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- Genre: Masters Thesis
- Creators: Johnston, Carol
- Creators: Davulcu, Hasan
- Status: Published
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
Continuous Delivery, as one of the youngest and most popular member of agile model family, has become a popular concept and method in software development industry recently. Instead of the traditional software development method, which requirements and solutions must be fixed before starting software developing, it promotes adaptive planning, evolutionary development and delivery, and encourages rapid and flexible response to change. However, several problems prevent Continuous Delivery to be introduced into education world. Taking into the consideration of the barriers, we propose a new Cloud based Continuous Delivery Software Developing System. This system is designed to fully utilize the whole life circle of software developing according to Continuous Delivery concepts in a virtualized environment in Vlab platform.
ContributorsDeng, Yuli (Author) / Huang, Dijiang (Thesis advisor) / Davulcu, Hasan (Committee member) / Chen, Yinong (Committee member) / Arizona State University (Publisher)
Created2013
Description
The body is capable of regulating hunger in several ways. Some of these hunger regulation methods are innate, such as genetics, and some, such as the responses to stress and to the smell of food, are innate but can be affected by body conditions such as BMI and physical activity. Further, some hunger regulation methods stem from learned behaviors originating from cultural pressures or parenting styles. These latter regulation methods for hunger can be grouped into the categories: emotion, environment, and physical. The factors that regulate hunger can also influence the incidence of disordered eating, such as eating in the absence of hunger (EAH). Eating in the absence of hunger can occur in one of two scenarios, continuous EAH or beginning EAH. College students are at a particularly high risk for EAH and weight gain due to stress, social pressures, and the constant availability of energy dense and nutrient poor food options. The purpose of this study is to validate a modified EAH-C survey in college students and to discover which of the three latent factors (emotion, environment, physical) best predicts continual and beginning EAH. To do so, a modified EAH-C survey, with additional demographic components, was administered to students at a major southwest university. This survey contained two questions, one each for continuing and beginning EAH, regarding 14 factors related to emotional, physical, or environmental reasons that may trigger EAH. The results from this study revealed that the continual and beginning EAH surveys displayed good internal consistency reliability. We found that for beginning and continuing EAH, although emotion is the strongest predictor of EAH, all three latent factors are significant predictors of EAH. In addition, we found that environmental factors had the greatest influence on an individual's likelihood to continue to eat in the absence of hunger. Due to statistical abnormalities and differing numbers of factors in each category, we were unable to determine which of the three factors exerted the greatest influence on an individual's likelihood to begin eating in the absence of hunger. These results can be utilized to develop educational tools aimed at reducing EAH in college students, and ultimately reducing the likelihood for unhealthy weight gain and health complications related to obesity.
ContributorsGoett, Taylor (Author) / Johnston, Carol (Thesis advisor) / Lee, Chong (Committee member) / Lespron, Christy (Committee member) / Arizona State University (Publisher)
Created2013
Description
Our research focuses on finding answers through decentralized search, for complex, imprecise queries (such as "Which is the best hair salon nearby?") in situations where there is a spatiotemporal constraint (say answer needs to be found within 15 minutes) associated with the query. In general, human networks are good in answering imprecise queries. We try to use the social network of a person to answer his query. Our research aims at designing a framework that exploits the user's social network in order to maximize the answers for a given query. Exploiting an user's social network has several challenges. The major challenge is that the user's immediate social circle may not possess the answer for the given query, and hence the framework designed needs to carry out the query diffusion process across the network. The next challenge involves in finding the right set of seeds to pass the query to in the user's social circle. One other challenge is to incentivize people in the social network to respond to the query and thereby maximize the quality and quantity of replies. Our proposed framework is a mobile application where an individual can either respond to the query or forward it to his friends. We simulated the query diffusion process in three types of graphs: Small World, Random and Preferential Attachment. Given a type of network and a particular query, we carried out the query diffusion by selecting seeds based on attributes of the seed. The main attributes are Topic relevance, Replying or Forwarding probability and Time to Respond. We found that there is a considerable increase in the number of replies attained, even without saturating the user's network, if we adopt an optimal seed selection process. We found the output of the optimal algorithm to be satisfactory as the number of replies received at the interrogator's end was close to three times the number of neighbors an interrogator has. We addressed the challenge of incentivizing people to respond by associating a particular amount of points for each query asked, and awarding the same to people involved in answering the query. Thus, we aim to design a mobile application based on our proposed framework so that it helps in maximizing the replies for the interrogator's query by diffusing the query across his/her social network.
