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- Creators: Barrett, The Honors College
- Creators: Poulenc, Francis, 1899-1963
Description
Kokuji are a specific type of character, or Sinograph, present in Japanese script. Kokuji are differentiated from “normal” Sinographs in Japanese, kanji, by the origin. Kokuji are Sinographs of Japanese origin while other kanji in Japanese are of Chinese origin. The purpose of this paper was to explore how this kind of character has changed since it was first identified and the implications these changes have on Japanese identity. This essay is split into three chapters past the introduction. The first chapter explains the terminology used in the rest of the paper, how Sinographs work, and explores similar phenomena in other scripts. The second chapter focuses on the status of kokuji during two periods of Japanese history, the Edo period (1603-1868) and the Meiji period (1868-1912). The Edo period is relevant because during this period kokuji were first recognized as entities separate from normal kanji. The Meiji period is important because it marks the shift into modern Japan, and it started the linguistic and orthographic reforms that would continue until the late twentieth century. The last chapter takes a closer look at the linguistic reforms that took place during the Taishō period and the Shōwa periods. The Taishō period has Japan still trying to become a “modern” nation and continues some of the language reform from the Meiji period. The Shōwa period post-World War II enacts many of the language reforms that shape modern Japanese language. Through these linguistic reforms we can figure out why kokuji have fallen out of use and why the remaining ones are somewhat common.
ContributorsReyes, Emiliano (Author) / Oh, Young (Thesis director) / Hedberg, William (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Experimentation with glaze materials resulted in 2 functional and interesting base glazes with multiple color variants each. A semi-matte stoneware glaze was created, however after being unable to replicate a specific coloring without drying out the glaze, it was discovered that using this glaze to spray over specific studio glazes produced a more pleasant color effect than the glaze by itself. A glossy clear glaze was created. The glaze crazed minimally, and color variants were created with the rare earth metals erbium, praseodymium, and neodymium, resulting in celadon-like glazes that were pink, green, and bluish purple respectively. Finally, A semi-matte stoneware glaze with high spodumene content was created with two specific color variations
ContributorsVilen, Zachary Kwochka (Author) / Beiner, Susan (Thesis director) / Steimle, Timothy (Committee member) / School of Life Sciences (Contributor) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
In vitro gametogenesis (IVG) research has been growing in countries like Japan, US, and China after the development of stem cell research and other scientific advancements as well as because of the perception of infertility as a domestic and international problem. IVG research’s progress has been deliberated internationally, with discussion of questions, challenges, and possibilities that have arisen and may arise in the future as the technology is adopted by different countries. The first section introduces the meaning of IVG, explains the importance of review by scientists and citizens for IVG, and describes a rise in infertility reported in multiple developed countries that could be addressed by IVG. The second section discusses IVG’s applications and implications using 5 ethical categories articulated by Obama’s Presidential Commission for the Study of Bioethical Issues: Public Beneficence, Responsible Stewardship, Intellectual Freedom and Responsibility, Democratic Deliberation, and Justice and Fairness. These five ethical principles were intended for analysis of emerging technologies, and IVG is an emerging technology with possible integration into clinical settings. Among the principles, it seemed that a major weak point of inquiry concerns LGBT+ and disability inclusion, especially of gender dysphoric and transgender people who may experience higher rates of infertility and have a harder time conceiving due to a mix of discrimination, gender dysphoria, and infertility due to hormone replacement therapy (HRT) treatment or gender/sex reassignment surgeries (GRSs/SRSs) that may impair or remove reproductive body parts. A number of other ethical considerations arise about this technology.
ContributorsVillarreal, Lance Edward (Author) / Maienschein, Jane (Thesis director) / Ellison, Karin (Committee member) / Wilson-Rawls, Jeanne (Committee member) / School of Life Sciences (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Abstract
Matrix Factorization techniques have been proven to be more effective in recommender systems than standard user based or item based methods. Using this knowledge, Funk SVD and SVD++ are compared by the accuracy of their predictions of Twitch streamer data.
Introduction
As watching video games is becoming more popular, those interested are becoming interested in Twitch.tv, an online platform for guests to watch streamers play video games and interact with them. A streamer is an person who broadcasts them-self playing a video game or some other thing for an audience (the guests of the website.) The site allows the guest to first select the game/category to view and then displays currently active streamers for the guest to select and watch. Twitch records the games that a streamer plays along with the amount of time that a streamer spends streaming that game. This is how the score is generated for a streamer’s game. These three terms form the streamer-game-score (user-item-rating) tuples that we use to train out models.
