Physical Sciences and Mathematics Commons^™

Automating Ciri Ratings Of Human Rights Reports Using Gate, Joshua M. Joiner

UNF Graduate Theses and Dissertations

This thesis involves parsing document-based reports from the United States Human Rights Reports and rating the human practices for various countries based on the CIRI (Cingranelli-Richards) Human Rights Data Project dataset. The United States Human Rights Reports are annual reports that cover internationally recognized human rights practices regarding individual, civil, political, and worker rights. Students, scholars, policymakers, and analysts used the CIRI data for practical and research purposes. CIRI analyzed the annual reports from 1981 to 2011 and then stopped releasing the dataset for any further years, but a possible reason is due to the manual process of scouring the …

Extracting A Relational Database Schema From A Document Database, Jared Thomas Wheeler

UNF Graduate Theses and Dissertations

As NoSQL databases become increasingly used, more methodologies emerge for migrating from relational databases to NoSQL databases. Meanwhile, there is a lack of methodologies that assist in migration in the opposite direction, from NoSQL to relational. As software is being iterated upon, use cases may change. A system which was originally developed with a NoSQL database may accrue needs which require Atomic, Consistency, Isolation, and Durability (ACID) features that NoSQL systems lack, such as consistency across nodes or consistency across re-used domain objects. Shifting requirements could result in the system being changed to utilize a relational database. While there are …

A Hybrid Approach To Music Recommendation: Exploiting Collaborative Music Tags And Acoustic Features, Jaime C. Kaufman

UNF Graduate Theses and Dissertations

Recommendation systems make it easier for an individual to navigate through large datasets by recommending information relevant to the user. Companies such as Facebook, LinkedIn, Twitter, Netflix, Amazon, Pandora, and others utilize these types of systems in order to increase revenue by providing personalized recommendations. Recommendation systems generally use one of the two techniques: collaborative filtering (i.e., collective intelligence) and content-based filtering.

Systems using collaborative filtering recommend items based on a community of users, their preferences, and their browsing or shopping behavior. Examples include Netflix, Amazon shopping, and Last.fm. This approach has been proven effective due to increased popularity, and …