Barrett, The Honors College Thesis/Creative Project Collection
Barrett, The Honors College at Arizona State University proudly showcases the work of undergraduate honors students by sharing this collection exclusively with the ASU community.
Barrett accepts high performing, academically engaged undergraduate students and works with them in collaboration with all of the other academic units at Arizona State University. All Barrett students complete a thesis or creative project which is an opportunity to explore an intellectual interest and produce an original piece of scholarly research. The thesis or creative project is supervised and defended in front of a faculty committee. Students are able to engage with professors who are nationally recognized in their fields and committed to working with honors students. Completing a Barrett thesis or creative project is an opportunity for undergraduate honors students to contribute to the ASU academic community in a meaningful way.
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- Creators: Meuth, Ryan
Description
Even in the largest public university in the country, computer related degrees such as Computer Science, Computer Systems Engineering and Software Engineering have low enrollment rates and high dropout rates. This is interesting because the careers that require these degrees are marketed as the highest paying and most powerful. The goal of this project was to find out what the students of Arizona State University (ASU) thought about these majors and why they did or did not pick them. A total of 206 students were surveyed from a variety of sources including upper level classes, lower level classes and Barrett, the Honors College. Survey questions asked why the students picked their current major, if they had a previous major and why did they switch, and if the students had considered one of the three computer related degrees. Almost all questions were open ended, meaning the students did not have multiple choice answers and instead could write as short or as long of a response as needed. Responses were grouped based on a set of initial hypotheses and any emerging trends. These groups were displayed in several different bar graphs broken down by gender, grade level and category of student (stayed in a computer related degree, left one, joined one or picked a non-computer related degree). Trends included students of all grade levels picking their major because they were passionate or interested in the subject. This may suggest that college students are set in their path and will not switch majors easily. Students also reported seeing computer related degrees as too difficult and intimidating. However, given the low (when compared to all of ASU) number of students surveyed, the conclusions and trends given cannot be representative of ASU as a whole. Rather, they are just representative of this sample population. Further work on this study, if time permitted, would be to try to survey more students and question some of the trends established to find more specific answers.
ContributorsMeza, Edward L (Author) / Meuth, Ryan (Thesis director) / Miller, Phillip (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
Description
This thesis dives into the world of machine learning by attempting to create an application that will accurately predict whether or not a sneaker will resell at a profit. To begin this study, I first researched different machine learning algorithms to determine which would be best for this project. After ultimately deciding on using an artificial neural network, I then moved on to collecting data, using StockX and Twitter. StockX is a platform where individuals can post and resell shoes, while also providing statistics and analytics about each pair of shoes. I used StockX to retrieve data about the actual shoe, which involved retrieving data for the network feature variables: gender, brand, and retail price. Additionally, I also retrieved the data for the average deadstock price for each shoe, which describes what the mean price of new, unworn shoes are selling for on StockX. This data was used with the retail price data to determine whether or not a shoe has been, on average, selling for a profit. I used Twitter’s API to retrieve links to different shoes on StockX along with retrieving the number of favorites and retweets each of those links had. These metrics were used to account for ‘hype’ of the shoe, with shoes traditionally being more profitable the larger the hype surrounding them. After preprocessing the data, I trained the model using a randomized 80% of the data. On average, the model had about a 65-70% accuracy range when tested with the remaining 20% of the data. Once the model was optimized, I saved it and uploaded it to a web application that took in user input for the five feature variables, tested the datapoint using the model, and outputted the confidence in whether or not the shoe would generate a profit.
From a technical perspective, I used Python for the whole project, while also using HTML/CSS for the front-end of the application. As for key packages, I used Keras, an open source neural network library to build the model; data preprocessing was done using sklearn’s various subpackages. All charts and graphs were done using data visualization libraries matplotlib and seaborn. These charts provided insight as to what the final dataset looked like. They showed how the brand distribution is relatively close to what it should be, while the gender distribution was heavily skewed. Future work on this project would involve expanding the dataset, automating the entirety of the data retrieval process, and finally deploying the project on the cloud for users everywhere to use the application.
