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- All Subjects: artificial intelligence
- Creators: Arizona State University
- Creators: School of International Letters and Cultures
Description
Parenting approaches that are firm yet warm (i.e., authoritative parenting) have been found to be robustly beneficial for mainstream White Americans youths, but do not demonstrate similarly consistent effects among Chinese Americans (CA) adolescents. Evidence suggests that CA adolescents interpret and experience parenting differently than their mainstream counterparts given differences in parenting values and child-rearing norms between traditional Chinese and mainstream American cultures. The current study tests the theory that prospective effects of parenting on psychological and academic functioning depends on adolescents' cultural frameworks for interpreting and understanding parenting. CA adolescents with values and expectations of parenting that are more consistent with mainstream American parenting norms were predicted to experience parenting similar to their White American counterparts (i.e., benefiting from a combination of parental strictness and warmth). In contrast, CA adolescents with parenting values and expectations more consistent with traditional Chinese parenting norms were predicted to experience parenting and its effects on academic and psychological outcomes differently than patterns documented in the mainstream literature. This study was conducted with a sample of Chinese American 9th graders (N = 500) from the Multicultural Family Adolescent Study. Latent Class Analysis (LCA), a person-centered approach to modeling CA adolescents' cultural frameworks for interpreting parenting, was employed using a combination of demographic variables (e.g., nativity, language use at home, mother's length of stay in the U.S.) and measures of parenting values and expectations (e.g., parental respect, ideal strictness & laxness). The study then examined whether prospective effects of parenting behaviors (strict control, warmth, and their interaction effect) on adolescent adjustment (internalizing and externalizing symptoms, substance use, and GPA) were moderated by latent class membership. The optimal LCA solution identified five distinct cultural frameworks for understanding parenting. Findings generally supported the idea that effects of parenting on CA adolescent adjustment depend on adolescents' cultural framework for parenting. The classic authoritative parenting effect (high strictness and warmth leads to positive outcomes) was found for the two most acculturated groups of adolescents. However, only one of these groups overtly endorsed mainstream American parenting values.
ContributorsLiu, Freda Fangfang (Author) / Gonzales, Nancy A. (Thesis advisor) / Tein, Jenn-Yun (Committee member) / Yoo, Hyung Chol (Committee member) / Barrera, Manuel (Committee member) / Arizona State University (Publisher)
Created2011
Description
This dissertation transforms a set of system complexity reduction problems to feature selection problems. Three systems are considered: classification based on association rules, network structure learning, and time series classification. Furthermore, two variable importance measures are proposed to reduce the feature selection bias in tree models. Associative classifiers can achieve high accuracy, but the combination of many rules is difficult to interpret. Rule condition subset selection (RCSS) methods for associative classification are considered. RCSS aims to prune the rule conditions into a subset via feature selection. The subset then can be summarized into rule-based classifiers. Experiments show that classifiers after RCSS can substantially improve the classification interpretability without loss of accuracy. An ensemble feature selection method is proposed to learn Markov blankets for either discrete or continuous networks (without linear, Gaussian assumptions). The method is compared to a Bayesian local structure learning algorithm and to alternative feature selection methods in the causal structure learning problem. Feature selection is also used to enhance the interpretability of time series classification. Existing time series classification algorithms (such as nearest-neighbor with dynamic time warping measures) are accurate but difficult to interpret. This research leverages the time-ordering of the data to extract features, and generates an effective and efficient classifier referred to as a time series forest (TSF). The computational complexity of TSF is only linear in the length of time series, and interpretable features can be extracted. These features can be further reduced, and summarized for even better interpretability. Lastly, two variable importance measures are proposed to reduce the feature selection bias in tree-based ensemble models. It is well known that bias can occur when predictor attributes have different numbers of values. Two methods are proposed to solve the bias problem. One uses an out-of-bag sampling method called OOBForest, and the other, based on the new concept of a partial permutation test, is called a pForest. Experimental results show the existing methods are not always reliable for multi-valued predictors, while the proposed methods have advantages.
ContributorsDeng, Houtao (Author) / Runger, George C. (Thesis advisor) / Lohr, Sharon L (Committee member) / Pan, Rong (Committee member) / Zhang, Muhong (Committee member) / Arizona State University (Publisher)
Created2011
Description
Real-world environments are characterized by non-stationary and continuously evolving data. Learning a classification model on this data would require a framework that is able to adapt itself to newer circumstances. Under such circumstances, transfer learning has come to be a dependable methodology for improving classification performance with reduced training costs and without the need for explicit relearning from scratch. In this thesis, a novel instance transfer technique that adapts a "Cost-sensitive" variation of AdaBoost is presented. The method capitalizes on the theoretical and functional properties of AdaBoost to selectively reuse outdated training instances obtained from a "source" domain to effectively classify unseen instances occurring in a different, but related "target" domain. The algorithm is evaluated on real-world classification problems namely accelerometer based 3D gesture recognition, smart home activity recognition and text categorization. The performance on these datasets is analyzed and evaluated against popular boosting-based instance transfer techniques. In addition, supporting empirical studies, that investigate some of the less explored bottlenecks of boosting based instance transfer methods, are presented, to understand the suitability and effectiveness of this form of knowledge transfer.
