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arXiv 2022年

作者： Rüdisser, H.T. Windisch, A. Amerstorfer, U.V. Möstl, C. Amerstorfer, T. Bailey, R.L. Reiss, M.A. Know-Center GmbH Inffeldgasse 13 Graz8010 Austria Institute of Physics University of Graz Universitätsplatz 5 Graz8010 Austria Institute of Interactive Systems and Data Science Graz University of Technology Inffeldgasse 13 Graz8010 Austria Department of Physics Washington University St. LouisMO63130 United States RL Community AI AUSTRIA Wollzeile 24/12 Vienna1010 Austria Space Research Institute Austrian Academy of Sciences Schmiedlstraße 6 Graz8042 Austria Zentralanstalt für Meteorologie und Geodynamik Hohe Warte 38 Vienna1190 Austria

Interplanetary coronal mass ejections (ICMEs) are one of the main drivers for space weather disturbances. In the past, different approaches have been used to automatically detect events in existing time series resulting from solar wind in situ observations. However, accurate and fast detection still remains a challenge when facing the large amount of data from different instruments. For the automatic detection of ICMEs we propose a pipeline using a method that has recently proven successful in medical image segmentation. Comparing it to an existing method, we find that while achieving similar results, our model outperforms the baseline regarding training time by a factor of approximately 20, thus making it more applicable for other datasets. The method has been tested on in situ data from the Wind spacecraft between 1997 and 2015 with a True Skill Statistic (TSS) of 0.64. Out of the 640 ICMEs, 466 were detected correctly by our algorithm, producing a total of 254 False Positives. Additionally, it produced reasonable results on datasets with fewer features and smaller training sets from Wind, STEREO-A and STEREO-B with True Skill Statistics of 0.56, 0.57 and 0.53, respectively. Our pipeline manages to find the start of an ICME with a mean absolute error (MAE) of around 2 hours and 56 minutes, and the end time with a MAE of 3 hours and 20 minutes. The relatively fast training allows straightforward tuning of hyperparameters and could therefore easily be used to detect other structures and phenomena in solar wind data, such as corotating interaction regions. © 2022, CC BY.

关键词： Image segmentation

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Evaluation of Data Augmentation and Loss Functions in Semantic Image Segmentation for Drilling Tool Wear Detection

arXiv

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arXiv 2023年

作者： Schlager, Elke Windisch, Andreas Hanna, Lukas Klünsner, Thomas Hagendorfer, Elias Jan Teppernegg, Tamara Know-Center GmbH Sandgasse 36 Graz8010 Austria Graz University of Technology Institute of Interactive Systems and Data Science Sandgasse 36 Graz8010 Austria Washington University in St. Louis Physics Department One Brookings Drive St. LouisMO63130 United States Reinforcement Learning Community AI Austria Wollzeile 24/12 Vienna1010 Austria Materials Center Leoben Forschung GmbH Roseggerstrasse 12 Leoben8700 Austria CERATIZIT Austria GmbH Metallwerk-Plansee-Straße 71 Breitenwang6600 Austria

Tool wear monitoring is crucial for quality control and cost reduction in manufacturing processes, of which drilling applications are one example. In this paper, we present a U-Net based semantic image segmentation pipeline, deployed on microscopy images of cutting inserts, for the purpose of wear detection. The wear area is differentiated in two different types, resulting in a multiclass classification problem. Joining the two wear types in one general wear class, on the other hand, allows the problem to be formulated as a binary classification task. Apart from the comparison of the binary and multiclass problem, also different loss functions, i. e., Cross Entropy, Focal Cross Entropy, and a loss based on the Intersection over Union (IoU), are investigated. Furthermore, models are trained on image tiles of different sizes, and augmentation techniques of varying intensities are deployed. We find, that the best performing models are binary models, trained on data with moderate augmentation and an IoU-based loss function. Copyright © 2023, The Authors. All rights reserved.

