In this work, we showcase a set of implemented multiplayer games and interactive installations based on Fun in Numbers (FinN). FinN allows the quick prototyping of applications that utilize input from multiple physica...
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ISBN:
(纸本)9781605588643
In this work, we showcase a set of implemented multiplayer games and interactive installations based on Fun in Numbers (FinN). FinN allows the quick prototyping of applications that utilize input from multiple physical sources (sensors and other means of interfacing), by offering a set of programming templates and services, such as proximity, localization and synchronization, that hide the underlying complexity.
Generic web search is designed to serve all users, independent of the individual needs and without any adaptation to personal requirements. We propose a novel technique1 that performs post-categorization to the result...
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ISBN:
(纸本)0769522742
Generic web search is designed to serve all users, independent of the individual needs and without any adaptation to personal requirements. We propose a novel technique1 that performs post-categorization to the results of popular search engines at the client's side. A user profile is built based on user's choices from a category hierarchy (explicitly given requirements) and user's search history (implicitly logged choices). Caching is utilized in order to provide improved responses. An experimental prototype has been implemented based on results coming from a popular search engine. The experimental results indicate strongly that the proposed mechanism is both effective and efficient.
This paper studies speech-driven Web retrieval models which accepts spoken search topics (queries) in the NTCIR-3 Web retrieval task. The major focus of this paper is on improving speech recognition accuracy of spoken...
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This paper studies speech-driven Web retrieval models which accepts spoken search topics (queries) in the NTCIR-3 Web retrieval task. The major focus of this paper is on improving speech recognition accuracy of spoken queries then improving retrieval accuracy in speech-driven Web retrieval. We experimentally evaluate the techniques of combining outputs of multiple LVCSR models in recognition of spoken queries. As model combination techniques, we compare the SVM learning technique conventional voting schemes such as ROVER. We show that the techniques of multiple LVCSR model combination can achieve improvement both in speech recognition and retrieval accuracies in speech-driven text retrieval. We also show that model combination by SVM learning outperforms conventional voting schemes both in speech recognition retrieval accuracies.
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