4,538 research outputs found

    Dataflow Programming and Acceleration of Computationally-Intensive Algorithms

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    The volume of unstructured textual information continues to grow due to recent technological advancements. This resulted in an exponential growth of information generated in various formats, including blogs, posts, social networking, and enterprise documents. Numerous Enterprise Architecture (EA) documents are also created daily, such as reports, contracts, agreements, frameworks, architecture requirements, designs, and operational guides. The processing and computation of this massive amount of unstructured information necessitate substantial computing capabilities and the implementation of new techniques. It is critical to manage this unstructured information through a centralized knowledge management platform. Knowledge management is the process of managing information within an organization. This involves creating, collecting, organizing, and storing information in a way that makes it easily accessible and usable. The research involved the development textual knowledge management system, and two use cases were considered for extracting textual knowledge from documents. The first case study focused on the safety-critical documents of a railway enterprise. Safety is of paramount importance in the railway industry. There are several EA documents including manuals, operational procedures, and technical guidelines that contain critical information. Digitalization of these documents is essential for analysing vast amounts of textual knowledge that exist in these documents to improve the safety and security of railway operations. A case study was conducted between the University of Huddersfield and the Railway Safety Standard Board (RSSB) to analyse EA safety documents using Natural language processing (NLP). A graphical user interface was developed that includes various document processing features such as semantic search, document mapping, text summarization, and visualization of key trends. For the second case study, open-source data was utilized, and textual knowledge was extracted. Several features were also developed, including kernel distribution, analysis offkey trends, and sentiment analysis of words (such as unique, positive, and negative) within the documents. Additionally, a heterogeneous framework was designed using CPU/GPU and FPGAs to analyse the computational performance of document mapping

    AI Lifecycle Zero-Touch Orchestration within the Edge-to-Cloud Continuum for Industry 5.0

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    The advancements in human-centered artificial intelligence (HCAI) systems for Industry 5.0 is a new phase of industrialization that places the worker at the center of the production process and uses new technologies to increase prosperity beyond jobs and growth. HCAI presents new objectives that were unreachable by either humans or machines alone, but this also comes with a new set of challenges. Our proposed method accomplishes this through the knowlEdge architecture, which enables human operators to implement AI solutions using a zero-touch framework. It relies on containerized AI model training and execution, supported by a robust data pipeline and rounded off with human feedback and evaluation interfaces. The result is a platform built from a number of components, spanning all major areas of the AI lifecycle. We outline both the architectural concepts and implementation guidelines and explain how they advance HCAI systems and Industry 5.0. In this article, we address the problems we encountered while implementing the ideas within the edge-to-cloud continuum. Further improvements to our approach may enhance the use of AI in Industry 5.0 and strengthen trust in AI systems

    Software Design Change Artifacts Generation through Software Architectural Change Detection and Categorisation

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    Software is solely designed, implemented, tested, and inspected by expert people, unlike other engineering projects where they are mostly implemented by workers (non-experts) after designing by engineers. Researchers and practitioners have linked software bugs, security holes, problematic integration of changes, complex-to-understand codebase, unwarranted mental pressure, and so on in software development and maintenance to inconsistent and complex design and a lack of ways to easily understand what is going on and what to plan in a software system. The unavailability of proper information and insights needed by the development teams to make good decisions makes these challenges worse. Therefore, software design documents and other insightful information extraction are essential to reduce the above mentioned anomalies. Moreover, architectural design artifacts extraction is required to create the developer’s profile to be available to the market for many crucial scenarios. To that end, architectural change detection, categorization, and change description generation are crucial because they are the primary artifacts to trace other software artifacts. However, it is not feasible for humans to analyze all the changes for a single release for detecting change and impact because it is time-consuming, laborious, costly, and inconsistent. In this thesis, we conduct six studies considering the mentioned challenges to automate the architectural change information extraction and document generation that could potentially assist the development and maintenance teams. In particular, (1) we detect architectural changes using lightweight techniques leveraging textual and codebase properties, (2) categorize them considering intelligent perspectives, and (3) generate design change documents by exploiting precise contexts of components’ relations and change purposes which were previously unexplored. Our experiment using 4000+ architectural change samples and 200+ design change documents suggests that our proposed approaches are promising in accuracy and scalability to deploy frequently. Our proposed change detection approach can detect up to 100% of the architectural change instances (and is very scalable). On the other hand, our proposed change classifier’s F1 score is 70%, which is promising given the challenges. Finally, our proposed system can produce descriptive design change artifacts with 75% significance. Since most of our studies are foundational, our approaches and prepared datasets can be used as baselines for advancing research in design change information extraction and documentation

