Barrett, The Honors College Thesis/Creative Project Collection

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Date Range
2014 2016

Malware that perform identity theft or steal bank credentials are becoming increasingly common and can cause millions of dollars of damage annually. A large area of research focus is the automated detection and removal of such malware, due to their large impact on millions of people each year. Such a detector will be beneficial to any industry that is regularly the target of malware, such as the financial sector. Typical detection approaches such as those found in commercial anti-malware software include signature-based scanning, in which malware executables are identified based on a unique signature or fingerprint developed for that malware. ...

Contributors
Anwar, Sajid, Chan, Tsz, Ahn, Gail-Joon, et al.
Created Date
2016-05

Twitter, the microblogging platform, has grown in prominence to the point that the topics that trend on the network are often the subject of the news and other traditional media. By predicting trends on Twitter, it could be possible to predict the next major topic of interest to the public. With this motivation, this paper develops a model for trends leveraging previous work with k-nearest-neighbors and dynamic time warping. The development of this model provides insight into the length and features of trends, and successfully generalizes to identify 74.3% of trends in the time period of interest. The model developed ...

Contributors
Marshall, Grant A, Liu, Huan, Morstatter, Fred, et al.
Created Date
2015-05

Epilepsy affects numerous people around the world and is characterized by recurring seizures, prompting the ability to predict them so precautionary measures may be employed. One promising algorithm extracts spatiotemporal correlation based features from intracranial electroencephalography signals for use with support vector machines. The robustness of this methodology is tested through a sensitivity analysis. Doing so also provides insight about how to construct more effective feature vectors.

Contributors
Ma, Owen, Bliss, Daniel, Berisha, Visar, et al.
Created Date
2015-05

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

A model has been developed to modify Euler-Bernoulli beam theory for wooden beams, using visible properties of wood knot-defects. Treating knots in a beam as a system of two ellipses that change the local bending stiffness has been shown to improve the fit of a theoretical beam displacement function to edge-line deflection data extracted from digital imagery of experimentally loaded beams. In addition, an Ellipse Logistic Model (ELM) has been proposed, using L1-regularized logistic regression, to predict the impact of a knot on the displacement of a beam. By classifying a knot as severely positive or negative, vs. mildly positive ...

Contributors
Sexton, Thurston Bryant, Takahashi, Timothy, Jones, Donald, et al.
Created Date
2015-05

In this paper, I will show that news headlines of global events can predict changes in stock price by using Machine Learning and eight years of data from r/WorldNews, a popular forum on Reddit.com. My data is confined to the top 25 daily posts on the forum, and due to the implicit filtering mechanism in the online community, these 25 posts are representative of the most popular news headlines and influential global events of the day. Hence, these posts shine a light on how large-scale social and political events affect the stock market. Using a Logistic Regression and a Naive ...

Contributors
Priniski, John Hunter, Haiyan, Wang, Hazel, Kwon, et al.
Created Date
2016-12

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

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

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

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

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.