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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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Investigation of Scribing Quality Defect of Thin Film Solar Cell Using Machine Vision

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IOP Conference Series: Materials Science and Engineering 2020年第1期932卷

作者： R Shuaimi R C Ismail M N Hazwan Hadzir M N M Isa A B Jambek First Solar Sdn. Bhd. Kulim Hi-Tech Park Kulim Kedah Malaysia School of Microelectronic Engineering Universiti Malaysia Perlis Arau Malaysia School of Computing and Informatics Albukhary International University Malaysia System Engineering and Energy Laboratory Universiti Kuala Lumpur Kulim Kedah

Laser micromachining provide significant effect in thin film solar industrial field especially in determining cell efficiency of each panels. However, there is an issue in determining scribing failure or defect on solar module. This research aims to investigate the defects of laser micromachining process in thin film solar module in manufacturing fields. Machine vision inspection system is used as inspection tools and to investigate the defect of laser micromachining in thin film solar cells. As a result, two major defects is define which is scribe line quality and scribe line position defects in every scribe line. By identifying the defect cause by laser micromachining through machine vision, quality control plan can be taken together to prevent reoccurrence.

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Automated Validation of Crowdsourced Data

Automated Validation of Crowdsourced Data

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Student Conference on Research and Development (SCOReD)

作者： Zailani Ibrahim Hazleen Aris Aishah Mansur College of Computer Science and Information Technology Universiti Tenaga Nasional Kajang Selangor Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Kajang Selangor DHL IT Services Cyberjaya Selangor

ISBN: (纸本)9781538691762;9781538691755

Ensuring the accuracy of information or data contributed by the crowd is amongst the challenges in crowdsourcing initiatives. Data that do not meet certain criteria set by the crowdsourcer are also submitted by the crowd in a crowdsourcing initiative due to its openness. Thus, there is a need to ensure that only valid data are being captured before the data are being processed further. However, manually validating the data is not practical due to the high volume of data involved in crowdsourcing and their unpredictable nature. Therefore, in this research, an automated algorithm to validate crowdsourced data was developed. The objective was to identify the processes needed to enable the validation of crowdsourced data to be performed automatically. Two types of validation were included; task validation and worker validation. Kuder-Richardson Formula 20 was used to compute validity of task and mean formula converted to percentage was used in computing worker validity. The algorithm was implemented by embedding it in a prototype crowdsourcing application called AsnafCircle that crowdsourced information on eligible asnaf (alms recipient) from the public. Evaluation showed that the algorithm was able to automatically compute values that determine task and worker validity. Evaluation by experts also conformed the necessity of the processes that constitute the algorithm. The presence of this algorithm will help to ensure validity of contributed data in crowdsourcing initiatives, hence, improving their reliability.

关键词： Task analysis Crowdsourcing Reliability Interviews Bibliographies Conferences Research and development

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Coordinate: A Spreadsheet-Programmable Augmented Reality Framework for Immersive Map-Based Visualizations

Coordinate: A Spreadsheet-Programmable Augmented Reality Fra...

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IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Aashiq Shaikh Linda Nguyen Alireza Bahremand Hannah Bartolomea Frank Liu Van Nguyen Derrick Anderson Robert LiKamWa School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ USA School of Arts Media and Engineering Arizona State University Tempe AZ USA School of Electrical Computer and Energy Engineering Arizona State University Tempe AZ USA Department of Psychology Arizona State University Tempe AZ USA School of Public Affairs Arizona State University Tempe AZ USA

ISBN: (数字)9781728156040

ISBN: (纸本)9781728156057

Augmented reality devices are opening up a new design space for immersive visualizations. 3D spatial content can be overlaid onto existing physical visualizations for new insights into the data. We present Coordinate, a collaborative analysis tool for augmented reality visualizations of map-based data designed for mobile devices. Coordinate leverages a spreadsheet-programmable web interface paired with a contemporary augmented reality infrastructure for an easy-to-use tool that can provide spatial information to multiple users. It also offers an immersive visualization experience that seeks to enrich presentations for business, educational, and scientific discussions.

