1,911 research outputs found
A Survey on Forensics and Compliance Auditing for Critical Infrastructure Protection
The broadening dependency and reliance that modern societies have on essential services
provided by Critical Infrastructures is increasing the relevance of their trustworthiness. However, Critical
Infrastructures are attractive targets for cyberattacks, due to the potential for considerable impact, not just
at the economic level but also in terms of physical damage and even loss of human life. Complementing
traditional security mechanisms, forensics and compliance audit processes play an important role in ensuring
Critical Infrastructure trustworthiness. Compliance auditing contributes to checking if security measures are
in place and compliant with standards and internal policies. Forensics assist the investigation of past security
incidents. Since these two areas significantly overlap, in terms of data sources, tools and techniques, they can
be merged into unified Forensics and Compliance Auditing (FCA) frameworks. In this paper, we survey the
latest developments, methodologies, challenges, and solutions addressing forensics and compliance auditing
in the scope of Critical Infrastructure Protection. This survey focuses on relevant contributions, capable of
tackling the requirements imposed by massively distributed and complex Industrial Automation and Control
Systems, in terms of handling large volumes of heterogeneous data (that can be noisy, ambiguous, and
redundant) for analytic purposes, with adequate performance and reliability. The achieved results produced
a taxonomy in the field of FCA whose key categories denote the relevant topics in the literature. Also, the
collected knowledge resulted in the establishment of a reference FCA architecture, proposed as a generic
template for a converged platform. These results are intended to guide future research on forensics and
compliance auditing for Critical Infrastructure Protection.info:eu-repo/semantics/publishedVersio
Multidisciplinary perspectives on Artificial Intelligence and the law
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
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The impact of enterprise social networking on knowledge sharing between academic staff in higher education
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonHigher education institutions have always considered knowledge sharing critical for research excellence and finding proper methods for sharing knowledge across academic staff has therefore been a major issue for universities and knowledge management research. Recent evidence shows that many universities have embraced enterprise social networking tools to improve communication, relationships, partnerships, and knowledge sharing. To date, there is little understanding of the critical factors for online knowledge sharing behaviour between academic staff, and the impact of these factors on work benefits for academic staff which differ between consumptive users and contributive users in higher education. This study employed the extended unified theory of acceptance and use of technology (UTAUT) to examine factors affecting knowledge sharing about the consumptive use and contributive use of enterprise social network (ESN) behaviour. The study adopts a critical realism philosophical approach and employed a grounded theory mixed methods. The conceptual model was validated through structural equation modelling based on an online survey of 254 academic staff using enterprise social networking as a part of their work in the United Kingdom. The findings have significant theoretical and practical implications for researchers and policy makers. The research has developed a cohesive ESN use model by extending and modifying the unified theory of acceptance and use of technology. The findings indicate significant differences around factors affecting consumptive and contributive usage patterns within ESNs. Due to advances in communication technologies, this research argues that a previous model suggested by Venkatesh et al. (2003) is no longer fit for purpose and the new communication tools can lead to improved knowledge in higher education. This research also makes valuable contributions to universities from a managerial viewpoint, suggesting that universities could help their scholars find a more comprehensive range of funding sources matching scholars' ideas
2023-2024 Catalog
The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation
Evaluating Architectural Safeguards for Uncertain AI Black-Box Components
Although tremendous progress has been made in Artificial Intelligence (AI), it entails new challenges. The growing complexity of learning tasks requires more complex AI components, which increasingly exhibit unreliable behaviour. In this book, we present a model-driven approach to model architectural safeguards for AI components and analyse their effect on the overall system reliability
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Policy options for food system transformation in Africa and the role of science, technology and innovation
As recognized by the Science, Technology and Innovation Strategy for Africa – 2024 (STISA-2024), science, technology and innovation (STI) offer many opportunities for addressing the main constraints to embracing transformation in Africa, while important lessons can be learned from successful interventions, including policy and institutional innovations, from those African countries that have already made significant progress towards food system transformation. This chapter identifies opportunities for African countries and the region to take proactive steps to harness the potential of the food and agriculture sector so as to ensure future food and nutrition security by applying STI solutions and by drawing on transformational policy and institutional innovations across the continent. Potential game-changing solutions and innovations for food system transformation serving people and ecology apply to (a) raising production efficiency and restoring and sustainably managing degraded resources; (b) finding innovation in the storage, processing and packaging of foods; (c) improving human nutrition and health; (d) addressing equity and vulnerability at the community and ecosystem levels; and (e) establishing preparedness and accountability systems. To be effective in these areas will require institutional coordination; clear, food safety and health-conscious regulatory environments; greater and timely access to information; and transparent monitoring and accountability systems
