Matching Items (616)
Filtering by
- Creators: Computer Science and Engineering Program
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Member of: Theses and Dissertations
Description
This paper investigates the influence of regulatory sentiment on investment-based crowdfunding across various global markets. Crowdfunding, a capital-raising method where individuals collectively invest in projects, businesses, or causes, has significantly evolved with the advent of digital platforms. The emergence of lending-based and investment-based crowdfunding has led to the development of diverse regulatory frameworks worldwide. This study focuses on the relationship between regulatory sentiment and two critical dimensions of crowdfunding markets: investment volume and platform count. By conducting a multivariate analysis using data from the Cambridge Center for Alternative Finance and GDP statistics from the OECD, the paper examines whether investor sentiment about regulation impacts these two variables across seven developed markets.
The research centers around three primary questions: the existence and nature of any statistically significant relationships between regulatory sentiment and investment volume/platform count; and which type of sentiment (adequate, excessive, or inadequate) has the strongest relationship with these variables. The analysis includes a detailed review of regulatory frameworks in the United States, United Kingdom, France, Germany, Spain, Italy, and Malaysia.
The findings reveal a statistically significant relationship between adequate and excessive regulatory sentiment and both investment volume and platform count, with adequate sentiment showing a positive impact and excessive sentiment demonstrating a negative effect. The results highlight the importance of balanced regulatory frameworks in fostering healthy crowdfunding ecosystems and provide insights into how investor perceptions of regulation can influence market dynamics. Future research could further explore these relationships, potentially using more objective measures of regulations and examining the bidirectional influence between market performance and regulatory sentiment.
ContributorsKonstantinov, Phillip (Author) / Lindsey, Laura (Thesis director) / Hertzel, Michael (Committee member) / Barrett, The Honors College (Contributor) / Department of Finance (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-12
Description
This work focuses on combining multiple different technologies to produce a scalable, full-stack music generation and sharing application meant to be deployed to a cloud environment while keeping operating costs as low as possible. The key feature of this app is that it allows users to generate tracks from scratch by providing a text description, or customize existing tracks by supplying both an audio file and a track description. Users will be able to share these tracks with other users, via this app, so that they can collaborate with others and jumpstart their creative process, allowing creators to produce more content for their fans. A web app was developed; Contak. This application requires a database, REST API, object storage, music generation artificial intelligence models, and a web application (GUI) to interact with the user. In order to define the best music generation model, a small exploratory study was conducted to compare the quality of different music generation models, including MusicGen, MusicLM, and Riffusion. Results found that the MusicGen model, selected for this work, outperformed the competing models: MusicLM and Riffusion. This exploratory study includes rankings of the three models based on how well each one adhered to a text description of a track. The purpose was to test the hypothesis that MusicGen produces higher quality music that adheres to text descriptions better than other models because it encodes audio at a higher bit rate (32 kHz). While the web app generates high quality tracks with above average text adherence, the main limitation of this work is the response time needed to generate tracks from existing audio using the currently available backend infrastructure, as this can take up to 7 minutes to complete. In the future, this app can be deployed to a cloud environment with GPU acceleration to improve response times and throughput. Additionally, new methods of input besides text and audio input can be implemented using MIDI instructions and the Magenta music model, providing increased track generation precision for advanced music creators with MIDI experience.
ContributorsZamora, Michael (Author) / Chavez Echeagaray, Maria (Thesis director) / Prim, Tadi (Committee member) / Day, Kimberly (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-12
Description
This project is centered around a decade-old video game called League of Legends, which is one of the most popular video games in esports. Due to its nature of being a complex team-based strategy game, intuitive human predictions of the game’s outcome are relatively unreliable. Many approaches have been adopted to assist intuitive human predictions in traditional team-based sports, such as the Least Squares Method and various supervised machine learning algorithms. These methods have been significantly outperforming human predictions. The objective of this research is, hence, to test whether the predictive models generated using these methods can achieve a similar level of reliability in a more complex game like League of Legends.
ContributorsWang, Jiahao (Author) / Zandieh, Michelle (Thesis director) / Lee, Inyoung (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / College of Integrative Sciences and Arts (Contributor)
Created2023-12
Description
Gerrymandering involves the purposeful manipulation of districts in order to gain some political advantage. Because legislators have a vested interest in continuing their tenure, they can easily hijack the redistricting process each decade for their and their political party's benefit. This threatens the cornerstone of democracy: a voter’s capability to select an elected official that accurately represents their interests. Instead, gerrymandering has legislators to choose their voters.
In recent years, the Supreme Court has heard challenges to state legislature-drawn districts, most recently in Allen v. Milligan for Alabama and Moore v. Harper for North Carolina. The highest court of the United States ruled that the two state maps were gerrymandered, and in coming to their decision, the 9 justices relied on a plethora of amicus briefs- one of which included the Markov Chain Monte Carlo method, a computational method used to find gerrymandering.
Because of how widespread gerrymandering has become on both sides of the political aisle, states have moved to create independent redistricting commissions. Qualitative research regarding the efficacy of independent commissions is present, but there is little research using the quantitative computational methods from these SCOTUS cases. As a result, my thesis will use the Markov Chain Monte Carlo method to answer if impartial redistricting commissions (like we have in Arizona) actually preclude unfair redistricting practices.
My completed project is located here:
https://dheetideliwala.github.io/honors-thesis/
ContributorsDeliwala, Dheeti (Author) / Bryan, Chris (Thesis director) / Strickland, James (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Politics and Global Studies (Contributor)
Created2023-12
ContributorsDeliwala, Dheeti (Author) / Bryan, Chris (Thesis director) / Strickland, James (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Politics and Global Studies (Contributor)
Created2023-12
ContributorsDeliwala, Dheeti (Author) / Bryan, Chris (Thesis director) / Strickland, James (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Politics and Global Studies (Contributor)
Created2023-12
Description
Vulkan is a modern, low-level, and low-overhead graphics library that allows for the distribution of work across CPU cores using multithreading. This multithreading is possible due to the near full control of the GPU that Vulkan allows. The additional control makes it possible to send multiple instructions to the GPU at the same time. There are a variety of techniques that can be used with Vulkan to effectively improve performance while multithreading instructions to the GPU. One of the challenges of multithreading is the lack of modern-day GPU hardware to support it, which leads to the purpose of this paper, to explore the practicality of multithreading techniques with Vulkan in today’s current computing environment.
ContributorsWahl, Ryan (Author) / Hansford, Dianne (Thesis director) / Kobayashi, Yoshihiro (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-12
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