Matching Items (67)
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- All Subjects: Machine Learning
- Creators: Computer Science and Engineering Program
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
- Resource Type: Text
Description
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 in this work provides understanding into why par- ticular words trend on Twitter.
ContributorsMarshall, Grant A (Author) / Liu, Huan (Thesis director) / Morstatter, Fred (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2015-05
Description
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 detection: extremist ideal diffusion through bots.
ContributorsKarlsrud, Mark C. (Author) / Liu, Huan (Thesis director) / Morstatter, Fred (Committee member) / Barrett, The Honors College (Contributor) / Computing and Informatics Program (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2015-05
Description
This paper explores how US Cold War nuclear testing in the Pacific Islands has been approached in three different regions \u2014affected Pacific Islands, the US, and Japan. Because the US has failed to adequately address its nuclear past in the Pacific Islands, and Pacific Islander narratives struggle to reach the international community on their own, my study considers the possibility of Pacific Islanders finding greater outlet for their perspectives within dominant Japanese narratives, which also feature nuclear memory. Whereas the US government has remained largely evasive and aloof about the consequences of its nuclear testing in the Pacific, Japan encourages active, anti-nuclear war memory that could be congruent with Pacific Islander interests. After examining historical events, surrounding context, and prevailing sentiments surrounding this issue in each region however, my study finds that even within Japanese narratives, Pacific Islander narratives can only go so far because of Japan's own nuclear power industry, its hierarchical relationship with the Pacific Islands, and Japan's strong ties to the US in what can be interpreted as enduring Cold War politics.
ContributorsHinze, Rie Victoria (Author) / Benkert, Volker (Thesis director) / Moore, Aaron (Committee member) / School of International Letters and Cultures (Contributor) / Computer Science and Engineering Program (Contributor) / School of Politics and Global Studies (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
Description
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, we have developed in collaboration with FDA analysts some image analysis-based machine intelligence to achieve species identification with up to 90% accuracy. The current project is a continuation of this development effort. Here we present an image analysis environment that allows practical deployment of the machine intelligence on computers with limited processing power and memory. Using this environment, users can prepare input sets by selecting images for analysis, and inspect these images through the integrated pan, zoom, and color analysis capabilities. After species analysis, the results panel allows the user to compare the analyzed images with referenced images of the proposed species. Further additions to this environment should include a log of previously analyzed images, and eventually extend to interaction with a central cloud repository of images through a web-based interface. Additional issues to address include standardization of image layout, extension of the feature-extraction algorithm, and utilizing image classification to build a central search engine for widespread usage.
ContributorsMartin, Daniel Luis (Author) / Ahn, Gail-Joon (Thesis director) / Doupé, Adam (Committee member) / Xu, Joshua (Committee member) / Computer Science and Engineering Program (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
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 this thesis, we use machine learning classification techniques to determine the effectiveness of a movie trailer in the promotion of its namesake. We accomplish this by creating a predictive model that automatically analyzes the audio and visual characteristics of a movie trailer to determine whether or not a film's opening will be successful by earning at least 35% of a film's production budget during its first U.S. box office weekend. Our predictive model performed reasonably well, achieving an accuracy of 68.09% in a binary classification. Accuracy increased to 78.62% when including genre in our predictive model.
ContributorsWilliams, Terrance D'Mitri (Author) / Pon-Barry, Heather (Thesis director) / Zafarani, Reza (Committee member) / Maciejewski, Ross (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2014-05
Description
In the sixty-seven years following the end of World War II, West Germany and Japan underwent a remarkable series of economic and social changes that irrevocably altered their respective ways of life. Formerly xenophobic, militaristic and highly socially stratified societies, both emerged from the 20th Century as liberal, prosperous and free. Both made great strides well beyond the expectations of their occupiers, and rebounded from the overwhelming destruction of their national economies within a few short decades. While these changes have yielded dramatic results, the wartime period still looms large in their respective collective memories. Therefore, an ongoing and diverse dialectical process would engage the considerable popular, official, and intellectual energy of their post-war generations. In West Germany, the term Vergangenheitsbewältigung (VGB) emerged to describe a process of coming to terms with the past, while the Japanese chose kako no kokufuku to describe their similar historical sojourns. Although intellectuals of widely varying backgrounds in both nations made great strides toward making Japanese and German citizens cognizant of the roles that their militaries played in gruesome atrocities, popular cinematic productions served to reiterate older, discredited assertions of the fundamental honor and innocence of the average soldier, thereby nurturing a historically revisionist line of reasoning that continues to compete for public attention. All forms of media would play an important role in sustaining this “apologetic narrative,” and cinema, among the most popular and visible of these mediums, was not excluded from this. Indeed, films would play a unique recurring role, like rhetorical time capsules, in offering a sanitized historical image of Japanese and German soldiers that continues to endure in modern times. Nevertheless, even as West Germany and Japan regained their sovereignty and re-examined their pasts with ever greater resolution and insight, their respective film industries continued to “reset” the clock, and accentuated the visibility and relevancy of apologetic forces still in existence within both societies. However, it is important to note that, when speaking of “Germans” and “Japanese,” that they are not meant to be thought of as being uniformly of one mind or another. Rather, the use of these words is meant as convenient shorthand to refer to the dominant forces in Japanese and German civil society at any given time over the course of their respective post- war histories. Furthermore, references to “Germany” during the Cold War period are to be understood to mean the Federal Republic of Germany, rather than their socialist counterpart, the German Democratic Republic, a nation that undertook its own coming to terms with the past in an entirely distinct fashion.
