2,473 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

    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

    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

    University bulletin 2023-2024

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    This catalog for the University of South Carolina at Beaufort lists information about the college, the academic calendar, admission policies, degree programs, faculty and course descriptions

    Stress detection in lifelog data for improved personalized lifelog retrieval system

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    Stress can be categorized into acute and chronic types, with acute stress having short-term positive effects in managing hazardous situations, while chronic stress can adversely impact mental health. In a biological context, stress elicits a physiological response indicative of the fight-or-flight mechanism, accompanied by measurable changes in physiological signals such as blood volume pulse (BVP), galvanic skin response (GSR), and skin temperature (TEMP). While clinical-grade devices have traditionally been used to measure these signals, recent advancements in sensor technology enable their capture using consumer-grade wearable devices, providing opportunities for research in acute stress detection. Despite these advancements, there has been limited focus on utilizing low-resolution data obtained from sensor technology for early stress detection and evaluating stress detection models under real-world conditions. Moreover, the potential of physiological signals to infer mental stress information remains largely unexplored in lifelog retrieval systems. This thesis addresses these gaps through empirical investigations and explores the potential of utilizing physiological signals for stress detection and their integration within the state-of-the-art (SOTA) lifelog retrieval system. The main contributions of this thesis are as follows. Firstly, statistical analyses are conducted to investigate the feasibility of using low-resolution data for stress detection and emphasize the superiority of subject-dependent models over subject-independent models, thereby proposing the optimal approach to training stress detection models with low-resolution data. Secondly, longitudinal stress lifelog data is collected to evaluate stress detection models in real-world settings. It is proposed that training lifelog models on physiological signals in real-world settings is crucial to avoid detection inaccuracies caused by differences between laboratory and free-living conditions. Finally, a state-of-the-art lifelog interactive retrieval system called \lifeseeker is developed, incorporating the stress-moment filter function. Experimental results demonstrate that integrating this function improves the overall performance of the system in both interactive and non-interactive modes. In summary, this thesis contributes to the understanding of stress detection applied in real-world settings and showcases the potential of integrating stress information for enhancing personalized lifelog retrieval system performance

    Utilising Convolutional Neural Networks for Pavement Distress Classification and Detection

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    This paper examines deep learning models for accurate and efficient identification and classification of pavement distresses. In it, a variety of related studies conducted on the topic as well as the various identification and classification methods proposed, such as edge detection, machine learning classification informed by statistical feature extraction, artificial neural networks, and real-time object detection systems, are discussed. The study investigates the effect of image processing techniques such as grayscaling, background subtraction, and image resizing on the performance and generalizability of the models. Using convolutional neural networks (CNN) architectures, this paper proposes a model that correctly classifies images into five pavement distress categories, namely fatigue (or alligator), longitudinal, transverse, patches, and craters, with an accuracy rate of 90.4% and a recall rate of 90.1%. The model is contrasted to a current state-of-the-art model based on the You Only Look Once framework as well as a baseline CNN model to demonstrate the impact of the image processing and architecture building techniques discussed on performance. The findings of this paper contribute to the fields of computer vision and infrastructure monitoring by demonstrating the efficacy of convolutional neural networks (CNNs) in image classification and the viability of using CNNbased models to automate pavement condition monitoring

    Fully-Automated Packaging Structure Recognition of Standardized Logistics Assets on Images

