834 research outputs found

    The Black Box of Enrollment Management: The Influence of Academic Capitalism and Values of the Public Good

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    The study addresses the widening income and racial access gap in higher education resulting from enrollment management teams’ operationalization of academic capitalism. The study focuses on the local, micro level, emphasizing how enrollment management leadership teams make sense of enrollment management, recognizing that enrollment management and the work of enrollment management stakeholders exist within an organizational space encompassing the values of both public good and academic capitalism. Using a case study methodology and critical sensemaking theory, the research explored how academic capitalism and values of the public good shaped enrollment management leadership teams’ sensemaking and sensegiving as they enacted decisions, actions, and practices to recruit and admit students. The main conclusion includes the critical role of the EMLTs and its members’ agency in public good enactments, especially driving the sensemaking process, and a more nuanced and complicated picture between academic capitalism and values of the public good in enrollment management. The study is the first to demonstrate that academic capitalism and the public good can coexist and overlap, in various ways, within the field of enrollment management despite existing literature’s overwhelming characterization of enrollment management as firmly existing within the space of academic capitalism. Recommendations for colleges and universities include leveraging capitalist tools to drive a public good agenda; using predictive data analytics to have a measured approach to increase access; balancing the use of tuition discounting; investing in hiring organizational actors who can operate with contradictory logics and share public good values; developing key public good metrics; diversifying revenue streams; and for wealthy institutions to be bold in their public good enactments

    dacl10k: Benchmark for Semantic Bridge Damage Segmentation

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    Reliably identifying reinforced concrete defects (RCDs)plays a crucial role in assessing the structural integrity, traffic safety, and long-term durability of concrete bridges, which represent the most common bridge type worldwide. Nevertheless, available datasets for the recognition of RCDs are small in terms of size and class variety, which questions their usability in real-world scenarios and their role as a benchmark. Our contribution to this problem is "dacl10k", an exceptionally diverse RCD dataset for multi-label semantic segmentation comprising 9,920 images deriving from real-world bridge inspections. dacl10k distinguishes 12 damage classes as well as 6 bridge components that play a key role in the building assessment and recommending actions, such as restoration works, traffic load limitations or bridge closures. In addition, we examine baseline models for dacl10k which are subsequently evaluated. The best model achieves a mean intersection-over-union of 0.42 on the test set. dacl10k, along with our baselines, will be openly accessible to researchers and practitioners, representing the currently biggest dataset regarding number of images and class diversity for semantic segmentation in the bridge inspection domain.Comment: 23 pages, 6 figure

    Machine Learning Algorithm for the Scansion of Old Saxon Poetry

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    Several scholars designed tools to perform the automatic scansion of poetry in many languages, but none of these tools deal with Old Saxon or Old English. This project aims to be a first attempt to create a tool for these languages. We implemented a Bidirectional Long Short-Term Memory (BiLSTM) model to perform the automatic scansion of Old Saxon and Old English poems. Since this model uses supervised learning, we manually annotated the Heliand manuscript, and we used the resulting corpus as labeled dataset to train the model. The evaluation of the performance of the algorithm reached a 97% for the accuracy and a 99% of weighted average for precision, recall and F1 Score. In addition, we tested the model with some verses from the Old Saxon Genesis and some from The Battle of Brunanburh, and we observed that the model predicted almost all Old Saxon metrical patterns correctly misclassified the majority of the Old English input verses

    The good, the bad, and the bloody. Conceptualisations of menstruation across genders and languages

