1,635 research outputs found

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    Exploring the Depths of Market Basket Analysis: A Comprehensive Guide to Transaction Analysis with FP-Growth and Apriori Algorithms

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    This research investigates the role of data science in understanding customer behavior and enhancing sales, focusing specifically on the application of Apriori and FP-Growth Algorithms at a retail store, Deli Point, in Labuan Bajo. It illuminates the impact of 'rubbish data' on transactional data analysis, emphasizing the need for robust data cleaning procedures to ensure accurate results. Utilizing the faster FP-Growth Algorithm, the study effectively analyzed customer purchasing patterns to identify optimal product combinations for sales improvement. It discovered that 'parsley local' and 'mint flores' items had the highest support with a value of 0.036, indicating that strategic placement of these items together could enhance sales. The rule between chicken leg bone, orange sunkist, and chicken breast boneless was found to have a high confidence value and a lift value higher than 1, implying a higher potential for these items to be sold when positioned near each other. This study contributes to understanding consumer behavior and provides insights for enhancing sales and competitiveness in the retail industry. An association rule involving 'chicken leg bone’, 'orange sunkist', and 'chicken breast boneless' demonstrated high confidence and a lift value above one, suggesting significant sales potential when these items are grouped together. This study not only contributes valuable insights into retail consumer behavior and effective product placement strategies but also underscores the transformative role of data science in optimizing sales and boosting competitiveness in the retail sector.This research investigates the role of data science in understanding customer behavior and enhancing sales, focusing specifically on the application of Apriori and FP-Growth Algorithms at a retail store, Deli Point, in Labuan Bajo. It illuminates the impact of 'rubbish data' on transactional data analysis, emphasizing the need for robust data cleaning procedures to ensure accurate results. Utilizing the faster FP-Growth Algorithm, the study effectively analyzed customer purchasing patterns to identify optimal product combinations for sales improvement. It discovered that 'parsley local' and 'mint flores' items had the highest support with a value of 0.036, indicating that strategic placement of these items together could enhance sales. The rule between chicken leg bone, orange sunkist, and chicken breast boneless was found to have a high confidence value and a lift value higher than 1, implying a higher potential for these items to be sold when positioned near each other. This study contributes to understanding consumer behavior and provides insights for enhancing sales and competitiveness in the retail industry. An association rule involving 'chicken leg bone’, 'orange sunkist', and 'chicken breast boneless' demonstrated high confidence and a lift value above one, suggesting significant sales potential when these items are grouped together. This study not only contributes valuable insights into retail consumer behavior and effective product placement strategies but also underscores the transformative role of data science in optimizing sales and boosting competitiveness in the retail sector

    The Application of Data Analytics Technologies for the Predictive Maintenance of Industrial Facilities in Internet of Things (IoT) Environments

