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arXiv 2020年

作者： Wohlin, Claes Felizardo, Katia Romero Mendes, Emilia Kalinowski, Marcos Department of Software Engineering Blekinge Institute of Technology Karlskrona Sweden Department of Computing Federal Technological University of Paraná Cornélio Procópio Brazil Department of Computer Science Blekinge Institute of Technology Karlskrona Sweden Department of Informatics Pontifical Catholic University of Rio de Janeiro Rio de Janeiro Brazil

Context: Systematic Literature Reviews (SLRs) have been adopted within Software engineering (SE) for more than a decade to provide meaningful summaries of evidence on several topics. Many of these SLRs are now potentially not fully up-to-date, and there are no standard proposals on how to update SLRs in SE. Objective: The objective of this paper is to propose guidelines on how to best search for evidence when updating SLRs in SE, and to evaluate these guidelines using an SLR that was not employed during the formulation of the guidelines. Method: To propose our guidelines, we compare and discuss outcomes from applying different search strategies to identify primary studies in a published SLR, an SLR update, and two replications in the area of effort estimation. These guidelines are then evaluated using an SLR in the area of software ecosystems, its update and a replication. Results: The use of a single iteration forward snowballing with Google Scholar, and employing as a seed set the original SLR and its primary studies is the most cost-effective way to search for new evidence when updating SLRs. Furthermore, the importance of having more than one researcher involved in the selection of papers when applying the inclusion and exclusion criteria is highlighted through the results. Conclusions: Our proposed guidelines formulated based upon an effort estimation SLR, its update and two replications, were supported when using an SLR in the area of software ecosystems, its update and a replication. Therefore, we put forward that our guidelines ought to be adopted for updating SLRs in SE. Copyright © 2020, The Authors. All rights reserved.

关键词： Software engineering

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High energy irradiation effects on silicon photonic passive devices

arXiv

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arXiv 2021年

作者： Zhou, Yue Bi, Dawei Wang, Songlin Wu, Longsheng Huang, Yi Zhang, Enxia Fleetwood, Daniel M. Wu, Aimin State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China Spallation Neutron Source Science Center Dongguan523803 China Department of Electrical Engineering and Computer Science Vanderbilt University NashvilleTN37235 United States

In this work, the radiation responses of silicon photonic passive devices built in silicon-on-insulator (SOI) technology are investigated through high energy neutron and 60Co γ-ray irradiation. The wavelengths of both micro-ring resonators (MRRs) and Mach-Zehnder interferometers (MZIs) exhibit blue shifts after high-energy neutron irradiation to a fluence of 1×1012n/cm2;the blue shift is smaller in MZI devices than in MRRs due to different waveguide widths. Devices with SiO2upper cladding layer show strong tolerance to irradiation. Neutron irradiation leads to slight changes in the crystal symmetry in the Si cores of the optical devices and accelerated oxidization for devices without SiO2cladding. A 2-μm top cladding of SiO2layer significantly improves the radiation tolerance of these passive photonic devices. Copyright © 2021, The Authors. All rights reserved.

关键词： Silicon photonics

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Erratum to:Recent Advances of Chimp Optimization Algorithm:Variants and Applications

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Journal of Bionic engineering 2024年第3期21卷 1618-1618页