From a technical perspective, I used Python for the whole project, while also using HTML/CSS for the front-end of the application. As for key packages, I used Keras, an open source neural network library to build the model; data preprocessing was done using sklearn’s various subpackages. All charts and graphs were done using data visualization libraries matplotlib and seaborn. These charts provided insight as to what the final dataset looked like. They showed how the brand distribution is relatively close to what it should be, while the gender distribution was heavily skewed. Future work on this project would involve expanding the dataset, automating the entirety of the data retrieval process, and finally deploying the project on the cloud for users everywhere to use the application.
ContributorsShah, Shail (Author) / Meuth, Ryan (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
The Internet has made it possible to exchange information at a rapid rate. With this extraordinary ability, media companies and various other organizations have been able to communicate thoughts and information to an extremely large audience. As a result, news subscribers are overwhelmed with biased information, which makes it very easy to be misinformed. Unfortunately, there is currently no way to stay truly informed without spending countless hours searching the Internet for different viewpoints and ultimately using that information to formulate a sound understanding. This project (nicknamed "Newsie") solves this problem by providing news subscribers with many news sources to every topic, thereby saving them time and ultimately paving a way to a more informed society. Since one of the main goals of this project is to provide information to the largest number of people, Newsie is designed with availability in mind. Unsurprisingly, the most accessible method of communication is the Internet \u2014 more specifically, a website. Users will be able to access Newsie via a webpage, and easily view to most recent headlines with their corresponding articles from several sources. Another goal of the project is to classify different articles and sources based on their bias. After reading articles, users will be able to vote on their biases. This provides a crowdsourced method of determining bias.
ContributorsAlimov, Robert Joseph (Author) / Meuth, Ryan (Thesis director) / Franceschini, Enos (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
Description
Machine learning is one of the fastest growing fields and it has applications in almost any industry. Predicting sports games is an obvious use case for machine learning, data is relatively easy to collect, generally complete data is available, and outcomes are easily measurable. Predicting the outcomes of sports events may also be easily profitable, predictions can be taken to a sportsbook and wagered on. A successful prediction model could easily turn a profit. The goal of this project was to build a model using machine learning to predict the outcomes of NBA games.
In order to train the model, data was collected from the NBA statistics website. The model was trained on games dating from the 2010 NBA season through the 2017 NBA season. Three separate models were built, predicting the winner, predicting the total points, and finally predicting the margin of victory for a team. These models learned on 80 percent of the data and validated on the other 20 percent. These models were trained for 40 epochs with a batch size of 15.
The model for predicting the winner achieved an accuracy of 65.61 percent, just slightly below the accuracy of other experts in the field of predicting the NBA. The model for predicting total points performed decently as well, it could beat Las Vegas’ prediction 50.04 percent of the time. The model for predicting margin of victory also did well, it beat Las Vegas 50.58 percent of the time.
In order to train the model, data was collected from the NBA statistics website. The model was trained on games dating from the 2010 NBA season through the 2017 NBA season. Three separate models were built, predicting the winner, predicting the total points, and finally predicting the margin of victory for a team. These models learned on 80 percent of the data and validated on the other 20 percent. These models were trained for 40 epochs with a batch size of 15.
The model for predicting the winner achieved an accuracy of 65.61 percent, just slightly below the accuracy of other experts in the field of predicting the NBA. The model for predicting total points performed decently as well, it could beat Las Vegas’ prediction 50.04 percent of the time. The model for predicting margin of victory also did well, it beat Las Vegas 50.58 percent of the time.
ContributorsGarretson, Samuel Burkett (Author) / Meuth, Ryan (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Department of Management and Entrepreneurship (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
The most important task for a beginning computer science student, in order for them to succeed in their future studies, is to learn to be able to understand code. One of the greatest indicators of student success in beginning programming courses is the ability to read code and predict its output, as this shows that the student truly understands what each line of code is doing. Yet few tools available to students today focus on helping students to improve their ability to read code. The goal of the random Python program generator is to give students a tool to practice this important skill.