    Rethink Digital Health Innovation: Understanding Socio-Technical Interoperability as Guiding Concept

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    Diese Dissertation sucht nach einem theoretischem GrundgerĂŒst, um komplexe, digitale Gesundheitsinnovationen so zu entwickeln, dass sie bessere Erfolgsaussichten haben, auch in der alltĂ€glichen Versorgungspraxis anzukommen. Denn obwohl es weder am Bedarf von noch an Ideen fĂŒr digitale Gesundheitsinnovationen mangelt, bleibt die Flut an erfolgreich in der Praxis etablierten Lösungen leider aus. Dieser unzureichende Diffusionserfolg einer entwickelten Lösung - gern auch als Pilotitis pathologisiert - offenbart sich insbesondere dann, wenn die geplante Innovation mit grĂ¶ĂŸeren Ambitionen und KomplexitĂ€t verbunden ist. Dem geĂŒbten Kritiker werden sofort ketzerische Gegenfragen in den Sinn kommen. Beispielsweise was denn unter komplexen, digitalen Gesundheitsinnovationen verstanden werden soll und ob es ĂŒberhaupt möglich ist, eine universale Lösungsformel zu finden, die eine erfolgreiche Diffusion digitaler Gesundheitsinnovationen garantieren kann. Beide Fragen sind nicht nur berechtigt, sondern mĂŒnden letztlich auch in zwei ForschungsstrĂ€nge, welchen ich mich in dieser Dissertation explizit widme. In einem ersten Block erarbeite ich eine Abgrenzung jener digitalen Gesundheitsinnovationen, welche derzeit in Literatur und Praxis besondere Aufmerksamkeit aufgrund ihres hohen Potentials zur Versorgungsverbesserung und ihrer resultierenden KomplexitĂ€t gewidmet ist. Genauer gesagt untersuche ich dominante Zielstellungen und welche Herausforderung mit ihnen einhergehen. Innerhalb der Arbeiten in diesem Forschungsstrang kristallisieren sich vier Zielstellungen heraus: 1. die UnterstĂŒtzung kontinuierlicher, gemeinschaftlicher Versorgungsprozesse ĂŒber diverse Leistungserbringer (auch als inter-organisationale Versorgungspfade bekannt); 2. die aktive Einbeziehung der Patient:innen in ihre Versorgungsprozesse (auch als Patient Empowerment oder Patient Engagement bekannt); 3. die StĂ€rkung der sektoren-ĂŒbergreifenden Zusammenarbeit zwischen Wissenschaft und Versorgungpraxis bis hin zu lernenden Gesundheitssystemen und 4. die Etablierung daten-zentrierter Wertschöpfung fĂŒr das Gesundheitswesen aufgrund steigender bzgl. VerfĂŒgbarkeit valider Daten, neuen Verarbeitungsmethoden (Stichwort KĂŒnstliche Intelligenz) sowie den zahlreichen Nutzungsmöglichkeiten. Im Fokus dieser Dissertation stehen daher weniger die autarken, klar abgrenzbaren Innovationen (bspw. eine Symptomtagebuch-App zur Beschwerdedokumentation). Vielmehr adressiert diese Doktorarbeit jene Innovationsvorhaben, welche eine oder mehrere der o.g. Zielstellung verfolgen, ein weiteres technologisches Puzzleteil in komplexe Informationssystemlandschaften hinzufĂŒgen und somit im Zusammenspiel mit diversen weiteren IT-Systemen zur Verbesserung der Gesundheitsversorgung und/ oder ihrer Organisation beitragen. In der Auseinandersetzung mit diesen Zielstellungen und verbundenen Herausforderungen der Systementwicklung rĂŒckte das Problem fragmentierter IT-Systemlandschaften des Gesundheitswesens in den Mittelpunkt. Darunter wird der unerfreuliche Zustand verstanden, dass unterschiedliche Informations- und Anwendungssysteme nicht wie gewĂŒnscht miteinander interagieren können. So kommt es zu Unterbrechungen von InformationsflĂŒssen und Versorgungsprozessen, welche anderweitig durch fehleranfĂ€llige ZusatzaufwĂ€nde (bspw. Doppeldokumentation) aufgefangen werden mĂŒssen. Um diesen EinschrĂ€nkungen der EffektivitĂ€t und Effizienz zu begegnen, mĂŒssen eben jene IT-System-Silos abgebaut werden. Alle o.g. Zielstellungen ordnen sich dieser defragmentierenden Wirkung unter, in dem sie 1. verschiedene Leistungserbringer, 2. Versorgungsteams und Patient:innen, 3. Wissenschaft und Versorgung oder 4. diverse Datenquellen und moderne Auswertungstechnologien zusammenfĂŒhren wollen. Doch nun kommt es zu einem komplexen Ringschluss. Einerseits suchen die in dieser Arbeit thematisierten digitalen Gesundheitsinnovationen Wege zur Defragmentierung der Informationssystemlandschaften. Andererseits ist ihre eingeschrĂ€nkte Erfolgsquote u.a. in eben jener bestehenden Fragmentierung begrĂŒndet, die sie aufzulösen suchen. Mit diesem Erkenntnisgewinn eröffnet sich der zweite Forschungsstrang dieser Arbeit, der sich mit der Eigenschaft der 'InteroperabilitĂ€t' intensiv auseinandersetzt. Er