477 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Current and Future Challenges in Knowledge Representation and Reasoning

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    Knowledge Representation and Reasoning is a central, longstanding, and active area of Artificial Intelligence. Over the years it has evolved significantly; more recently it has been challenged and complemented by research in areas such as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl Perspectives workshop was held on Knowledge Representation and Reasoning. The goal of the workshop was to describe the state of the art in the field, including its relation with other areas, its shortcomings and strengths, together with recommendations for future progress. We developed this manifesto based on the presentations, panels, working groups, and discussions that took place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge Representation: its origins, goals, milestones, and current foci; its relation to other disciplines, especially to Artificial Intelligence; and on its challenges, along with key priorities for the next decade

    Intégration des méthodes formelles dans le développement des RCSFs

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    In this thesis, we have relied on formal techniques in order to first evaluate WSN protocols and then to propose solutions that meet the requirements of these networks. The thesis contributes to the modelling, analysis, design and evaluation of WSN protocols. In this context, the thesis begins with a survey on WSN and formal verification techniques. Focusing on the MAC layer, the thesis reviews proposed MAC protocols for WSN as well as their design challenges. The dissertation then proceeds to outline the contributions of this work. As a first proposal, we develop a stochastic generic model of the 802.11 MAC protocol for an arbitrary network topology and then perform probabilistic evaluation of the protocol using statistical model checking. Considering an alternative power source to operate WSN, energy harvesting, we move to the second proposal where a protocol designed for EH-WSN is modelled and various performance parameters are evaluated. Finally, the thesis explores mobility in WSN and proposes a new MAC protocol, named "Mobility and Energy Harvesting aware Medium Access Control (MEH-MAC)" protocol for dynamic sensor networks powered by ambient energy. The protocol is modelled and verified under several features

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    Managing healthcare transformation towards P5 medicine (Published in Frontiers in Medicine)

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    Health and social care systems around the world are facing radical organizational, methodological and technological paradigm changes to meet the requirements for improving quality and safety of care as well as efficiency and efficacy of care processes. In this they’re trying to manage the challenges of ongoing demographic changes towards aging, multi-diseased societies, development of human resources, a health and social services consumerism, medical and biomedical progress, and exploding costs for health-related R&D as well as health services delivery. Furthermore, they intend to achieve sustainability of global health systems by transforming them towards intelligent, adaptive and proactive systems focusing on health and wellness with optimized quality and safety outcomes. The outcome is a transformed health and wellness ecosystem combining the approaches of translational medicine, 5P medicine (personalized, preventive, predictive, participative precision medicine) and digital health towards ubiquitous personalized health services realized independent of time and location. It considers individual health status, conditions, genetic and genomic dispositions in personal social, occupational, environmental and behavioural context, thus turning health and social care from reactive to proactive. This requires the advancement communication and cooperation among the business actors from different domains (disciplines) with different methodologies, terminologies/ontologies, education, skills and experiences from data level (data sharing) to concept/knowledge level (knowledge sharing). The challenge here is the understanding and the formal as well as consistent representation of the world of sciences and practices, i.e. of multidisciplinary and dynamic systems in variable context, for enabling mapping between the different disciplines, methodologies, perspectives, intentions, languages, etc. Based on a framework for dynamically, use-case-specifically and context aware representing multi-domain ecosystems including their development process, systems, models and artefacts can be consistently represented, harmonized and integrated. The response to that problem is the formal representation of health and social care ecosystems through an system-oriented, architecture-centric, ontology-based and policy-driven model and framework, addressing all domains and development process views contributing to the system and context in question. Accordingly, this Research Topic would like to address this change towards 5P medicine. Specifically, areas of interest include, but are not limited: • A multidisciplinary approach to the transformation of health and social systems • Success factors for sustainable P5 ecosystems • AI and robotics in transformed health ecosystems • Transformed health ecosystems challenges for security, privacy and trust • Modelling digital health systems • Ethical challenges of personalized digital health • Knowledge representation and management of transformed health ecosystems Table of Contents: 04 Editorial: Managing healthcare transformation towards P5 medicine Bernd Blobel and Dipak Kalra 06 Transformation of Health and Social Care Systems—An Interdisciplinary Approach Toward a Foundational Architecture Bernd Blobel, Frank Oemig, Pekka Ruotsalainen and Diego M. Lopez 26 Transformed Health Ecosystems—Challenges for Security, Privacy, and Trust Pekka Ruotsalainen and Bernd Blobel 36 Success Factors for Scaling Up the Adoption of Digital Therapeutics Towards the Realization of P5 Medicine Alexandra Prodan, Lucas Deimel, Johannes Ahlqvist, Strahil Birov, Rainer Thiel, Meeri Toivanen, Zoi Kolitsi and Dipak Kalra 49 EU-Funded Telemedicine Projects – Assessment of, and Lessons Learned From, in the Light of the SARS-CoV-2 Pandemic Laura Paleari, Virginia Malini, Gabriella Paoli, Stefano Scillieri, Claudia Bighin, Bernd Blobel and Mauro Giacomini 60 A Review of Artificial Intelligence and Robotics in Transformed Health Ecosystems Kerstin Denecke and Claude R. Baudoin 73 Modeling digital health systems to foster interoperability Frank Oemig and Bernd Blobel 89 Challenges and solutions for transforming health ecosystems in low- and middle-income countries through artificial intelligence Diego M. López, Carolina Rico-Olarte, Bernd Blobel and Carol Hullin 111 Linguistic and ontological challenges of multiple domains contributing to transformed health ecosystems Markus Kreuzthaler, Mathias Brochhausen, Cilia Zayas, Bernd Blobel and Stefan Schulz 126 The ethical challenges of personalized digital health Els Maeckelberghe, Kinga Zdunek, Sara Marceglia, Bobbie Farsides and Michael Rigb

