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 121
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
Bright Summer, a one-movement piece for orchestra, was composed in Arizona, and completed in February 2013. The piece is approximately twelve minutes long. The motivation for writing this piece was the death of my mother the year before, in 2012. The prevailing mood of this work is bright and pleasant, expressing my mother's cheerful personality when she was alive. It also portrays bright summer days which resemble my mother's spirit. Thus, soundscape plays an important role in this work. It depicts summer breeze, rustling sounds of leaves, and, to translate a Korean saying, "high blue skies." This soundscape opens the piece as well as closes it. In the middle section, the fast upbeat themes represent my mother's witty and optimistic personality. The piece also contains the presence of a hymn tune, The Love of God is Greater Far, which informs the motivic content and also functions as the climax of the piece. It was my mother's favorite hymn and we used to sing it together following her conversion to Christianity. The piece contains three main sections, which are held together by transitional material based on the soundscape and metric modulations. Unlike my earlier works, Bright Summer is tonal, with upper tertian harmonies prevailing throughout the piece. However, the opening and closing soundscapes do not have functional harmonies. For example, tertian chords appear and vanish silently, leaving behind some resonant sounds without any harmonic progression. Overall, the whole piece is reminiscent of my mother who lived a beautiful life.
ContributorsKim, JeeYeon (Composer) / DeMars, James (Thesis advisor) / Hackbarth, Glenn (Committee member) / Rogers, Rodney (Committee member) / Levy, Benjamin (Committee member) / Rockmaker, Jody (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
Norwegian composer Ola Gjeilo (b. 1978) is highly regarded as an accomplished and prolific composer of choral music. His creative output includes works for chorus, solo piano, and wind symphony. His unique style infuses elements of cinematic music, jazz and improvisation, with particularly intriguing selections of text. This study examines the factors that influence Gjeilo's compositional techniques, and the musical interpretations of conductor Charles Bruffy in his preparation for The Phoenix Chorale's recording Northern Lights: Choral Works by Ola Gjeilo. The eleven works discussed in this study are: The Ground, Evening Prayer, Ubi caritas, Prelude, Northern Lights, The Spheres, Tota pulchra es, Serenity, Phoenix (Agnus Dei), Unicornis captivatur, and Dark Night of the Soul. As a relatively new and young composer, there is very little published literature on Gjeilo and his works. This study provides an intimate glance into the creative process of the composer. By composing in multiple styles and with a variety of inspirational sources, Gjeilo creates a fresh approach toward composition of new choral music. His style is revealed through interviews and numerous collaborations with conductors and performers who have prepared and performed his music, as well through an examination of the eleven works recorded by The Phoenix Chorale.
ContributorsGarrison, Ryan Derrick (Author) / Reber, William (Thesis advisor) / Saucier, Catherine (Committee member) / Rockmaker, Jody (Committee member) / Doan, Jerry (Committee member) / Arizona State University (Publisher)
Created2013
Description
This thesis presents a new arrangement of Richard Peaslee's trombone solo "Arrows of Time" for brass band. This arrangement adapts Peaslee's orchestration - and subsequent arrangement by Dr. Joshua Hauser for wind ensemble - for the modern brass band instrumentation and includes a full score. A brief biography of Richard Peaslee and his work accompanies this new arrangement, along with commentary on the orchestration of "Arrows of Time", and discussion of the evolution and adaptation of the work for wind ensemble by Dr. Hauser. The methodology used to adapt these versions for the brass band completes the background information.
ContributorsMalloy, Jason Patrick (Author) / Ericson, John (Thesis advisor) / Oldani, Robert (Committee member) / Rockmaker, Jody (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
The German pianist and composer Johannes Brahms (1883-1897) wrote more than 122 works for a wide variety of ensembles and genres. Despite this remarkable productivity, and his widely heralded talent for innovation and technique as a composer, few of his works have been arranged for solo guitar, and these have focused primarily on his simpler, more melodic works. Conventional wisdom is that his music is "too dense" to be played on the guitar. As a result, there are no arrangements of orchestral works by Brahms in the standard repertoire for the guitar. In arranging Brahms's Serenade in D Major, movt. 1 for the guitar, I provide a counter argument that not all of Brahms's orchestral music is too dense all of the time. In Part I, I provide a brief overview of the history of, and sources for, the Serenade. Part II describes a step-by-step guide through the process of arranging orchestral repertoire for the solo guitar. Part III is an examination of the editing process that utilizes examples from the guitar arrangement of the Serenade in order to illustrate the various techniques and considerations that are part of the editing process. Part IV is a performance edition of the arrangement. In summary, the present arrangement of Brahms's Serenade, op.11 is the beginning of a conversation about why the "guitar world" should be incorporating the music of Brahms into the standard repertoire. The lessons learned, and the technical challenges discovered, should help inform future arrangers and guitar performers for additional compositions by Brahms.
ContributorsLanier, William Hudson (Author) / Koonce, Frank (Thesis advisor) / Micklich, Albie (Committee member) / Rockmaker, Jody (Committee member) / Arizona State University (Publisher)
Created2013
Description
Nelson Rolihlahla Mandela was born July 18, 1918 into the Madiba clan in Mvezo, Transkei, South Africa. Mandela was a lawyer by trade and a freedom fighter who envisioned freedom and equality for all South Africans regardless of race. In 1965, Mandela was imprisoned at Robben Island for twenty-seven years for treason and terrorist activities against the South African apartheid regime: he was assigned prison numbers 46664. In 1992, Mandela was released from prison and two years later not only became the first democratically elected president of South Africa, but also its first black president. "Madiba 46664" is an eight-minute chamber work scored for flute, oboe, clarinet in B-flat, and bassoon; vibraphone, and two percussionists; piano; violins, violas, and celli. The work blends traditional South African rhythms of the drumming culture with elements of Western harmony and form in contrasting textures of homophony, polyphony and antiphony. "Madiba 46664" utilizes Mandela's prison number, birthdate and age (at the time the composition process began in 2013) for the initial generation of meter, rhythm, harmony, melody, and form. The work also shares intercultural concepts that can be seen in the works of three contemporary African composers, South Africans Jeanne Zaidel-Rudolph and Andile Khumalo, and Nigerian Ayo Oluranti. Each section represents a period of Mandela's life as a freedom fighter, a prisoner, and a president. The inspiration stems from the composer's discussions with Mandela soon after his release from prison and prior to his presidency. These lively discussions pertained to the state of traditional music in then apartheid South Africa and led to this creation. The conversations also played a role in the creative process.
ContributorsMabingnai, Collette Sipho (Composer) / DeMars, James (Thesis advisor) / Hackbarth, Glenn (Committee member) / Humphreys, Jere (Committee member) / Rockmaker, Jody (Committee member) / Rogers, Rodney (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