School of Engineering Department of Computer Science and Engineering 96 AI Meets Big Data: User Analytics and Personalized Recommendation Based on Location Data Supervisor: CHAN Gary Shueng Han / CSE Student: YANG Lin / COMP Course: UROP1100, Spring UROP2100, Summer Image restoration is an elementary tool for numerous low-level visionrelated tasks. It involves generating a high-quality clean image from a given low-quality degraded version. The degradation may occur during the picture capturing, transmitting, and preserving, resulting in different restoration tasks, such as denoising, deraining, dehazing, and deblurring. In this article, the development of image restoration tasks is reviewed, and two representative models will be focused on. One of them is an MLP-based model, which shows stateof-the-art performance on various restoration tasks, and the other one is a transformer-based model, which is all-in-one and degradation-blind. In the end, the future challenges of image restoration will be discussed. AI Meets Big Data: User Analytics and Personalized Recommendation Based on Location Data Supervisor: CHAN Gary Shueng Han / CSE Student: ZHOU Yanting / MATH-CS Course: UROP1100, Fall UROP3100, Summer Pervasive positioning is to locate an object anywhere seamlessly on a country scale. With the aim of connecting every party involved in a localization process, this standard contains a set of communication protocols, data organization and specification for site signals, and map data organization standard. In this summer semester, we carried on the development of the SDK APIs for application developers and corresponding Web APIs for site owners to respond queries. More specifically, we completed the preliminary development of the SDK APIs for initialized handshaking, indoor localization, and map display. The SDK functions give reasonable outputs when we tested them under the pre-set site servers and real collected data.