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Description
Spotify, one of the most popular music streaming services, has many
algorithms for recommending new music to users. However, at the
core of their recommendations is the collaborative filtering algorithm,
which recommends music based on what other people with similar
tastes have listened to [1]. While this can produce highly relevant
content recommendations, it tends to promote only popular content
[2]. The popularity bias inherent in collaborative-filtering based
systems can overlook music that fits a user’s taste, simply because
nobody else is listening to it. One possible solution to this problem is
to recommend music based on features of the music itself, and
recommend songs which have similar features. Here, a method for
extracting high-level features representing the mood of a song is
presented, with the aim of tailoring music recommendations to an
individual's mood, and providing music recommendations with
diversity in popularity.
algorithms for recommending new music to users. However, at the
core of their recommendations is the collaborative filtering algorithm,
which recommends music based on what other people with similar
tastes have listened to [1]. While this can produce highly relevant
content recommendations, it tends to promote only popular content
[2]. The popularity bias inherent in collaborative-filtering based
systems can overlook music that fits a user’s taste, simply because
nobody else is listening to it. One possible solution to this problem is
to recommend music based on features of the music itself, and
recommend songs which have similar features. Here, a method for
extracting high-level features representing the mood of a song is
presented, with the aim of tailoring music recommendations to an
individual's mood, and providing music recommendations with
diversity in popularity.
ContributorsGomez, Luis Angel (Author) / Kevin, Burger (Thesis director) / Alberto, Hernández (Committee member) / Arts, Media and Engineering Sch T (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Despite efforts to recruit and retain female engineering students, only about 21.3% of bachelor’s degrees each year in engineering and computer science are awarded to women. The purpose of this synthesis is to understand the ways in which current research has explored how self-identity, engineering identity, and sense of belonging influence undergraduate women’s persistence. Analysis is focused around 4 themes that emerged: (1) Sense of Self: Self-Efficacy, Expectancy Value Theory; (2) Culture of Engineering: Engineering Identity; (3) Stereotype Threat; (4) Interdisciplinary Studies to Expand the Culture of Engineering. Conclusions of this synthesis may be used as opportunities for future engagement with these topics.
ContributorsTapia, Kayla (Author) / Ganesh, Tirupalavanam (Thesis director) / Velez, Jennifer (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
The goal of this product was to create a highly customizable application in which any individual, musician or not, can create a harmony for the user’s melody. This Automating Music Composer is built on the underlying rules of music composition, rules that are unique for each type of music available. This program is built on rules that are similar to how a Finite State Machine works (Fig 1). Each state represents a different chord in a given key, where the first roman numeral represents the first note in the chord progression. Each transition represents the action that can be taken by the chord progression, or the next note that can be reached by the current note. The user is able to manipulate these rules and styles, adjust different musical parameters to their liking, and is able to input their own melody, which then will output a unique harmony. This product aims to bridge the gap between predictive technologies and musical composition. Allowing the user to be more involved in the composition process helps the program to act as a tool for the user, rather than a separate entity that simply gives the user a completed recording. This allows the user to appreciate and understand what they are helping to produce more than they would if they were to simply be an inactive consumer of a random music composer. This product is meant to feel like an extension of the user, rather than a separate tool.
ContributorsKumar, Dhantin (Co-author) / Lopez, Christian (Co-author) / Nakamura, Mutsumi (Thesis director) / Blount, Andrew (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
The process of cooking a turkey is a yearly task that families undertake in order to deliver a delicious centerpiece to a Thanksgiving meal. While other dishes accompany and comprise the traditional Thanksgiving supper, focusing on creating a turkey that satisfies the tastes of all guests is difficult, as preferences vary. Over the years, many cooking methods and preparation variations have come to light. This thesis studies these cooking methods and preparation variations, as well as the effects on the crispiness of the skin, the juiciness of the meat, the tenderness of the meat, and the overall taste, to simplify the choices that home cooks have to prepare a turkey that best fits their tastes. Testing and evaluation reveal that among deep-frying, grilling, and oven roasting turkey, a number of preparation variations show statistically significant changes relative to a lack of these preparation variations. For crispiness, fried turkeys are statistically superior, scoring about 1.5 points higher than other cooking methods on a 5 point scale. For juiciness, the best preparation variation was using an oven bag, with the oven roasted turkey scoring about 4.5 points on a 5 point scale. For tenderness, multiple methods are excellent, with the best three preparation variations in order being spatchcocking, brining, and using an oven bag, each of these preparation variations are just under a 4 out of 5. Finally, testing reaffirms that judges tend to have different subjective tastes, with some having different perceptions and opinions on some criteria, while statistically agreeing on others: there was 67% agreement among judges on crispiness and tenderness, while there was only 17% agreement on juiciness. Evaluation of these cooking methods, as well as their respective preparation variations, addresses the question of which methods are worthwhile endeavors for cooks.
