Abstractions to support dynamic adaptation of communication frameworks for user-centric communication

The convergence of data, audio and video on IP networks is changing the way individuals, groups and organizations communicate. This diversity of communication media presents opportunities for creating synergistic collaborative communications. This form of collaborative communication is however not without its challenges. The increasing number of communication service providers coupled with a combinatorial mix of offered services, varying Quality-of-Service and oscillating pricing of services increases the complexity for the user to manage and maintain ‘always best’ priced or performance services. Consumers have to manually manage and adapt their communication in line with differences in services across devices, networks and media while ensuring that the usage remain consistent with their intended goals. This dissertation proposes a novel user-centric approach to address this problem. The proposed approach aims to reduce the aforementioned complexity to the user by (1) providing high-level abstractions and a policy based methodology for automated selection of the communication services guided by high-level user policies and (2) providing services through the seamless integration of multiple communication service providers and providing an extensible framework to support the integration of multiple communication service providers. The approach was implemented in the Communication Virtual Machine (CVM), a model-driven technology for realizing communication applications. The CVM includes the Network Communication Broker, the layer responsible for providing a network-independent API to the upper layers of CVM. The initial prototype for the NCB supported only a single communication framework which limited the number, quality and types of services available. Experimental evaluation of the approach show the additional overhead of the approach is minimal compared to the individual communication services frameworks. Additionally the automated approach proposed out performed the individual communication services frameworks for cross framework switching.

User-centric music information retrieval

The rapid growth of the Internet and the advancements of the Web technologies have made it possible for users to have access to large amounts of on-line music data, including music acoustic signals, lyrics, style/mood labels, and user-assigned tags. The progress has made music listening more fun, but has raised an issue of how to organize this data, and more generally, how computer programs can assist users in their music experience. An important subject in computer-aided music listening is music retrieval, i.e., the issue of efficiently helping users in locating the music they are looking for. Traditionally, songs were organized in a hierarchical structure such as genre->artist->album->track, to facilitate the users’ navigation. However, the intentions of the users are often hard to be captured in such a simply organized structure. The users may want to listen to music of a particular mood, style or topic; and/or any songs similar to some given music samples. This motivated us to work on user-centric music retrieval system to improve users’ satisfaction with the system. The traditional music information retrieval research was mainly concerned with classification, clustering, identification, and similarity search of acoustic data of music by way of feature extraction algorithms and machine learning techniques. More recently the music information retrieval research has focused on utilizing other types of data, such as lyrics, user-access patterns, and user-defined tags, and on targeting non-genre categories for classification, such as mood labels and styles. This dissertation focused on investigating and developing effective data mining techniques for (1) organizing and annotating music data with styles, moods and user-assigned tags; (2) performing effective analysis of music data with features from diverse information sources; and (3) recommending music songs to the users utilizing both content features and user access patterns.