Prior research on text collections of religious documents has demonstrated that viable recommender systems in the area are lacking, if not non-existent, for some datasets. For example, both www.LDS.org and scriptures.byu.edu are websites designed for religious use. Although they provide users with the ability to search for documents based on keywords, they do not provide the ability to discover documents based on similarity. Consequently, these systems would greatly benefit from a recommender system. This work provides a framework for evaluating recommender systems and is flexible enough for use with either website. Such a framework would identify the best recommender system that provides users another way to explore and discover documents related to their current interests, given a starting document. The framework created for this thesis, RelRec, is attractive because it compares two different recommender systems. Documents are considered relevant if they are among the nearest neighbors, where "nearest" is defined by a particular system's similarity formula. We use RelRec to compare output of two particular recommender systems on our selected data collection. RelRec shows that LDA recommeder outperforms the TF-IDF recommender in terms of coverage, making it preferable for LDS-based document collections.

Thesis (M.A. (African Languages)) --University of the North, 2002 / Refer to the document

Applying data analytics for talent acquisition and retention has been identified as one of the most urgent challenges facing HR leaders around the world; however, it is also one of the challenges that firms are least prepared to tackle. Our research strives to narrow such a capability gap between the urgency and readiness of data-driven human resource management. First, we predict interfirm competitors for human capital in the labor market utilizing the rich information contained in over 89,000 LinkedIn users' profiles. Using employee migrations across firms, we derive and analyze a human capital flow network. We leverage this network to extract global cues about interfirm human capital overlap through structural equivalence and community classification. The online employee profiles also provide rich data on the explicit knowledge base of firms and allow us to measure the interfirm human capital overlap in terms of similarity in their employees' skills. We validate our proposed human capital overlap metrics in a predictive analytics framework using future employee migrations as an indicator of labor market competition. The results show that our proposed metrics have superior predictive power over conventional firm-level economic and human resource measures. Second, we estimate the effect of skilled immigrants on the native U.S. workers' turnover probability. We apply unsupervised machine learning to categorize employees' self-reported skills and find that skilled immigrants disproportionately specialize in IT. In contrast, the native workers predominantly focus on management and analyst skills. Utilizing the randomness in the H-1B visa lottery system and a 2SLS design, we find that a 1 percentage point increase in a firm's proportion of skilled immigrant employees leads to a decrease of 0.69 percentage points in a native employee's turnover risk. However, this beneficial crowding-in effect varies for native workers with different skills. Our methodology highlights the need to account for a multifaceted view of the skilled immigration's effect on native workers. Finally, we also propose a set of features and models that are able to effectively predict future employee turnover outcomes. Our predictive models can provide significant utility to managers by identifying individuals with the highest turnover risks.

This thesis is grounded in my work done through the Harvey Mudd College Clinic Program as Project Manager of the PilotCity Clinic Team. PilotCity is a startup whose mission is to transform small to mid-sized cities into centers of innovation by introducing employer partnerships and work-based learning to high school classrooms. The team was tasked with developing software and algorithms to automate PilotCity's programming and to extract educational insights from unstructured data sources like websites, syllabi, resumes, and more. The team helped engineer a web application to expand and facilitate PilotCity's usership, designed a recommender system to automate the process of matching employers to high school classrooms, and packaged a topic modeling module to extract educational priorities from more complex data such as syllabi, course handbooks, or other educational text data. Finally, the team explored automatically generating supplementary course resources using insights from topic models. This thesis will detail the team's process from beginning to final deliverables including the methods, implementation, results, challenges, future directions, and impact of the project.

What do politicians talk about when discussing foreign affairs? Are these topics different from the ones in the newspapers? Finally, can unsupervised methods be used to help us understand these problems? Answering these questions is of paramount importance to understanding the relationship between foreign policy and mass media. Based on this discussion, this research has three main objectives: (a) to verify whether unsupervised methods can be used to analyze documents on international issues; (b) to understand the issues that politicians talk about when dealing with foreign affairs; and (c) to understand when and with which periodicity the mass media publish news on certain international topics. To do so, I created two new corpora, one with news articles published in the international section of two major Brazilian newspapers; and a corpus with all speeches made within the two Committees on Foreign Affairs of the National Congress of Brazil. I ran a topic model using Latent Dirichlet Allocation (LDA) in both. The results of this topic model show that LDA can be used to distinguish different international issues that appear in both political discourse and the mass media in Brazil. Additionally, I found that the LDA model can be used to identify when some topics are debated and for how long. The findings also demonstrate that Brazilian politicians and Brazilian newspapers are neither isolated nor unstable in what regards international issues. / Sobre o que os políticos falam quando discutem temas internacionais? Esses tópicos são diferentes daqueles que aparecem nos jornais? Finalmente, métodos não supervisionados podem ser usados para nos ajudar a entender esses problemas? Responder a essas perguntas é de suma importância para entender a relação entre política externa e mídia de massa. Com base nessa discussão, esta pesquisa tem três objetivos principais: (a) verificar se os métodos não supervisionados podem ser usados para analisar documentos sobre questões internacionais; (b) compreender sobre que assuntos os políticos falam quando lidam com relações exteriores; e (c) entender quando e por quanto tempo a mídia de massa publica notícias sobre determinados tópicos internacionais. Para tanto, eu criei dois novos corpora, um com notícias publicadas no caderno internacional de dois dos principais jornais brasileiros; e um corpus com todos os discursos feitos dentro das duas Comissões de Relações Exteriores do Congresso Brasileiro. Executei um modelo de tópico usando Latent Dirichlet Allocation (LDA) em ambos. Os resultados desse modelo de tópico mostram que ele pode ser usado para distinguir diferentes questões internacionais que aparecem tanto no discurso político como na mídia de massa no Brasil. Além disso, o modelo pode ser usado para identificar quando alguns tópicos são debatidos e por quanto tempo. Os resultados também demonstram que tanto os políticos como os jornais brasileiros não são isolados nem instáveis em relação a questões internacionais.

