ASU Electronic Theses and Dissertations
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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- Creators: Xue, Guoliang
- Creators: Mobasher, Barzin
Description
Data mining, also known as big data analysis, has been identified as a critical and challenging process for a variety of applications in real-world problems. Numerous datasets are collected and generated every day to store the information. The rise in the number of data volumes and data modality has resulted in the increased demand for data mining methods and strategies of finding anomalies, patterns, and correlations within large data sets to predict outcomes. Effective machine learning methods are widely adapted to build the data mining pipeline for various purposes like business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The major challenges for effectively and efficiently mining big data include (1) data heterogeneity and (2) missing data. Heterogeneity is the natural characteristic of big data, as the data is typically collected from different sources with diverse formats. The missing value is the most common issue faced by the heterogeneous data analysis, which resulted from variety of factors including the data collecting processing, user initiatives, erroneous data entries, and so on. In response to these challenges, in this thesis, three main research directions with application scenarios have been investigated: (1) Mining and Formulating Heterogeneous Data, (2) missing value imputation strategy in various application scenarios in both offline and online manner, and (3) missing value imputation for multi-modality data. Multiple strategies with theoretical analysis are presented, and the evaluation of the effectiveness of the proposed algorithms compared with state-of-the-art methods is discussed.
Contributorsliu, Xu (Author) / He, Jingrui (Thesis advisor) / Xue, Guoliang (Thesis advisor) / Li, Baoxin (Committee member) / Tong, Hanghang (Committee member) / Arizona State University (Publisher)
Created2021
Description
Alkali-activated aluminosilicates, commonly known as "geopolymers", are being increasingly studied as a potential replacement for Portland cement. These binders use an alkaline activator, typically alkali silicates, alkali hydroxides or a combination of both along with a silica-and-alumina rich material, such as fly ash or slag, to form a final product with properties comparable to or better than those of ordinary Portland cement. The kinetics of alkali activation is highly dependent on the chemical composition of the binder material and the activator concentration. The influence of binder composition (slag, fly ash or both), different levels of alkalinity, expressed using the ratios of Na2O-to-binders (n) and activator SiO2-to-Na2O ratios (Ms), on the early age behavior in sodium silicate solution (waterglass) activated fly ash-slag blended systems is discussed in this thesis. Optimal binder composition and the n values are selected based on the setting times. Higher activator alkalinity (n value) is required when the amount of slag in the fly ash-slag blended mixtures is reduced. Isothermal calorimetry is performed to evaluate the early age hydration process and to understand the reaction kinetics of the alkali activated systems. The differences in the calorimetric signatures between waterglass activated slag and fly ash-slag blends facilitate an understanding of the impact of the binder composition on the reaction rates. Kinetic modeling is used to quantify the differences in reaction kinetics using the Exponential as well as the Knudsen method. The influence of temperature on the reaction kinetics of activated slag and fly ash-slag blends based on the hydration parameters are discussed. Very high compressive strengths can be obtained both at early ages as well as later ages (more than 70 MPa) with waterglass activated slag mortars. Compressive strength decreases with the increase in the fly ash content. A qualitative evidence of leaching is presented through the electrical conductivity changes in the saturating solution. The impact of leaching and the strength loss is found to be generally higher for the mixtures made using a higher activator Ms and a higher n value. Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy (ATR-FTIR) is used to obtain information about the reaction products.
