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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- All Subjects: artificial intelligence
Description
I study the performance of hedge fund managers, using quarterly stock holdings from 1995 to 2010. I use the holdings-based measure built on Ferson and Mo (2012) to decompose a manager's overall performance into stock selection and three components of timing ability: market return, volatility, and liquidity. At the aggregate level, I find that hedge fund managers have stock picking skills but no timing skills, and overall I do not find strong evidence to support their superiority. I show that the lack of abilities is driven by the large fluctuations of timing performance with market conditions. I find that conditioning information, equity capital constraints, and priority in stocks to liquidate can partly explain the weak evidence. At the individual fund level, bootstrap analysis results suggest that even top managers' abilities cannot be separated from luck. Also, I find that hedge fund managers exhibit short-horizon persistence in selectivity skill.
ContributorsKang, MinJeong (Author) / Aragon, George O. (Thesis advisor) / Hertzel, Michael G (Committee member) / Boguth, Oliver (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
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
Objective of this thesis project is to build a prototype using Linear Temporal Logic specifications for generating a 2D motion plan commanding an iRobot to fulfill the specifications. This thesis project was created for Cyber Physical Systems Lab in Arizona State University. The end product of this thesis is creation of a software solution which can be used in the academia and industry for research in cyber physical systems related applications. The major features of the project are: creating a modular system for motion planning, use of Robot Operating System (ROS), use of triangulation for environment decomposition and using stargazer sensor for localization. The project is built on an open source software called ROS which provides an environment where it is very easy to integrate different modules be it software or hardware on a Linux based platform. Use of ROS implies the project or its modules can be adapted quickly for different applications as the need arises. The final software package created and tested takes a data file as its input which contains the LTL specifications, a symbols list used in the LTL and finally the environment polygon data containing real world coordinates for all polygons and also information on neighbors and parents of each polygon. The software package successfully ran the experiment of coverage, reachability with avoidance and sequencing.
ContributorsPandya, Parth (Author) / Fainekos, Georgios (Thesis advisor) / Dasgupta, Partha (Committee member) / Lee, Yann-Hang (Committee member) / Arizona State University (Publisher)
Created2013
Description
Linear Temporal Logic is gaining increasing popularity as a high level specification language for robot motion planning due to its expressive power and scalability of LTL control synthesis algorithms. This formalism, however, requires expert knowledge and makes it inaccessible to non-expert users. This thesis introduces a graphical specification environment to create high level motion plans to control robots in the field by converting a visual representation of the motion/task plan into a Linear Temporal Logic (LTL) specification. The visual interface is built on the Android tablet platform and provides functionality to create task plans through a set of well defined gestures and on screen controls. It uses the notion of waypoints to quickly and efficiently describe the motion plan and enables a variety of complex Linear Temporal Logic specifications to be described succinctly and intuitively by the user without the need for the knowledge and understanding of LTL specification. Thus, it opens avenues for its use by personnel in military, warehouse management, and search and rescue missions. This thesis describes the construction of LTL for various scenarios used for robot navigation using the visual interface developed and leverages the use of existing LTL based motion planners to carry out the task plan by a robot.
