Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
Displaying 1 - 10 of 122
Description
The increasing popularity of Twitter renders improved trustworthiness and relevance assessment of tweets much more important for search. However, given the limitations on the size of tweets, it is hard to extract measures for ranking from the tweet's content alone. I propose a method of ranking tweets by generating a reputation score for each tweet that is based not just on content, but also additional information from the Twitter ecosystem that consists of users, tweets, and the web pages that tweets link to. This information is obtained by modeling the Twitter ecosystem as a three-layer graph. The reputation score is used to power two novel methods of ranking tweets by propagating the reputation over an agreement graph based on tweets' content similarity. Additionally, I show how the agreement graph helps counter tweet spam. An evaluation of my method on 16~million tweets from the TREC 2011 Microblog Dataset shows that it doubles the precision over baseline Twitter Search and achieves higher precision than current state of the art method. I present a detailed internal empirical evaluation of RAProp in comparison to several alternative approaches proposed by me, as well as external evaluation in comparison to the current state of the art method.
ContributorsRavikumar, Srijith (Author) / Kambhampati, Subbarao (Thesis advisor) / Davulcu, Hasan (Committee member) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2013
Description
The overall goal of this research project was to assess the feasibility of investigating the effects of microgravity on mineralization systems in unit gravity environments. If possible to perform these studies in unit gravity earth environments, such as earth, such systems can offer markedly less costly and more concerted research efforts to study these vitally important systems. Expected outcomes from easily accessible test environments and more tractable studies include the development of more advanced and adaptive material systems, including biological systems, particularly as humans ponder human exploration in deep space. The specific focus of the research was the design and development of a prototypical experimental test system that could preliminarily meet the challenging design specifications required of such test systems. Guided by a more unified theoretical foundation and building upon concept design and development heuristics, assessment of the feasibility of two experimental test systems was explored. Test System I was a rotating wall reactor experimental system that closely followed the specifications of a similar test system, Synthecon, designed by NASA contractors and thus closely mimicked microgravity conditions of the space shuttle and station. The latter includes terminal velocity conditions experienced by both innate material systems, as well as, biological systems, including living tissue and humans but has the ability to extend to include those material test systems associated with mineralization processes. Test System II is comprised of a unique vertical column design that offered more easily controlled fluid mechanical test conditions over a much wider flow regime that was necessary to achieving terminal velocities under free convection-less conditions that are important in mineralization processes. Preliminary results indicate that Test System II offers distinct advantages in studying microgravity effects in test systems operating in unit gravity environments and particularly when investigating mineralization and related processes. Verification of the Test System II was performed on validating microgravity effects on calcite mineralization processes reported earlier others. There studies were conducted on calcite mineralization in fixed-wing, reduced gravity aircraft, known as the `vomit comet' where reduced gravity conditions are include for very short (~20second) time periods. Preliminary results indicate that test systems, such as test system II, can be devised to assess microgravity conditions in unit gravity environments, such as earth. Furthermore, the preliminary data obtained on calcite formation suggest that strictly physicochemical mechanisms may be the dominant factors that control adaptation in materials processes, a theory first proposed by Liu et al. Thus the result of this study may also help shine a light on the problem of early osteoporosis in astronauts and long term interest in deep space exploration.
ContributorsSeyedmadani, Kimia (Author) / Pizziconi, Vincent (Thesis advisor) / Towe, Bruce (Committee member) / Alford, Terry (Committee member) / Arizona State University (Publisher)
Created2013
Description
As crystalline silicon solar cells continue to get thinner, the recombination of carriers at the surfaces of the cell plays an ever-important role in controlling the cell efficiency. One tool to minimize surface recombination is field effect passivation from the charges present in the thin films applied on the cell surfaces. The focus of this work is to understand the properties of charges present in the SiNx films and then to develop a mechanism to manipulate the polarity of charges to either negative or positive based on the end-application. Specific silicon-nitrogen dangling bonds (·Si-N), known as K center defects, are the primary charge trapping defects present in the SiNx films. A custom built corona charging tool was used to externally inject positive or negative charges in the SiNx film. Detailed Capacitance-Voltage (C-V) measurements taken on corona charged SiNx samples confirmed the presence of a net positive or negative charge density, as high as +/- 8 x 1012 cm-2, present in the SiNx film. High-energy (~ 4.9 eV) UV radiation was used to control and neutralize the charges in the SiNx films. Electron-Spin-Resonance (ESR) technique was used to detect and quantify the density of neutral K0 defects that are paramagnetically active. The density of the neutral K0 defects increased after UV treatment and decreased after high temperature annealing and charging treatments. Etch-back C-V measurements on SiNx films showed that the K centers are spread throughout the bulk of the SiNx film and not just near the SiNx-Si interface. It was also shown that the negative injected charges in the SiNx film were stable and present even after 1 year under indoor room-temperature conditions. Lastly, a stack of SiO2/SiNx dielectric layers applicable to standard commercial solar cells was developed using a low temperature (< 400 °C) PECVD process. Excellent surface passivation on FZ and CZ Si substrates for both n- and p-type samples was achieved by manipulating and controlling the charge in SiNx films.
