AlbertoPeon/voms-admin-client
The VOMS Administration service client
Developer Profile
AlbertoPeon
Discovered public repositories for AlbertoPeon in the GitHub catalog.
AlbertoPeon/clojure-stuff
Public repository.
AlbertoPeon/python-novaclient
Client library for OpenStack Compute API
AlbertoPeon/coursera
Script for downloading Coursera.org videos and naming them.
AlbertoPeon/voms-admin-server
The VOMS Administration service
AlbertoPeon/mcollective-vagrant
Vagrant based MCollective testing framework
AlbertoPeon/vagrant-examples
A collection of sample Vagrant configurations using Puppet. Starting out simple and getting more complex.
AlbertoPeon/finatra
finagle + sinatra
AlbertoPeon/foreman
an application that automates the lifecycle of servers
AlbertoPeon/reactive-examples
Unofficial Scala worksheet examples to accompany the Coursera Reactive Programming course
AlbertoPeon/nemo
File browser for Cinnamon
AlbertoPeon/puppet-voms
Configures VOMS clients in particular /etc/vomses/* and /etc/grid-security/vomsdir/*
AlbertoPeon/reddit
the code that powers reddit.com
AlbertoPeon/openspending
The core OpenSpending application
AlbertoPeon/bubbleworks
Simple working bubbletree.js example
AlbertoPeon/bubbletree
Radial Bubble Tree Visualization
AlbertoPeon/python-google-places
Simple wrapper around the new 'experimental' Google Places API
AlbertoPeon/hw4_rottenpotatoes
Public repository.
AlbertoPeon/hw3_rottenpotatoes
HW 3 skeleton for doing BDD with RottenPotatoes
AlbertoPeon/Statistical-NLP-Application-for-analyzing-messages-between-individuals
The main goal of this project is to create an application able to filter and classify a big amount of messages (such as tweets or e-mails) according to the value of their fields. By doing this, the user can discard those which are irrelevant and focus on the important ones and, finally, establish connections between them . In order to achieve this, the user can tag messages and individuals (who are the senders and/or receivers of the messages) manually, but also in an automatic way using Natural Language Processing algorithms to detect similar messages. The application will be developed in Java, using Java Swing for the graphic user interface and a SQLite database for saving the generated data. Additionally, the application will work as a framework for adding more techniques of detection of similar messages, in order to compare the results of each technique and select the most appropriate one for the kind of message we are working with.