Titlebook: Ordinal Analysis with an Introduction to Proof Theory; Toshiyasu Arai Textbook 2020 The Editor(s) (if applicable) and The Author(s), under

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Toshiyasu Araites to the client. In the state of the art, it is clear that pushing delivers a lower latency compared to pulling, however, this has not been tested for an Open Data usage scenario where 15?k clients are not an exception. Also, there are no general guidelines when to use a polling or push-based appr

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Toshiyasu Araice. Voice-based interaction like dialogue systems is the current emerging trend in these systems. Natural Language Understanding (NLU) model can identify the end-users’ intentions in the utterances provided to spoken dialogue systems. The utility of dialogue systems is reliant on the quality of NLU

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ch can be developed, deployed, and scaled independently. The service decomposition is the act of decomposing (breaking) a coarse-grained service into a set of fine-grained services that collectively perform the functionality of the original service. The service decomposition introduces additional ov

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Toshiyasu Araimand structure, or are not publicly available in English language which limits the development of goal-oriented dialogue systems for smart homes. In this paper, we propose a generic method to create training data for the NLU component using a generative grammar-based approach. Our method outputs, Vo

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Toshiyasu Arai graph incrementally when the resources change. This paper proposes such a dependence graph-based, client-only resource preloading technique. For better user experience, we decide the preloading order based on those factors that affect user perception such as the size or the location of images in th

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Toshiyasu Araimand structure, or are not publicly available in English language which limits the development of goal-oriented dialogue systems for smart homes. In this paper, we propose a generic method to create training data for the NLU component using a generative grammar-based approach. Our method outputs, Vo

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Toshiyasu Araiat SSE excels above polling in three aspects: lower CPU usage on the server, lower latency on the client and more than double the number of clients that can be served. However, considering that users can perceive a certain maximum latency on the client (MAL) of an update acceptable, we describe in t

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