ContributorsSharma, Vivek (Author) / Bowden, Stuart (Thesis advisor) / Schroder, Dieter (Committee member) / Honsberg, Christiana (Committee member) / Roedel, Ronald (Committee member) / Alford, Terry (Committee member) / Arizona State University (Publisher)
Created2013
Description
Automating aspects of biocuration through biomedical information extraction could significantly impact biomedical research by enabling greater biocuration throughput and improving the feasibility of a wider scope. An important step in biomedical information extraction systems is named entity recognition (NER), where mentions of entities such as proteins and diseases are located within natural-language text and their semantic type is determined. This step is critical for later tasks in an information extraction pipeline, including normalization and relationship extraction. BANNER is a benchmark biomedical NER system using linear-chain conditional random fields and the rich feature set approach. A case study with BANNER locating genes and proteins in biomedical literature is described. The first corpus for disease NER adequate for use as training data is introduced, and employed in a case study of disease NER. The first corpus locating adverse drug reactions (ADRs) in user posts to a health-related social website is also described, and a system to locate and identify ADRs in social media text is created and evaluated. The rich feature set approach to creating NER feature sets is argued to be subject to diminishing returns, implying that additional improvements may require more sophisticated methods for creating the feature set. This motivates the first application of multivariate feature selection with filters and false discovery rate analysis to biomedical NER, resulting in a feature set at least 3 orders of magnitude smaller than the set created by the rich feature set approach. Finally, two novel approaches to NER by modeling the semantics of token sequences are introduced. The first method focuses on the sequence content by using language models to determine whether a sequence resembles entries in a lexicon of entity names or text from an unlabeled corpus more closely. The second method models the distributional semantics of token sequences, determining the similarity between a potential mention and the token sequences from the training data by analyzing the contexts where each sequence appears in a large unlabeled corpus. The second method is shown to improve the performance of BANNER on multiple data sets.
ContributorsLeaman, James Robert (Author) / Gonzalez, Graciela (Thesis advisor) / Baral, Chitta (Thesis advisor) / Cohen, Kevin B (Committee member) / Liu, Huan (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
Microwave dielectrics are widely used to make resonators and filters in telecommunication systems. The production of thin films with high dielectric constant and low loss could potentially enable a marked reduction in the size of devices and systems. However, studies of these materials in thin film form are very sparse. In this research, experiments were carried out on practical high-performance dielectrics including ZrTiO4-ZnNb2O6 (ZTZN) and Ba(Co,Zn)1/3Nb2/3O3 (BCZN) with high dielectric constant and low loss tangent. Thin films were deposited by laser ablation on various substrates, with a systematical study of growth conditions like substrate temperature, oxygen pressure and annealing to optimize the film quality, and the compositional, microstructural, optical and electric properties were characterized. The deposited ZTZN films were randomly oriented polycrystalline on Si substrate and textured on MgO substrate with a tetragonal lattice change at elevated temperature. The BCZN films deposited on MgO substrate showed superior film quality relative to that on other substrates, which grow epitaxially with an orientation of (001) // MgO (001) and (100) // MgO (100) when substrate temperature was above 500 oC. In-situ annealing at growth temperature in 200 mTorr oxygen pressure was found to enhance the quality of the films, reducing the peak width of the X-ray Diffraction (XRD) rocking curve to 0.53o and the χmin of channeling Rutherford Backscattering Spectrometry (RBS) to 8.8% when grown at 800oC. Atomic Force Microscopy (AFM) was used to study the topography and found a monotonic decrease in the surface roughness when the growth temperature increased. Optical absorption and transmission measurements were used to determine the energy bandgap and the refractive index respectively. A low-frequency dielectric constant of 34 was measured using a planar interdigital measurement structure. The resistivity of the film is ~3×1010 ohm·cm at room temperature and has an activation energy of thermal activated current of 0.66 eV.
