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International Conference on New Trends in Computing Sciences (ICTCS)

作者： Dyana Rashid Ibrahim Rawan Ghnemat Amjad Hudaib Dept. of Computer Science Princess Sumaya University for Technology Amman Jordan Dept. of Software Engineering University of Jordan Amman Jordan

ISBN: (纸本)9781538605288

software testing is the most important task in software production and it takes a lot of time, cost and effort. Thus, we need to reduce these resources. software Defect Prediction (SDP) mechanisms are used to enhance the work of SQA process through the prediction of defective modules, many approaches have been conducted by researchers in order to predict the fault-proneness modules. This paper proposed an approach for the SDP purpose, it employs two existed algorithms to have a high performance, that are the Bat-based search Algorithm (BA) for the feature selection process, and the Random Forest algorithm (RF) for the prediction purpose. This paper also has tested a number of feature selection algorithms and classifiers to see their effectiveness in this problem.

关键词： Classification algorithms Prediction algorithms software Feature extraction Radio frequency Algorithm design and analysis software algorithms

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Considerations and Recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 1 — In vivo small-animal imaging

arXiv

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arXiv 2022年

作者： Jelescu, Ileana O. Grussu, Francesco Ianus, Andrada Hansen, Brian Barrett, Rachel L.C. Aggarwal, Manisha Michielse, Stijn Nasrallah, Fatima Syeda, Warda Wang, Nian Veraart, Jelle Roebroeck, Alard Bagdasarian, Andrew F. Eichner, Cornelius Sepehrband, Farshid Zimmermann, Jan Soustelle, Lucas Bowman, Christien Tendler, Benjamin C. Hertanu, Andreea Jeurissen, Ben Verhoye, Marleen Frydman, Lucio de Looij, Yohan van Hike, David Dunn, Jeff F. Miller, Karla Landman, Bennett A. Shemesh, Noam Anderson, Adam McKinnon, Emilie Farquharson, Shawna Acqua, Flavio Dell Pierpaoli, Carlo Drobnjak, Ivana Leemans, Alexander Harkins, Kevin D. Descoteaux, Maxime Xu, Duan Huang, Hao Santin, Mathieu D. Grant, Samuel C. Obenaus, Andre Kim, Gene S. Wu, Dan Le Bihan, Denis Blackband, Stephen J. Ciobanu, Luisa Fieremans, Els Bai, Ruiliang Leergaard, Trygve B. Zhang, Jiangyang Dyrby, Tim B. Johnson, G. Allan Cohen-Adad, Julien Budde, Matthew D. Schilling, Kurt G. Department of Radiology Lausanne University Hospital University of Lausanne Lausanne Switzerland CIBM Center for Biomedical Imaging Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland Radiomics Group Vall d’Hebron Institute of Oncology Vall d’Hebron Barcelona Hospital Campus Barcelona Spain Queen Square MS Centre Queen Square Institute of Neurology Faculty of Brain Sciences University College London London United Kingdom Champalimaud Research Champalimaud Foundation Lisbon Portugal Center of Functionally Integrative Neuroscience Aarhus University Aarhus Denmark Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom NatBrainLab Department of Forensics and Neurodevelopmental Sciences Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine BaltimoreMD United States Maastricht University Medical Center Maastricht Netherlands The Queensland Brain Institute The University of Queensland Queensland Australia Melbourne Neuropsychiatry Centre The University of Melbourne ParkvilleVIC Australia Department of Radiology and Imaging Sciences Indiana University IN United States Stark Neurosciences Research Institute Indiana University School of Medicine IN United States Center for Biomedical Imaging NYU Grossman School of Medicine New YorkNY United States Faculty of psychology and Neuroscience Maastricht University Maastricht Netherlands Department of Chemical & Biomedical Engineering FAMU-FSU College of Engineering Florida State University TallahasseeFL United States Center for Interdisciplinary Magnetic Resonance National HIgh Magnetic Field Laboratory TallahasseeFL United States Department of Neuropsychology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany USC Stevens Neuroimaging and Informatics Instit

Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected recommendations and guidelines from the diffusion community, on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We then give guidelines for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including pre-processing, model-fitting, and tractography. Finally, we provide an online resource which lists publicly available preclinical dMRI datasets and software packages, to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. While we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal herein is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge. © 2022, CC BY-NC-ND.