ContributorsVenkatesan, Ashok (Author) / Panchanathan, Sethuraman (Thesis advisor) / Li, Baoxin (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2011
Description
TaxiWorld is a Matlab simulation of a city with a fleet of taxis which operate within it, with the goal of transporting passengers to their destinations. The size of the city, as well as the number of available taxis and the frequency and general locations of fare appearances can all be set on a scenario-by-scenario basis. The taxis must attempt to service the fares as quickly as possible, by picking each one up and carrying it to its drop-off location. The TaxiWorld scenario is formally modeled using both Decentralized Partially-Observable Markov Decision Processes (Dec-POMDPs) and Multi-agent Markov Decision Processes (MMDPs). The purpose of developing formal models is to learn how to build and use formal Markov models, such as can be given to planners to solve for optimal policies in problem domains. However, finding optimal solutions for Dec-POMDPs is NEXP-Complete, so an empirical algorithm was also developed as an improvement to the method already in use on the simulator, and the methods were compared in identical scenarios to determine which is more effective. The empirical method is of course not optimal - rather, it attempts to simply account for some of the most important factors to achieve an acceptable level of effectiveness while still retaining a reasonable level of computational complexity for online solving.
ContributorsWhite, Christopher (Author) / Kambhampati, Subbarao (Thesis advisor) / Gupta, Sandeep (Committee member) / Varsamopoulos, Georgios (Committee member) / Arizona State University (Publisher)
Created2011
Description
Action language C+ is a formalism for describing properties of actions, which is based on nonmonotonic causal logic. The definite fragment of C+ is implemented in the Causal Calculator (CCalc), which is based on the reduction of nonmonotonic causal logic to propositional logic. This thesis describes the language of CCalc in terms of answer set programming (ASP), based on the translation of nonmonotonic causal logic to formulas under the stable model semantics. I designed a standard library which describes the constructs of the input language of CCalc in terms of ASP, allowing a simple modular method to represent CCalc input programs in the language of ASP. Using the combination of system F2LP and answer set solvers, this method achieves functionality close to that of CCalc while taking advantage of answer set solvers to yield efficient computation that is orders of magnitude faster than CCalc for many benchmark examples. In support of this, I created an automated translation system Cplus2ASP that implements the translation and encoding method and automatically invokes the necessary software to solve the translated input programs.
ContributorsCasolary, Michael (Author) / Lee, Joohyung (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Baral, Chitta (Committee member) / Arizona State University (Publisher)
Created2011
Description
The purpose of this study was to examine the attitudes and opinions of Navajo students toward the Navajo language and culture programs within the schools they were attending. Although in the final year of the No Child Left Behind, a majority of the 265 schools on and near the Navajo reservation have not been making Adequate Yearly Progress, a concern for the parents, teachers, administrators, school board members, and the Navajo Nation. The study entailed conducting a survey at five schools; three of which were not meeting the requirements of the No Child Left Behind. The purpose of the survey instrument (27 questions) administered to the students at the five schools was to examine their attitudes and opinions as to participating in Navajo language and culture programs, to determine if the programs assisted them in their academic achievements, and to examine whether these programs actually made a difference for schools in their Adequate Yearly Progress requirement Approximately 87% of 99 Navajo students, 55 boys and 58 girls, ages 9 through 14, Grades 3 through 8, who lived off the reservation in Flagstaff, Arizona and Gallup, New Mexico, and took the survey knew and spoke Navajo, but less fluently and not to a great extent. However, the students endorsed learning Navajo and strongly agreed that the Navajo language and culture should be part of the curriculum. Historically there have been schools such as the Rock Point Community School, Rough Rock Demonstration School, Borrego Pass Community School, and Ramah Community School that have been successful in their implementation of bilingual programs. The question presently facing Navajo educators is what type of programs would be successful within the context of the No Child Left Behind federal legislation. Can there be replications of successful Navajo language and culture programs into schools that are not making Adequate Yearly Progress?
ContributorsTsosie, David J (Author) / Spencer, Dee A. (Thesis advisor) / Appleton, Nicholas A. (Committee member) / Koerperich, Robbie (Committee member) / Arizona State University (Publisher)
Created2013
Description
Simultaneously culture heroes and stumbling buffoons, Tricksters bring cultural tools to the people and make the world more habitable. There are common themes in these figures that remain fruitful for the advancement of culture, theory, and critical praxis. This dissertation develops a method for opening a dialogue with Trickster figures. It draws from established literature to present a newly conceived and more flexible Trickster archetype. This archetype is more than a collection of traits; it builds on itself processually to form a method for analysis. The critical Trickster archetype includes the fundamental act of crossing borders; the twin ontologies of ambiguity and liminality; the particular tactics of humor, duplicity, and shape shifting; and the overarching cultural roles of culture hero and stumbling buffoon. Running parallel to each archetypal element, though, are Trickster's overarching critical spirit of Quixotic utopianism and underlying telos of manipulating human relationships. The character 'Q' from Star Trek: The Next Generation is used to demonstrate the critical Trickster archetype. To be more useful for critical cultural studies, Trickster figures must also be connected to their socio-cultural and historical contexts. Thus, this dissertation offers a second set of analytics, a dialogical method that connects Tricksters to the worlds they make more habitable. This dialogical method, developed from the work of M. M. Bakhtin and others, consists of three analytical tools: utterance, intertextuality, and chronotope. Utterance bounds the text for analysis. Intertextuality connects the utterance, the text, to its context. Chronotope suggests particular spatio-temporal relationships that help reveal the cultural significance of a dialogical performance. Performance artists Andre Stitt, Ann Liv Young, and Steven Leyba are used to demonstrate the method of Trickster dialogics. A concluding discussion of Trickster's unique chronotope reveals its contributions to conceptions of utopia and futurity. This dissertation offers theoretical advancements about the significance and tactics of subversive communication practices. It offers a new and unique method for cultural and performative analyses that can be expanded into different kinds of dialogics. Trickster dialogics can also be used generatively to direct and guide the further development of performative praxis.
ContributorsSalinas, Chema (Author) / de la Garza, Amira (Thesis advisor) / Carlson, Cheree (Committee member) / Olson, Clark (Committee member) / Ellsworth, Angela (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
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
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