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Annual IEEE Symposium on computer-Based Medical Systems

作者： Leila C.C. Bergamasco Hellyan Oliveira Helton Bíscaro Harry Wechsler Fátima L.S. Nunes Polytechnic School - Graduate Program in Electrical Engineering University of Sao Paulo São Paulo SP Brasil Laboratory of Health Informatics Applications University of Sao Paulo São Paulo SP Brasil Department of Computer Science George Mason University Fairfax VA USA

Three-dimensional models have been used to aid medical diagnoses, using images generated by modalities like Magnetic Resonance Imaging. They can provide a more complete vision of objects since their depth is taken into account. Content-based Image Retrieval (CBIR) has also been used to aid the diagnosis. One important step in Three-dimensional CBIR (Model Retrieva) systems is the comparison between two models by using a set of features extracted and stored in a database. In this paper we present a novel method to compare two models, using the Bipartite graphs technique, with the aim to improve the retrieval precision. This technique retrieves 3D medical models of the left ventricle in order to aid the diagnosis of Congestive Heart Failure. Results showed that the novel method improved the precision by 10% when compared to the Similarity Function of Euclidean and Manhattan distance. These results confirmed that bipartite graph techniques can be used to improve the accuracy of Model Retrieval systems.

关键词： Solid modeling Three-dimensional displays Computational modeling Medical diagnostic imaging Feature extraction Bipartite graph

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Metadata Correction: Importance-Performance Analysis of Personal Health Records in Taiwan: A Web-Based Survey

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Journal of medical Internet research 2017年第6期19卷 e209页

作者： Hsiao-Hsien Rau Yi-Syuan Wu Chi-Ming Chu Fu-Chung Wang Min-Huei Hsu Chi-Wen Chang Kang-Hua Chen Yen-Liang Lee Senyeong Kao Yu-Lung Chiu Hsyien-Chia Wen Anis Fuad Chien-Yeh Hsu Hung-Wen Chiu Graduate Institute of Biomedical Informatics Taipei Medical University Taipei Taiwan. Administration Department Healthconn Corp Taipei Taiwan. Institute of Medical Informatics Department of Computer Science of Information Engineering National Cheng Kung University Tainan Taiwan. School Of Public Health National Defense Medical Center Taiepi Taiwan. National Health Insurance Administration Ministry of Health and Welfare Taiepi Taiwan. School of Nursing College of Medicine Chang Gung University Linkou Taiwan. Division of Endocrinology Department of Pediatrics Linkou Chang Gung Memorial Hospital Linkou Taiwan. Internet of Things Laboratory Chunghwa Telecom Laboratories TaoYuen Taiwan. School of Health Care Administration Taipei Medical University Taipei Taiwan. Department of Biostatistics Epidemiology and Population Health Faculty of Medicine Universitas Gadjah Mada Yogyakarta Indonesia. Department of Information Management National Taipei University of Nursing and Health Sciences Taipei Taiwan. Master Program in Global Health and Development Taipei Medical University Taipei Taiwan.

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Performance Evaluation of Hypervisors for HPC Applications

Performance Evaluation of Hypervisors for HPC Applications

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IEEE International Conference on Systems, Man and Cybernetics

作者： David Beserra Felipe Oliveira Jean Araujo Felipe Fernandes Alberto Araújo Patrícia Endo Paulo Maciel Edward David Moreno Graduate Program in Computer Science University of Sergipe Aracaju Brazil Academic Unit of Garanhuns Federal Rural University of Pernambuco Garanhuns Brazil Informatics Center Federal University of Pernambuco Recife Brazil Graduate in Systems and Computing Military Institute of Engineering Rio de Janeiro Brazil University of Pernambuco Caruaru Brazil

ISBN: (纸本)9781479986989

High Performance Computing (HPC) aggregates computing power in order to solve large and complex problems in different knowledge areas. Nowadays, HPC users can utilize virtualized infrastructures as a low-cost alternative to deploy their applications. However, virtualization brings some challenges for HPC, specially in regard to overhead caused by hyper visors. In this work, our main goal is to analyze the performance of two hyper visors (KVM and Virtual Box) under HPC activities, considering full virtualization, and Para virtualization approaches. We used the HPC Challenge Benchmark (HPCC) to evaluate processor, RAM, inter-process communication and network communication performance. Our results show KVM in Para virtualization mode has a similar performance of a native cluster.

关键词： Virtualization Virtual machine monitors Bandwidth Benchmark testing Random access memory Performance evaluation Kernel

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Feature extraction for identification of sugarcane rust disease

Feature extraction for identification of sugarcane rust dise...

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2014 International Conference on Information, Communication Technology and System, ICTS 2014

作者： Dewi, Ratih Kartika Ginardi, R. V. Hari Information Technology and Computer Science Program Brawijaya University Malang Indonesia Dept. of Informatics Faculty of Information Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9781479968572

This research propose an image pattern classification to identify rust disease in sugarcane leaf with a combination of texture and color feature extraction. The purpose of this research is to find appropriate features that can identify sugarcane rust disease. Firstly, normal and diseased images are collected and pre-processed. Then, features of shape, color and texture are extracted from these images. After that, these images are classified by support vector machine classifier. A combination of several features are used to evaluate the appropriate features to find distinctive features for identification of rust disease. When a single feature is used, shape feature has the lowest accuracy of 51% and texture feature has the highest accuracy of 96.5%. A combination of texture and color feature extraction results a highest classification accuracy of 97.5%. A combination of texture and color feature extraction with polynomial kernel results in 98.5 % classification accuracy. © 2014 IEEE.