关键词： Invertebrates

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Ontology Powered Knowledge Modeling for a Smart Home

Ontology Powered Knowledge Modeling for a Smart Home

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IEEE GCC Conference & Exhibition

作者： Reem Al-Osaimi Nor Abdul Karim Dept. of Software Engineering at the College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia

ISBN: (纸本)9781538627570

The development and expansion of ontology is highly important in building smart applications such as a smart home. This research seeks to identify and develop an extension framework for personalized smart home ontology that can be used in an intelligent home to reduce and manage the energy consumption. The ontology framework was developed based on a methodology known as Natalya and Deborah Methodology. In the design, the researcher added the home appliances as sub classes, and grouped them based on their functional properties. This approach was not used before in the previous work. With this approach, any kind of home appliances can be semantically defined. In order to validate the proposed framework, the researcher developed a prototype to check the consistency and the correction of a Smart Home Ontology and Semantic Web Rule (SWRL). The ontology was validated through the pellet reasoning engine and Simple Protocol and RDF Query language (SPARQL) query to test and correct any logical mistake in the SWRL rules or in the ontology created.

关键词： Ontologies Smart homes Engines Home appliances Semantic Web Cognition

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Enhancing Supervised Classifications with Metamorphic Relations

Enhancing Supervised Classifications with Metamorphic Relati...

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IEEE/ACM International Workshop on Metamorphic Testing (MET)

作者： Liming Xu Dave Towey Andrew P. French Steve Benford Zhi Quan Zhou Tsong Yueh Chen International Doctoral Innovation Centre University of Nottingham Ningbo China Ningbo Zhejiang China AIOP Group University of Nottingham Ningbo China Ningbo Zhejiang China School of Computer Science University of Nottingham Nottingham UK School of Computing and Information Technology University of Wollongong Wollongong NSW Australia Dept. of Computer Science and Software Engineering Swinburne University of Technology Hawthorn VIC Australia

We report on a novel use of metamorphic relations (MRs) in machine learning: instead of conducting metamorphic testing, we use MRs for the augmentation of the machine learning algorithms themselves. In particular, we report on how MRs can enable enhancements to an image classification problem of images containing hidden visual markers (“Artcodes”). Working on an original classifier, and using the characteristics of two different categories of images, two MRs, based on separation and occlusion, were used to improve the performance of the classifier. Our experimental results show that the MR-augmented classifier achieves better performance than the original classifier, algorithms, and extending the use of MRs beyond the context of software testing.

关键词： Machine learning Testing software Forestry Histograms Visualization computer science

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Web-Based Application to Collect and Analyze Users Data for Keystroke Biometric Authentication

Web-Based Application to Collect and Analyze Users Data for ...

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IEEE Ukraine Conference on Electrical and computer engineering

作者： Alieksieiev Vasyl Elena Sharapova Olena Ivanova Gorelov Denis Synytsia Yuliia Computer Radio Engineering and Systems of Technical Security of Information dept.(CRESTSI) Kharkiv National University of Radioelectronics (KNURE) Software Engineering Dept. Marvell Semiconductor Inc.

ISBN: (纸本)9781509030071

Keystroke dynamics is rapidly developing and promising field of the research. User authentication systems based on this approach is not require the installation of additional equipment. It makes them flexible and significantly reduces the cost of implementation and subsequent exploitation. This paper describes the web application developed to collect and analyze individual characteristics of user keystroke based on time calculation of digraphs. That approach has several advantages such as simplicity to use, ability to obtain data at any time, to integrate application into an existing web resource, application is cross platform, has modular architecture and provide great scalability.

关键词： Web application Authentication Keystroke dynamics Data collection Digraph Modularity Cross platform

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Understanding and Predicting Trends in Urban Freight Transport

Understanding and Predicting Trends in Urban Freight Transpo...

