2,020 research outputs found

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    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

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    UMSL Bulletin 2022-2023

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    The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp

    A Critical Review Of Post-Secondary Education Writing During A 21st Century Education Revolution

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    Educational materials are effective instruments which provide information and report new discoveries uncovered by researchers in specific areas of academia. Higher education, like other education institutions, rely on instructional materials to inform its practice of educating adult learners. In post-secondary education, developmental English programs are tasked with meeting the needs of dynamic populations, thus there is a continuous need for research in this area to support its changing landscape. However, the majority of scholarly thought in this area centers on K-12 reading and writing. This paucity presents a phenomenon to the post-secondary community. This research study uses a qualitative content analysis to examine peer-reviewed journals from 2003-2017, developmental online websites, and a government issued document directed toward reforming post-secondary developmental education programs. These highly relevant sources aid educators in discovering informational support to apply best practices for student success. Developmental education serves the purpose of addressing literacy gaps for students transitioning to college-level work. The findings here illuminate the dearth of material offered to developmental educators. This study suggests the field of literacy research is fragmented and highlights an apparent blind spot in scholarly literature with regard to English writing instruction. This poses a quandary for post-secondary literacy researchers in the 21st century and establishes the necessity for the literacy research community to commit future scholarship toward equipping college educators teaching writing instruction to underprepared adult learners

    Improving patient safety by learning from near misses – insights from safety-critical industries

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    Background Patients are at risk of being harmed by the very processes meant to help them. To improve patient safety, healthcare organisations attempt to identify the factors that contribute to incidents and take action to optimise conditions to minimise repeats. However, improvements in patient safety have not matched those observed in other safety-critical industries. One difference between healthcare and other safety-critical industries may be how they learn from near misses when seeking to make safety improvements. Near misses are incidents that almost happened, but for an interruption in the sequence of events. Management of near misses includes their identification, reporting and investigation, and the learning that results. Safety theory suggests that acting on near misses will lead to actions to help prevent incidents. However, evidence also suggests that healthcare has yet to embrace the learning potential that patient safety near misses offer. The aims of this research, in support of this thesis, were to explore how best healthcare can learn from patient safety near misses to improve patient safety, and to identify what guidance non-healthcare safety-critical industries, which have implemented effective near-miss management systems, can offer healthcare. As this research progressed the aims were updated to include consideration of whether healthcare should seek to learn from patient safety near misses. Methods This research took a mixed-methods approach augmented by scoping reviews of the healthcare (study 1) and non-healthcare safety-critical industry (study 3) literature. A qualitative case study (study 2) was undertaken to explore the management of patient safety near misses in the English National Health Service. Seventeen interviews were undertaken with patient safety leads across acute hospitals, ambulance trusts, mental health trusts, primary care, and national bodies. A questionnaire was also used to help access the views of frontline staff. A grounded theory (study 4) was used to develop a set of principles, based on learning from non-healthcare safety-critical industries, around how best near misses can be managed. Thirty-five interviews were undertaken across aviation, maritime, and rail, with nuclear later added as per the theoretical sampling. Results The scoping reviews contributed 125 healthcare and 108 non-healthcare safety-critical industry academic articles, published internationally between 2000 and 2022, to the evidence gained from the qualitative case study and grounded theory. Safety cultures and maturity with safety management processes were found to vary in and across the different industries, and there was a reluctance for healthcare to learn about safety and near misses from other industries. Healthcare has yet to establish effective processes to manage patient safety near misses. There is an absence of evidence that learning has led to improvements in patient safety. The definition of a patient safety near miss varies, and organisations focus their efforts on reporting and investigating incidents, with limited attention to patient safety near misses. In non-healthcare safety-critical industries, near-miss management is more established, but process maturity varies in and across industries. Near misses are often defined specifically for an industry, but there is limited evidence that learning from them has improved safety. Information about near misses are commonly aggregated and may contribute to company and industry safety management systems. Exploration of the definition of a patient safety near miss led to the identification of the features of a near miss. The features have not been previously defined in the manner presented in this thesis. A patient safety near miss is context-specific and complex, involves interruptions, highlights system vulnerabilities, and is delineated from an incident by whether events reach a patient. Across healthcare and non-healthcare safety-critical industries the impact of learning from near misses is often assumed or extrapolated based on the common cause hypothesis. The hypothesis is regularly cited in safety literature and is used as the basis for justifying a focus on patient safety near misses. However, the validity of the hypothesis has been questioned and has not been validated for different patient safety near miss and incident types. Conclusions The research findings challenge long-held beliefs that learning from patient safety near misses will lead to improvements in patient safety. These beliefs are based on traditional safety theory that is unlikely to now be valid in the complexity of modern-day systems where incidents are the result of multiple factors and can emerge without apparent warning. Further research is required to understand the relationship between learning from patient safety near misses and patient safety, and whether the common cause hypothesis is valid for different types of healthcare safety event. While there are questions about the value of learning directly from patient safety near misses, the contribution of near misses to safety management systems in non-healthcare safety-critical industries looks to be beneficial for safety improvement. Safety management systems have yet to be implemented in the National Health Service and future research should look to understand how best this may be achieved and their value. In the meantime, patient safety near misses may help healthcare’s understanding of systems and their optimisation to create barriers to incidents and build resilience. This research offers an evidence-based definition of a patient safety near miss and describes principles to support identification, reporting, prioritisation, investigation, aggregation, learning, and action to help improve patient safety

