xiaofengzhu's repositories
d3
A JavaScript visualization library for HTML and SVG.
Wikipedia
A Pythonic wrapper for the Wikipedia API
VisualizePost
Visualize Posts from Facebook
oddHackathon
NU Hackathon
three.js
JavaScript 3D library.
jstree
jquery tree plugin
ioio
Software, firmware and hardware of the IOIO - I/O for Android
corenlp
A python wrapper for the Stanford CoreNLP java library.
django
The Web framework for perfectionists with deadlines.
stemmer
Erlang implementation of Porter algorithm (an algorithm for suffix stripping)
Mining-the-Social-Web-2nd-Edition
The official online compendium for Mining the Social Web, 2nd Edition (O'Reilly, 2013)
iir
Machine Learning / Natural Language Processing / Information Retrieval
parquet-examples
Example programs and scripts for accessing parquet files
brewer
EECS 394 Cource Project
purple_team
EECS 394 Cource Project
ollie
Ollie is a open information extractor that uses bootstrapped dependency paths.
vagrant
Virtual Development Environment
datasci_course_materials
Public repository for course materials for the Spring 2013 session of Introduction to Data Science, an online coursera course.
guide
用ViewPager实现欢迎引导页面 .
chat
a barebones/simple chat room server in go
BigDataR_Examples
Data Science and Machine Learning Examples for Data Science Linux
Human-Navigation-Algorithm
Human navigation has been a topic of interest in spatial cognition from the past few decades. It has been experimentally observed that humans accomplish the task of way-finding a destination in an unknown environment by recognizing landmarks. Investigations using network analytic techniques reveal that humans, when asked to way-find their destination, learn the top ranked nodes of a network. In this paper we report a study simulating the strategy used by humans to recognize the centers of a network. We show that the paths obtained from our simulation has the same properties as the paths obtained in human based experiment. The simulation thus performed leads to a novel way of path-finding in a network. We discuss the performance of our method and compare it with the existing techniques to find a path between a pair of nodes in a network