    Integration of heterogeneous data sources and automated reasoning in healthcare and domotic IoT systems

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    In recent years, IoT technology has radically transformed many crucial industrial and service sectors such as healthcare. The multi-facets heterogeneity of the devices and the collected information provides important opportunities to develop innovative systems and services. However, the ubiquitous presence of data silos and the poor semantic interoperability in the IoT landscape constitute a significant obstacle in the pursuit of this goal. Moreover, achieving actionable knowledge from the collected data requires IoT information sources to be analysed using appropriate artificial intelligence techniques such as automated reasoning. In this thesis work, Semantic Web technologies have been investigated as an approach to address both the data integration and reasoning aspect in modern IoT systems. In particular, the contributions presented in this thesis are the following: (1) the IoT Fitness Ontology, an OWL ontology that has been developed in order to overcome the issue of data silos and enable semantic interoperability in the IoT fitness domain; (2) a Linked Open Data web portal for collecting and sharing IoT health datasets with the research community; (3) a novel methodology for embedding knowledge in rule-defined IoT smart home scenarios; and (4) a knowledge-based IoT home automation system that supports a seamless integration of heterogeneous devices and data sources

    Compositional synthesis of reactive systems

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    Synthesis is the task of automatically deriving correct-by-construction implementations from formal specifications. While it is a promising path toward developing verified programs, it is infamous for being hard to solve. Compositionality is recognized as a key technique for reducing the complexity of synthesis. So far, compositional approaches require extensive manual effort. In this thesis, we introduce algorithms that automate these steps. In the first part, we develop compositional synthesis techniques for distributed systems. Providing assumptions on other processes' behavior is fundamental in this setting due to inter-process dependencies. We establish delay-dominance, a new requirement for implementations that allows for implicitly assuming that other processes will not maliciously violate the shared goal. Furthermore, we present an algorithm that computes explicit assumptions on process behavior to address more complex dependencies. In the second part, we transfer the concept of compositionality from distributed to single-process systems. We present a preprocessing technique for synthesis that identifies independently synthesizable system components. We extend this approach to an incremental synthesis algorithm, resulting in more fine-grained decompositions. Our experimental evaluation shows that our techniques automate the required manual efforts, resulting in fully automated compositional synthesis algorithms for both distributed and single-process systems.Synthese ist die Aufgabe korrekte Implementierungen aus formalen Spezifikation abzuleiten. Sie ist zwar ein vielversprechender Weg für die Entwicklung verifizierter Programme, aber auch dafür bekannt schwer zu lösen zu sein. Kompositionalität gilt als eine Schlüsseltechnik zur Verringerung der Komplexität der Synthese. Bislang erfordern kompositionale Ansätze einen hohen manuellen Aufwand. In dieser Dissertation stellen wir Algorithmen vor, die diese Schritte automatisieren. Im ersten Teil entwickeln wir kompositionale Synthesetechniken für verteilte Systeme. Aufgrund der Abhängigkeiten zwischen den Prozessen ist es in diesem Kontext von grundlegender Bedeutung, Annahmen über das Verhalten der anderen Prozesse zu treffen. Wir etablieren Delay-Dominance, eine neue Anforderung für Implementierungen, die es ermöglicht, implizit anzunehmen, dass andere Prozesse das gemeinsame Ziel nicht böswillig verletzen. Darüber hinaus stellen wir einen Algorithmus vor, der explizite Annahmen über das Verhalten anderer Prozesse ableitet, um komplexere Abhängigkeiten zu berücksichtigen. Im zweiten Teil übertragen wir das Konzept der Kompositionalität von verteilten auf Einzelprozesssysteme. Wir präsentieren eine Vorverarbeitungmethode für die Synthese, die unabhängig synthetisierbare Systemkomponenten identifiziert. Wir erweitern diesen Ansatz zu einem inkrementellen Synthesealgorithmus, der zu feineren Dekompositionen führt. Unsere experimentelle Auswertung zeigt, dass unsere Techniken den erforderlichen manuellen Aufwand automatisieren und so zu vollautomatischen Algorithmen für die kompositionale Synthese sowohl für verteilte als auch für Einzelprozesssysteme führen
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