The main purpose of this study was to compare the strategies used by Chinese- speaking students when confronted with familiar versus unfamiliar topics in a multiple-choice format reading comprehension test. The focus was on describing what students do when they are taking reading comprehension tests by asking students to verbalize their thoughts. The strategies were further compared with participants' level of familiarity with different reading topics and their reading scores. Twenty Chinese-speaking participants at the University of Iowa performed three tasks: a topical knowledge vocabulary assessment that served as an indicator of each participant's topical knowledge about the four selected content areas in this study (law, business, language teaching, and engineering); two Test of English as a Foreign Language (TOEFL) internet-based test (iBT) practice reading comprehension passages, one with a familiar topic and the other with an unfamiliar topic, and both with retrospective think-aloud protocols; and an interview related to participants' test-taking strategies. Two stages of analysis, qualitative and quantitative, were undertaken in this study. For the qualitative analysis, all verbal reports provided by participants in the think-aloud protocols and the interviews were recorded and transcribed. Six categories of strategies emerged: general approaches to reading the passages, identification of important information by the discourse structure of the passages, vocabulary/sentence-in-context approaches, multiple-choice test-management strategies, test-wiseness, and background knowledge. For the quantitative analysis, an analysis of variance (ANOVA) with repeated measures was completed to determine if there were significant differences based on the frequency of strategy use and level of topic familiarity. The results showed that the types of test-taking strategies adopted by Chinese-speaking graduate students remained similar when they read passages with familiar versus unfamiliar topics. However, participants all reported feeling more relief and more confidence when reading passages related to their background knowledge. The second ANOVA employed a split-plot statistical design to examine whether there were significant differences based on participants' strategy use and their reading scores as measured by the iBT reading comprehension tests. High scorers employed strategies in categories one, two, three, and four significantly more frequently than low scorers. However, low scorers adopted significantly more strategies in category five than high scorers. In category six, high and low scorers seemed to use a similar number of strategies. Findings that emerged from the two perspectives are discussed; implications related to test-taking and reading pedagogy are provided in the conclusion.

Phishing is a social engineering attack to deceive and persuade people to divulge private information like usernames and passwords, account details (including bank account details), and social security numbers. Phishers typically utilize e-mail, chat, text messages, or social media. Despite the presence of automatic anti-phishing filters, phishing messages reach online users' inboxes. Understanding the influence of phishing techniques and individual differences on susceptibility and detection accuracy is an important step toward creating comprehensive behavioral and organizational anti-phishing awareness programs. This dissertation seeks to achieve a dual purpose in a series of three essays. Essay 1 seeks to explore the nature of phishing threats that including identifying attack intentions, and psychological and design techniques of phishing attacks. Essay 2 seeks to understand the relative influence of attack techniques and individual phishing experiential traits on people's phishing susceptibility. Essay 3 seeks to understand an individual's cognitive and affective differences that differentiate between an individual's phishing detection accuracy.

The most recent trend in the studies of LF intervention effects makes crucial reference to focusing effects on the interveners, and this paper critically examines the representative analyses of the focus-based approach. While each analysis has its own merits and shortcomings, I argue that a pragmatic analysis that does not make appeal to syntactic configurations is better equipped to deal with many of the complex and delicate facts surrounding intervention effects.

The paper investigates focus marking devices in the scarcely documented North-Ghanaian Gur language Konkomba. The two particles lé and lá occur under specific focus conditions and are therefore regarded as focus markers in the sparse literature. Comparing the distribution and obligatoriness of both alleged focus markers however, I show that one of the particles, lé, is better analyzed as a connective particle, i.e. as a syntactic rather than as a genuine pragmatic marker, and that comparable syntactic focus marking strategies for sentence-initial constituents are also known from related languages.

The aim of this study was to quantitatively measure refutation text's power for conceptual change while qualitatively discovering students' preference of refutation or expository text structures. This study also sought to examine if religious interest levels predict conceptual change. Participants for this study were 9th, 10th-, 11th-, and 12th-grade seminary students from the private religious educational system of The Church of Jesus Christ of Latter-day Saints (LDS). The study was conducted in two sessions. Session 1 involved pretesting, interventions, and posttesting. Session 2 involved delayed posttesting and participant interviews. Results were predominately measured quantitatively with some qualitative interview analysis added to enrich the study. This research study provides insight into the refutation text effects in LDS religious education. Results of the study showed significant differences in conceptual change between participants reading refutation texts and those reading expository texts. In every case, the refutation text group performed higher on posttests than did the expository group. Results also showed participant preference toward refutation text structures. Furthermore, the study found significant correlations that verify topic interest as a possible predictor of conceptual change. Insights are valuable in aiding curriculum developers in implementing effective ways to teach doctrinal principles by utilizing refutation text interventions. The advantages of this research study add to educational research and identify areas for improvement and exploration in further research. This study of refutation text effects in religious education also broadens researchers' understanding of refutation text's power for conceptual change in subjects outside of K-12 science. Results of this study are of interest to researchers, teachers, curriculum writers, and LDS seminary teachers and administrators.