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- Genre: Academic theses
- Genre: Masters Thesis
- Creators: Davulcu, Hasan
- Creators: Johnston, Carol
- Creators: Liu, Huan
- Status: Published
Description
Background: Previous research suggests a healthy eater schema (i.e., identifying yourself as a healthy eater) may be a useful concept to target in interventions. A "stealth" intervention that discussed the moral issues related to food worked better at promoting healthful eating than an intervention focused on the health benefits. No research has explored the relationship between moral foundations, a theoretical model focused on delineating core "foundations" for making a moral decision, and healthy eater self-identity or self-efficacy. Purpose: We explored the relationship between moral foundations (i.e., harm/care, fairness/reciprocity, in-group/loyalty, authority/respect, & purity/sanctity) and health eater self-identity and fruit and vegetable self-efficacy (FVSE). Methods: 542 participants completed an online cross-sectional survey, which included moral foundations (i.e., MFQ), political views, healthy eater self-identity (i.e., HESS), and FVSE measures. Logistic regression was used to assess the relationship between moral foundations between healthy eater self-identity after controlling for age, gender, major, BMI, and political beliefs. OLS regression was used to explore the relationship between self-efficacy and the moral foundations after controlling for the covariates. Results: 75.6% of the sample were college students, with a mean age of 25.27 (SD=8.61). 25.1% of students were nutrition majors. Harm/care, authority/respect, and ingroup/loyalty were significantly associated with healthy eater schema, (i.e., OR=1.7, p<.001, OR=1.5, p=.009, and OR=1.4, p=.027, respectively). Ingroup/loyalty, authority/respect, and purity/sanctity were related to FVSE (p=.006, p=.002, p=.04, respectively). Conclusion: Among college students, harm/care and authority/respect were associated with a healthy eater schema. Future research should explore possible uses of these moral foundations in interventions (e.g., a plant-based diet based on reduced harm to animals or eating fewer processed views based on "traditional" values).
ContributorsKiser, Sarah (Author) / Hekler, Eric B. (Thesis advisor) / Ohri-Vachaspati, Punam (Committee member) / Wharton, Christopher (Christopher Mack), 1977- (Committee member) / Johnston, Carol (Committee member) / Arizona State University (Publisher)
Created2013
Description
Contemporary online social platforms present individuals with social signals in the form of news feed on their peers' activities. On networks such as Facebook, Quora, network operator decides how that information is shown to an individual. Then the user, with her own interests and resource constraints selectively acts on a subset of items presented to her. The network operator again, shows that activity to a selection of peers, and thus creating a behavioral loop. That mechanism of interaction and information flow raises some very interesting questions such as: can network operator design social signals to promote a particular activity like sustainability, public health care awareness, or to promote a specific product? The focus of my thesis is to answer that question. In this thesis, I develop a framework to personalize social signals for users to guide their activities on an online platform. As the result, we gradually nudge the activity distribution on the platform from the initial distribution p to the target distribution q. My work is particularly applicable to guiding collaborations, guiding collective actions, and online advertising. In particular, I first propose a probabilistic model on how users behave and how information flows on the platform. The main part of this thesis after that discusses the Influence Individuals through Social Signals (IISS) framework. IISS consists of four main components: (1) Learner: it learns users' interests and characteristics from their historical activities using Bayesian model, (2) Calculator: it uses gradient descent method to compute the intermediate activity distributions, (3) Selector: it selects users who can be influenced to adopt or drop specific activities, (4) Designer: it personalizes social signals for each user. I evaluate the performance of IISS framework by simulation on several network topologies such as preferential attachment, small world, and random. I show that the framework gradually nudges users' activities to approach the target distribution. I use both simulation and mathematical method to analyse convergence properties such as how fast and how close we can approach the target distribution. When the number of activities is 3, I show that for about 45% of target distributions, we can achieve KL-divergence as low as 0.05. But for some other distributions KL-divergence can be as large as 0.5.
ContributorsLe, Tien D (Author) / Sundaram, Hari (Thesis advisor) / Davulcu, Hasan (Thesis advisor) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2014
Description
The wide adoption and continued advancement of information and communications technologies (ICT) have made it easier than ever for individuals and groups to stay connected over long distances. These advances have greatly contributed in dramatically changing the dynamics of the modern day workplace to the point where it is now commonplace to see large, distributed multidisciplinary teams working together on a daily basis. However, in this environment, motivating, understanding, and valuing the diverse contributions of individual workers in collaborative enterprises becomes challenging. To address these issues, this thesis presents the goals, design, and implementation of Taskville, a distributed workplace game played by teams on large, public displays. Taskville uses a city building metaphor to represent the completion of individual and group tasks within an organization. Promising results from two usability studies and two longitudinal studies at a multidisciplinary school demonstrate that Taskville supports personal reflection and improves team awareness through an engaging workplace activity.
