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Visual-Textual Video Synopsis Generation, Aidean Sharghi Karganroodi
Visual-Textual Video Synopsis Generation, Aidean Sharghi Karganroodi
Electronic Theses and Dissertations
In this dissertation we tackle the problem of automatic video summarization. Automatic summarization techniques enable faster browsing and indexing of large video databases. However, due to the inherent subjectivity of the task, no single video summarizer fits all users unless it adapts to individual user's needs. To address this issue, we introduce a fresh view on the task called "Query-focused'' extractive video summarization. We develop a supervised model that takes as input a video and user's preference in form of a query, and creates a summary video by selecting key shots from the original video. We model the problem as …
A Study Of Perceptions On Incident Response Exercises, Information Sharing, Situational Awareness, And Incident Response Planning In Power Grid Utilities, Joseph Garmon
Electronic Theses and Dissertations
The power grid is facing increasing risks from a cybersecurity attack. Attacks that shut off electricity in Ukraine have already occurred, and successful compromises of the power grid that did not shut off electricity to customers have been privately disclosed in North America. The objective of this study is to identify how perceptions of various factors emphasized in the electric sector affect incident response planning. Methods used include a survey of 229 power grid personnel and the use of partial least squares structural equation modeling to identify causal relationships. This study reveals the relationships between perceptions by personnel responsible for …
Blockchain-Driven Secure And Transparent Audit Logs, Ashar Ahmad
Blockchain-Driven Secure And Transparent Audit Logs, Ashar Ahmad
Electronic Theses and Dissertations
In enterprise business applications, large volumes of data are generated daily, encoding business logic and transactions. Those applications are governed by various compliance requirements, making it essential to provide audit logs to store, track, and attribute data changes. In traditional audit log systems, logs are collected and stored in a centralized medium, making them prone to various forms of attacks and manipulations, including physical access and remote vulnerability exploitation attacks, and eventually allowing for unauthorized data modification, threatening the guarantees of audit logs. Moreover, such systems, and given their centralized nature, are characterized by a single point of failure. To …
Parameter Estimation Of Stochastic Models Against Probabilistic Temporal Logic Behavioral Specifications, Arfeen Khalid
Parameter Estimation Of Stochastic Models Against Probabilistic Temporal Logic Behavioral Specifications, Arfeen Khalid
Electronic Theses and Dissertations
The inherent behavioral variability exhibited by stochastic systems makes it a challenging task for human experts to manually analyze them. Computational modeling of such systems helps in investigating and predicting the behaviors of their underlying processes but at the same time introduces the presence of several unknown parameters. A key challenge faced in this scenario is to determine the values of these unknown parameters against known behavioral specifications. The solutions that have been presented so far estimate the parameters of a given model against a single specification whereas a correct model is expected to satisfy all the behavioral specifications when …
Collaborative Artificial Intelligence Algorithms For Medical Imaging Applications, Naji Khosravan
Collaborative Artificial Intelligence Algorithms For Medical Imaging Applications, Naji Khosravan
Electronic Theses and Dissertations
In this dissertation, we propose novel machine learning algorithms for high-risk medical imaging applications. Specifically, we tackle current challenges in radiology screening process and introduce cutting-edge methods for image-based diagnosis, detection and segmentation. We incorporate expert knowledge through eye-tracking, making the whole process human-centered. This dissertation contributes to machine learning, computer vision, and medical imaging research by: 1) introducing a mathematical formulation of radiologists level of attention, and sparsifying their gaze data for a better extraction and comparison of search patterns. 2) proposing novel, local and global, image analysis algorithms. Imaging based diagnosis and pattern analysis are "high-risk" Artificial Intelligence …