Digital Traces of the Mind::Using Smartphones to Capture Signals of Well-Being in Individuals
General context and questions Adolescents and young adults typically use their smartphone several hours a day. Although there are concerns about how such behaviour might affect their well-being, the popularity of these powerful devices also opens novel opportunities for monitoring well-being in daily life. If successful, monitoring well-being in daily life provides novel opportunities to develop future interventions that provide personalized support to individuals at the moment they require it (just-in-time adaptive interventions). Taking an interdisciplinary approach with insights from communication, computational, and psychological science, this dissertation investigated the relation between smartphone app use and well-being and developed machine learning models to estimate an individual’s well-being based on how they interact with their smartphone. To elucidate the relation between smartphone trace data and well-being and to contribute to the development of technologies for monitoring well-being in future clinical practice, this dissertation addressed two overarching questions:RQ1: Can we find empirical support for theoretically motivated relations between smartphone trace data and well-being in individuals? RQ2: Can we use smartphone trace data to monitor well-being in individuals?Aims The first aim of this dissertation was to quantify the relation between the collected smartphone trace data and momentary well-being at the sample level, but also for each individual, following recent conceptual insights and empirical findings in psychological, communication, and computational science. A strength of this personalized (or idiographic) approach is that it allows us to capture how individuals might differ in how smartphone app use is related to their well-being. Considering such interindividual differences is important to determine if some individuals might potentially benefit from spending more time on their smartphone apps whereas others do not or even experience adverse effects. The second aim of this dissertation was to develop models for monitoring well-being in daily life. The present work pursued this transdisciplinary aim by taking a machine learning approach and evaluating to what extent we might estimate an individual’s well-being based on their smartphone trace data. If such traces can be used for this purpose by helping to pinpoint when individuals are unwell, they might be a useful data source for developing future interventions that provide personalized support to individuals at the moment they require it (just-in-time adaptive interventions). With this aim, the dissertation follows current developments in psychoinformatics and psychiatry, where much research resources are invested in using smartphone traces and similar data (obtained with smartphone sensors and wearables) to develop technologies for detecting whether an individual is currently unwell or will be in the future. Data collection and analysis This work combined novel data collection techniques (digital phenotyping and experience sampling methodology) for measuring smartphone use and well-being in the daily lives of 247 student participants. For a period up to four months, a dedicated application installed on participants’ smartphones collected smartphone trace data. In the same time period, participants completed a brief smartphone-based well-being survey five times a day (for 30 days in the first month and 30 days in the fourth month; up to 300 assessments in total). At each measurement, this survey comprised questions about the participants’ momentary level of procrastination, stress, and fatigue, while sleep duration was measured in the morning. Taking a time-series and machine learning approach to analysing these data, I provide the following contributions: Chapter 2 investigates the person-specific relation between passively logged usage of different application types and momentary subjective procrastination, Chapter 3 develops machine learning methodology to estimate sleep duration using smartphone trace data, Chapter 4 combines machine learning and explainable artificial intelligence to discover smartphone-tracked digital markers of momentary subjective stress, Chapter 5 uses a personalized machine learning approach to evaluate if smartphone trace data contains behavioral signs of fatigue. Collectively, these empirical studies provide preliminary answers to the overarching questions of this dissertation.Summary of results With respect to the theoretically motivated relations between smartphone trace data and wellbeing (RQ1), we found that different patterns in smartphone trace data, from time spent on social network, messenger, video, and game applications to smartphone-tracked sleep proxies, are related to well-being in individuals. The strength and nature of this relation depends on the individual and app usage pattern under consideration. The relation between smartphone app use patterns and well-being is limited in most individuals, but relatively strong in a minority. Whereas some individuals might benefit from using specific app types, others might experience decreases in well-being when spending more time on these apps. With respect to the question whether we might use smartphone trace data to monitor well-being in individuals (RQ2), we found that smartphone trace data might be useful for this purpose in some individuals and to some extent. They appear most relevant in the context of sleep monitoring (Chapter 3) and have the potential to be included as one of several data sources for monitoring momentary procrastination (Chapter 2), stress (Chapter 4), and fatigue (Chapter 5) in daily life. Outlook Future interdisciplinary research is needed to investigate whether the relationship between smartphone use and well-being depends on the nature of the activities performed on these devices, the content they present, and the context in which they are used. Answering these questions is essential to unravel the complex puzzle of developing technologies for monitoring well-being in daily life.<br/
Automatic Generation of Personalized Recommendations in eCoaching
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
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