关键词： Wear of materials

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The PLATO mission

引用

Experimental Astronomy 2025年第3期59卷 1-111页

作者： Rauer, Heike Aerts, Conny Cabrera, Juan Deleuil, Magali Erikson, Anders Gizon, Laurent Goupil, Mariejo Heras, Ana Walloschek, Thomas Lorenzo-Alvarez, Jose Marliani, Filippo Martin-Garcia, César Mas-Hesse, J. Miguel O’Rourke, Laurence Osborn, Hugh Pagano, Isabella Piotto, Giampaolo Pollacco, Don Ragazzoni, Roberto Ramsay, Gavin Udry, Stéphane Appourchaux, Thierry Benz, Willy Brandeker, Alexis Güdel, Manuel Janot-Pacheco, Eduardo Kabath, Petr Kjeldsen, Hans Min, Michiel Santos, Nuno Smith, Alexis Suarez, Juan-Carlos Werner, Stephanie C. Aboudan, Alessio Abreu, Manuel Acuña, Lorena Adams, Moritz Adibekyan, Vardan Affer, Laura Agneray, François Agnor, Craig Aguirre Børsen-Koch, Victor Ahmed, Saad Aigrain, Suzanne Al-Bahlawan, Ashraf Alcacera Gil, Ma de los Angeles Alei, Eleonora Alencar, Silvia Alexander, Richard Alfonso-Garzón, Julia Alibert, Yann Allende Prieto, Carlos Almeida, Leonardo Alonso Sobrino, Roi Altavilla, Giuseppe Althaus, Christian Alvarez Trujillo, Luis Alonso Amarsi, Anish Ammler-von Eiff, Matthias Amôres, Eduardo Andrade, Laerte Antoniadis-Karnavas, Alexandros António, Carlos Aparicio del Moral, Beatriz Appolloni, Matteo Arena, Claudio Armstrong, David Aroca Aliaga, Jose Asplund, Martin Audenaert, Jeroen Auricchio, Natalia Avelino, Pedro Baeke, Ann Baillié, Kevin Balado, Ana Ballber Balagueró, Pau Balestra, Andrea Ball, Warrick Ballans, Herve Ballot, Jerome Barban, Caroline Barbary, Gaële Barbieri, Mauro Barceló Forteza, Sebastià Barker, Adrian Barklem, Paul Barnes, Sydney Barrado Navascues, David Barragan, Oscar Baruteau, Clément Basu, Sarbani Baudin, Frederic Baumeister, Philipp Bayliss, Daniel Bazot, Michael Beck, Paul G. Belkacem, Kevin Bellinger, Earl Benatti, Serena Benomar, Othman Bérard, Diane Bergemann, Maria Bergomi, Maria Bernardo, Pierre Biazzo, Katia Bignamini, Andrea Bigot, Lionel Billot, Nicolas Binet, Martin Biondi, David Biondi, Federico Birch, Aaron C. Bitsch, Bertram Bluhm Ceballos, Paz Victoria Bódi, Attila Bognár, Zsófia Boisse, Isabelle Bolmont, Emeline Bonanno, Alfio Bonavita, Institut für Planetenforschung Deutsches Zentrum für Luft- und Raumfahrt DLR Rutherfordstrasse 2 Berlin 12489 Germany Fachbereich Geowissenschaften Institut für Geologische Freie Universität Berlin Malteserstr. 74-100 Berlin 12249 Germany Institute of Astronomy KU Leuven Leuven Belgium CNRS CNES LAM Aix Marseille University Marseille France Max Planck Institute for Solar System Research Göttingen Germany LESIA Paris Observatory PSL University Sorbonne University Paris Cité University CNRS Meudon France ESTEC European Space Agency Noordwijk Netherlands ESAC European Space Agency Madrid Spain Centro de Astrobiología CSIC-INTA Campus ESAC Madrid Spain Center for Space and Habitability University of Bern Bern Switzerland INAF - Osservatorio Astrofisico di Catania Catania Italy Department of Physics and Astronomy University of Padua Padua Italy Department of Physics University of Warwick Coventry United Kingdom Astronomical Observatory of Padova INAF - Italian National Institute for Astrophysics Padua Italy Armagh Observatory and Planetarium College Hill Armagh United Kingdom Geneve Observatory University of Geneve Geneve Switzerland IAS University of Paris-Saclay Orsay France AlbaNove University Centre Stockholm University Stockholm Sweden Department of Astrophysics University of Vienna Vienna Austria Department of Astronomy University of Sao Paulo Sao Paulo Brazil Astronomical Institute of the Czech Academy of Sciences Ondřejov Czech Republic Department of Physics and Astronomy Aarhus University Aarhus Denmark Institute for Space Research SRON Leiden Netherlands Instituto de Astrofísica e Ciências do Espaço CAUP Universidade do Porto Porto Portugal Departamento de Física e Astronomia Faculdade da Ci ncias Universidade do Porto Porto Portugal Mullard Space Science Laboratory University College London Holmbury Saint Mary United Kingdom Dept. Theoretical Physics and the Cosmos University of Granada Granada Spain Centre for Plan

