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- Creators: Computer Science and Engineering Program
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Description
The goal of this project is to measure the effects of the use of dynamic circuit technology within quantum neural networks. Quantum neural networks are a type of neural network that utilizes quantum encoding and manipulation techniques to learn to solve a problem using quantum or classical data. In their current form these neural networks are linear in nature, not allowing for alternative execution paths, but using dynamic circuits they can be made nonlinear and can execute different paths. We measured the effects of these dynamic circuits on the training time, accuracy, and effective dimension of the quantum neural network across multiple trials to see the impacts of the nonlinear behavior.
ContributorsLynch, Brian (Author) / De Luca, Gennaro (Thesis director) / Chen, Yinong (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-12
Description
The significance of precise and prompt documentation of procedures within medical residency programs is important for medical residents due to its direct influence on the evaluation of competency, appraisal of the residency program, and the provision of high-quality healthcare. The motivation of the research study is driven by the concerns expressed by medical professionals in the residency program. The research in this honors thesis explored complex difficulties encountered by residents at medical hospitals in relation to the documentation of medical procedures. The study comprised of three parts: an in-depth literature survey specifically with respect to the duties and lives of Residents and survey of previously conducted structured interviews of Residents at hospitals.
The study revealed that the current logging processes at hospitals, have become cumbersome, inefficient, time-intensive, and reduced motivation to log the procedures correctly and in a timely manner. Frequently, the procedures are logged from memory and therefore accuracy of the data is unknown. This study did not find evidence that the data logged was used for continuous improvement of processes and the improvement of the curriculum for Residents. The thesis has made a detailed system requirement based on the understanding of the subject and a detailed analysis of current methods and technologies used.
Given the permeance of Artificial Intelligence (AI) and software such as ChatGPT, a literature survey of use of AI/ChatGPT was also undertaken. AI technology may provide an opportunity to streamline data logging and analysis. As the technology progresses and legal and ethical issues are resolved, many AI technologies and recommendations from this paper could become part of ongoing Engineering Projects in Community Service (EPICS) projects at Arizona State University (ASU).
ContributorsMoudgalya, Sudhanva (Author) / Malpe, Adwith (Thesis director) / Schoepf, Jared (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-12
Description
This project uses All of Us Data to analyze how well of a predictor APOE ε4 is in the Latinx community, a high grandparent care community. APOE is used as a predictor for Alzheimer’s disease, but it is unknown, due to the lack of studies, how strong of a predictor it will be for Latinx individuals. This project aims to understand if the increased risk of Alzheimer’s disease among Hispanics is associated with a different level of ε4 gene frequency.
ContributorsPerez, Julianna (Author) / Holechek, Susan (Thesis director) / Lopez, Gilberto (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Life Sciences (Contributor)
Created2023-05
Description
Command and Control (C2) tactics are commonly used by ethical hackers and other offensive security professionals to emulate a realistic adversary attack on a network. This helps security teams measure how prepared they are for a real attack. This thesis documents the creative process of designing and creating Meltout, an open-source C2 framework written in the Rust programming language.
ContributorsShinno, Thaddeus (Author) / Meuth, Ryan (Thesis director) / Shoshitaishvili, Yan (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-05
Description
American Sign Language (ASL) is used for Deaf and Hard of Hearing (DHH) individuals to communicate and learn in a classroom setting. In ASL, fingerspelling and gestures are two primary components used for communication. Fingerspelling is commonly used for words that do not have a specifically designated sign or gesture. In technical contexts, such as Computer Science curriculum, there are many technical terms that fall under this category. Most of its jargon does not have standardized ASL gestures; therefore, students, educators, and interpreters alike have been reliant on fingerspelling, which poses challenges for all parties. This study investigates the efficacy of both fingerspelling and gestures with fifteen technical terms that do have standardized gestures. The terms’ fingerspelling and gesture are assessed based on preference, ease of use, ease of learning, and time by research subjects who were selected as DHH individuals familiar with ASL.
The data is collected in a series of video recordings by research subjects as well as a post-participation questionnaire. Each research subject has produced thirty total videos, two videos to fingerspell and gesture each technical term. Afterwards, they completed a post-participation questionnaire in which they indicated their preference and how easy it was to learn and use both fingerspelling and gestures. Additionally, the videos have been analyzed to determine the time difference between fingerspelling and gestures. Analysis reveals that gestures are favored over fingerspelling as they are generally preferred, considered easier to learn and use, and faster. These results underscore the significance for standardized gestures in the Computer Science curriculum for accessible learning that enhances communication and promotes inclusion.
