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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Description
This dissertation delves into some EFL stakeholders' understanding of spiritual identities and power relations associated with these identities as performed in an undergraduate EFL teacher education program at a Christian university in Indonesia. This study is motivated by an ongoing debate over the place of spirituality, especially Christianity, in ELT. In this project, religions are considered to be windows through which one's spirituality is viewed and expressed. Spiritually associated relations of power indicate discrepancies due to positioning of one person committed to a spiritual view in relation to those having similar or different spiritual views. The purpose of exploring spiritually associated identities and power relations is to provide empirical evidence which supports the following arguments. The integration of spirituality in ELT, or lack thereof, can be problematic. More importantly, however, spirituality can be enriching for some EFL teachers and students alike, and be presented together with critical ELT. To explore the complexity of power relations associated with some EFL stakeholders' spiritual identities, I analyzed data from classroom observations, four focus group discussions from February to April 2014, and individual interviews with 23 teachers and students from February to September 2014. Findings showed that Christian and non-Christian English teachers had nuanced views regarding the place of prayer in ELT-related activities, professionalism in ELT, and ways of negotiating spiritually associated power relations in ELT contexts. Students participating in this study performed their spiritual identities in ways that can be perceived as problematic (e.g., by being very dogmatic or evangelical) or self-reflexive. Classroom observations helped me to see more clearly how Christian English teachers interacted with their students from different religious backgrounds. In one class, a stimulating dialogue seemed to emerge when a teacher accommodated both critical and religious views to be discussed. This project culminates in my theorization of the praxis of critical spiritual pedagogy in ELT. Central to this praxis are (a) raising the awareness of productive power and power relations associated with spiritual identities; (b) learning how to use defiant discourses in negotiating spiritually associated power relations; and (c) nurturing self-reflexivity critically and spiritually.
ContributorsMambu, Joseph Ernest (Author) / Matsuda, Paul Kei (Thesis advisor) / Friedrich, Patricia (Committee member) / Prior, Matthew T. (Committee member) / Arizona State University (Publisher)
Created2014
Description
Feature representations for raw data is one of the most important component in a machine learning system. Traditionally, features are \textit{hand crafted} by domain experts which can often be a time consuming process. Furthermore, they do not generalize well to unseen data and novel tasks. Recently, there have been many efforts to generate data-driven representations using clustering and sparse models. This dissertation focuses on building data-driven unsupervised models for analyzing raw data and developing efficient feature representations.
Simultaneous segmentation and feature extraction approaches for silicon-pores sensor data are considered. Aggregating data into a matrix and performing low rank and sparse matrix decompositions with additional smoothness constraints are proposed to solve this problem. Comparison of several variants of the approaches and results for signal de-noising and translocation/trapping event extraction are presented. Algorithms to improve transform-domain features for ion-channel time-series signals based on matrix completion are presented. The improved features achieve better performance in classification tasks and in reducing the false alarm rates when applied to analyte detection.
Developing representations for multimedia is an important and challenging problem with applications ranging from scene recognition, multi-media retrieval and personal life-logging systems to field robot navigation. In this dissertation, we present a new framework for feature extraction for challenging natural environment sounds. Proposed features outperform traditional spectral features on challenging environmental sound datasets. Several algorithms are proposed that perform supervised tasks such as recognition and tag annotation. Ensemble methods are proposed to improve the tag annotation process.
To facilitate the use of large datasets, fast implementations are developed for sparse coding, the key component in our algorithms. Several strategies to speed-up Orthogonal Matching Pursuit algorithm using CUDA kernel on a GPU are proposed. Implementations are also developed for a large scale image retrieval system. Image-based "exact search" and "visually similar search" using the image patch sparse codes are performed. Results demonstrate large speed-up over CPU implementations and good retrieval performance is also achieved.
Simultaneous segmentation and feature extraction approaches for silicon-pores sensor data are considered. Aggregating data into a matrix and performing low rank and sparse matrix decompositions with additional smoothness constraints are proposed to solve this problem. Comparison of several variants of the approaches and results for signal de-noising and translocation/trapping event extraction are presented. Algorithms to improve transform-domain features for ion-channel time-series signals based on matrix completion are presented. The improved features achieve better performance in classification tasks and in reducing the false alarm rates when applied to analyte detection.