ContributorsSwaminathan, Neelakantan (Author) / Sundaram, Hari (Thesis advisor) / Davulcu, Hasan (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
With the advent of social media (like Twitter, Facebook etc.,) people are easily sharing their opinions, sentiments and enforcing their ideologies on others like never before. Even people who are otherwise socially inactive would like to share their thoughts on current affairs by tweeting and sharing news feeds with their friends and acquaintances. In this thesis study, we chose Twitter as our main data platform to analyze shifts and movements of 27 political organizations in Indonesia. So far, we have collected over 30 million tweets and 150,000 news articles from RSS feeds of the corresponding organizations for our analysis. For Twitter data extraction, we developed a multi-threaded application which seamlessly extracts, cleans and stores millions of tweets matching our keywords from Twitter Streaming API. For keyword extraction, we used topics and perspectives which were extracted using n-grams techniques and later approved by our social scientists. After the data is extracted, we aggregate the tweet contents that belong to every user on a weekly basis. Finally, we applied linear and logistic regression using SLEP, an open source sparse learning package to compute weekly score for users and mapping them to one of the 27 organizations on a radical or counter radical scale. Since, we are mapping users to organizations on a weekly basis, we are able to track user's behavior and important new events that triggered shifts among users between organizations. This thesis study can further be extended to identify topics and organization specific influential users and new users from various social media platforms like Facebook, YouTube etc. can easily be mapped to existing organizations on a radical or counter-radical scale.
ContributorsPoornachandran, Sathishkumar (Author) / Davulcu, Hasan (Thesis advisor) / Sen, Arunabha (Committee member) / Woodward, Mark (Committee member) / Arizona State University (Publisher)
Created2013
Description
Dietary protein is known to increase postprandial thermogenesis more so than carbohydrates or fats, probably related to the fact that amino acids have no immediate form of storage in the body and can become toxic if not readily incorporated into body tissues or excreted. It is also well documented that subjects report greater satiety on high- versus low-protein diets and that subject compliance tends to be greater on high-protein diets, thus contributing to their popularity. What is not as well known is how a high-protein diet affects resting metabolic rate over time, and what is even less well known is if resting metabolic rate changes significantly when a person consuming an omnivorous diet suddenly adopts a vegetarian one. This pilot study sought to determine whether subjects adopting a vegetarian diet would report decreased satiety or demonstrate a decreased metabolic rate due to a change in protein intake and possible increase in carbohydrates. Further, this study sought to validate a new device called the SenseWear Armband (SWA) to determine if it might be sensitive enough to detect subtle changes in metabolic rate related to diet. Subjects were tested twice on all variables, at baseline and post-test. Independent and related samples tests revealed no significant differences between or within groups for any variable at any time point in the study. The SWA had a strong positive correlation to the Oxycon Mobile metabolic cart but due to a lack of change in metabolic rate, its sensitivity was undetermined. These data do not support the theory that adopting a vegetarian diet results in a long-term change in metabolic rate.
