School of Engineering Department of Computer Science and Engineering 92 AI Meets Big Data: User Analytics and Personalized Recommendation Based on Location Data Supervisor: CHAN Gary Shueng Han / CSE Student: HO Ka Shuen / COMP Course: UROP1000, Summer Global Navigation Satellite System (GNSS) provides positioning service that determines users’ location using data received from satellites. With a more accurate system, new user experience could be provided and urge for development of innovation especially in the era of intelligence technology and automation. This report explores ways to analysis smartphone location data and produce higher accuracy positions with decimeter or even centimeter resolution. Two approaches were examined, weighted least square line with outlier detection and coordinate with nearest neighbors. Location measurement error was reduced as a result but there is more algorithm to be explored that could minimize error more significantly. AI Meets Big Data: User Analytics and Personalized Recommendation Based on Location Data Supervisor: CHAN Gary Shueng Han / CSE Student: KAO Shiu-hong / DSCT Course: UROP1100, Fall The traditional architecture of the Convolution Neural Network (CNN) has been one of the most common and powerful neural networks, especially for dealing with image information. However, some of its shortcomings, such as redundant filters and parameters, are believed to exist, contributing to unnecessary computation, and reducing the efficiency of the network. Dynamic neural networks, on the other hand, provide a more flexible architecture based on different samples. In this report, traditional Convolution as well as the other two pixel-wise dynamic networks, specifically, Involution and Conditionally Parameterized Convolutions (CondConv), are introduced. The basic architectures of these networks are firstly specified. Afterward, an experiment will be conducted to compare the efficiency and performance of distinct networks.
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