Barrett, The Honors College Thesis/Creative Project Collection
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.
Displaying 41 - 50 of 63
Description
Internal and external validity of the BIDR was examined in college students and with forensic clients. The study also investigated the equivalence of the original format of the BIDR and the revised (the PDS). Results showed the IM scales of the BIDR and the PDS can be regarded as equivalent, but the SDE scales can not. Correlations with concurrent validity scales were generally stronger for the IM scale than the SDE scale. For both groups, the SDE and IM scales were substantially correlated with each other. Analyses of the undergraduate data did not support Paulhus' intention of two major factors for either the BIDR or the PDS; but did show this pattern for forensic data.
ContributorsCottle, Jamie Beth (Author) / Lanyon, Richard (Thesis director) / Presson, Clark (Committee member) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2001-12
Description
Machine learning is one of the fastest growing fields and it has applications in almost any industry. Predicting sports games is an obvious use case for machine learning, data is relatively easy to collect, generally complete data is available, and outcomes are easily measurable. Predicting the outcomes of sports events may also be easily profitable, predictions can be taken to a sportsbook and wagered on. A successful prediction model could easily turn a profit. The goal of this project was to build a model using machine learning to predict the outcomes of NBA games.
In order to train the model, data was collected from the NBA statistics website. The model was trained on games dating from the 2010 NBA season through the 2017 NBA season. Three separate models were built, predicting the winner, predicting the total points, and finally predicting the margin of victory for a team. These models learned on 80 percent of the data and validated on the other 20 percent. These models were trained for 40 epochs with a batch size of 15.
The model for predicting the winner achieved an accuracy of 65.61 percent, just slightly below the accuracy of other experts in the field of predicting the NBA. The model for predicting total points performed decently as well, it could beat Las Vegas’ prediction 50.04 percent of the time. The model for predicting margin of victory also did well, it beat Las Vegas 50.58 percent of the time.
In order to train the model, data was collected from the NBA statistics website. The model was trained on games dating from the 2010 NBA season through the 2017 NBA season. Three separate models were built, predicting the winner, predicting the total points, and finally predicting the margin of victory for a team. These models learned on 80 percent of the data and validated on the other 20 percent. These models were trained for 40 epochs with a batch size of 15.
The model for predicting the winner achieved an accuracy of 65.61 percent, just slightly below the accuracy of other experts in the field of predicting the NBA. The model for predicting total points performed decently as well, it could beat Las Vegas’ prediction 50.04 percent of the time. The model for predicting margin of victory also did well, it beat Las Vegas 50.58 percent of the time.
ContributorsGarretson, Samuel Burkett (Author) / Meuth, Ryan (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Department of Management and Entrepreneurship (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
In this update to the ESPBot, we have introduced new libraries for a small OLED display and a beeper. This functionality can be easily expanded to multiple beepers and displays, but requires more GPIO pins, or for the user to not use some of the infrared sensors or the ultrasonic sensor. We have also relocated some of the pins. The display can be updated to display 1 of 4 predefined shapes, or to display user-defined text. New shapes can be added by defining new methods within display.ino and calling the appropriate functions while parsing the JSON data in viple.ino. The beeper can be controlled by user-defined input to play any frequency for any amount of time. There is also a function added to play the happy birthday song. More songs can be added by defining new methods within beeper.ino and calling the appropriate functions while parsing the JSON data in viple.ino. More functionality can be added to allow the user to input a list of frequencies along with a list of time so the user can define their own songs or sequences on the fly.
ContributorsWelfert, Monica Michelle (Co-author) / Nguyen, Van (Co-author) / Chen, Yinong (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-12
Description
Machine learning is the process of training a computer with algorithms to learn from data and make informed predictions. In a world where large amounts of data are constantly collected, machine learning is an important tool to analyze this data to find patterns and learn useful information from it. Machine learning applications expand to numerous fields; however, I chose to focus on machine learning with a business perspective for this thesis, specifically e-commerce.
The e-commerce market utilizes information to target customers and drive business. More and more online services have become available, allowing consumers to make purchases and interact with an online system. For example, Amazon is one of the largest Internet-based retail companies. As people shop through this website, Amazon gathers huge amounts of data on its customers from personal information to shopping history to viewing history. After purchasing a product, the customer may leave reviews and give a rating based on their experience. Performing analytics on all of this data can provide insights into making more informed business and marketing decisions that can lead to business growth and also improve the customer experience.
