vijaym123/BlameGame
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Discovered public repositories for vijaym123 in the GitHub catalog.
Public repository.
Command line utility for generating books and exercises using GitHub/Git and Markdown
A CSS button library built using Sass and Compass
a graph visualization library using web workers and jQuery
EECS-337-Food
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Public repository.
Public repository.
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Public repository.
This project is on collecting data from govtrack.us and analysing it.
Code repo for vijaym.in website
a jekyll/octopress theme which incorporates bootstrap, html5boilerplate, SEO practices, and a strong hero.
Twitter for Python!
UI design for "A Weight-Based Personalized Recommendation using Idiosyncratic and Collaborative Filtering" project.
This is the code for our final year project on a novel recommendation algorithm.
A Coding Social Experiment
A python application that detects and highlights the heart-rate of an individual (using only their own webcam) in real-time.
mirror of git://git.videolan.org/ffmpeg.git
TwitMiner is a Machine learning contest conducted by Computer Science and Automation department of Indian Institute of Science, Bangalore. The challenge is to predict whether a particular tweet text can be classified to a category of ‘Politics’ or ‘Sports’.
The Open Source Framework for Machine Vision
Public repository.
My CV
A Javascript/Coffeescript companion library to SimpleCV
Public repository.
python screenshot
Google Drive CLI interface
Public repository.
Public repository.
Summer Of Code Page
A dominating set for a graph G = (V, E) is a subset D of V, where the set can be reached from any other vertices in less than or equal k steps with a probability involved is greater than half.
We present in this article, a novel strategy to partially predict the order of node arrivals in such an evolved network. We show that our proposed method outperforms other centrality measure based approaches.
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
This project is based
Hello World in Python