HCI International 2017
Vancouver, Canada, 9 - 14 July 2017
Vancouver Convention Centre
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Distributed, Ambient and Pervasive Interactions Best Paper Award

5th International Conference on Distributed, Ambient and Pervasive Interactions Best Paper Award. Details in text following the image.

Best Paper Award for the 5th International Conference on Distributed, Ambient and Pervasive Interactions, in the context of HCI International 2017, 9 - 14 July 2017, Vancouver, Canada

 

Certificate for best paper award of the 5th International Conference on Distributed, Ambient and Pervasive Interactions. Details in text following the image

Certificate for Best Paper Award of the 5th International Conference on Distributed, Ambient and Pervasive Interactions
conferred to

Kenro Aihara (National Institute of Informatics / The Graduate University for Advanced Studies, Japan),
Piao Bin (National Institute of Informatics, Japan), Hajime Imura (Hokkaido University, Japan),
Atsuhiro Takasu (National Institute of Informatics / The Graduate University for Advanced Studies, Japan),
and Yuzuru Tanaka (Hokkaido University, Japan)

for the paper entitled

"A Smart City Application for Sharing Up-to-date Road Surface Conditions Detected from Crowdsourced Data"

Presented in the context of
HCI International 2017
9 - 14 July 2017, Vancouver, Canada

Paper Abstract
"This paper introduces a smart city application to share road conditions. The application is based on a mobile sensing framework to collect sensor data reflecting personal-scale, or microscopic, roadside phenomena using crowdsourcing. To collect data, a driving recorder smartphone application that records not only sensor data but also videos from the driver’s view is used. To extract specific roadside phenomena, collected data are integrated and analyzed at the service platform. One example is estimating road surface conditions. The paper shows our method to estimate road surface type (RST) and road surface shape (RSS). Features are defined in Sequential Forward Floating Search (SFFS) algorithm from collected data. By using random forest as classifier, average recall was about 91% in the 50km/h – 80km/h range. The result may support to build a service that provides detected road conditions from up-to-date crowdsourced mobile sensing application."

The full paper is available through SpringerLink, provided that you have proper access rights.

 

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