Matching Items (5)
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- All Subjects: Machine Learning
- Resource Type: Text
Description
In this work we analyze just what makes the topic of third party voting so intriguing to voters and why it is different than voting for one of the major parties in American politics. First, we will discuss briefly the history of politics in America and what makes it exciting. Next, we will outline some of the works by other political and economic professionals such as Hotelling, Lichtman and Rietz. Finally, using the framework described beforehand this paper will analyze the different stances that voters, candidates, and others involved in the political process of voting have regarding the topic of third party voting.
ContributorsMcElroy, Elizabeth (Co-author) / Beardsley, James (Co-author) / Foster, William (Thesis director) / Goegan, Brian (Committee member) / Department of Economics (Contributor) / School of International Letters and Cultures (Contributor) / Economics Program in CLAS (Contributor) / School of Politics and Global Studies (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Cannabis use has been purported to cause an amotivation-like syndrome among users. The purpose of this study was to investigate whether third party observers noticed amotivation among cannabis users. Participants in this study were 72 undergraduate university students, with a mean age of M=19.20 years old (SD=2.00). Participants nominated Informants who knew them well and these informants completed a version of the 18-item Apathy Evaluation Scale. Results indicated that more frequent cannabis use was associated with higher informant-reported levels of amotivation, even when controlling for age, sex, psychotic-like experiences, SES, alcohol use, tobacco use, other drug use, and depression symptoms (β=0.34, 95% CI: 0.04, 0.64, p=.027). A lack of motivation severe enough to be visible by a third party has the potential to have negative social impacts on individuals who use cannabis regularly.
ContributorsWhite, Makita Marie (Author) / Meier, Madeline (Thesis director) / Glenberg, Arthur (Committee member) / Pardini, Dustin (Committee member) / School of Art (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
2018, Google researchers published the BERT (Bidirectional Encoder Representations from Transformers) model, which has since served as a starting point for hundreds of NLP (Natural Language Processing) related experiments and other derivative models. BERT was trained on masked-language modelling (sentence prediction) but its capabilities extend to more common NLP tasks, such as language inference and text classification. Naralytics is a company that seeks to use natural language in order to be able to categorize users who create text into multiple categories – which is a modified version of classification. However, the text that Naralytics seeks to pull from exceed the maximum token length of 512 tokens that BERT supports – so this report discusses the research towards multiple BERT derivatives that seek to address this problem – and then implements a solution that addresses the multiple concerns that are attached to this kind of model.
ContributorsNgo, Nicholas (Author) / Carter, Lynn (Thesis director) / Lee, Gyou-Re (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / Economics Program in CLAS (Contributor)
Created2023-05
Description
A defense-by-randomization framework is proposed as an effective defense mechanism against different types of adversarial attacks on neural networks. Experiments were conducted by selecting a combination of differently constructed image classification neural networks to observe which combinations applied to this framework were most effective in maximizing classification accuracy. Furthermore, the reasons why particular combinations were more effective than others is explored.
ContributorsMazboudi, Yassine Ahmad (Author) / Yang, Yezhou (Thesis director) / Ren, Yi (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Economics Program in CLAS (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Using Machine Learning Classification Techniques to Predict Recessionary Periods in the U.S. Economy
Description
The goal of this research project is to determine how beneficial machine learning (ML) techniquescan be in predicting recessions. Past work has utilized a multitude of classification methods from Probit models to linear Support Vector Machines (SVMs) and obtained accuracies nearing 60-70%, where some models even predicted the Great Recession based off data from the previous 50 years. This paper will build on past work, by starting with less complex classification techniques that are more broadly used in recession forecasting and end by incorporating more complex ML models that produce higher accuracies than their more primitive counterparts. Many models were tested in this analysis and the findings here corroborate past work that the SVM methodology produces more accurate results than currently used probit models, but adds on that other ML models produced sufficient accuracy as well.
ContributorsHogan, Carter (Author) / McCulloch, Robert (Thesis director) / Pereira, Claudiney (Committee member) / Barrett, The Honors College (Contributor) / School of International Letters and Cultures (Contributor) / Economics Program in CLAS (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2022-05