SPINNING NARRATIVES ACROSS POLITICAL DIVIDES: HARNESSING THE CULTURAL POWER OF A STORY WELL-TOLD

This dissertation explores how two American storytellers, considered by many in their to be exemplary in their craft, rely on narrative strategies to communicate to their audiences on divisive political topics in a way that both invokes feelings of pleasure and connection and transcends party identification and ideological divides. Anna Quindlen, through her political columns and op-eds, and Aaron Sorkin, through his television show The West Wing, have won over a politically diverse fan base in spite of the fact that their writing espouses liberal political viewpoints. By telling stories that entertain, first and foremost, Quindlen and Sorkin are able to have a material impact on their audiences on both dry and controversial topics, accomplishing that which 19th Century writer and activist Harriet Farley made her practice: writing in such a way to gain the access necessary to “do good by stealth.” This dissertation will argue that it is their skilled use of storytelling elements, which capitalize on the cultural relationship humans have with storytelling, that enables Quindlen and Sorkin to achieve this. The dissertation asks: How do stories shape the beliefs, perspectives, and cognitive functions of humans? How do stories construct culture and interact with cultural values? What is the media’s role in shaping society? What gives stories their power to unite as a medium? What is the significance of the experience of reading or hearing a well-told story, of how it feels? What are the effects of Quindlen’s and Sorkin’s writing on audience members and the political world at large? What is lost when a simplistic narrative structure is followed? Who is left out and what is overlooked? The literature that informs the answers to these questions will cross over and through several academic disciplines: American Studies, British Cultural Studies, Communication, Folklore, Journalism, Literature, Media Studies, Popular Culture, and Social Psychology. The chapters will also explore scholarship on the subjects of narratology and schema theory.

DISSIMILAR CONTEMPORARIES: SELECTED PIANO MUSIC OF SERGEI RACHMANINOFF AND SERGEI PROKOFIEV

Virtual contemporaries, Sergei Rachmaninoff and Sergei Prokofiev were pianists, steeped in the traditions of Russian pianism; recordings of both pianists-composers playing their own works are available. Although the composers can be perceived as having little in common, in fact both composed in classical forms, both had a strong lyrical sense and both had an unbreakable connection with their Russian heritage. Rachmaninoff was the last great representative of Russian late Romanticism as well as one of the finest pianists of his generation. He cultivated a sweepingly passionate and melodious idiom, with pronounced lyrical quality, expressive breath and structural ingenuity. Prokofiev, on the other hand, tried to push the Russian Romantic traditions to a point of exacerbation and caricature before experimenting with various kinds of modernism. Stressing simplicity, he helped invent Neo-Classicism. His melodies are essentially tonal with wide skips and sweeping long lines. Harmonically, he used triadic harmony full of dissonances, strange inversions, unusual spacings, and jarring juxtapositions. Writing in classical forms, he incorporated rhythmic vitality and lyrical elements into his music. I have chosen to perform five works by each composer, written in a variety of genres, including the sonata, the toccata, variations, the concerto. I also have divided the pieces into three recital programs to show the idiosyncratic characteristics of the composers. I have endeavored to select pieces based on the technical and artistic challenges that they offer, thereby allowing me to grow as a pianist and an artist. My goal is to gain a thorough understanding of not only the pieces but also the musical styles of both composers.

User Behavioral Modeling of Web-based Systems for Continuous User Authentication

Authentication plays an important role in how we interact with computers, mobile devices, the web, etc. The idea of authentication is to uniquely identify a user before granting access to system privileges. For example, in recent years more corporate information and applications have been accessible via the Internet and Intranet. Many employees are working from remote locations and need access to secure corporate files. During this time, it is possible for malicious or unauthorized users to gain access to the system. For this reason, it is logical to have some mechanism in place to detect whether the logged-in user is the same user in control of the user's session. Therefore, highly secure authentication methods must be used. We posit that each of us is unique in our use of computer systems. It is this uniqueness that is leveraged to "continuously authenticate users" while they use web software. To monitor user behavior, n-gram models are used to capture user interactions with web-based software. This statistical language model essentially captures sequences and sub-sequences of user actions, their orderings, and temporal relationships that make them unique by providing a model of how each user typically behaves. Users are then continuously monitored during software operations. Large deviations from "normal behavior" can possibly indicate malicious or unintended behavior. This approach is implemented in a system called Intruder Detector (ID) that models user actions as embodied in web logs generated in response to a user's actions. User identification through web logs is cost-effective and non-intrusive. We perform experiments on a large fielded system with web logs of approximately 4000 users. For these experiments, we use two classification techniques; binary and multi-class classification. We evaluate model-specific differences of user behavior based on coarse-grain (i.e., role) and fine-grain (i.e., individual) analysis. A specific set of metrics are used to provide valuable insight into how each model performs. Intruder Detector achieves accurate results when identifying legitimate users and user types. This tool is also able to detect outliers in role-based user behavior with optimal performance. In addition to web applications, this continuous monitoring technique can be used with other user-based systems such as mobile devices and the analysis of network traffic.