ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- Creators: Li, Baoxin
- Creators: Chassin, Laurie
Description
Research has shown that a developmental process of maturing out of alcohol involvement occurs during young adulthood, and that this process is related to both young adult role transitions (e.g., marriage) and personality developmental (e.g., decreased disinhibition and neuroticism). The current study extended past research by testing whether protective marriage and personality effects on maturing out were stronger among more severe late adolescent drinkers, and whether protective marriage effects were stronger among those who experienced more personality development. Parental alcoholism and gender were tested as moderators of marriage, personality, and late adolescent drinking effects on maturing out; and as distal predictors mediated by these effects. Participants were a subsample (N = 844; 51% children of alcoholics; 53% male, 71% non-Hispanic Caucasian, 27% Hispanic; Chassin, Barrera, Bech, & Kossak-Fuller, 1992) from a larger longitudinal study of familial alcoholism. Hypotheses were tested with latent growth models characterizing alcohol consumption and drinking consequence trajectories from late adolescence to adulthood (age 17-40). Past findings were replicated by showing protective effects of becoming married, sensation-seeking reductions, and neuroticism reductions on the drinking trajectories. Moderation tests showed that protective marriage effects on the drinking trajectories were stronger among those with higher pre-marriage drinking in late adolescence (i.e., higher growth intercepts). This might reflect role socialization mechanisms such that more severe drinking produces more conflict with the demands of new roles (i.e., role incompatibility), thus requiring greater drinking reductions to resolve this conflict. In contrast, little evidence was found for moderation of personality effects by late adolescent drinking or for moderation of marriage effects by personality. Parental alcoholism findings suggested complex moderated mediation pathways. Parental alcoholism predicted less drinking reduction through decreasing the likelihood of marriage (mediation) and muting marriage's effect on the drinking trajectories (moderation), but parental alcoholism also predicted more drinking reduction through increasing initial drinking in late adolescence (mediation). The current study provides new insights into naturally occurring processes of recovery during young adulthood and suggests that developmentally-tailored interventions for young adults could harness these natural recovery processes (e.g., by integrating role incompatibility themes and addressing factors that block role effects among those with familial alcoholism).
ContributorsLee, Matthew R. (Author) / Chassin, Laurie (Thesis advisor) / Corbin, William R. (Committee member) / Mackinnon, David P (Committee member) / Presson, Clark C. (Committee member) / Arizona State University (Publisher)
Created2013
Description
Past literature has indicated that the majority of people with alcohol problems never seek treatment and that this is especially true of women. Relatively few studies have investigated how different types of alcohol-related consequences longitudinally predict men and women's perceived need for treatment and their utilization of treatment services. The current study sought to expand the literature by examining whether gender moderates the links between four frequently endorsed types of consequences and perceived need for or actual utilization of treatment. Two-hundred thirty-seven adults ages 21-36 completed a battery of questionnaires at two time points five years apart. Results indicated that there were four broad types of consequences endorsed by both men and women. Multiple-group models and Wald chi square tests indicated that there were no significant relationships between consequences and treatment outcomes. No gender moderation was found but post-hoc power analyses indicated that the study was underpowered to detect moderation. Researchers need to continue to study factors that predict utilization of alcohol treatment services and the process of recovery so that treatment providers can better address the needs of people with alcohol-related consequences in the areas of referral procedures, clinical assessment, and treatment service provision and planning.
ContributorsBeltran Gonzalez, Iris (Author) / Chassin, Laurie (Thesis advisor) / Tein, Jenn-Yun (Committee member) / Corbin, William (Committee member) / Barrera, Jr., Manuel (Committee member) / Arizona State University (Publisher)
Created2013
Description
The rapid escalation of technology and the widespread emergence of modern technological equipments have resulted in the generation of humongous amounts of digital data (in the form of images, videos and text). This has expanded the possibility of solving real world problems using computational learning frameworks. However, while gathering a large amount of data is cheap and easy, annotating them with class labels is an expensive process in terms of time, labor and human expertise. This has paved the way for research in the field of active learning. Such algorithms automatically select the salient and exemplar instances from large quantities of unlabeled data and are effective in reducing human labeling effort in inducing classification models. To utilize the possible presence of multiple labeling agents, there have been attempts towards a batch mode form of active learning, where a batch of data instances is selected simultaneously for manual annotation. This dissertation is aimed at the development of novel batch mode active learning algorithms to reduce manual effort in training classification models in real world multimedia pattern recognition applications. Four major contributions are proposed in this work: $(i)$ a framework for dynamic batch mode active learning, where the batch size and the specific data instances to be queried are selected adaptively through a single formulation, based on the complexity of the data stream in question, $(ii)$ a batch mode active learning strategy for fuzzy label classification problems, where there is an inherent imprecision and vagueness in the class label definitions, $(iii)$ batch mode active learning algorithms based on convex relaxations of an NP-hard integer quadratic programming (IQP) problem, with guaranteed bounds on the solution quality and $(iv)$ an active matrix completion algorithm and its application to solve several variants of the active learning problem (transductive active learning, multi-label active learning, active feature acquisition and active learning for regression). These contributions are validated on the face recognition and facial expression recognition problems (which are commonly encountered in real world applications like robotics, security and assistive technology for the blind and the visually impaired) and also on collaborative filtering applications like movie recommendation.