ContributorsNikkila, Shawn (Author) / Sundaram, Hari (Thesis advisor) / Byrne, Daragh (Committee member) / Davulcu, Hasan (Committee member) / Olson, Loren (Committee member) / Arizona State University (Publisher)
Created2013
Description
Most data cleaning systems aim to go from a given deterministic dirty database to another deterministic but clean database. Such an enterprise pre–supposes that it is in fact possible for the cleaning process to uniquely recover the clean versions of each dirty data tuple. This is not possible in many cases, where the most a cleaning system can do is to generate a (hopefully small) set of clean candidates for each dirty tuple. When the cleaning system is required to output a deterministic database, it is forced to pick one clean candidate (say the "most likely" candidate) per tuple. Such an approach can lead to loss of information. For example, consider a situation where there are three equally likely clean candidates of a dirty tuple. An appealing alternative that avoids such an information loss is to abandon the requirement that the output database be deterministic. In other words, even though the input (dirty) database is deterministic, I allow the reconstructed database to be probabilistic. Although such an approach does avoid the information loss, it also brings forth several challenges. For example, how many alternatives should be kept per tuple in the reconstructed database? Maintaining too many alternatives increases the size of the reconstructed database, and hence the query processing time. Second, while processing queries on the probabilistic database may well increase recall, how would they affect the precision of the query processing? In this thesis, I investigate these questions. My investigation is done in the context of a data cleaning system called BayesWipe that has the capability of producing multiple clean candidates per each dirty tuple, along with the probability that they are the correct cleaned version. I represent these alternatives as tuples in a tuple disjoint probabilistic database, and use the Mystiq system to process queries on it. This probabilistic reconstruction (called BayesWipe–PDB) is compared to a deterministic reconstruction (called BayesWipe–DET)—where the most likely clean candidate for each tuple is chosen, and the rest of the alternatives discarded.
ContributorsRihan, Preet Inder Singh (Author) / Kambhampati, Subbarao (Thesis advisor) / Liu, Huan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2013
Description
There has been a lot of research in the field of artificial intelligence about thinking machines. Alan Turing proposed a test to observe a machine's intelligent behaviour with respect to natural language conversation. The Winograd schema challenge is suggested as an alternative, to the Turing test. It needs inferencing capabilities, reasoning abilities and background knowledge to get the answer right. It involves a coreference resolution task in which a machine is given a sentence containing a situation which involves two entities, one pronoun and some more information about the situation and the machine has to come up with the right resolution of a pronoun to one of the entities. The complexity of the task is increased with the fact that the Winograd sentences are not constrained by one domain or specific sentence structure and it also contains a lot of human proper names. This modification makes the task of association of entities, to one particular word in the sentence, to derive the answer, difficult. I have developed a pronoun resolver system for the confined domain Winograd sentences. I have developed a classifier or filter which takes input sentences and decides to accept or reject them based on a particular criteria. Once the sentence is accepted. I run parsers on it to obtain the detailed analysis. Furthermore I have developed four answering modules which use world knowledge and inferencing mechanisms to try and resolve the pronoun. The four techniques I use are : ConceptNet knowledgebase, Search engine pattern counts,Narrative event chains and sentiment analysis. I have developed a particular aggregation mechanism for the answers from these modules to arrive at a final answer. I have used caching technique for the association relations that I obtain for different modules, so as to boost the performance. I run my system on the standard ‘nyu dataset’ of Winograd sentences and questions. This dataset is then restricted, by my classifier, to 90 sentences. I evaluate my system on this 90 sentence dataset. When I compare my results against the state of the art system on the same dataset, I get nearly 4.5 % improvement in the restricted domain.
