This is a group project for MIS 586 (Big Data Analytics) at the University of Arizona. In this project, we aimed to explore the Pokémon spawning pattern with temporal and spatial features, and co-occurrence behaviors with network metrics.

We later utilized those features and metrics to develop a C5.0 Decision Tree prediction model for recommending the next most-likely spawned Pokémon for a given set of features.

This is another group project for MIS 586 (Big Data Analytics) at the University of Arizona. In this project, we aimed utilize network analysis to answer the question: are Asian teams represented on social media (Twitter) like they are in the competitive scene?

Twitter API for Python, Tweepy, is used for data collection in this project. We also used the network analysis tool, Gephi, to calculate network metrics.

This is a course project for MIS 507 (Software Design) at the University of Arizona. This project aimed to develop a educational game system specifically for elementary schools with multiple design patterns. Java was the major programming language selected to complete the project.

This is a course project for MIS 507 (Web Mining) at the University of Arizona. This project aims to forecast stock price trend in a long term fashion through stock features extracted from Quandl and sentiments extracted from Reddit world news.

The model was trained on historical stock price data of 60 companies from 6 different industries combined with the top 25 news headlines on Reddit world news (on a daily basis for the last 8 years). These features were used to predict stock price trends through C5.0 Decision Tree with accuracy slightly greater than 70%.

This is a course project for Statistical Consulting at the National Cheng Kung University. This project explores the 5-year medical patterns of individuals who undergoes dialysis as treatment for renal failure after such patterns.

Taiwan's National Health Insurance Research Data - LHID2000 is used to complete this study. Data visualization was leveraged to display spatial and temporal patterns, and a Lasso Logistic Regression was was also used to construct a prediction model.

This Python program aimes to simulate how memory (256 bytes) and register (16 bytes) interact while running machine code instructions. The program displays changes in Memory, Register, Program Counter, and Instruction Register step-by-step. The program was written under Jupyter Notebook (which is why there are many "In[#]" throughout my code) with Python 3. The GUI package used is tkinter.

This program was developed for educational purpose for students in CSIM department at Soochow University in Taipei, Taiwan.

This is a course project for MIS 531 (Enterprise Database Management) at the University of Arizona. This project aimed to build a database system and develope commonly used query for CATS Academics, a non-academis department at the University of Arizona, to help them manage the room usage and reservation of their new facility.

Abstract— Cyber-attacks cost the global economy over $450 billion annually. To combat this issue, researchers and practitioners put enormous efforts into developing Cyber Threat Intelligence, or the process of identifying emerging threats and key hackers. However, the reliance on internal network data to has resulted in inherently reactive intelligence. CTI experts have urged the importance of proactively studying the large, ever- evolving online hacker community. Despite their CTI value, collecting data from hacker community platforms is a non-trivial task. In this paper, we summarize our efforts in systematically identifying and automatically collecting a large-scale of hacker forums, carding shops, Internet-Relay-Chat, and DarkNet Marketplaces. We also present our efforts to provide this data to the larger CTI community via the AZSecure Hacker Assets Portal. With our methodology, we collected 102 platforms for a total of 43,902,913 records. To the best of our knowledge, this compilation of hacker community data is the largest such collection in academia, and can enable a numerous novel and valuable proactive CTI research inquiries.