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arXiv 2019年

作者： Blumreiter, Mathias Greenyer, Joel Garcia, Francisco Javier Chiyah Klös, Verena Schwammberger, Maike Sommer, Christoph Vogelsang, Andreas Wortmannz, Andreas Institute for Software Systems Hamburg University of Technology Software Engineering Group Leibniz Universität Hannover Germany Heriot-Watt University Software and Embedded Systems Engineering Technische Universität Berlin Germany Department of Computing Science University of Oldenburg Heinz Nixdorf Institute Dept. of Computer Science Paderborn University Automated Systems Engineering Technologies Technische Universität Berlin Germany Software Engineering RWTH Aachen University

With the increasing complexity of Cyber-Physical systems. their behavior and decisions become increasingly difficult to understand and comprehend for users and other stakeholders. Our vision is to build self-explainable systems.that can, at run-time, answer questions about the system's past, current, and future behavior. As hitherto no design methodology or reference framework exists for building such systems. we propose the Monitor, Analyze, Build, Explain (MAB-EX) framework for building self-explainable systems.that leverage requirements- A nd explainability models at run-time. The basic idea of MAB-EX is to first Monitor and Analyze a certain behavior of a system, then Build an explanation from explanation models and convey this EXplanation in a suitable way to a stakeholder. We also take into account that new explanations can be learned, by updating the explanation models, should new and yet unexplainable behavior be detected by the system. Copyright © 2019, The Authors. All rights reserved.

关键词： Embedded systems

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Error correction for information retrieval of czech documents 10

Error correction for information retrieval of czech document...

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10th International Conference on Agents and Artificial Intelligence, ICAART 2018

作者： Martínek, Jiří Král, Pavel Dept. of Computer Science and Engineering Faculty of Applied Sciences University of West Bohemia Plzeň Czech Republic NTIS - New Technologies for the Information Society Faculty of Applied Sciences University of West Bohemia Plzeň Czech Republic

ISBN: (纸本)9789897582752

This paper proposes a novel system for information retrieval over a set of scanned documents in the Czech language. The documents are in the form of raster images and thus they are first converted into the text form by optical character recognition (OCR). Then OCR errors are corrected and the corrected texts are indexed and stored into a fulltext database. The database provides a possibility of searching over these documents. This paper describes all components of the above mentioned system with a particular focus on the proposed OCR correction method. We experimentally show that the proposed approach is efficient, because it corrects a significant number of errors. We also create a small Czech corpus to evaluate OCR error correction methods which represent another contribution of this paper. Copyright © 2018 by SCITEPRESS – science and Technology Publications, Lda. All rights reserved

关键词： Error correction

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Overcoming the disconnect between energy system and climate modelling

arXiv

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arXiv 2022年

作者： Craig, Michael T. Wohland, Jan Stoop, Laurens P. Kies, Alexander Pickering, Bryn Bloomfield, Hannah C. Browell, Jethro De Felice, Matteo Dent, Chris J. Deroubaix, Adrien Frischmuth, Felix Gonzalez, Paula L.M. Grochowicz, Aleksander Gruber, Katharina Härtel, Philipp Kittel, Martin Kotzur, Leander Labuhn, Inga Lundquist, Julie K. Pflugradt, Noah van der Wiel, Karin Zeyringer, Marianne Brayshaw, David J. School for Environment and Sustainability University of Michigan United States Institute for Environmental Decisions ETH Zurich Switzerland Helmholtz-Zentrum Hereon Germany Information and Computing Science Copernicus Institute of Sustainable Development Utrecht University Netherlands TenneT TSO B.V. Arnhem Netherlands Royal Netherlands Meteorological Institute Netherlands Frankfurt Institute for Advanced Studies Germany Department of Electrical and Computer Engineering Aarhus University Denmark ETH Zürich Switzerland School of Geographical Sciences University of Bristol United Kingdom Department of Meteorology University of Reading United Kingdom University of Glasgow United Kingdom European Commission Joint Research Centre Netherlands School of Mathematics University of Edinburg United Kingdom The Alan Turing Institute United Kingdom Max Planck Institute Germany Fraunhofer Institute for Energy Economics and Energy System Technology Germany Department of Mathematics University of Oslo Norway University of Natural Resources and Life Sciences Austria German Institute for Economic Research Germany Institute of Energy and Climate Research Techno-economic Systems Analysis Germany University of Bremen Institute of Geography Germany Dept. of Atmospheric and Oceanic Science University of Colorado Boulder United States National Renewable Energy Laboratory United States Renewable and Sustainable Energy Institute United States Forschungszentrum Jülich Germany Department of Technology Systems University of Oslo Norway