untersucht, wie diese Eigenschaft eine zentrale Rolle fĂŒr Innovationsvorhaben in der Digital Health DomĂ€ne einnehmen soll. Denn InteroperabilitĂ€t beschreibt, vereinfacht ausgedrĂŒckt, die FĂ€higkeit von zwei oder mehreren Systemen miteinander gemeinsame Aufgaben zu erfĂŒllen. Sie reprĂ€sentiert somit das Kernanliegen der identifizierten Zielstellungen und ist Dreh- und Angelpunkt, wenn eine entwickelte Lösung in eine konkrete Zielumgebung integriert werden soll. Von einem technisch-dominierten Blickwinkel aus betrachtet, geht es hierbei um die GewĂ€hrleistung von validen, performanten und sicheren Kommunikationsszenarien, sodass die o.g. InformationsflussbrĂŒche zwischen technischen Teilsystemen abgebaut werden. Ein rein technisches InteroperabilitĂ€tsverstĂ€ndnis genĂŒgt jedoch nicht, um die Vielfalt an Diffusionsbarrieren von digitalen Gesundheitsinnovationen zu umfassen. Denn beispielsweise das Fehlen adĂ€quater VergĂŒtungsoptionen innerhalb der gesetzlichen Rahmenbedingungen oder eine mangelhafte PassfĂ€higkeit fĂŒr den bestimmten Versorgungsprozess sind keine rein technischen Probleme. Vielmehr kommt hier eine Grundhaltung der Wirtschaftsinformatik zum Tragen, die Informationssysteme - auch die des Gesundheitswesens - als sozio-technische Systeme begreift und dabei Technologie stets im Zusammenhang mit Menschen, die sie nutzen, von ihr beeinflusst werden oder sie organisieren, betrachtet. Soll eine digitale Gesundheitsinnovation, die einen Mehrwert gemĂ€ĂŸ der o.g. Zielstellungen verspricht, in eine existierende Informationssystemlandschaft der Gesundheitsversorgung integriert werden, so muss sie aus technischen sowie nicht-technischen Gesichtspunkten 'interoperabel' sein. Zwar ist die Notwendigkeit von InteroperabilitĂ€t in der Wissenschaft, Politik und Praxis bekannt und auch positive Bewegungen der DomĂ€ne hin zu mehr InteroperabilitĂ€t sind zu verspĂŒren. Jedoch dominiert dabei einerseits ein technisches VerstĂ€ndnis und andererseits bleibt das Potential dieser Eigenschaft als Leitmotiv fĂŒr das Innovationsmanagement bislang weitestgehend ungenutzt. An genau dieser Stelle knĂŒpft nun der Hauptbeitrag dieser Doktorarbeit an, in dem sie eine sozio-technische Konzeptualisierung und Kontextualisierung von InteroperabilitĂ€t fĂŒr kĂŒnftige digitale Gesundheitsinnovationen vorschlĂ€gt. Literatur- und expertenbasiert wird ein Rahmenwerk erarbeitet - das Digital Health Innovation Interoperability Framework - das insbesondere Innovatoren und Innovationsfördernde dabei unterstĂŒtzen soll, die Diffusionswahrscheinlichkeit in die Praxis zu erhöhen. Nun sind mit diesem Framework viele Erkenntnisse und Botschaften verbunden, die ich fĂŒr diesen Prolog wie folgt zusammenfassen möchte: 1. Um die Entwicklung digitaler Gesundheitsinnovationen bestmöglich auf eine erfolgreiche Integration in eine bestimmte Zielumgebung auszurichten, sind die Realisierung eines neuartigen Wertversprechens sowie die GewĂ€hrleistung sozio-technischer InteroperabilitĂ€t die zwei zusammenhĂ€ngenden Hauptaufgaben eines Innovationsprozesses. 2. Die GewĂ€hrleistung von InteroperabilitĂ€t ist eine aktiv zu verantwortende Managementaufgabe und wird durch projektspezifische Bedingungen sowie von externen und internen Dynamiken beeinflusst. 3. Sozio-technische InteroperabilitĂ€t im Kontext digitaler Gesundheitsinnovationen kann ĂŒber sieben, interdependente Ebenen definiert werden: Politische und regulatorische Bedingungen; Vertragsbedingungen; Versorgungs- und GeschĂ€ftsprozesse; Nutzung; Information; Anwendungen; IT-Infrastruktur. 4. Um InteroperabilitĂ€t auf jeder dieser Ebenen zu gewĂ€hrleisten, sind Strategien differenziert zu definieren, welche auf einem Kontinuum zwischen KompatibilitĂ€tsanforderungen aufseiten der Innovation und der Motivation von Anpassungen aufseiten der Zielumgebung verortet werden können. 5. Das Streben nach mehr InteroperabilitĂ€t fördert sowohl den nachhaltigen Erfolg der einzelnen digitalen Gesundheitsinnovation als auch die Defragmentierung existierender Informationssystemlandschaften und trĂ€gt somit zur Verbesserung des Gesundheitswesens bei. Zugegeben: die letzte dieser fĂŒnf Botschaften trĂ€gt eher die FĂ€rbung einer Überzeugung, als dass sie ein Ergebnis wissenschaftlicher BeweisfĂŒhrung ist. Dennoch empfinde ich diese, wenn auch persönliche Erkenntnis als Maxim der DomĂ€ne, der ich mich zugehörig fĂŒhle - der IT-Systementwicklung des Gesundheitswesens

