Computer Science University of Memphis Memphis USA Department of Computer Science Northern Kentucky University Highland Heights USA Department of Biological Sciences Northern Kentucky University Highland Heights USA Department of Biology Western Kentucky University Bowling Green USA Interdisciplinary Studies Program: Specialization in Bioinformatics University of Louisville Louisville USA United States Department of Agriculture Agricultural Research Service United States Meat Animal Research Center Clay Center USA Department of Biochemistry and Molecular Genetics School of Medicine University of Louisville Louisville USA Biomedical Informatics Core Children’s Foundation Research Institute Memphis USA Department of Pediatrics University of Tennessee Health Science Center Memphis USA Department of Electrical and Computer Engineering University of Louisville Louisville USA Department of Computer Science University of Kentucky Lexington USA Division of Biomedical Informatics College of Medicine University of Kentucky Lexington USA Department of Electrical Engineering and Computer Science University of Tennessee Knoxville USA Department of Electrical E

I1 Proceedings of the Fifteenth Annual UT- KBRIN Bioinformatics Summit 2016 Eric C. Rouchka, Julia H. Chariker, Benjamin J. Harrison, Juw Won Park P1 CC-PROMISE: Projection onto the Most Interesting Statistical Evidence (PROMISE) with Canonical Correlation to integrate gene expression and methylation data with multiple pharmacologic and clinical endpoints Xueyuan Cao, Stanley Pounds, Susana Raimondi, James Downing, Raul Ribeiro, Jeffery Rubnitz, Jatinder Lamba P2 Integration of microRNA-mRNA interaction networks with gene expression data to increase experimental power Bernie J Daigle, Jr. P3 Designing and writing software for in silico subtractive hybridization of large eukaryotic genomes Deborah Burgess, Stephanie Gehrlich, John C Carmen P4 Tracking the molecular evolution of Pax gene Nicholas Johnson; Chandrakanth Emani P5 Identifying genetic differences in thermally dimorphic and state specific fungi using in silico genomic comparison Stephanie Gehrlich, Deborah Burgess, John C Carmen P6 Identification of conserved genomic regions and variation therein amongst Cetartiodactyla species using next generation sequencing Kalpani De Silva, Michael P Heaton, Theodore S Kalbfleisch P7 Mining physiological data to identify patients with similar medical events and phenotypes Teeradache Viangteeravat, Rahul Mudunuri, Oluwaseun Ajayi, Fatih Şen, Eunice Y Huang P8 Smart brief for home health monitoring Mohammad Mohebbi, Luaire Florian, Douglas J Jackson, John F Naber P9 Side-effect term matching for computational adverse drug reaction predictions AKM Sabbir, Sally R Ellingson P10 Enrichment vs robustness: A comparison of transcriptomic data clustering metrics Yuping Lu, Charles A Phillips, Michael A Langston P11 Deep neural networks for transcriptome-based cancer classification Rahul K Sevakula, Raghuveer Thirukovalluru, Nishchal K. Verma, Yan Cui P12 Motif discovery using K-means clustering Mohammed Sayed, Juw Won Park P13 Large scale discovery of active enhancers from nasce

关键词： True Negative Differential Expression UCSC Genome Browser Laboratory Information Management System Biomedical informatics

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Computational collective intelligence – Technologies and applications 6thinternational conference ICCCI 2014 preface

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 8733 LNAI卷 V-VI页

作者： Hwang, Dosam Jung, Jason J. Nguyen, Ngoc Thanh Yeungnam University Department of Computer Engineering Dae-Dong Gyeungsan712-749 Korea Republic of Wroclaw University of Technology Institute of Informatics Wybrzeże Wyspiańskiego 27 Wroclaw50-370 Poland

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