The our problem’s solution is similar to the purpose of the Netflix prize; however, as opposed to suggesting a user a movie, the goal is to suggest a user a game. We built a model to predict the score that a streamer will have for a game. The score field in our data is fundamentally different from a movie rating in Netflix because the way a user influences a game’s score is by actively streaming it, not by giving it an score based off opinion. The dataset being used it the Twitch.tv dataset provided by Isaac Jones [1]. Also, the only data used in training the models is in the form of the streamer-game-score (user-item-rating) tuples. It will be known if these data points with limited information will be able to give an accurate prediction of a streamer’s score for a game. SVD and SVD++ are the baseis of the models being trained and tested. Scikit’s Surprise library in Python3 is used for the implementation of the models.
Matrix Factorization techniques have been proven to be more effective in recommender systems than standard user based or item based methods. Using this knowledge, Funk SVD and SVD++ are compared by the accuracy of their predictions of Twitch streamer data.
Introduction
As watching video games is becoming more popular, those interested are becoming interested in Twitch.tv, an online platform for guests to watch streamers play video games and interact with them. A streamer is an person who broadcasts them-self playing a video game or some other thing for an audience (the guests of the website.) The site allows the guest to first select the game/category to view and then displays currently active streamers for the guest to select and watch. Twitch records the games that a streamer plays along with the amount of time that a streamer spends streaming that game. This is how the score is generated for a streamer’s game. These three terms form the streamer-game-score (user-item-rating) tuples that we use to train out models.
The our problem’s solution is similar to the purpose of the Netflix prize; however, as opposed to suggesting a user a movie, the goal is to suggest a user a game. We built a model to predict the score that a streamer will have for a game. The score field in our data is fundamentally different from a movie rating in Netflix because the way a user influences a game’s score is by actively streaming it, not by giving it an score based off opinion. The dataset being used it the Twitch.tv dataset provided by Isaac Jones [1]. Also, the only data used in training the models is in the form of the streamer-game-score (user-item-rating) tuples. It will be known if these data points with limited information will be able to give an accurate prediction of a streamer’s score for a game. SVD and SVD++ are the baseis of the models being trained and tested. Scikit’s Surprise library in Python3 is used for the implementation of the models.
ContributorsAitken, Connor Dalton (Author) / Liu, Huan (Thesis director) / Jones, Isaac (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Propaganda bots are malicious bots on Twitter that spread divisive opinions and support political accounts. This project is based on detecting propaganda bots on Twitter using machine learning. Once I began to observe patterns within propaganda followers on Twitter, I determined that I could train algorithms to detect these bots. The paper focuses on my development and process of training classifiers and using them to create a user-facing server that performs prediction functions automatically. The learning goals of this project were detailed, the focus of which was to learn some form of machine learning architecture. I needed to learn some aspect of large data handling, as well as being able to maintain these datasets for training use. I also needed to develop a server that would execute these functionalities on command. I wanted to be able to design a full-stack system that allowed me to create every aspect of a user-facing server that can execute predictions using the classifiers that I design.
Throughout this project, I decided on a number of learning goals to consider it a success. I needed to learn how to use the supporting libraries that would help me to design this system. I also learned how to use the Twitter API, as well as create the infrastructure behind it that would allow me to collect large amounts of data for machine learning. I needed to become familiar with common machine learning libraries in Python in order to create the necessary algorithms and pipelines to make predictions based on Twitter data.
This paper details the steps and decisions needed to determine how to collect this data and apply it to machine learning algorithms. I determined how to create labelled data using pre-existing Botometer ratings, and the levels of confidence I needed to label data for training. I use the scikit-learn library to create these algorithms to best detect these bots. I used a number of pre-processing routines to refine the classifiers’ precision, including natural language processing and data analysis techniques. I eventually move to remotely-hosted versions of the system on Amazon web instances to collect larger amounts of data and train more advanced classifiers. This leads to the details of my final implementation of a user-facing server, hosted on AWS and interfacing over Gmail’s IMAP server.
The current and future development of this system is laid out. This includes more advanced classifiers, better data analysis, conversions to third party Twitter data collection systems, and user features. I detail what it is I have learned from this exercise, and what it is I hope to continue working on.
Throughout this project, I decided on a number of learning goals to consider it a success. I needed to learn how to use the supporting libraries that would help me to design this system. I also learned how to use the Twitter API, as well as create the infrastructure behind it that would allow me to collect large amounts of data for machine learning. I needed to become familiar with common machine learning libraries in Python in order to create the necessary algorithms and pipelines to make predictions based on Twitter data.
