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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

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Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition

arXiv

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arXiv 2023年

作者： de Franca, F.O. Virgolin, M. Kommenda, M. Majumder, M.S. Cranmer, M. Espada, G. Ingelse, L. Fonseca, A. Landajuela, M. Petersen, B. Glatt, R. Mundhenk, N. Lee, C.S. Hochhalter, J.D. Randall, D.L. Kamienny, P. Zhang, H. Dick, G. Simon, A. Burlacu, B. Kasak, Jaan Machado, Meera Wilstrup, Casper La Cava, W.G. Federal University of ABC Santo Andre Brazil Evolutionary Intelligence Group Centrum Wiskunde & Informatica Science Park 123 Amsterdam Netherlands Computational Health Informatics Program Boston Children's Hospital Harvard Medical School Boston United States University of Applied Sciences Upper Austria Hagenberg Austria Center for Computational Astrophysics Flatiron Institute Department of Astrophysical Sciences Princeton University United States LASIGE Faculdade de Ciências Universidade de Lisboa Lisboa Portugal Computational Engineering Division Lawrence Livermore National Laboratory Livermore United States University of Utah Department of Mechanical Engineering Utah United States Meta FAIR France Victoria University of Wellington School of Engineering and Computer Science New Zealand University of otago Department of Information Science New Zealand Institut für Angewandte Physik Universität Tübingen Max Planck Institute for Intelligent Systems Tübingen Germany Abzu AI Orient Plads 1 Nordhavn 2150 Denmark

Symbolic regression searches for analytic expressions that accurately describe studied phenomena. The main attraction of this approach is that it returns an interpretable model that can be insightful to users. Historically, the majority of algorithms for symbolic regression have been based on evolutionary algorithms. However, there has been a recent surge of new proposals that instead utilize approaches such as enumeration algorithms, mixed linear integer programming, neural networks, and Bayesian optimization. In order to assess how well these new approaches behave on a set of common challenges often faced in real-world data, we hosted a competition at the 2022 Genetic and Evolutionary Computation Conference consisting of different synthetic and real-world datasets which were blind to entrants. For the real-world track, we assessed interpretability in a realistic way by using a domain expert to judge the trustworthiness of candidate models. We present an in-depth analysis of the results obtained in this competition, discuss current challenges of symbolic regression algorithms and highlight possible improvements for future competitions. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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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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A new integrated neurosymbolic approach for crop-yield prediction using environmental data and satellite imagery at field scale

Artificial Intelligence in Geosciences

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Artificial Intelligence in Geosciences 2025年第1期6卷

作者： Meghraoui, Khadija Racharak, Teeradaj Ait El Kadi, Kenza Bensiali, Saloua Sebari, Imane Research Unit of Geospatial Technologies for A Smart Decision. IAV Hassan II Rabat10101 Morocco Informatics Tohoku University Miyagi Japan School of Information Science Japan Advanced Institute of Science and Technology Ishikawa Japan School of Geomatics and Surveying Engineering. IAV Hassan II Rabat10101 Morocco Department of Applied Statistics and Computer Science. IAV Hassan II Rabat10101 Morocco

Crop-yield is a crucial metric in agriculture, essential for effective sector management and improving the overall production process. This indicator is heavily influenced by numerous environmental factors, particularly those related to soil and climate, which present a challenging task due to the complex interactions involved. In this paper, we introduce a novel integrated neurosymbolic framework that combines knowledge-based approaches with sensor data for crop-yield prediction. This framework merges predictions from vectors generated by modeling environmental factors using a newly developed ontology focused on key elements and evaluates this ontology using quantitative methods, specifically representation learning techniques, along with predictions derived from remote sensing imagery. We tested our proposed methodology on a public dataset centered on corn, aiming to predict crop-yield. Our developed smart model achieved promising results in terms of crop-yield prediction, with a root mean squared error (RMSE) of 1.72, outperforming the baseline models. The ontology-based approach achieved an RMSE of 1.73, while the remote sensing-based method yielded an RMSE of 1.77. This confirms the superior performance of our proposed approach over those using single modalities. This integrated neurosymbolic approach demonstrates that the fusion of statistical and symbolic artificial intelligence (AI) represents a significant advancement in agricultural applications. It is particularly effective for crop-yield prediction at the field scale, thus facilitating more informed decision-making in advanced agricultural practices. Additionally, it is acknowledged that results might be further improved by incorporating more detailed ontological knowledge and testing the model with higher-resolution imagery to enhance prediction accuracy. © 2025 The Authors

