RDA and Linked Data: A New Way to Look at the Henson Collection

In 2005, the University of Maryland acquired over 70 digital videos spanning 35 years of Jim Henson’s groundbreaking work in television and film. To support in-house discovery and use, the collection was cataloged in detail using AACR2 and MARC21, and a web-based finding aid was also created. In the past year, I created an "r-ball" (a linked data set described using RDA) of these same resources. The presentation will compare and contrast these three ways of accessing the Jim Henson Works collection, with insights gleaned from providing resource discovery using RIMMF (RDA in Many Metadata Formats).

Towards Proactive Context-aware Computing and Systems

A primary goal of context-aware systems is delivering the right information at the right place and right time to users in order to enable them to make effective decisions and improve their quality of life. There are three key requirements for achieving this goal: determining what information is relevant, personalizing it based on the users’ context (location, preferences, behavioral history etc.), and delivering it to them in a timely manner without an explicit request from them. These requirements create a paradigm that we term as “Proactive Context-aware Computing”. Most of the existing context-aware systems fulfill only a subset of these requirements. Many of these systems focus only on personalization of the requested information based on users’ current context. Moreover, they are often designed for specific domains. In addition, most of the existing systems are reactive - the users request for some information and the system delivers it to them. These systems are not proactive i.e. they cannot anticipate users’ intent and behavior and act proactively without an explicit request from them. In order to overcome these limitations, we need to conduct a deeper analysis and enhance our understanding of context-aware systems that are generic, universal, proactive and applicable to a wide variety of domains. To support this dissertation, we explore several directions. Clearly the most significant sources of information about users today are smartphones. A large amount of users’ context can be acquired through them and they can be used as an effective means to deliver information to users. In addition, social media such as Facebook, Flickr and Foursquare provide a rich and powerful platform to mine users’ interests, preferences and behavioral history. We employ the ubiquity of smartphones and the wealth of information available from social media to address the challenge of building proactive context-aware systems. We have implemented and evaluated a few approaches, including some as part of the Rover framework, to achieve the paradigm of Proactive Context-aware Computing. Rover is a context-aware research platform which has been evolving for the last 6 years. Since location is one of the most important context for users, we have developed ‘Locus’, an indoor localization, tracking and navigation system for multi-story buildings. Other important dimensions of users’ context include the activities that they are engaged in. To this end, we have developed ‘SenseMe’, a system that leverages the smartphone and its multiple sensors in order to perform multidimensional context and activity recognition for users. As part of the ‘SenseMe’ project, we also conducted an exploratory study of privacy, trust, risks and other concerns of users with smart phone based personal sensing systems and applications. To determine what information would be relevant to users’ situations, we have developed ‘TellMe’ - a system that employs a new, flexible and scalable approach based on Natural Language Processing techniques to perform bootstrapped discovery and ranking of relevant information in context-aware systems. In order to personalize the relevant information, we have also developed an algorithm and system for mining a broad range of users’ preferences from their social network profiles and activities. For recommending new information to the users based on their past behavior and context history (such as visited locations, activities and time), we have developed a recommender system and approach for performing multi-dimensional collaborative recommendations using tensor factorization. For timely delivery of personalized and relevant information, it is essential to anticipate and predict users’ behavior. To this end, we have developed a unified infrastructure, within the Rover framework, and implemented several novel approaches and algorithms that employ various contextual features and state of the art machine learning techniques for building diverse behavioral models of users. Examples of generated models include classifying users’ semantic places and mobility states, predicting their availability for accepting calls on smartphones and inferring their device charging behavior. Finally, to enable proactivity in context-aware systems, we have also developed a planning framework based on HTN planning. Together, these works provide a major push in the direction of proactive context-aware computing.

Approaching and Executing Premiere, Interpretive, and Historical Works

The spectrum of vocal music spans time, genres, styles, and is infinitely vast. New works are ever evolving and expanding, new artistic ideas are revealed from older works, and interest renewed from the tried and true. As a vocal musician in present day, I aspired to find a common thread amidst the boundless spectrum of works to be performed—whether I was hearkening back to a time of old, dissecting pieces by composers who have opened the door to personal artistry, or learning to sing a new work never performed or heard before. The Mercuriality of Song unearths more differences than commonalities in preparation, despite the fact that my voice remains the constant— differences which were expected, often surprising, but nevertheless new and rewarding in their challenges. Three performances (a world-premiere, a lieder recital, and an early music recital) comprise the basis for my investigation into comparing methods and processes of different periods via program notes, laying the foundation for initial preparation from an historical context. An amalgam of genres and stylistic differences along with performance planning culminate this exploration of vocal discovery and implementation.