关键词： Feature extraction

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Negative Attitudes toward minimalistic Robots with intragroup communication styles

Negative Attitudes toward minimalistic Robots with intragrou...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Marlena R. Fraune Selma Šabanović Cognitive Science Program Indiana University USA Informatics and Computer Science Department Indiana University Indiana IN

With robots becoming more prevalent in daily life, it is important to understand human attitudes toward robots not only when humans interact with them directly, as most research examines, but also when people are indirectly exposed to robots performing nonsocial tasks (e.g., cleaning) in their vicinity. Because minimalistic robots are at present more likely to be found in households than human-like robots, this study examined human reactions to nonsocial, nonanthropomorphic robots. The specific focus of this study was on how robot communication style during human-robot co-location affects human perceptions of a group of robots. This paper also evaluates the relationship between participants' scores on the Negative Attitudes toward Robots Scale (NARS) and their behavioral response to and perceptions of robots in their environment. Our results suggest that robot communication style did not affect perceptions of robots and that responses on the NARS may not translate directly to behavior toward robots.

关键词： Atmospheric measurements Particle measurements Semantics Legged locomotion Educational institutions Psychology

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EAR-Math: Evaluation of Audio Rendered Mathematics

EAR-Math: Evaluation of Audio Rendered Mathematics

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8th International Conference on Universal Access in Human-computer Interaction, UAHCI 2014 - Held as Part of 16th International Conference on Human-computer Interaction, HCI International 2014

作者： Kacorri, Hernisa Riga, Paraskevi Kouroupetroglou, Georgios National and Kapodistrian University of Athens Department of Informatics and Telecommunications Panepistimiopolis Ilisia 15784 Athens Greece Doctoral Program in Computer Science Graduate Center 365 Fifth Ave New York NY 10016 United States

ISBN: (纸本)9783319074399

Audio rendering of mathematical expressions has accessibility benefits for people with visual impairment. Seeking a systematic way to measure participants' perception of the rendered formulae with audio cues, we investigate the design of performance metrics to capture the distance between reference and perceived math expressions. We propose EAR-Math, a methodological approach for user-based evaluation of math rendering against a baseline. EAR-Math measures systems' performance using three fine-grained error rates based on the structural elements, arithmetic operators, numbers and identifiers in a formula. The proposed methodology and metrics were successfully applied in a pilot study, where 5 sighted and 2 blind participants evaluated 39 stimuli rendered by MathPlayer in Greek. In the obtained results, we observed that structural elements had the highest mean and variance of errors, which improved from 18% in the first attempt to 10% and 7% in two following attempts. © 2014 Springer International Publishing.

关键词： Artificial intelligence

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2015 Brainhack Proceedings Abstracts

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GIGAscience 2016年第1期5卷 1-26页

作者： [Anonymous] Computational Neuroimaging Lab Center for Biomedical Imaging and Neuromodulation Nathan Kline Institute for Psychiatric Research Orangeburg USA Center for the Developing Brain Child Mind Institute New York USA Centre de Recherche Institut Universitaire de Gériatrie de Montréal Montréal Canada Department of computer science and operational research University of Montreal Montreal Canada Max Planck Research Group for Neuroanatomy & Connectivity Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany SRI International Menlo Park USA Department of Psychiatry and Behavioral Sciences Stanford University Stanford USA Functional Imaging Unit Center for Diagnostic Radiology Unviersity Medicine Greifswald Greifswald Germany Université de Montréal Montréal Canada University of Massachusetts Medical School Worcester USA University of California Berkeley USA Institut Pasteur Paris France University of California San Diego La Jolla USA University of Washington Seattle USA Poldrack Lab Department of Psychology Stanford University Stanford USA Montréal Neurological Institute McGill University and Institute of Pscychology Montréal Canada Douglas Mental Health Institute Montréal Canada Otto-von-Guericke University Magdeburg Germany Montreal Neurological Institute McGill University Montreal Canada University of Hawaii John A. Burns School of Medicine Honolulu USA Oregon Health & Science University Portland USA University of Greifswald Greifswald Germany Duke Institute for Brain Sciences Durham USA Department of Psychology and Neuroscience University of Colorado Boulder USA Institute of Cognitive Neuroscience University College London London United Kingdom Section on Functional Imaging Methods Laboratory of Brain and Cognition National Institute of Mental Health Bethedsa USA City University of New York-Hunter College New York USA Monash Institute of Cognitive and Clinical Neurosciences Monash University Melbourne Australia McGill Centre fo