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    In industrial production environments, the maintenance of equipment has a decisive influence on costs and on the plannability of production capacities. In particular, unplanned failures during production times cause high costs, unplanned downtimes and possibly additional collateral damage. Predictive Maintenance starts here and tries to predict a possible failure and its cause so early that its prevention can be prepared and carried out in time. In order to be able to predict malfunctions and failures, the industrial plant with its characteristics, as well as wear and ageing processes, must be modelled. Such modelling can be done by replicating its physical properties. However, this is very complex and requires enormous expert knowledge about the plant and about wear and ageing processes of each individual component. Neural networks and machine learning make it possible to train such models using data and offer an alternative, especially when very complex and non-linear behaviour is evident. In order for models to make predictions, as much data as possible about the condition of a plant and its environment and production planning data is needed. In Industrial Internet of Things (IIoT) environments, the amount of available data is constantly increasing. Intelligent sensors and highly interconnected production facilities produce a steady stream of data. The sheer volume of data, but also the steady stream in which data is transmitted, place high demands on the data processing systems. If a participating system wants to perform live analyses on the incoming data streams, it must be able to process the incoming data at least as fast as the continuous data stream delivers it. If this is not the case, the system falls further and further behind in processing and thus in its analyses. This also applies to Predictive Maintenance systems, especially if they use complex and computationally intensive machine learning models. If sufficiently scalable hardware resources are available, this may not be a problem at first. However, if this is not the case or if the processing takes place on decentralised units with limited hardware resources (e.g. edge devices), the runtime behaviour and resource requirements of the type of neural network used can become an important criterion. This thesis addresses Predictive Maintenance systems in IIoT environments using neural networks and Deep Learning, where the runtime behaviour and the resource requirements are relevant. The question is whether it is possible to achieve better runtimes with similarly result quality using a new type of neural network. The focus is on reducing the complexity of the network and improving its parallelisability. Inspired by projects in which complexity was distributed to less complex neural subnetworks by upstream measures, two hypotheses presented in this thesis emerged: a) the distribution of complexity into simpler subnetworks leads to faster processing overall, despite the overhead this creates, and b) if a neural cell has a deeper internal structure, this leads to a less complex network. Within the framework of a qualitative study, an overall impression of Predictive Maintenance applications in IIoT environments using neural networks was developed. Based on the findings, a novel model layout was developed named Sliced Long Short-Term Memory Neural Network (SlicedLSTM). The SlicedLSTM implements the assumptions made in the aforementioned hypotheses in its inner model architecture. Within the framework of a quantitative study, the runtime behaviour of the SlicedLSTM was compared with that of a reference model in the form of laboratory tests. The study uses synthetically generated data from a NASA project to predict failures of modules of aircraft gas turbines. The dataset contains 1,414 multivariate time series with 104,897 samples of test data and 160,360 samples of training data. As a result, it could be proven for the specific application and the data used that the SlicedLSTM delivers faster processing times with similar result accuracy and thus clearly outperforms the reference model in this respect. The hypotheses about the influence of complexity in the internal structure of the neuronal cells were confirmed by the study carried out in the context of this thesis

