Barrett

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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Using Smart-Device Metrics to Inform Music Streaming

Description

This project seeks to motivate runners by creating an application that selectively plays music based on smartwatch metrics. This is done by analyzing metrics collected through a person’s smartwatch such as heart rate or running power and then selecting the music that best fits their workout’s intensity. This way, as…

This project seeks to motivate runners by creating an application that selectively plays music based on smartwatch metrics. This is done by analyzing metrics collected through a person’s smartwatch such as heart rate or running power and then selecting the music that best fits their workout’s intensity. This way, as the workout becomes harder for the user, increasingly motivating music is played.
ContributorsDoyle, Niklas (Author) / Osburn, Steven (Thesis director) / Miller, Phillip (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Music, Dance and Theatre (Contributor)
Created2023-05

Doyle_Spring_2023.pdf

Description
This project seeks to motivate runners by creating an application that selectively plays music based on smartwatch metrics. This is done by analyzing metrics collected through a person’s smartwatch such as heart rate or running power and then selecting the music that best fits their workout’s intensity. This way, as…

This project seeks to motivate runners by creating an application that selectively plays music based on smartwatch metrics. This is done by analyzing metrics collected through a person’s smartwatch such as heart rate or running power and then selecting the music that best fits their workout’s intensity. This way, as the workout becomes harder for the user, increasingly motivating music is played.
ContributorsDoyle, Niklas (Author) / Osburn, Steven (Thesis director) / Miller, Phillip (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Music, Dance and Theatre (Contributor)
Created2023-05

Machine Learning-Based Approach to Predictive Modeling for Energy Access

Description
Energy poverty is a pressing issue in agricultural areas that affects the livelihoods of millions of people worldwide. The lack of access to modern energy services in rural communities hinders the development of the agricultural sector and limits economic opportunities. To address this issue, this thesis aims to develop a…

Energy poverty is a pressing issue in agricultural areas that affects the livelihoods of millions of people worldwide. The lack of access to modern energy services in rural communities hinders the development of the agricultural sector and limits economic opportunities. To address this issue, this thesis aims to develop a predictive modeling framework using machine learning techniques to identify feasible interventions that can improve energy access in specific rural agricultural regions. Machine learning plays a pivotal role in addressing energy poverty in rural agricultural regions. By leveraging the power of advanced data analytics and predictive modeling, machine learning algorithms can analyze vast datasets related to energy usage, agricultural practices, geographic factors, and socioeconomic conditions. These algorithms can uncover valuable insights and patterns that are not readily apparent through traditional analytical methods. Moreover, machine learning enables the development of predictive models that can forecast energy demand and identify optimal strategies for improving energy access in rural areas. These models can take into account various variables, such as crop cycles, weather conditions, and community needs, to recommend interventions that are tailored to the specific requirements of each region.
ContributorsKonatam, Saisumana (Author) / Osburn, Steven (Thesis director) / Kerner, Hanah (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-12

PyAntiPhish: A Python-Based Machine Learning Detector of Phishing Websites and An Examination of Relevant URL-Based Features

Description
Phishing is one of most common and effective attack vectors in modern cybercrime. Rather than targeting a technical vulnerability in a computer system, phishing attacks target human behavioral or emotional tendencies through manipulative emails, text messages, or phone calls. Through PyAntiPhish, I attempt to create my own version of an…

Phishing is one of most common and effective attack vectors in modern cybercrime. Rather than targeting a technical vulnerability in a computer system, phishing attacks target human behavioral or emotional tendencies through manipulative emails, text messages, or phone calls. Through PyAntiPhish, I attempt to create my own version of an anti-phishing solution, through a series of experiments testing different machine learning classifiers and URL features. With an end-goal implementation as a Chromium browser extension utilizing Python-based machine learning classifiers (those available via the scikit-learn library), my project uses a combination of Python, TypeScript, Node.js, as well as AWS Lambda and API Gateway to act as a solution capable of blocking phishing attacks from the web browser.
ContributorsYang, Branden (Author) / Osburn, Steven (Thesis director) / Malpe, Adwith (Committee member) / Ahn, Gail-Joon (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05

ptr_linked_list_cpp.pdf

ContributorsPalmer, Rock (Author) / Osburn, Steven (Thesis director) / Platt, Dane (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05

ptr_linked_list_h.pdf

ContributorsPalmer, Rock (Author) / Osburn, Steven (Thesis director) / Platt, Dane (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05

helper_functions_h.pdf

ContributorsPalmer, Rock (Author) / Osburn, Steven (Thesis director) / Platt, Dane (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05

string_mapping_h.pdf

ContributorsPalmer, Rock (Author) / Osburn, Steven (Thesis director) / Platt, Dane (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05

truth_table_cpp.pdf

ContributorsPalmer, Rock (Author) / Osburn, Steven (Thesis director) / Platt, Dane (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05

linked_list_cpp.pdf

ContributorsPalmer, Rock (Author) / Osburn, Steven (Thesis director) / Platt, Dane (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05