Qiang Yang

TL;DR: This paper proposes a novel criterion based on empirical prediction error and its variance to better capture the consistency across domains in CF settings and embeds this criterion into a boosting framework to perform selective knowledge transfer.

TL;DR: The first attempt to implement three basic DP architectures in the deployed telecommunication (telco) big data platform for data mining applications finds that all DP architectures have less than 5% loss of prediction accuracy when the weak privacy guarantee is adopted, which implies that real-word industrial data mining systems cannot work well under such a strong privacy guarantee recommended by previous research works.

TL;DR: An automatic UAV-based inspection system is presented and implemented for asset assessment and defect detection for large-scale PV systems and the defect detection through image processing algorithms based on first order derivative of Gaussian function and feature matching is carried out.

TL;DR: This work proposes to match data distributions in the Hilbert space, which, given a pre-defined empirical kernel map, can be formulated as aligning kernel matrices across domains, and introduces the novel concept of surrogate kernel based on the Mercer's theorem.

TL;DR: It is demonstrated that with the proposed framework, the simulator car agents can transfer knowledge to the RC cars in real-time, with 27% increase in the average distance with obstacles and 42% decrease in the collision counts.