ContributorsChakraborty, Shayok (Author) / Panchanathan, Sethuraman (Thesis advisor) / Balasubramanian, Vineeth N. (Committee member) / Li, Baoxin (Committee member) / Mittelmann, Hans (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
Juvenile offenders suffer from substance use disorders at higher rates than adolescents in the general public. Substance use disorders also predict an increased risk for re-offending. Therefore, it is important that these juveniles, in particular, receive the appropriate substance use disorder treatment. The present study used logistic regression to test whether race/ethnicity would moderate the match between substance use disorder diagnosis and the receipt of a substance use disorder related service in a sample of male, serious juvenile offenders. Results showed that among those with a substance use disorder diagnosis, there were no race/ethnicity differences in the receipt of the appropriate service. However, among those without a substance use disorder diagnosis, non-Hispanic Caucasians were more likely to receive substance use service than were Hispanics or African-Americans. Post-hoc analyses revealed that when using a broader definition of substance use problems, significant differences by race/ethnicity in the prediction of service receipt were only observed at low levels of substance use problems. These findings shed light on how race/ethnicity may play a role in the recommendation of substance use disorder services in the juvenile justice system.
ContributorsMansion, Andre (Author) / Chassin, Laurie (Thesis advisor) / Dishion, Thomas (Committee member) / Knight, George (Committee member) / Arizona State University (Publisher)
Created2013
Description
In recent years, machine learning and data mining technologies have received growing attention in several areas such as recommendation systems, natural language processing, speech and handwriting recognition, image processing and biomedical domain. Many of these applications which deal with physiological and biomedical data require person specific or person adaptive systems. The greatest challenge in developing such systems is the subject-dependent data variations or subject-based variability in physiological and biomedical data, which leads to difference in data distributions making the task of modeling these data, using traditional machine learning algorithms, complex and challenging. As a result, despite the wide application of machine learning, efficient deployment of its principles to model real-world data is still a challenge. This dissertation addresses the problem of subject based variability in physiological and biomedical data and proposes person adaptive prediction models based on novel transfer and active learning algorithms, an emerging field in machine learning. One of the significant contributions of this dissertation is a person adaptive method, for early detection of muscle fatigue using Surface Electromyogram signals, based on a new multi-source transfer learning algorithm. This dissertation also proposes a subject-independent algorithm for grading the progression of muscle fatigue from 0 to 1 level in a test subject, during isometric or dynamic contractions, at real-time. Besides subject based variability, biomedical image data also varies due to variations in their imaging techniques, leading to distribution differences between the image databases. Hence a classifier learned on one database may perform poorly on the other database. Another significant contribution of this dissertation has been the design and development of an efficient biomedical image data annotation framework, based on a novel combination of transfer learning and a new batch-mode active learning method, capable of addressing the distribution differences across databases. The methodologies developed in this dissertation are relevant and applicable to a large set of computing problems where there is a high variation of data between subjects or sources, such as face detection, pose detection and speech recognition. From a broader perspective, these frameworks can be viewed as a first step towards design of automated adaptive systems for real world data.
