Matching Items (61)
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 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 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 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
One of the major sources of authentication is through the use of username and password systems. Ideally, each password is a unique identifier known by a single individual. In reality however, exposed passwords from past data breaches reveal vulnerabilities that are traceable to passwords created today. Vulnerabilities include repetitions of characters, words, character sequences, and phrases that are used in a password. This project was observed in English to highlight the vulnerabilities that can come from utilizing the English language. However, the vulnerabilities highlighted in this project can also be applicable in languages across the world. It was observed that through the common types of digital attacks, brute force attack and dictionary attack work effectively against weak passwords. Brute force attack revealed that a user could expose an alphanumeric password of length eight in as little as one and a half days. In addition, dictionary attacks revealed that an alphanumeric password of length eight can be exposed in a shorter amount of time if the password contains a single long word or phrase thought to be secure. During this attack analysis, it found that passwords become significantly more secure in the utilization of alphanumeric passwords of minimal length of eight. In addition, the password must also not be a particular phrase or word with simplistic characteristics for adequate strength against dictionary attack. The solution to using username and password systems is to create a password utilizing as many characters as possible while still retaining memorability. If creating a password of this type is not feasible, there is a need to use technological solutions to keep the current system of username and passwords as secure as possible under daily life. Otherwise, there will be a need to replace the username and password system altogether before it becomes insecure by technology.
ContributorsTipton, Tony T (Co-author) / Tipton, Tony (Co-author) / Meuth, Ryan (Thesis director) / Tirupalavanam, Ganesh (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Behavioral economics suggests that emotions can affect an individual’s decision making. Recent research on this idea’s application on large societies hints that there may exist some correlation or maybe even some causation relationship between public sentiment—at least what can be pulled from Twitter—and the movement of the stock market. One major result of consistent research on whether or not public sentiment can predict the movement of the stock market is that public sentiment, as a feature, is becoming more and more valid as a variable for stock-market-based machine learning models. While raw values typically serve as invaluable points of data, when training a model, many choose to “engineer” new features for their models—deriving rates of change or range values to improve model accuracy.
Since it doesn’t hurt to attempt to utilize feature extracted values to improve a model (if things don’t work out, one can always use their original features), the question may arise: how could the results of feature extraction on values such as sentiment affect a model’s ability to predict the movement of the stock market? This paper attempts to shine some light on to what the answer could be by deriving TextBlob sentiment values from Twitter data, and using Granger Causality Tests and logistic and linear regression to test if there exist a correlation or causation between the stock market and features extracted from public sentiment.
Since it doesn’t hurt to attempt to utilize feature extracted values to improve a model (if things don’t work out, one can always use their original features), the question may arise: how could the results of feature extraction on values such as sentiment affect a model’s ability to predict the movement of the stock market? This paper attempts to shine some light on to what the answer could be by deriving TextBlob sentiment values from Twitter data, and using Granger Causality Tests and logistic and linear regression to test if there exist a correlation or causation between the stock market and features extracted from public sentiment.
ContributorsYu, James (Author) / Meuth, Ryan (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
For our creative project, we initially wanted to work on a web application that would allow people with busy schedules to easily create and share events while also discovering other events that may interest them. With that in mind, we created the Group Event Planner App, a full stack project that lays down a foundation for all of our goals while focusing primarily on the proposed recommendation algorithms that enable its users to discover events that are likely to pique their interest. The development of our recommendation algorithms took inspiration from existing implementations, such as those at Amazon, YouTube, and Netflix, and resulted in a creative amalgamation.
ContributorsRussell, Preston (Co-author) / Sonnier, Connor (Co-author) / Chen, Yinong (Thesis director) / Meuth, Ryan (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
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
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 most important task for a beginning computer science student, in order for them to succeed in their future studies, is to learn to be able to understand code. One of the greatest indicators of student success in beginning programming courses is the ability to read code and predict its output, as this shows that the student truly understands what each line of code is doing. Yet few tools available to students today focus on helping students to improve their ability to read code. The goal of the random Python program generator is to give students a tool to practice this important skill.
The program writes randomly generated, syntactically correct Python 3 code in order to provide students infinite examples from which to study. The end goal of the project is to create an interactive tool where beginning programming students can click a button to generate a random code snippet, check if what they predict the output to be is correct, and get an explanation of the code line by line. The tool currently lacks a front end, but it currently is able to write Python code that includes assignment statements, delete statements, if statements, and print statements. It supports boolean, float, integer, and string variable types.
The program writes randomly generated, syntactically correct Python 3 code in order to provide students infinite examples from which to study. The end goal of the project is to create an interactive tool where beginning programming students can click a button to generate a random code snippet, check if what they predict the output to be is correct, and get an explanation of the code line by line. The tool currently lacks a front end, but it currently is able to write Python code that includes assignment statements, delete statements, if statements, and print statements. It supports boolean, float, integer, and string variable types.
ContributorsDiLorenzo, Kaitlyn (Author) / Meuth, Ryan (Thesis director) / Miller, Phillip (Committee member) / School of International Letters and Cultures (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
If you’ve ever found yourself uttering the words “Honey, I forgot the—” or “how did I miss the—" when coming home from the grocery store, then you’re not alone. This everyday problem that we disregard as part of life may not seem like much, but it is the driving force behind my honors thesis.
Shopping Buddy is a complete Amazon Web Services solution to this problem which is so innate to the human condition. Utilizing Alexa to keep track of your pantry, this web application automates the daunting task of creating your shopping list, putting the power of the cloud at your fingertips while keeping your complete shopping list only a click away.
Say goodbye to the nights of spaghetti without the parmesan that you left on the store shelf or the strawberries that you forgot for the strawberry shortcake. With this application, you will no longer need to rely on your memory of what you think is in the back of your fridge nor that pesky shopping list that you always end up losing when you need it the most. Accessible from any web enabled device, Shopping Buddy has got your back through all your shopping adventures to come.
Shopping Buddy is a complete Amazon Web Services solution to this problem which is so innate to the human condition. Utilizing Alexa to keep track of your pantry, this web application automates the daunting task of creating your shopping list, putting the power of the cloud at your fingertips while keeping your complete shopping list only a click away.
Say goodbye to the nights of spaghetti without the parmesan that you left on the store shelf or the strawberries that you forgot for the strawberry shortcake. With this application, you will no longer need to rely on your memory of what you think is in the back of your fridge nor that pesky shopping list that you always end up losing when you need it the most. Accessible from any web enabled device, Shopping Buddy has got your back through all your shopping adventures to come.
ContributorsMathews, Nicolle (Author) / Meuth, Ryan (Thesis director) / Chen, Yinong (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05