    Ein Framework zur Analyse komplexer Produktportfolios mittels Machine Learning

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    Die Nachfrage der Kunden nach individualisierten Produkten, die Globalisierung, neue Konsummuster sowie kürzere Produktlebenszyklen führen dazu, dass Unternehmen immer mehr Varianten anbieten. Aufgrund der Arbeitsteilung und der unterschiedlichen Perspektiven können einzelne Entwickler die Komplexität des Produktportfolios nicht durchdringen. Dennoch sind die heutigen Verfahren im Produktportfolio- und Variantenmanagement geprägt durch manuelle und erfahrungsbasierte Aktivitäten. Eine systematische Analyse und Optimierung des Produktportfolios sind damit nicht möglich. Unternehmen benötigen stattdessen intelligente Lösungen, welche das gespeicherte Wissen in Daten nutzen und einsetzen, um Entscheidungen über Innovation, Differenzierung und Elimination von Produktvarianten zu unterstützen. Zielstellung dieses Forschungsvorhabens ist die Entwicklung eines Frameworks zur Analyse komplexer Produktportfolios mittels Machine Learning. Machine Learning ermöglicht es, Wissen aus Daten unterschiedlicher Lebenszyklusphasen einer Produktvariante automatisiert zu generieren und zur Unterstützung des Produktportfolio- und Variantenmanagements einzusetzen. Für die Unterstützung der Entscheidungen über Produktvarianten ist Wissen über deren Abhängigkeiten und Beziehungen sowie die Eigenschaften der einzelnen Elemente erforderlich. Dadurch soll ein Beitrag zur besseren Handhabung komplexer Produktportfolios geleistet werden. Das Framework zur Analyse komplexer Produktportfolios mittels Machine Learning besteht aus drei Bausteinen, die das zentrale Ergebnis dieser Arbeit darstellen. Zuerst wird in Baustein 1 auf die Wissensbedarfe bei der Analyse und Anpassung komplexer Produktportfolios eingegangen. Anschließend werden in Baustein 2 die Daten, welche für Entscheidungen und somit für die Wissensgenerierung im Produktportfolio- und Variantenmanagement erforderlich sind, beschrieben und charakterisiert. Abschließend findet in Baustein 3 die Datenvorbereitung und die Implementierung der Machine Learning Verfahren statt. Es wird auf unterschiedliche Verfahren eingegangen und eine Unterstützung bei der Auswahl und Evaluation der Algorithmen sowie die Möglichkeiten zum Einsatz des generierten Wissens für die Analyse komplexer Produktportfolios aufgezeigt. Das Framework wird in einer Fallstudie bei einem Industriepartner aus der Nutzfahrzeugbranche mit einem besonders komplexen Produktportfolio angewendet. Dabei werden die drei Anwendungsfälle Prognose von „marktspezifischen und technischen Eigenschaften der Produktvarianten“, Ermittlung von „Ähnlichkeiten von Produktvarianten“ und Identifikation von „Korrelationen zwischen Merkmalsausprägungen“ mit realen Daten des Industriepartners umgesetzt. Das Framework sowie die in der Fallstudie beim Industriepartner erzielten Ergebnisse werden anschließend Experten im Produktportfolio- und Variantenmanagement vorgestellt. Diese bewerten die Ergebnisse hinsichtlich der funktionalen Eigenschaften sowie dem Mehrwert aus Sicht der Forschung und industriellen Praxis anhand zuvor definierter Kriterien.:1 Einführung 1.1 Motivation 1.2 Komplexe Produktportfolios: Eine Industrieperspektive 1.3 Zielsetzung und Forschungsfragen 1.4 Aufbau der Arbeit 2 Grundlagen zur Analyse von Produktportfolios mittels Machine Learning 2.1 Komplexe Produktportfolios 2.1.1 Terminologie komplexer Produktportfolios 2.1.2 Strukturierung komplexer Produktportfolios 2.1.3 Analyse und Anpassung komplexer Produktportfolios 2.1.4 Zusammenfassung: Komplexe Produktportfolios 2.2 Machine Learning 2.2.1 Machine Learning als Teil der künstlichen Intelligenz 2.2.2 Terminologie Machine Learning 2.2.3 Wissensgenerierung mit Machine Learning 2.2.4 Datenanalyseprozess 2.2.5 Machine Learning Verfahren und Algorithmen 2.2.6 Zusammenfassung: Machine Learning 3 Ansätze zur Analyse von Produktportfolios mittels Machine Learning 3.1 Kriterien zur Bewertung bestehender Ansätze 3.2 Bestehende Ansätze aus der Literatur 3.2.1 Einsatz überwachter Lernverfahren 3.2.2 Einsatz unüberwachter Lernverfahren 3.2.3 Einsatz kombinierter Lernverfahren 3.3 Resultierender Forschungsbedarf 4 Forschungsvorgehen 4.1 Design Research Methodology (DRM) 4.2 Vorgehen und Methodeneinsatz 4.3 Kriterien für die Entwicklung des Frameworks 4.4 Schlussfolgerungen zum Forschungsvorgehen 5 Framework zur Analyse komplexer Produktportfolios 5.1 Übersicht über das Framework 5.2 Baustein 1: Wissensbedarfe zur Analyse komplexer Produktportfolios 5.2.1 Informationssuche 5.2.2 Formulierung von Alternativen 5.2.3 Prognose 5.2.4 Kriterien zur Auswahl der Wissensbedarfe 5.3 Baustein 2: Datenbasierte Beschreibung komplexer Produktportfolios 5.3.1 Produktdatenmodell 5.3.2 Vertriebsdaten 5.3.3 Nutzungsdaten 5.4 Baustein 3: Systematische Generierung und Einsatz von Wissen 5.4.1 Baustein 3.0: Vorbereitung von Produktportfoliodaten 5.4.2 Baustein 3.1: Regressionsanalyse 5.4.3 Baustein 3.2: Klassifikationsanalyse 5.4.4 Baustein 3.3: Clusteranalyse 5.4.5 Baustein 3.4: Assoziationsanalyse 5.5 Anwendung des Frameworks 5.6 Schlussfolgerung zum Framework 6 Validierung des Frameworks 6.1 Konzept der Validierung 6.2 Baustein 1: Wissensbedarfe zur Analyse komplexer Produktportfolios 6.3 Baustein 2: Datenbasierte Beschreibung komplexer Produktportfolios 6.4 Baustein 3: Systematische Generierung und Einsatz von Wissen 6.4.1 Marktspezifische und technische Produkteigenschaften 6.4.2 Ähnlichkeiten von Produktvarianten 6.4.3 Korrelationen zwischen Merkmalsausprägungen 6.5 Erfolgsvalidierung mit einer Expertenbefragung 6.6 Schlussfolgerung zur Validierung 7 Diskussion 7.1 Nutzen und Einschränkungen 7.2 Ergebnisbeitrag für die Forschung 7.3 Ergebnisbeitrag für die Industrie 8 Zusammenfassung und Ausblick 8.1 Zusammenfassung 8.2 Ausblick 9 Literaturverzeichnis 10 Abbildungsverzeichnis 11 Tabellenverzeichnis Anhang A-