This paper details the steps and decisions needed to determine how to collect this data and apply it to machine learning algorithms. I determined how to create labelled data using pre-existing Botometer ratings, and the levels of confidence I needed to label data for training. I use the scikit-learn library to create these algorithms to best detect these bots. I used a number of pre-processing routines to refine the classifiers’ precision, including natural language processing and data analysis techniques. I eventually move to remotely-hosted versions of the system on Amazon web instances to collect larger amounts of data and train more advanced classifiers. This leads to the details of my final implementation of a user-facing server, hosted on AWS and interfacing over Gmail’s IMAP server.
The current and future development of this system is laid out. This includes more advanced classifiers, better data analysis, conversions to third party Twitter data collection systems, and user features. I detail what it is I have learned from this exercise, and what it is I hope to continue working on.
ContributorsPeterson, Austin (Author) / Yang, Yezhou (Thesis director) / Sadasivam, Aadhavan (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Mental illness creates a unique challenge for police. Changes in medical infrastructure have left many mentally ill without adequate access to resources or treatment. They often face additional challenges of substance abuse and homelessness. This has led to increasingly frequent contact with police and a shift from mental illness being treated as a health problem to being treated as a police problem. Police are unable to provide treatment, and are frustrated by the amount of their time consumed by persons with mental illness (PMI) and by the amount of time and effort it takes to connect them with treatment. Due to the unpredictable behavior often caused by mental illness and the way police are trained to deal with uncooperative behavior, persons suffering from mental illness are subject to the use of force by police at a disproportionate rate. Police are trying to combat these problems with the implementation of advanced training and the development of Crisis Intervention Teams and Mobile Response Units, as well as increasing connections with local medical facilities to promote treatment over arrest. Other strategies have been experimented with, both in the United States and across the globe, but there is currently a limited amount of research on how effective these programs are. Anecdotally, the most successful programs seem to be those that take a comprehensive approach to mental illness, creating solutions that include police, medical facilities, courts, dispatchers, first responders, and the community. Due to the limits of programs confined to one institution, it is recommended that treatment be expanded and police receive advanced training in dealing with mentally ill people, as well as involving others in the criminal justice and medical communities so that they provide a coordinated response to PMI.
ContributorsMcveety, Matthew James (Author) / Fradella, Hank (Thesis director) / Scott, Michael (Committee member) / School of Public Affairs (Contributor) / School of Criminology and Criminal Justice (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Adolescent survivors of sex trafficking are at risk for poor health outcomes and may be less likely to adopt healthy lifestyle behaviors such as physical activity. Survivors of childhood traumas may be less likely to engage in physical activity due to lack of self-efficacy. The present study was a case-series of a pre-post, single-arm physical activity intervention to test whether the program could increase self-efficacy among adolescent survivors of sex trafficking. The intervention was 8-weeks of 60-minute aerobic physical activity classes offered three times per week at a residential center for adolescent girls who are survivors of sex trafficking and sexual abuse. The primary outcome was physical activity-related self-efficacy as measured by the Sport Competence subscale of the Physical Self Perception Profile (PSPP) questionnaire. Secondary outcomes included self-reported physical activity, emotional eating, and sleep habits. All outcomes were measured at baseline, 4 weeks, and 8 weeks. Five participants were enrolled in the study. Two participants experienced an increase in the Sport Competence subscale of the PSPP Questionnaire by Week 4 of the study and then a decrease by Week 8 of the study. Another participant experienced no change in the Sport Competence subscale score. Scores for the last two participants could not be determined due to invalid data. These findings suggest that more research is needed on enhancing healthy behaviors among adolescent sex trafficking survivors.
ContributorsRichardson, Jamielee (Author) / Petrov, Megan (Thesis director) / Calvin, Samantha (Committee member) / College of Health Solutions (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
The purpose of this project was to create an algorithm to improve firearm aiming. In order to do so, a simulation of exterior ballistics – the bullet’s behavior between the firearm muzzle and the target – was created in MATLAB. The simulation of bullet trajectory included consideration of three forces: gravity, air drag, and Coriolis ‘force’. An overall equation of motion for the bullet in flight, comprising the effects of the aforementioned forces, was constructed using formulae and theory given in R. L. McCoy’s Modern Exterior Ballistics. For the project, a reference frame was defined based on firearm muzzle and target positions, and an aim vector described by two angles was defined to describe the direction of the firearm’s barrel. The simulations of bullet trajectory take into account eleven parameters: the two aim angles, initial bullet speed (commonly referred to as muzzle velocity), 3-D Cartesian components of wind velocity, air density, bullet diameter, bullet mass, latitude of the firing area, and azimuth of fire (a quantified compass direction of fire).
