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Miao Chen


Publications by Miao Chen (bibliography)

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Chen, Sicheng, Chen, Miao, Kunz, Andreas M., Yantac, Asim Evren, Bergmark, Mathias, Sundin, Anders and Fjeld, Morten (2013): SEMarbeta: Mobile Sketch-Gesture-Video Remote Support for Car Drivers. In: Schmidt, Albrecht, Bulling, Andreas and Holz, Christian (eds.) Proceedings of the 4th Augmented Human International Conference AH 2013 March 7-8, 2013, Stuttgart, Germany. pp. 69-76. http://www.hcilab.org/ah2013/

Uneven knowledge distribution is often an issue in remote support systems, creating the occasional need for additional information layers that extend beyond plain videoconference and shared workspaces. This paper introduces SEMarbeta, a remote support system designed for car drivers in need of help from an officebound professional expert. We introduce a design concept and its technical implementation using low-cost hardware and techniques inspired by augmented reality research. In this setup, the driver uses a portable Android tablet PC while the expert mechanic uses a stationary computer equipped with a video camera capturing his gestures and sketches. Hence, verbal instructions can be combined with supportive gestures and sketches added by the expert mechanic to the car_s video display. To validate this concept, we carried out a user study involving two typical automotive repair tasks: checking engine oil and examining fuses. Based on these tasks and following a between-group (drivers and expert mechanics) design, we compared voice-only with additional sketch- and gesture-overlay on video screenshots measuring objective and perceived quality of help. Results indicate that sketch- and gesture-overlay can benefit remote car support in typical breakdown situations.

© All rights reserved Chen et al. and/or ACM Press

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Chen, Miao and Liu, Xiaozong (2011): Predicting popularity of online distributed applications: iTunes app store case analysis. In: Proceedings of the 2011 iConference 2011. pp. 661-663. http://dx.doi.org/10.1145/1940761.1940859

Online distributed applications are becoming more and more important for users nowadays. There are an increasing number of individuals and companies developing applications and selling them online. In the past couple of years, Apple Inc. has successfully built an online application distribution platform -- iTunes App Store, which is facilitated by their fashionable hardware such like iPad or iPhone. Unlike other traditional selling networks, iTunes has some unique features to advertise their application, for example, daily application ranking, application recommendation, free trial application usage, application update, and user comments. All of these make us wonder what makes an application popular in the iTunes store and why users are interested in some specific type of applications. We plan to answer these questions by using machine learning techniques.

© All rights reserved Chen and Liu and/or ACM Press

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