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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How does the streaming of esports affect business, viewership, and its longevity

Description
Esports streaming has become an entertainment giant and promises to continue to grow in the coming years. Streaming platforms, such as Twitch and Youtube, have become a haven for community and competition, blending the two into a novel form of interaction that fuels business. This study will analyze how the…

Esports streaming has become an entertainment giant and promises to continue to grow in the coming years. Streaming platforms, such as Twitch and Youtube, have become a haven for community and competition, blending the two into a novel form of interaction that fuels business. This study will analyze how the streaming of esports has influenced business in the technological realm of electronic games and contributed to the field’s longevity. It questions how we, as a society, view community in the online world which itself has become a site for the expansion of how people interact. The study also incorporates the idea of business into the market of technological electronic game-based communities and how competition through esports has also been a fuel for both. Through literature analysis and data collection, the goal of this research would be to increase knowledge on the understanding of streaming esports and help predict what foundation it might take as a whole later on.
ContributorsLatimer, Travis D (Author) / Ingram-Waters, Mary (Thesis director) / Pierce, John (Committee member) / Computer Science and Engineering Program (Contributor) / WPC Graduate Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
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Marketing Masculinity: The Electric Motorcycle

Description
This research analyzes the masculine culture surrounding motorcycles to provide commentary on the appeal of electric motorcycles. More specifically, it examines the importance of masculine characteristics in advertising motorcycle identity. It analyzes the presented masculinity, to predict market sway, and the features necessary to create a compelling product.…

This research analyzes the masculine culture surrounding motorcycles to provide commentary on the appeal of electric motorcycles. More specifically, it examines the importance of masculine characteristics in advertising motorcycle identity. It analyzes the presented masculinity, to predict market sway, and the features necessary to create a compelling product. Through the analysis of commercials and websites for various motorcycle brands the target audience is discovered and used to predict the appeal of electric motorcycles. The presented masculinity is found to be targeting very specific populations of motorcyclists, where manufacturers believe electric motorcycles will be accepted most readily. Manufacturers are effectively entering the market through these demographics and can use them as a foothold to persuade others of the benefits of electric motorcycle technology.
ContributorsAnderson, Curtis Brian (Author) / Ingram-Waters, Mary (Thesis director) / Schmidt, Peter (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
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Convoluted Processes: The Use and Misuse of Machine Learning in Data Analysis and Prediction

Description

With the rapid increase of technological capabilities, particularly in processing power and speed, the usage of machine learning is becoming increasingly widespread, especially in fields where real-time assessment of complex data is extremely valuable. This surge in popularity of machine learning gives rise to an abundance of potential research and…

With the rapid increase of technological capabilities, particularly in processing power and speed, the usage of machine learning is becoming increasingly widespread, especially in fields where real-time assessment of complex data is extremely valuable. This surge in popularity of machine learning gives rise to an abundance of potential research and projects on further broadening applications of artificial intelligence. From these opportunities comes the purpose of this thesis. Our work seeks to meaningfully increase our understanding of current capabilities of machine learning and the problems they can solve. One extremely popular application of machine learning is in data prediction, as machines are capable of finding trends that humans often miss. Our effort to this end was to examine the CVE dataset and attempt to predict future entries with Random Forests. The second area of interest lies within the great promise being demonstrated by neural networks in the field of autonomous driving. We sought to understand the research being put out by the most prominent bodies within this field and to implement a model on one of the largest standing datasets, Berkeley DeepDrive 100k. This thesis describes our efforts to build, train, and optimize a Random Forest model on the CVE dataset and a convolutional neural network on the Berkeley DeepDrive 100k dataset. We document these efforts with the goal of growing our knowledge on (and usage of) machine learning in these topics.
ContributorsSelzer, Cora (Author) / Smith, Zachary (Co-author) / Ingram-Waters, Mary (Thesis director) / Rendell, Dawn (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2022-05