Zhibin Chen's repositories
Shared-learning-materials
正在搭架子 作为公共分享资料
masters_thesis
Detecting activity on the Bitcoin network using machine learning and graph analytics
Forum_Sensitive_Text_Filter
A simple hole forum packed with front-end, after-end and filter algorithms. For PKU course No.04831670(Computer Network and Web Tech).
lm-chinese-evaluation-harness
A framework for few-shot evaluation of autoregressive language models.
TechkillForQSGS
太阳神三国杀·学科杀 扩展包开源
ZacharyChenpk.github.io
Academic personal website.
ckiptagger
CKIP Neural Chinese Word Segmentation, POS Tagging, and NER
FBDQA-2021S
Financial Big Data and Quantitative Analytics, Spring 2021.
GNNPapers
Must-read papers on graph neural networks (GNN)
mona2artifact
将莫娜占卜铺导出的圣遗物列表导入原魔计算器
nonmonotonic_text
Non-Monotonic Sequential Text Generation
ROS_project
An implementation of a disaster relief robot. For PKU Course No.04830250(Introduction to Artificial Intelligence).
thesis-bitcoin-clustering
The Bitcoin currency is a publicly available, transparent, large scale network in which every single transaction can be analysed. Multiple tools are used to extract binary information, pre-process data and train machine learning models from the decentralised blockchain. As Bitcoin popularity increases both with consumers and businesses alike, this paper looks at the threat to privacy faced by users through commercial adoption by deriving user attributes, transaction properties and inherent idioms of the network. We define the Bitcoin network protocol, describe heuristics for clustering, mine the web for publicly available user information and finally train supervised learning models. We show that two machine learning algorithms perform successfully in clustering the Bitcoin transactions based on only graphical metrics measured from the transaction network. The Logistic Regression algorithm achieves an F1 score of 0.731 and the Support Vector Machines achieves an F1 score of 0.727. This work demonstrates the value of machine learning and network analysis for business intelligence; on the other hand it also reveals the potential threats to user privacy.