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Barrett, The Honors College Thesis/Creative Project Collection


Barrett, the Honors College accepts high performing, academically engaged 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, supervised and defended in front of a faculty committee. The thesis or creative project allows students to explore an intellectual interest and produce an original piece of scholarly research. The thesis or creative project is a student’s opportunity to explore areas of academic interest with greater intensity than is possible in a single course. It is also an opportunity to engage with professors, nationally recognized in their fields and specifically interested and committed to working with honors students. This work provides tangible evidence of a student’s research, writing and creative skills to graduate schools and/or prospective employers.


Date Range
2014 2018


Divergence functions are both highly useful and fundamental to many areas in information theory and machine learning, but require either parametric approaches or prior knowledge of labels on the full data set. This paper presents a method to estimate the divergence between two data sets in the absence of fully labeled data. This semi-labeled case is common in many domains where labeling data by hand is expensive or time-consuming, or wherever large data sets are present. The theory derived in this paper is demonstrated on a simulated example, and then applied to a feature selection and classification problem from pathological ...

Contributors
Gilton, Davis Leland, Berisha, Visar, Cochran, Douglas, et al.
Created Date
2016-05

Prior research has confirmed that supervised learning is an effective alternative to computationally costly numerical analysis. Motivated by NASA’s use of abort scenario matrices to aid in mission operations and planning, this paper applies supervised learning to trajectory optimization in an effort to assess the accuracy of a less time-consuming method of producing the magnitude of delta-v vectors required to abort from various points along a Near Rectilinear Halo Orbit. Although the utility of the study is limited, the accuracy of the delta-v predictions made by a Gaussian regression model is fairly accurate after a relatively swift computation time, paving ...

Contributors
Smallwood, Sarah Lynn, Peet, Matthew, Liu, Huan, et al.
Created Date
2018-05

Food safety is vital to the well-being of society; therefore, it is important to inspect food products to ensure minimal health risks are present. A crucial phase of food inspection is the identification of foreign particles found in the sample, such as insect body parts. The presence of certain species of insects, especially storage beetles, is a reliable indicator of possible contamination during storage and food processing. However, the current approach to identifying species is visual examination by human analysts; this method is rather subjective and time-consuming. Furthermore, confident identification requires extensive experience and training. To aid this inspection process, ...

Contributors
Martin, Daniel Luis, Ahn, Gail-Joon, Doupé, Adam, et al.
Created Date
2016-05

Understanding the necessary skills required to work in an industry is a difficult task with many potential uses. By being able to predict the industry of a person based on their skills, professional social networks could make searching better with automated tagging, advertisers can target more carefully, and students can better find a career path that fits their skillset. The aim in this project is to apply deep learning to the world of professional networking. Deep Learning is a type of machine learning that has recently been making breakthroughs in the analysis of complex datasets that previously were not of ...

Contributors
Andrew, Benjamin, Thiel, Alex, Sodemann, Angela, et al.
Created Date
2017-12

Bots tamper with social media networks by artificially inflating the popularity of certain topics. In this paper, we define what a bot is, we detail different motivations for bots, we describe previous work in bot detection and observation, and then we perform bot detection of our own. For our bot detection, we are interested in bots on Twitter that tweet Arabic extremist-like phrases. A testing dataset is collected using the honeypot method, and five different heuristics are measured for their effectiveness in detecting bots. The model underperformed, but we have laid the ground-work for a vastly untapped focus on bot ...

Contributors
Karlsrud, Mark C., Liu, Huan, Morstatter, Fred, et al.
Created Date
2015-05

Social media sites are platforms in which individuals discuss a wide range of topics and share a huge amount of information about themselves and their interests. So much of this information is encoded through unstructured text that users post on the these types of sites. There has been a considerable amount of work done in respect to sentiment analysis on these sites to infer users’ opinions and preferences. However there is a gap where it may be difficult to infer how a user feels about particular pages or topics that they have not conveyed their sentiment for in a observable ...

Contributors
Baird, James Daniel, Liu, Huan, Wang, Suhang, et al.
Created Date
2018-05

This thesis dives into the world of artificial intelligence by exploring the functionality of a single layer artificial neural network through a simple housing price classification example while simultaneously considering its impact from a data management perspective on both the software and hardware level. To begin this study, the universally accepted model of an artificial neuron is broken down into its key components and then analyzed for functionality by relating back to its biological counterpart. The role of a neuron is then described in the context of a neural network, with equal emphasis placed on how it individually undergoes training ...

Contributors
Richards, Nicholas Giovanni, Miller, Phillip, Meuth, Ryan, et al.
Created Date
2018-05

Due to the popularity of the movie industry, a film's opening weekend box-office performance is of great interest not only to movie studios, but to the general public, as well. In hopes of maximizing a film's opening weekend revenue, movie studios invest heavily in pre-release advertisement. The most visible advertisement is the movie trailer, which, in no more than two minutes and thirty seconds, serves as many people's first introduction to a film. The question, however, is how can we be confident that a trailer will succeed in its promotional task, and bring about the audience a studio expects? In ...

Contributors
Williams, Terrance D'Mitri, Pon-Barry, Heather, Zafarani, Reza, et al.
Created Date
2014-05

This work details the bootstrap estimation of a nonparametric information divergence measure, the Dp divergence measure, using a power law model. To address the challenge posed by computing accurate divergence estimates given finite size data, the bootstrap approach is used in conjunction with a power law curve to calculate an asymptotic value of the divergence estimator. Monte Carlo estimates of Dp are found for increasing values of sample size, and a power law fit is used to relate the divergence estimates as a function of sample size. The fit is also used to generate a confidence interval for the estimate ...

Contributors
Kadambi, Pradyumna Sanjay, Berisha, Visar, Bliss, Daniel, et al.
Created Date
2016-05

Social media users are inundated with information. Especially on Instagram--a social media service based on sharing photos--where for many users, missing important posts is a common issue. By creating a recommendation system which learns each user's preference and gives them a curated list of posts, the information overload issue can be mediated in order to enhance the user experience for Instagram users. This paper explores methods for creating such a recommendation system. The proposed method employs a learning model called ``Factorization Machines" which combines the advantages of linear models and latent factor models. In this work I derived features from ...

Contributors
Fakhri, Kian, Liu, Huan, Morstatter, Fred, et al.
Created Date
2016-12