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    Menstruation is a particularly prominent aspect of the life of the many bodies that experience it. From menarche in adolescence to menopause in later life, the implications are not only biological and medical, but also social, cultural and political. Myths, religions, cultures, medicine, and scholarship from diverse fields have concerned themselves with this event for decades, indeed centuries, creating the complex interplay that now informs the menstrual experience and discourse. Yet, beyond anthropologically exploring the status of ‘taboo’ that keeps menstruation hidden, the metaphors in this discourse remain to be fully analysed from a cognitive linguistics perspective. Furthermore, there has been little acknowledgement of gender and linguistic variance within that discourse, particularly of trans individuals and speakers of Arabic and its dialects. There is a wealth of metaphorical expressions that were born within this complex landscape, and that are now used to think and speak about menses, particularly in some types of language and among certain populations. This project aims to fill this gap as it focuses on uncovering the conceptual metaphors of menstruation that exist in everyday language, while including menstruators and non-menstruators alike, as well as speakers of Arabic and its dialect, using a Conceptual Metaphor Theory-based investigation of these metaphors. For this purpose, a survey of participants is first used to gather data which is examined through a semantic tagger, the Historical Thesaurus, and the Mapping Metaphor online tool. This analysis results in the identification of several conceptual metaphors pertaining to the domains PART OF NATURE or NATURAL PART OF LIFE, SOMETHING DIRTY or UPKEEP, PURIFICATION, A PERIOD OF TIME or A PERIOD OF THE HEALTH CYCLE, A HABIT, BLOOD, BLESSING AND TORMENT, A VISITOR, and THE COLOUR RED. Menstruators and non-menstruators rely on those domains and engage in creative coinages of new expressions to create a linguistic point that is informed by their purposes in communication and from which they are able to communicate exactly what and how they want, whether it is to comply with or to defy convention and taboo. Therefore, the usage of menstrual metaphors, beyond its tabooed background and its reflections of societal constraints, is first and foremost a strategic tool for menstruators in particular to be able to accomplish any communicative goal they have in a manner that they deem safe and suitable

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    The Potential of Visual ChatGPT For Remote Sensing

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    Recent advancements in Natural Language Processing (NLP), particularly in Large Language Models (LLMs), associated with deep learning-based computer vision techniques, have shown substantial potential for automating a variety of tasks. One notable model is Visual ChatGPT, which combines ChatGPT's LLM capabilities with visual computation to enable effective image analysis. The model's ability to process images based on textual inputs can revolutionize diverse fields. However, its application in the remote sensing domain remains unexplored. This is the first paper to examine the potential of Visual ChatGPT, a cutting-edge LLM founded on the GPT architecture, to tackle the aspects of image processing related to the remote sensing domain. Among its current capabilities, Visual ChatGPT can generate textual descriptions of images, perform canny edge and straight line detection, and conduct image segmentation. These offer valuable insights into image content and facilitate the interpretation and extraction of information. By exploring the applicability of these techniques within publicly available datasets of satellite images, we demonstrate the current model's limitations in dealing with remote sensing images, highlighting its challenges and future prospects. Although still in early development, we believe that the combination of LLMs and visual models holds a significant potential to transform remote sensing image processing, creating accessible and practical application opportunities in the field

    Generations: Creative Computation, Community, and the Rhetorical Canon

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    “Generations: Creative Computation, Community, and the Rhetorical Canon” investigates how computational poets and artists use the intrinsic rhetoricity of generative computational processes for social critique and community-building, through a renewal of the classical rhetorical canon. Computer-generated poetry and art is often created using the same technological mechanisms (full-stack development, procurement and manipulations of ‘big data’) as the algorithms and social norms it sets out to critique. These conditions of production provide a unique rhetorical perspective for revisiting the classical rhetorical canons—invention, arrangement, style, memory, and delivery. From this vantage point that views classical rhetorical theory in contemporary digital context, I detail ways that computer-generated texts relate to concerns of social critique and enable digital communities. “Generations” demonstrates the rhetorical possibilities and limitations of computer-generated creative texts as artistic correctives in response to specific harms (like neoliberal individualism and data colonialism) of contemporary digital life. It also demonstrates the ways that these texts are created in community with others, a salient feature of the genre that amplifies its capacity for social engagement.Doctor of Philosoph

    Iterative musical collaboration as palimpsest: Suite Inversée and The Headroom Project

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    Suite inversée is a musical work, co-composed by the two authors asynchronously online by means of file transfer alone and digitally presented using a self-made web app called The Headroom Project. The Headroom Project mediates the compositional project during creation as well as allowing the listener to browse a historical thread that weaves through the developmental process: through this app, each audio file that was shared between the two composers can be heard and considered both in and out of the context of its creation. The framework of the project provided the opportunity for the authors to reflect on issues of remote digital collaboration and the palimpsest nature of a work revealed in varying stages of evolution through a novel mode of presentation. This paper discusses the mode of creation by situating it within narratives of composition and technology
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