关键词： Mean square error

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sbi reloaded: a toolkit for simulation-based inference workflows

arXiv

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arXiv 2024年

作者： Boelts, Jan Deistler, Michael Gloeckler, Manuel Tejero-Cantero, Álvaro Lueckmann, Jan-Matthis Moss, Guy Steinbach, Peter Moreau, Thomas Muratore, Fabio Linhart, Julia Durkan, Conor Vetter, Julius Miller, Benjamin Kurt Herold, Maternus Ziaeemehr, Abolfazl Pals, Matthijs Gruner, Theo Bischoff, Sebastian Krouglova, Anastasia N. Gao, Richard Lappalainen, Janne K. Mucsányi, Bálint Pei, Felix Schulz, Auguste Stefanidi, Zinovia Rodrigues, Pedro L.C. Schröder, Cornelius Zaid, Faried Abu Beck, Jonas Kapoor, Jaivardhan Greenberg, David S. Gonçalves, Pedro J. Macke, Jakob H. Machine Learning in Science University of Tübingen Germany Tübingen AI Center Germany TransferLab AppliedAI Institute for Europe Germany ML Colab Cluster ML in Science University of Tübingen Germany Google Research United States Helmholtz-Zentrum Dresden-Rossendorf Germany Université Paris-Saclay INRIA CEA Palaiseau France Robert Bosch GmbH Germany School of Informatics University of Edinburgh United Kingdom University of Amsterdam Netherlands Research and Innovation Center BMW Group Germany Institute for Applied Mathematics and Scientific Computing University of the Bundeswehr Munich Germany Aix Marseille INSERM INS France TU Darmstadt Hessian.AI Germany University Hospital Tübingen M3 Research Center Germany Faculty of Science KU Leuven B-3000 Belgium Imec Belgium Methods of Machine Learning University of Tübingen Germany Neuroscience Institute Carnegie Mellon University United States Université Grenoble Alpes INRIA CNRS Grenoble INP LJK France Hertie Institute for AI in Brain Health University of Tübingen Germany Institute of Coastal Systems - Analysis and Modeling Helmholtz AI Germany Departments of Computer Science Electrical Engineering KU Leuven Belgium Department Empirical Inference Max Planck Institute for Intelligent Systems Tübingen Germany

Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a significant challenge. Simulation-based inference (SBI) addresses this by enabling Bayesian inference for simulators, identifying parameters that match observed data and align with prior knowledge. Unlike traditional Bayesian inference, SBI only needs access to simulations from the model and does not require evaluations of the likelihood-function. In addition, SBI algorithms do not require gradients through the simulator, allow for massive parallelization of simulations, and can perform inference for different observations without further simulations or training, thereby amortizing inference. Over the past years, we have developed, maintained, and extended sbi, a PyTorch-based package4 that implements Bayesian SBI algorithms based on neural networks. The sbi toolkit implements a wide range of inference methods, neural network architectures, sampling methods, and diagnostic tools. In addition, it provides well-tested default settings but also offers flexibility to fully customize every step of the simulation-based inference workflow. Taken together, the sbi toolkit enables scientists and engineers to apply state-of-the-art SBI methods to black-box simulators, opening up new possibilities for aligning simulations with empirically observed data. © 2024, CC BY-SA.