The program writes randomly generated, syntactically correct Python 3 code in order to provide students infinite examples from which to study. The end goal of the project is to create an interactive tool where beginning programming students can click a button to generate a random code snippet, check if what they predict the output to be is correct, and get an explanation of the code line by line. The tool currently lacks a front end, but it currently is able to write Python code that includes assignment statements, delete statements, if statements, and print statements. It supports boolean, float, integer, and string variable types.
The program writes randomly generated, syntactically correct Python 3 code in order to provide students infinite examples from which to study. The end goal of the project is to create an interactive tool where beginning programming students can click a button to generate a random code snippet, check if what they predict the output to be is correct, and get an explanation of the code line by line. The tool currently lacks a front end, but it currently is able to write Python code that includes assignment statements, delete statements, if statements, and print statements. It supports boolean, float, integer, and string variable types.
ContributorsDiLorenzo, Kaitlyn (Author) / Meuth, Ryan (Thesis director) / Miller, Phillip (Committee member) / School of International Letters and Cultures (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
In June of 2016, the United Kingdom held a referendum for its citizens to decide whether to remain a part of the European Union or take their leave. The vote was close but ultimately the U.K. decided to leave, triggering the two-year process of negotiations that would shape the U.K.’s departure (Brexit). The question of what will become of the border between Northern Ireland and the Republic of Ireland is heavy with implications for the national identity of people living on either side of the border, and this makes it one of the more pressing concerns in Brexit discourse. This research analyzes how national identity is used as a rhetorical tactic in media to influence and persuade readers to vote in accordance with the author’s political goals. It does so by evaluating how borders shape national identity and analyzing newspaper articles from the two highest circulating Northern Irish daily newspapers (The Irish News and the Belfast Telegraph) during the week leading up to the June 23rd, 2016 referendum. In analyzing news articles relating to the Irish border issue of Brexit from The Irish News and the Belfast Telegraph during the time frame of June 16th-23rd, 2016, four analytical categories of how identity-related rhetoric was used were discovered: fear, self-interest, Irish Nationalism, and a negative association of the past. Further, it was hypothesized and confirmed the political leanings of the papers influenced which type of rhetorical tactic was used. In the broad realm of Brexit and media related discussion, this research could help strengthen understanding of how traditional media uses national identity to persuade readers to and influence voting behavior in the midst of such a divisive referendum.
Key Words: Brexit, Irish border, national identity, rhetoric, newspapers
Key Words: Brexit, Irish border, national identity, rhetoric, newspapers
ContributorsCaldwell, Tara (Author) / O'Flaherty, Katherine (Thesis director) / Ripley, Charles (Committee member) / School of Social Transformation (Contributor) / School of Politics and Global Studies (Contributor, Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
If you’ve ever found yourself uttering the words “Honey, I forgot the—” or “how did I miss the—" when coming home from the grocery store, then you’re not alone. This everyday problem that we disregard as part of life may not seem like much, but it is the driving force behind my honors thesis.
Shopping Buddy is a complete Amazon Web Services solution to this problem which is so innate to the human condition. Utilizing Alexa to keep track of your pantry, this web application automates the daunting task of creating your shopping list, putting the power of the cloud at your fingertips while keeping your complete shopping list only a click away.
Say goodbye to the nights of spaghetti without the parmesan that you left on the store shelf or the strawberries that you forgot for the strawberry shortcake. With this application, you will no longer need to rely on your memory of what you think is in the back of your fridge nor that pesky shopping list that you always end up losing when you need it the most. Accessible from any web enabled device, Shopping Buddy has got your back through all your shopping adventures to come.
Shopping Buddy is a complete Amazon Web Services solution to this problem which is so innate to the human condition. Utilizing Alexa to keep track of your pantry, this web application automates the daunting task of creating your shopping list, putting the power of the cloud at your fingertips while keeping your complete shopping list only a click away.
Say goodbye to the nights of spaghetti without the parmesan that you left on the store shelf or the strawberries that you forgot for the strawberry shortcake. With this application, you will no longer need to rely on your memory of what you think is in the back of your fridge nor that pesky shopping list that you always end up losing when you need it the most. Accessible from any web enabled device, Shopping Buddy has got your back through all your shopping adventures to come.
