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3rd International Conference on Biomedical and Health informatics, ICBHI 2017

作者： Livitckaia, K. Koutkias, V. Maglaveras, N. Kouidi, E. van Gils, M. Chouvarda, I. Lab of Computing Medical Informatics & Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Aristotle University Campus Thessaloniki54124 Greece Institute of Applied Biosciences Centre for Research and Technology Hellas Thermi Thessaloniki Greece Laboratory of Sports Medicine School of Physical Education and Sports Sciences Aristotle University of Thessaloniki Thessaloniki Greece VTT Technical Research Centre of Finland Ltd. Tampere Finland

ISBN: (纸本)9789811074189

Physical fitness is one of the main therapeutic recommendations for patients with heart disease. Yet, adherence to physical activity regimen in both daily life and rehabilitation programs remains to be low. Elaborating on the analysis of factors associated with physical activity behavior, we noticed that the concept of adherence is complex, and its evaluation depends on a specific behavior and its settings. Evaluation of adherence to exercise in leisure time and exercise in the settings of cardiac rehabilitation program requires an application of different instruments. In this paper, we present a summary of findings from the literature analysis regarding types, settings and evaluation instruments of physical activity adherence in daily life and cardiac rehabilitation settings. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Heart

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Learning performance in inverse Ising problems with sparse teacher couplings

arXiv

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arXiv 2019年

作者： Abbara, Alia Kabashima, Yoshiyuki Obuchi, Tomoyuki Xu, Yingying Laboratoire de Physique de l'Ecole Normale Supérieure 24 rue Lhomond Paris75005 France Department of Mathematical and Computing Science Tokyo Institute of Technology 2-12-1 Ookayama Meguro-ku Tokyo Japan Department of Systems Science Graduate School of Informatics Kyoto University Yoshida Hon-machi Sakyo-ku Kyoto-shi Kyoto606-8501 Japan

We investigate the learning performance of the pseudolikelihood maximization method for inverse Ising problems. In the teacher-student scenario under the assumption that the teacher's couplings are sparse and the student does not know the graphical structure, the learning curve and order parameters are assessed in the typical case using the replica and cavity methods from statistical mechanics. Our formulation is also applicable to a certain class of cost functions having locality;the standard likelihood does not belong to that class. The derived analytical formulas indicate that the perfect inference of the presence/absence of the teacher's couplings is possible in the thermodynamic limit taking the number of spins N as infinity while keeping the dataset size M proportional to N, as long as α = M/N > 2. Meanwhile, the formulas also show that the estimated coupling values corresponding to the truly existing ones in the teacher tend to be overestimated in the absolute value, manifesting the presence of estimation bias. These results are considered to be exact in the thermodynamic limit on locally tree-like networks, such as the regular random or Erdős-Rényi graphs. Numerical simulation results fully support the theoretical predictions. Additional biases in the estimators on loopy graphs are also discussed. Copyright © 2019, The Authors. All rights reserved.

关键词： Couplings

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Feature Selection Using Multivariate Adaptive Regression Splines in Telecommunication Fraud Detection

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IOP Conference Series: Materials Science and Engineering 2020年第1期864卷

作者： Muhalim Mohamed Amin Anazida Zainal Nurulhuda Firdaus Mohd. Azmi Nor Azizah Ali School of Computing Faculty of Engineering Universiti Teknologi Malaysia (UTM) Johor Malaysia Cyber Threat Intelligence Lab Information Assurance & Security Research Group UTM Johor Malaysia Razak Faculty of Technology and Informatics UTM Kuala Lumpur Malaysia Applied Industrial Analytics Research Group UTM Johor Malaysia

Feature selection determines the most significant features for a given task while rejecting the noisy, irrelevant and redundant features of the dataset that might mislead the classifier. Besides, the technique diminishes the dimensionality of the attribute of the dataset, thus reducing computation time and improving prediction performance. This paper aims to perform a feature selection for classification more accurately with an optimal features subset using Multivariate Adaptive Regression Splines (MARS) in Spline Model (SM) classifier. A comparative study of prediction performance was conducted with other classifiers including Decision Tree (DT), Neural Network (NN) and Support Vector Machine (SVM) with similar optimal feature subset produced by MARS. From the results, the MARS technique demonstrated the features reduction up to 87.76% and improved the classification accuracy. Based on the comparative analysis conducted, the Spline classifier shows better performance by achieving the highest accuracy (97.44%) compared to other classifiers.

