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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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A Novel Method for Extrinsic Parameters Estimation between a Single-Line Scan LiDAR and a Camera

A Novel Method for Extrinsic Parameters Estimation between a...

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IEEE Intelligent Vehicles Symposium

作者： Pakapoj Tulsuk Panu Srestasathiern Miti Ruchanurucks Teera Phatrapornnant Hiroshi Nagahashi Faculty of Electrical Engineering Kasetsart University Thailand Geo-Informatics and Space Technology Development Agency Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Thailand Department of Information Processing Tokyo Institute of Technology Japan

ISBN: (纸本)9781479936397

This paper presents a novel method for extrinsic parameters estimation of a single line scan LiDAR and a camera. Using a checkerboard, the calibration setup is simple and practical. Particularly, the proposed calibration method is based on resolving geometry of the checkerboard that visible to the camera and the LiDAR. The calibration setup geometry is described by planes, lines and points. Our novelty is a new hypothesis of the geometry which is the orthogonal distances between LiDAR points and the line from the intersection between the checkerboard and LiDAR scan plane. To evaluate the performance of the proposed method, we compared our proposed method with the state of the art method i.e. Zhang and Pless [1]. The experimental results showed that the proposed method yielded better results.

关键词： planes lines points

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AN ADAPTIVE PROJECTED SUBGRADIENT BASED ALGORITHM FOR ROBUST SUBSPACE TRACKING

AN ADAPTIVE PROJECTED SUBGRADIENT BASED ALGORITHM FOR ROBUST...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Symeon Chouvardas Yiannis Kopsinis Sergios Theodoridis Computer Technology Institute and Press N. Kazantzaki Str University Campus 26504 Rio Greece University of Athens Dept. of Informatics and Telecommunications Athens 15784 Greece

ISBN: (纸本)9781479928941

In this paper, an Adaptive Projected Subgradient Method (APSM) based algorithm for robust subspace tracking is introduced. A properly chosen cost function is constructed at each time instance and the goal is to seek for points, which belong to the zero level set of this function;i.e., the set of points which score a zero loss. In each iteration, an outlier detection mechanism is employed, in order to conclude whether the current data vector contains outlier noise or not. Furthermore, a sparsity-promoting greedy algorithm is employed for the outlier vector estimation allowing the purification of the corrupted data from the outlier noise prior further processing. A theoretical analysis is carried out and experiments within the context of robust subspace estimation exhibit the enhanced performance of the proposed scheme compared to a recently developed state of the art algorithm.

关键词： state of the art outliers algorithms zero loss corrupted data tracking greedy algorithm Set of points Cost functions Subspace

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A FLOOD MAPPING ALGORITHM FROM CLOUD CONTAMINATED MODIS TIME-SERIES DATA USING A MARKOV RANDOM FIELD MODEL

A FLOOD MAPPING ALGORITHM FROM CLOUD CONTAMINATED MODIS TIME...

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IEEE International Geoscience and Remote Sensing Symposium

作者： T. Kasetkasem P. Phuhinkong P. Rakwatin T. Chanwimaluang I. Kumazawa Department of Electrical Engineering Kasetsart University Bangkok Thailand Geo-Informatics and Space Technology Development Agency (Public Organization) Bangkok Thailand National Electronics and Computer Technology Center Pathumthani Thailand Tokyo Institute of Technology Yokohama Japan

This paper addresses the problem of flood detection from the cloud-contaminated MODIS time-series data. Although MODIS data can provide almost daily coverage over the large area with the medium resolution. The use of MODIS data for flood mapping in the tropical regions is a challenging task due to the cloud contamination. Since the floods usually occur in the connected regions over a certain period of time, we employed the Markov random field model to characterize this property. For our experiments, the classification accuracy of flooded and non-flooded areas can be significantly increased by incorporating the MRF model.

关键词： MODIS Clouds Accuracy Floods Data models Markov processes Remote sensing

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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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eBPlatform: An IoT-based system for NCD patients homecare in China

eBPlatform: An IoT-based system for NCD patients homecare in...