The user inputs target position, muzzle position, and estimated environmental parameters to the system. Then, an aim vector would be calculated to hit the target under estimated conditions. Because the eleven trajectory parameters likely cannot all be precisely known, this solution will have some error. In real life, the system would use feedback from real shots of a firearm to correct for this error. For this project, a real-world proxy simulation was created that had built-in random error and variations in the parameters. The correction algorithm uses the error data from all previous shots to calculate adjustments to the original aim vector, so that each successive shot becomes more accurate. The system was tested with specifications of a common rifle platform, with estimated parameters and variations for a location in Tempe, AZ (since data for an urban area is readily available compared to a point in the wilderness). Results from this testing revealed that the system can “hit” a 2-meter-radius circular target in under 30 shots. When the errors and variations in parameters were halved for the real-world stand-in simulation, the system could “hit” a circular target with 0.55 meter radius in less than 25 shots. After analysis, it was found that the corrected aim angles converged on values, suggesting that the correction algorithm functions as intended (taking into account all past shots). Generally, it was found that any reduction of the means and standard deviations of parameter error improved the ability of the system to hit smaller targets, or hit the same target with less shots.
The user inputs target position, muzzle position, and estimated environmental parameters to the system. Then, an aim vector would be calculated to hit the target under estimated conditions. Because the eleven trajectory parameters likely cannot all be precisely known, this solution will have some error. In real life, the system would use feedback from real shots of a firearm to correct for this error. For this project, a real-world proxy simulation was created that had built-in random error and variations in the parameters. The correction algorithm uses the error data from all previous shots to calculate adjustments to the original aim vector, so that each successive shot becomes more accurate. The system was tested with specifications of a common rifle platform, with estimated parameters and variations for a location in Tempe, AZ (since data for an urban area is readily available compared to a point in the wilderness). Results from this testing revealed that the system can “hit” a 2-meter-radius circular target in under 30 shots. When the errors and variations in parameters were halved for the real-world stand-in simulation, the system could “hit” a circular target with 0.55 meter radius in less than 25 shots. After analysis, it was found that the corrected aim angles converged on values, suggesting that the correction algorithm functions as intended (taking into account all past shots). Generally, it was found that any reduction of the means and standard deviations of parameter error improved the ability of the system to hit smaller targets, or hit the same target with less shots.
ContributorsReyes, Joshua De Leon (Author) / Grewal, Anoop Singh (Thesis director) / Murthy, Raghavendra N. (Committee member) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Individual’s outcomes are significantly affected by occupation share changes. This is supported by regression analysis of the Displace Workers Survey a supplement to the Current Population Survey – a nationally representative panel data set. Regression analysis is used to demonstrate that individuals in occupations that are increasing as a share of the total number of workers are better off across the gamut of outcomes than their counterparts in occupations that are decreasing as a share of the total number of workers. Workers in occupations with increasing shares are more likely to find jobs quickly, less likely to experience significant wage losses, less likely to change occupation, and less likely to go on to welfare.
ContributorsGaughan, Patrick Michael (Author) / Veramendi, Gregory (Thesis director) / Foster, William (Committee member) / Department of Finance (Contributor) / Department of Information Systems (Contributor) / Department of Economics (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Islamism, not Islam, is a wide-ranging school of political ideology. As defined by this Fred Hillday it is a “determined choice of an Islamic doctrine, rather than the simple fact of being born Muslim.” The Muslim Brotherhood in Egypt at its height was one of the most influential and largest Islamist political organizations, but as this thesis will show, its influenced has waned to the point of nonexistence. A large portion of this paper will focus on the history of the Muslim Brotherhood, its leading ideologues such as Hasan al-Banna and Sayyid Qutb, as well as its political strategies that allowed it to become the most powerful organized political force in the country at the time of Mubarak’s ousting. The political experience of Islamists in Morocco, Tunisia and Sudan will be compared to the Muslim Brotherhood in Egypt. Moroccan and Tunisian Islamists rebranded themselves as Muslim Democrats in parliamentary political systems post-Arab Spring. Sudan, which saw an Islamist autocracy similar to what the Brotherhood wanted to effect in Egypt, has become a failed state. The 2013 Coup that ended the Brotherhood and Morsi’s time in power also marked the end of the kind of Islamism the Brotherhood promoted, or at least the end of its political popularity in the mainstream across the Middle East.
ContributorsPerle, Daniel Michael (Author) / Souad, Ali (Thesis director) / Abdullahi, Gallab (Committee member) / Walter Cronkite School of Journalism & Mass Comm (Contributor) / School of Politics and Global Studies (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05