Energy system models underpin decisions by energy system planners and operators. Energy system modelling faces a transformation: accounting for changing meteorological conditions imposed by climate change. To enable that transformation, a community of practice in energy-climate modelling has started to form that aims to better integrate energy system models with weather and climate models. Here, we evaluate the disconnects between the energy system and climate modelling communities, then lay out a research agenda to bridge those disconnects. In the near-term, we propose interdisciplinary activities for expediting uptake of future climate data in energy system modelling. In the long-term, we propose a transdisciplinary approach to enable development of (1) energy-system-tailored climate datasets for historical and future meteorological conditions and (2) energy system models that can effectively leverage those datasets. This agenda increases the odds of meeting ambitious climate mitigation goals by systematically capturing and mitigating climate risk in energy sector decision making. © 2022, CC BY-NC-ND.

关键词： Decision making

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Domain-Adversarial Graph Neural Networks for Text Classification

Domain-Adversarial Graph Neural Networks for Text Classifica...

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IEEE International Conference on Data Mining (ICDM)

作者： Man Wu Shirui Pan Xingquan Zhu Chuan Zhou Lei Pan Dept. of Computer & Electrical Engineering and Computer Science Florida Atlantic University Boca Raton USA Faculty of Information Technology Monash University Melbourne Australia Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Information Technology Deakin University Geelong VIC Australia

Text classification, in cross-domain setting, is a challenging task. On the one hand, data from other domains are often useful to improve the learning on the target domain; on the other hand, domain variance and hierarchical structure of documents from words, key phrases, sentences, paragraphs, etc. make it difficult to align domains for effective learning. To date, existing cross-domain text classification methods mainly strive to minimize feature distribution differences between domains, and they typically suffer from three major limitations - (1) difficult to capture semantics in non-consecutive phrases and long-distance word dependency because of treating texts as word sequences, (2) neglect of hierarchical coarse-grained structures of document for feature learning, and (3) narrow focus of the domains at instance levels, without using domains as supervisions to improve text classification. This paper proposes an end-to-end, domain-adversarial graph neural networks (DAGNN), for cross-domain text classification. Our motivation is to model documents as graphs and use a domain-adversarial training principle to lean features from each graph (as well as learning the separation of domains) for effective text classification. At the instance level, DAGNN uses a graph to model each document, so that it can capture non-consecutive and long-distance semantics. At the feature level, DAGNN uses graphs from different domains to jointly train hierarchical graph neural networks in order to learn good features. At the learning level, DAGNN proposes a domain-adversarial principle such that the learned features not only optimally classify documents but also separates domains. Experiments on benchmark datasets demonstrate the effectiveness of our method in cross-domain classification tasks.

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Alert system for timely medication administration

Alert system for timely medication administration

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2018 International Conference on Artificial Intelligence, ICAI 2018 at 2018 World Congress in computer science, computer engineering and Applied Computing, CSCE 2018

作者： Puente, C. Sobrino, A. Villa-Monte, A. Olivas, J.A. Advanced Technical Faculty of Engineering ICAI Pontificia Comillas University Madrid Spain Faculty of Philosophy University of Santiago de Compostela La Coruna Spain Institute of Research in Computer Science LIDI Faculty of Computer Science National University of La Plata Buenos Aires Argentina Information Technologies and Systems Dept. University of Castilla-La Mancha Ciudad Real Spain

ISBN: (纸本)1601324804

Time has an extraordinary relevance in medicine. The diagnoses of diseases or prescription of medicines frequently presents temporary references. The etiology of a disease frequently depends on when such a symptom appeared and the prescription of some drugs is severely constrained to certain temporary annotations. Every year, the health of the patients suffers due to the bad administration of medicines and an appreciable amount of money is lost by that bad practice. In this paper we want to contribute to mend this problem. In that vein, we approach a release software for controlling the medicine doses administration with an intuitive traffic-light code in order to help the patient to consume the drugs according to the temporary restrictions indicated by the doctor. That software involves three steps. The first one encodes into a database the guidelines of the IHS BCMA for timely medication administration. The second part is in charge to locate the timely restrictions associated to a given illness. By last, the software display to the user the time alarms associated to aprescribed treatment. CSREA Press ©.