作者： Mohammad Sh.Daoud Mohammad Shehab Laith Abualigah Mohammad Alshinwan Mohamed Abd Elaziz Mohd Khaled Yousef Shambour Diego Oliva Mohammad A.Alia Raed Abu Zitar College of Engineering Al Ain University112612 Abu DhabiUnited Arab Emirates College of Computer Sciences and Informatics Amman Arab UniversityAmman 11953Jordan Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq 25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied Science Research Center Applied Science Private UniversityAmman 11931Jordan School of Computer Sciences Universiti Sains Malaysia11800 Pulau PinangMalaysia Faculty of Information Technology Applied Science Private UniversityAmman 11931Jordan Department of Mathematics Faculty of ScienceZagazig UniversityZagazig 7120001Egypt The Custodian of the Two Holy Mosques Institute for Hajj and Umrah Research Umm Al-Qura UniversityMakkah 24382Saudi Arabia Depto.de Innovacio′n Basada en la Informacio′n y el Conocimiento Universidad de GuadalajaraCUCEI45129 GuadalajaraMexico Cyber Security Department Faculty of Science and Information TechnologyAl Zaytoonah University of JordanAmman 11733Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi38044 Abu DhabiUnited Arab Emirates School of Engineering and Technology Sunway University Malaysia27500 Petaling JayaMalaysia

Erratum to:J Bionic Eng https://***/10.1007/*** this article the statement in the Funding information section was incorrectly given as‘22UQU4361183DSR03’and should have read‘23UQU4361183DSR03’.

关键词： statement section correctly

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Explainable AI: Definition and attributes of a good explanation for health AI

arXiv

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arXiv 2024年

作者： Kyrimi, Evangelia McLachlan, Scott Wohlgemut, Jared M. Perkins, Zane B. Lagnado, David A. Marsh, William Gimson, Alexander Shafti, Ali Ercole, Ari Banerjee, Amitava Glocker, Ben Schafer, Burkhard Gatsonis, Constantine Grosan, Crina Sent, Danielle Berman, David S. Glass, David O'Regan, Declan P. Letsios, Dimitrios Morrissey, Dylan Pisirir, Erhan Leofante, Francesco Soyel, Hamit Williamson, Jon Grieman, Keri Dube, Kudakwashe Mardsen, Max Nagendran, Myura Tai, Nigel Kostopoulou, Olga Jones, Owain Curzon, Paul Stoner, Rebecca S. Tandle, Sankalp Joshi, Shalmali Mossadegh, Somayyeh Buijsman, Stefan Miller, Tim Madai, Vince Istvan School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom School of Nursing Midwifery and Palliative Care King's College London London United Kingdom Centre for Trauma Sciences Blizard Institute Queen Mary University of London London United Kingdom Royal London Hospital Barts Health NHS Trust London United Kingdom Department of Experiment Psychology University College London London United Kingdom Cambridge Liver Unit Addenbrooke's Hospital Cambridge University Hospitals Hills Road Box 210 Cambridge United Kingdom University of Cambridge United Kingdom Cambridge Consultants Ltd. United Kingdom Division of Anaesthesia Cambridge Centre for AI in Medicine University of Cambridge Cambridge United Kingdom Magdelene College University of Cambridge United Kingdom Institute of Health Informatics University College London London United Kingdom Department of Computing Imperial College London London United Kingdom SCRIPT Centre for IT and IP Law School of Law University of Edinburgh United Kingdom Center for Statistical Sciences School of Public Health Brown University Providence United States Research Group Artificial Intelligence HU University of Applied Sciences Utrecht Netherlands Jheronimus Academy of Data Science Tilburg University Eindhoven University of Technology s-Hertogenbosch Netherlands Centre for Theoretical Physics Queen Mary University of London London United Kingdom School of Computing Ulster University Belfast United Kingdom MRC Laboratory of Medical Sciences Imperial College London London United Kingdom Department of Informatics King's College London United Kingdom Physiotherapy Department Barts Health NHS Trust London United Kingdom Sport and Exercise Medicine Queen Mary University of London London United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Computing Imperial College London United Kingdom Departmen

