This study explores ways of visualizing metadata in virtual reality through means other than text. Specifically, we focus on the metadata of a 3D object as a search result and how these visualizations can support understanding of the object. The study draws on research from the field of information science and is positioned in the domain of cultural heritage. There are few studies about human information interaction in virtual reality or metadata visualization in these environments. However, metadata can be a useful tool in the information seeking search process. Because its visualization should depend on purpose, ways of visualizing metadata other than through text are in need of exploration. This study answers the following question: How can descriptive metadata of a 3D search result object be visualized in VR to support users’ understanding of the object? A research through design approach was employed. Data was collected through interviews about objects with visitors at a museum. These interview data were analyzed using a thematic analysis, and the results of that analysis were triangulated with the objects’ metadata in a database. This triangulation resulted in sets of characteristics and techniques that, as expressed by interviewees, could help them gain an understanding of an object of historical and cultural significance. We then selected a smaller set of characteristics for implementation using a prioritization matrix, and an object to visualize metadata for. Brainstorming sessions generated ways of visualizing these characteristics using relevant techniques. The prototype is a virtual reality experience that allows the user to navigate three layers of information with visualizations of the 3D object’s characteristics. The prototype was interactively demonstrated with four participants and their feedback was documented. The discussion is about the definition of metadata, unforeseen designs, the search process, the role of virtual reality in information seeking, how metadata visualizations can support understanding of an object and whether our do, our choice of using research through design as the approach and the study’s ethical and societal implications and its limitations. We conclude that metadata of a search result as an object in VR can be visualized by segmenting the VR experience into different layers of information, specifying a sequence in which the layers are presented that builds a narrative and provides users with multisensory feedback. We encourage future evaluative and comparative studies.

The purpose of this study is to determine if elementary school libraries in west central Florida provide diverse, inclusive collections of LGBTQAI+ material in a safe space, and, if they do, to ascertain the librarians' understanding of, and satisfaction with, how that material is cataloged and classified. I wanted to know whether the literature was easily located, or, conversely, if the library classification tools and practices either misrepresented non-normative students or failed to represent them at all, given the potential impacts on students' self-acceptance. To answer these questions, I surveyed 41 private, independent, and public librarians, 10 of whom volunteered for in-depth, semi-structured interviews. During the interviews, I explored how the librarians used lists, thesauri, tagging, ratings, and spine labels to supplement their catalog searches. As none of this work happens in a vacuum, I also examined the paradigm in which the librarians do their work. I approached this study as a humanist, through a feminist lens, using Queer Theory and Whiteness studies. I was not seeking a unitary truth but rather a thick description of each librarian's perspectives and decisions concerning their collection management and cataloging procedures, contextualized within their daily demands as school librarians and, in the case of the public school librarians, technology specialists. My research found a group of librarians dedicated to their work, each of whom approached the management of LGBTQAI+ literature from their own positionality, ranging from those who denied that elementary students needed this literature at all, to those who wanted to provide it but were restricted by time, budget, and parental and administration censorship, to those who boldly proclaimed "I'm not afraid."

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Low-cost network-connected smart outlets are now available for monitoring, controlling, and scheduling the energy usage of electrical devices. As a result, such smart outlets are being integrated into automated home management systems, which remotely control them by analyzing and interpreting their data. However, to effectively interpret data and control devices, the system must know the type of device that is plugged into each smart outlet. Existing systems require users to manually input and maintain the outlet metadata that associates a device type with a smart outlet. Such manual operation is time-consuming and error-prone: users must initially inventory all outlet-to-device mappings, enter them into the management system, and then update this metadata every time a new device is plugged in or moves to a new outlet. To address the problem, we propose AutoPlug, a system that automatically identifies and tracks the devices plugged into smart outlets in real time without user intervention. AutoPlug combines machine learning techniques with time-series analysis of device energy data in real time to accurately identify and track devices on startup, and as they move from outlet-to-outlet. We show that AutoPlug achieves ∼90% identification accuracy on real data collected from 13 distinct device types, while also detecting when a device changes outlets with an accuracy >90%. We implement an AutoPlug prototype on Raspberry Pi and deploy it live in a real home for a period of 20 days. We show that its performance enables it to monitor up to 25 outlets withlatency.

