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17th International Workshop on Ontology Matching, OM 2022

作者： Pour, Mina Abd Nikooie Algergawy, Alsayed Buche, Patrice Castro, Leyla J. Chen, Jiaoyan Dong, Hang Fallatah, Omaima Faria, Daniel Fundulaki, Irini Hertling, Sven He, Yuan Horrocks, Ian Huschka, Martin Ibanescu, Liliana Jiménez-Ruiz, Ernesto Karam, Naouel Laadhar, Amir Lambrix, Patrick Li, Huanyu Li, Ying Michel, Franck Nasr, Engy Paulheim, Heiko Pesquita, Catia Saveta, Tzanina Shvaiko, Pavel Trojahn, Cassia Verhey, Chantelle Wu, Mingfang Yaman, Beyza Zamazal, Ondrej Zhou, Lu Linköping University Swedish E-Science Research Center Linköping Sweden Heinz Nixdorf Chair for Distributed Information Systems Friedrich Schiller University Jena Germany UMR IATE INRAE University of Montpellier France ZB MED Information Centre for Life Sciences Germany Department of Computer Science The University of Manchester United Kingdom Department of Computer Science University of Oxford United Kingdom Information School The University of Sheffield Sheffield United Kingdom University of Lisbon Portugal Institute of Computer Science-FORTH Heraklion Greece Data and Web Science Group University of Mannheim Germany Fraunhofer Institute for High-Speed Dynamics Ernst-Mach-Institut EMI Germany Université Paris-Saclay INRAE AgroParisTech UMR MIA Paris-Saclay France City University of London United Kingdom Fraunhofer FOKUS Institute for Applied Informatics University of Leipzig Germany University of Stuttgart Germany University of Gävle Sweden University Côte d'Azur CNRS Inria France Albert Ludwig University of Freiburg Germany LASIGE Faculdade de Ciências Universidade de Lisboa Portugal Trentino Digitale SpA Trento Italy Institut de Recherche en Informatique de Toulouse France World Data System International Technology Office United States Australian Research Data Commons Australia ADAPT Centre Trinity College Dublin Ireland Prague University of Economics and Business Czech Republic TigerGraph Inc. United States SIRIUS University of Oslo Norway

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities. The OAEI 2022 campaign offered 14 tracks and was attended by 18 participants. This paper is an overall presentation of that campaign. © 2022 Copyright for this paper by its authors.

关键词： Ontology

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GENEVIZ: A Visual Tool for the Construction and Blockchain-Based Validation of SFC Packages 16th

GENEVIZ: A Visual Tool for the Construction and Blockchain-B...

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16th International Conference on the Economics of Grids, Clouds, systems, and Services, GECON 2019

作者： Franco, Muriel F. Bucher, Martin J. J. Scheid, Eder J. Granville, Lisandro Z. Stiller, Burkhard Communication Systems Group CSG Department of Informatics IfI University of Zürich UZH Binzmühlestrasse 14 Zürich8050 Switzerland Computer Networks Group Institute of Informatics INF Federal University of Rio Grande do Sul UFRGS Av. Bento Gonçalves Porto Alegre9500 Brazil

ISBN: (纸本)9783030360269

Network Functions Virtualization (NFV) decouples the network package performed by network functions from dedicated hardware appliance by running Virtual Network Functions (VNF) on commercial off-the-shelf hardware. Network operators can create customized network services by chaining multiple VNFs, defining a so-called Service Function Chaining (SFC). Because NFV became technically mature recently, the building of such SFCs still needs in-depth knowledge about NFV technology and its descriptors. Furthermore, there is a lack of tools that help to simplify the creation of SFCs. This paper, introduces GENEVIZ, a tool that provides a user-friendly interface for the creation of new SFCs as well as for importing and adjusting acquired SFCs (e.g., from marketplaces of VNFs), in order to create new SFCs based on existing ones. Therefore, this work addresses as well data integrity and provides the functionality to store and validate SFCs through the use of blockchains. Three case studies are presented to provide evidence of the technical feasibility of the solution proposed. © Springer Nature Switzerland AG 2019.

