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- All Subjects: Computer Science
- Creators: Computer Science and Engineering Program
Description
Multi-view learning, a subfield of machine learning that aims to improve model performance by training on multiple views of the data, has been studied extensively in the past decades. It is typically applied in contexts where the input features naturally form multiple groups or views. An example of a naturally multi-view context is a data set of websites, where each website is described not only by the text on the page, but also by the text of hyperlinks pointing to the page. More recently, various studies have demonstrated the initial success of applying multi-view learning on single-view data with multiple artificially constructed views. However, there lacks a systematic study regarding the effectiveness of such artificially constructed views. To bridge this gap, this thesis begins by providing a high-level overview of multi-view learning with the co-training algorithm. Co-training is a classic semi-supervised learning algorithm that takes advantage of both labelled and unlabelled examples in the data set for training. Then, the thesis presents a web-based tool developed in Python allowing users to experiment with and compare the performance of multiple view construction approaches on various data sets. The supported view construction approaches in the web-based tool include subsampling, Optimal Feature Set Partitioning, and the genetic algorithm. Finally, the thesis presents an empirical comparison of the performance of these approaches, not only against one another, but also against traditional single-view models. The findings show that a simple subsampling approach combined with co-training often outperforms both the other view construction approaches, as well as traditional single-view methods.
ContributorsAksoy, Kaan (Author) / Maciejewski, Ross (Thesis director) / He, Jingrui (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-12
Description
Karate is a Japanese martial art that originated approximately a century ago, with heavy influence from Chinese martial arts at the time. Although it was originally created as a form of self-defense, many today practice it for sport. Organizations such as the World Karate Federation (WKF) and USA Karate establish rules for competitions as well as host tournaments for practitioners of all ages and skill levels to participate in. Dojos will often host small, local tournaments for their students to practice and sharpen their competition skills. Smaller tournaments often do not have the same tools and technologies that larger tournaments do. Sign-ups are typically done in-person and payments are cash-only, which can be inconvenient for those who are extremely busy or forgetful. Another issue with hosting local tournaments is that the software used to run the timer is a desktop application, called Karate Semaphore. In the case of technical difficulties, installing the software on another machine can be extremely time-consuming and delay the progression of the tournament. Not to mention, Karate Semaphore was created following the 2012 WKF rules—meaning it is currently out of date, as it does not contain any features supporting new rules.
For my creative project, I designed a website through which smaller, local tournament registration and management are possible. Users can register for tournaments through the registration page. Registered users can check their registration is successful by viewing a table of all competitors. If the list of competitors is too long, they can filter results based on search criteria. Tournament management will be possible via a functioning timer following WKF rules which keeps track of both the match’s score as well as time.
For my creative project, I designed a website through which smaller, local tournament registration and management are possible. Users can register for tournaments through the registration page. Registered users can check their registration is successful by viewing a table of all competitors. If the list of competitors is too long, they can filter results based on search criteria. Tournament management will be possible via a functioning timer following WKF rules which keeps track of both the match’s score as well as time.
ContributorsRuan, Shirley (Author) / Sarwat, Mohamed (Thesis director) / Chen, Yinong (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
We consider programmable matter as a collection of simple computational elements (or particles) that self-organize to solve system-wide problems of movement, configuration, and coordination. Here, we focus on the compression problem, in which the particle system gathers as tightly together as possible, as in a sphere or its equivalent in the presence of some underlying geometry. Within this model a configuration of particles can be represented as a unique closed self-avoiding walk on the triangular lattice. In this paper we will examine the bias parameter of a Markov chain based algorithm that solves the compression problem under the geometric amoebot model, for particle systems that begin in a connected configuration with no holes. This bias parameter, $\lambda$, determines the behavior of the algorithm. It has been shown that for $\lambda > 2+\sqrt{2}$, with all but exponentially small probability, the algorithm achieves compression. Additionally the same algorithm can be used for expansion for small values of $\lambda$; in particular, for all $0 < \lambda < \sqrt{\tau}$, where $\lim_{n\to\infty} {(p_n)^{1
}}=\tau$. This research will focus on improving approximations on the lower bound of $\tau$. Toward this end we will examine algorithmic enumeration, and series analysis for self-avoiding polygons.
}}=\tau$. This research will focus on improving approximations on the lower bound of $\tau$. Toward this end we will examine algorithmic enumeration, and series analysis for self-avoiding polygons.
ContributorsLough, Kevin James (Author) / Richa, Andrea (Thesis director) / Fishel, Susanna (Committee member) / School of Mathematical and Statistical Sciences (Contributor, Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
DescriptionA two-way deterministic finite pushdown automaton ("2PDA") is developed for the Lua language. This 2PDA is evaluated against both a purpose-built Lua syntax test suite and the test suite used by the reference implementation of Lua, and fully passes both.