ContributorsKarim, Bushra (Author) / Gupta, Sandeep (Thesis director) / Hossain, Sameena (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of International Letters and Cultures (Contributor)
Created2024-05
Description
Nations censor specific information in accordance with their political, legal, and cultural standards. Each country adopts unique approaches and regulations for censorship, whether it involves moderating online content or prohibiting protests. This paper seeks to study the underlying motivations for the disparate behaviors exhibited by authorities and individuals. To achieve this, we develop a mathematical model designed to understand the dynamics between authority figures and individuals, analyzing their behaviors under various conditions. We argue that individuals essentially act in three phases - compliance, self-censoring, and defiance when faced with different situations under their own desires and the authority's parameters. We substantiate our findings by conducting different simulations on the model and visualizing the outcomes. Through these simulations, we realize why individuals exhibit behaviors falling into one of three categories, who are influenced by factors such as the level of surveillance imposed by the authority, the severity of punishments, the tolerance for dissent, or the individuals' boldness. This also helped us to understand why certain populations in a country exhibit defiance, self-censoring behavior, or compliance as they interact with each other and behave under specific conditions within a small network world.
ContributorsNahar, Anish Ashish (Author) / Daymude, Joshua (Thesis director) / Forrest, Stephanie (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
Every year, Arizona mobile home residents suffer hundreds of fatalities and severe illnesses due to the effects of extreme heat within their homes exacerbated by high energy costs, a lack of energy-efficient infrastructure, and underlying socio-economic issues. Many of these deaths and severe illnesses can be prevented via active monitoring and reporting of temperature and humidity data from these living spaces. The team will design, build, test, and implement a Heat Warning Detection System (HWDS) to mitigate heat-related illnesses and deaths. The HWDS will detect when temperature and humidity levels have reached a dangerous threshold and will issue notifications to the emergency contacts of the resident over SMS and/or email. This will allow for timely preventative measures to be taken to ensure the safety of the resident. The team will investigate the ideal threshold to notify the mobile home residents.
HWDS will require minimal user interaction. Apart from the initial physical installation of the device, the user will have to provide a list of emergency contacts that they would like the system to notify in the event that HWDS detects dangerous conditions in their residence. By deploying prototypes of HWDS to volunteer participant homes, we will be able to validate the functionality of the system as well as the usability of the physical device by homeowners. HWDS provides homeowners and their loved ones with the opportunity to take preventative measures before being exposed to conditions that could potentially have more severe implications. In the spirit of promoting accessibility and prevention among the most vulnerable communities in Greater Phoenix, our team partners with the Knowledge Exchange for Resilience at ASU (KER) to interface with organizations such as the Arizona Association of Manufactured Home, RV & Park Model Owners (AAMHO) to promote legislation and subsidies aimed towards making solutions such as ours more financially viable for the communities that need it most.
ContributorsYeager, William (Author) / Ward, Trenton (Co-author) / Drake, Thomas (Co-author) / Schoepf, Jared (Thesis director) / Solís, Patricia (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
Deforestation in the Amazon rainforest has the potential to have devastating effects
on ecosystems on both a local and global scale, making it one of the most environmentally
threatening phenomena occurring today. In order to minimize deforestation in the Ama-
zon and its consequences, it is helpful to analyze its occurrence using machine learning
architectures such as the U-Net. The U-Net is a type of Fully Convolutional Network that
has shown significant capability in performing semantic segmentation. It is built upon
a symmetric series of downsampling and upsampling layers that propagate feature infor-
mation into higher spatial resolutions, allowing for the precise identification of features
on the pixel scale. Such an architecture is well-suited for identifying features in satellite
imagery. In this thesis, we construct and train a U-Net to identify deforested areas in
satellite imagery of the Amazon through semantic segmentation.
ContributorsDouglas, Liam (Author) / Giel, Joshua (Co-author) / Espanol, Malena (Thesis director) / Cochran, Douglas (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05
Description
Every year, Arizona mobile home residents suffer hundreds of fatalities and severe illnesses due to the effects of extreme heat within their homes exacerbated by high energy costs, a lack of energy-efficient infrastructure, and underlying socio-economic issues. Many of these deaths and severe illnesses can be prevented via active monitoring and reporting of temperature and humidity data from these living spaces. The team will design, build, test, and implement a Heat Warning Detection System (HWDS) to mitigate heat-related illnesses and deaths. The HWDS will detect when temperature and humidity levels have reached a dangerous threshold and will issue notifications to the emergency contacts of the resident over SMS and/or email. This will allow for timely preventative measures to be taken to ensure the safety of the resident. The team will investigate the ideal threshold to notify the mobile home residents.
HWDS will require minimal user interaction. Apart from the initial physical installation of the device, the user will have to provide a list of emergency contacts that they would like the system to notify in the event that HWDS detects dangerous conditions in their residence. By deploying prototypes of HWDS to volunteer participant homes, we will be able to validate the functionality of the system as well as the usability of the physical device by homeowners. HWDS provides homeowners and their loved ones with the opportunity to take preventative measures before being exposed to conditions that could potentially have more severe implications. In the spirit of promoting accessibility and prevention among the most vulnerable communities in Greater Phoenix, our team partners with the Knowledge Exchange for Resilience at ASU (KER) to interface with organizations such as the Arizona Association of Manufactured Home, RV & Park Model Owners (AAMHO) to promote legislation and subsidies aimed towards making solutions such as ours more financially viable for the communities that need it most.
ContributorsWard, Trenton (Author) / Yeager, William (Co-author) / Drake, Thomas (Co-author) / Schoepf, Jared (Thesis director) / Solís, Patricia (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2024-05