    Tradition and Innovation in Construction Project Management

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    This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

    Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges and Future Directions

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    Sixth-generation (6G) networks anticipate intelligently supporting a wide range of smart services and innovative applications. Such a context urges a heavy usage of Machine Learning (ML) techniques, particularly Deep Learning (DL), to foster innovation and ease the deployment of intelligent network functions/operations, which are able to fulfill the various requirements of the envisioned 6G services. Specifically, collaborative ML/DL consists of deploying a set of distributed agents that collaboratively train learning models without sharing their data, thus improving data privacy and reducing the time/communication overhead. This work provides a comprehensive study on how collaborative learning can be effectively deployed over 6G wireless networks. In particular, our study focuses on Split Federated Learning (SFL), a technique recently emerged promising better performance compared with existing collaborative learning approaches. We first provide an overview of three emerging collaborative learning paradigms, including federated learning, split learning, and split federated learning, as well as of 6G networks along with their main vision and timeline of key developments. We then highlight the need for split federated learning towards the upcoming 6G networks in every aspect, including 6G technologies (e.g., intelligent physical layer, intelligent edge computing, zero-touch network management, intelligent resource management) and 6G use cases (e.g., smart grid 2.0, Industry 5.0, connected and autonomous systems). Furthermore, we review existing datasets along with frameworks that can help in implementing SFL for 6G networks. We finally identify key technical challenges, open issues, and future research directions related to SFL-enabled 6G networks

    Automatic Generation of Personalized Recommendations in eCoaching

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    Denne avhandlingen omhandler eCoaching for personlig livsstilsstøtte i sanntid ved bruk av informasjons- og kommunikasjonsteknologi. Utfordringen er å designe, utvikle og teknisk evaluere en prototyp av en intelligent eCoach som automatisk genererer personlige og evidensbaserte anbefalinger til en bedre livsstil. Den utviklede løsningen er fokusert på forbedring av fysisk aktivitet. Prototypen bruker bærbare medisinske aktivitetssensorer. De innsamlede data blir semantisk representert og kunstig intelligente algoritmer genererer automatisk meningsfulle, personlige og kontekstbaserte anbefalinger for mindre stillesittende tid. Oppgaven bruker den veletablerte designvitenskapelige forskningsmetodikken for å utvikle teoretiske grunnlag og praktiske implementeringer. Samlet sett fokuserer denne forskningen på teknologisk verifisering snarere enn klinisk evaluering.publishedVersio