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International Conference on Mobile Data Management

作者： Petar Mrazovic Bahaeddin Eravci Josep Lluis Larriba-Pey Hakan Ferhatosmanoglu Mihhail Matskin Dept. of Software and Computer Systems Royal Institute of Technology Stockholm Sweden Dept. of Computer Engineering Bilkent University Ankara Turkey Dept. of Computer Architecture Polytechnic University of Catalonia Barcelona Spain Max Planck Institute for Informatics Saarbruecken Germany

ISBN: (纸本)9781538639337

Among different components of urban mobility, urban freight transport is usually considered as the least sustainable. Limited traffic infrastructures and increasing demands in dense urban regions lead to frequent delivery runs with smaller freight vehicles. This increases the traffic in urban areas and has negative impacts upon the quality of life in urban populations. Data driven optimizations are essential to better utilize existing urban transport infrastructures and to reduce the negative effects of freight deliveries for the cities. However, there is limited work and data driven research on urban delivery areas and freight transportation networks. In this paper, we collect and analyse data on urban freight deliveries and parking areas towards an optimized urban freight transportation system. Using a new check-in based mobile parking system for freight vehicles, we aim to understand and optimize freight distribution processes. We explore the relationship between areas' availability patterns and underlying traffic behaviour in order to understand the trends in urban freight transport. By applying the detected patterns we predict the availabilities of loading/unloading areas, and thus open up new possibilities for delivery route planning and better managing of freight transport infrastructures.

关键词： Loading Urban areas Mobile communication Market research Mobile applications Sociology Statistics

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Wireless low power toxic gas detector based on ADuCM360

Wireless low power toxic gas detector based on ADuCM360

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International SoC Design Conference, ISOCC

作者： Minming Gu ZhenPing Xia Yan Lei Lin Zhang Jieming Ma Dept. of Electronic & Information Engineering Suzhou University of Science and Technology Suzhou China Dept. of Computer Science and Software Engineering Xi'an Jiaotong-Liverpool University (XJTLU) Suzhou China

In order to avoid toxic gas poisoning incident, explosion and improve people's quality of life, this project designed the wireless low power toxic gas detector based on ADuCM360 with sensor technology and wireless transmission technology. The system consists of power management module, sensor module, signal conditioning module and wireless communication module. Analysis and experimental results show that this toxic gas detector can monitor and store the toxic gas concentration, temperature and humidity in the room for a long time. You will be able to understand the detection of toxic gases without necessity of visiting the site in person.

关键词： Wireless communication Wireless sensor networks Gas detectors Temperature sensors Monitoring Gases Hardware

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Planning for performance: Persistent collective operations for MPI 17

Planning for performance: Persistent collective operations f...

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24th European MPI Users" Group Meeting, EuroMPI 2017

作者： Morgan, Bradley Holmes, Daniel J. Skjellum, Anthony Bangalore, Purushotham Sridharan, Srinivas Auburn University United States EPCC University of Edinburgh Scotland United Kingdom Dept. of Computer Science and Software Engineering Auburn University United States Dept. of Computer Science University of Alabama at Birmingham BirminghamAL35294-1170 United States SimCenter University of Tennessee ChattanoogaTN United States Intel Corporation 23-56P Outer Ring Road Bangalore560017 India

ISBN: (纸本)9781450348492

Advantages of nonblocking collective communication in MPI have been established over the past quarter century, even predating MPI-1. For regular computations with fixed communication patterns, more optimizations can be revealed through the use of persistence (planned transfers) not currently available in the MPI-3 API except for a limited form of point-to-point persistence (aka half-channels) standardized since MPI-1. This paper covers the design, prototype implementation of LibPNBC (based on LibNBC), and MPI-4 standardization status of persistent nonblocking collective operations. We provide early performance results, using a modified version of NBCBench and an example illustrating the potential performance enhancements for such operations. Persistent operations allow MPI implementations to make intelligent choices about algorithm and resource utilization once and amortize this decision cost across many uses in a long-running program. Evidence that this approach is of value is provided. As with non-persistent, nonblocking collective operations, the requirement for strong progress and blocking completion notification are jointly needed to maximize the benefit of such operations (e.g., overlap of communication with computation or other communication). Further enhancement of the current implementation prototype as well as additional opportunities to enhance performance through the application of these new APIs comprise future work. © 2017 Association for Computing Machinery.