ContributorsLi, You (Author) / Newman, Nathan (Thesis advisor) / Alford, Terry (Committee member) / Singh, Rakesh (Committee member) / Arizona State University (Publisher)
Created2013
Description
Ball Grid Array (BGA) using lead-free or lead-rich solder materials are widely used as Second Level Interconnects (SLI) in mounting packaged components to the printed circuit board (PCB). The reliability of these solder joints is of significant importance to the performance of microelectronics components and systems. Product design/form-factor, solder material, manufacturing process, use condition, as well as, the inherent variabilities present in the system, greatly influence product reliability. Accurate reliability analysis requires an integrated approach to concurrently account for all these factors and their synergistic effects. Such an integrated and robust methodology can be used in design and development of new and advanced microelectronics systems and can provide significant improvement in cycle-time, cost, and reliability. IMPRPK approach is based on a probabilistic methodology, focusing on three major tasks of (1) Characterization of BGA solder joints to identify failure mechanisms and obtain statistical data, (2) Finite Element analysis (FEM) to predict system response needed for life prediction, and (3) development of a probabilistic methodology to predict the reliability, as well as, the sensitivity of the system to various parameters and the variabilities. These tasks and the predictive capabilities of IMPRPK in microelectronic reliability analysis are discussed.
ContributorsFallah-Adl, Ali (Author) / Tasooji, Amaneh (Thesis advisor) / Krause, Stephen (Committee member) / Alford, Terry (Committee member) / Jiang, Hanqing (Committee member) / Mahajan, Ravi (Committee member) / Arizona State University (Publisher)
Created2013
Description
Data mining is increasing in importance in solving a variety of industry problems. Our initiative involves the estimation of resource requirements by skill set for future projects by mining and analyzing actual resource consumption data from past projects in the semiconductor industry. To achieve this goal we face difficulties like data with relevant consumption information but stored in different format and insufficient data about project attributes to interpret consumption data. Our first goal is to clean the historical data and organize it into meaningful structures for analysis. Once the preprocessing on data is completed, different data mining techniques like clustering is applied to find projects which involve resources of similar skillsets and which involve similar complexities and size. This results in "resource utilization templates" for groups of related projects from a resource consumption perspective. Then project characteristics are identified which generate this diversity in headcounts and skillsets. These characteristics are not currently contained in the data base and are elicited from the managers of historical projects. This represents an opportunity to improve the usefulness of the data collection system for the future. The ultimate goal is to match the product technical features with the resource requirement for projects in the past as a model to forecast resource requirements by skill set for future projects. The forecasting model is developed using linear regression with cross validation of the training data as the past project execution are relatively few in number. Acceptable levels of forecast accuracy are achieved relative to human experts' results and the tool is applied to forecast some future projects' resource demand.
ContributorsBhattacharya, Indrani (Author) / Sen, Arunabha (Thesis advisor) / Kempf, Karl G. (Thesis advisor) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Contemporary online social platforms present individuals with social signals in the form of news feed on their peers' activities. On networks such as Facebook, Quora, network operator decides how that information is shown to an individual. Then the user, with her own interests and resource constraints selectively acts on a subset of items presented to her. The network operator again, shows that activity to a selection of peers, and thus creating a behavioral loop. That mechanism of interaction and information flow raises some very interesting questions such as: can network operator design social signals to promote a particular activity like sustainability, public health care awareness, or to promote a specific product? The focus of my thesis is to answer that question. In this thesis, I develop a framework to personalize social signals for users to guide their activities on an online platform. As the result, we gradually nudge the activity distribution on the platform from the initial distribution p to the target distribution q. My work is particularly applicable to guiding collaborations, guiding collective actions, and online advertising. In particular, I first propose a probabilistic model on how users behave and how information flows on the platform. The main part of this thesis after that discusses the Influence Individuals through Social Signals (IISS) framework. IISS consists of four main components: (1) Learner: it learns users' interests and characteristics from their historical activities using Bayesian model, (2) Calculator: it uses gradient descent method to compute the intermediate activity distributions, (3) Selector: it selects users who can be influenced to adopt or drop specific activities, (4) Designer: it personalizes social signals for each user. I evaluate the performance of IISS framework by simulation on several network topologies such as preferential attachment, small world, and random. I show that the framework gradually nudges users' activities to approach the target distribution. I use both simulation and mathematical method to analyse convergence properties such as how fast and how close we can approach the target distribution. When the number of activities is 3, I show that for about 45% of target distributions, we can achieve KL-divergence as low as 0.05. But for some other distributions KL-divergence can be as large as 0.5.
