Mobile devices have acceleratedly penetrated into our daily lives. Though they were originally designed as a communication tool or for personal use, and due to the rapid availability of wireless network technologies, people have begun to use mobile devices for supporting collaborative work and learning. There is, however, a serious problem in mobile devices related to their user interfaces. In this paper, we try to alleviate the problem and propose intuitive techniques for information transfer, which is one of the typical usages of mutually-connected computers. Our system, Toss-It, enables a user to send information from the user's PDA to other electronic devices with a "toss" or "swing" action, like a user would toss a ball or deal cards to others. The implementation of Toss-It consists of three principle parts-gesture recognition, location recognition, and file transfer. We especially describe the details of gesture recognition and location recognition. We then evaluate the practicability and usability of Toss-It through the experiments. We also discuss user scenarios describing how Toss-It can support users' collaborative activities.
This paper describes a new manipulation technique for small-screen mobile devices. The proposed technique, called HybridTouch, uses a touchpad attached to the rear surface of a PDA. A user can manipulate the PDA by simultaneously touching the front surface with a stylus pen held by the dominant hand and the rear surface with a finger of the nondominant hand. User studies were conducted via applications augmented by HybridTouch, and proved that users could perform manipulation tasks intuitively.
We propose a computing platform for parallel machine learning. Learning from large-scale data has become common, so that parallelization techniques are increasingly applied to machine learning algorithms in order to reduce calculation time. Problems of parallelization are implementation costs and calculation overheads. Firstly, we formulate MapReduce programming model specialized in parallel machine learning. It represents learning algorithms as iterations of following two phases: applying data to machine learning models and updating model parameters. This model is able to describe various kinds of machine learning algorithms, such as k-means clustering, EM algorithm, and linear SVM, with comparable implementation cost to the original MapReduce. Secondly, we propose a fast machine learning platform which reduces the processing overheads at iterative procedures of machine learning. Machine learning algorithms iteratively read the same training data in the data application phase. Our platform keeps the training data in local memories of each worker during iterative procedures, which leads to acceleration of data access. We evaluate performance of our platform on three experiments. Our platform executes k-means clustering 2.85 to 118 times faster than the MapReduce approach, and shows 9.51 times speedup with 40 processing cores against 8 cores. We also show the performance of Variational Bayes clustering and linear SVM implemented on our platform.
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