关键词： Conferences Artificial intelligence Virtual reality 6G mobile communication Integrated circuits

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Towards Real Time Segmentation of Large-Scale 4D Micro/Nanotomography Images in the Sirius Synchrotron Light Source

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Microscopy and Microanalysis 2018年第S2期24卷 90-91页

作者： Spina, Thiago V Vasconcelos, Gustavo J Q Goncalves, Henrique M Libel, Gustavo C Pedrini, Helio Carvalho, Tiago J Archilha, Nathaly L Brazilian Synchrotron Light Laboratory (LNLS) Brazilian Center for Research in Energy and Materials (CNPEM) Campinas Brazil Institute of Computing University of Campinas Campinas Brazil Department of Informatics Federal University of Technology - Parana Curitiba Brazil Federal Institute of Education Science and Technology of Sao Paulo - Campinas Campinas Brazil

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An environment for sustainable research software in Germany and beyond: Current state, open challenges, and call for action

arXiv

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arXiv 2020年

作者： Anzt, Hartwig Bach, Felix Druskat, Stephan Löffler, Frank Loewe, Axel Renard, Bernhard Y. Seemann, Gunnar Struck, Alexander Achhammer, Elke Aggarwal, Piush Appel, Franziska Bader, Michael Brusch, Lutz Busse, Christian Chourdakis, Gerasimos Dabrowski, Piotr W. Ebert, Peter Flemisch, Bernd Friedl, Sven Fritzsch, Bernadette Funk, Maximilian D. Gast, Volker Goth, Florian Grad, Jean-Noël Hermann, Sibylle Hohmann, Florian Janosch, Stephan Kutra, Dominik Linxweiler, Jan Muth, Thilo Peters-Kottig, Wolfgang Rack, Fabian Raters, Fabian H.C. Rave, Stephan Reina, Guido Reißig, Malte Ropinski, Timo Schaarschmidt, Joerg Seibold, Heidi Thiele, Jan P. Uekerman, Benjamin Unger, Stefan Weeber, Rudolf Germany Innovative Computing Lab University of Tennessee KnoxvilleTN United States Department of English Studies Friedrich Schiller University Jena Germany Germany Department of Computer Science Humboldt-Universität zu Berlin Germany Heinz Nixdorf Chair for Distributed Information Systems Friedrich Schiller University Jena Germany Michael Stifel Center Jena Germany Center for Computation and Technology Louisiana State University Baton Rouge United States Germany Robert Koch Institute Berlin Germany Hasso Plattner Institute Faculty of Digital Engineering University of Potsdam Germany Institute for Experimental Cardiovascular Medicine University Heart Centre Freiburg Bad Krozingen Germany Faculty of Medicine University of Freiburg Freiburg Germany Matters of Activity. Image Space Material. Cluster of Excellence at Humboldt-Universität zu Berlin Germany Technische Universität München Germany Language Technology Lab Universität Duisburg-Essen Germany Germany Department of Informatics Technical University of Munich Germany Technische Universität Dresden Germany Deutsches Krebsforschungszentrum Heidelberg Germany Scientific Computing in Computer Science Technical University Munich Germany School of Computing Communication and Business Hochschule für Technik und Wirtschaft Berlin Germany Center for Bioinformatics Saar Saarland Informatics Campus Germany Institute for Modelling Hydraulic and Environmental Systems University of Stuttgart Germany Berlin Institute of Health Germany Scientific Computing Alfred Wegener Institute Helmholtz Center for Polar and Marine Research Bremerhaven Germany Max-Planck-Gesellschaft e.V. Institut für Theoretische Physik und Astrophysik Universität Würzburg Germany Institut für Computerphysik University of Stuttgart Germany University Library University of Stuttgart Germany Universität Bremen Germany Max Planck Institute of Molecular Cell Biology and Genetics Dresden Germany European Molecular Biology Laboratory Heidelb

Research software has become a central asset in academic research. It optimizes existing and enables new research methods, implements and embeds research knowledge, and constitutes an essential research product in itself. Research software must be sustainable in order to understand, replicate, reproduce, and build upon existing research or conduct new research effectively. In other words, software must be available, discoverable, usable, and adaptable to new needs, both now and in the future. Research software therefore requires an environment that supports sustainability. Hence, a change is needed in the way research software development and maintenance are currently motivated, incentivized, funded, structurally and infrastructurally supported, and legally treated. Failing to do so will threaten the quality and validity of research. In this paper, we identify challenges for research software sustainability in Germany and beyond, in terms of motivation, selection, research software engineering personnel, funding, infrastructure, and legal aspects. Besides researchers, we specifically address political and academic decision-makers to increase awareness of the importance and needs of sustainable research software practices. In particular, we recommend strategies and measures to create an environment for sustainable research software, with the ultimate goal to ensure that software-driven research is valid, reproducible and sustainable, and that software is recognized as a first class citizen in research. This paper is the outcome of two workshops run in Germany in 2019, at deRSE19 - the first International Conference of Research Software Engineers in Germany - and a dedicated DFG-supported follow-up workshop in Berlin. Copyright © 2020, The Authors. All rights reserved.

