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ContributorsHilliker, Jacob (Author) / Li, Baoxin (Thesis director) / Libman, Jeffrey (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2021-12
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ContributorsHilliker, Jacob (Author) / Li, Baoxin (Thesis director) / Libman, Jeffrey (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2021-12
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Description

Among classes in the Computer Science curriculum at Arizona State University, Automata Theory is widely considered to be one of the most difficult. Many Computer Science concepts have strong visual components that make them easier to understand. Binary trees, Dijkstra's algorithm, pointers, and even more basic concepts such as arrays

Among classes in the Computer Science curriculum at Arizona State University, Automata Theory is widely considered to be one of the most difficult. Many Computer Science concepts have strong visual components that make them easier to understand. Binary trees, Dijkstra's algorithm, pointers, and even more basic concepts such as arrays all have very strong visual components. Not only that, but resources for them are abundantly available online. Automata Theory, on the other hand, is the first Computer Science course students encounter that has a significant focus on deep theory. Many
of the concepts can be difficult to visualize, or at least take a lot of effort to do so. Furthermore, visualizers for finite state machines are hard to come by. Because I thoroughly enjoyed learning about Automata Theory and parsers, I wanted to create a program that involved the two. Additionally, I thought creating a program for visualizing automata would help students who struggle with Automata Theory develop a stronger understanding of it.

ContributorsSmith, Andrew (Author) / Burger, Kevin (Thesis director) / Meuth, Ryan (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2021-12
Description

Among classes in the Computer Science curriculum at Arizona State University, Automata Theory is widely considered to be one of the most difficult. Many Computer Science concepts have strong visual components that make them easier to understand. Binary trees, Dijkstra's algorithm, pointers, and even more basic concepts such as arrays

Among classes in the Computer Science curriculum at Arizona State University, Automata Theory is widely considered to be one of the most difficult. Many Computer Science concepts have strong visual components that make them easier to understand. Binary trees, Dijkstra's algorithm, pointers, and even more basic concepts such as arrays all have very strong visual components. Not only that, but resources for them are abundantly available online. Automata Theory, on the other hand, is the first Computer Science course students encounter that has a significant focus on deep theory. Many
of the concepts can be difficult to visualize, or at least take a lot of effort to do so. Furthermore, visualizers for finite state machines are hard to come by. Because I thoroughly enjoyed learning about Automata Theory and parsers, I wanted to create a program that involved the two. Additionally, I thought creating a program for visualizing automata would help students who struggle with Automata Theory develop a stronger understanding of it.

ContributorsSmith, Andrew (Author) / Burger, Kevin (Thesis director) / Meuth, Ryan (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2021-12
Description

Among classes in the Computer Science curriculum at Arizona State University, Automata Theory is widely considered to be one of the most difficult. Many Computer Science concepts have strong visual components that make them easier to understand. Binary trees, Dijkstra's algorithm, pointers, and even more basic concepts such as arrays

Among classes in the Computer Science curriculum at Arizona State University, Automata Theory is widely considered to be one of the most difficult. Many Computer Science concepts have strong visual components that make them easier to understand. Binary trees, Dijkstra's algorithm, pointers, and even more basic concepts such as arrays all have very strong visual components. Not only that, but resources for them are abundantly available online. Automata Theory, on the other hand, is the first Computer Science course students encounter that has a significant focus on deep theory. Many
of the concepts can be difficult to visualize, or at least take a lot of effort to do so. Furthermore, visualizers for finite state machines are hard to come by. Because I thoroughly enjoyed learning about Automata Theory and parsers, I wanted to create a program that involved the two. Additionally, I thought creating a program for visualizing automata would help students who struggle with Automata Theory develop a stronger understanding of it.

ContributorsSmith, Andrew (Author) / Burger, Kevin (Thesis director) / Meuth, Ryan (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2021-12
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Description

This thesis explores how large scale cyber exercises work in the 21st century, going in-depth on Exercise Cyber Shield, the Department of Defense’s largest unclassified cyber defense exercise run by the Army National Guard. It highlights why these cyber exercises are so relevant, going over several large scale cyber attacks

This thesis explores how large scale cyber exercises work in the 21st century, going in-depth on Exercise Cyber Shield, the Department of Defense’s largest unclassified cyber defense exercise run by the Army National Guard. It highlights why these cyber exercises are so relevant, going over several large scale cyber attacks that have occurred in the past year and the impact they caused. This research aims to illuminate the intricacies around cyber exercise assessment involving manual vs automated scoring systems; this is brought back to work on creating an automated scoring engine for Exercise Cyber Shield. This thesis provides an inside look behind the scenes of the operations of the largest unclassified cyber defense exercise in the United States, including conversations with the Exercise Officer-In-Charge of Cyber Shield as well as a cyber exercise expert working on assessment of Exercise Cyber Shield, and the research also includes information from past final reports for Cyber Shield. Issues that these large scale cyber exercises have faced over the years are brought to light, and attempts at solutions are discussed.

