GPS data analysis is one of the main operators in geographical information systems.However, because of security and privacy issues, we often face situations where GPS data cannot be obtained frequently.Such situations and the measurement errors of GPS coordinates make identifying user behaviors challenging.In this work, we assume this setting and tackle the classification problem of target and non-target categories for the first time.Target categories are store categories in the scope of a service provider, whereas nontarget ones are those that are not in.Given a GPS point, this problem estimates which category this location belongs to, so it is a binary classification problem.This problem has two main difficulties.First, we cannot obtain labeled data of the non-target categories.Second, many GPS data have error ranges and no labels, i.e., they do not clarify where the users stay.To solve the problem while addressing these difficulties, we propose a new classification method based on machine learning.We exploit GPS and check-in data to obtain user feature vectors at a given time.Our loss function considers nonstay information on each store category to identify the non-target space in the feature space.From these techniques, we compute the probability of staying in one of the (non-)target categories.We conduct experiments on real-world datasets, and the results show the effectiveness of our method.

Order Dependencies (ODs) have many applications, such as query optimization, data integration, and data cleaning. Although many works addressed the problem of discovering OD (and its variants), they do not consider datasets with missing values, a standard observation in real-world datasets. This paper introduces the novel notion of Embedded ODs to deal with missing values, and we propose an efficient algorithm for validating embedded ODs. We conduct experiments on real-world datasets, and the results confirm the efficiency of our algorithm.

Wire-driven haptic devices can easily achieve highspeed operation because of the low inertia of light and thin wires. Therefore, it is expected to be widely used as a motion support system in fields such as sports training. However, usually these wires are driven by controlling each wire's tension using servo motors, which raises concerns about the safety in human-robot interaction. Thus, we propose to support the user by controlling servo brakes instead of motors, which can ensure safety. In this paper, we introduce a passive wire-driven system without any motors for realistic motion support in sports training. The system can measure the user motion and support the user while following a target form by a novel passive control method considering the orientation as well as the position using 7 wires' lengths and the brake tensions. We also conduct error evaluation experiments and show that a tennis beginner using this system can accurately follow the target form.

This paper proposes a mm3-class dual-use near-field antenna that can be used for both magnetic-field based communication and electrical-field based distance sensing. The proposed antenna consists of two spiral coils, and they are used as a coil antenna in communication mode and signal electrodes in distance sensing mode. We evaluated the performance of the communication mode with a prototype antenna. The measured S21 is −8.3 dB to −45.1 dB in the range from 6 mm to 24 mm, which is highly correlated to 3D full-wave electromagnetic simulation. With this antenna, we performed BER evaluation with commercial transceiver boards showing that the proposed antenna could be used for ASK/OOK signaling. We also confirmed that the proposed antenna could be used for cm-scale node-to-node distance sensing through capacitive coupling.