ContributorsBudukh, Tejas Ulhas (Author) / Baral, Chitta (Thesis advisor) / VanLehn, Kurt (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Biological organisms are made up of cells containing numerous interconnected biochemical processes. Diseases occur when normal functionality of these processes is disrupted, manifesting as disease symptoms. Thus, understanding these biochemical processes and their interrelationships is a primary task in biomedical research and a prerequisite for activities including diagnosing diseases and drug development. Scientists studying these interconnected processes have identified various pathways involved in drug metabolism, diseases, and signal transduction, etc. High-throughput technologies, new algorithms and speed improvements over the last decade have resulted in deeper knowledge about biological systems, leading to more refined pathways. Such pathways tend to be large and complex, making it difficult for an individual to remember all aspects. Thus, computer models are needed to represent and analyze them. The refinement activity itself requires reasoning with a pathway model by posing queries against it and comparing the results against the real biological system. Many existing models focus on structural and/or factoid questions, relying on surface-level information. These are generally not the kind of questions that a biologist may ask someone to test their understanding of biological processes. Examples of questions requiring understanding of biological processes are available in introductory college level biology text books. Such questions serve as a model for the question answering system developed in this thesis. Thus, the main goal of this thesis is to develop a system that allows the encoding of knowledge about biological pathways to answer questions demonstrating understanding of the pathways. To that end, a language is developed to specify a pathway and pose questions against it. Some existing tools are modified and used to accomplish this goal. The utility of the framework developed in this thesis is illustrated with applications in the biological domain. Finally, the question answering system is used in real world applications by extracting pathway knowledge from text and answering questions related to drug development.
ContributorsAnwar, Saadat (Author) / Baral, Chitta (Thesis advisor) / Inoue, Katsumi (Committee member) / Chen, Yi (Committee member) / Davulcu, Hasan (Committee member) / Lee, Joohyung (Committee member) / Arizona State University (Publisher)
Created2014
Description
ABSTRACT Vitamin C plays an important role in fatty acid metabolism because it is required for carnitine synthesis. Vitamin C has been shown to have an inverse relationship with weight and body fat percent in a number of studies. However, there has been limited research exploring the relationship between vitamin C status and fat oxidation. This cross-sectional study investigates the relationship between plasma vitamin C and fat oxidation in 69 participants and between plasma vitamin C and body fatness in 82 participants. Participants were measured for substrate utilization via indirect calorimetry while at rest and measured for body fatness via DEXA scan. Participants provided a single fasting blood draw for analysis of plasma vitamin C. Results did not show a significant association between vitamin C and fat oxidation while at rest, therefore the data do not support the hypothesis that vitamin C status affects fat oxidation in a resting state. However, a significant inverse association was found between vitamin C and both total body fat percent and visceral fat.
ContributorsObermeyer, Lindsay (Author) / Johnston, Carol (Thesis advisor) / Hall, Rick (Committee member) / Swan, Pamela (Committee member) / Arizona State University (Publisher)
Created2014
Description
As the size and scope of valuable datasets has exploded across many industries and fields of research in recent years, an increasingly diverse audience has sought out effective tools for their large-scale data analytics needs. Over this period, machine learning researchers have also been very prolific in designing improved algorithms which are capable of finding the hidden structure within these datasets. As consumers of popular Big Data frameworks have sought to apply and benefit from these improved learning algorithms, the problems encountered with the frameworks have motivated a new generation of Big Data tools to address the shortcomings of the previous generation. One important example of this is the improved performance in the newer tools with the large class of machine learning algorithms which are highly iterative in nature. In this thesis project, I set about to implement a low-rank matrix completion algorithm (as an example of a highly iterative algorithm) within a popular Big Data framework, and to evaluate its performance processing the Netflix Prize dataset. I begin by describing several approaches which I attempted, but which did not perform adequately. These include an implementation of the Singular Value Thresholding (SVT) algorithm within the Apache Mahout framework, which runs on top of the Apache Hadoop MapReduce engine. I then describe an approach which uses the Divide-Factor-Combine (DFC) algorithmic framework to parallelize the state-of-the-art low-rank completion algorithm Orthogoal Rank-One Matrix Pursuit (OR1MP) within the Apache Spark engine. I describe the results of a series of tests running this implementation with the Netflix dataset on clusters of various sizes, with various degrees of parallelism. For these experiments, I utilized the Amazon Elastic Compute Cloud (EC2) web service. In the final analysis, I conclude that the Spark DFC + OR1MP implementation does indeed produce competitive results, in both accuracy and performance. In particular, the Spark implementation performs nearly as well as the MATLAB implementation of OR1MP without any parallelism, and improves performance to a significant degree as the parallelism increases. In addition, the experience demonstrates how Spark's flexible programming model makes it straightforward to implement this parallel and iterative machine learning algorithm.