关键词： Neural networks

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Automobile Maintenance Modelling Using gcForest

Automobile Maintenance Modelling Using gcForest

引用

IEEE International Conference on Automation Science and engineering (CASE)

作者： Chong Chen Ying Liu Xianfang Sun Carla Di Cairano-Gilfedder Scott Titmus Institute of Mechanical and Manufacturing Engineering School of Engineering Cardiff University Cardiff UK School of Computer Science and Informatics Cardiff University Queen’s Buildings Cardiff UK Applied Research BT Technology Ipswich UK Rivus Solutions (formerly BT Fleet) Solihull UK

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

Automobile maintenance has gained increasing attention in recent years. If the failure time of an automobile can be predicted, it can bring tangible benefits to automobile fleet management. The Multi-Grained Cascade Forest (gcForest) is a tree-based deep learning algorithm, which was originally developed for image classification, but is potentially a helpful tool in automobile maintenance. This study aims to introduce the gcForest into automobile maintenance based on automobile historical maintenance data and geographical information system (GIS) data. The experimental results reveal that the gcForest shows merits in automobile time-between-failure (TBF) modelling, while it requires less computational cost.

关键词： Automobiles Maintenance engineering Data models Predictive models Forestry Machine learning Prediction algorithms

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The Who in Code-Switching: A Case Study for Predicting Egyptian Arabic–English Code-Switching Levels Based on Character Profiles

International Journal of Asian Language Processing

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International Journal of Asian Language Processing 2022年第1期32卷

作者： Injy Hamed Alia El Bolock Cornelia Herbert Slim Abdennadher Ngoc Thang Vu https://***/0000-0002-9171-9461 Institute for Natural Language Processing University of Stuttgart Stuttgart Germany Department of Applied Emotional and Motivational Psychology Institute of Psychology and Education Faculty of Engineering Computer Science and Psychology Ulm University Ulm Germany Faculty of Informatics & Computer Science German International University Cairo Egypt Computer Science Department The German University in Cairo Cairo Egypt

Code-switching (CS) is a common linguistic phenomenon exhibited by multilingual individuals, where they tend to alternate between languages within one single conversation. CS is a complex phenomenon that not only encompasses linguistic challenges, but also contains a great deal of complexity in terms of its dynamic behavior across speakers. Given that the factors giving rise to CS vary from one country to another, as well as from one person to another, CS is found to be a speaker-dependent behavior, where the frequency by which the foreign language is embedded differs across speakers. While several researchers have looked into predicting CS behavior from a linguistic point of view, research is still lacking in the task of predicting the user CS behavior from sociological and psychological perspectives. We provide an empirical user study, where we investigate the correlations between users’ CS levels and character traits. We conduct interviews with bilinguals and gather information on their profiles, including their demographics, personality traits, and traveling experiences. We then use machine learning (ML) to predict users’ CS levels based on their profiles, where we identify the main influential factors in the modeling process. We experiment with both classification as well as regression tasks. Our results show that the CS behavior is affected by the relation between speakers, travel experiences, as well as Neuroticism and Extraversion personality traits.

关键词： Code-switching Code-mixing Character Computing Personality Arabic–English Code-switching

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On Biased Compression for Distributed Learning

arXiv

引用

arXiv 2020年

作者： Beznosikov, Aleksandr Horváth, Samuel Richtárik, Peter Safaryan, Mher Computer Electrical and Math. Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Skolkovo Institute of Science and Technology Moscow121205 Russia School of Applied Mathematics and Informatics Moscow Institute of Physics and Technology Moscow141701 Russia

In the last few years, various communication compression techniques have emerged as an indispensable tool helping to alleviate the communication bottleneck in distributed learning. However, despite the fact biased compressors often show superior performance in practice when compared to the much more studied and understood unbiased compressors, very little is known about them. In this work we study three classes of biased compression operators, two of which are new, and their performance when applied to (stochastic) gradient descent and distributed (stochastic) gradient descent. We show for the first time that biased compressors can lead to linear convergence rates both in the single node and distributed settings. We prove that distributed compressed SGD method, employed with error feedback mechanism, enjoys the ergodic rate O ( δLexp[−µKδL ] + (CKµ+δD) ) , where δ ≥ 1 is a compression parameter which grows when more compression is applied, L and µ are the smoothness and strong convexity constants, C captures stochastic gradient noise (C = 0 if full gradients are computed on each node) and D captures the variance of the gradients at the optimum (D = 0 for over-parameterized models). Further, via a theoretical study of several synthetic and empirical distributions of communicated gradients, we shed light on why and by how much biased compressors outperform their unbiased variants. Finally, we propose several new biased compressors with promising theoretical guarantees and practical performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Stochastic systems