ContributorsChithiraputhiran, Sundara Raman (Author) / Neithalath, Narayanan (Thesis advisor) / Rajan, Subramaniyam D (Committee member) / Mobasher, Barzin (Committee member) / Arizona State University (Publisher)
Created2012
Description
Nowadays, wireless communications and networks have been widely used in our daily lives. One of the most important topics related to networking research is using optimization tools to improve the utilization of network resources. In this dissertation, we concentrate on optimization for resource-constrained wireless networks, and study two fundamental resource-allocation problems: 1) distributed routing optimization and 2) anypath routing optimization. The study on the distributed routing optimization problem is composed of two main thrusts, targeted at understanding distributed routing and resource optimization for multihop wireless networks. The first thrust is dedicated to understanding the impact of full-duplex transmission on wireless network resource optimization. We propose two provably good distributed algorithms to optimize the resources in a full-duplex wireless network. We prove their optimality and also provide network status analysis using dual space information. The second thrust is dedicated to understanding the influence of network entity load constraints on network resource allocation and routing computation. We propose a provably good distributed algorithm to allocate wireless resources. In addition, we propose a new subgradient optimization framework, which can provide findgrained convergence, optimality, and dual space information at each iteration. This framework can provide a useful theoretical foundation for many networking optimization problems. The study on the anypath routing optimization problem is composed of two main thrusts. The first thrust is dedicated to understanding the computational complexity of multi-constrained anypath routing and designing approximate solutions. We prove that this problem is NP-hard when the number of constraints is larger than one. We present two polynomial time K-approximation algorithms. One is a centralized algorithm while the other one is a distributed algorithm. For the second thrust, we study directional anypath routing and present a cross-layer design of MAC and routing. For the MAC layer, we present a directional anycast MAC. For the routing layer, we propose two polynomial time routing algorithms to compute directional anypaths based on two antenna models, and prove their ptimality based on the packet delivery ratio metric.
ContributorsFang, Xi (Author) / Xue, Guoliang (Thesis advisor) / Yau, Sik-Sang (Committee member) / Ye, Jieping (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2013
Description
The field of cyber-defenses has played catch-up in the cat-and-mouse game of finding vulnerabilities followed by the invention of patches to defend against them. With the complexity and scale of modern-day software, it is difficult to ensure that all known vulnerabilities are patched; moreover, the attacker, with reconnaissance on their side, will eventually discover and leverage them. To take away the attacker's inherent advantage of reconnaissance, researchers have proposed the notion of proactive defenses such as Moving Target Defense (MTD) in cyber-security. In this thesis, I make three key contributions that help to improve the effectiveness of MTD.
First, I argue that naive movement strategies for MTD systems, designed based on intuition, are detrimental to both security and performance. To answer the question of how to move, I (1) model MTD as a leader-follower game and formally characterize the notion of optimal movement strategies, (2) leverage expert-curated public data and formal representation methods used in cyber-security to obtain parameters of the game, and (3) propose optimization methods to infer strategies at Strong Stackelberg Equilibrium, addressing issues pertaining to scalability and switching costs. Second, when one cannot readily obtain the parameters of the game-theoretic model but can interact with a system, I propose a novel multi-agent reinforcement learning approach that finds the optimal movement strategy. Third, I investigate the novel use of MTD in three domains-- cyber-deception, machine learning, and critical infrastructure networks. I show that the question of what to move poses non-trivial challenges in these domains. To address them, I propose methods for patch-set selection in the deployment of honey-patches, characterize the notion of differential immunity in deep neural networks, and develop optimization problems that guarantee differential immunity for dynamic sensor placement in power-networks.
First, I argue that naive movement strategies for MTD systems, designed based on intuition, are detrimental to both security and performance. To answer the question of how to move, I (1) model MTD as a leader-follower game and formally characterize the notion of optimal movement strategies, (2) leverage expert-curated public data and formal representation methods used in cyber-security to obtain parameters of the game, and (3) propose optimization methods to infer strategies at Strong Stackelberg Equilibrium, addressing issues pertaining to scalability and switching costs. Second, when one cannot readily obtain the parameters of the game-theoretic model but can interact with a system, I propose a novel multi-agent reinforcement learning approach that finds the optimal movement strategy. Third, I investigate the novel use of MTD in three domains-- cyber-deception, machine learning, and critical infrastructure networks. I show that the question of what to move poses non-trivial challenges in these domains. To address them, I propose methods for patch-set selection in the deployment of honey-patches, characterize the notion of differential immunity in deep neural networks, and develop optimization problems that guarantee differential immunity for dynamic sensor placement in power-networks.
ContributorsSengupta, Sailik (Author) / Kambhampati, Subbarao (Thesis advisor) / Bao, Tiffany (Youzhi) (Committee member) / Huang, Dijiang (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2020