关键词： Network function virtualization

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Applying the FAIR Principles to computational workflows

arXiv

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arXiv 2024年

作者： Wilkinson, Sean R. Aloqalaa, Meznah Belhajjame, Khalid Crusoe, Michael R. de Paula Kinoshita, Bruno Gadelha, Luiz Garijo, Daniel Ragnar Gustafsson, Ove Johan Juty, Nick Kanwal, Sehrish Khan, Farah Zaib Köster, Johannes Gehlen, Karsten Petersvon Pouchard, Line Rannow, Randy K. Soiland-Reyes, Stian Soranzo, Nicola Sufi, Shoaib Sun, Ziheng Vilne, Baiba Wouters, Merridee A. Yuen, Denis Goble, Carole Oak Ridge Leadership Computing Facility Oak Ridge National Laboratory Oak RidgeTN United States Department of Computer Science University of Manchester Manchester United Kingdom LAMSADE PSL Paris Dauphine University Paris France Berlin Germany Earth Sciences Barcelona Supercomputing Center Barcelona Spain Heidelberg Germany Ontology Engineering Group Universidad Politécnica de Madrid Madrid Spain Australian BioCommons University of Melbourne MelbourneVIC Australia ParkvilleVIC Australia University Medicine Essen University of Duisburg-Essen Essen Germany Data Management Department Deutsches Klimarechenzentrum GmbH Hamburg Germany Center for Computing Research Sandia National Laboratories AlbuquerqueNM United States Silverdraft Supercomputing BoiseID United States Informatics Institute University of Amsterdam Amsterdam Netherlands Earlham Institute Norwich United Kingdom Center for Spatial Information Science and Systems Department of Geography and Geoinformation Science George Mason University FairfaxVA United States Bioinformatics Group Riga Stradins University Riga Latvia School of Clinical Medicine University of New South Wales KensingtonNSW Australia Ontario Institute for Cancer Research TorontoON Canada

Recent trends within computational and data sciences show an increasing recognition and adoption of computational workflows as tools for productivity and reproducibility that also democratize access to platforms and processing know-how. As digital objects to be shared, discovered, and reused, computational workflows benefit from the FAIR principles, which stand for Findable, Accessible, Interoperable, and Reusable. The Workflows Community Initiative’s FAIR Workflows Working group (WCI-FW), a global and open community of researchers and developers working with computational workflows across disciplines and domains, has systematically addressed the application of both FAIR data and software principles to computational workflows. We present recommendations with commentary that reflects our discussions and justifies our choices and adaptations. These are offered to workflow users and authors, workflow management system developers, and providers of workflow services as guidelines for adoption and fodder for discussion. The FAIR recommendations for workflows that we propose in this paper will maximize their value as research assets and facilitate their adoption by the wider community. © 2024, CC BY.

关键词： Application programs

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Packaging research artefacts with RO-Crate

arXiv

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arXiv 2021年

作者： Soiland-Reyes, Stian Sefton, Peter Crosas, Mercè Castro, Leyla Jael Coppens, Frederik Fernández, José M. Garijo, Daniel Grüning, Björn Rosa, Marco La Leo, Simone Carragáin, Eoghan Ó. Portier, Marc Trisovic, Ana Groth, Paul Goble, Carole Department of Computer Science University of Manchester United Kingdom Informatics Institute University of Amsterdam Netherlands Faculty of Science University Technology Sydney Australia Institute for Quantitative Social Science Harvard University CambridgeMA United States ZB MED Information Centre for Life Sciences Cologne Germany VIB-UGent Center for Plant Systems Biology Gent Belgium Barcelona Supercomputing Center Barcelona Spain Ontology Engineering Group Universidad Politécnica de Madrid Madrid Spain Bioinformatics Group Department of Computer Science Albert-Ludwigs-University Freiburg Freiburg Germany PARADISEC Melbourne Australia Italy University College Cork Ireland Vlaams Instituut voor de Zee Oostende Belgium Institute for Quantitative Social Science Harvard University CambridgeMA United States Informatics Institute University of Amsterdam Netherlands Department of Computer Science University of Manchester United Kingdom

