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Smart home is one of the most important applications of ubiquitous computing. In this work, we propose an infrastructure of Vietnamese Smart homes as well as a training framework for activity recognition and forecast. In this framework, active learning technique is applied and a new mining algorithm is proposed. In addition to activity recognition, a forecast mechanism is also added into the smart...
Activity clustering and recognition is one of the most important research trends about smart home. Taking place inside a sensor smart home, activities differ from each other at typical characteristics such as sensor sets triggered as well as temporal ones. In this work, we present a smart home infrastructure and propose a method of calculating neighborhood radius for clustering and recognizing in-home...
In this paper we summarize AAIA'14 Data Mining Competition: Key risk factors for Polish State Fire Service which was held between February 3, 2014 and May 5, 2014 at the Knowledge Pit platform http://challenge.mimuw.edu.pl/. We describe the scope and background of this competition and we explain in details the evaluation procedure. We also briefly overview the results of this analytical challenge,...
This paper presents an improved hand tracking system using pixel-based hierarchical-feature AdaBoosting (PBHFA), skin color segmentation, and codebook (CB) background cancelation. The proposed PBH feature significantly reduces the training time by a factor of at least 1440 compared to the traditional Haar-like feature. Moreover, lower computation and high tracking accuracy are also provided simultaneously...
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