Developing representations for multimedia is an important and challenging problem with applications ranging from scene recognition, multi-media retrieval and personal life-logging systems to field robot navigation. In this dissertation, we present a new framework for feature extraction for challenging natural environment sounds. Proposed features outperform traditional spectral features on challenging environmental sound datasets. Several algorithms are proposed that perform supervised tasks such as recognition and tag annotation. Ensemble methods are proposed to improve the tag annotation process.
To facilitate the use of large datasets, fast implementations are developed for sparse coding, the key component in our algorithms. Several strategies to speed-up Orthogonal Matching Pursuit algorithm using CUDA kernel on a GPU are proposed. Implementations are also developed for a large scale image retrieval system. Image-based "exact search" and "visually similar search" using the image patch sparse codes are performed. Results demonstrate large speed-up over CPU implementations and good retrieval performance is also achieved.
ContributorsSattigeri, Prasanna S (Author) / Spanias, Andreas (Thesis advisor) / Thornton, Trevor (Committee member) / Goryll, Michael (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2014
Description
Sparse learning is a powerful tool to generate models of high-dimensional data with high interpretability, and it has many important applications in areas such as bioinformatics, medical image processing, and computer vision. Recently, the a priori structural information has been shown to be powerful for improving the performance of sparse learning models. A graph is a fundamental way to represent structural information of features. This dissertation focuses on graph-based sparse learning. The first part of this dissertation aims to integrate a graph into sparse learning to improve the performance. Specifically, the problem of feature grouping and selection over a given undirected graph is considered. Three models are proposed along with efficient solvers to achieve simultaneous feature grouping and selection, enhancing estimation accuracy. One major challenge is that it is still computationally challenging to solve large scale graph-based sparse learning problems. An efficient, scalable, and parallel algorithm for one widely used graph-based sparse learning approach, called anisotropic total variation regularization is therefore proposed, by explicitly exploring the structure of a graph. The second part of this dissertation focuses on uncovering the graph structure from the data. Two issues in graphical modeling are considered. One is the joint estimation of multiple graphical models using a fused lasso penalty and the other is the estimation of hierarchical graphical models. The key technical contribution is to establish the necessary and sufficient condition for the graphs to be decomposable. Based on this key property, a simple screening rule is presented, which reduces the size of the optimization problem, dramatically reducing the computational cost.
ContributorsYang, Sen (Author) / Ye, Jieping (Thesis advisor) / Wonka, Peter (Thesis advisor) / Wang, Yalin (Committee member) / Li, Jing (Committee member) / Arizona State University (Publisher)
Created2014
Description
Due to government initiatives, education in the classroom has focused on high stakes test scores measuring student achievement on basic skills. The purpose of this action research study was to augment fourth grade students' knowledge of basic content by teaching greater meaning and depth of understanding--to teach critical thinking using Socratic circles. Using a constructivist approach, a comprehensive plan was designed and implemented that included an age-appropriate platform for argument and inquiry, a process that required critical thinking skills, and allowed the intellectual standards for critical thinking to be developed and measured. Ten students representing the academic levels of the whole class were selected and participated in seven Socratic circles. Over a period of 15 weeks, a mixed methods approach was employed to determine how students were able to apply the intellectual standards to reasoning during Socratic circles, how this innovation provoked participation in student-centered dialogue, and how Socratic circles improved students' evaluation of competing ideas during their reasoned discourse. Results suggested that Comprehensive Socratic Circles increased participation in reasoned discourse. Students' ability to evaluate competing ideas improved, and their application of the intellectual standards for critical thinking to their reasoning increased. Students also increased their use of student-centered dialogue across the sessions. These findings suggest that Socratic circles is a flexible and effective teaching strategy that fosters critical thinking in fourth graders.
ContributorsCleveland, Julie (Author) / Rotheram-Fuller, Erin (Thesis advisor) / Dinn-You Liou, Daniel (Committee member) / Lansdowne, Kimberly (Committee member) / Arizona State University (Publisher)
Created2015
Description
With the rise of social media, hundreds of millions of people spend countless hours all over the globe on social media to connect, interact, share, and create user-generated data. This rich environment provides tremendous opportunities for many different players to easily and effectively reach out to people, interact with them, influence them, or get their opinions. There are two pieces of information that attract most attention on social media sites, including user preferences and interactions. Businesses and organizations use this information to better understand and therefore provide customized services to social media users. This data can be used for different purposes such as, targeted advertisement, product recommendation, or even opinion mining. Social media sites use this information to better serve their users.