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Cyber security after COVID 19: A review

引用

AIP Conference Proceedings 2023年第1期2839卷

作者： Muthana Alisawi Layth Hammood Alaan Ghazi Sirwan Saber Abdullah Aras Al-Dawoodi Abbas Hussein Ali Ashraf Nabeel Almallah Nidhal Mohsin Hazzaa Yousif Mohammed Wahab Asmaa Yaseen Nawaf Department of Computer science college of computer science and information technology University of Kirkuk. Kirkuk Iraq Computer science informatics institute Gazi university Çankaya Ankara 06690 Turkey Institute of science faculty of technology computer engineering Gazi university Çankaya Ankara 06690 Turkey Department of Computer Technical Engineering Al-Qalam university College Kirkuk Iraq Faculty of Applied and Human Sciences University Malaysia Perlis Perlis Malaysia Faculty of Electronic Engineering Technology University Malaysia Perlis Perlis Malaysia Information Technology and Communication Department Rafidain Bank Baghdad Iraq Department of Quality Assurance and Academic Performance University of Kirkuk Kirkuk Iraq Department of computer science college of computer and mathematics University of Mosul Mosul Iraq Gazi University - Turkey Çankaya Ankara 06690 Turkey

Nowadays, when talking about cybersecurity, the talk is associated with the most important event in the world during the year 2020, which is COVID-19. Although the digital world exists parallel to the real world, humanity did not have to rely on the Internet to accomplish its tasks. However, the situation changed after the Corona pandemic, as the world found itself in front of one option, which is to work remotely to overcome the impact of the pandemic. Therefore, with this massive and sudden digital transformation on the global level, cyber criminals have many opportunities through cyber vulnerabilities that appear due to the rapid shift to remote work. In this study, we presented an accurate review of the effects that accompanied the occurrence of the Coronavirus pandemic on cybersecurity. Moreover, the world has seen a growing number of cyber-attacks due to these effects, which led to attract the attention to the importance of cyber security to ensure full benefit from digital services without exposure to damage. Also, we indicated in this paper a number of recommendations that contribute to mitigating the damages of cyber-attacks or preventing their occurrence. Furthermore, Cyber security should be a complete approach and not limited to the technical side, where the awareness of the community about cyber risks should contribute to preventing cyber criminals from achieving their goals.

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Disability in space - What ESA should have aimed for

arXiv

引用

arXiv 2021年

作者： Heinicke, Christiane Kaczmarzyk, Marcin Tannert, Benjamin Wasniowski, Aleksander Perycz, Malgorzata Schöning, Johannes Am Fallturm 2 Bremen28359 Germany Rzeszow University of Technology The Faculty of Civil and Environmental Engineering and Architecture Department of Building Engineering Poznańska Street 2 Rzeszow35-084 Poland Hochschule Bremen - City University of Applied Science Applied Media Informatics Flughafenallee 10 Bremen28199 Germany Space Is More ul. Mikulskiego 17 Wroclaw52-420 Poland Institute of Computer Science Polish Academy of Sciences Department of Artificial Intelligence Lab of Computational Biology Jana Kazimierza 5 st. Warsaw01-248 Poland University of Bremen Human-Computer Interaction Bibliothekstraße 5 Bremen28359 Germany

With announcing their "Parastronaut Feasibility Project", the European Space Agency ESA promises to make every reasonable effort to send astronauts with disability to space. This could spark hope in the 15% of all people who live with some form of disability. However, the fine-print of the announcement sets overly tight boundaries to "acceptable" disabilities, allowing only malformations that are expected to have a positive impact on the parastronaut's performance in space. By doing so, ESA is following decade-old traditions in space flight, with some inclusion for the sake of inclusion and public opinion. Instead of accommodating people with disabilities, ESA should send a strong signal for inclusion and truly challenge people with disabilities so they can and will tease out the best of their qualities. © 2021, CC BY.

关键词： Manned space flight

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