An increasing number of researchers support reproducibility by including pointers to and descriptions of datasets, software and methods in their publications. However, scientific articles may be ambiguous, incomplete and difficult to process by automated systems. In this paper we introduce RO-Crate, an open, community-driven, and lightweight approach to packaging research artefacts along with their metadata in a machine readable manner. RO-Crate is based on *** annotations in JSON-LD, aiming to establish best practices to formally describe metadata in an accessible and practical way for their use in a wide variety of situations. An RO-Crate is a structured archive of all the items that contributed to a research outcome, including their identifiers, provenance, relations and annotations. As a general purpose packaging approach for data and their metadata, RO-Crate is used across multiple areas, including bioinformatics, digital humanities and regulatory sciences. By applying"just enough" Linked Data standards, RO-Crate simplifies the process of making research outputs FAIR while also enhancing research reproducibility. © 2021, CC BY.

关键词： Metadata

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Learning Graph Edit Distance by Graph Neural Networks

arXiv

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arXiv 2020年

作者： Riba, Pau Fischer, Andreas Lladós, Josep Fornés, Alicia Computer Vision Center Universitat Autònoma de Barcelona Spain Department of Informatics DIVA Group University of Fribourg Fribourg Switzerland Institute of Complex Systems University of Applied Sciences and Arts Western Switzerland Fribourg Switzerland

The emergence of geometric deep learning as a novel framework to deal with graph-based representations has faded away traditional approaches in favor of completely new methodologies. In this paper, we propose a new framework able to combine the advances on deep metric learning with traditional approximations of the graph edit distance. Hence, we propose an efficient graph distance based on the novel field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure, and thus, leveraging this information for its use on a distance computation. The performance of the proposed graph distance is validated on two different scenarios. On the one hand, in a graph retrieval of handwritten words i.e. keyword spotting, showing its superior performance when compared with (approximate) graph edit distance benchmarks. On the other hand, demonstrating competitive results for graph similarity learning when compared with the current state-of-the-art on a recent benchmark dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph neural networks

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AI recognition of patient race in medical imaging: a modelling study

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The Lancet Digital Health 2022年第6期4卷 e406-e414页

作者： Gichoya, Judy Wawira Banerjee, Imon Bhimireddy, Ananth Reddy Burns, John L Celi, Leo Anthony Chen, Li-Ching Correa, Ramon Dullerud, Natalie Ghassemi, Marzyeh Huang, Shih-Cheng Kuo, Po-Chih Lungren, Matthew P Palmer, Lyle J Price, Brandon J Purkayastha, Saptarshi Pyrros, Ayis T Oakden-Rayner, Lauren Okechukwu, Chima Seyyed-Kalantari, Laleh Trivedi, Hari Wang, Ryan Zaiman, Zachary Zhang, Haoran Department of Radiology Emory University Atlanta GA United States Department of Computer Science Emory University Atlanta GA United States School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ United States School of Informatics and Computing Indiana University–Purdue University Indianapolis IN United States Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA United States Department of Medicine Beth Israel Deaconess Medical Center Boston MA United States Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Computer Science University of Toronto Toronto ON Canada Stanford University School of Medicine Palo Alto CA United States Australian Institute for Machine Learning University of Adelaide Adelaide SA Australia School of Public Health University of Adelaide Adelaide SA Australia Florida State University College of Medicine Tallahassee FL United States Dupage Medical Group Hinsdale IL United States Department of Computer Science Georgia Institute of Technology Atlanta GA United States Lunenfeld-Tanenbaum Research Institute Sinai Health Toronto ON Canada Vector Institute for Artificial Intelligence Toronto ON Canada