Despite the importance of personal information, in many cases people do not reveal this information to the public. Predicting the hidden or missing information is a common response to this challenge. In this thesis, we address the problem of predicting user attributes and future or missing links using an egocentric approach. The current research proposes novel concepts and approaches to better understand social media users in twofold including, a) their attributes, preferences, and interests, and b) their future or missing connections and interactions. More specifically, the contributions of this dissertation are (1) proposing a framework to study social media users through their attributes and link information, (2) proposing a scalable algorithm to predict user preferences; and (3) proposing a novel approach to predict attributes and links with limited information. The proposed algorithms use an egocentric approach to improve the state of the art algorithms in two directions. First by improving the prediction accuracy, and second, by increasing the scalability of the algorithms.
Despite the importance of personal information, in many cases people do not reveal this information to the public. Predicting the hidden or missing information is a common response to this challenge. In this thesis, we address the problem of predicting user attributes and future or missing links using an egocentric approach. The current research proposes novel concepts and approaches to better understand social media users in twofold including, a) their attributes, preferences, and interests, and b) their future or missing connections and interactions. More specifically, the contributions of this dissertation are (1) proposing a framework to study social media users through their attributes and link information, (2) proposing a scalable algorithm to predict user preferences; and (3) proposing a novel approach to predict attributes and links with limited information. The proposed algorithms use an egocentric approach to improve the state of the art algorithms in two directions. First by improving the prediction accuracy, and second, by increasing the scalability of the algorithms.
ContributorsAbbasi, Mohammad Ali, 1975- (Author) / Liu, Huan (Thesis advisor) / Davulcu, Hasan (Committee member) / Ye, Jieping (Committee member) / Agarwal, Nitin (Committee member) / Arizona State University (Publisher)
Created2014
Description
Users often join an online social networking (OSN) site, like Facebook, to remain social, by either staying connected with friends or expanding social networks. On an OSN site, users generally share variety of personal information which is often expected to be visible to their friends, but sometimes vulnerable to unwarranted access from others. The recent study suggests that many personal attributes, including religious and political affiliations, sexual orientation, relationship status, age, and gender, are predictable using users' personal data from an OSN site. The majority of users want to remain socially active, and protect their personal data at the same time. This tension leads to a user's vulnerability, allowing privacy attacks which can cause physical and emotional distress to a user, sometimes with dire consequences. For example, stalkers can make use of personal information available on an OSN site to their personal gain. This dissertation aims to systematically study a user vulnerability against such privacy attacks.
A user vulnerability can be managed in three steps: (1) identifying, (2) measuring and (3) reducing a user vulnerability. Researchers have long been identifying vulnerabilities arising from user's personal data, including user names, demographic attributes, lists of friends, wall posts and associated interactions, multimedia data such as photos, audios and videos, and tagging of friends. Hence, this research first proposes a way to measure and reduce a user vulnerability to protect such personal data. This dissertation also proposes an algorithm to minimize a user's vulnerability while maximizing their social utility values.
To address these vulnerability concerns, social networking sites like Facebook usually let their users to adjust their profile settings so as to make some of their data invisible. However, users sometimes interact with others using unprotected posts (e.g., posts from a ``Facebook page\footnote{The term ''Facebook page`` refers to the page which are commonly dedicated for businesses, brands and organizations to share their stories and connect with people.}''). Such interactions help users to become more social and are publicly accessible to everyone. Thus, visibilities of these interactions are beyond the control of their profile settings. I explore such unprotected interactions so that users' are well aware of these new vulnerabilities and adopt measures to mitigate them further. In particular, {\em are users' personal attributes predictable using only the unprotected interactions}? To answer this question, I address a novel problem of predictability of users' personal attributes with unprotected interactions. The extreme sparsity patterns in users' unprotected interactions pose a serious challenge. Therefore, I approach to mitigating the data sparsity challenge by designing a novel attribute prediction framework using only the unprotected interactions. Experimental results on Facebook dataset demonstrates that the proposed framework can predict users' personal attributes.