Proposals of artificial intelligence (AI) solutions based on increasingly complex and accurate predictive models are becoming ubiquitous across many disciplines. As the complexity of these models grows, transparency and users' understanding often diminish. This suggests that accurate prediction alone is insufficient for making an AI-based solution truly useful. In the development of healthcare systems, this introduces new issues related to accountability and safety. Understanding how and why an AI system makes a recommendation may require complex explanations of its inner workings and reasoning processes. Although research on explainable AI (XAI) has significantly increased in recent years and there is high demand for XAI in medicine, defining what constitutes a good explanation remains ad hoc, and providing adequate explanations continues to be challenging. To fully realize the potential of AI, it is critical to address two fundamental questions about explanations for safety-critical AI applications, such as health-AI: (1) What is an explanation in health-AI? and (2) What are the attributes of a good explanation in health-AI? In this study, we examined published literature and gathered expert opinions through a two-round Delphi study. The research outputs include (1) a definition of what constitutes an explanation in health-AI and (2) a comprehensive list of attributes that characterize a good explanation in health-AI. © 2024, CC BY.

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Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA

arXiv

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arXiv 2023年

作者： Yang, Kaiyuan Musio, Fabio Ma, Yihui Juchler, Norman Paetzold, Johannes C. Al-Maskari, Rami Höher, Luciano Li, Hongwei Bran Hamamci, Ibrahim Ethem Sekuboyina, Anjany Shit, Suprosanna Huang, Houjing Prabhakar, Chinmay de la Rosa, Ezequiel Waldmannstetter, Diana Kofler, Florian Navarro, Fernando Menten, Martin Ezhov, Ivan Rueckert, Daniel Vos, Iris Ruigrok, Ynte Velthuis, Birgitta Kuijf, Hugo Hämmerli, Julien Wurster, Catherine Bijlenga, Philippe Westphal, Laura Bisschop, Jeroen Colombo, Elisa Baazaoui, Hakim Makmur, Andrew Hallinan, James Wiestler, Bene Kirschke, Jan S. Wiest, Roland Montagnon, Emmanuel Letourneau-Guillon, Laurent Galdran, Adrian Galati, Francesco Falcetta, Daniele Zuluaga, Maria A. Lin, Chaolong Zhao, Haoran Zhang, Zehan Ra, Sinyoung Hwang, Jongyun Park, Hyunjin Chen, Junqiang Wodzinski, Marek Müller, Henning Shi, Pengcheng Liu, Wei Ma, Ting Yalçin, Cansu Hamadache, Rachika E. Salvi, Joaquim Llado, Xavier Estrada, Uma Maria Lal-Trehan Abramova, Valeriia Giancardo, Luca Oliver, Arnau Liu, Jialu Huang, Haibin Cui, Yue Lin, Zehang Liu, Yusheng Zhu, Shunzhi Patel, Tatsat R. Tutino, Vincent M. Orouskhani, Maysam Wang, Huayu Mossa-Basha, Mahmud Zhu, Chengcheng Rokuss, Maximilian R. Kirchhoff, Yannick Disch, Nico Holzschuh, Julius Isensee, Fabian Maier-Hein, Klaus Sato, Yuki Hirsch, Sven Wegener, Susanne Menze, Bjoern Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Center for Computational Health Zurich University of Applied Sciences Zurich Switzerland Department of Neuroradiology University Hospital of Zurich Zurich Switzerland Department of Neurosurgery Zhongnan Hospital of Wuhan University Wuhan China Department of Computing Imperial College London London United Kingdom Helmholtz Center Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Harvard Medical School Boston United States School of Medicine Technical University of Munich Munich Germany Department of Informatics Technical University of Munich Munich Germany Helmholtz AI Helmholtz Munich Munich Germany Image Sciences Institute UMC Utrecht Utrecht Netherlands Department of Neurology UMC Utrecht Utrecht Netherlands Department of Radiology UMC Utrecht Utrecht Netherlands Department of Clinical Neurosciences Division of Neurosurgery Geneva University Hospitals Geneva Switzerland Department of Neurology University Hospital of Zurich Zurich Switzerland Department of Physiology University of Toronto Toronto Canada Department of Neurosurgery University Hospital of Zurich Zurich Switzerland Department of Diagnostic Imaging National University Hospital Singapore Department of Diagnostic and Interventional Neuroradiology University Hospital Berne University of Berne Berne Switzerland Montreal Canada Universitat Pompeu Fabra Barcelona Spain EURECOM Biot France Institute of Medical Technology Peking University Health Science Center Beijing China Hangzhou Genlight Medtech Co. Ltd. Hangzhou China Department of Artificial Intelligence Sungkyunkwan University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Seoul Korea Republic of Shanghai MediWorks Precision Instruments Co. Ltd. Shanghai China Institute of Informatics HES-SO Valais-Wallis Switzerland Department of Measurement and Electronics AGH University

