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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.

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.

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.

As computer users increasingly rely on digital tools for daily tasks, the complexity of their working environments continues to grow. Modern knowledge workers must navigate a diverse array of digital resources, including documents, websites, applications, and other information. This complexity presents challenges in managing multiple activities to maintain productivity, such as handling interruptions, resuming tasks, curating resources, recalling context, retrieving previously closed digital resources, and fostering self-reflection. Despite these challenges, there has been limited research on leveraging visual cues to help users reconstruct their previous mental contexts, retrieve digital resources, and enhance self-reflection for behavioral change. Therefore, this Ph.D. dissertation addresses these gaps by focusing on: (1) investigating the existing challenges users face in curating digital resources, (2) designing and implementing supportive applications for task resumption, (3) developing methods that utilize screenshots and metadata for reconstructing mental context and retrieving resources, and (4) enhancing the processes of self-reflection and behavioral change to improve overall productivity. / Doctor of Philosophy / In our increasingly digital world, people are spending more time on computers to complete their daily tasks. However, as there are increasing resources and information involved, managing tasks has become more complex because individuals need to juggle various digital tools at the same time, like documents, websites, and applications. This can lead to difficulties such as dealing with interruptions, picking up where they left off, organizing information, remembering what they were working on, and reflecting on their habits to make positive changes. Surprisingly, there has been little research on how common-seen visual tools, like screenshots, can help people better handle these challenges. Screenshots can serve as visual reminders, helping users remember what they were doing and easily find the resources they need. Hence, this Ph.D. dissertation explores these issues by: (1) examining the problems people face when organizing digital resources, (2) creating tools to help users quickly resume their tasks, (3) using screenshots and additional information to help users remember their previous work, and (4) improving the way people reflect on their behavior to encourage positive changes.

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?

No published studies to date examined the practices in creation or adoption of metadata in cultural heritage institutions or evaluated metadata in bibliographic databases in the Arabian Gulf counties and assessed its potential interoperability in the aggregation that would provide a central point of access to bibliographic databases of cultural heritage institutions. This exploratory study aimed to address this gap with the goal of: (1) developing understanding of the current state of information representation and knowledge organization in cultural heritage collections in Arabian Gulf countries, and (2) exploring perspectives for future developments such as creating regional large-scale portals similar to Digital Public Library of America, Europeana etc. that facilitate discovery by aggregating metadata and possible barriers to these developments.. The study is focused on a Kuwaiti, Qatari, and Omani libraries, museums, and archives. The mixed-methods research combined semi-structured interviews of the bibliographic database managers at 15 cultural heritage institutions and in-depth content analysis of a sample of 412 metadata records that represent items in these bibliographic databases for accuracy, completeness, consistency, use of knowledge organization systems, etc. This study findings make a research contribution important for evaluating the feasibility and planning of future aggregations of cultural heritage bibliographic databases. Results provided insights into possible ways to achieve interoperability in metadata for such digital portals in the Arabian Gulf region.