ContributorsStevens, Kevin A (Author) / Shoshitaishvili, Yan (Thesis director) / Wang, Ruoyu (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Learning to code is a skill that is becoming increasing needed as technology advances, yet is absent in traditional education. This thesis aims to provide a resource for middle school teachers to introduce programming skills and concepts to their students over several lessons designed to fit within the constraints of a standard class period. By targeting students in middle school, if they develop an interest, they will have enough time in middle or high school to prepare themselves for a degree in Computer Science or to complete a programming boot camp after they graduate high school. Additionally, middle school students are old enough to understand challenging programming concepts and work together to solve a programming challenge. The programming language and environment, VIPLE, will be used to teach the concepts in the lessons as it is a graphical programming language, which removes many of the common challenges faced by young students in learning to code, like dealing with syntax or remembering keywords for coding blocks.
ContributorsBelt, Emily (Author) / Chen, Yinong (Thesis director) / Miller, Cindy (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Alife is an event searching and event publishing website written in C# using the MVC software design pattern. Alife aims to offer a platform for student organizations to publish their events while enabling ASU students to browse, search, and filter events based on date, location, keywords, and category tags. Alife can also retrieve events information from the official ASU Event website, parse the keywords of the events and assign category tags to them. Alife project explores many concepts of Distributed Service-Oriented software development, such as server-side development, MVC architecture, client-side development, database integration, web service development and consuming.
ContributorsWu, Mengqi (Author) / Chen, Yinong (Thesis director) / Feng, Xuerong (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
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
The nonprofit organization, I Am Zambia, works to give supplemental education to young women in Lusaka. I Am Zambia is creating sustainable change by educating these females, who can then lift their families and communities out of poverty. The ultimate goal of this thesis was to explore and implement high level systematic problem solving through basic and specialized computational thinking curriculum at I Am Zambia in order to give these women an even larger stepping stool into a successful future.
To do this, a 4-week long pilot curriculum was created, implemented, and tested through an optional class at I Am Zambia, available to women who had already graduated from the year-long I Am Zambia Academy program. A total of 18 women ages 18-24 chose to enroll in the course. There were a total of 10 lessons, taught over 20 class period. These lessons covered four main computational thinking frameworks: introduction to computational thinking, algorithmic thinking, pseudocode, and debugging. Knowledge retention was tested through the use of a CS educational tool, QuizIt, created by the CSI Lab of School of Computing, Informatics and Decision Systems Engineering at Arizona State University. Furthermore, pre and post tests were given to assess the successfulness of the curriculum in teaching students the aforementioned concepts. 14 of the 18 students successfully completed the pre and post test.
Limitations of this study and suggestions for how to improve this curriculum in order to extend it into a year long course are also presented at the conclusion of this paper.
To do this, a 4-week long pilot curriculum was created, implemented, and tested through an optional class at I Am Zambia, available to women who had already graduated from the year-long I Am Zambia Academy program. A total of 18 women ages 18-24 chose to enroll in the course. There were a total of 10 lessons, taught over 20 class period. These lessons covered four main computational thinking frameworks: introduction to computational thinking, algorithmic thinking, pseudocode, and debugging. Knowledge retention was tested through the use of a CS educational tool, QuizIt, created by the CSI Lab of School of Computing, Informatics and Decision Systems Engineering at Arizona State University. Furthermore, pre and post tests were given to assess the successfulness of the curriculum in teaching students the aforementioned concepts. 14 of the 18 students successfully completed the pre and post test.
Limitations of this study and suggestions for how to improve this curriculum in order to extend it into a year long course are also presented at the conclusion of this paper.
ContributorsGriffin, Hadley Meryl (Author) / Hsiao, Sharon (Thesis director) / Mutsumi, Nakamura (Committee member) / Arts, Media and Engineering Sch T (Contributor) / Computer Science and Engineering Program (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
DescriptionThe goal of this study is to equip administrators and instructors with a deeper understanding of the apparent cheating problem in Computer Science courses, with proposed solutions to lower academic dishonesty from the students’ perspective.
ContributorsAl Yasari, Farah (Co-author) / Alyasari, Farah (Co-author) / Tadayon-Navabi, Farideh (Thesis director) / Bazzi, Rida (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Spatial audio can be especially useful for directing human attention. However, delivering spatial audio through speakers, rather than headphones that deliver audio directly to the ears, produces the issue of crosstalk, where sounds from each of the two speakers reach the opposite ear, inhibiting the spatialized effect. A research team at Meteor Studio has developed an algorithm called Xblock that solves this issue using a crosstalk cancellation technique. This thesis project expands upon the existing Xblock IoT system by providing a way to test the accuracy of the directionality of sounds generated with spatial audio. More specifically, the objective is to determine whether the usage of Xblock with smart speakers can provide generalized audio localization, which refers to the ability to detect a general direction of where a sound might be coming from. This project also expands upon the existing Xblock technique to integrate voice commands, where users can verbalize the name of a lost item using the phrase, “Find [item]”, and the IoT system will use spatial audio to guide them to it.
ContributorsSong, Lucy (Author) / LiKamWa, Robert (Thesis director) / Berisha, Visar (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05