    Empirical essays on agency problems in venture capital

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    In the first essay, we explore the potential agency conflict between limited partners and general partners in venture capital firms due to changes in investment style. Investment style refers to the characteristics of a venture capital funds portfolio, such as the portfolio companies stage of development, location, and industry. While investment style can significantly impact the risk and return profile of a fund, it is usually not explicitly agreed upon by limited and general partners. We argue that changes in investment style, known as style drifts, can reveal information about the risk-taking behavior of venture capitalists and present empirical evidence in support of this claim. To determine whether style drifts constitute an agency conflict, we consider two sets of hypotheses. The first set posits that style drifts are intentional decisions to take on more risk, potentially driven by incentives related to compensation or employment. The second set suggests that style drifts may occur because of competitive pressure and may not necessarily be indicative of an intent to increase risk. Our findings suggest that style drifts are likely to create an agency conflict, as the evidence supports the hypothesis that well-performing venture capitalists increase investment risk to benefit from higher compensation potential via carried interest when they feel confident, they will be able to raise a follow-on fund securing their base income via management fees. Additionally, we examine the impact of style drifts on individual investments and fund performance and find that overall, style drifts hurt a funds exit rate, indicating the potential for increased risk. In the second essay, we examine the relationship between venture capitalists and entrepreneurs, specifically focusing on the role of information asymmetry in the funding process. Using text classification and text mining techniques we analyze the content and level of detail in capital allocation plans provided by entrepreneurs to investors, which serve as a proxy for private informational updates that are typically not widely available. Our analysis shows that investors do consider the content and specificity of these updates when making valuation decisions and that both positive information signals and more detailed information are related to higher valuations. We also investigate the effect of the relative level of information asymmetry between venture capitalists and entrepreneurs on the value of these updates, finding that they are more impactful in situations where there is a higher level of information asymmetry. The results of our study have practical implications for entrepreneurs, as we find that the negative impact of negative information signals can be offset by providing highly specific information and that the value of an informational update is influenced by the existing level of information asymmetry. In the third essay, I explore the impact of university affiliations on the initial matching process between venture capitalists and founders, the involvement of the investor during the funding relationship, and the eventual startup performance and investment exit success. University affiliations can influence the funding relationship through two channels: first, attending a top university may serve as a signal of founder quality to venture capitalists, helping them to avoid adverse selection; second, shared alumni networks may establish trust and reduce information asymmetry between otherwise unknown individuals. Using a dataset of 42,101 investments involving 38,452 unique venture capitalists and founders, I find that educational ties between venture capitalists and founders have a positive effect on the funding relationship, including the initial matching, the level of involvement of the investor during the funding relationship, and the eventual startup performance and investment exit success. The effect of sharing an educational background between a venture capitalist and a founder is about five times larger than the effect of a founder attending a top university. Further, the results also show that educational ties are more valuable the more exclusive they are, and that redundant ties between the founding team and the investors have diminishing value for the investment decision.Im ersten Aufsatz wird der potenzielle Agency-Konflikt zwischen Limited Partners und Risikokapitalgebern in Risikokapitalgesellschaften aufgrund von Änderungen des Investitionsstils untersucht. Der Investitionsstil bezieht sich auf die Merkmale des Portfolios eines Risikokapitalfonds, wie z.B. das Entwicklungsstadium, den Standort und die Branche der Portfoliounternehmen. Der Anlagestil kann sich zwar erheblich auf das Risiko- und Ertragsprofil eines Fonds auswirken, wird aber in der Regel nicht ausdrücklich von den Limited Partners und Risikokapitalgebern vereinbart. Wir argumentieren, dass Veränderungen im Anlagestil, die so genannten Style Drifts, Aufschluss über das Risikoverhalten von Risikokapitalgebern geben können, und präsentieren empirische Belege zur Unterstützung dieser Behauptung. Um festzustellen, ob Style Drifts einen Agency-Konflikt darstellen, prüfen wir zwei Hypothesen. Die erste Hypothese besagt, dass Style Drifts absichtliche Entscheidungen zur Übernahme von mehr Risiko sind, die möglicherweise durch Anreize im Zusammenhang mit der Vergütung bedingt sind. Die zweite Hypothese besagt, dass Style Drifts als Folge von Wettbewerbsdruck auftreten können und nicht unbedingt auf eine beabsichtigte Risikoerhöhung hindeuten. Unsere Ergebnisse deuten darauf hin, dass Style Drifts wahrscheinlich zu einem Agency-Konflikt führen, da die Ergebnisse die Hypothese stützen, dass leistungsstarke Risikokapitalgeber das Anlagerisiko erhöhen, um von einem höheren Vergütungspotenzial über Carried Interest zu profitieren, wenn sie sich sicher sind, dass sie in der Lage sein werden, einen Folgefonds einzuwerben, der ihr Grundeinkommen über Managementgebühren sichert. Darüber hinaus untersuchen wir die Auswirkungen von Style Drifts auf einzelne Investitionen und die Fondsperformance und stellen fest, dass Style Drifts insgesamt die Erfolgsquote eines Fonds beeinträchtigen, was auf ein erhöhtes Risiko hinweist. Im zweiten Aufsatz untersuchen wir die Beziehung zwischen Risikokapitalgebern und Unternehmern und konzentrieren uns dabei insbesondere auf die Rolle der Informationsasymmetrie im Finanzierungsprozess. Mithilfe von Textklassifizierungs- und Textmining-Techniken analysieren wir den Inhalt und den Detaillierungsgrad von Kapitalallokationsplänen, die den Investoren von den Unternehmern zur Verfügung gestellt werden. Unsere Analyse zeigt, dass Investoren den Inhalt und die Spezifität dieser Informationsaktualisierungen bei ihren Bewertungsentscheidungen berücksichtigen und dass sowohl positive Informationssignale als auch detailliertere Informationen mit höheren Bewertungen verbunden sind. Wir untersuchen auch die Auswirkungen des relativen Ausmaßes der Informationsasymmetrie zwischen Risikokapitalgebern und Unternehmern auf den Wert dieser Informationsaktualisierungen und stellen fest, dass sie in Situationen mit einem höheren Maß an Informationsasymmetrie größere Auswirkungen haben. Die Ergebnisse unserer Studie haben praktische Auswirkungen für Unternehmer, da wir feststellen, dass die negativen Auswirkungen negativer Informationssignale durch die Bereitstellung hochspezifischer Informationen ausgeglichen werden können und dass der Wert einer Informationsaktualisierung vom bestehenden Grad der Informationsasymmetrie beeinflusst wird. Im dritten Aufsatz untersuche ich die Auswirkungen von Universitätszugehörigkeiten auf den anfänglichen Matching-Prozess zwischen Risikokapitalgebern und Gründern, die Beteiligung des Investors während der Finanzierungsbeziehung und die letztendliche Performance des Start-ups und den Erfolg der Investition beim Ausstieg. Die Zugehörigkeit zu einer Universität kann die Finanzierungsbeziehung über zwei Kanäle beeinflussen: Erstens kann der Besuch einer Spitzenuniversität den Risikokapitalgebern als Signal für die Qualität des Gründers dienen und ihnen helfen, Adverse Selection zu vermeiden; zweitens können gemeinsame Alumni-Netzwerke Vertrauen schaffen und Informationsasymmetrien zwischen ansonsten unbekannten Personen verringern. Anhand eines Datensatzes von 42.101 Investitionen, an denen 38.452 Risikokapitalgeber und Gründer beteiligt waren, stelle ich fest, dass sich Bildungsbeziehungen zwischen Risikokapitalgebern und Gründern positiv auf die Finanzierungsbeziehung auswirken, einschließlich des anfänglichen Matchings, des Umfangs des Engagements des Investors während der Finanzierungsbeziehung und der letztendlichen Startup-Performance und des Erfolgs beim Ausstieg aus der Investition. Der Effekt eines gemeinsamen Bildungshintergrunds zwischen einem Risikokapitalgeber und einem Gründer ist etwa fünfmal so groß wie der Effekt eines Gründers, der eine Spitzenuniversität besucht hat. Außerdem zeigen die Ergebnisse, dass Bildungsbeziehungen umso wertvoller sind, je exklusiver sie sind, und dass redundante Beziehungen zwischen dem Gründerteam und den Investoren einen abnehmenden Wert für die Investitionsentscheidung haben

