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Foundational Competencies and Responsibilities of a Research software Engineer

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arXiv 2023年

作者： Goth, Florian Alves, Renato Braun, Matthias Castro, Leyla Jael Chourdakis, Gerasimos Christ, Simon Cohen, Jeremy Druskat, Stephan Erxleben, Fredo Grad, Jean-Noël Hagdorn, Magnus Hodges, Toby Juckeland, Guido Kempf, Dominic Lamprecht, Anna-Lena Linxweiler, Jan Löffler, Frank Martone, Michele Schwarzmeier, Moritz Seibold, Heidi Thiele, Jan Philipp Waldow, Harald von Wittke, Samantha Institute for Theoretical Physics and Astrophysics University of Würzburg Germany European Molecular Biology Laboratory Heidelberg Germany Cluster of Excellence IntCDC University of Stuttgart Germany ZB MED Information Centre for Life Sciences Cologne Germany School of Computation Information and Technology Technical University of Munich Garching Germany Institute for Parallel and Distributed Systems University of Stuttgart Stuttgart Germany Leibniz University Hannover Department of Cell Biology and Biophysics Computational Biology Germany Imperial College London London United Kingdom Institute for Software Technology Berlin Germany Humboldt-Universität zu Berlin Department of Computer Science Berlin Germany Helmholtz-Zentrum Dresden-Rossendorf Germany Institute for Computational Physics University of Stuttgart Germany Geschäftsbereich IT Charité Universitätsmedizin Berlin Germany The Carpentries United States Heidelberg University Scientific Software Center Germany Institute of Computer Science University of Potsdam Germany Technische Universität Braunschweig Germany Michael Stifel Center Jena Friedrich Schiller University Jena Germany Leibniz Supercomputing Centre Garching Germany Mathematical Modeling and Analysis TU Darmstadt Germany IGDORE Munich Germany Weierstrass Institute Berlin Germany Leibniz University Hannover Institute of Applied Mathematics Scientific Computing Hannover Germany Johann Heinrich von Thünen Institute Centre for Information Management Germany

The term Research software Engineer, or RSE, emerged a little over 10 years ago as a way to represent individuals working in the research community but focusing on software development. The term has been widely adopted and there are a number of high-level definitions of what an RSE is. However, the roles of RSEs vary depending on the institutional context they work in. At one end of the spectrum, RSE roles may look similar to a traditional research role. At the other extreme, they resemble that of a software engineer in industry. Most RSE roles inhabit the space between these two extremes. Therefore, providing a straightforward, comprehensive definition of what an RSE does and what experience, skills and competencies are required to become one is challenging. In this community paper we define the broad notion of what an RSE is, explore the different types of work they undertake, and define a list of foundational competencies as well as values that outline the general profile of an RSE. Further research and training can build upon this foundation of skills and focus on various aspects in greater detail. We expect that graduates and practitioners will have a larger and more diverse set of skills than outlined here. On this basis, we elaborate on the progression of these skills along different dimensions. We look at specific types of RSE roles, propose recommendations for organisations, and give examples of future specialisations. An appendix details how existing curricula fit into this framework. © 2023, CC BY.

关键词： computer aided software engineering

Unified resilience model using deep learning for assessing power system performance

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Heliyon 2025年第4期11卷 e42802页

作者： Artemchuk, Volodymyr Garbuz, Iurii Jamil Alsayaydeh, Jamil Abedalrahim Shkarupylo, Vadym Oliinyk, Andrii Bin Yusof, Mohd Faizal Herawan, Safarudin Gazali Department of Mathematical and Econometric Modelling G.E. Pukhov Institute for Modelling in Energy Engineering of the NAS of Ukraine Kyiv Ukraine Department of Environmental Protection Technologies and Radiation Safety Center for Information-analytical and Technical Support of Nuclear Power Facilities Monitoring of the NAS of Ukraine Kyiv Ukraine Department of Іnformation Systems in Economics Kyiv National Economic University Named After Vadym Hetman Kyiv Ukraine Department of Computerized Control System National Aviation University Kyiv Ukraine Department of Engineering Technology Fakulti Teknologi Dan Kejuruteraan Elektronik Dan Komputer (FTKEK) Universiti Teknikal Malaysia Melaka (UTeM) Melaka 76100 Malaysia Department of Computer Systems Networks and Cybersecurity of the National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Department of Software Tools Faculty of Computer Science and Technology Zaporizhzhia Polytechnic National University Zaporizhzhia Ukraine Research Section Faculty of Resilience Rabdan Academy Abu Dhabi United Arab Emirates Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta 11480 Indonesia