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DARE: A Reflective Platform Designed to Enable Agile Data-Driven Research on the Cloud

DARE: A Reflective Platform Designed to Enable Agile Data-Dr...

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IEEE International Conference on e-Science and Grid computing

作者： Iraklis Klampanos Athanasios Davvetas André Gemünd Malcolm Atkinson Antonios Koukourikos Rosa Filgueira Amrey Krause Alessandro Spinuso Angelos Charalambidis Federica Magnoni Emanuele Casarotti Christian Pagé Mike Lindner Andreas Ikonomopoulos Vangelis Karkaletsis Institute of Informatics and Telecommunications NCSR "Demokritos" Greece Fraunhofer Institute for Algorithms and Scientific Computing School of Informatics The University of Edinburgh UK NCSR "Demokritos" EPCC The University of Edinburgh UK Koninklijk Nederlands Meteorologisch Instituut (KNMI) The Netherlands Istituto Nazionale Geofisica e Vulcanologia (INGV) Italy Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique Karlsruher Institut f??r Technologie (KIT) Germany Institute of Nuclear & Radiological Sciences and Technology Energy & Safety NCSR "Demokritos" Greece

ISBN: (数字)9781728124513

ISBN: (纸本)9781728124520

The DARE platform has been designed to help research developers deliver user-facing applications and solutions over diverse underlying e-infrastructures, data and computational contexts. The platform is Cloud-ready, and relies on the exposure of APIs, which are suitable for raising the abstraction level and hiding complexity. At its core, the platform implements the cataloguing and execution of fine-grained and Python-based dispel4py workflows as services. Reflection is achieved via a logical knowledge base, comprising multiple internal catalogues, registries and semantics, while it supports persistent and pervasive data provenance. This paper presents design and implementation aspects of the DARE platform, as well as it provides directions for future development.

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Upperbounds on the probability of finding marked connected components using quantum walks

arXiv

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arXiv 2019年

作者： Glos, Adam Nahimovs, Nikolajs Balakirev, Konstantin Khadiev, Kamil Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland Institute of Informatics Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Center for Quantum Computer Science Faculty of Computing University of Latvia Raina bulv. 19 RigaLV-1586 Latvia Higher School of Economics Kochnovskiy passage 3 Moscow Russia Kazan Federal University Kremlevskaya 18 Kazan Russia Smart Quantum Technologies Ltd. K. Marksa 5 Kazan Russia

Finding a marked vertex in a graph can be a complicated task when using quantum walks. Recent results show that for two or more adjacent marked vertices search by quantum walk with Grover's coin may have no speed-up over classical exhaustive search. In this paper, we analyze the probability of finding a marked vertex and prove several upper bounds for various sets of marked vertices. All upper bounds are given in explicit form. Copyright © 2019, The Authors. All rights reserved.

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An Energy Efficient Mobility Management Mechanism in Software Defined Networking (SDN)

An Energy Efficient Mobility Management Mechanism in Softwar...

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International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE)

作者： Nurul Hazrina Shahba binti Mohammad Shahrun Alaelddin Fuad Yousif Mohammed Rudy Ramlie S. H. Shah Newaz Au Thien Wan School of Computing and Informatics Universiti Teknologi Brunei (UTB) Brunei Darussalam Gadong Korea Advanced Institute of Science and Technology (KAIST) Daejeon South Korea KAIST Institute for Information Technology Convergence Daejeon South Korea

ISBN: (纸本)9781538641248

Software Delined Networking (SDN) has paved the way for cost effective, energy efficient, and flexible and dynamic network structure. In SDN, a controller sets routing path for the data plane consisting of several switches. If an incoming packet is not known to a switch, the switch needs to invoke the controller, in turn the controller facilitates forwarding path for that packet. In case when a station is mobile (e.g., mobile phone or laptop), the controller needs to update the SDN forwarding path whenever the Mobile Terminal (MT) gets attached with a new switch. This will result in increasing location update overhead at not only the controller but also all the intermediate switches between the main controller and Leaf Switch (LS). Some of the existing research efforts consider both SDN forwarding path update and Layer 3 location update whenever a MT attaches with a new LS. In our study, we have realized that if mobile IP over SDN is managed, a network operator will end up imposing additional location update cost. In this paper, a novel overlay SDN topology is proposed in order to manage location update of MTs. In this contribution, a MT maintains its location update with a Root Switch (RS), which is in charge of tracking location of MTs within a cluster of cells, as long as the MT stays within the cluster (regional area). However, if the MT moves out of the regional area, in our proposal, there will be a location update at the main controller. Results obtained through numerical analysis reveal that proposed overlay topology based location update mechanism in SDN can lead to reduced location update cost noticeably.