ContributorsChattopadhyay, Rita (Author) / Panchanathan, Sethuraman (Thesis advisor) / Ye, Jieping (Thesis advisor) / Li, Baoxin (Committee member) / Santello, Marco (Committee member) / Arizona State University (Publisher)
Created2013
Description
Currently, to interact with computer based systems one needs to learn the specific interface language of that system. In most cases, interaction would be much easier if it could be done in natural language. For that, we will need a module which understands natural language and automatically translates it to the interface language of the system. NL2KR (Natural language to knowledge representation) v.1 system is a prototype of such a system. It is a learning based system that learns new meanings of words in terms of lambda-calculus formulas given an initial lexicon of some words and their meanings and a training corpus of sentences with their translations. As a part of this thesis, we take the prototype NL2KR v.1 system and enhance various components of it to make it usable for somewhat substantial and useful interface languages. We revamped the lexicon learning components, Inverse-lambda and Generalization modules, and redesigned the lexicon learning algorithm which uses these components to learn new meanings of words. Similarly, we re-developed an inbuilt parser of the system in Answer Set Programming (ASP) and also integrated external parser with the system. Apart from this, we added some new rich features like various system configurations and memory cache in the learning component of the NL2KR system. These enhancements helped in learning more meanings of the words, boosted performance of the system by reducing the computation time by a factor of 8 and improved the usability of the system. We evaluated the NL2KR system on iRODS domain. iRODS is a rule-oriented data system, which helps in managing large set of computer files using policies. This system provides a Rule-Oriented interface langauge whose syntactic structure is like any procedural programming language (eg. C). However, direct translation of natural language (NL) to this interface language is difficult. So, for automatic translation of NL to this language, we define a simple intermediate Policy Declarative Language (IPDL) to represent the knowledge in the policies, which then can be directly translated to iRODS rules. We develop a corpus of 100 policy statements and manually translate them to IPDL langauge. This corpus is then used for the evaluation of NL2KR system. We performed 10 fold cross validation on the system. Furthermore, using this corpus, we illustrate how different components of our NL2KR system work.
ContributorsKumbhare, Kanchan Ravishankar (Author) / Baral, Chitta (Thesis advisor) / Ye, Jieping (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2013
Description
We solve the problem of activity verification in the context of sustainability. Activity verification is the process of proving the user assertions pertaining to a certain activity performed by the user. Our motivation lies in incentivizing the user for engaging in sustainable activities like taking public transport or recycling. Such incentivization schemes require the system to verify the claim made by the user. The system verifies these claims by analyzing the supporting evidence captured by the user while performing the activity. The proliferation of portable smart-phones in the past few years has provided us with a ubiquitous and relatively cheap platform, having multiple sensors like accelerometer, gyroscope, microphone etc. to capture this evidence data in-situ. In this research, we investigate the supervised and semi-supervised learning techniques for activity verification. Both these techniques make use the data set constructed using the evidence submitted by the user. Supervised learning makes use of annotated evidence data to build a function to predict the class labels of the unlabeled data points. The evidence data captured can be either unimodal or multimodal in nature. We use the accelerometer data as evidence for transportation mode verification and image data as evidence for recycling verification. After training the system, we achieve maximum accuracy of 94% when classifying the transport mode and 81% when detecting recycle activity. In the case of recycle verification, we could improve the classification accuracy by asking the user for more evidence. We present some techniques to ask the user for the next best piece of evidence that maximizes the probability of classification. Using these techniques for detecting recycle activity, the accuracy increases to 93%. The major disadvantage of using supervised models is that it requires extensive annotated training data, which expensive to collect. Due to the limited training data, we look at the graph based inductive semi-supervised learning methods to propagate the labels among the unlabeled samples. In the semi-supervised approach, we represent each instance in the data set as a node in the graph. Since it is a complete graph, edges interconnect these nodes, with each edge having some weight representing the similarity between the points. We propagate the labels in this graph, based on the proximity of the data points to the labeled nodes. We estimate the performance of these algorithms by measuring how close the probability distribution of the data after label propagation is to the probability distribution of the ground truth data. Since labeling has a cost associated with it, in this thesis we propose two algorithms that help us in selecting minimum number of labeled points to propagate the labels accurately. Our proposed algorithm achieves a maximum of 73% increase in performance when compared to the baseline algorithm.
ContributorsDesai, Vaishnav (Author) / Sundaram, Hari (Thesis advisor) / Li, Baoxin (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Levels of heavy episodic drinking peak during emerging adulthood and contribute to the experience of negative consequences. Previous research has identified a number of trait-like personality characteristics that are associated with drinking. Studies of the Acquired Preparedness Model have supported positive expectancies, and to a lesser extent negative expectancies, as mediators of the relation between trait-like characteristics and alcohol outcomes. However, expectancies measured via self-report may reflect differences in learned expectancies in spite of similar alcohol-related responses, or they may reflect true individual differences in subjective responses to alcohol. The current study addressed this gap in the literature by assessing the relative roles of expectancies and subjective response as mediators within the APM in a sample of 236 emerging adults (74.7% male) participating in a placebo-controlled alcohol challenge study. The study tested four mediation models collapsed across beverage condition as well as eight separate mediation models with four models (2 beverage by 2 expectancy/subjective response) for each outcome (alcohol use and alcohol-related problems). Consistent with previous studies, SS was positively associated with alcohol outcomes in models collapsed across beverage condition. SS was also associated with positive subjective response in collapsed models and in the alcohol models. The hypothesized negative relation between SS and sedation was not significant. In contrast to previous studies, neither stimulation nor sedation predicted either weekly drinking or alcohol-related problems. While stimulation and alcohol use appeared to have a positive and significant association, this relation did not hold when controlling for SS, suggesting that SS and stimulation account for shared variability in drinking behavior. Failure to find this association in the placebo group suggests that, while explicit positive expectancies are related to alcohol use after controlling for levels of sensation seeking, implicit expectancies (at least as assessed by a placebo manipulation) are not. That the relation between SS and stimulation held only in the alcohol condition in analyses separate by beverage condition indicates that sensation seeking is a significant predictor of positive subjective response to alcohol (stimulation), potentially above and beyond expectancies.