关键词： Application programming interfaces (API)

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Event reconstruction using pattern spectra and convolutional neural networks for the Cherenkov Telescope Array 7

Event reconstruction using pattern spectra and convolutional...

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7th Heidelberg International Symposium on High-Energy Gamma-Ray Astronomy, Gamma 2022

作者： Wilkinson, M.H.F. Peletier, R.F. Abdalla, H. Abe, H. Abe, S. Abusleme, A. Acero, F. Acharyya, A. Acín Portella, V. Ackley, K. Adam, R. Adams, C. Adhikari, S.S. Aguado-Ruesga, I. Agudo, I. Aguilera, R. Aguirre-Santaella, A. Aharonian, F. Alberdi, A. Alfaro, R. Alfaro, J. Alispach, C. Aloisio, R. Alves Batista, R. Amans, J.-P. Amati, L. Amato, E. Ambrogi, L. Ambrosi, G. Ambrosio, M. Ammendola, R. Anderson, J. Anduze, M. Angüner, E.O. Antonelli, L.A. Antonuccio, V. Antoranz, P. Anutarawiramkul, R. Aragunde Gutierrez, J. Aramo, C. Araudo, A. Araya, M. Arbet-Engels, A. Arcaro, C. Arendt, V. Armand, C. Armstrong, T. Arqueros, F. Arrabito, L. Arsioli, B. Artero, M. Asano, K. Ascasíbar, Y. Aschersleben, J. Ashley, M. Attinà, P. Aubert, P. Singh, C.B. Baack, D. Babic, A. Backes, M. Baena, V. Bajtlik, S. Baktash, A. Balazs, C. Balbo, M. Ballester, O. Ballet, J. Balmaverde, B. Bamba, A. Bandiera, R. Baquero Larriva, A. Barai, P. Barbier, C. Barbosa Martins, V. Barcelo, M. Barkov, M. Barnard, M. Baroncelli, L. Barres de Almeida, U. Barrio, J.A. Bastieri, D. Batista, P.I. Batkovic, I. Bauer, C. Bautista-González, R. Baxter, J. Becciani, U. Becerra González, J. Becherini, Y. Beck, G. Becker Tjus, J. Bednarek, W. Belfiore, A. Bellizzi, L. Belmont, R. Benbow, W. Berge, D. Bernardini, E. Bernardos, M.I. Bernlöhr, K. Berti, A. Berton, M. Bertucci, B. Beshley, V. Bhatt, N. Bhattacharyya, S. Bhattacharyya, W. Bhattacharyya, S. Bi, B. Bicknell, G. Biederbeck, N. Bigongiari, C. Biland, A. Bird, R. Bissaldi, E. Biteau, J. Bitossi, M. Blanch, O. Blank, M. Blazek, J. Bobin, J. Boccato, C. Bocchino, F. Boehm, C. Bohacova, M. Boisson, C. Boix, J. Bolle, J.-P. Bolmont, J. Bonanno, G. Bonavolontà, C. Bonneau Arbeletche, L. Bonnoli, G. Bordas, P. Borkowski, J. Bórquez, S. Bose, R. Bose, D. Bosnjak, Z. Bottacini, E. Böttcher, M. Botticella, M.T. Boutonnet, C. Bouyjou, F. Bozhilov, V. Bozzo, E. Brahimi, L. Braiding, C. Brau-Nogué, S. Breen, S. Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen PO Box 407 GroningenNL-9700 AK Netherlands Centre for Space Research North-West University Potchefstroom2520 South Africa Institute for Cosmic Ray Research University of Tokyo 5-1-5 Kashiwa-no-ha Chiba Kashiwa277-8582 Japan Pontificia Universidad Católica de Chile Av. Libertador Bernardo O’Higgins 340 Santiago Chile AIM CEA CNRS Université Paris-Saclay Université Paris Diderot Sorbonne Paris Cité CEA Paris-Saclay IRFU/DAp Bat 709 Orme des Merisiers Gif-sur-Yvette91191 France Centre for Advanced Instrumentation Dept. of Physics Durham University South Road DurhamDH1 3LE United Kingdom Port d’Informació Científica Edifici D Carrer de l’Albareda Cerdanyola del Vallès Bellaterrra08193 Spain School of Physics and Astronomy Monash University MelbourneVIC3800 Australia Laboratoire Leprince-Ringuet École Polytechnique UMR 7638 CNRS IN2P3 Institut Polytechnique de Paris Palaiseau91128 France Department of Physics Columbia University 538 West 120th Street New YorkNY10027 United States University of Oslo Department of Physics Sem Saelandsvei 24 - PO Box 1048 Blindern OsloN-0316 Norway EMFTEL department IPARCOS Universidad Complutense de Madrid Madrid28040 Spain Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada18008 Spain Campus UAB Carrer de Can Magrans s/n Cerdanyola del Vallés08193 Spain Instituto de Física Teórica UAM CSIC Departamento de Física Teórica Universidad Autónoma de Madrid c/ Nicolás Cabrera 13-15 Campus de Cantoblanco UAM Madrid28049 Spain Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin02 Ireland Universidad Nacional Autónoma de México Delegación Coyoacán Ciudad de México04510 Mexico University of Geneva Département de Physique Nucléaire et Corpusculaire 24 rue du Général-Dufour Genève1211 4 Switzerland INFN Dipartimento di Scienze Fisiche e Chimiche U