PLATO (PLAnetary Transits and Oscillations of stars) is ESA’s M3 mission designed to detect and characterise extrasolar planets and perform asteroseismic monitoring of a large number of stars. PLATO will detect small planets (down to <2REarth) around bright stars (<11 mag), including terrestrial planets in the habitable zone of solar-like stars. With the complement of radial velocity observations from the ground, planets will be characterised for their radius, mass, and age with high accuracy (5%, 10%, 10% for an Earth-Sun combination respectively). PLATO will provide us with a large-scale catalogue of well-characterised small planets up to intermediate orbital periods, relevant for a meaningful comparison to planet formation theories and to better understand planet evolution. It will make possible comparative exoplanetology to place our Solar System planets in a broader context. In parallel, PLATO will study (host) stars using asteroseismology, allowing us to determine the stellar properties with high accuracy, substantially enhancing our knowledge of stellar structure and evolution. The payload instrument consists of 26 cameras with 12cm aperture each. For at least four years, the mission will perform high-precision photometric measurements. Here we review the science objectives, present PLATO‘s target samples and fields, provide an overview of expected core science performance as well as a description of the instrument and the mission profile towards the end of the serial production of the flight cameras. PLATO is scheduled for a launch date end 2026. This overview therefore provides a summary of the mission to the community in preparation of the upcoming operational phases. © The Author(s) 2025.

关键词： Asteroseismology Exoplanets PLATO mission

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Classification of job offers of the World Wide Web

Classification of job offers of the World Wide Web

引用

2018 International Conference on Computing, Mathematics and Engineering Technologies, iCoMET 2018

作者： Ul Haq Dar, Ehtesham Dorn, Jurgen Institute of Software Technology and Interactive System E-Commerce Group Vienna University of Technology Vienna Austria

ISBN: (纸本)9781538613702

To automate the retrieval of online job opportunities of a specific domain, text classification is an only viable method. In this paper, we investigated eight classifiers and defined a new preprocessing method for job offers classification, and study their accuracies and generalization behaviors. Different job offers websites are used to collect data. We organized data into six different dataset groups, by applying different existing and a new preprocessing method. Classifiers are regularized to avoid high variance, and their accuracies and generalization errors are evaluated. All the classifiers showed more than 0.9 accuracy, but their generalization errors varied. Ridge Regression and Stochastic Gradient Decent generalized well on new data - for all the dataset groups. On the contrary Random Forest and Perceptron tenacious toward high variance. Remaining classifiers exhibited both behaviors for different dataset groups. We found two classifiers that generalized well to new data. Our finding highlight the potential use of classifiers to automate the job offers retrieval from the World Wide Web. © 2018 IEEE.

关键词： Classification (of information)

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Ontology based classification system for online job offers

Ontology based classification system for online job offers

引用

2018 International Conference on Computing, Mathematics and Engineering Technologies, iCoMET 2018

作者： Ul Haq Dar, Ehtesham Dorn, Jurgen Institute of Software Technology and Interactive System E-Commerce Group Vienna University of Technology Vienna Austria

ISBN: (纸本)9781538613702

The significance of employment in a setup of society is quite evident. Publishing jobs online opened the research opportunities to study different methods for the automation of job offers retrieval - from the internet. We need a classification mechanism, from machine learning or some other discipline, to automate job offers retrieval. In this paper, we devised an ontology-based classifier for job offers classification. More than 5000 job offers were collected from different job offers websites. The ontology-based classification works in three stages, concepts are extracted from job offers text description as feature vectors, the minimum threshold for the classification is calculated, and a classification model is developed. Rather using machine learning algorithms, we used an ontology for classification. We evaluated this classifier according to machine learning evaluation model, training, and test dataset. Our classifier showed more than 0.9 accuracy, precision, and recall for both training and test dataset. Our finding paves the way to automate the job offers classification and retrieval. © 2018 IEEE.