    Evaluating the sustainability and resiliency of local food systems

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    With an ever-rising global population and looming environmental challenges such as climate change and soil degradation, it is imperative to increase the sustainability of food production. The drastic rise in food insecurity during the COVID-19 pandemic has further shown a pressing need to increase the resiliency of food systems. One strategy to reduce the dependence on complex, vulnerable global supply chains is to strengthen local food systems, such as by producing more food in cities. This thesis uses an interdisciplinary, food systems approach to explore aspects of sustainability and resiliency within local food systems. Lifecycle assessment (LCA) was used to evaluate how farm scale, distance to consumer, and management practices influence environmental impacts for different local agriculture models in two case study locations: Georgia, USA and England, UK. Farms were grouped based on urbanisation level and management practices, including: urban organic, peri-urban organic, rural organic, and rural conventional. A total of 25 farms and 40 crop lifecycles were evaluated, focusing on two crops (kale and tomatoes) and including impacts from seedling production through final distribution to the point of sale. Results were extremely sensitive to the allocation of composting burdens (decomposition emissions), with impact variation between organic farms driven mainly by levels of compost use. When composting burdens were attributed to compost inputs, the rural conventional category in the U.S. and the rural organic category in the UK had the lowest average impacts per kg sellable crop produced, including the lowest global warming potential (GWP). However, when subtracting avoided burdens from the municipal waste stream from compost inputs, trends reversed entirely, with urban or peri-urban farm categories having the lowest impacts (often negative) for GWP and marine eutrophication. Overall, farm management practices were the most important factor driving environmental impacts from local food supply chains. A soil health assessment was then performed on a subset of the UK farms to provide insight to ecosystem services that are not captured within LCA frameworks. Better soil health was observed in organically-farmed and uncultivated soils compared to conventionally farmed soils, suggesting higher ecosystem service provisioning as related to improved soil structure, flood mitigation, erosion control, and carbon storage. However, relatively high heavy metal concentrations were seen on urban and peri-urban farms, as well as those located in areas with previous mining activity. This implies that there are important services and disservices on farms that are not captured by LCAs. Zooming out from a focus on food production, a qualitative methodology was used to explore experiences of food insecurity and related health and social challenges during the COVID-19 pandemic. Fourteen individuals receiving emergency food parcels from a community food project in Sheffield, UK were interviewed. Results showed that maintaining food security in times of crisis requires a diverse set of individual, household, social, and place-based resources, which were largely diminished or strained during the pandemic. Drawing upon social capital and community support was essential to cope with a multiplicity of hardship, highlighting a need to develop community food infrastructure that supports ideals of mutual aid and builds connections throughout the food supply chain. Overall, this thesis shows that a range of context-specific solutions are required to build sustainable and resilient food systems. This can be supported by increasing local control of food systems and designing strategies to meet specific community needs, whilst still acknowledging a shared global responsibility to protect ecosystem, human, and planetary health

