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: Computer Science and Engineering Program
Description
An application called "Productivity Heatmap" was created with this project with the goal of allowing users to track how productive they are over the course of a day and week, input through scheduled prompts separated by 30 minutes to 4 hours, depending on preference. The result is a heat map colored according to a user's productivity at particular times of each day during the week. The aim is to allow a user to have a visualization on when he or she is best able to be productive, given that every individual has different habits and life patterns. This application was made completely in Google's Android Studio environment using Java and XML, with SQLite being used for database management. The application runs on any Android device, and was designed to be a balance of providing useful information to a user while maintaining an attractive and intuitive interface. This thesis explores the creation of a functional mobile application for mass distribution, with a particular set of end users in mind, namely college students. Many challenges in the form of learning a new development environment were encountered and overcome, as explained in the report. The application created is a core functionality proof-of-concept of a much larger personal project in creating a versatile and useful mobile application for student use. The principles covered are the creation of a mobile application, meeting requirements specified by others, and investigating the interest generated by such a concept. Beyond this thesis, testing will be done, and future enhancements will be made for mass-market consumption.
ContributorsWeser, Matthew Paul (Author) / Nelson, Brian (Thesis director) / Balasooriya, Janaka (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Fiddlevent is an event searching website written in Ruby on Rails. Fiddlevent enables any person to go online and find local events that interest him. Fiddlevent also enables merchants to post their events online. Fiddlevent explores all challenges of website development, such as project management, database design, user interface design, deployment and the software development lifecycle. Fiddlevent aims to utilize best practices for website and software development.
ContributorsThornton, Christopher Gordon (Author) / Balasooriya, Janaka (Thesis director) / Nakamura, Mutsumi (Committee member) / Hurst, Charles (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2013-05
Description
Web-application development constantly changes \u2014 new programming languages, testing tools and programming methodologies are often proposed. The focus of this project is on the tool Selenium and the fairly new technique known as High Volume Automated Testing (HVAT). Both of these techniques were used to test the Just-in-Time Teaching and Learning Classroom Management System software. Selenium was used with a black-box testing technique and HVAT was employed in a white-box testing technique. Two of the major functionalities of this software were examined, which include the login and the professor functionality. The results of the black-box testing technique showed parts of the login component contain bugs, but the professor component is clean. HVAT white-box testing revealed error free implementation on the code level. We present an analysis on a new technique for HVAT testing with Selenium.
ContributorsEjaz, Samira (Author) / Balasooriya, Janaka (Thesis director) / Nakamura, Mutsumi (Committee member) / Wilkerson, Kelly (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2013-05
Description
We created an Android application, Impromp2, which allows users to search for and save events of interest to them in the Phoenix area. The backend, built on the Parse platform, gathers events daily using Web services and stores them in a database. Impromp2 was designed to improve upon similarly-purposed apps available for Android devices in several key ways, especially in user interface design and data interaction capability. This is a full-stack software project that explores databases and their performance considerations, Web services, user interface design, and the challenges of app development for a mobile platform.
ContributorsNorth, Joseph Robert (Author) / Balasooriya, Janaka (Thesis director) / Nakamura, Mutsumi (Committee member) / Faucon, Philippe (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2015-05
Description
My proposed project is an educational application that will seek to simplify the<br/>process of internalizing the chord symbols most commonly seen by those learning<br/>musical improvisation. The application will operate like a game, encouraging the<br/>user to identify chord tones within time limits and award points for successfully<br/>doing so.
ContributorsOwens, Kevin Bradyn (Author) / Balasooriya, Janaka (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Behavioral economics suggests that emotions can affect an individual’s decision making. Recent research on this idea’s application on large societies hints that there may exist some correlation or maybe even some causation relationship between public sentiment—at least what can be pulled from Twitter—and the movement of the stock market. One major result of consistent research on whether or not public sentiment can predict the movement of the stock market is that public sentiment, as a feature, is becoming more and more valid as a variable for stock-market-based machine learning models. While raw values typically serve as invaluable points of data, when training a model, many choose to “engineer” new features for their models—deriving rates of change or range values to improve model accuracy.
Since it doesn’t hurt to attempt to utilize feature extracted values to improve a model (if things don’t work out, one can always use their original features), the question may arise: how could the results of feature extraction on values such as sentiment affect a model’s ability to predict the movement of the stock market? This paper attempts to shine some light on to what the answer could be by deriving TextBlob sentiment values from Twitter data, and using Granger Causality Tests and logistic and linear regression to test if there exist a correlation or causation between the stock market and features extracted from public sentiment.
Since it doesn’t hurt to attempt to utilize feature extracted values to improve a model (if things don’t work out, one can always use their original features), the question may arise: how could the results of feature extraction on values such as sentiment affect a model’s ability to predict the movement of the stock market? This paper attempts to shine some light on to what the answer could be by deriving TextBlob sentiment values from Twitter data, and using Granger Causality Tests and logistic and linear regression to test if there exist a correlation or causation between the stock market and features extracted from public sentiment.
ContributorsYu, James (Author) / Meuth, Ryan (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
There exist many very effective calendar platforms out there, from Google Calendar, to Microsoft’s Outlook, and various implementations by other service providers. While all those services serve their purpose, they may be missing in the capacity to be easily portable for some, or the capacity to offer to the user a ranking of their various events and tasks in order of priority. This is that, while some of these services do offer reliable support for portability on smaller devices, it could be even more beneficial to the user to constantly have an idea of which calendar entry they should prioritize at a given point in time, based on the necessities of each entry and regardless of which entry occurs first on a chronologic line. Many of these capacities are missing in the technology currently used at ASU for course management. This project attempts to address this issue by providing a Software Application that offers to store a user’s calendar events and present those events back to the user after arranging them by order of priority. The project makes use of technologies such as Fibrease, Angular and Android to make the service available through a web browser as well as an Android mobile client. We explore possible avenues of implementations to make the services of this platform accessible and usable through other existing platforms such as Blackboard or Canvas. We also consider ways to incorporate this software into the already existing workflow of other web platforms such as Google Calendar, Blackboard or Canvas, by allowing one platform to be aware of any item creation or update from the other platform, and thus removing the necessity of creating one calendar entry multiple times in different platforms.
ContributorsNdombe, Kelly (Author) / Chen, Yinong (Thesis director) / Balasooriya, Janaka (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Appointment scheduling in health care systems is a well-established domain, however, the top commercial services neglect scheduling analytics. This project explores the benefit of utilizing data analysis to equip health care offices with insights on how to improve their existing schedules. The insights are generated by comparing patients’ preferred appointment times with the current schedule coverage and calculating utilization of past appointments. While untested in the field, the project yielded promising results using generated sample data as a proof of concept for the benefits of using data analytics to remove deficiencies in a health care office’s schedule.
ContributorsBowman, Jedde James (Author) / Chen, Yinong (Thesis director) / Balasooriya, Janaka (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
The aim of this project was to provide college applicants with the ability to apply using a video instead of an essay. These videos are analyzed automatically and their scripts are taken and submitted with the application. This was implemented through the use of Amazon Web Services (AWS) and their S3 buckets along with their speech to text transcription service. This type of application process can give admissions teams the opportunity to get to know who will potentially be attending their university and allows the applicants to express themselves to admissions teams in a new and unique way.
ContributorsStephan, Meagan (Co-author) / Pratt, Devan (Co-author) / Chen, Yinong (Thesis director) / Balasooriya, Janaka (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-12
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