A user vulnerability can be managed in three steps: (1) identifying, (2) measuring and (3) reducing a user vulnerability. Researchers have long been identifying vulnerabilities arising from user's personal data, including user names, demographic attributes, lists of friends, wall posts and associated interactions, multimedia data such as photos, audios and videos, and tagging of friends. Hence, this research first proposes a way to measure and reduce a user vulnerability to protect such personal data. This dissertation also proposes an algorithm to minimize a user's vulnerability while maximizing their social utility values.
To address these vulnerability concerns, social networking sites like Facebook usually let their users to adjust their profile settings so as to make some of their data invisible. However, users sometimes interact with others using unprotected posts (e.g., posts from a ``Facebook page\footnote{The term ''Facebook page`` refers to the page which are commonly dedicated for businesses, brands and organizations to share their stories and connect with people.}''). Such interactions help users to become more social and are publicly accessible to everyone. Thus, visibilities of these interactions are beyond the control of their profile settings. I explore such unprotected interactions so that users' are well aware of these new vulnerabilities and adopt measures to mitigate them further. In particular, {\em are users' personal attributes predictable using only the unprotected interactions}? To answer this question, I address a novel problem of predictability of users' personal attributes with unprotected interactions. The extreme sparsity patterns in users' unprotected interactions pose a serious challenge. Therefore, I approach to mitigating the data sparsity challenge by designing a novel attribute prediction framework using only the unprotected interactions. Experimental results on Facebook dataset demonstrates that the proposed framework can predict users' personal attributes.
ContributorsGundecha, Pritam S (Author) / Liu, Huan (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Ye, Jieping (Committee member) / Barbier, Geoffrey (Committee member) / Arizona State University (Publisher)
Created2015
Description
This dissertation examines Japanese preschool teachers' cultural practices and beliefs about the pedagogy of social-emotional development. The study is an interview-based, ethnographic study, which is based on the video-cued mutivocal ethnographic method. This study focuses on the emic terms that Japanese preschool teachers use to explain their practices, such as amae (dependency), omoiyari (empathy), sabishii (loneliness), mimamoru (watching and waiting) and garari (peripheral participation). My analysis suggests that sabishii, amae, and omoiyari form a triad of emotional exchange that has a particular cultural patterning and salience in Japan and in the Japanese approach to the socialization of emotions in early childhood. Japanese teachers think about the development of the class as a community, which is different from individual-centric Western pedagogical perspective that gives more attention to each child's development. Mimamoru is a pedagogical philosophy and practice in Japanese early childhood education. A key component of Japanese teachers' cultural practices and beliefs about the pedagogy of social-emotional development is that the process requires the development not only of children as individuals, but also of children in a preschool class as a community. In addition, the study suggests that at a deeper level these emic concepts reflect more general Japanese cultural notions of time, space, sight, and body. This dissertation concludes with the argument that teachers' implicit cultural practices and beliefs is "A cultural art of teaching." Teachers' implicit cultural practices and beliefs are harmonized in the teachers' mind and body, making connections between them, and used depending on the nuances of a situation, as informed by teachers' conscious and unconscious thoughts. The study has also shown evidence of similar practices and logic vertically distributed within Japanese early childhood education, from the way teachers act with children, to the way directors act with teachers, to the way government ministries act with directors, to the way deaf and hearing educators act with their deaf and hearing students. Because these practices are forms of bodily habitus and implicit Japanese culture, it makes sense that they are found across fields of action.