ContributorsScott, Caitlin (Author) / Corbin, William (Thesis advisor) / Shiota, Michelle (Committee member) / Chassin, Laurie (Committee member) / Arizona State University (Publisher)
Created2012
Description
The hypothalamus pituitary adrenal (HPA) axis and the human genome are important components of the biological etiology of externalizing disorders. By studying the associations between specific genetic variants, diurnal cortisol, and externalizing symptoms we can begin to unpack this complex etiology. It was hypothesized that genetic variants from the corticotropine releasing hormone receptor 1 (CRHR1), FK506 binding protein 51 (FKBP5), catechol-O-methyl transferase (COMT), and dopamine transporter (DAT1) genes and diurnal cortisol intercepts and slopes would separately predict externalizing symptoms. It was also hypothesized that genetic variants would moderate the association between cortisol and externalizing. Participants were 800 twins (51% boys), 88.5% Caucasian, M=7.93 years (SD=0.87) participating in the Wisconsin Twin Project. Hierarchical Linear Modeling (HLM) was used to separate the variance associated with state and trait cortisol measured across three consecutive days and trait cortisol measures were used. There were no main effects of genes on externalizing symptoms. The evening cortisol intercept, the morning cortisol slope and the evening cortisol slope predicted externalizing, but only in boys, such that boys with higher cortisol and flatter slopes across the day also had more externalizing symptoms. The morning cortisol intercept and CRHR1 rs242924 interacted to predict externalizing in both boys and girls, with GG carriers significantly higher compared to TT carriers at one standard deviation below the mean of morning cortisol. For boys only there was a significant interaction between the DAT1 variable number tandem repeat (VNTR) and the afternoon slope and a significant slope for 9/9 carriers and 9/10 carriers such that when the slope was more steep, boys carrying a nine had fewer externalizing symptoms but when the slope was less steep, they had more. Results confirm a link between diurnal trait cortisol and externalizing in boys, as well as moderation of that association by genetic polymorphisms. This is the first study to empirically examine this association and should encourage further research on the biological etiology of externalizing disorder symptoms.
ContributorsSwann, Gregory (Author) / Lemery-Chalfant, Kathryn (Thesis advisor) / Chassin, Laurie (Committee member) / Doane-Sampey, Leah (Committee member) / Arizona State University (Publisher)
Created2012
Description
Surgery as a profession requires significant training to improve both clinical decision making and psychomotor proficiency. In the medical knowledge domain, tools have been developed, validated, and accepted for evaluation of surgeons' competencies. However, assessment of the psychomotor skills still relies on the Halstedian model of apprenticeship, wherein surgeons are observed during residency for judgment of their skills. Although the value of this method of skills assessment cannot be ignored, novel methodologies of objective skills assessment need to be designed, developed, and evaluated that augment the traditional approach. Several sensor-based systems have been developed to measure a user's skill quantitatively, but use of sensors could interfere with skill execution and thus limit the potential for evaluating real-life surgery. However, having a method to judge skills automatically in real-life conditions should be the ultimate goal, since only with such features that a system would be widely adopted. This research proposes a novel video-based approach for observing surgeons' hand and surgical tool movements in minimally invasive surgical training exercises as well as during laparoscopic surgery. Because our system does not require surgeons to wear special sensors, it has the distinct advantage over alternatives of offering skills assessment in both learning and real-life environments. The system automatically detects major skill-measuring features from surgical task videos using a computing system composed of a series of computer vision algorithms and provides on-screen real-time performance feedback for more efficient skill learning. Finally, the machine-learning approach is used to develop an observer-independent composite scoring model through objective and quantitative measurement of surgical skills. To increase effectiveness and usability of the developed system, it is integrated with a cloud-based tool, which automatically assesses surgical videos upload to the cloud.
ContributorsIslam, Gazi (Author) / Li, Baoxin (Thesis advisor) / Liang, Jianming (Thesis advisor) / Dinu, Valentin (Committee member) / Greenes, Robert (Committee member) / Smith, Marshall (Committee member) / Kahol, Kanav (Committee member) / Patel, Vimla L. (Committee member) / Arizona State University (Publisher)
Created2013