ContributorsKrouse, Brian (Author) / Ye, Jieping (Thesis advisor) / Liu, Huan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2014
Description
In contemporary society, sustainability and public well-being have been pressing challenges. Some of the important questions are:how can sustainable practices, such as reducing carbon emission, be encouraged? , How can a healthy lifestyle be maintained?Even though individuals are interested, they are unable to adopt these behaviors due to resource constraints. Developing a framework to enable cooperative behavior adoption and to sustain it for a long period of time is a major challenge. As a part of developing this framework, I am focusing on methods to understand behavior diffusion over time. Facilitating behavior diffusion with resource constraints in a large population is qualitatively different from promoting cooperation in small groups. Previous work in social sciences has derived conditions for sustainable cooperative behavior in small homogeneous groups. However, how groups of individuals having resource constraint co-operate over extended periods of time is not well understood, and is the focus of my thesis. I develop models to analyze behavior diffusion over time through the lens of epidemic models with the condition that individuals have resource constraint. I introduce an epidemic model SVRS ( Susceptible-Volatile-Recovered-Susceptible) to accommodate multiple behavior adoption. I investigate the longitudinal effects of behavior diffusion by varying different properties of an individual such as resources,threshold and cost of behavior adoption. I also consider how behavior adoption of an individual varies with her knowledge of global adoption. I evaluate my models on several synthetic topologies like complete regular graph, preferential attachment and small-world and make some interesting observations. Periodic injection of early adopters can help in boosting the spread of behaviors and sustain it for a longer period of time. Also, behavior propagation for the classical epidemic model SIRS (Susceptible-Infected-Recovered-Susceptible) does not continue for an infinite period of time as per conventional wisdom. One interesting future direction is to investigate how behavior adoption is affected when number of individuals in a network changes. The affects on behavior adoption when availability of behavior changes with time can also be examined.
ContributorsDey, Anindita (Author) / Sundaram, Hari (Thesis advisor) / Turaga, Pavan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2013
Description
ABSTRACT The hormone leptin is an important regulator of body weight and energy balance, while nitric oxide (NO) produced in the blood vessels is beneficial for preventing disease-induced impaired vasodilation and hypertension. Elevations in the free radical superoxide can result in impaired vasodilation through scavenging of NO. Omega 3 is a polyunsaturated fatty acid that is beneficial at reducing body weight and in lowering many cardiovascular risk factors like atherosclerosis. The present study was designed to examine the change in plasma concentrations of leptin, nitric oxide, and the antioxidant superoxide dismutase in addition to examining the association between leptin and NO in healthy normal weight adult female subjects before and following omega 3 intakes. Participants were randomly assigned to either a fish oil group (600 mg per day) or a control group (1000 mg of coconut oil per day) for 8 weeks. Results showed no significant difference in the percent change of leptin over the 8 week supplementation period for either group (15.3±31.9 for fish oil group, 7.83±27 for control group; p=0.763). The percent change in NO was similarly not significantly altered in either group (-1.97±22 decline in fish oil group, 11.8±53.9 in control group; p=0.960). Likewise, the percent change in superoxide dismutase for each group was not significant following 8 weeks of supplementation (fish oil group: 11.94±20.94; control group: 11.8±53.9; p=0.362). The Pearson correlation co-efficient comparing the percent change of both leptin and NO was r2= -0.251 demonstrating a mildly negative, albeit insignificant, relationship between these factors. Together, these findings suggest that daily supplementation with 600 mg omega 3 in healthy females is not beneficial for improving these cardiovascular risk markers. Future studies in this area should include male subjects as well as overweight subjects with larger doses of fish oil that are equivalent to three or more servings per week. The importance of gender cannot be underestimated since estrogen has protective effects in the vasculature of females that may have masked any further protective effects of the fish oil. In addition, overweight individuals are often leptin-resistant and develop impaired vasodilation resulting from superoxide-mediated scavenging of nitric oxide. Therefore, the reported antioxidant and weight loss properties of omega 3 supplementation may greatly benefit overweight individuals.
ContributorsAlanbagy, Samer (Author) / Sweazea, Karen (Thesis advisor) / Johnston, Carol (Committee member) / Shepard, Christina (Committee member) / Lespron, Christy (Committee member) / Arizona State University (Publisher)
Created2014