For this thesis, I have trained binary classification models on a publicly available product review dataset from Amazon to predict whether a review has a positive or negative sentiment. The sentiment analysis process includes analyzing and encoding the human language, then extracting the sentiment from the resulting values. In the business world, sentiment analysis provides value by revealing insights into customer opinions and their behaviors. In this thesis, I will explain how to perform a sentiment analysis and analyze several different machine learning models. The algorithms for which I compared the results are KNN, Logistic Regression, Decision Trees, Random Forest, Naïve Bayes, Linear Support Vector Machines, and Support Vector Machines with an RBF kernel.
The e-commerce market utilizes information to target customers and drive business. More and more online services have become available, allowing consumers to make purchases and interact with an online system. For example, Amazon is one of the largest Internet-based retail companies. As people shop through this website, Amazon gathers huge amounts of data on its customers from personal information to shopping history to viewing history. After purchasing a product, the customer may leave reviews and give a rating based on their experience. Performing analytics on all of this data can provide insights into making more informed business and marketing decisions that can lead to business growth and also improve the customer experience.
For this thesis, I have trained binary classification models on a publicly available product review dataset from Amazon to predict whether a review has a positive or negative sentiment. The sentiment analysis process includes analyzing and encoding the human language, then extracting the sentiment from the resulting values. In the business world, sentiment analysis provides value by revealing insights into customer opinions and their behaviors. In this thesis, I will explain how to perform a sentiment analysis and analyze several different machine learning models. The algorithms for which I compared the results are KNN, Logistic Regression, Decision Trees, Random Forest, Naïve Bayes, Linear Support Vector Machines, and Support Vector Machines with an RBF kernel.
ContributorsMadaan, Shreya (Author) / Meuth, Ryan (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Dean, W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
For my thesis project, I have developed a cash register web application for the Arizona State University Barrett Dining Hall. I previously worked at the Barrett Dining Hall, and I would occasionally step in as a cashier. This work is how I came to be familiar with the system and all its inefficiencies. The system requires multiple user inputs to implement even the most basic of tasks, is not user-friendly, and therefore very prone to error. In the event that multiple incorrect inputs are entered, the software will freeze, and the user will have to turn off the computer and turn it back on. In theory, this application is an improvement over the software system that is currently in place in that the user interface has been specifically designed to be user-friendly. This application reduces the number of required user inputs by automating certain tasks (such as pricing and determining the meal period), thereby reducing the chance of user error. It is also an improvement in that it allows students to log in to the system to view how many meals they have left, how much M&G is in their account, and how many guest passes they have left. This functionality is extremely important because this is a feature that is not currently in place, and is something that students have actively complained about. Currently, if students want to check on their meal plan, they have to either physically go to a dining hall and ask the cashier, or call a toll-free number. The two technologies used to develop this application are C# and XML. These technologies were chosen because I wanted to learn something new for this project to broaden my knowledge. I also happened to be taking a class at the start of this project that utilized C# and XML for Web Applications, and it seemed like the perfect opportunity to transfer over the skills I had been learning.
ContributorsLewis, Q. Mariha Paishance (Author) / Chen, Yinong (Thesis director) / Nakamura, Mutsumi (Committee member) / School of International Letters and Cultures (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This project is a full integrated development environment implementing the LEGv8 assembly language standard, to be used in classroom settings. The LEGv8 assembly language is defined by the ARM edition of "Computer Organization and Design: The Hardware/Software Interface" by David A. Patterson and John L. Hennessy as a more approachable alternative to the full ARMv8 instruction set. The MIPS edition of that same book is used in the Computer Organization course at ASU. This class makes heavy use of the "MARS" MIPS simulator, which allows students to write and run their own MIPS assembly programs. Writing assembly language programs is a key component of the course, as assembly programs have many design difficulties as compared to a high-level language. This project is a fork of the MARS project. The interface and functionality remain largely the same aside from the change to supporting the LEGv8 syntax and instruction set. Faculty used to the MARS environment from teaching Computer Organization should only have to adjust to the new language standard, as the editor and environment will be familiar. The available instructions are basic arithmetic/logical operations, memory interaction, and flow control. Both floating-point and integer operations are supported, with limited support of conditional execution. Only branches can be conditionally executed, per LEGv8. Directives remain in the format supported by MARS, as documentation on ARM-style directives is both sparse and agreeable to this standard. The operating system functions supported by the MARS simulator also remain, as there is no generally standardized requirements for operating system interactions.