    Traffic light detection and V2I communications of an autonomous vehicle with the traffic light for an effective intersection navigation using MAVS simulation

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    Intersection Navigation plays a significant role in autonomous vehicle operation. This paper focuses on enhancing autonomous vehicle intersection navigation through advanced computer vision and Vehicle-to-Infrastructure (V2I) communication systems. The research unfolds in two phases. In the first phase, an approach utilizing YOLOv8s is proposed for precise traffic light detection and recognition, trained on the Small-Scale Traffic Light Dataset (S2TLD). The second phase establishes seamless connectivity between autonomous vehicles and traffic lights in a simulated Mississippi State University Autonomous Vehicle Simulation (MAVS) environment resembling a small city with multiple intersections. This V2I system enables the transmission of Signal Phase and Timing (SPaT) messages to vehicles, providing information on current traffic light phases and time until the next phase change which enables the vehicles to adjust their speed and behavior in real-time. The simulation demonstrates accurate traffic light detection, with vehicles receiving SPaT messages, showcasing the system’s effectiveness in a multi-intersection scenario

    Navigating the Skies: An Exploration of Stakeholder Perspectives on Rules for Orbital Traffic Coordination using Grounded Theory and Case Study Research Methodologies

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    This dissertation explored standards, rules, or regulations ( rules ) of orbital traffic coordination to reduce the risk of collisions in space between active space objects. The research questions explored topics associated with areas for potential implementation of rules include maneuvering capabilities, liability and insurance, zoning, right-of-way, and tracking of objects in space. The researcher utilized an exploratory qualitative research method because of the developing field of study and a growing domain for potential regulation. The research design is a mixture of a case study for bounding and structuring the data collection and grounded theory for a rigorous and well-defined analysis approach. The primary data source is semi-structured interviews used to explore the perspectives of three stakeholder groups with a vested interest in space traffic management. The three groups are space industry, space insurance industry, and space law and policy experts. Amongst the three groups, 19 interviews were conducted. The data were analyzed to summarize and compare the different perspectives of each group and across the groups. From the summarized perspectives, the intent was to recommend a set of rules, but participants offered few specific rules. Instead, the dissertation’s results present shared considerations across the six research questions to provide the current state of thinking across the community. Results from this dissertation will provide valuable insight to policymakers beyond feedback generally received during comment periods associated with federal rulemaking. National space traffic management legal frameworks need to harmonize globally to optimize space transportation operations and practices. This dissertation contributes to a larger global effort to standardize and solidify rules defining interactions between space operators by capturing the perspectives of experts primarily in and concerning the United States

    SoRTS: Learned Tree Search for Long Horizon Social Robot Navigation

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    The fast-growing demand for fully autonomous robots in shared spaces calls for the development of trustworthy agents that can safely and seamlessly navigate in crowded environments. Recent models for motion prediction show promise in characterizing social interactions in such environments. Still, adapting them for navigation is challenging as they often suffer from generalization failures. Prompted by this, we propose Social Robot Tree Search (SoRTS), an algorithm for safe robot navigation in social domains. SoRTS aims to augment existing socially aware motion prediction models for long-horizon navigation using Monte Carlo Tree Search. We use social navigation in general aviation as a case study to evaluate our approach and further the research in full-scale aerial autonomy. In doing so, we introduce XPlaneROS, a high-fidelity aerial simulator that enables human-robot interaction. We use XPlaneROS to conduct a first-of-its-kind user study where 26 FAA-certified pilots interact with a human pilot, our algorithm, and its ablation. Our results, supported by statistical evidence, show that SoRTS exhibits a comparable performance to competent human pilots, significantly outperforming its ablation. Finally, we complement these results with a broad set of self-play experiments to showcase our algorithm's performance in scenarios with increasing complexity.Comment: arXiv admin note: substantial text overlap with arXiv:2304.0142

    A Changing Landscape:On Safety & Open Source in Automated and Connected Driving

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