The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited public datasets with annotations on CoW anatomy, especially for CTA. Therefore we organized the TopCoW Challenge in 2023 with the release of an annotated CoW dataset. The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology. It was also the first large dataset with paired MRA and CTA from the same patients. TopCoW challenge formalized the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. We invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. The top performing teams managed to segment many CoW components to Dice scores around 90%, but with lower scores for communicating arteries and rare variants. There were also topological mistakes for predictions with high Dice scores. Additional topological analysis revealed further areas for improvement in detecting certain CoW components and matching CoW variant topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically. © 2023, CC BY-NC-ND.

关键词： Large datasets

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Middle East and North African Health informatics Association (MENAHIA): Strengthening Health informatics Ecosystems in the Region and Beyond

引用

Yearbook of Medical informatics 2020年第1期29卷 290-294页

作者： Alhuwail, Dari Al-Jafar, Eiman Alshammari, Riyad Al-Shorbaji, Najeeb El Hassan, Osama Al-Hamdan, Zaid Alshammari, Abdulwahhab Luque, Luis Fernandez Salmi, Salim A.L. Taweel, Adel AlKhalifa, Khalid Househ, Mowafa Al Thani, Dena A. Department of Information Science College of Life Sciences Kuwait University Kuwait Health Informatics Unit Dasman Diabetes Institute Kuwait Kuwait Health Informatics Association Kuwait Department of Health Informatics and Information Management College of Allied Health Kuwait University Kuwait Health Informatics Department College of Public Health and Health Informatics King Saud bin Abdulaziz University for Health Sciences Riyadh Saudi Arabia King Abdullah International Medical Research Center Riyadh Saudi Arabia Ministry of the National Guard- Health Affairs Riyadh Saudi Arabia Saudi Health Informatics Association Saudi Arabia eHealth Development Association Amman Jordan Health Informatics & Smart Health Department Dubai Health Authority Dubai United Arab Emirates Jordan University of Science and Technology Irbid Jordan King Abdullah International Medical Research Center Ministry of the National Guard Health Affairs Riyadh Saudi Arabia Qatar Computing Research Institute Hamad bin Khalifa University Qatar Oman College of Health Sciences Ministry of Health Oman Department of Computer Science Birzeit University Birzeit Palestine Supreme Council of Health Bahrain Information and Computing Technology Division College of Science and Engineering Hamad Bin Khalifa University Qatar

Over the years, Health informatics initiatives in the Middle East and North African region have been thriving with a number of different educational, scientific, professional, and technological developments taking place. Within the past year, Health informatics initiatives in the region have focused on the educational and capacity building efforts. This regional report provides an overview of the wide spectrum of initiatives and developments that are taking place within the region which are supported by the Middle East and North African Health informatics Association (MENAHIA). © 2020 Thieme Medical Publishers, Inc.. All rights reserved.

