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Ensemble Machine Learning At The Edge Using The Codec Classifier Structure And Weak Learners Guided By Mutual Information, Aj Beckwith
All Graduate Theses and Dissertations, Fall 2023 to Present
The Codec Classifier is a low-computation, low-memory tree ensemble method that dramatically improves feasibility of image classification on resource-constrained edge devices. It achieves advantages over other tree ensemble methods due the separation of encoder and decoder tasks in the classifier. The encoder partitions feature space, and the decoder labels the regions in the partition. This functional separation of tasks enables the encoder design (partitioning) to be guided by maximizing the mutual information (MI) between class labels and the features (i.e. the encoded representation of the data) without regard to the error performance of the classifier. Experiments show maximizing MI leads …
Do Poor Countries Catch Up To Rich Countries? Structural Change In The World-Economy, 1816-1916, Jared Walker
Do Poor Countries Catch Up To Rich Countries? Structural Change In The World-Economy, 1816-1916, Jared Walker
All Graduate Theses and Dissertations, Fall 2023 to Present
Do poor countries catch up to rich countries? To answer that question, countries were divided into upper class (core), middle class (semi-periphery), and lower class (periphery) based on degree of industrialization as indicated by primary energy consumption data. Findings indicated twenty-three upward transitions and five downward transitions during the period examined. Asymmetrical upward mobility was understood in the context of geographic expansion of the system. This sufficiently increased the population of the lower class (periphery) to support larger populations in the middle class (semi-periphery) and upper class (core). Nevertheless, probability analysis indicated a stable system characterized by high levels of …
Adversarially Reweighted Sequence Anomaly Detection With Limited Log Data, Kevin Vulcano
Adversarially Reweighted Sequence Anomaly Detection With Limited Log Data, Kevin Vulcano
All Graduate Theses and Dissertations, Fall 2023 to Present
In the realm of safeguarding digital systems, the ability to detect anomalies in log sequences is paramount, with applications spanning cybersecurity, network surveillance, and financial transaction monitoring. This thesis presents AdvSVDD, a sophisticated deep learning model designed for sequence anomaly detection. Built upon the foundation of Deep Support Vector Data Description (Deep SVDD), AdvSVDD stands out by incorporating Adversarial Reweighted Learning (ARL) to enhance its performance, particularly when confronted with limited training data. By leveraging the Deep SVDD technique to map normal log sequences into a hypersphere and harnessing the amplification effects of Adversarial Reweighted Learning, AdvSVDD demonstrates remarkable efficacy …