ContributorsMathews, Nicolle (Author) / Meuth, Ryan (Thesis director) / Chen, Yinong (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
A common design of multi-agent robotic systems requires a centralized master node, which coordinates the actions of all the agents. The multi-agent system designed in this project enables coordination between the robots and reduces the dependence on a single node in the system. This design change reduces the complexity of the central node, and makes the system more adaptable to changes in its topology. The final goal of this project was to have a group of robots collaboratively claim positions in pre-defined formations, and navigate to the position using pose data transmitted by a localization server.
Planning coordination between robots in a multi-agent system requires each robot to know the position of the other robots. To address this, the localization server tracked visual fiducial markers attached to the robots and relayed their pose to every robot at a rate of 20Hz using the MQTT communication protocol. The robots used this data to inform a potential fields path planning algorithm and navigate to their target position.
This project was unable to address all of the challenges facing true distributed multi-agent coordination and needed to make concessions in order to meet deadlines. Further research would focus on shoring up these deficiencies and developing a more robust system.
Planning coordination between robots in a multi-agent system requires each robot to know the position of the other robots. To address this, the localization server tracked visual fiducial markers attached to the robots and relayed their pose to every robot at a rate of 20Hz using the MQTT communication protocol. The robots used this data to inform a potential fields path planning algorithm and navigate to their target position.
This project was unable to address all of the challenges facing true distributed multi-agent coordination and needed to make concessions in order to meet deadlines. Further research would focus on shoring up these deficiencies and developing a more robust system.
ContributorsThibeault, Quinn (Author) / Meuth, Ryan (Thesis director) / Chen, Yinong (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
As modern advancements in medical technology continue to increase overall life expectancy, hospitals and healthcare systems are finding new and more efficient ways of storing extensive amounts of patient healthcare information. This progression finds people increasingly dependent on hospitals as the primary providers of medical data, ranging from immunization records to surgical history. However, the benefits of carrying a copy of personal health information are becoming increasingly evident. This project aims to create a simple, secure, and cohesive application that stores and retrieves user health information backed by Google’s Firebase cloud infrastructure. Data was collected to both explore the current need for such an application, and to test the usability of the product. The former was done using a multiple-choice survey distributed through social media to understand the necessity for a patient-held health file (PHF). Subsequently, user testing was performed with the intent to track the success of our application in meeting those needs. According to the data, there was a trend that suggested a significant need for a healthcare information storage device. This application, allowing for efficient and simple medical information storage and retrieval, was created for a target audience of those seeking to improve their medical information awareness, with a primary focus on the elderly population. Specific correlations between the frequency of physician visits and app usage were identified to target the potential use cases of our app. The outcome of this project succeeded in meeting the significant need for increased patient medical awareness in the healthcare community.
ContributorsUpponi, Rohan Sachin (Co-author) / Somayaji, Vasishta (Co-author) / McDaniel, Troy (Thesis director) / Meuth, Ryan (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Debate provides a valuable educational opportunity for students to learn a variety of portable research and public speaking skills, but many of its goals are hindered by the lack of a dedicated software. Currently, the primary tool for research and presentation of evidence is paperlessdebate.com's Verbatim, which is built as a template for Microsoft Word. While functional, Verbatim suffers from several shortcomings; its reliance on Word means that it cannot be fully cross-platform, and it also means that it is difficult to streamline Verbatim's workflow for the particular needs of debaters. Thus, the goal of this project was to fill the need for a stand-alone, cross platform application that debaters (and coaches) can use to research and present evidence. The bulk of the project consisted of creating a specialized editor, including a variety of features catered towards usability in a range of debate contexts. Additionally, the software is integrated with a back end database such that it can also replace the mixture of storage solutions (such as Dropbox and Microsoft's OneDrive) that teams currently use to maintain and share their data. In order to make the software more extensible and to improve its accessibility, it is released as free open source software under the GNU General Public License v3.0. This paper describes the core features of the application and the motivation behind those features' implementations, and briefly includes a discussion of the companion mobile app for Android devices. It also reviews the technologies that were used to create the software's implementation.
ContributorsRedman, Colin (Author) / Symonds, Adam (Thesis director) / Meuth, Ryan (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05