关键词： IP networks Control systems Topology Routing Routing protocols Network topology Numerical analysis

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RETRACTED ARTICLE: Utilizing Index-Based Periodic High Utility Mining to Study Frequent Itemsets

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Arabian Journal for Science and Engineering 2021年第3期48卷 4123-4123页

作者： Setiawan, Roy Le, Dac-Nhuong Rajan, Regin Subramani, Thirukumaran Sharma, Dilip Kumar Ponnam, Vidya Sagar Kumar, Kailash Akbar Batcha, Syed Musthafa Dadheech, Pankaj Sengan, Sudhakar Department Management Universitas Kristen Petra Jawa Timur Indonesia School of Computer Science/Institute of Research and Development Duy Tan University Danang Vietnam Department of Computer Science and Engineering Adhiyamaan College of Engineering Hosur India Department of Computer Science and Engineering KL University Vijayawada India Jaypee University of Engineering and Technology Guna India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India College of Computing and Informatics Saudi Electronic University Riyadh Kingdom of Saudi Arabia Department of Information Technology M. Kumarasamy College of Engineering Karur India Department of Computer Science and Engineering Swami Keshvanand Institute of Technology Management and Gramothan (SKIT) Jaipur India Department of Computer Science and Engineering PSN College of Engineering and Technology Tirunelveli India

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High-fidelity prototyping for mobile electronic data collection forms through design and user evaluation

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JMIR Human Factors 2019年第1期6卷 e11852页

作者： Mugisha, Alice Babic, Ankica Wakholi, Peter Tylleskär, Thorkild Center for International Health Department of Global Public Health and Primary Care University of Bergen Årstadveien 21 Overlege Danielssens building Bergen 5020 Norway Department of Information Technology School of Computing and Informatics Technology Makerere University Kampala Uganda Department of Information and Media Studies Faculty of Social Sciences University of Bergen Bergen Norway Department of Biomedical Engineering University of Linköping Linköping Sweden

Background: Mobile data collection systems are often difficult to use for nontechnical or novice users. This can be attributed to the fact that developers of such tools do not adequately involve end users in the design and development of product features and functions, which often creates interaction challenges. Objective: The main objective of this study was to assess the guidelines for form design using high-fidelity prototypes developed based on end-user preferences. We also sought to investigate the association between the results from the System Usability Scale (SUS) and those from the Study Tailored Evaluation Questionnaire (STEQ) after the evaluation. In addition, we sought to recommend some practical guidelines for the implementation of the group testing approach particularly in low-resource settings during mobile form design. Methods: We developed a Web-based high-fidelity prototype using Axure RP 8. A total of 30 research assistants (RAs) evaluated this prototype in March 2018 by completing the given tasks during 1 common session. An STEQ comprising 13 affirmative statements and the commonly used and validated SUS were administered to evaluate the usability and user experience after interaction with the prototype. The STEQ evaluation was summarized using frequencies in an Excel sheet while the SUS scores were calculated based on whether the statement was positive (user selection minus 1) or negative (5 minus user selection). These were summed up and the score contributions multiplied by 2.5 to give the overall form usability from each participant. Results: Of the RAs, 80% (24/30) appreciated the form progress indication, found the form navigation easy, and were satisfied with the error messages. The results gave a SUS average score of 70.4 (SD 11.7), which is above the recommended average SUS score of 68, meaning that the usability of the prototype was above average. The scores from the STEQ, on the other hand, indicated a 70% (21/30) level of agreement wi

关键词： Group user testing High-fidelity prototype Mobile electronic data collection forms Usability evaluation