关键词： Ontology

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Vehicle management in a modular production context using Deep Q-Learning

arXiv

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arXiv 2022年

作者： Pouget, Lucain Hasenbichler, Timo Auer, Jakob Lichtenegger, Klaus Windisch, Andreas Skalar Systems GmbH Dr.-Robert-Graf-Straße 9 Graz8010 Austria FH JOANNEUM University of Applied Sciences Data Science and Artificial Intelligence Eckertstraße 30i Graz8020 Austria Know-Center GmbH Inffeldgasse 13 Graz8010 Austria Institute of Interactive Systems and Data Science Graz University of Technology Inffeldgasse 13 Graz8010 Austria Physics Department Washington University in St. Louis One Brookings Drive St. LouisMO63130 United States RL Community AI Austria Wollzeile 24/12 Vienna1010 Austria

We investigate the feasibility of deploying Deep-Q based deep reinforcement learning agents to job-shop scheduling problems in the context of modular production facilities, using discrete event simulations for the environment. These environments are comprised of a source and sink for the parts to be processed, as well as (several) workstations. The agents are trained to schedule automated guided vehicles to transport the parts back and forth between those stations in an optimal fashion. Starting from a very simplistic setup, we increase the complexity of the environment and compare the agents' performances with well established heuristic approaches, such as first-in-first-out based agents, cost tables and a nearest-neighbor approach. We furthermore seek particular configurations of the environments in which the heuristic approaches struggle, to investigate to what degree the Deep-Q agents are affected by these challenges. We find that Deep-Q based agents show comparable performance as the heuristic baselines. Furthermore, our findings suggest that the DRL agents exhibit an increased robustness to noise, as compared to the conventional approaches. Overall, we find that DRL agents constitute a valuable approach for this type of scheduling problems. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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A Brief Review of Explainable Artificial Intelligence in Healthcare

SSRN

引用

SSRN 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalo, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Swinburne University of Technology HawthornVIC3122 Australia Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, The Authors. All rights reserved.

关键词： Decision making

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Agent-based simulations for protecting nursing homes with prevention and vaccination strategies

arXiv

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arXiv 2020年

作者： Lasser, Jana Zuber, Johannes Sorger, Johannes Dervic, Elma Ledebur, Katharina Lindner, Simon David Klager, Elisabeth Kletečka-Pulker, Maria Willschke, Harald Stangl, Katrin Stadtmann, Sarah Haslinger, Christian Klimek, Peter Wochele-Thoma, Thomas Graz University of Technology Institute for Interactive Systems and Data Science Inffeldgasse 16C Graz8010 Austria Complexity Science Hub Vienna Josefstädterstraße 39 Vienna1080 Austria Medical University Vienna Section for Science of Complex Systems Center for Medical Statistics Informatics and Intelligent Systems Spitalgasse 23 Vienna1090 Austria Research Institute of Molecular Pathology Campus-Vienna-Biocenter 1 Vienna1030 Austria Ludwig Boltzmann Institute for Digital Health and Patient Safety Spitalgasse 23 BT86 Vienna1090 Austria University Vienna Institut für Ethik und Recht in der Medizin Spitalgasse 2-4 Vienna1090 Austria Caritas Erzdiözese Wien Albrechtskreithgasse 19-21 Vienna1160 Austria Hygienefachkraft-unlimited Oswaldgasse 14-22/2/11 Vienna1120 Austria

Due to its high lethality amongst the elderly, the safety of nursing homes has been of central importance during the COVID-19 pandemic. With test procedures becoming available at scale, such as antigen or RT-LAMP tests, and increasing availability of vaccinations, nursing homes might be able to safely relax prohibitory measures while controlling the spread of infections (meaning an average of one or less secondary infections per index case). Here, we develop a detailed agent-based epidemiological model for the spread of SARS-CoV-2 in nursing homes to identify optimal prevention strategies. The model is microscopically calibrated to high-resolution data from nursing homes in Austria, including detailed social contact networks and information on past outbreaks. We find that the effectiveness of mitigation testing depends critically on the timespan between test and test result, the detection threshold of the viral load for the test to give a positive result, and the screening frequencies of residents and employees. Under realistic conditions and in absence of an effective vaccine, we find that preventive screening of employees only might be sufficient to control outbreaks in nursing homes, provided that turnover times and detection thresholds of the tests are low enough. If vaccines that are moderately effective against infection and transmission are available, control is achieved if 80% or more of the inhabitants are vaccinated, even if no preventive testing is in place and residents are allowed to have visitors. Since these results strongly depend on vaccine efficacy against infection, retention of testing infrastructures, regular voluntary screening and sequencing of virus genomes is advised to enable early identification of new variants of concern. © 2020, CC BY-NC-SA.

