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.
Displaying 1 - 10 of 88
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
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
Libby Larsen is one of the most performed and acclaimed composers today. She is a spirited, compelling, and sensitive composer whose music enhances the poetry of America's most prominent authors. Notable among her works are song cycles for soprano based on the poetry of female writers, among them novelist and poet Willa Cather (1873-1947). Larsen has produced two song cycles on works from Cather's substantial output of fiction: one based on Cather's short story, "Eric Hermannson's Soul," titled Margaret Songs: Three Songs from Willa Cather (1996); and later, My Antonia (2000), based on Cather's novel of the same title. In Margaret Songs, Cather's poetry and short stories--specifically the character of Margaret Elliot--combine with Larsen's unique compositional style to create a surprising collaboration. This study explores how Larsen in these songs delves into the emotional and psychological depths of Margaret's character, not fully formed by Cather. It is only through Larsen's music and Cather's poetry that Margaret's journey through self-discovery and love become fully realized. This song cycle is a glimpse through the eyes of two prominent female artists on the societal pressures placed upon Margaret's character, many of which still resonate with women in today's culture. This study examines the work Margaret Songs by discussing Willa Cather, her musical influences, and the conditions surrounding the writing of "Eric Hermannson's Soul." It looks also into Cather's influence on Libby Larsen and the commission leading to Margaret Songs. Finally, a description of the musical, dramatic, and textual content of the songs completes this interpretation of the interactions of Willa Cather, Libby Larsen, and the character of Margaret Elliot.
ContributorsMcLain, Christi Marie (Author) / FitzPatrick, Carole (Thesis advisor) / Dreyfoos, Dale (Committee member) / Holbrook, Amy (Committee member) / Ryan, Russell (Committee member) / Arizona State University (Publisher)
Created2013
Description
Arnold Schoenberg's 1908-09 song cycle, Das Buch der hängenden Gärten [The Book of the Hanging Gardens], opus 15, represents one of his most decisive early steps into the realm of musical modernism. In the midst of personal and artistic crises, Schoenberg set texts by Stefan George in a style he called "pantonality," and described his composition as radically new. Though stylistically progressive, however, Schoenberg's musical achievement had certain ideologically conservative roots: the composer numbered among turn-of-the-century Viennese artists and thinkers whose opposition to the conventional and the popular--in favor of artistic autonomy and creativity--concealed a reactionary misogyny. A critical reading of Hanging Gardens through the lens of gender reveals that Schoenberg, like many of his contemporaries, incorporated strong frauenfeindlich [anti-women] elements into his work, through his modernist account of artistic creativity, his choice of texts, and his musical settings. Although elements of Hanging Gardens' atonal music suggest that Schoenberg valued gendered-feminine principles in his compositional style, a closer analysis of the work's musical language shows an intact masculinist hegemony. Through his deployment of uncanny tonal reminiscences, underlying tonal gestures, and closed forms in Hanging Gardens, Schoenberg ensures that the feminine-associated "excesses" of atonality remain under masculine control. This study draws upon the critical musicology of Susan McClary while arguing that Schoenberg's music is socially contingent, affected by the gender biases of his social and literary milieux. It addresses likely influences on Schoenberg's worldview including the philosophy of Otto Weininger, Freudian psychoanalysis, and a complex web of personal relationships. Finally, this analysis highlights the relevance of Schoenberg's world and its constructions of gender to modern performance practice, and argues that performers must consider interrelated historical, textual, and musical factors when interpreting Hanging Gardens in new contexts.