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Reading race: AI recognizes patient's racial identity in medical images

arXiv

引用

arXiv 2021年

作者： Banerjee, I. Bhimireddy, A.R. Burns, J.L. Celi, L.A. Chen, L. Correa, R. Dullerud, N. Ghassemi, M. Gichoya, J.W. Huang, S. Kuo, P. Lungren, M.P. Price, B.J. Purkayastha, S. Pyrros, A.A. Oakden-Rayner, L. Okechukwu, C. Seyyed-Kalantari, L. Trivedi, H. Wang, R. Zaiman, Z. Zhang, H. Department of Biomedical Informatics Emory University 100 Woodruff Circle AtlantaGA30322 United States School of Informatics and Computing Indiana University Purdue University 535 W Michigan St. IT 475E IndianapolisIN46202 United States Institute for Medical Engineering and Science Massachusetts Institute of Technology 45 Carleton Street E25- 330 CambridgeMA02142 United States Department of Medicine Beth Israel Deaconess Medical Center 330 Brookline Avenue BostonMA02215 United States Department of Computer Science National Tsing Hua University No. 101 Section 2 Kuang-Fu Road Hsinchu30013 Taiwan Department of Computer Science Emory University 201 Dowman Drive AtlantaGA30322 United States Department of Computer Science University of Toronto 214 College St TorontoONM5T 3A1 Canada Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Department of Radiology Emory University 1364 Clifton Rd AtlantaGA30322 United States Stanford University School of Medicine 291 Campus Drive Palo AltoCA94305 United States Florida State University College of Medicine 1115 W Call St TallahasseeFL32304 United States Australian Institute for Machine Learning University of Adelaide AIML Building Lot Fourteen Cnr North Terrace and Frome Road AdelaideSA5000 Australia Department of Computer Science Georgia Institute of Technology 266 Ferst Drive AtlantaGA30332 United States Lunenfeld-Tanenbaum Research Institute Sinai Health 600 University Ave TorontoONM5G 1X5 Canada Dupage Medical Group 40 S Clay St HinsdaleIL60521 United States

Background: In medical imaging, prior studies have demonstrated disparate AI performance by race, yet there is no known correlation for race on medical imaging that would be obvious to the human expert interpreting the images. We conduct a comprehensive evaluation of AI's ability to recognize patient's racial identity from medical images. Methods: Using private and public datasets we evaluate: A) performance quantification of deep learning models to detect race from medical images, including the ability of these models to generalize to external environments and across multiple imaging modalities, B) assessment of possible confounding anatomic and phenotype population features, such as disease distribution and body habitus as predictors of race, and C) investigation into the underlying mechanism by which AI models can recognize race. Findings: Standard deep learning models can be trained to predict race from medical images with high performance across multiple imaging modalities. Our findings hold under external validation conditions, as well as when models are optimized to perform clinically motivated tasks. We demonstrate this detection is not due to trivial proxies or imaging-related surrogate covariates for race, such as underlying disease distribution. Finally, we show that performance persists over all anatomical regions and frequency spectrum of the images suggesting that mitigation efforts will be challenging and demand further study. Interpretation: We emphasize that model ability to predict self-reported race is itself not the issue of importance. However, our findings that AI can trivially predict self-reported race - even from corrupted, cropped, and noised medical images - in a setting where clinical experts cannot, creates an enormous risk for all model deployments in medical imaging: if an AI model secretly used its knowledge of self-reported race to misclassify all Black patients, radiologists would not be able to tell using the same data the model ha

关键词： Medical imaging

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Condition Monitoring and Reporting Framework for Wireless Sensor Network-based Automatic Weather Stations

Condition Monitoring and Reporting Framework for Wireless Se...

引用

IST-Africa

作者： Mary Nsabagwa Isaac Mugume Robert Kasumba Joshua Muhumuza Steven Byarugaba Eugene Tumwesigye Julianne Sansa Otim School of Computing and Informatics Technology Makerere University P.O.BOX 7062 Kampala Uganda School of Forestry Environmental and Geographical Sciences Makerere University P.O.BOX 7062 Kampala Uganda

ISBN: (纸本)9781538671658;9781905824601

Automatic Weather Stations (AWSs) consist of sensors that automatically collect and transmit weather data. AWS efficiency is achieved if the amount of down time is minimized. Keeping track of AWS status is a daunting task, aggravated by increase in the observation network density. The purpose of this paper is to present a condition monitoring and reporting framework which relies on AWS sensor data to establish AWS health. The framework consists of four major components including a data receiver, analyser, problem classifier and reporter. On receiving the sensor data including input voltage, date and weather data, the analyser determines if there is a problem while the classifier uses a decision table to determine the probable cause of the problem. Based on the identified problems, the reporter logs each problem in a database and sends an SMS or email to the concerned person. Sending an SMS or email is performed on new problems or on problems not attended to within 24 hours in order to minimize the number of reports.

关键词： Sensors Wireless sensor networks Condition monitoring Monitoring Logic gates Wind speed

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