4,706 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

    Graduate Catalog of Studies, 2023-2024

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    Fairness Testing: A Comprehensive Survey and Analysis of Trends

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    Unfair behaviors of Machine Learning (ML) software have garnered increasing attention and concern among software engineers. To tackle this issue, extensive research has been dedicated to conducting fairness testing of ML software, and this paper offers a comprehensive survey of existing studies in this field. We collect 100 papers and organize them based on the testing workflow (i.e., how to test) and testing components (i.e., what to test). Furthermore, we analyze the research focus, trends, and promising directions in the realm of fairness testing. We also identify widely-adopted datasets and open-source tools for fairness testing

    Jews in East Norse Literature

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    This book explores the portrayal of Jews and Judaism in medieval Danish and Swedish literary and visual culture. Drawing on over 100 manuscripts and incunabula as well as runic inscriptions and religious art, the author describes the various, often contradictory, images ranging from antisemitism and anti-Judaism to the elevation of Jews as morally exemplary figures. It includes new editions of 54 East Norse texts with English translations

    Reshaping Higher Education for a Post-COVID-19 World: Lessons Learned and Moving Forward

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    ‘Inner qualities versus inequalities’: A case study of student change learning about Aboriginal health using sequential, explanatory mixed methods

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    Racism and lack of self-determination in health care perpetuate injury and injustice to Aboriginal people. To instil cultural safety at individual, organisational, community and systems levels, a key site of action has been health professional education that seeks to elicit reflexivity, cultural humility and a working understanding of Aboriginal health concepts. Studies in Aboriginal community settings show Family Well Being (FWB) empowerment education is effective in supporting personal and collective reflexivity and transformation through empowering life skills development. Implementation of FWB within educational settings shows early signs of effectiveness among students. Yet knowledge of the steps and processes of student change is lacking. This mixed methods explanatory case study sought to measure and understand change in postgraduate students of a leading Australian university learning about Aboriginal health and wellbeing through blended delivery, including through face-to-face immersion in FWB in an urban classroom. Three interrelated studies investigated fidelity and acceptability of the program, measured and analysed growth and empowerment in students, and explained processes of change observed, through thematic analysis of asynchronous online discussions using lenses based on transformative learning and empowerment. Researcher reflexivity was promoted by Aboriginal supervision. Over six years, 194 students enrolled in two different Aboriginal public health courses, 85 of them in the FWB course. As well as achieving program fidelity and acceptability, pre/post-course change in students across a range of emotional empowerment, personal growth and life-long learning processes was measured in the FWB group. Thematic analysis revealed students’ fluid and recursive processes of transformative learning in their professional selves and capacities to act in domains important to Aboriginal health. This case study contributes new knowledge critical to strengthening health professional capabilities for ever more complex, uncertain and emotionally demanding sites of practice, and to work in empowering ways—with, not for, Aboriginal people and communities

    Augmented Behavioral Annotation Tools, with Application to Multimodal Datasets and Models: A Systematic Review

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    Annotation tools are an essential component in the creation of datasets for machine learning purposes. Annotation tools have evolved greatly since the turn of the century, and now commonly include collaborative features to divide labor efficiently, as well as automation employed to amplify human efforts. Recent developments in machine learning models, such as Transformers, allow for training upon very large and sophisticated multimodal datasets and enable generalization across domains of knowledge. These models also herald an increasing emphasis on prompt engineering to provide qualitative fine-tuning upon the model itself, adding a novel emerging layer of direct machine learning annotation. These capabilities enable machine intelligence to recognize, predict, and emulate human behavior with much greater accuracy and nuance, a noted shortfall of which have contributed to algorithmic injustice in previous techniques. However, the scale and complexity of training data required for multimodal models presents engineering challenges. Best practices for conducting annotation for large multimodal models in the most safe and ethical, yet efficient, manner have not been established. This paper presents a systematic literature review of crowd and machine learning augmented behavioral annotation methods to distill practices that may have value in multimodal implementations, cross-correlated across disciplines. Research questions were defined to provide an overview of the evolution of augmented behavioral annotation tools in the past, in relation to the present state of the art. (Contains five figures and four tables)

    Endogenous measures for contextualising large-scale social phenomena: a corpus-based method for mediated public discourse

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    This work presents an interdisciplinary methodology for developing endogenous measures of group membership through analysis of pervasive linguistic patterns in public discourse. Focusing on political discourse, this work critiques the conventional approach to the study of political participation, which is premised on decontextualised, exogenous measures to characterise groups. Considering the theoretical and empirical weaknesses of decontextualised approaches to large-scale social phenomena, this work suggests that contextualisation using endogenous measures might provide a complementary perspective to mitigate such weaknesses. This work develops a sociomaterial perspective on political participation in mediated discourse as affiliatory action performed through language. While the affiliatory function of language is often performed consciously (such as statements of identity), this work is concerned with unconscious features (such as patterns in lexis and grammar). This work argues that pervasive patterns in such features that emerge through socialisation are resistant to change and manipulation, and thus might serve as endogenous measures of sociopolitical contexts, and thus of groups. In terms of method, the work takes a corpus-based approach to the analysis of data from the Twitter messaging service whereby patterns in users’ speech are examined statistically in order to trace potential community membership. The method is applied in the US state of Michigan during the second half of 2018—6 November having been the date of midterm (i.e. non-Presidential) elections in the United States. The corpus is assembled from the original posts of 5,889 users, who are nominally geolocalised to 417 municipalities. These users are clustered according to pervasive language features. Comparing the linguistic clusters according to the municipalities they represent finds that there are regular sociodemographic differentials across clusters. This is understood as an indication of social structure, suggesting that endogenous measures derived from pervasive patterns in language may indeed offer a complementary, contextualised perspective on large-scale social phenomena

    Religion, Education, and the ‘East’. Addressing Orientalism and Interculturality in Religious Education Through Japanese and East Asian Religions

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    This work addresses the theme of Japanese religions in order to rethink theories and practices pertaining to the field of Religious Education. Through an interdisciplinary framework that combines the study of religions, didactics and intercultural education, this book puts the case study of Religious Education in England in front of two ‘challenges’ in order to reveal hidden spots, tackle unquestioned assumptions and highlight problematic areas. These ‘challenges’, while focusing primarily on Japanese religions, are addressed within the wider contexts of other East Asian traditions and of the modern historical exchanges with the Euro-American societies. As result, a model for teaching Japanese and other East Asian religions is discussed and proposed in order to fruitfully engage issues such as orientalism, occidentalism, interculturality and critical thinking

    Designing an AI-enabled bundling generator in an automotive case study

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    Procurement and marketing are the main boundary-spanning functions of an organization. Some studies highlight that procurement is less likely to benefit from artificial intelligence emphasizing its potential in other functions, i.e., in marketing. A case study in the automotive industry of the bundling problem utilizing the design science approach is conducted from the perspective of the buying organization contributing to theory and practice. We rely on information processing theory to create a practical tool that is augmenting the skills of expert buyers through a recommendation engine to make better decisions in a novel way to further save costs. Thereby, we are adding to the literature on spend analysis that has mainly been looking backward using historical data of purchasing orders and invoices to infer saving potentials in the future – our study supplements this approach with forward-looking planning data with inherent challenges of precision and information-richness
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