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2014 IEEE Global Communications Conference

作者： Yu Liu Jianwei Niu Lianjun Yang Lei Shu School of Computer Science and ngineering Beihang University Beijing China Lab of Medical Informatics Institute of Microelectronics of CAS Beijing China Guangdong University of Petrochemical Technology Guangdong China

ISBN: (纸本)9781479935130

The number of Non-communicable disease (NCD) patients in China is growing rapidly, which is far beyond the capacity of the national health and social security system. Community health stations do not have enough doctors to take care of their patients in traditional ways. In order to establish a bridge between doctors and patients, we propose eBPlatform, which is an information system based on the Internet of Things (IoT) technology for homecare of the NCD patients. The eBox is a sensor which can be deployed in the patient's home for blood pressure measurement, blood sugar measurement and ECG signals collection. Some services are running on the remote server, which can receive the samples, filter and analyze the ECG signals. The uploaded data will be pushed to a web portal, with which doctors provide treatments online. The system requirements, design and implementation of hardware and software are discussed respectively. Finally, we investigate a case study with 50 NCD patients for half a year in Beijing. The results show that eBPlatform can increase the efficiency of the doctor and make a big progress to eliminate the numerical imbalance between community medical practitioners and NCD patients.

关键词： Electrocardiography Medical services Blood pressure Biomedical monitoring Servers Pressure measurement Biosensors

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A Fluctuation-Based Modelling Approach to Quantification of the Technical Debt on Mobile Cloud-Based Service Level

A Fluctuation-Based Modelling Approach to Quantification of ...

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IEEE Globecom Workshops

作者： Georgios Skourletopoulos Constandinos X. Mavromoustakis George Mastorakis Joel J. P. C. Rodrigues Periklis Chatzimisios Jordi Mongay Batalla Department of Computer Science University of Nicosia Nicosia Cyprus Department of Informatics Engineering Technological Educational Institute of Crete Heraklion Crete Greece Instituto de Telecomunica??es University of Beira Interior Covilh? Portugal CSSN Research Lab Alexander Technological Educational Institute of Thessaloniki Thessaloniki Greece Warsaw University of Technology Nowowiejska Str. 15/19 00-665 Warsaw Poland

ISBN: (纸本)9781467395274

Enterprise mobility has become a top technology priority for companies over recent years and many organizations are accelerating the adoption of mobile cloud application models. The mobile cloud can be considered as a marketplace, where the mobile services of the mobile cloud-based system architectures can be leased off via the cloud. In this context, this paper elaborates on a novel fluctuation-based quantification model, which is based on a cost-benefit appraisal, adopting a non-linear and asymmetric approach. The proposed model aims to predict the incurrence and the risk of entering into a new technical debt (TD) in the future and provide insights to inform effective investment decision making. The lease of a cloud-based mobile service was considered, when developing the formula, and the research approach is investigated with respect to the cost that derives from the unused capacity. The probability of overutilization or underutilization of the selected service is examined, as fluctuations in the number of users are forecasted. A quantification tool has been also developed as a proof of concept, implementing the proposed model and intending to quantify and evaluate the technical debt on mobile cloud-based service level, when fluctuations in the demand occur.

关键词： fluctuation-based modelling non-linearity technical debt quantification technical debt evaluation mobile cloud-based service level selection lease web services

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A fast method of statistical assessment for combinatorial hypotheses based on frequent itemset enumeration

A fast method of statistical assessment for combinatorial hy...

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European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2014

作者： Minato, Shin-Ichi Uno, Takeaki Tsuda, Koji Terada, Aika Sese, Jun Graduate School of Information Science and Technology Hokkaido University Sapporo 060-0814 Japan JST ERATO Minato Discrete Structure Manipulation System Project Sapporo 060-0814 Japan National Institute of Informatics Tokyo 101-8430 Japan Graduate School of Frontier Sciences University of Tokyo Kashiwa 277-8561 Japan Computational Biology Research Center National Institute of Advanced Industrial Science and Technology Tokyo 135-0064 Japan Department of Computer Science Ochanomizu University Tokyo 112-8610 Japan

ISBN: (纸本)9783662448502

In many scientific communities using experiment databases, one of the crucial problems is how to assess the statistical significance (p-value) of a discovered hypothesis. Especially, combinatorial hypothesis assessment is a hard problem because it requires a multiple-testing procedure with a very large factor of the p-value correction. Recently, Terada et al. proposed a novel method of the p-value correction, called "Limitless Arity Multiple-testing Procedure" (LAMP), which is based on frequent itemset enumeration to exclude meaninglessly infrequent itemsets which will never be significant. The LAMP makes much more accurate p-value correction than previous method, and it empowers the scientific discovery. However, the original LAMP implementation is sometimes too time-consuming for practical databases. We propose a new LAMP algorithm that essentially executes itemset mining algorithm once, while the previous one executes many times. Our experimental results show that the proposed method is much (10 to 100 times) faster than the original LAMP. This algorithm enables us to discover significant p-value patterns in quite short time even for very large-scale databases. © 2014 Springer-Verlag.

关键词： Database systems

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