I1 Introduction to the 2015 Brainhack Proceedings R. Cameron Craddock, Pierre Bellec, Daniel S. Margules, B. Nolan Nichols, Jörg P. Pfannmöller A1 Distributed collaboration: the case for the enhancement of Brainspell’s interface AmanPreet Badhwar, David Kennedy, Jean-Baptiste Poline, Roberto Toro A2 Advancing open science through NiData Ben Cipollini, Ariel Rokem A3 Integrating the Brain Imaging Data Structure (BIDS) standard into C-PAC Daniel Clark, Krzysztof J. Gorgolewski, R. Cameron Craddock A4 Optimized implementations of voxel-wise degree centrality and local functional connectivity density mapping in AFNI R. Cameron Craddock, Daniel J. Clark A5 LORIS: DICOM anonymizer Samir Das, Cécile Madjar, Ayan Sengupta, Zia Mohades A6 Automatic extraction of academic collaborations in neuroimaging Sebastien Dery A7 NiftyView: a zero-footprint web application for viewing DICOM and NIfTI files Weiran Deng A8 Human Connectome Project Minimal Preprocessing Pipelines to Nipype Eric Earl, Damion V. Demeter, Kate Mills, Glad Mihai, Luka Ruzic, Nick Ketz, Andrew Reineberg, Marianne C. Reddan, Anne-Lise Goddings, Javier Gonzalez-Castillo, Krzysztof J. Gorgolewski A9 Generating music with resting-state fMRI data Caroline Froehlich, Gil Dekel, Daniel S. Margulies, R. Cameron Craddock A10 Highly comparable time-series analysis in Nitime Ben D. Fulcher A11 Nipype interfaces in CBRAIN Tristan Glatard, Samir Das, Reza Adalat, Natacha Beck, Rémi Bernard, Najmeh Khalili-Mahani, Pierre Rioux, Marc-Étienne Rousseau, Alan C. Evans A12 DueCredit: automated collection of citations for software, methods, and data Yaroslav O. Halchenko, Matteo Visconti di Oleggio Castello A13 Open source low-cost device to register dog’s heart rate and tail movement Raúl Hernández-Pérez, Edgar A. Morales, Laura V. Cuaya A14 Calculating the Laterality Index Using FSL for Stroke Neuroimaging Data Kaori L. Ito, Sook-Lei Liew A15 Wrapping FreeSurfer 6 for use in high-performance computing environments Hans J. Johns

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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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Identification of K most vulnerable nodes in multi-layered network using a new model of interdependency

Identification of K most vulnerable nodes in multi-layered n...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Arunabha Sen Anisha Mazumder Joydeep Banerjee Arun Das Randy Compton Computer Science and Engineering Program School of Computing Informatics and Decision System Engineering Arizona State University Tempe Arizona

The critical infrastructures of a nation including the power grid and the communication network are highly interdependent. Recognizing the need for a deeper understanding of the interdependency in a multi-layered network, significant efforts have been made by the research community in the last few years to achieve this goal. Accordingly a number of models have been proposed and analyzed. Unfortunately, most of the models are over simplified and, as such, they fail to capture the complex interdependency that exists between entities of the power grid and the communication networks involving a combination of conjunctive and disjunctive relations. To overcome the limitations of existing models, we propose a new model that is able to capture such complex interdependency relations. Utilizing this model, we provide techniques to identify the K most “vulnerable” nodes of an interdependent network. We show that the problem can be solved in polynomial time in some special cases, whereas for some others, the problem is NP-complete. We establish that this problem is equivalent to computation of a fixed point of a multilayered network system and we provide a technique for its computation utilizing Integer Linear programming. Finally, we evaluate the efficacy of our technique using real data collected from the power grid and the communication network that span the Maricopa County of Arizona.

关键词： Equations Communication networks Mathematical model Power grids Power system faults Power system protection Nonhomogeneous media

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On the impact of coding parameters on storage requirement of region-based fault tolerant distributed file system design

On the impact of coding parameters on storage requirement of...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Sujogya Banerjee Arun Das Anisha Mazumder Zahra Derakhshandeh Arunabha Sen Computer Science and Engineering Program School of Computing Informatics and Decision System Engineering Arizona State University Tempe Arizona

Advances in technology have resulted in Internet-scale deployment of storage systems such as peer-to-peer storage and cloud storage, where data is distributed over multiple storage nodes in a networked environment. In these environments the storage nodes are often commodity machines and are susceptible to failure. The notion of fault domain, introduced by Microsoft Azure, captures the fault-tolerance aspects of a data center. A fault domain is defined as a set of servers all of which become inaccessible when a single fault (such as the failure of a switch or a router) occurs in the data center. As such a fault domain can be viewed as a spatially correlated or region based failure. In order to enhance reliability through redundancy, maximum distance separable (MDS) codes such as Reed-Solomon codes and (N, K) codings are utilized. In this paper we present analytical results demonstrating that the choice of the coding parameters N and K may have significant impact on storage that will be necessary to achieve reliability. We present a polynomial time algorithm for optimal storage allocation in a mesh network and we conduct extensive experimentation to evaluate the impact of the coding parameters N and K on the storage requirement to provide all region fault tolerance with varying size of the mesh and the fault region.

关键词： Color Image color analysis Encoding Silicon Peer-to-peer computing Fault tolerant systems Fault tolerance

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