Background: Previous studies in medical imaging have shown disparate abilities of artificial intelligence (AI) to detect a person's race, yet there is no known correlation for race on medical imaging that would be obvious to human experts when interpreting the images. We aimed to conduct a comprehensive evaluation of the ability of AI to recognise a patient's racial identity from medical images. Methods: Using private (Emory CXR, Emory Chest CT, Emory Cervical Spine, and Emory Mammogram) and public (MIMIC-CXR, CheXpert, National Lung Cancer Screening Trial, RSNA Pulmonary Embolism CT, and Digital Hand Atlas) datasets, we evaluated, first, performance quantification of deep learning models in detecting race from medical images, including the ability of these models to generalise to external environments and across multiple imaging modalities. Second, we assessed possible confounding of anatomic and phenotypic population features by assessing the ability of these hypothesised confounders to detect race in isolation using regression models, and by re-evaluating the deep learning models by testing them on datasets stratified by these hypothesised confounding variables. Last, by exploring the effect of image corruptions on model performance, we investigated the underlying mechanism by which AI models can recognise race. Findings: In our study, we show that standard AI deep learning models can be trained to predict race from medical images with high performance across multiple imaging modalities, which was sustained under external validation conditions (x-ray imaging [area under the receiver operating characteristics curve (AUC) range 0·91–0·99], CT chest imaging [0·87–0·96], and mammography [0·81]). We also showed that this detection is not due to proxies or imaging-related surrogate covariates for race (eg, performance of possible confounders: body-mass index [AUC 0·55], disease distribution [0·61], and breast density [0·61]). Finally, we provide evidence to show that t

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Case Study on Design and Evaluation of a Multi-Soft-Core Processor

Case Study on Design and Evaluation of a Multi-Soft-Core Pro...

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International Conference and Workshop on Computing and Communication (IEMCON)

作者： Michael Kirchhoff Lothar Wagner Bernd Däne Detlef Streitferdt Wolfgang Fengler Group for Computer Architecture and Embedded Systems Technische Universität Ilmenau Ilmenau Germany Group for Software Systems and Process Informatics Technische Universität Ilmenau Ilmenau Germany

ISBN: (纸本)9781728125312

Modern FPGAs (Field Programmable Gate Arrays) are becoming increasingly important when it comes to embedded system development. Within these FPGAs, soft-core processors are often used to solve a wide range of different tasks. Soft-core processors are a cost-effective and time-efficient way to realize embedded systems. The trend for soft-core processors, as well as mainstream CPUs (central processing units), leads to multi-core architectures. Both the necessary memory architectures and the compilers play an important role in this process. In this paper, a novel method that aims at minimizing the necessary memory resources on the FPGA while maximizing the processing speed of any given algorithm is described. In the first step, an application-specializable multi-soft-core processor architecture is presented that is capable of solving problems while adhering to hard real-time deadlines. Its special architecture and other necessary features are discussed. Furthermore, a method for the generation of optimized machine code for each processor core as well as hard real-time compatible deadlock handling mechanisms are presented. Selected algorithms are implemented to demonstrate the functionality and efficiency of the realized approach for different configurations of the multi-soft-core processor architecture.

关键词： Microprocessors Multicore processing Memory architecture Multiprocessor interconnection Field programmable gate arrays Field programmable gate arrays Memory architecture macroprocessor Parallel processor interconnection PROCESSOR soft-core Processor Core embedded system development Processor architectures Mechanical Code central processing units hard real-time

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Dynamic Resilient Network Games with Applications to Multi-Agent Consensus

arXiv

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arXiv 2020年

作者： Nugraha, Yurid Cetinkaya, Ahmet Hayakawa, Tomohisa Ishii, Hideaki Zhu, Quanyan Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo152-8552 Japan Information Systems Architecture Science Research Division National Institute of Informatics Tokyo101-8430 Japan Department of Computer Science Tokyo Insitute of Technology Yokohama226-8502 Japan Department of Electrical and Computer Engineering New York University BrooklynNY11201 United States

A cyber security problem in a networked system formulated as a resilient graph problem based on a game-theoretic approach is considered. The connectivity of the underlying graph of the network system is reduced by an attacker who removes some of the edges whereas the defender attempts to recover them. Both players are subject to energy constraints so that their actions are restricted and cannot be performed continuously. For this two-stage game, which is played repeatedly over time, we characterize the optimal strategies for the attacker and the defender in terms of edge connectivity and the number of connected components of the graph. The resilient graph game is then applied to a multi-agent consensus problem. We study how the attacks and the recovery on the edges affect the consensus process. Finally, we also provide numerical simulation to illustrate the results. Copyright © 2020, The Authors. All rights reserved.

