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- Genre: Masters Thesis
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
This study investigates the effectiveness of the use of Concept-Based Instruction (CBI) to facilitate the acquisition of Spanish mood distinctions by second semester second language learners of Spanish. The study focuses on the development of Spanish mood choice and the types of explanations (Rule-of-Thumb vs. Concept-based) used by five students before and after being exposed to Concept-Based Instruction regarding the choice of Spanish mood following various modalities .The students in this study were presented with a pedagogical treatment on Spanish mood choice that included general theoretical concepts based on Gal'perin's (1969, 1992) didactic models and acts of verbalization, which form part of a Concept-Based pedagogical approach. In order to ascertain the effectiveness of the use of concept-based tools to promote the ability to use Spanish mood appropriately over time, a pre and post-test was administered to the group in which students were asked to respond to prompts containing modalities that elicit the indicative and subjunctive moods, indicate their level of confidence in their response, and verbalize in writing a reason for their choice. The development of these abilities in learners exposed to CBI was assessed by comparing pre and post-test scores examining both forms and explanations for the indicative and subjunctive modality prompts given. Results showed that students continued to rely on Rule-of-Thumb explanations of mood choice but they did expand their use of conceptually-based reasoning. Although the quantitative and qualitative analyses of the results indicate that most students did improve their ability to make appropriate mood choices (forms and explanations) after the CBI treatment, the increased use of conceptually-based explanations for their mood choices led to both correct and incorrect responses.
ContributorsBeus, Eric (Author) / Lafford, Barbara (Thesis advisor) / Beas, Omar (Committee member) / Cerron-Palomino, Alvaro (Committee member) / Arizona State University (Publisher)
Created2013
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
Description
Time adolescents spend in organized or informal skill based activities after school is associated with a variety of positive developmental outcomes. Little is known about how siblings might shape adolescents' motivation to participate in after-school activities. The current study applied the expectancy value model and ecological theory to understand if sibling behaviors were related to adolescents' after-school activities for 34 Mexican origin families. Qualitative and quantitative results suggested siblings engaged in five promoting behaviors (i.e., support, provider of information, role modeling, comparison, co-participation) and three inhibiting behaviors (i.e., babysitting, transportation, and negativity) towards adolescent activity participation. Furthermore, sibling behaviors differed by adolescent characteristics (i.e., cultural orientation, familism, and neighborhood) and sibling characteristics (i.e., gender, age). The results provide evidence of the various promoting and inhibiting socialization behaviors sibling might use to influence adolescents' activity motivation.
ContributorsPrice, Chara Dale (Author) / Simpkins, Sandra (Thesis advisor) / Updegraff, Kimberly (Committee member) / Menjivar, Cecilia (Committee member) / Arizona State University (Publisher)
Created2012
Description
This study was designed to influence consumer habits, specifically those relating to purchases of fruits, vegetables, and junk food. Previous studies have clearly shown the ineffectiveness of simply describing the health benefits of eating more fruits and vegetables (F/V). In contrast, this study aimed to change the result by changing the message: providing participants with insight into the hidden agendas of food companies and grocery stores, provide useful tips on how to include children when selecting F/V, and emphasizing the importance of parental modeling in regard to food purchases. Participants of this study were separated into two groups, the tour group and the education group. The tour group was guided through a grocery store where they learned about sales tactics and manipulations used by grocery stores and food companies to influence purchases. Education group participants were provided with an education session focusing on USDA and FDA handouts displaying current educational suggestions for increasing F/V consumption. Grocery store receipts were collected and analyzed to track the progress of both groups. The goal of the study was to identify a method of informing consumers that will produce a significant change in behavior. Increasing F/V consumption, even in relatively small amounts, would be an important step forward in improving the diet and overall health of Americans. This study was the first of its kind to measure purchasing patterns objectively (through analysis of purchase receipts, rather than personal opinion/evaluation surveys) and in a wide-scope retail environment that includes all grocery store purchases by participants. Significant increases or decreases in the amount of money spent on F/V, or the amount (pounds) of F/V purchased were not seen, however a small correlation (r = 0.133) exists when comparing the weight of F/V purchased pre/post intervention. Data from Food Frequency Questionnaires shows participants consuming significantly higher amounts of F/V post intervention (p=0.043). The tour group and education group experienced an average increase of 0.7 servings per day. Future interventions might benefit by extending their scope to include cooking demonstrations, in-home interventions, and education on healthy eating outside of the home.
ContributorsKinsfather, Diana (Author) / Johnston, Carol (Thesis advisor) / Hekler, Eric (Committee member) / Tetreault, Colin (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
Objective. Both the civic education literature and the political ambition literature leave a gap in addressing the impact of political science coursework on political ambition. I address this gap by specifying the relationships between civic education, political knowledge, and political ambition. Methods. I employ paired t tests, chi-square tests, and Fisher's exact probability tests on an original dataset of 174 paired pre- and post-test survey responses. My survey improves upon prior works in the ambition literature (Fox and Lawless 2013) by virtue of its field experiment design. Results. My findings indicate that political science coursework has a positive impact on political knowledge, but only among women, and that political science coursework has a negative impact (among women) on one of the most valid measures of political ambition—how likely one is to run for office in the future. Conclusions/Implications. The results have negative normative implications for those trying to use political education as an instrument to reduce the gender gap (see Lawless and Fox 2010, Fox and Lawless 2013) in political ambition. This suggests the need to explore further options for increasing political ambition, particularly among women.
ContributorsWiezel, Adi (Author) / Kittilson, Miki (Thesis advisor) / Fridkin, Kim (Committee member) / Woodall, Gina (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