关键词： Artificial intelligence

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Streaming Process Discovery for Lambda Architecture-based Process Monitoring Platform

Streaming Process Discovery for Lambda Architecture-based Pr...

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International Scientific and Technical Conference on computer science and information technologies

作者： Anatoliy Batyuk Volodymyr Voityshyn Dept. of Automated Control Systems Institute of Computer Science and Information Technologies Lviv Polytechnic National University Lviv Ukraine

Having precise information about how business processes are performed in real-life is an important competitive advantage for a modern company. The digital footprint left by processes in IT systems.can be transformed to event data for further analysis by process mining techniques. One of the biggest challenge of process mining is to deal with streaming event data and provide operational support for the on-going processes. Current paper is devoted to a streaming process discovery algorithm implemented by the authors upon a near real-time process monitoring platform which is based on the lambda architecture.

关键词： Process mining Streaming process discovery Lambda architecture Heuristic Miner Event data Event logs XES Digital footprint

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Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge

arXiv

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arXiv 2022年

作者： Ali, Sharib Ghatwary, Noha Jha, Debesh Isik-Polat, Ece Polat, Gorkem Yang, Chen Li, Wuyang Galdran, Adrian Ballester, Miguel-Ángel González Thambawita, Vajira Hicks, Steven Poudel, Sahadev Lee, Sang-Woong Jin, Ziyi Gan, Tianyuan Yu, ChengHui Yan, JiangPeng Yeo, Doyeob Lee, Hyunseok Tomar, Nikhil Kumar Haithmi, Mahmood Ahmed, Amr Riegler, Michael A. Daul, Christian Halvorsen, Pål Rittscher, Jens Salem, Osama E. Lamarque, Dominique Cannizzaro, Renato Realdon, Stefano de Lange, Thomas East, James E. Institute of Biomedical Engineering Department of Engineering Science University of Oxford OxfordOX3 7DQ United Kingdom Oxford National Institute for Health Research Biomedical Research Centre OxfordOX4 2PG United Kingdom Big Data Institute University of Oxford Li Ka Shing Centre for Health Information and Discovery OxfordOX3 7LF United Kingdom Translational Gastroenterology Unit Nuffield Department of Medicine Experimental Medicine Div. John Radcliffe Hospital University of Oxford OxfordOX3 9DU United Kingdom Computer Engineering Department Arab Academy for Science and Technology Alexandria1029 Egypt SimulaMet Oslo0167 Norway Department of Computer Science UiT The Arctic University of Norway Hansine Hansens veg 18 Tromsø9019 Norway Medical Department Sahlgrenska University Hospital-Mölndal Blå stråket 5 Göteborg413 45 Sweden Veneto Institute of Oncology IOV-IRCCS Via Gattamelata 64 Padua35128 Italy CRO Centro Riferimento Oncologico IRCCS Aviano Italy Via Franco Gallini 2 PN Aviano33081 Italy Université de Versailles St-Quentin en Yvelines Hôpital Ambroise Paré 9 Av. Charles de Gaulle Boulogne-Billancourt92100 France Faculty of Medicine University of Alexandria Alexandria21131 Egypt Graduate School of Informatics Middle East Technical University Ankara06800 Turkey City University of Hong Kong Kowloon Hong Kong BCN MedTech Dept. of Information and Communication Technologies Universitat Pompeu Fabra Barcelona08018 Spain Department of IT Convergence Engineering Gachon University Seongnam13120 Korea Republic of College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou310027 China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China Department of Automation Tsinghua University Beijing100084 China Smart Sensing & Diagnosis Research Division Korea Atomic Energy Research Institute 34057 Korea Republic of Daegu-Gyeongbuk Medical Innovation Foundation Medical Device Deve

Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, location, and surface largely affect identification, localisation, and characterisation. Moreover, colonoscopic surveillance and removal of polyps (referred to as polypectomy) are highly operator-dependent procedures. There exist a high missed detection rate and incomplete removal of colonic polyps due to their variable nature, the difficulties to delineate the abnormality, the high recurrence rates, and the anatomical topography of the colon. There have been several developments in realising automated methods for both detection and segmentation of these polyps using machine learning. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets that come from different centres, modalities and acquisition systems. To test this hypothesis rigorously we curated a multi-centre and multi-population dataset acquired from multiple colonoscopy systems.and challenged teams comprising machine learning experts to develop robust automated detection and segmentation methods as part of our crowd-sourcing Endoscopic computer vision challenge (EndoCV) 2021. In this paper, we analyse the detection results of the four top (among seven) teams and the segmentation results of the five top teams (among 16). Our analyses demonstrate that the top-ranking teams concentrated on accuracy (i.e., accuracy > 80% on overall Dice score on different validation sets) over real-time performance required for clinical applicability. We further dissect the methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets. © 2022, CC BY-NC-SA.

关键词： Endoscopy

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Hybrid Training Data for Historical Text OCR

Hybrid Training Data for Historical Text OCR

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International Conference on Document Analysis and Recognition

作者： Jiří Martínek Ladislav Lenc Pavel Král Anguelos Nicolaou Vincent Christlein University of West Bohemia Plzen Czech Republic Dept. of Computer Science & Engineering University of West Bohemia Plzeň Czech Republic NTIS - New Technologies for the Information Society University of West Bohemia Plzeň Czech Republic Pattern Recognition Lab Friedrich-Alexander University Erlangen-Nürnberg

Current optical character recognition (OCR) systems.commonly make use of recurrent neural networks (RNN) that process whole text lines. Such systems.avoid the task of character segmentation necessary for character-based approaches. A disadvantage of this approach is a need of a large amount of annotated data. This can be solved by sing generated synthetic data instead of costly manually annotated ones. Unfortunately, such data is often not suitable for historical documents particularly for quality reasons. This work presents a hybrid approach for generating annotated data for OCR at a low cost. We first collect a small dataset of isolated characters from historical document images. Then, we generate historical looking text lines from the generated characters. Another contribution lies in the design and implementation of an OCR system based on a convolutional-LSTM network. We first pre-train this system on hybrid data. Afterwards, the network is fine-tuned with real printed text lines. We demonstrate that this training strategy is efficient for obtaining state-of-the-art results. We also show that the score of the proposed system is comparable or even better in comparison to several state-of-the-art systems.

关键词： Optical character recognition software Training Recurrent neural networks Tools Hybrid power systems Text recognition Image segmentation

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Metrics Reloaded: Recommendations for image analysis validation

arXiv

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arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

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A de-centralized framework for data sharing, ontology matching and distributed analytics 1

A de-centralized framework for data sharing, ontology matchi...

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1st International Workshop on Semantic Web technologies for Health Data Management, SWH 2018

作者： Pezoulas, Vasileios C. Kourou, Konstantina D. Exarchos, Themis P. An-dronikou, Vassiliki Varvarigou, Theodora Tzioufas, Athanasios G. De Vita, Salvatore Fotiadis, Dimitrios I. Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering University of Ioannina IoanninaGR 45110 Greece Dept. of Informatics Ionian University CorfuGR 49100 Greece Division of Communication Electronic and Information Engineering School of Electrical and Computer Engineering National Technical University of Athens AthensGR 15780 Greece Dept. of Pathophysiology Faculty of Medicine National and Kapodistrian University of Athens AthensGR 15772 Greece Clinic of Rheumatology Dept. of Medical and Biological Sciences Udine University UdineIT 33100 Italy

In this paper we present the fundamental concept, as well as, the objectives and the architecture of HarmonicSS EU project. The proposed architecture envisages to address (i) the lack of patient stratification in large patient cohorts, (ii) the dataset heterogeneity across these cohorts, and (iii) the incomplete understanding of disease pathogenesis. In order to do so, ontology matching mechanisms are recruited for harmonizing the clinical data, distributed data analytics services have been designed for the de-centralized analysis of patient data, and different health policies can be assessed, all of them with respect to the data protection guidelines posed by the upcoming General Data Protection Regulation (GDPR). The HarmonicSS architecture has been employed towards the development of a data sharing, harmonization and distributed analytics framework for the recruitment and analysis of heterogeneous regional, national and international longitudinal cohorts of patients with primary Sjögren’s syndrome (pSS). The outcomes can be used by researchers, clinicians, patients, and health policy makers. © 2018 CEUR-WS. All rights reserved.

关键词： Large dataset

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