关键词： Bahrain Health informatics Jordan Kuwait Middle East Morocco North Africa Oman Pakistan Palestine Qatar Saudi Arabia United Arab Emirates

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Harnessing artificial intelligence to fill global shortfalls in biodiversity knowledge

Nature Reviews Biodiversity

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Nature Reviews Biodiversity 2025年第3期1卷 166-182页

作者： Laura J. Pollock David Rolnick Justin Kitzes Sara Beery Kaitlyn M. Gaynor Marta A. Jarzyna Tanya Berger-Wolf Oisin Mac Aodha Bernd Meyer Graham W. Taylor Devis Tuia Department of Biology McGill University Montréal Quebec Canada Mila—Quebec AI Institute Montréal Quebec Canada School of Computer Science McGill University Montréal Quebec Canada Department of Biological Sciences University of Pittsburgh Pittsburgh PA USA Faculty of AI and Decision Making Massachusetts Institute of Technology Cambridge MA USA Department of Botany University of British Columbia Vancouver British Columbia Canada Department of Zoology University of British Columbia Vancouver British Columbia Canada Department of Evolution Ecology and Organismal Biology The Ohio State University Columbus OH USA Translational Data Analytics Institute The Ohio State University Columbus OH USA Department of Computer Science and Engineering The Ohio State University Columbus OH USA Department of Electrical & Computer Engineering The Ohio State University Columbus OH USA School of Informatics University of Edinburgh Edinburgh UK Department of Data Science and AI Monash University Clayton Victoria Australia School of Engineering University of Guelph Guelph Ontario Canada Vector Institute Toronto Ontario Canada School of Architecture Civil and Environmental Engineering Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland

Large, well described gaps exist in both what we know and what we need to know to address the biodiversity crisis. Artificial intelligence (AI) offers new potential for filling these knowledge gaps, but where the biggest and most influential gains could be made remains unclear. To date, biodiversity-related uses of AI have largely focused on tracking and monitoring of wildlife populations. Rapid progress is being made in the use of AI to build phylogenetic trees and species distribution models. However, AI also has considerable unrealized potential in the re-evaluation of important ecological questions, especially those that require the integration of disparate and inherently complex data types, such as images, video, text, audio and DNA. This Review describes the current and potential future use of AI to address seven clearly defined shortfalls in biodiversity knowledge. Recommended steps for AI-based improvements include the re-use of existing image data and the development of novel paradigms, including the collaborative generation of new testable hypotheses. The resulting expansion of biodiversity knowledge could lead to science spanning from genes to ecosystems — advances that might represent our best hope for meeting the rapidly approaching 2030 targets of the Global Biodiversity Framework.

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Traffic Analysis in Vehicular Adhoc Networks using Flow and Network Level Analysis

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Microprocessors and Microsystems 2020年

作者： Karunkuzhali, D. Prasad, M. Singh, Gurpreet Kaur, Amanpreet Periasamy, J.K. Ramprabu, G. Department of Information Technology Panimalar Engineering College ChennaiTamilnadu India School of Computing & Informatics College of Engineering & Technology Dilla University Dilla Ethiopia Near ITI Chowk Rajpura District Patiala Punjab140401 India University Institute of Engineering Chandigarh University Gharaun District Mohali Punjab140413 India Department of Computer Science and Engineering Sri Sairam Engineering College ChennaiTamilnadu India Department of Electronics and Communication Engineering Bonam Venkata Chalamayya Institute of Technology & Science Amalapuram East GodavariAndhra Pradesh India

Distributed communication approach has been predominantly used in Vehicular Adhoc Networks (VANETs) where communication between Road Side Unit (RSU) and vehicles is difficulty due to mobility of vehicle and its minimal contact duration. Block-Chain Routing and Security using Hash (BCRSH) assures that network level analysis in vehicle navigation is robust against attack by using Hash functions. RSU shares the Hash function with each vehicle that passes in its sub-terrain. Geographically separated RSU shares the previous hash to subsequent RSU analogous to Block chain methodology. The paired communication between RSU and vehicle is being framed with a Merkle tree approach within the time interval to provide safe navigation. Network level analysis has been simulated with network simulator-2 for packet delivery ratio, energy consumption and trust values. Flow level analysis has been incorporated with Markovian queues with JSim simulator. © 2020 Elsevier B.V.