The Cherenkov Telescope Array (CTA) is the future observatory for ground-based imaging atmospheric Cherenkov telescopes. Each telescope will provide a snapshot of gamma-ray induced particle showers by capturing the induced Cherenkov emission at ground level. The simulation of such events provides camera images that can be used as training data for convolutional neural networks (CNNs) to differentiate signals from background events and to determine the energy of the initial gamma-ray events. Pattern spectra are commonly used tools for image classification and provide the distributions of the sizes and shapes of features comprising an image. The application of pattern spectra on a CNN allows the selection of relevant combinations of features within an image. In this work, we generate pattern spectra from simulated gamma-ray images to train a CNN for signal-background separation and energy reconstruction for CTA. We compare our results to a CNN trained with CTA images and find that the pattern spectra-based analysis is computationally less expensive but not competitive with the purely CTA images-based analysis. Thus, we conclude that the CNN must rely on additional features in the CTA images not captured by the pattern spectra. © Copyright owned by the author(s)

关键词： Cosmology

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Modeling and Simulation of Top and Bottom Lid Driven Cavity using Lattice Boltzmann Method

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IOP Conference Series: Materials Science and engineering 2019年第5期546卷

作者： K A Yuana E P Budiana Deendarlianto Indarto Mechanical Engineering Department Faculty of Engineering Gadjah Mada University Bulaksumur Sleman Yogyakarta Indonesia. Networking and Software Engineering Dept. Computer Science Faculty Amikom University Yogyakarta Ring Road Utara Sleman Yogyakarta Indonesia. Mechanical Enginering Department Faculty of Engineering Sebelas Maret University Jl. Ir. Sutami 36A Jebres Surakarta Central Java Indonesia

Simulation of computational fluid dynamic (CFD) in isothermal fluid flow is very important in engineering and modeling problem. The simulation can explain the closed-flow behavior as complement with experimental field. Lid Driven Cavity as the tool for CFD benchmark is important to investigate. Lattice Boltzmann Method (LBM) as non-conventional CFD is used as present method investigation. The keys parameters of lid driven cavity problem are Reynolds number and the parts of moving boundaries that driven the cavity. In the present investigation was used the variations of Reynolds numbers: 500, 1000 and 2000. The lid that driven the cavity are top and bottom part. The results presented in visual flow pattern and in two dimensional velocity plot.

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