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    Innerhalb einer logistischen Lieferkette müssen vielfältige Transportgüter an zahlreichen Knotenpunkten bearbeitet, wiedererkannt und kontrolliert werden. Dabei ist oft ein großer manueller Aufwand erforderlich, um die Paketidentität oder auch die Packstruktur zu erkennen oder zu verifizieren. Solche Schritte sind notwendig, um beispielsweise eine Lieferung auf ihre Vollständigkeit hin zu überprüfen. Wir untersuchen die Konzeption und Implementierung eines Verfahrens zur vollständigen Automatisierung der Erkennung der Packstruktur logistischer Sendungen. Ziel dieses Verfahrens ist es, basierend auf einem einzigen Farbbild, eine oder mehrere Transporteinheiten akkurat zu lokalisieren und relevante Charakteristika, wie beispielsweise die Gesamtzahl oder die Anordnung der enthaltenen Packstücke, zu erkennen. Wir stellen eine aus mehreren Komponenten bestehende Bildverarbeitungs-Pipeline vor, die diese Aufgabe der Packstrukturerkennung lösen soll. Unsere erste Implementierung des Verfahrens verwendet mehrere Deep Learning Modelle, genauer gesagt Convolutional Neural Networks zur Instanzsegmentierung, sowie Bildverarbeitungsmethoden und heuristische Komponenten. Wir verwenden einen eigenen Datensatz von Echtbildern aus einer Logistik-Umgebung für Training und Evaluation unseres Verfahrens. Wir zeigen, dass unsere Lösung in der Lage ist, die korrekte Packstruktur in etwa 85% der Testfälle unseres Datensatzes zu erkennen, und sogar eine höhere Genauigkeit erzielt wird, wenn nur die meist vorkommenden Packstücktypen betrachtet werden. Für eine ausgewählte Bilderkennungs-Komponente unseres Algorithmus vergleichen wir das Potenzial der Verwendung weniger rechenintensiver, eigens designter Bildverarbeitungsmethoden mit den zuvor implementierten Deep Learning Verfahren. Aus dieser Untersuchung schlussfolgern wir die bessere Eignung der lernenden Verfahren, welche wir auf deren sehr gute Fähigkeit zur Generalisierung zurückführen. Außerdem formulieren wir das Problem der Objekt-Lokalisierung in Bildern anhand selbst gewählter Merkmalspunkte, wie beispielsweise Eckpunkte logistischer Transporteinheiten. Ziel hiervon ist es, Objekte präziser zu lokalisieren, als dies insbesondere im Vergleich zur Verwendung herkömmlicher umgebender Rechtecke möglich ist, während gleichzeitig die Objektform durch bekanntes Vorwissen zur Objektgeometrie forciert wird. Wir stellen ein spezifisches Deep Learning Modell vor, welches die beschriebene Aufgabe löst im Fall von Objekten, welche durch vier Eckpunkte beschrieben werden können. Das dabei entwickelte Modell mit Namen TetraPackNet wird evaluiert mittels allgemeiner und anwendungsbezogener Metriken. Wir belegen die Anwendbarkeit der Lösung im Falle unserer Bilderkennungs-Pipeline und argumentieren die Relevanz für andere Anwendungsfälle, wie beispielweise Kennzeichenerkennung

    Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks

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    We introduce Florence-2, a novel vision foundation model with a unified, prompt-based representation for a variety of computer vision and vision-language tasks. While existing large vision models excel in transfer learning, they struggle to perform a diversity of tasks with simple instructions, a capability that implies handling the complexity of various spatial hierarchy and semantic granularity. Florence-2 was designed to take text-prompt as task instructions and generate desirable results in text forms, whether it be captioning, object detection, grounding or segmentation. This multi-task learning setup demands large-scale, high-quality annotated data. To this end, we co-developed FLD-5B that consists of 5.4 billion comprehensive visual annotations on 126 million images, using an iterative strategy of automated image annotation and model refinement. We adopted a sequence-to-sequence structure to train Florence-2 to perform versatile and comprehensive vision tasks. Extensive evaluations on numerous tasks demonstrated Florence-2 to be a strong vision foundation model contender with unprecedented zero-shot and fine-tuning capabilities

    A Critical Discourse Analysis Of Hbcus And Their Place In Science And Technology From 1979-80 As Told By Four National Newspapers

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    This study was an investigation of how national newspapers contributed to the reproduction of racism as they reported on Historically Black Colleges and Universities (HBCUs) and the need for more Black Americans in STEM programs. The existence of racism in newspaper discourse reaffirms the long-standing perception that HBCUs, and the Black Americans they serve, do not deserve full educational participation in society. The lack of diversity in STEM fields represents a key area where a critical exploration of how HBCUs are described is needed. Specifically, four national newspapers, the Los Angeles Times, the New York Times, the Wall Street Journal, and the Washington Post, printed during the period of March 7, 1979, to December 12, 1980 were explored. Critical race theory provided the theoretical foundation of the study to explain why racism is a continued aspect of society that limits the STEM access of HBCUs. The research question for the study sought to understand the constructed images of HBCUs and Black students present in national newspaper discourse with respect to STEM topics. Using a critical discourse analysis approach, the study included 15 articles relevant to the topic. A key marker of relevant discourse was the passing of the 1980 National Science Foundation Authorization and Science and Technology Equal Opportunities Act, which provided HBCU students with additional access to science and technology curriculums and degrees. The study found discourse that represented a battle for HBCU continued existence, images of Black students as academically incapable, and implicit uses of racism to uphold notions of White supremacy. Implications to the field include a need for a more critical lens to be taken when framing events about HBCUs and Black students as these contribute to the collective perception of these groups as inferior
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