Energy resilience in renewable energy sources dissemination components such as batteries and inverters is crucial for achieving high operational fidelity. Resilience factors play a vital role in determining the performance of power systems, regardless of their operating environment and interruptions. This article introduces a Unified Resilience Model (URM) using Deep Learning (DL) to enhance power system performance. The proposed model analyzes environmental factors impacting the resilience of batteries and energy storage devices. This deep learning approach trains performance-impacting factors using previously known low resilience drain data. The learning output is utilized to augment various strengthening factors, thereby improving resilience. Drain mitigation and performance improvements are combined for direct impact verification. This process validates the model's fidelity in enhancing power system performance, with a specific focus on the impact of weather factors. © 2025 The Authors

关键词： Deep learning Energy resilience Fidelity Weather impact

Using the PerFECt Framework to Establish an Onlife Community for Theatre in Mathematics to Teach Principles of Computing

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Using the PerFECt Framework to Establish an Onlife Community...

IEEE Annual International computer software and applications Conference (COMPSAC)

作者： Nektarios Moumoutzis Dessislava Paneva-Marinova Chara Xanthaki Polyxeni Arapi Nikos Pappas Stavros Christodoulakis Institute of Mathematics & Informatics Bulgarian Academy of Sciences (IMI-BAS) Institute of Mathematics & Informatics Bulgarian Academy of Sciences (IMI-BAS) Sofia Bulgaria Computer Science Teacher in Secondary Education Chania Greece Lab. of Distr. Multimedia Inf. Systems & Applications School of Electrical and Computer Engineering Technical University of Crete (TUC/MUSIC) Chania Greece

ISBN: (数字)9781728173030

ISBN: (纸本)9781728173047

The new reality brought about by digital technologies is characterized by new qualities, new opportunities for action, new community affordances, that are captures by the term onlife. This term was initially proposed in the Onlife Manifesto and used in this paper to signify certain qualities that are important in understanding modern digital platforms and applications as well as guide the efforts for effectively extending to provide new capabilities to their users. Specific design principles are presented exemplified by the presentation of the design of a Community of Practice for teachers involved in the pilot activities of the Erasmus+ project TIM - Theatre in Mathematics that promote the adoption of Theatre in teaching Mathematics in Primary and Lower Secondary Education targeting 8-14 years old students.

关键词： Mathematics Education Electronic mail computers software Conferences Informatics

F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning

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arXiv 2020年

作者： Li, Wenhao Jin, Bo Wang, Xiangfeng Yan, Junchi Zha, Hongyuan School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China School of Software Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201804 China School of Computer Science and Technology Key Laboratory of Mathematics and Engineering Applications Ministry of Education East China Normal University Shanghai200062 China Department of Computer Science and Engineering Key Laboratory of Artificial Intelligence Ministry of Education Shanghai Jiao Tong University Shanghai200240 China

Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractical in complicated applications due to non-interactivity between agents, the curse of dimensionality, and computation complexity. Hence, several decentralized MARL algorithms are motivated. However, existing decentralized methods only handle the fully cooperative setting where massive information needs to be transmitted in training. The block coordinate gradient descent scheme they used for successive independent actor and critic steps can simplify the calculation, but it causes serious bias. This paper proposes a flexible fully decentralized actor-critic MARL framework, which can combine most of the actor-critic methods and handle large-scale general cooperative multi-agent settings. A primal-dual hybrid gradient descent type algorithm framework is designed to learn individual agents separately for decentralization. From the perspective of each agent, policy improvement and value evaluation are jointly optimized, which can stabilize multi-agent policy learning. Furthermore, the proposed framework can achieve scalability and stability for the large-scale environment. This framework also reduces information transmission by the parameter sharing mechanism and novel modeling-other-agents methods based on theory-of-mind and online supervised learning. Sufficient experiments in cooperative Multiagent Particle Environment and StarCraft II show that the proposed decentralized MARL instantiation algorithms perform competitively against conventional centralized and decentralized methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Gradient methods

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

A Review of Artificial Fish Swarm Algorithms: Recent Advances and applications

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arXiv 2020年

作者： Pourpanah, Farhad Wang, Ran Lim, Chee Peng Wang, Xi-Zhao Yazdani, Danial College of Mathematics and Statistics Guangdong Key Lab. of Intelligent Information Processing Shenzhen University China Department of Electrical and Computer Engineering University of Windsor Canada College of Mathematics and Statistics Shenzhen Key Lab. of Advanced Machine Learning and Applications Guangdong Key Lab. of Intelligent Information Processing Shenzhen University China Institute for Intelligent Systems Research and Innovation Deakin University Australia College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University China School of Computer Science and Engineering Southern University of Science and Technology China

The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological behaviors of fish schooling in nature, viz., the preying, swarming and following behaviors. Owing to a number of salient properties, which include flexibility, fast convergence, and insensitivity to the initial parameter settings, the family of AFSA has emerged as an effective Swarm Intelligence (SI) methodology that has been widely applied to solve real-world optimization problems. Since its introduction in 2002, many improved and hybrid AFSA models have been developed to tackle continuous, binary, and combinatorial optimization problems. This paper aims to present a concise review of the continuous AFSA, encompassing the original ASFA, its improvements and hybrid models, as well as their associated applications. We focus on articles published in high-quality journals since 2013. Our review provides insights into AFSA parameters modifications, procedure and sub-functions. The main reasons for these enhancements and the comparison results with other hybrid methods are discussed. In addition, hybrid, multi-objective and dynamic AFSA models that have been proposed to solve continuous optimization problems are elucidated. We also analyse possible AFSA enhancements and highlight future research directions for advancing AFSA-based models. Copyright © 2020, The Authors. All rights reserved.

关键词： Multiobjective optimization

FAIR for AI: An interdisciplinary and international community building perspective

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arXiv 2022年

作者： Huerta, E.A. Blaiszik, Ben Brinson, L. Catherine Bouchard, Kristofer E. Diaz, Daniel Doglioni, Caterina Duarte, Javier M. Emani, Murali Foster, Ian Fox, Geoffrey Harris, Philip Heinrich, Lukas Jha, Shantenu Katz, Daniel S. Kindratenko, Volodymyr Kirkpatrick, Christine R. Lassila-Perini, Kati Madduri, Ravi K. Neubauer, Mark S. Psomopoulos, Fotis E. Roy, Avik Rübel, Oliver Zhao, Zhizhen Zhu, Ruike Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science University of Chicago ChicagoIL60637 United States Globus University of Chicago ChicagoIL60637 United States Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Scientific Data Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Biological Systems & Engineering Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Helen Wills Neuroscience Institute University of California Berkeley BerkeleyCA94720 United States Department of Physics University of California La Jolla San DiegoCA92093 United States Lund University Department of Physics Box 118 Lund221 00 Sweden School of Physics & Astronomy The University of Manchester ManchesterM13 9PL United Kingdom Leadership Computing Facility Argonne National Laboratory LemontIL60439 United States Biocomplexity Institute Department of Computer Science University of Virginia CharlottesvilleVA22904 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Technical University Munich Arcisstraße 21 München80333 Germany Computational Science Initiative Brookhaven National Laboratory UptonNY11973 United States Electrical and Computer Engineering Rutgers The State University of New Jersey PiscatawayNJ08854 United States National Center for Supercomputing Applications University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Computer Science University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical & Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States School of Information Sciences University of Illinois at Urbana-Champaign UrbanaIL61801 United States San Diego Supercomputer Center University of California La Jolla San DiegoCA

A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been reinterpreted or extended to include the software, tools, algorithms, and workflows that produce data. FAIR principles are now being adapted in the context of AI models and datasets. Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. The material for this report builds on the FAIR for AI Workshop held at Argonne National Laboratory on June 7, 2022. Copyright © 2022, The Authors. All rights reserved.