ContributorsMoore, Amy (Author) / Johnston, Carol (Thesis advisor) / Appel, Christy (Thesis advisor) / Gaesser, Glenn (Committee member) / Arizona State University (Publisher)
Created2012
Description
A semiconductor supply chain modeling and simulation platform using Linear Program (LP) optimization and parallel Discrete Event System Specification (DEVS) process models has been developed in a joint effort by ASU and Intel Corporation. A Knowledge Interchange Broker (KIBDEVS/LP) was developed to broker information synchronously between the DEVS and LP models. Recently a single-echelon heuristic Inventory Strategy Module (ISM) was added to correct for forecast bias in customer demand data using different smoothing techniques. The optimization model could then use information provided by the forecast model to make better decisions for the process model. The composition of ISM with LP and DEVS models resulted in the first realization of what is now called the Optimization Simulation Forecast (OSF) platform. It could handle a single echelon supply chain system consisting of single hubs and single products In this thesis, this single-echelon simulation platform is extended to handle multiple echelons with multiple inventory elements handling multiple products. The main aspect for the multi-echelon OSF platform was to extend the KIBDEVS/LP such that ISM interactions with the LP and DEVS models could also be supported. To achieve this, a new, scalable XML schema for the KIB has been developed. The XML schema has also resulted in strengthening the KIB execution engine design. A sequential scheme controls the executions of the DEVS-Suite simulator, CPLEX optimizer, and ISM engine. To use the ISM for multiple echelons, it is extended to compute forecast customer demands and safety stocks over multiple hubs and products. Basic examples for semiconductor manufacturing spanning single and two echelon supply chain systems have been developed and analyzed. Experiments using perfect data were conducted to show the correctness of the OSF platform design and implementation. Simple, but realistic experiments have also been conducted. They highlight the kinds of supply chain dynamics that can be evaluated using discrete event process simulation, linear programming optimization, and heuristics forecasting models.
ContributorsSmith, James Melkon (Author) / Sarjoughian, Hessam S. (Thesis advisor) / Davulcu, Hasan (Committee member) / Fainekos, Georgios (Committee member) / Arizona State University (Publisher)
Created2012
Description
Objective: Vinegar consumption studies have demonstrated possible therapeutic effects in reducing HbA1c and postprandial glycemia. The purpose of the study was to closely examine the effects of a commercial vinegar drink on daily fluctuations in fasting glucose concentrations and postprandial glycemia, and on HbA1c, in individuals at risk for Type 2 Diabetes Mellitus (T2D). Design: Thirteen women and one man (21-62 y; mean, 46.0±3.9 y) participated in this 12-week parallel-arm trial. Participants were recruited from a campus community and were healthy and not diabetic by self-report. Participants were not prescribed oral hypoglycemic medications or insulin; other medications were allowed if use was stable for > 3 months. Subjects were randomized to one of two groups: VIN (8 ounces vinegar drink providing 1.5 g acetic acid) or CON (1 vinegar pill providing 0.04 g acetic acid). Treatments were taken twice daily immediately prior to the lunch and dinner meals. Venous blood samples were drawn at trial weeks 0 and 12 to measure insulin, fasting glucose, and HbA1c. Subjects recorded fasting glucose and 2-h postprandial glycemia concentrations daily using a glucometer. Results: The VIN group showed significant reductions in fasting capillary blood glucose concentrations (p=0.05) that were immediate and sustained throughout the duration of the study. The VIN group had reductions in 2-h postprandial glucose (mean change of −7.6±6.8 mg/dL over the 12-week trial), but this value was not significantly different than that for the CON group (mean change of 3.3±5.3 mg/dL over the 12-week trial, p=0.232). HbA1c did not significantly change (p=0.702), but the reduction in HbA1c in the VIN group, −0.14±0.1%, may have physiological relevance. Conclusions: Significant reductions in HbA1c were not observed after daily consumption of a vinegar drink containing 1.5 g acetic acid in non-diabetic individuals. However, the vinegar drink did significantly reduce fasting capillary blood glucose concentrations in these individuals as compared to a vinegar pill containing 0.04 g acetic acid. These results support a therapeutic effect for vinegar in T2D prevention and progression, specifically in high-risk populations.