关键词： Hospitals

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Execution-based model profiling 1

引用

6th IFIP WG 2.6 International Symposium on Data-DrivenProcess Discovery and Analysis, SIMPDA 2016

作者： Mazak, Alexandra Wimmer, Manuel Patsuk-Bösch, Polina Institute of Software Technology and Interactive Systems TU Wien Favoritenstrasse 9-11 Vienna1040 Austria

ISBN: (数字)9783319741611

ISBN: (纸本)9783319741604

In model-driven engineering (MDE), models are mostly used in prescriptive ways for system engineering. While prescriptive models are indeed an important ingredient to realize a system, for later phases in the systems’ lifecycles additional model types are beneficial to use. Unfortunately, current MDE approaches mostly neglect the information upstream in terms of descriptive models from operations to (re)design phases. To tackle this limitation, we propose execution-based model profiling as a continuous process to improve prescriptive models at design-time through runtime information. This approach incorporates knowledge in terms of model profiles from execution logs of the running system. To accomplish this, we combine techniques of process mining with runtime models of MDE. In the course of a case study, we make use of a traffic light system example to demonstrate the feasibility and benefits of the introduced execution-based model profiling approach. © IFIP International Federation for Information Processing 2018 Published by Springer International Publishing AG 2018. All Rights Reserved.

关键词： Life cycle

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Navigating the challenges in creating complex data systems: a development philosophy

arXiv

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arXiv 2022年

作者： Dittmer, Sören Roberts, Michael Gilbey, Julian Biguri, Ander Preller, Jacobus Rudd, James H.F. Aston, John A.D. Schönlieb, Carola-Bibiane Selby, Ian Breger, Anna Thorpe, Matthew Weir-McCall, Jonathan R. Gkrania-Klotsas, Effrossyni Korhonen, Anna Jefferson, Emily Langs, Georg Yang, Guang Prosch, Helmut Stanczuk, Jan Tang, Jing Babar, Judith Sánchez, Lorena Escudero Teare, Philip Patel, Mishal Wassin, Marcel Holzer, Markus Walton, Nicholas Lió, Pietro Shadbahr, Tolou Sala, Evis Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Addenbrooke’s Hospital Cambridge University Hospitals NHS Trust Cambridge United Kingdom Department of Medicine University of Cambridge Cambridge United Kingdom Department of Pure Mathematics and Mathematical Statistics University of Cambridge Cambridge United Kingdom ZeTeM University of Bremen Bremen Germany Department of Radiology University of Cambridge Cambridge United Kingdom Faculty of Mathematics University of Vienna Austria Department of Mathematics University of Manchester Manchester United Kingdom Royal Papworth Hospital Cambridge Royal Papworth Hospital NHS Foundation Trust Cambridge United Kingdom Language Technology Laboratory University of Cambridge Cambridge United Kingdom Population Health and Genomics School of Medicine University of Dundee Dundee United Kingdom Department of Biomedical Imaging and Image-guided Therapy Computational Imaging Research Lab Medical University of Vienna Vienna Austria National Heart and Lung Institute Imperial College London London United Kingdom Research Program in Systems Oncology Faculty of Medicine University of Helsinki Helsinki Finland Data Science & Artificial Intelligence AstraZeneca Cambridge United Kingdom Clinical Pharmacology & Safety Sciences AstraZeneca Cambridge United Kingdom Contextflow GmbH Vienna Austria Institute of Astronomy University of Cambridge Cambridge United Kingdom Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom

In this perspective, we argue that despite the democratization of powerful tools for data science and machine learning over the last decade, developing the code for a trustworthy and effective data science system (DSS) is getting harder. Perverse incentives and a lack of widespread software engineering (SE) skills are among many root causes we identify that naturally give rise to the current systemic crisis in reproducibility of DSSs. We analyze why SE and building large complex systems is, in general, hard. Based on these insights, we identify how SE addresses those difficulties and how we can apply and generalize SE methods to construct DSSs that are fit for purpose. We advocate two key development philosophies, namely that one should incrementally grow – not biphasically plan and build – DSSs, and one should always employ two types of feedback loops during development: one which tests the code’s correctness and another that evaluates the code’s efficacy. Copyright © 2022, The Authors. All rights reserved.

关键词： software engineering

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