ContributorsGinger, Kerry Anne (Author) / FitzPatrick, Carole (Thesis advisor) / Dreyfoos, Dale (Committee member) / Mook, Richard (Committee member) / Norton, Kay (Committee member) / Ryan, Russell (Committee member) / Arizona State University (Publisher)
Created2012
Description
ABSTRACT This document introduces singers and voice teachers to Dr. Alfred A. Tomatis's listening training method with a particular emphasis on its relevance to singers. After presenting an overview of Tomatis's work in the field of audio-psycho-phonology (circa 1947 through the 1990s) and specific ways that aspects of his theory are relevant to singers' performance skills, this project investigates the impact of listening training on singers by examining published research. The studies described in this document have investigated the impact of listening training on elements of the singer's skill set, including but not limited to measures of vocal quality such as intonation, vocal control, intensity, and sonority, as well as language pronunciation and general musicianship. Anecdotal evidence, presented by performers and their observers, is also considered. The evidence generated by research studies and anecdotal reports strongly favors Tomatis-based listening training as a valid way to improve singers' performance abilities.
ContributorsHurley, Susan Lynn (Author) / Doan, Jerry (Thesis advisor) / Dreyfoos, Dale (Committee member) / Kopta, Anne (Committee member) / Norton, Kay (Committee member) / Thompson, Billie M (Committee member) / Arizona State University (Publisher)
Created2012
Description
The trained singer utilizes an awareness of her body as an instrument. When she becomes pregnant, her body changes in numerous ways to support the pregnancy. Many of these changes have great impact on her ability to sing during the pregnancy and postpartum periods. The voice may be altered positively or negatively by the release of hormones. The body undergoes many changes that affect the posture and breathing required for singing. Most notably, the abdominal muscles are greatly impacted by the pregnancy. They are stretched by the growing uterus, and this affects their function. In addition, the linea alba (the connective tissue between the halves of the rectus abdominis) is softened by hormonal increases and subject to stretching as the uterus grows, predisposing it to weakness. Since the other abdominal muscles attach to the linea alba via connective tissue, maintaining the integrity of the linea alba during pregnancy and postpartum is vital to the operational function of the abdominal muscles. Protecting the vulnerable linea alba must be deliberately undertaken in two parts. First, conscious exercise is needed to preserve the linea alba during pregnancy and to rehabilitate it after pregnancy. Targeted exercises strengthen the transverse abdominis and shorten and approximate the two halves of the rectus abdominis. Second, modifications in daily movement are necessary to protect the linea alba while performing routine activities. Cesarean sections present additional surgical concerns for singers, including abdominal incisions, use of medication, and the rare need for general anesthesia via intubation. Recovery from a cesarean can be difficult due to abdominal pain, yet steps may be taken to speed healing at the hospital and at home. This paper provides an overview of how pregnancy affects the singer, discusses the effects of pregnancy and cesarean section, and provides a plan to protect the abdominal muscles during pregnancy and rehabilitate them in the postpartum period. It combines information from the fields of physical therapy, medicine, and surgery into a guide for the singer and voice teacher.
ContributorsWill, Andrea Pitman (Author) / Doan, Jerry (Thesis advisor) / Elgar Kopta, Anne (Thesis advisor) / Dreyfoos, Dale (Committee member) / Mills, Robert (Committee member) / Oldani, Robert W (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
This paper and its accompanying recital examine three solo vocal works by Italian composer Alfredo Casella (1883-1947): "Larmes" from Cinq Mélodies (Op. 2); "Mort, ta servante est à ma porte" from L'adieu à la vie: Quatre lyriques funèbres extraites du "Gitanjali" de Rabindranath Tagore (Op. 26); and "Amante sono, vaghiccia, di voi" from Tre canzoni trecentesche (Op. 36). Each of these songs is discussed as representative of Casella's three compositional periods. A fourth song, "Ecce odor filii mei" from Tre canti sacri per baritono et organo (Op. 66), is also examined, as an end-of-life composition. Some of the more important solo vocal works composed in each period are mentioned to show where the four selected songs fit into Casella's compositional output and to suggest music for further study or repertoire.
ContributorsGordon, Stefan (Author) / Mills, Robert (Thesis advisor) / Holbrook, Amy (Committee member) / FitzPatrick, Carole (Committee member) / Dreyfoos, Dale (Committee member) / Arizona State University (Publisher)
Created2014