ContributorsWhite, Josiah Jeremiah (Author) / Meuth, Ryan (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
Gift-giving economies are economic models that freely give resources rather than barter for them or purchase them from market. Need-based transfers fit into this economic model by freely giving resources on the basis of need, provided the giver can spare the resources. The Maasai are an East African pastoral tribe that practices need-based transfers through a tradition they call osotua. If they have a partner with an established osotua relationship, then they will give any amount of cattle that partner request, provided they can spare the cattle. Cheating each other is unheard of in this tradition, but for this simulation I am introducing cheating into this economic model through feigning need. If a cheater is not in need, they will act like they are in need. If they are in need, then the cheater will request more cattle than what they need to survive. I am testing two different responses to cheating: walking-away and punishing. In the walk-away condition, the victim ends their osotua partnership and establishes a new one. In the punishment condition, a portion of the cheater's stolen cattle is destroyed.
ContributorsDunkelberger, Sophia (Author) / Aktipis, Athena (Thesis director) / Presson, Clark (Committee member) / Johnson, Kathryn (Committee member) / School of Social and Behavioral Sciences (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
This paper details the specification and implementation of a single-machine blockchain simulator. It also includes a brief introduction on the history & underlying concepts of blockchain, with explanations on features such as decentralization, openness, trustlessness, and consensus. The introduction features a brief overview of public interest and current implementations of blockchain before stating potential use cases for blockchain simulation software. The paper then gives a brief literature review of blockchain's role, both as a disruptive technology and a foundational technology. The literature review also addresses the potential and difficulties regarding the use of blockchain in Internet of Things (IoT) networks, and also describes the limitations of blockchain in general regarding computational intensity, storage capacity, and network architecture. Next, the paper gives the specification for a generic blockchain structure, with summaries on the behaviors and purposes of transactions, blocks, nodes, miners, public & private key cryptography, signature validation, and hashing. Finally, the author gives an overview of their specific implementation of the blockchain using C/C++ and OpenSSL. The overview includes a brief description of all the classes and data structures involved in the implementation, including their function and behavior. While the implementation meets the requirements set forward in the specification, the results are more qualitative and intuitive, as time constraints did not allow for quantitative measurements of the network simulation. The paper concludes by discussing potential applications for the simulator, and the possibility for future hardware implementations of blockchain.
ContributorsRauschenbach, Timothy Rex (Author) / Vrudhula, Sarma (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
Bioscience High School, a small magnet high school located in Downtown Phoenix and a STEAM (Science, Technology, Engineering, Arts, Math) focused school, has been pushing to establish a computer science curriculum for all of their students from freshman to senior year. The school's Mision (Mission and Vision) is to: "..provide a rigorous, collaborative, and relevant academic program emphasizing an innovative, problem-based curriculum that develops literacy in the sciences, mathematics, and the arts, thus cultivating critical thinkers, creative problem-solvers, and compassionate citizens, who are able to thrive in our increasingly complex and technological communities." Computational thinking is an important part in developing a future problem solver Bioscience High School is looking to produce. Bioscience High School is unique in the fact that every student has a computer available for him or her to use. Therefore, it makes complete sense for the school to add computer science to their curriculum because one of the school's goals is to be able to utilize their resources to their full potential. However, the school's attempt at computer science integration falls short due to the lack of expertise amongst the math and science teachers. The lack of training and support has postponed the development of the program and they are desperately in need of someone with expertise in the field to help reboot the program. As a result, I've decided to create a course that is focused on teaching students the concepts of computational thinking and its application through Scratch and Arduino programming.
ContributorsLiu, Deming (Author) / Meuth, Ryan (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The benefits of earning a college degree are clear, yet nearly half of first-time, full-time college students do not complete their degree within 6 years. Completing college is no simple task and students must regularly make decisions not to prefer the smaller, immediate rewards over the larger, future reward of graduation (i.e. temporal discounting). Recent research shows initial support that temporal discounting can be reduced by heightening future self-continuity. This thesis study pilot tested an ecological, scalable approach to increase future-self continuity by heightening three components, similarity, vividness and positivity, within the framework of social media. Participants completed measures of these components before and after simulating the creation of a social media profile for themselves 10 years after college graduation. A significant increase in perceived similarity to the future self from Time 1 to Time 2 was detected in a within-subjects test. The findings in this study are encouraging and may inform the development of interventions aimed at increasing future self-continuity in college students.
ContributorsSmyth, Heather Lynn (Author) / Kwan, Virginia S. Y. (Thesis director) / Presson, Clark (Committee member) / Okun, Morris (Committee member) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05