ContributorsSrinivas, Shashank (Author) / Fainekos, Georgios (Thesis advisor) / Baral, Chitta (Committee member) / Burleson, Winslow (Committee member) / Arizona State University (Publisher)
Created2013
Description
"YouTube Shakespeares" is a study of Shakespeare online videos and the people who create, upload, and view them on YouTube. Employing an interdisciplinary approach, this work is a remix of theories and methodologies from literary, performance, (social) media, fan, and Internet studies that expands the field of Shakespeare studies. This dissertation explores the role of YouTube users and their activities, the expansion of literary research methods onto digital media venues, YouTube as site of Shakespeare performance, and YouTube Shakespeares' fan communities. It analyzes a broad array of Shakespeare visual performances including professional and user-generated mashups, remixes, film clips, auditions, and high school performances. A rich avenue for the study of people's viewing and reception of Shakespeare, YouTube tests the (un)limitations of Shakespeare adaptation. This work explores the ethical implications of researching performances that include human subjects, arguing that their presence frequently complicates common concepts of public and private identities. Although YouTube is a "published" forum for social interactivity and video repository, this work urges digital humanities scholars to recognize and honor the human users entailed in the videos not as text, but as human subjects. Shifting the study focus to human subjects demands a revision of research methods and publications protocols as the researcher repositions herself into the role of virtual ethnographer. "YouTube Shakespeares" develops its own ethics-based, online research method, which includes seeking Institutional Board Review approval and online interviews. The second half of the dissertation shifts from methodology to theorizing YouTube Shakespeares' performance spaces as analogs to the interactive and imaginary areas of Shakespeare's early modern theatre. Additionally, this work argues that YouTube Shakespeares' creators and commentators are fans. "YouTube Shakespeares" is one of the first Shakespeare-centric studies to employ fan studies as a critical lens to explore the cultural significance and etiquette of people's online Shakespeare performance activities. The work ends with a conversation about the issues of ephemerality, obsolescence, and concerns about the instability of digital and online materials, noting the risk of evidentiary loss of research materials is far outweighed by a scholarly critical registration of YouTube in the genealogy of Shakespeare performance.
ContributorsFazel, Valerie Margaret (Author) / Thompson, Ayanna (Thesis advisor) / Ryner, Bradley (Committee member) / Fox, Cora (Committee member) / Arizona State University (Publisher)
Created2013
Description
Answer Set Programming (ASP) is one of the most prominent and successful knowledge representation paradigms. The success of ASP is due to its expressive non-monotonic modeling language and its efficient computational methods originating from building propositional satisfiability solvers. The wide adoption of ASP has motivated several extensions to its modeling language in order to enhance expressivity, such as incorporating aggregates and interfaces with ontologies. Also, in order to overcome the grounding bottleneck of computation in ASP, there are increasing interests in integrating ASP with other computing paradigms, such as Constraint Programming (CP) and Satisfiability Modulo Theories (SMT). Due to the non-monotonic nature of the ASP semantics, such enhancements turned out to be non-trivial and the existing extensions are not fully satisfactory. We observe that one main reason for the difficulties rooted in the propositional semantics of ASP, which is limited in handling first-order constructs (such as aggregates and ontologies) and functions (such as constraint variables in CP and SMT) in natural ways. This dissertation presents a unifying view on these extensions by viewing them as instances of formulas with generalized quantifiers and intensional functions. We extend the first-order stable model semantics by by Ferraris, Lee, and Lifschitz to allow generalized quantifiers, which cover aggregate, DL-atoms, constraints and SMT theory atoms as special cases. Using this unifying framework, we study and relate different extensions of ASP. We also present a tight integration of ASP with SMT, based on which we enhance action language C+ to handle reasoning about continuous changes. Our framework yields a systematic approach to study and extend non-monotonic languages.
ContributorsMeng, Yunsong (Author) / Lee, Joohyung (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Baral, Chitta (Committee member) / Fainekos, Georgios (Committee member) / Lifschitz, Vladimir (Committee member) / Arizona State University (Publisher)
Created2013
Description
The integration of yoga into the music curriculum has the potential of offering many immediate and life-long benefits to musicians. Yoga can help address issues such as performance anxiety and musculoskeletal problems, and enhance focus and awareness during musical practice and performance. Although the philosophy of yoga has many similarities to the process of learning a musical instrument, the benefits of yoga for musicians is a topic that has gained attention only recently. This document explores several ways in which the practice and philosophy of yoga can be fused with saxophone pedagogy as one way to prepare students for a healthy and successful musical career. A six-week study at Arizona State University was conducted to observe the effects of regular yoga practice on collegiate saxophone students. Nine participants attended a sixty-minute "yoga for musicians" class twice a week. Measures included pre- and post- study questionnaires as well as personal journals kept throughout the duration of the study. These self-reported results showed that yoga had positive effects on saxophone playing. It significantly increased physical comfort and positive thinking, and improved awareness of habitual patterns and breath control. Student participants responded positively to the idea of integrating such a course into the music curriculum. The integration of yoga and saxophone by qualified professionals could also be a natural part of studio class and individual instruction. Carrie Koffman, professor of saxophone at The Hartt School, University of Hartford, has established one strong model for the combination of these disciplines. Her methods and philosophy, together with the basics of Western-style hatha yoga, clinical reports on performance injuries, and qualitative data from the ASU study are explored. These inquiries form the foundation of a new model for integrating yoga practice regularly into the saxophone studio.
