UROP Proceedings 2020-21

School of Engineering Department of Computer Science and Engineering 124 Algorithms and Games in Android Devices Supervisor: HUI Pan / CSE Student: ZHAO Jiachen / COSC Course: UROP1100, Spring Gamification is a method to motivate users by applying game elements to non-game situations. Building a satisfying gamified platform has advantages compared to ordinary ones, for example, more user activities and better outcomes. However, an unsuccessful gamification may have countereffects, and waste the efforts paid on it. In this study, we conduct a qualitative research to do sentiment analysis on the posts from Meta Stack Overflow. We will use a pre-trained natural language processing (NLP) model Bidirectional Encoder Representations from Transformers (BERT) to analyze the post texts to see whether gamification of Stack Overflow is satisfying and find out a pattern between questions and answers. Algorithms and Games in Android Devices Supervisor: HUI Pan / CSE Student: ZHU Zhengjie / COMP Course: UROP1100, Summer It is well known that whenever people download a new application, the onboarding screen (show the key features of this application) is always the first part. Whether users continue to use the app is often affected by this small part. In other words, if the guide interface is good, it is likely to make user continue to explore this app. I collected 51 apps from the Google Store. A total of 417 UI elements from these guide interface. A questionnaire based on four criteria (later will be talked) was designed, 21 respondents were invited to complete the questionnaire, and 1,071 pieces of feedback data were collected. Then analyze the data to draw some valuable conclusions. Finally, a flow chart is designed for the developer's reference. Human Faces Generation using GAN Supervisor: HUI Pan / CSE Student: KHAN Umaash Ahmed / RMBI Course: UROP1100, Fall Although much work has been done in computer vision privacy protection field, there are not enough resources comparing the advantages and disadvantages of different methods in the privacy protection field. In this UROP project, we identify 3 privacy protection methods in computer vision applications and evaluate each one of them. The 3 methods identified are face cartoonization, facial attribute cartoonization (nose, eyes, lips) and FakeGAN. For evaluation, we focus on 2 main aspects. These are user experience and identifiable possibilities. For user experience, the output pictures should maintain as much original emotion and meaning as possible while for the identifiable possibilities, the output pictures should be as different from the original as possible.

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