Matching Items (11)
Filtering by
Description
Global violent conflict has become an increasing problem in recent decades, especially in the African continent. Civil wars, terrorism, riots, and political violence has wrought havoc not only on civilian lives, but also on economic foundations. Trade networks are a way to measure these economic foundations. To summarize trade networks clustering coefficient as well as trade quantity/value summation measures are used. To understand effects of global trade on violent conflict, Pearson product-moment correlations are utilized. This work details a comparison of African national economies and violent conflict events using clustering coefficient, trade summation measures and Pearson correlation coefficient.
ContributorsKadambi, Sagarika Sanjay (Author) / Maciejewski, Ross (Thesis director) / Shutters, Shade (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
With the population size growing rapidly at Arizona State University, students are more likely to get sick and miss school when living on campus. The purpose of this project was to design a mobile web application called, SeeSick, that would visualize the spread of illness on the ASU Tempe campus. This application would provide students with information that could help prevent the spread of illness and allow them to take actionable steps for staying healthy. To accomplish the design and testing of this application, research was conducted on how technology is currently used by students when they are sick, how to design an effective user interface for ASU students, how to physically visualize the spread of the flu on an app, and if an application like this would be useful. The visualizations are created from a user input form and from Twitter data scraping and are displayed on a heat map of the Tempe campus. 126 students were surveyed before the development of the application and once the application was functional, 87 students were interviewed for user testing. Through trial-and-error design and testing, the application was analyzed to determine if it would be used and change behavior. The design of SeeSick successfully provided users with a way to visualize the spread of symptoms on campus and presented them personalized feedback about their symptoms. 62% of students interviewed found the application to be useful and 84% of participants found it easy to use. However, 57% of students said their behavior would not change while using SeeSick. Of the students who tested the application, SeeSick was found to be useful, easy to use, but would not cause behavior change. The current version supports the goal to create a mobile application that tracks the spread of the flu on campus, however it was not tested enough to determine if it would change behavior. With further development and larger testing groups, SeeSick could be improved to not only track the spread of illness on a hyper-local level, but also create actionable steps to prevent the spread of illness.
ContributorsChartier, McKinsey Lynne (Author) / Hekler, Eric (Thesis director) / Maciejewski, Ross (Committee member) / Barrett, The Honors College (Contributor)
Created2014-12
Description
This work explores the development of a visual analytics tool for geodemographic exploration in an online environment. We mine 78 million records from the United States white pages, link the location data to demographic data (specifically income) from the United States Census Bureau, and allow users to interactively compare distributions of names with regards to spatial location similarity and income. In order to enable interactive similarity exploration, we explore methods of pre-processing the data as well as on-the-fly lookups. As data becomes larger and more complex, the development of appropriate data storage and analytics solutions has become even more critical when enabling online visualization. We discuss problems faced in implementation, design decisions and directions for future work.
ContributorsIbarra, Jose Luis (Author) / Maciejewski, Ross (Thesis director) / Mack, Elizabeth (Committee member) / Longley, Paul (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2014-05
Description
Speech recognition in games is rarely seen. This work presents a project, a 2D computer game named "The Emblems" which utilizes speech recognition as input. The game itself is a two person strategy game whose goal is to defeat the opposing player's army. This report focuses on the speech-recognition aspect of the project. The players interact on a turn-by-turn basis by speaking commands into the computer's microphone. When the computer recognizes a command, it will respond accordingly by having the player's unit perform an action on screen.
ContributorsNguyen, Jordan Ngoc (Author) / Kobayashi, Yoshihiro (Thesis director) / Maciejewski, Ross (Committee member) / Barrett, The Honors College (Contributor) / Computing and Informatics Program (Contributor) / Computer Science and Engineering Program (Contributor)
Created2014-05
Description
The project, "The Emblems: OpenGL" is a 2D strategy game that incorporates Speech Recognition for control and OpenGL for computer graphics. Players control their own army by voice commands and try to eliminate the opponent's army. This report focuses on the 2D art and visual aspects of the project. There are different sprites for the player's army units and icons within the game. The game also has a grid for easy unit placement.
ContributorsHsia, Allen (Author) / Kobayashi, Yoshihiro (Thesis director) / Maciejewski, Ross (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2014-05
Description
Due to the popularity of the movie industry, a film's opening weekend box-office performance is of great interest not only to movie studios, but to the general public, as well. In hopes of maximizing a film's opening weekend revenue, movie studios invest heavily in pre-release advertisement. The most visible advertisement is the movie trailer, which, in no more than two minutes and thirty seconds, serves as many people's first introduction to a film. The question, however, is how can we be confident that a trailer will succeed in its promotional task, and bring about the audience a studio expects? In this thesis, we use machine learning classification techniques to determine the effectiveness of a movie trailer in the promotion of its namesake. We accomplish this by creating a predictive model that automatically analyzes the audio and visual characteristics of a movie trailer to determine whether or not a film's opening will be successful by earning at least 35% of a film's production budget during its first U.S. box office weekend. Our predictive model performed reasonably well, achieving an accuracy of 68.09% in a binary classification. Accuracy increased to 78.62% when including genre in our predictive model.