ContributorsLe, Tien D (Author) / Sundaram, Hari (Thesis advisor) / Davulcu, Hasan (Thesis advisor) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2014
Description
Most data cleaning systems aim to go from a given deterministic dirty database to another deterministic but clean database. Such an enterprise pre–supposes that it is in fact possible for the cleaning process to uniquely recover the clean versions of each dirty data tuple. This is not possible in many cases, where the most a cleaning system can do is to generate a (hopefully small) set of clean candidates for each dirty tuple. When the cleaning system is required to output a deterministic database, it is forced to pick one clean candidate (say the "most likely" candidate) per tuple. Such an approach can lead to loss of information. For example, consider a situation where there are three equally likely clean candidates of a dirty tuple. An appealing alternative that avoids such an information loss is to abandon the requirement that the output database be deterministic. In other words, even though the input (dirty) database is deterministic, I allow the reconstructed database to be probabilistic. Although such an approach does avoid the information loss, it also brings forth several challenges. For example, how many alternatives should be kept per tuple in the reconstructed database? Maintaining too many alternatives increases the size of the reconstructed database, and hence the query processing time. Second, while processing queries on the probabilistic database may well increase recall, how would they affect the precision of the query processing? In this thesis, I investigate these questions. My investigation is done in the context of a data cleaning system called BayesWipe that has the capability of producing multiple clean candidates per each dirty tuple, along with the probability that they are the correct cleaned version. I represent these alternatives as tuples in a tuple disjoint probabilistic database, and use the Mystiq system to process queries on it. This probabilistic reconstruction (called BayesWipe–PDB) is compared to a deterministic reconstruction (called BayesWipe–DET)—where the most likely clean candidate for each tuple is chosen, and the rest of the alternatives discarded.
ContributorsRihan, Preet Inder Singh (Author) / Kambhampati, Subbarao (Thesis advisor) / Liu, Huan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2013
Description
As the size and scope of valuable datasets has exploded across many industries and fields of research in recent years, an increasingly diverse audience has sought out effective tools for their large-scale data analytics needs. Over this period, machine learning researchers have also been very prolific in designing improved algorithms which are capable of finding the hidden structure within these datasets. As consumers of popular Big Data frameworks have sought to apply and benefit from these improved learning algorithms, the problems encountered with the frameworks have motivated a new generation of Big Data tools to address the shortcomings of the previous generation. One important example of this is the improved performance in the newer tools with the large class of machine learning algorithms which are highly iterative in nature. In this thesis project, I set about to implement a low-rank matrix completion algorithm (as an example of a highly iterative algorithm) within a popular Big Data framework, and to evaluate its performance processing the Netflix Prize dataset. I begin by describing several approaches which I attempted, but which did not perform adequately. These include an implementation of the Singular Value Thresholding (SVT) algorithm within the Apache Mahout framework, which runs on top of the Apache Hadoop MapReduce engine. I then describe an approach which uses the Divide-Factor-Combine (DFC) algorithmic framework to parallelize the state-of-the-art low-rank completion algorithm Orthogoal Rank-One Matrix Pursuit (OR1MP) within the Apache Spark engine. I describe the results of a series of tests running this implementation with the Netflix dataset on clusters of various sizes, with various degrees of parallelism. For these experiments, I utilized the Amazon Elastic Compute Cloud (EC2) web service. In the final analysis, I conclude that the Spark DFC + OR1MP implementation does indeed produce competitive results, in both accuracy and performance. In particular, the Spark implementation performs nearly as well as the MATLAB implementation of OR1MP without any parallelism, and improves performance to a significant degree as the parallelism increases. In addition, the experience demonstrates how Spark's flexible programming model makes it straightforward to implement this parallel and iterative machine learning algorithm.
ContributorsKrouse, Brian (Author) / Ye, Jieping (Thesis advisor) / Liu, Huan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2014