ContributorsVance, Jarod (Co-author) / Lacsa, Jeremy (Co-author) / Green, Matthew (Thesis director) / Taylor, David (Committee member) / Chemical Engineering Program (Contributor) / 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
In this project, I investigated the impact of virtual reality on memory retention. The investigative approach to see the impact of virtual reality on memory retention, I utilized the memorization technique called the memory palace in a virtual reality environment. For the experiment, due to Covid-19, I was forced to be the only subject. To get effective data, I tested myself within randomly generated environments with a completely unique set of objects, both outside of a virtual reality environment and within one. First I conducted a set of 10 tests on myself by going through a virtual environment on my laptop and recalling as many objects I could within that environment. I recorded the accuracy of my own recollection as well as how long it took me to get through the data. Next I conducted a set of 10 tests on myself by going through the same virtual environment, but this time with an immersive virtual reality(VR) headset and a completely new set of objects. At the start of the project it was hypothesized that virtual reality would result in a higher memory retention rate versus simply going through the environment in a non-immersive environment. In the end, the results, albeit with a low test rate, leaned more toward showing the hypothesis to be true rather than not.
ContributorsDu, Michael Shan (Author) / Kobayashi, Yoshihiro (Thesis director) / McDaniel, Troy (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
In recent years, the development of new Machine Learning models has allowed for new technological advancements to be introduced for practical use across the world. Multiple studies and experiments have been conducted to create new variations of Machine Learning models with different algorithms to determine if potential systems would prove to be successful. Even today, there are still many research initiatives that are continuing to develop new models in the hopes to discover potential solutions for problems such as autonomous driving or determining the emotional value from a single sentence. One of the current popular research topics for Machine Learning is the development of Facial Expression Recognition systems. These Machine Learning models focus on classifying images of human faces that are expressing different emotions through facial expressions. In order to develop effective models to perform Facial Expression Recognition, researchers have gone on to utilize Deep Learning models, which are a more advanced implementation of Machine Learning models, known as Neural Networks. More specifically, the use of Convolutional Neural Networks has proven to be the most effective models for achieving highly accurate results at classifying images of various facial expressions. Convolutional Neural Networks are Deep Learning models that are capable of processing visual data, such as images and videos, and can be used to identify various facial expressions. The purpose of this project, I focused on learning about the important concepts of Machine Learning, Deep Learning, and Convolutional Neural Networks to implement a Convolutional Neural Network that was previously developed by a recommended research paper.
ContributorsFrace, Douglas R (Author) / Demakethepalli Venkateswara, Hemanth Kumar (Thesis director) / McDaniel, Troy (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
This thesis examines the applications of the Internet of Things and Artificial Intelligence within small-to-medium sized retail businesses. These technologies have become a common aspect of a modern business environment, yet there remains a level of unfamiliarity with these concepts for business owners to fully utilize these tools. The complexity behind IoT and AI has been simplified to provide benefits for a brick and mortar business store in regards to security, logistics, profit optimization, operations, and analytics. While these technologies can contribute to a business’s success, they potentially come with a high and unattainable financial cost. In order to investigate which aspects of businesses can benefit the most from these technologies, interviews with small-to-medium business owners were conducted and paired with an analysis of published research. These interviews provided specific pain points and issues that could potentially be solved by these technologies. The analysis conducted in this thesis gives a detailed summary of this research and provides a business model for two small businesses to optimize their Internet of Things and Artificial Intelligence to solve these pain points, while staying in their financial budget.
ContributorsAldrich, Lauren (Co-author) / Bricker, Danielle (Co-author) / Sebold, Brent (Thesis director) / Vermeer, Brandon (Committee member) / Computer Science and Engineering Program (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
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
Customers in the modern world are accustomed to having immediate and simple access to an immense amount of information, and demand this immediacy in all businesses, especially in the restaurant industry. Now more than ever, restaurants are relying on third party delivery services such as UberEATS, Postmates, and GrubHub to satiate the appetite of their delivery market, and while this may seem like the natural progression, not all restaurant owners are comfortable moving in this direction. Pain points range from not wanting a third party to represent their business or the lack of supervision over the food in transit, and the time it takes to navigate the delivery landscape, to the fact that some food just doesn’t “travel” well. In addition to this, food delivery services can cause increased stress on a kitchen, and dig into the bottom line of an already slim restaurant margin. Simply put, customer reliance on these applications puts apprehensive restaurant owners at a competitive disadvantage.Our solution is simple—we want business owners to be able to take advantage of the huge market provided by third party delivery services, without the fear of compromising their brand. At DLVR Consulting, we listen to specific pain points of a customer and alleviate them through solutions developed by our in-house food, restaurant, and branding experts. Whether creating an entirely new “delivery” brand, menu curation, or payment processing service, we give the customer exactly what they need to feel comfortable using third-party delivery applications. In this plan, we will first take a deep dive into the problem and opportunity identified by both third-party research and first-hand interviews with successful restaurant owners and operators. After exploring the problem, we will propose our solution, who we will target with said solution, and what makes this solution unique and sellable. From here we will begin to explore the execution of our ideas, including our sales and marketing plans which will work in conjunction with our go-to-market strategy. We will explore key milestones and metrics we hope to meet in the coming year, as well as the team which will be taking DLVR from a plan to an implemented business. We will take a look at our three year financial forecast, and break this down further to monthly revenue, direct costs, and expenses. We will finish by taking a look at our required funding, and how we will attempt to gain said funding.
ContributorsClancy, Kevin (Co-author, Co-author) / Sebold, Brent (Thesis director) / Clancy, Keith (Committee member) / Computer Science and Engineering Program (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05