关键词： Multi agent systems

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Generation and validation of frame conditions in formal models 1

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6th International Conference on Model-Driven Engineering and Software Development, MODELSWARD 2018

作者： Niemann, Philipp Przigoda, Nils Wille, Robert Drechsler, Rolf Group for Computer Architecture University of Bremen Bremen Germany Cyber-Physical Systems DFKI GmbH Bremen Germany Siemens Mobility GmbH Braunschweig Germany Institute for Integrated Circuits Johannes Kepler University Linz Linz Austria

ISBN: (数字)9783030110307

ISBN: (纸本)9783030110291

Operation contracts are a popular description means in behavioral system modeling. Pre- and postconditions are used to describe the effects on model elements (such as attributes, links, etc.) that are enforced by an operation. However, it is usually not clearly stated what model elements may be affected by an operation call and what shall remain unchanged—although this information is essential in order to obtain a comprehensive description. A promising solution to this so-called frame problem is to define additional frame conditions. However, properly defining frame conditions which complete the model description in the intended way can be a non-trivial, tedious, and error-prone task. While in general there are several tools and methods for obtaining formal model descriptions and also a broad variety of approaches for the validation and verification of the generated models, corresponding methods for frame conditions have not received significant attention so far. In this work, we provide a comprehensive overview of recently proposed approaches that close this gap and support the designer in generating and validating frame conditions. © 2019, Springer Nature Switzerland AG.

关键词： Artificial intelligence

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One pathogen does not an epidemic make: A review of interacting contagions, diseases, beliefs, and stories

arXiv

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arXiv 2025年

作者： Hébert-Dufresne, Laurent Ahn, Yong-Yeol Allard, Antoine Crothers, Jessica W. Dodds, Peter Sheridan Galesic, Mirta Ghanbarnejad, Fakhteh Gravel, Dominique Hammond, Ross A. Lerman, Kristina Lovato, Juniper Openshaw, John J. Redner, S. Scarpino, Samuel V. St-Onge, Guillaume Tangherlini, Timothy R. Young, Jean-Gabriel Vermont Complex Systems Institute University of Vermont BurlingtonVT United States Translational Global Infectious Disease Research Center University of Vermont BurlingtonVT United States Department of Computer Science University of Vermont BurlingtonVT United States Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Département de physique de génie physique et d’optique Université Laval QuébecQC Canada Department of Pathology and Laboratory Medicine Robert Larner M.D. College of Medicine University of Vermont BurlingtonVT United States Santa Fe Institute Santa FeNM United States Complexity Science Hub Vienna Austria School of Technology and Architecture SRH University of Applied Sciences Heidelberg Leipzig Germany Département de biologie Université de Sherbrooke SherbrookeQC Canada Brown School Washington University in St. Louis St. LouisMO United States The Brookings Institution WashingtonDC United States Information Sciences Institute University of Southern California United States Division of Infectious Diseases and Geographic Medicine Stanford University StanfordCA United States Institute for Experiential AI Northeastern University BostonMA United States Laboratory for the Modeling of Biological and Socio-technical Systems Northeastern University BostonMA United States The Roux Institute Northeastern University PortlandME United States Department of Scandinavian School of Information University of California Berkeley BerkeleyCA United States Department of Mathematics & Statistics University of Vermont BurlingtonVT United States

From pathogens and computer viruses to genes and memes, contagion models have found widespread utility across the natural and social sciences. Despite their success and breadth of adoption, the approach and structure of these models remain surprisingly siloed by field. Given the siloed nature of their development and widespread use, one persistent assumption is that a given contagion can be studied in isolation, independently from what else might be spreading in the population. In reality, countless contagions of biological and social nature interact within hosts (interacting with existing beliefs, or the immune system) and across hosts (interacting in the environment, or affecting transmission mechanisms). Additionally, from a modeling perspective, we know that relaxing these assumptions has profound effects on the physics and translational implications of the models. Here, we review mechanisms for interactions in social and biological contagions, as well as the models and frameworks developed to include these interactions in the study of the contagions. We highlight existing problems related to the inference of interactions and to the scalability of mathematical models and identify promising avenues of future inquiries. In doing so, we highlight the need for interdisciplinary efforts under a unified science of contagions and for removing a common dichotomy between social and biological contagions. Copyright © 2025, The Authors. All rights reserved.

关键词： Histology

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