School of Engineering Department of Computer Science and Engineering 123 A De-Polarization System for Social Media Supervisor: HUI Pan / CSE Student: YU Yue / QSA Course: UROP2100, Spring The report consists of two parts. The first project is the continued project from the last semester which aims to build a Chrome extension, Debubble, to help the users explore more ideas based on which they are reading on the news sites. We made some improvements including the UI reconstitution, A/B testing set, and user survey module on the basis of the last semester’s work and published the fourth version on the Chrome Store. The second project investigates the user activities in a Reddit community called WallStreetBet (WSB) in the timespan when the short squeeze of the stocks such as GameStop (GME), AMC Entertainment (AMC), and BlackBerry (BB) appears. By dividing the users into different groups based on the time they joined in the discussion in the WSB community, we measure the correlation between different user group’s activity and the stock ticker’s volatility. The research result reports the significant difference between the new users and the old users in terms of the user activities including the frequency of mentioning the stock ticker’s name and the frequency of using emoji. A De-Polarization System for Social Media Supervisor: HUI Pan / CSE Student: ZAU Ka Ming / CPE XU Xiao / SENG LIU Yuehuai / SENG Course: UROP1100, Summer UROP1100, Summer UROP1100, Summer The project we did is focusing on the online psychologist. Several accounts were chosen from Instagram. Then, we did some analysis between the users and the online professional to establish the relationship between the online material and the reaction of users. Firstly, we did a qualitative analysis for 30 accounts that therapists own. Then, we develop the codebook. After that, we rated many accounts to judge whether these accounts are more about emotional or informational support. Algorithms and Games in Android Devices Supervisor: HUI Pan / CSE Student: IP Si Hou / COMP Course: UROP2100, Fall With the rise of feed forward neural networks, a massive number of researches in computer vision immerses. Generative adversarial networks are one of the innovative approaches in recent years. By adapting the concept, the style of the image can be converted from one style to another by the usage of a discriminator and generator. CartoonGAN is one of the approaches to converting images from the real world into a cartoon style. In this project, we want to further investigate the style transfer of cartoons. We would like to perform a real-time style transfer of cartoons. We would like to make a real-time GAN that shows both temporal consistency and preserving the style and content of the input scene.