    Measuring the impact of COVID-19 on hospital care pathways

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    Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted

    KARTAL: Web Application Vulnerability Hunting Using Large Language Models

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    Broken Access Control is the most serious web application security risk as published by Open Worldwide Application Security Project (OWASP). This category has highly complex vulnerabilities such as Broken Object Level Authorization (BOLA) and Exposure of Sensitive Information. Finding such critical vulnerabilities in large software systems requires intelligent and automated tools. State-of-the-art (SOTA) research including hybrid application security testing tools, algorithmic bruteforcers, and artificial intelligence has shown great promise in detection. Nevertheless, there exists a gap in research for reliably identifying logical and context-dependant Broken Access Control vulnerabilities. We propose KARTAL, a novel method for web application vulnerability detection using a Large Language Model (LLM). It consists of 3 components: Fuzzer, Prompter, and Detector. The Fuzzer is responsible for methodically collecting application behaviour. The Prompter processes the data from the Fuzzer and formulates a prompt. The Detector uses an LLM which we have finetuned for detecting vulnerabilities. In the study, we investigate the performance, key factors, and limitations of the proposed method. We experiment with finetuning three types of decoder-only pre-trained transformers for detecting two sophisticated vulnerabilities. Our best model attained an accuracy of 87.19%, with an F1 score of 0.82. By using hardware acceleration on a consumer-grade laptop, our fastest model can make up to 539 predictions per second. The experiments on varying the training sample size demonstrated the great learning capabilities of our model. Every 400 samples added to training resulted in an average MCC score improvement of 19.58%. Furthermore, the dynamic properties of KARTAL enable inference-time adaption to the application domain, resulting in reduced false positives
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