ETDs

This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.

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Inside the Box: Analysing Cyber-physical Systems, Exploiting Models and Specifications

Description
The notion of the safety of a system when placed in an environment with humans and other machines has been one of the primary concerns of practitioners while deploying any cyber-physical system (CPS). Such systems, also called safety-critical systems, need to be exhaustively tested for erroneous behavior. This generates the…

The notion of the safety of a system when placed in an environment with humans and other machines has been one of the primary concerns of practitioners while deploying any cyber-physical system (CPS). Such systems, also called safety-critical systems, need to be exhaustively tested for erroneous behavior. This generates the need for coming up with algorithms that can help ascertain the behavior and safety of the system by generating tests for the system where they are likely to falsify. In this work, three algorithms have been presented that aim at finding falsifying behaviors in cyber-physical Systems. PART-X intelligently partitions while sampling the input space to provide probabilistic point and region estimates of falsification. PYSOAR-C and LS-EMIBO aims at finding falsifying behaviors in gray-box systems when some information about the system is available. Specifically, PYSOAR-C aims to find falsification while maximizing coverage using a two-phase optimization process, while LS-EMIBO aims at exploiting the structure of a requirement to find falsifications with lower computational cost compared to the state-of-the-art. This work also shows the efficacy of the algorithms on a wide range of complex cyber-physical systems. The algorithms presented in this thesis are available as python toolboxes.
ContributorsKhandait, Tanmay Bhaskar (Author) / Pedrielli, Giulia (Thesis advisor) / Fainekos, Georgios (Thesis advisor) / Gopalan, Nakul (Committee member) / Arizona State University (Publisher)
Created2022
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Bayesian Methods for Tuning Hyperparameters of Loss Functions in Machine Learning

Description
The introduction of parameterized loss functions for robustness in machine learning has led to questions as to how hyperparameter(s) of the loss functions can be tuned. This thesis explores how Bayesian methods can be leveraged to tune such hyperparameters. Specifically, a modified Gibbs sampling scheme is used to generate a…

The introduction of parameterized loss functions for robustness in machine learning has led to questions as to how hyperparameter(s) of the loss functions can be tuned. This thesis explores how Bayesian methods can be leveraged to tune such hyperparameters. Specifically, a modified Gibbs sampling scheme is used to generate a distribution of loss parameters of tunable loss functions. The modified Gibbs sampler is a two-block sampler that alternates between sampling the loss parameter and optimizing the other model parameters. The sampling step is performed using slice sampling, while the optimization step is performed using gradient descent. This thesis explores the application of the modified Gibbs sampler to alpha-loss, a tunable loss function with a single parameter $\alpha \in (0,\infty]$, that is designed for the classification setting. Theoretically, it is shown that the Markov chain generated by a modified Gibbs sampling scheme is ergodic; that is, the chain has, and converges to, a unique stationary (posterior) distribution. Further, the modified Gibbs sampler is implemented in two experiments: a synthetic dataset and a canonical image dataset. The results show that the modified Gibbs sampler performs well under label noise, generating a distribution indicating preference for larger values of alpha, matching the outcomes of previous experiments.
ContributorsCole, Erika Lingo (Author) / Sankar, Lalitha (Thesis advisor) / Lan, Shiwei (Thesis advisor) / Pedrielli, Giulia (Committee member) / Hahn, Paul (Committee member) / Arizona State University (Publisher)
Created2022