Popularity Prediction for Social Media over Arbitrary Time Horizons
Daniel Haimovich, Dima Karamshuk, Thomas Leeper, Evgeniy Riabenko, Milan Vojnovic
ACM User Interface Software and Technology Symposium (UIST)
In this paper, we present Acustico, a passive acoustic sensing approach that enables tap detection and 2D tap localization on uninstrumented surfaces using a wrist-worn device. Our technique uses a novel application of acoustic time differences of arrival (TDOA) analysis. We adopt a sensor fusion approach by taking both “surface waves” (i.e., vibrations through surface) and “sound waves” (i.e., vibrations through air) into analysis to improve sensing resolution. We carefully design a sensor configuration to meet the constraints of a wristband form factor. We built a wristband prototype with four acoustic sensors, two accelerometers and two microphones. Through a 20- participant study, we evaluated the performance of our proposed sensing technique for tap detection and localization. Results show that our system reliably detects taps with an F1-score of 0.9987 across different environmental noises and yields high localization accuracies with root-mean-square-errors of 7.6mm (X-axis) and 4.6mm (Y-axis) across different surfaces and tapping techniques.
Daniel Haimovich, Dima Karamshuk, Thomas Leeper, Evgeniy Riabenko, Milan Vojnovic
Liqi Yan, Qifan Wang, Yiming Cu, Fuli Feng, Xiaojun Quan, Xiangyu Zhang, Dongfang Liu
Barlas Oğuz, Kushal Lakhotia, Anchit Gupta, Patrick Lewis, Vladimir Karpukhin, Aleksandra Piktus, Xilun Chen, Sebastian Riedel, Wen-tau Yih, Sonal Gupta, Yashar Mehdad