ContributorsWilliams, Terrance D'Mitri (Author) / Pon-Barry, Heather (Thesis director) / Zafarani, Reza (Committee member) / Maciejewski, Ross (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2014-05
Description
Elections in the United States are highly decentralized with vast powers given to the states to control laws surrounding voter registration, primary procedures, and polling places even in elections of federal officials. There are many individual factors that predict a person's likelihood of voting including race, education, and age. Historically disenfranchised groups are still disproportionately affected by restrictive voter registration and ID laws which can suppress their turnout. Less understood is how election-day polling place accessibility affects turnout. Absentee and early voting increase accessibility for all voters, but 47 states still rely on election-day polling places. I study how the geographic allocation of polling places and the number of voters assigned to each (polling place load) in Maricopa County, Arizona has affected turnout in primary and general elections between 2006 and 2016 while controlling for the demographics of voting precincts. This represents a significant data problem; voting precincts changed three times during the time studied and polling places themselves can change every election. To aid in analysis, I created a visualization that allows for the exploration of polling place load, precinct demographics, and polling place accessibility metrics in a map view of the county. I find through a spatial regression model that increasing the load on a polling place can decrease the election-day turnout and prohibitively large distances to the polling place have a similar effect. The effect is more pronounced during general elections and is present at varying levels during each of the 12 elections studied. Finally, I discuss how early voting options appear to have little positive effect on overall turnout and may in fact decrease it.
ContributorsHansen, Brett Joseph (Author) / Maciejewski, Ross (Thesis director) / Grubesic, Anthony (Committee member) / Economics Program in CLAS (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
Education has been at the forefront of many issues in Arizona over the past several years with concerns over lack of funding sparking the Red for Ed movement. However, despite the push for educational change, there remain many barriers to education including a lack of visibility for how Arizona schools are performing at a legislative district level. While there are sources of information released at a school district level, many of these are limited and can become obscure to legislators when such school districts lie on the boundary between 2 different legislative districts. Moreover, much of this information is in the form of raw spreadsheets and is often fragmented between government websites and educational organizations. As such, a visualization dashboard that clearly identifies schools and their relative performance within each legislative district would be an extremely valuable tool to legislative bodies and the Arizona public. Although this dashboard and research are rough drafts of a larger concept, they would ideally increase transparency regarding public information about these districts and allow legislators to utilize the dashboard as a tool for greater understanding and more effective policymaking.
ContributorsColyar, Justin Dallas (Author) / Michael, Katina (Thesis director) / Maciejewski, Ross (Committee member) / Tate, Luke (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Multi-view learning, a subfield of machine learning that aims to improve model performance by training on multiple views of the data, has been studied extensively in the past decades. It is typically applied in contexts where the input features naturally form multiple groups or views. An example of a naturally multi-view context is a data set of websites, where each website is described not only by the text on the page, but also by the text of hyperlinks pointing to the page. More recently, various studies have demonstrated the initial success of applying multi-view learning on single-view data with multiple artificially constructed views. However, there lacks a systematic study regarding the effectiveness of such artificially constructed views. To bridge this gap, this thesis begins by providing a high-level overview of multi-view learning with the co-training algorithm. Co-training is a classic semi-supervised learning algorithm that takes advantage of both labelled and unlabelled examples in the data set for training. Then, the thesis presents a web-based tool developed in Python allowing users to experiment with and compare the performance of multiple view construction approaches on various data sets. The supported view construction approaches in the web-based tool include subsampling, Optimal Feature Set Partitioning, and the genetic algorithm. Finally, the thesis presents an empirical comparison of the performance of these approaches, not only against one another, but also against traditional single-view models. The findings show that a simple subsampling approach combined with co-training often outperforms both the other view construction approaches, as well as traditional single-view methods.
ContributorsAksoy, Kaan (Author) / Maciejewski, Ross (Thesis director) / He, Jingrui (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-12
Description
Collecting accurate collective decisions via crowdsourcing
is challenging due to cognitive biases, varying
worker expertise, and varying subjective scales. This
work investigates new ways to determine collective decisions
by prompting users to provide input in multiple
formats. A crowdsourced task is created that aims
to determine ground-truth by collecting information in
two different ways: rankings and numerical estimates.
Results indicate that accurate collective decisions can
be achieved with less people when ordinal and cardinal
information is collected and aggregated together
using consensus-based, multimodal models. We also
show that presenting users with larger problems produces
more valuable ordinal information, and is a more
efficient way to collect an aggregate ranking. As a result,
we suggest input-elicitation to be more widely considered
for future work in crowdsourcing and incorporated
into future platforms to improve accuracy and efficiency.
is challenging due to cognitive biases, varying
worker expertise, and varying subjective scales. This
work investigates new ways to determine collective decisions
by prompting users to provide input in multiple
formats. A crowdsourced task is created that aims
to determine ground-truth by collecting information in
two different ways: rankings and numerical estimates.
Results indicate that accurate collective decisions can
be achieved with less people when ordinal and cardinal
information is collected and aggregated together
using consensus-based, multimodal models. We also
show that presenting users with larger problems produces
more valuable ordinal information, and is a more
efficient way to collect an aggregate ranking. As a result,
we suggest input-elicitation to be more widely considered
for future work in crowdsourcing and incorporated
into future platforms to improve accuracy and efficiency.
ContributorsKemmer, Ryan Wyeth (Author) / Escobedo, Adolfo (Thesis director) / Maciejewski, Ross (Committee member) / Computing and Informatics Program (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05