关键词： Vehicular ad hoc networks

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Force Prediction in Robotized Ultrasonic Sheet Metal Forming Based on Finite Element Analysis and Artificial Neural Networks

Research Square

引用

Research Square 2021年

作者： Ostasevicius, Vytautas Paulauskaite-Taraseviciene, Agne Paleviciute, Ieva Jurenas, Vytautas Griskevicius, Paulius Eidukynas, Darius Kizauskiene, Laura Institute of Mechatronics Kaunas University of Technology Studentu 56-122 KaunasLT-51424 Lithuania Department of Applied Informatics Kaunas University of Technology Studentu 50-214 KaunasLT-51368 Lithuania Institute of Mechatronics Kaunas University of Technology Studentu 56-105 KaunasLT-51424 Lithuania Institute of Mechatronics Kaunas University of Technology Studentu 56-107 KaunasLT-51368 Lithuania Department of Mechanical Engineering Kaunas University of Technology Studentu 56-301 KaunasLT-51424 Lithuania Institute of Mechatronics Kaunas University of Technology Studentu 56-125 KaunasLT-51424 Lithuania Centre of Real Time Computer Systems Kaunas University of Technology Barsausko 59-51423 KaunasLT-51368 Lithuania

The forces acting in the process of single point incremental forming (SPIF) change the geometry of the sheet metal. The tool-workpiece interaction process is non-linear due to the large deformations of the sheet metal, which determine the plastic behaviour, as well as the evolutionary boundary conditions resulting from the contact between the tool and the sheet. Instead of lubricating the contact surface of the forming tool and the sheet metal, an innovative environmentally friendly method to reduce the coefficient of friction by vibrating the sheet has been proposed. The finite element method (FEM) allowed a virtual evaluation of the deformation parameters of the SPIF process in order to determine the destructive loads. The FEM was chosen as a deterministic numerical tool to evaluate the set of defect parameters induced by forming forces. The paper also proposes a method for predicting the formation force using an artificial neural network (ANN), assuming that such a model is generalized to implicit data. In this context, an empirical analysis of the implementation of the ANN technique is performed. © 2021, CC BY.

关键词： Sheet metal

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Optimal loads scheduling using the intelligent optimization approach

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AIP Conference Proceedings 2024年第1期3051卷

作者： Bilal Naji Alhasnawi Nidhal Abd Mohammed Basil H. Jasim Sabah Mohammed Mlket Almutoki Hussein M. Hathal Thulfiqar H. Mandeel Ali M. Jasim Rabeh Abbassi Vladimír Bureš Bishoy E. Sedhom Department of Electricity Techniques Al-Furat Al-Awsat Technical University Technical Institute Samawa Iraq Electrical Engineering Department Basrah University Basrah 61001 Iraq Electrical Engineering Department College of Engineering Mustansiriyah University Baghdad 10047 Iraq Department of Computer Technical Engineering College of Information Technology Imam Ja’afar Al-Sadiq University Al-Muthanna 66002 Iraq Department of Communications Engineering Iraq University College Basrah 61001 Iraq Department of Electrical Engineering College of Engineering University of Ha’il Ha’il City 81451 Saudi Arabia Faculty of Informatics and Management University of Hradec Králové 50003 Hradec Králové Czech Republic Electrical Engineering Department Faculty of Engineering Mansoura University Mansoura 35516 Egypt

Because of the need for energy growing exponentially, prices are constantly going up, which impacts both residential and business consumers. Researchers are looking for workable solutions to this problem, such as off-peak scheduling that reduces peak energy consumption while maintaining reasonable energy costs. The system design issues that regulate decision-making for the best operation management are optimized in this research using an Improved Crow Search Algorithm (ICSA). The simulation’s results show that the suggested algorithm-based ICSA reduces carbon emissions, peak-to-average ratio (PAR), and electricity costs. Using the ICSA method, the scheduling controller suggested in this study was successful in achieving an energy savings of 30.58%.

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