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A common challenge faced by students is that they often have questions about course material that they cannot ask during lecture time. There are many ways for students to have these questions answered, such as office hours and online discussion boards. However, office hours may be at inconvenient times or locations, and online discussion boards are difficult to navigate and may be inactive. The purpose of this project was to create an Alexa skill that allows users to ask their Alexa-equipped device a question concerning their course material and to receive an answer retrieved from discussion board data. User questions are mapped to discussion board posts by use of the cosine similarity algorithm. In this algorithm, posts from the discussion board and the user’s question are converted into mathematical vectors, with each term in the vector corresponding to a word. The values of these terms are computed based on the word’s frequency within the vector’s corresponding document, the frequency of that word within all the documents, and the length of the document. After the question and candidate posts are converted into vectors, the algorithm determines the post most similar to the user’s question by computing the angle between the vectors. With the most similar discussion board post determined, the user receives the replies to the post, if any, as their answer. Users are able to indicate to their Alexa device whether they were satisfied by the answer, and if they were unsatisfied then they are given the opportunity to either rephrase their question or to have the question sent to a database of unanswered questions. The professor can view and answer the questions in this database on a website hosted by use of Amazon’s Simple Storage Service. The Alexa skill does well at answering questions that have already been asked in the discussion board. However, the skill depends heavily on the user’s word choice. Two questions that are semantically identical but different in phrasing are often given different answers. This is because the cosine algorithm measures similarity on the basis of word overlap, not semantic meaning, and thus the application never truly “understands” what type of answer the user desires. Improving the performance of this Alexa skill will require a more advanced question answering algorithm, but the limitations of Amazon Web Services as a development platform make implementing such an algorithm difficult. Nevertheless, this project has created the basis of a question answering Alexa skill by demonstrating a feasible way that the resources offered by Amazon can be utilized in order to build such an application.
ContributorsBaker, Matthew Elias (Author) / Chen, Yinong (Thesis director) / Balasooriya, Janaka (Committee member) / Historical, Philosophical & Religious Studies (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
The United States Supreme Court decided Ramos v. Louisiana in 2020, requiring all states to convict criminal defendants by a unanimous jury. However, this case only applied to petitioners on direct, and not collateral, appeal. In this thesis, I argue that the Ramos precedent should apply to people on collateral appeal as well, exploring the implications of such a decision and the criteria that should be used to make the decision in the case before the court, Edwards v. Vannoy (2021). Ultimately, I find that because the criteria currently used to determine retroactivity of new criminal precedents does not provide a clear answer to the question posed in Edwards, the Court should give more weight to the defendant's freedoms pursuant to the presumption of innocence while considering the potential for any disastrous outcomes.
ContributorsCaldwell, Rachel Lillian (Author) / Hoekstra, Valerie (Thesis director) / Bender, Paul (Committee member) / Historical, Philosophical & Religious Studies (Contributor) / School of Social Transformation (Contributor, Contributor) / Historical, Philosophical & Religious Studies, Sch (Contributor) / School of Politics and Global Studies (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
This study analyzed currently existing statute at the state, federal, and international level to ultimately build a criteria of recommendations for policymakers to consider when building regulations for facial recognition technology usage by law enforcement agencies within the United States.
ContributorsHong, Susan Suggi (Author) / Royal, K (Thesis director) / Marchant, Gary (Committee member) / Historical, Philosophical & Religious Studies (Contributor) / School of Politics and Global Studies (Contributor, Contributor) / Historical, Philosophical & Religious Studies, Sch (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
In this thesis, we analyze the case, Swain, et al. v. Bixby Village, et al., the Ahwatukee Lakes Golf Course case, and the legal findings surrounding it. First, this thesis examines the history of the case and its ongoing litigation. Next, the background information on select definitions and other related cases is examined. Finally, this thesis analyzes three main points addressed in the Appellate Court’s Opinion on the case and presents potential next steps and recommendations for an equitable solution on both sides of this and future cases concerning land restricted to golf course use.
ContributorsEngler, Joelle Samantha (Co-author) / Asher, Rebecca (Co-author) / Gammage, Grady (Thesis director) / Stapp, Mark (Committee member) / Cassidy, Delilah (Committee member) / Historical, Philosophical & Religious Studies (Contributor) / School of Accountancy (Contributor) / Department of Economics (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05