关键词： Software design

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energy Efficient Sink Placement in Wireless Sensor Networks by Brain Storm Optimization Algorithm

Energy Efficient Sink Placement in Wireless Sensor Networks ...

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International Wireless Communications and Mobile computing Conference, IWCMC

作者： Eva Tuba Dana Simian Edin Dolicanin Raka Jovanovic Milan Tuba Faculty of Informatics and Computing Singidunum University Belgrade Serbia Faculty of Science Lucian Blaga University Sibiu Romania Department of Technical Sciences State University of Novi Pazar Novi Pazar Serbia Qatar Environment and Energy Research Institute Hamad bin Khalifa University Doha Qatar

Wireless sensor networks represent one of the most promising technologies whose use has significantly increased in the past years. They are used in various applications such as health care monitoring, surveillance and monitoring in agriculture, industrial monitoring, habitat and underwater monitoring, etc. Deployment of the wireless sensor networks introduces number of hard optimization problems. Placement of the elements such as sensors, gateways, sinks and base stations, depend on different conditions and constraints such as signal propagation, distance, energy preservation, reliability. In this paper, we propose a method based on brain storm optimization algorithm for placing multiple sinks in a network consisting of regular sensors and gateways with higher battery capacity. Regular sensor nodes are statically organized in clusters around gateways considering not only distance but also energy efficiency. Gateways communicate with sinks for which optimal positions need to be determined. Location problems are in general difficult and in this case requirement of minimizing the energy additionally complicates the problem. The simulation results report estimation of the network life time. Obtained results have shown that our proposed method outperformed particle swarm optimization based method from literature in terms of mentioned metrics.

关键词： Logic gates Optimization Wireless sensor networks Mathematical model Storms Clustering algorithms Particle swarm optimization

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A Graph Matching Algorithm for User Authentication in Data Networks using Image-based Physical Unclonable Functions

A Graph Matching Algorithm for User Authentication in Data N...

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computing Conference

作者： Ali Valehi Abolfazl Razi Bertrand Cambou Weijie Yu Michael Kozicki School of Informatics Computing and Cyber Security (SICCS) Northern Arizona University School of Electrical Computing and Energy Engineering (ECEE) Arizona State University

ISBN: (纸本)9781509054442

Recently, Physically Unclonable Functions (PUFs) received considerable attention in order to developing security mechanisms for applications such as Internet of Things (IoT) by exploiting the natural randomness in device-specific characteristics. This approach complements and improves the conventional security algorithms that are vulnerable to security attacks due to recent advances in computational technology and fully automated hacking systems. In this project, we propose a new authentication mechanism based on a specific implementation of PUF using metallic dendrites. Dendrites are nanomaterial devices that contain unique, complex and unclonable patterns (similar to human DNAs). We propose a method to process dendrite images. The proposed framework comprises several steps including denoising, skeletonizing, pruning and feature points extraction. The feature points are represented in terms of a tree-based weighted algorithm that converts the authentication problem to a graph matching problem. The test object is compared against a database of valid patterns using a novel algorithm to perform user identification and authentication. The proposed method demonstrates a high level of accuracy and a low computational complexity that grows linearly with the number of extracted points and database size. It also significantly reduces the required in-network storage capacity and communication rates to maintain database of users in large-scale networks.

关键词： Information-Security Image-Processing Graph-Matching Authentication

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A Comprehensive Review of Existing Risk Assessment Models in Cloud computing

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Journal of Physics: Conference Series 2018年第1期1018卷

作者： Ahmad Amini Norziana Jamil Institute of Informatics and Computing in Energy (IICE) Universiti Tenaga Nasional Jalan IKRAM-UNITEN Selangor Malaysia

Cloud computing is a popular paradigm in information technology and computing as it offers numerous advantages in terms of economical saving and minimal management effort. Although elasticity and flexibility brings tremendous benefits, it still raises many information security issues due to its unique characteristic that allows ubiquitous computing. Therefore, the vulnerabilities and threats in cloud computing have to be identified and proper risk assessment mechanism has to be in place for better cloud computing management. Various quantitative and qualitative risk assessment models have been proposed but up to our knowledge, none of them is suitable for cloud computing environment. This paper, we compare and analyse the strengths and weaknesses of existing risk assessment models. We then propose a new risk assessment model that sufficiently address all the characteristics of cloud computing, which was not appeared in the existing models.