ContributorsZhao, Henry (Author) / Chavez Echeagaray, Maria Elena (Thesis director) / Rhodes, Brad (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2021-12
Description
Narrative generation is an important field due to the high demand for stories in video game design and also in stories used in learning tools in the classroom. As these stories should contain depth, it is desired for these stories to ideally be more descriptive. There are tools that hel

Narrative generation is an important field due to the high demand for stories in video game design and also in stories used in learning tools in the classroom. As these stories should contain depth, it is desired for these stories to ideally be more descriptive. There are tools that help with the creation of these stories, such as planning, which requires a domain as input, or GPT-3, which requires an input prompt to generate the stories. However, other aspects to consider are the coherence and variation of stories. To save time and effort and create multiple possible stories, we combined both planning and the Large Language Model (LLM) GPT-3 similar to how they were used in TattleTale to generate such stories while examining whether descriptive input prompts to GPT-3 affect the outputted stories. The stories generated are readable to the general public and overall, the prompts do not consistently affect descriptiveness of outputs across all stories tested. For this work, three stories with three variants each were created and tested for descriptiveness. To do so, adjectives, adverbs, prepositional phrases, and suboordinating conjunctions were counted using Natural Language Processing (NLP) tool spaCy for Part Of Speech (POS) tagging. This work has shown that descriptiveness is highly correlated with the amount of words in the story in general, so running GPT-3 to obtain longer stories is a feasible option to consider in order to obtain more descriptive stories. The limitations of GPT-3 have an impact on the descriptiveness of resulting stories due to GPT-3’s inconsistency and transformer architecture, and other methods of narrative generation such as simple planning could be more useful.
ContributorsDozier, Courtney (Author) / Chavez-Echeagary, Maria Elena (Thesis director) / Benjamin, Victor (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-12
Description

The field of quantum computing is an exciting area of research that allows quantum mechanics such as superposition, interference, and entanglement to be utilized in solving complex computing problems. One real world application of quantum computing involves applying it to machine learning problems. In this thesis, I explore the effects

The field of quantum computing is an exciting area of research that allows quantum mechanics such as superposition, interference, and entanglement to be utilized in solving complex computing problems. One real world application of quantum computing involves applying it to machine learning problems. In this thesis, I explore the effects of choosing different circuit ansatz and optimizers on the performance of a variational quantum classifier tasked with binary classification.

ContributorsHsu, Brightan (Author) / De Luca, Gennaro (Thesis director) / Chen, Yinong (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-12
Description
The growth in online job boards has made it easier than ever to find and apply for roles online. Unfortunately, since said job boards are, mainly, designed for hiring companies and not job applicants, the applicant interface is high friction and frustrating. With each company (and often each

The growth in online job boards has made it easier than ever to find and apply for roles online. Unfortunately, since said job boards are, mainly, designed for hiring companies and not job applicants, the applicant interface is high friction and frustrating. With each company (and often each job) that a job-seeker applies for, they need to fill out an application form asking for the same information they have already provided countless times. This thesis explores the effectiveness of FuseApply, a web application and accompanying Chrome extension that reduces the friction involved in filling out these forms by automatically filling out a portion of job applications for users. Results from user experience testing with eleven Arizona State University (ASU) School of Computing and Augmented Intelligence students on real-world job applications demonstrated significant time savings and thus added value for users. On average, FuseApply saved users 33.09 seconds in time completing online job application forms, compared with manually filling them out. A one-tail T-test confirmed that this difference is statistically significant. Users also showed noticeable reduction in frustration with FuseApply. 72.7% of applicants said that they would use FuseApply in the future when applying for jobs, and comments were also positive. Business viability is less clear, as 63.6% of applicants said they would not pay for the software. Results demonstrate that FuseApply is useful and valuable software, but cast doubt on monetization plans.
ContributorsO'Scannlain-Miller, Henry (Author) / Elena Chavez-Echeagaray, Maria (Thesis director) / Benjamin, Victor (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-12
Description
Not enough students are earning bachelor’s degrees in Computer Science, which is shocking as computing jobs are growing by the thousands (Zampa, 2016). These jobs have high-paying salaries and are not going to fade from the future any time soon, that is why the falling rates of computer science graduates

Not enough students are earning bachelor’s degrees in Computer Science, which is shocking as computing jobs are growing by the thousands (Zampa, 2016). These jobs have high-paying salaries and are not going to fade from the future any time soon, that is why the falling rates of computer science graduates are alarming. The working hypothesis on why so few college students major in computer science is that most think that it is too hard to learn (Wang, 2017). But I believe the real reason lies in that computer science is not an educational subject that is taught before university, which is too late for most students because by ages 12 to 13 (about seventh to eighth grade) they have decided that computer science concepts are “too difficult” for them to learn (Learning, 2022). Implementing a computer science-based education at an earlier age can possibly circumvent this seen development where students begin to lose confidence and doubt their abilities to learn computer science. This can be done easily by integrating computer science into academic subjects that are already taught in elementary schools such as science, math, and language arts as computer science uses logic, syntax, and other skills that are broadly applicable. Thus, I have created a introductory lesson plan for an elementary school class that incorporates learning how to code with robotics to promote learning computer science principles and destigmatize that it is “too hard” to learn in university.
ContributorsWong, Erika (Author) / Hedges, Craig (Thesis director) / Fischer, Adelheid (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-05