ContributorsAdams, Allison Dromgold (Author) / Norton, Kay (Thesis advisor) / Hill, Gary (Committee member) / McAllister, Timothy (Committee member) / Micklich, Albie (Committee member) / Standley, Eileen (Committee member) / Arizona State University (Publisher)
Created2012
Description
The Holocaust and the effects it has had upon witnesses has been a topic of study for nearly six decades; however, few angles of research have been conducted relating to the long-term effects of the Holocaust upon the children and grandchildren of Holocaust survivors--the After Generations. The After Generations are considered the proof--the living legacies--that their parents and grandparents survived. Growing up with intimate knowledge of the atrocities that occurred during the Holocaust, members of the After Generations not only carry with them their family's story, but also their own vicarious experience(s) of trauma. From this legacy comes a burden of responsibility to those who perished, their survivor parents/grandparents, the stories that were shared, as well as to future generations. Using grounded theory method, this study not only explores the long-term effects of the Holocaust upon members of the After Generations, but what it means to responsibly remember the stories from the Holocaust, as well as how individuals might ethically represent such stories/memories. Findings that developed out of an axial analysis of interview transcripts and journal writing, as well as the later development of a performance script, are embodied in a manner that allows the actual language and experiences of the participants to be collectively witnessed both symbolically and visually. Through their desire to remember, members of the After Generations demonstrate how they plan to carry on traditions, live lives that honor those that came before them, and maintain hope for the future. In so doing, the stories shared reveal the centrality of the Holocaust in the lives of members of the After Generations through their everyday choices to responsibly and actively remember through their art, writings, life-work, as well as from within their work in their local communities. Such acts of remembrance are important to the education of others as well as to the construction and maintenance of the After Generations' identities. The representation of these voices acts as a reminder of how hatred and its all-consuming characteristics can affect not only the person targeted, but multiple generations, as well.
ContributorsRath, Sandra (Author) / de la Garza, Sarah Amira (Thesis advisor) / Underiner, Tamara (Committee member) / Corey, Frederick C. (Committee member) / Eisenberg, Judith (Committee member) / Arizona State University (Publisher)
Created2012
Description
This thesis introduces the Model-Based Development of Multi-iRobot Toolbox (MBDMIRT), a Simulink-based toolbox designed to provide the means to acquire and practice the Model-Based Development (MBD) skills necessary to design real-time embedded system. The toolbox was developed in the Cyber-Physical System Laboratory at Arizona State University. The MBDMIRT toolbox runs under MATLAB/Simulink to simulate the movements of multiple iRobots and to control, after verification by simulation, multiple physical iRobots accordingly. It adopts the Simulink/Stateflow, which exemplifies an approach to MBD, to program the behaviors of the iRobots. The MBDMIRT toolbox reuses and augments the open-source MATLAB-Based Simulator for the iRobot Create from Cornell University to run the simulation. Regarding the mechanism of iRobot control, the MBDMIRT toolbox applies the MATLAB Toolbox for the iRobot Create (MTIC) from United States Naval Academy to command the physical iRobots. The MBDMIRT toolbox supports a timer in both the simulation and the control, which is based on the local clock of the PC running the toolbox. In addition to the build-in sensors of an iRobot, the toolbox can simulate four user-added sensors, which are overhead localization system (OLS), sonar sensors, a camera, and Light Detection And Ranging (LIDAR). While controlling a physical iRobot, the toolbox supports the StarGazer OLS manufactured by HAGISONIC, Inc.
ContributorsSu, Shih-Kai (Author) / Fainekos, Georgios E (Thesis advisor) / Sarjoughian, Hessam S. (Committee member) / Artemiadis, Panagiotis K (Committee member) / Arizona State University (Publisher)
Created2012