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From FAIR to CURE: Guidelines for Computational Models of Biological Systems

arXiv

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arXiv 2025年

作者： Sauro, Herbert M. Agmon, Eran Blinov, Michael L. Gennari, John H. Hellerstein, Joe Heydarabadipour, Adel Hunter, Peter Jardine, Bartholomew E. May, Elebeoba Nickerson, David P. Smith, Lucian P. Bader, Gary D. Bergmann, Frank Boyle, Patrick M. Dräger, Andreas Faeder, James R. Feng, Song Freire, Juliana Fröhlich, Fabian Glazier, James A. Gorochowski, Thomas E. Helikar, Tomas Hoops, Stefan Imoukhuede, Princess Keating, Sarah M. Konig, Matthias Laubenbacher, Reinhard Loew, Leslie M. Lopez, Carlos F. Lytton, William W. McCulloch, Andrew Mendes, Pedro Myers, Chris J. Myers, Jerry G. Mulugeta, Lealem Niarakis, Anna van Niekerk, David D. Olivier, Brett G. Patrie, Alexander A. Quardokus, Ellen M. Radde, Nicole Rohwer, Johann M. Sahle, Sven Schaff, James C. Sego, T.J. Shin, Janis Snoep, Jacky L. Vadigepalli, Rajanikanth Wiley, H. Steve Waltemath, Dagmar Moraru, Ion Department of Bioengineering University of Washington SeattleWA98195-5061 United States Intelligent Systems Engineering and Biocomplexity Institute Indiana University Street BloomingtonIN47408 United States Center for Cardiovascular Biology University of Washington SeattleWA98195-5061 United States eScience Institute University of Washington SeattleWA98195-5061 United States Institute for Stem Cell and Regenerative Medicine University of Washington SeattleWA98195-5061 United States School of Biological Sciences University of Bristol 24 Tyndall Avenue BristolBS8 1TQ United Kingdom Departments of Physiology & Pharmacology Neurology Downstate Health Science University BrooklynNY11203 United States Department of Neurology Kings County Hospital BrooklynNY11203 United States NASA-John H. Glenn Research Center MS 110-3 21000 Brookpark Road ClevelandOH44135 United States Center for Cell Analysis and Modeling UConn Health 263 Farmington Avenue FarmingtonCT06030-6406 United States Amsterdam Institute for Life and Environment Vrije Universiteit Amsterdam De Boelelaan 1108 Amsterdam1081 HZ Netherlands Department of Pathology and Genomic Medicine Thomas Jefferson University 1020 Locust St PhiladelphiaPA19107 United States Biological Sciences Division Pacific Northwest National Laboratory 902 Battelle Blvd RichlandWA99354 United States Auckland Bioengineering Institute University of Auckland Auckland1010 New Zealand Center of Integrative Biology University of Toulouse III-Paul Sabatier 165 Rue Marianne Grunberg-Manago Toulouse31400 France Lifeware Group Inria Building Alan Turing 1 Rue Honoré d’Estienne d’Orves Palaiseau91120 France Multiscale Modeling Group Altos Labs Redwood CityCA94065 United States partner site Tübingen Tübingen Germany Ottfried-Müller-Str. 37 Tübingen72076 Germany 06120 Germany Department of Computer Science Center for Data Science New York University New YorkNY11201 United States Department of Bi

Guidelines for managing scientific data have been established under the FAIR principles requiring that data be Findable, Accessible, Interoperable, and Reusable. In many scientific disciplines, especially computational biology, both data and models are key to progress. For this reason, and recognizing that such models are a very special type of "data", we argue that computational models, especially mechanistic models prevalent in medicine, physiology and systems biology, deserve a complementary set of guidelines. We propose the CURE principles, emphasizing that models should be Credible, Understandable, Reproducible, and Extensible. We delve into each principle, discussing verification, validation, and uncertainty quantification for model credibility;the clarity of model descriptions and annotations for understandability;adherence to standards and open science practices for reproducibility;and the use of open standards and modular code for extensibility and reuse. We outline recommended and baseline requirements for each aspect of CURE, aiming to enhance the impact and trustworthiness of computational models, particularly in biomedical applications where credibility is paramount. Our perspective underscores the need for a more disciplined approach to modeling, aligning with emerging trends such as Digital Twins and emphasizing the importance of data and modeling standards for interoperability and reuse. Finally, we emphasize that given the non-trivial effort required to implement the guidelines, the community moves to automate as many of the guidelines as possible. © 2025, CC BY.

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