ContributorsHayashi, Akiko (Author) / Tobin, Joseph (Thesis advisor) / Eisenberg, Nancy (Committee member) / Nakagawa, Kathryn (Committee member) / Fischman, Gustavo (Committee member) / Swadener, Elizabeth (Committee member) / Arizona State University (Publisher)
Created2011
Description
Multi-label learning, which deals with data associated with multiple labels simultaneously, is ubiquitous in real-world applications. To overcome the curse of dimensionality in multi-label learning, in this thesis I study multi-label dimensionality reduction, which extracts a small number of features by removing the irrelevant, redundant, and noisy information while considering the correlation among different labels in multi-label learning. Specifically, I propose Hypergraph Spectral Learning (HSL) to perform dimensionality reduction for multi-label data by exploiting correlations among different labels using a hypergraph. The regularization effect on the classical dimensionality reduction algorithm known as Canonical Correlation Analysis (CCA) is elucidated in this thesis. The relationship between CCA and Orthonormalized Partial Least Squares (OPLS) is also investigated. To perform dimensionality reduction efficiently for large-scale problems, two efficient implementations are proposed for a class of dimensionality reduction algorithms, including canonical correlation analysis, orthonormalized partial least squares, linear discriminant analysis, and hypergraph spectral learning. The first approach is a direct least squares approach which allows the use of different regularization penalties, but is applicable under a certain assumption; the second one is a two-stage approach which can be applied in the regularization setting without any assumption. Furthermore, an online implementation for the same class of dimensionality reduction algorithms is proposed when the data comes sequentially. A Matlab toolbox for multi-label dimensionality reduction has been developed and released. The proposed algorithms have been applied successfully in the Drosophila gene expression pattern image annotation. The experimental results on some benchmark data sets in multi-label learning also demonstrate the effectiveness and efficiency of the proposed algorithms.
ContributorsSun, Liang (Author) / Ye, Jieping (Thesis advisor) / Li, Baoxin (Committee member) / Liu, Huan (Committee member) / Mittelmann, Hans D. (Committee member) / Arizona State University (Publisher)
Created2011
Description
ABSTRACT études; written for violin ensemble, which include violin duets, trios, and quartets, are less numerous than solo études.; These works rarely go by the title "étude;," and have not been the focus of much scholarly research. Ensemble études; have much to offer students, teachers and composers, however, because they add an extra dimension to the learning, teaching, and composing processes. This document establishes the value of ensemble études; in pedagogy and explores applications of the repertoire currently available. Rather than focus on violin duets, the most common form of ensemble étude;, it mainly considers works for three and four violins without accompaniment. Concentrating on the pedagogical possibilities of studying études; in a group, this document introduces creative ways that works for violin ensemble can be used as both études; and performance pieces. The first two chapters explore the history and philosophy of the violin étude; and multiple-violin works, the practice of arranging of solo études; for multiple instruments, and the benefits of group learning and cooperative learning that distinguish ensemble étude; study from solo étude; study. The third chapter is an annotated survey of works for three and four violins without accompaniment, and serves as a pedagogical guide to some of the available repertoire. Representing a wide variety of styles, techniques and levels, it illuminates an historical association between violin ensemble works and pedagogy. The fourth chapter presents an original composition by the author, titled Variations on a Scottish Folk Song: étude; for Four Violins, with an explanation of the process and techniques used to create this ensemble étude.; This work is an example of the musical and technical integration essential to étude; study, and demonstrates various compositional traits that promote cooperative learning. Ensemble études; are valuable pedagogical tools that deserve wider exposure. It is my hope that the information and ideas about ensemble études; in this paper and the individual descriptions of the works presented will increase interest in and application of violin trios and quartets at the university level.
ContributorsLundell, Eva Rachel (Contributor) / Swartz, Jonathan (Thesis advisor) / Rockmaker, Jody (Committee member) / Buck, Nancy (Committee member) / Koonce, Frank (Committee member) / Norton, Kay (Committee member) / Arizona State University (Publisher)
Created2011
Description
In many classication problems data samples cannot be collected easily, example in drug trials, biological experiments and study on cancer patients. In many situations the data set size is small and there are many outliers. When classifying such data, example cancer vs normal patients the consequences of mis-classication are probably more important than any other data type, because the data point could be a cancer patient or the classication decision could help determine what gene might be over expressed and perhaps a cause of cancer. These mis-classications are typically higher in the presence of outlier data points. The aim of this thesis is to develop a maximum margin classier that is suited to address the lack of robustness of discriminant based classiers (like the Support Vector Machine (SVM)) to noise and outliers. The underlying notion is to adopt and develop a natural loss function that is more robust to outliers and more representative of the true loss function of the data. It is demonstrated experimentally that SVM's are indeed susceptible to outliers and that the new classier developed, here coined as Robust-SVM (RSVM), is superior to all studied classier on the synthetic datasets. It is superior to the SVM in both the synthetic and experimental data from biomedical studies and is competent to a classier derived on similar lines when real life data examples are considered.
ContributorsGupta, Sidharth (Author) / Kim, Seungchan (Thesis advisor) / Welfert, Bruno (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2011