Learning objects (LO) reuse is one topical area in instructional design that is gaining popularity in the education economy. It hinges on high hopes and promises to transform how learning occurs in the information age. This study attempted to identify and interrogate the core characteristics of reusable learning objects and conceptualize them as innovations in the curriculum development process. The goal was to synthesize existing knowledge on learning objects, weave streams of literature and research to focus on core arising issues, and then develop an instructional design tool that can help learners easily and effectively find reusable learning objects. The learning objects could be categorized and deconstructed to the levels of their instructional design transformations with regard to macro and micro-level reusability. The researcher used combinatorial developmental research with integrative literature review methodologies to design and develop a metadata management tool. This study involved an in depth review of literature on learning objects, reusable learning objects and their associated metadata management schemes through the integrative literature review approach. Results and data from the integrative literature review were then utilized to design and develop a tool addressing meta-tagging schemes, metadata management, search, and access of learning objects. The researcher identified characteristics of learning objects within the reuse process and discussed best practices, reuse procedures and modeling, based on the analysis of existing cases such as the Open-Knowledge-Initiative (OKI) projects to aid in the tool development. Integrative analysis running concurrently with the development process allowed for rigorous identification and alignment of key factors in the learning objects reuse universe. If fully developed, the metadata management tool could contribute to effective metadata management for learning objects often reused by learning designers, deliverers, and consumers. / Doctor of Philosophy / Learning Objects (LO) reuse is gaining popularity in the field of instructional design. This is because it could transform how learning occurs in today's information age. In this study, I outlined the important characteristics of reusable learning objects and set them up as creative and re-creative products in the curriculum development process. My goal was to combine and reproduce existing literature on LOs that would allow me to develop an instructional design tool to help learning content designers, deliverers, and consumers to easily tag, search, then find reusable learning objects. I reviewed literature on learning objects, reusable learning objects and their associated metadata management schemes then used this data to design and develop the tool addressing meta tagging schemes, metadata management, search, and accessibility of learning objects. The tool allows LO categorization and deconstruction to the largest and smallest granular levels of their instructional reusability. I combined a developmental research method with an integrative literature review method to design and develop the prototype of a tool known as metadata management tool (mmt) for reusable learning objects. If successful, the metadata management tool developed could contribute to an effective metadata management for learning objects often reused by learning designers, deliverers, and consumers.

As more artifacts are created in a digital format, there is a need to have metadata associated with the artifacts to increase the chance for resource discovery by others. This is especially the case with non-textual artifacts. Once these artifacts have descriptive metadata associated with them, they have the potential to become learning objects which can be used by others in their own teaching and research. This study explored the design and development of a performance support tool to create descriptive metadata by users that are most familiar with the non-textual learning objects, yet may not have an understanding of the various metadata schemas and standards required by other institutional/knowledge repositories and search engines. In order to create such a tool, certain features need to be included in order for users to create appropriate metadata. The tool needs to have Unicode character support in order metadata entry, display and searching. Research found that characteristics such as controlled vocabularies, tooltips, validation rules, and having a relevant image on the same screen as the metadata form help users to create appropriate and accurate metadata; yet, no existing tool was found that contained all of these features to assist faculty in describing their non-textual learning objects. These characteristics were operationalized in the design and development of the performance support tool. Findings from the evaluation of the tool indicate that the owner of the learning objects was able to create a customized, non-standard metadata form that users were then able to use to create appropriate and accurate descriptive metadata. / Ph. D.

Hardware-Assisted Trusted Execution Environment technologies have become a crucial component in providing security for cloud-based computing. One of such hardware-assisted countermeasures is Intel Software Guard Extension (SGX). Using additional dedicated hardware and a new set of CPU instructions, SGX is able to provide isolated execution of code within trusted hardware containers called enclaves. By utilizing private encrypted memory and various integrity authentication mechanisms, it can provide confidentiality and integrity guarantees to protected data. In spite of dedicated hardware, these extra layers of security add a significant performance overhead. Decryption of data using secret OTPs, which are generated by modified Counter Mode Encryption AES blocks, results in a significant latency overhead that contributes to the overall SGX performance loss. This thesis introduces a metadata prediction extension to SGX based on local metadata releveling and prediction mechanisms. Correct prediction of metadata allows to speculatively precompute OTPs, which can be immediately used in decryption of incoming ciphertext data. This hides a significant part of decryption latency and results in faster SGX performance without any changes to the original SGX security guarantees. / Master of Science / With the exponential growth of cloud computing, where critical data processing is happening on third-party computer systems, it is important to ensure data confidentiality and integrity against third-party access. Sometimes that may include not only external attackers, but also insiders, like cloud computing providers themselves. While software isolation using Virtual Machines is the most common method of achieving runtime security in cloud computing, numerous shortcomings of software-only countermeasures force companies to demand extra layers of security. Recently adopted general purpose hardware-assisted technology like Intel Software Guard Extension (SGX) add that extra layer of security at the significant performance overhead. One of the major contributors to the SGX performance overhead is data decryption latency. This work proposes a novel algorithm to speculatively predict metadata that is used during decryption. This allows the processor to hide a significant portion of decryption latency, thus improving the overall performance of Intel SGX without compromising security.

No / Many data warehouses are currently underutilized by managers and knowledge workers. Can high-quality end-user metadata help to increase levels of adoption and use?