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Okwire’shon:’a, or trees of the forest, guide the methodology and epistemology of my doctoral research. The Rotinonhsonni creation history tells us that all life is made from the clay of the earth (Mother Earth or First Woman), and therefore everything in Creation shares an origin in and a connection to the earth. Thus, Rotinonhsonni thoughtways understand trees to be part of an interconnected network of land-based knowledge that spans from time immemorial to the present. As extensions of First Woman, trees are literally my relations, my ancestors. While onkwehonwe (original peoples) have long been able to tap into the knowledge of the land (and many still do), colonialism has significantly disrupted our landed and place-based relationships and consequently our ability to read the land. This, in turn, disrupts the ability of onkwehonwe to live within the principles of Kayanerekowa. My dissertation explores, through juxtapositions of Rotinonhsonni oral histories, contemporary Indigenous literature, and a series of trees, the possibility of (re)learning to read and communicate with the land. Using a trans-Indigenous methodology, my project examines three branches of land-centered philosophy within Indigenous literature: enacting creation stories; spirit agency; and internalized ecological holism. By reading different Indigenous texts across from Rotinonhsonni epic teachings, my trans-Indigenous methodology affirms Indigenous alliances with the environment and with each other, their long-standing presence on and stewardship of the land, and the value and validity of knowledge that is ancestral, adaptive, and alive. I argue that by carrying forward land-centered knowledge contemporary Indigenous literature stimulates an awareness of the land and nonhuman societies as cognizant and in communication with us. Renewing relations and modes of relationality to the land in this way re-energizes Kayanerekowa, and has the potential to strengthen Indigenous efforts for self-determination, knowledge resurgence, land reclamation, and nation-to-nation alliances. / Thesis / Doctor of Philosophy (PhD) / This project demonstrates a cyclical process of reading between a small selection of contemporary Indigenous literatures, Indigenous oral histories and cosmologies, and a series of trees indigenous to Turtle Island. Tree-readings are attempted through three methods: aesthetic (metaphoric) interpretation; analysis through Indigenous oral histories; and, listening to the thoughts of the trees themselves. Each tree’s teachings are then bundled together as a framework for reading a work of Indigenous narrative art that demonstrates similar principles and emphases. The overall aim of this work is to model how landed processes of coming to know develop an awareness of the land and all nonhuman societies as alive, thinking, and possessing agency. In a Rotinonhsonni (Six Nations) context, this renewal of landed consciousness strengthens the principles of righteousness, reason, and power, which sustain the Kayanerekowa (Great Law of Peace).

Creating 360-degree 3D content has gained traction in the past few years, being used for Virtual Reality environments. However, creating such content is challenging because it requires a multi-camera setup or a collection of images from different perspectives. This paper proposes 3D Pano Inpainting, a pipeline capable of transforming a single equirectangular panoramic RGBD image into a complete 360° 3D virtual reality scene represented as a textured mesh. Our methodology is as follows: we estimate a consistent depth map for the input panorama; we use a pre built framework to convert the image and its depth map into a textured mesh with inpainted background edges; we account for wrapping the resulting mesh around the viewer’s perspective for better immersion in VR headsets. Additionally, we evaluate our method’s effectiveness in producing consistent novel views through the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and learned perceptual image patch similarity (LPIPS) between a rendering produced from the ground truth image and depth map to that produced from our model. Furthermore, we compare our model’s scores with those of a non-inpainted textured mesh.

<p dir="ltr">The use of connected health technology is becoming increasingly significant in the field of healthcare. Artificial Intelligence- augmented workflows connected to treatment guidelines promise more inclusive care delivery. The AI/ML-based connected health informatics plays an integral role in every stage of medical product development, from initial discovery to providing guidance to healthcare providers, and finally to delivering patient care. The exponential growth of meta data and the rapid advancement of connected health technologies provide greater opportunities for novel healthcare solutions, delivery mechanisms, and clinical trial designs.</p><p dir="ltr">However, it poses complexity of the AI/ML-specific challenges besides all the challenges SaMD products face. The regulations for AI/ML-based connected solutions have yet to mature. The AI/ML SaMD development process requires additional considerations such as data quality and management, continuous deployment, and validation.</p><p><br></p><p dir="ltr">This study delves into the integration of Machine Learning (ML) with medical software devices and how the current lifecycle models fit the needs of the AI industry. AI/ML-based SaMD development process artifacts are identified through the theory and AI/ML SaMD regulations and standards requirements. Moreover, this study analyzes collected data from interviews, surveys, and an experimental case study to identify success factors in building quality and agility for AI/ML-based SaMD development projects.</p><p dir="ltr">Incorporating of Artificial Intelligence (AI) in healthcare requires continuous deployment and validation processes, which may not be in line with the current workflow, capability, or authority of regulators. This research also highlights that model governance and technology access can be key challenges in implementing AI/ML development process artifacts, especially when integrated into connected health solutions.</p><p dir="ltr">This work sets the foundation for future research to reduce bottlenecks in the machine-learning process. The focus should be on aiding model governance to streamline development and ensure machine reliability. A suitable software toolchain is necessary for exploratory data analysis, data integration, documentation, model governance, monitoring, version control, and integration with other software and services within a connected health solution. Additionally, conducting more focused research on security and privacy in the context of connected health would be valuable.</p>

no / When developing multivariate data classification and clustering methodologies for data mining, it is clear that most literature contributions only really consider data that contain consistently the same attributes. There are however many cases in current big data analytics applications where for same topic and even same source data sets there are differing attributes being measured, for a multitude of reasons (whether the specific design of an experiment or poor data quality and consistency). We define this class of data a dimensionally inconsistent multivariate data, a topic that can be considered a subclass of the Big Data Variety research. This paper explores some classification methodologies commonly used in multivariate classification and clustering tasks and considers how these traditional methodologies could be adapted to compare dimensionally inconsistent data sets. The study focuses on adapting two similarity measures: Robinson-Foulds tree distance metrics and Variation of Information; for comparing clustering of hierarchical cluster algorithms (such clusters are derived from the raw multivariate data). The results from experiments on engineering data highlight that adapting pairwise measures to exclude non-common attributes from the traditional distance metrics may not be the best method of classification. We suggest that more specialised metrics of similarity are required to address challenges presented by dimensionally inconsistent multivariate data, with specific applications for big engineering data analytics. / Jaguar Land-Rover

People pursuing LGBTQ+ rights in the United States have faced triumphs and setbacks over time but now face equality-restricting legislation in several regions. Previous researchers have studied LGBTQ+ college students and LGBTQ+ identity as a master status, but less on how queer identity competes for salience with other identities. This study uses qualitative and quantitative secondary data on student responses to a university-wide climate survey (n=1699) conducted in the Fall semester of 2022. I analyze LGBTQ+ and non-LGBTQ+ student experiences and satisfaction with their school. The quantitative results of this research find that LGBTQ+ students have a markedly different campus experience, while the qualitative results find that student identity takes precedence over gender and sexual identity when assessing East Tennessee State University (ETSU). Data analysis includes personal narratives from LGBTQ+ and non-LGBTQ+ students on their college experiences with belongingness, academics, and campus political landscape.