ContributorsPiscopo, Michael (Author) / Benkert, Volker (Thesis director) / Moore, Aaron (Committee member) / Machander, Sina (Committee member) / Barrett, The Honors College (Contributor) / College of Liberal Arts and Sciences (Contributor)
Created2012-12
Description
With the development of technology, there has been a dramatic increase in the number of machine learning programs. These complex programs make conclusions and can predict or perform actions based off of models from previous runs or input information. However, such programs require the storing of a very large amount of data. Queries allow users to extract only the information that helps for their investigation. The purpose of this thesis was to create a system with two important components, querying and visualization. Metadata was stored in Sedna as XML and time series data was stored in OpenTSDB as JSON. In order to connect the two databases, the time series ID was stored as a metric in the XML metadata. Queries should be simple, flexible, and return all data that fits the query parameters. The query language used was an extension of XQuery FLWOR that added time series parameters. Visualization should be easily understood and be organized in a way to easily find important information and details. Because of the possibility of a large amount of data being returned from a query, a multivariate heat map was used to visualize the time series results. The two programs that the system performed queries on was Energy Plus and Epidemic Simulation Data Management System. By creating such a system, it would be easier for people of the project's fields to find the relationship between metadata that leads to the desired results over time. Over the time of the thesis project, the overall software was completed, however the software must be optimized in order to take the enormous amount of data expected from the system.
ContributorsTse, Adam Yusof (Author) / Candan, Selcuk (Thesis director) / Chen, Xilun (Committee member) / Barrett, The Honors College (Contributor) / School of Music (Contributor) / Computer Science and Engineering Program (Contributor)
Created2015-05
Description
In this paper, I introduce the fake news problem and detail how it has been exacerbated<br/>through social media. I explore current practices for fake news detection using natural language<br/>processing and current benchmarks in ranking the efficacy of various language models. Using a<br/>Twitter-specific benchmark, I attempt to reproduce the scores of six language models<br/>demonstrating their effectiveness in seven tweet classification tasks. I explain the successes and<br/>challenges in reproducing these results and provide analysis for the future implications of fake<br/>news research.
ContributorsChang, Ariz Bay (Author) / Liu, Huan (Thesis director) / Tahir, Anique (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
This paper is centered on the use of generative adversarial networks (GANs) to convert or generate RGB images from grayscale ones. The primary goal is to create sensible and colorful versions of a set of grayscale images by training a discriminator to recognize failed or generated images and training a generator to attempt to satisfy the discriminator. The network design is described in further detail below; however there are several potential issues that arise including the averaging of a color for certain images such that small details in an image are not assigned unique colors leading to a neutral blend. We attempt to mitigate this issue as much as possible.
ContributorsMarkabawi, Jah (Co-author) / Masud, Abdullah (Co-author) / Lobo, Ian (Co-author) / Koleber, Keith (Co-author) / Yang, Yingzhen (Thesis director) / Wang, Yancheng (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
This paper is centered on the use of generative adversarial networks (GANs) to convert or generate RGB images from grayscale ones. The primary goal is to create sensible and colorful versions of a set of grayscale images by training a discriminator to recognize failed or generated images and training a generator to attempt to satisfy the discriminator. The network design is described in further detail below; however there are several potential issues that arise including the averaging of a color for certain images such that small details in an image are not assigned unique colors leading to a neutral blend. We attempt to mitigate this issue as much as possible.
ContributorsMasud, Abdullah Bin (Co-author) / Koleber, Keith (Co-author) / Lobo, Ian (Co-author) / Markabawi, Jah (Co-author) / Yang, Yingzhen (Thesis director) / Wang, Yancheng (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05