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AirTransNote augments activities in classrooms by sharing student notes written on regular paper using wireless communications. AirTransNote uses digital pens to free students from the need to use PCs for note taking. To improve the effectiveness of the system, we used a portable remote controller with an RFID tag reader. Teachers can select notes for students by using the controller as "magic wand." The selection interface is intuitive for both teachers and students. Also, the system can save time for setting up devices before lectures. The system promotes an augmented classroom, enabling interactive lectures in regular classrooms using natural styles.

In this study, we explore context-aware cross-device interactions between a smartphone and smartwatch. We present 24 contexts, and then examine and prioritize suitable user interfaces (UIs) for each. In addition, we present example applications, including a map, notification management system, multitasking application, music player, and video chat application, each of which has its own context-aware UIs. To support these context-aware UIs, we investigate the performance of our context recognizer in which recognition is based on machine-learning using the accelerometers in a smartphone and smartwatch. We conduct seven different evaluations using four machine-learning algorithms: J48 decision tree, sequential minimal optimization (SMO)-based support vector machine (SVM), random forest, and multilayer perceptron. With each algorithm, we conduct a long-interval experiment to examine the level of accuracy at which each context is recognized using data previously collected for training. The results show that SMO-based SVM is suitable for recognizing the 24 contexts considered in this study.

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