关键词： Information management

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Preface

CEUR Workshop Proceedings 2022年 3091卷

作者： Kovalev, Igor Semenkin, Eugene Schwenker, Friedhelm Shaidurov, Vladimir Ganchev, Todor Škraba, Andrej Stanimirović, Predrag Kolehmainen, Mikko Kronberger, Gabriel Affenzeller, Michael Brester, Christina Ryzhikov, Ivan Kazakovtsev, Lev Sopov, Evgenii Semenkina, Maria Akhmedova, Shakhnaz Stanovov, Vladimir Krasnoyarsk Regional Science and Technology City Hall Department of Computer Science of Siberian Federal University Krasnoyarsk Russia Siberian Institute of Applied System Analysis named after A.N. Antamoshkin The Department ofsystem analysis and operations research of Reshetnev University Krasnoyarsk Russia Institute of Neural Information Processing Ulm University Ulm Germany The Institute of Computational Modeling SB RAS Krasnoyarsk Russia Technical University of Varna Varna Bulgaria University of Maribor UM Faculty of Organizational Sciences Cybernetics and Decision Support Systems Laboratory Slovenia Department of Computer Science Niš Serbia Department of Environmental and Biological Sciences Environmental Informatics University of Eastern Finland Kuopio Finland University of Applied Sciences Upper Austria Hagenberg Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Hagenberg Austria University of Eastern Finland UEF · Department of Environmental Science Kuopio Finland The Department of Systems Analysis Reshetnev University of Science and Technology Krasnoyarsk Russia Siberian Institute of Applied System Analysis Krasnoyarsk Russia University of Applied Sciences Upper Austria and Softwarepark Hagenberg Austria Reshetnev Siberian States University of Science and Technology Siberian Institute of Applied System Analysis Krasnoyarsk Russia SibSAU · Institute of computer science and telecommunications Krasnoyarsk Russia

Introduction of Fog Computations to Measuring System Collection and Accounting of Data of Distributed Internet of Things

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Journal of Physics: Conference Series 2021年第1期2096卷

作者： V D Munister A L Zolkin T G Aygumov V E Ozhiganov Mariupol State University Department of Enterprise Economics State educational institution of higher professional education "Donetsk National Technical University" Ph.D. in Engineering Science Senior Lecturer of Computer and Information Sciences Department of the Povolzhskiy State University of Telecommunications and Informatics Department of computer engineering software and automated systems Dagestan State Technical University Federal State Unitary Enterprise RUSSIAN FEDERAL NUCLEAR CENTER All-Russian Research Institute of Experimental Physics FSUE RFNC – VNIIEF

The article deals with the problems of speed and timeliness of the development of control actions from digital management systems. Generalization of researches is given. The role of mathematical applications of game theory for the role in the implementation of fog and boundary computations in the paradigm of the development of digital signal processing systems as well as their optimization value are substantiated.A particular generalization is developed and is given from the processing of an encoded signal sequence that demonstrates the operation of a humidity sensor from the viewpoint of bringing it to the basis of an antagonistic game in order to determine the optimal strategy of the device's behaviour at a given time *** practical significance of the study is confirmed by a new approach to the implementation of economic methods for analysis of probabilistic outcomes within the framework of game theory in an algorithm for analysis of a digital signal for a linear comparator of recorded physical quantities in the form of digital levels. The value of such analysis metrics in network-centric distributed systems of the Internet of things in multiplexing environments is given.

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