    “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy

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    Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT’s capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT’s use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts

    Securing the Internet of Things: A Study on Machine Learning-Based Solutions for IoT Security and Privacy Challenges

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    The Internet of Things (IoT) is a rapidly growing technology that connects and integrates billions of smart devices, generating vast volumes of data and impacting various aspects of daily life and industrial systems. However, the inherent characteristics of IoT devices, including limited battery life, universal connectivity, resource-constrained design, and mobility, make them highly vulnerable to cybersecurity attacks, which are increasing at an alarming rate. As a result, IoT security and privacy have gained significant research attention, with a particular focus on developing anomaly detection systems. In recent years, machine learning (ML) has made remarkable progress, evolving from a lab novelty to a powerful tool in critical applications. ML has been proposed as a promising solution for addressing IoT security and privacy challenges. In this article, we conducted a study of the existing security and privacy challenges in the IoT environment. Subsequently, we present the latest ML-based models and solutions to address these challenges, summarizing them in a table that highlights the key parameters of each proposed model. Additionally, we thoroughly studied available datasets related to IoT technology. Through this article, readers will gain a detailed understanding of IoT architecture, security attacks, and countermeasures using ML techniques, utilizing available datasets. We also discuss future research directions for ML-based IoT security and privacy. Our aim is to provide valuable insights into the current state of research in this field and contribute to the advancement of IoT security and privacy

    Attributes in Cloud Service Descriptions : A comprehensive Content Analysis

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    The exponential growth of cloud services can make it challenging for customers to find the best available service. This problem is further aggregated by not comprehensive and non-standardized service descriptions on cloud providers’ websites. This issue has not yet been adequately researched. In response to this gap and following the call (Lehner & Floerecke, 2023) to analyse IT service catalogues directed toward external customers, the purpose of this work is to examine the attribute usage in customer-facing service descriptions available on providers’ websites. A literature review thereby identified 76 different attributes used for cloud service description. Although there are a vast number of attributes used for cloud service descriptions, a core of attributes that were named in most papers, could be detected. In a following step, a content analysis of 100 service descriptions available on cloud providers’ websites was performed to understand, how frequently each attribute was used in the cloud service description from Cloud providers in general and also differentiated by size, cloud service model (IaaS, PaaS, SaaS), and geographical location of the provider. The majority of attributes of the literature review could thereby be found in the content analysis as well. 15 more attributes have been added to the initial list as they could not be matched to any of the attributes from the literature. In addition, it could be verified that criteria such as size, service model, and geographical location have a significant impact on the attribute usage for service descriptions. Finally, expert interviews were conducted to get additional insights. The consent of the expert is that the main purpose of cloud service descriptions available on cloud providers’ websites is not necessarily to inform customers, but to attract and convince them. The insights of this work can provide valuable information to customers as well as cloud providers to understand, which attributes are currently used or not used for cloud service descriptions on provider’s websites. This research provides valuable information for both customers and cloud providers by identifying which attributes are currently used or not used for cloud service descriptions and can serve as a foundation for further research

    AI: Limits and Prospects of Artificial Intelligence

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    The emergence of artificial intelligence has triggered enthusiasm and promise of boundless opportunities as much as uncertainty about its limits. The contributions to this volume explore the limits of AI, describe the necessary conditions for its functionality, reveal its attendant technical and social problems, and present some existing and potential solutions. At the same time, the contributors highlight the societal and attending economic hopes and fears, utopias and dystopias that are associated with the current and future development of artificial intelligence
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