ContributorsQuagliano, Samantha (Author) / Johnston, Carol (Thesis advisor) / Appel, Christy (Committee member) / Dixon, Kathleen (Committee member) / Arizona State University (Publisher)
Created2013
Description
Objective: The purpose of this randomized parallel arm trial was to demonstrate the effects of daily fish oil supplementation (600mg per day for eight weeks) on body composition and body mass in young healthy women, aged 18-38, at a large southwestern university. Design: 26 non-obese (mean BMI 23.7±0.6 kg/m2), healthy women (18-38y; mean, 23.5±1.1 y) from a southwestern Arizona university campus community completed the study. Subjects were healthy, non-smokers, consuming less than 3.5 oz of fish per week according to self-report. Participants were randomized to one of two groups: FISH (600 mg omega-3 fatty acids provided in one gel capsule per day), or CON (1000 mg coconut oil placebo provided in one gel capsule per day). Body weight, BMI, and percent body fat were measured using a stadiometer and bioelectrical impedance scale at the screening visit and intervention weeks 1, 4, and 8. 24-hour dietary recalls were also performed at weeks 1 and 8. Results: 8 weeks of omega-3 fatty acid supplementation did not significantly alter body weight (p=0.830), BMI (p=1.00), or body fat percentage (p=0.600) as compared to placebo. Although not statistically significant, 24-hour dietary recalls performed at the beginning and end of the intervention revealed a trend towards increased caloric intake in the FISH group and decreased caloric intake in the CON group throughout the course of the study (p=0.069). If maintained, this difference in caloric intake could have physiological relevance. Conclusions: Omega-3 fatty acids do not significantly alter body weight or body composition in healthy young females. These findings do not refute the current recommendations for Americans to consume at least 8 oz of omega-3-rich seafood per week, supplying 250 mg EPA and DHA per day. More research is needed to investigate the potential for omega-3 fatty acids to modulate daily caloric intake.
ContributorsTeran, Bianca (Author) / Johnston, Carol (Thesis advisor) / Johnson, Melinda (Committee member) / Ohri-Vachaspati, Punam (Committee member) / Arizona State University (Publisher)
Created2013
Description
In 2002, a scientifically derived food guide pyramid for vegetarians, the Modified Food Guide for Lacto-ovo-vegetarians and Vegans was published and well received. Now that 10 years have passed, new scientific literature regarding the bioavailability of the nutrients of key concern in vegetarian diets has been published, and the graphical format of the nation's food guide has evolved from a pyramid shape into a circular plate. The objective of this research was to examine the post-2002 literature regarding the bioavailability of key nutrients in vegetarian diets; to use this information to update the recommendations made in the 2002 Modified Food Guide Pyramid for Lacto-ovo-vegetarians and Vegans; and to adapt this revised food plan to the new USDA MyPlate format. This process involved reviewing the scientific literature to determine if the DRIs for the nutrients of key concern in vegetarian diets are adequate for the vegetarian population and using this information to develop new recommendations for vegetarians if necessary, analyzing the nutrient content of representative foods in different food groups, reconfiguring the food groups so that foods with like nutrient components were grouped together, determining the number of servings of each food group required to meet vegetarians' nutrient requirements at three caloric levels, and developing sample menus. A circular plate graphic, the Vegetarian Plate, was designed to illustrate the recommendations of this updated food guide. This updated, scientifically derived food guide provides a sound base for diet planning for lacto-ovo-vegetarians and vegans. Further research is needed to assess the Vegetarian Plate's adequacy for children, pregnant and lactating women, athletes, and individuals with medical conditions or chronic diseases.
ContributorsFladell, Lauren (Author) / Johnston, Carol (Thesis advisor) / Vaughan, Linda (Committee member) / Shepard, Christina (Committee member) / Arizona State University (Publisher)
Created2013