Free add entropy data software for linux

video-entropyd is a program to add entropy data from video4linux devices to the kernel random driver.
For security reasons (when doing network traffic or generating secure keys for example) one wants as much entropy-data in the kernel random-driver as possible. The random-driver takes partially care for this. But in situations in where theres a lot of demand for entropy-data, it might not be able to gather enough entropy-data by itself.
Thats where this program is for: adding entropy-data to the kernel-driver. It does that by fetching 2 images from a video4linux-device (with a random delay in between), calculating the difference between those two and then calculating the number of information-bits in that data. After that, the data with the number-of-entropy-bits is submitted to the kernel-random-driver.
After that, the program exits (when run from crontab) or sleeps a random time before redoing the same process.
Enhancements:
- This version checks the quality of the data it feeds to the /dev/random-device to see if the data is really random.
- Extra logging has been added.

ENTROPY stands for Emerging Network To Reduce Orwellian Potency Yield and as such describes the main goal of the project.
Main features:
- ENTROPY is developed as a response to increasing censorship and surveillance in the internet. The program connects your computer to a network of machines which all run this software. The ENTROPY network is running parallel to the WWW and also other internet services like FTP, email, ICQ. etc.
- For the user the ENTROPY network looks like a collection of WWW pages. The difference to the WWW however is that there are no accesses to central servers. And this is why there is no site operator who could log who downloaded what and when. Every computer taking part in the ENTROPY network (every node) is at the same time server, router for other nodes, caching proxy and client for the user: that is You.
- After you gained some experience with the ENTROPY network, there are command line tools for you to insert whole directory trees into the network as a ENTROPY site. So ENTROPY does for you what a webspace provider does for you in the WWW - but without the storage and bandwidth costs and without any regulation or policy as to what kind of content you are allowed to publish. Everyone can contribute his own ENTROPY site for everybody else to browse through. The contents is stored in a distributed manner across all available and reachable nodes and no one can find out about who put up what contents into the network. Even if your node is not actively running, your contents can be retrieved by others -- without knowing that it was actually you who published the files. Of course this is only true if you do not publish your name (or leave your name or other personal data in the files you publish)
Enhancements:
- Bugfixes were made.
- This release is not backwards compatible.

desktop-data-manager includes a clipboard history for many different types of content (text, images, ...) and an application to take screenshots from a single window/subwindow, rectangle of the screen or the whole desktop.

The two applications are designed to work well together. For more details please visit my homepage.

Notes: If you find some bugs/problems with the application please contact me at einki(at)users.sourceforge.net. The Program is programmed to work in a GTK/GNOME environment but at least the basic functionalities will work in KDE too.

AI::DecisionTree is Perl module for automatically Learns Decision Trees.

SYNOPSIS

use AI::DecisionTree;
my $dtree = new AI::DecisionTree;

# A set of training data for deciding whether to play tennis
$dtree->add_instance
(attributes => {outlook => sunny,
temperature => hot,
humidity => high},
result => no);

$dtree->add_instance
(attributes => {outlook => overcast,
temperature => hot,
humidity => normal},
result => yes);

... repeat for several more instances, then:
$dtree->train;

# Find results for unseen instances
my $result = $dtree->get_result
(attributes => {outlook => sunny,
temperature => hot,
humidity => normal});

The AI::DecisionTree module automatically creates so-called "decision trees" to explain a set of training data. A decision tree is a kind of categorizer that use a flowchart-like process for categorizing new instances. For instance, a learned decision tree might look like the following, which classifies for the concept "play tennis":

OUTLOOK
/ |
/ |
/ |
sunny/ overcast rainy
/ |
HUMIDITY | WIND
/ *no* /
/ /
high/ normal /
/ strong/ weak
*no* *yes* /
*no* *yes*

(This example, and the inspiration for the AI::DecisionTree module, come directly from Tom Mitchells excellent book "Machine Learning", available from McGraw Hill.)

A decision tree like this one can be learned from training data, and then applied to previously unseen data to obtain results that are consistent with the training data.

The usual goal of a decision tree is to somehow encapsulate the training data in the smallest possible tree. This is motivated by an "Occams Razor" philosophy, in which the simplest possible explanation for a set of phenomena should be preferred over other explanations. Also, small trees will make decisions faster than large trees, and they are much easier for a human to look at and understand. One of the biggest reasons for using a decision tree instead of many other machine learning techniques is that a decision tree is a much more scrutable decision maker than, say, a neural network.

The current implementation of this module uses an extremely simple method for creating the decision tree based on the training instances. It uses an Information Gain metric (based on expected reduction in entropy) to select the "most informative" attribute at each node in the tree. This is essentially the ID3 algorithm, developed by J. R. Quinlan in 1986. The idea is that the attribute with the highest Information Gain will (probably) be the best attribute to split the tree on at each point if were interested in making small trees.

Add-ons Sidebar is a Firefox extension that lets you view your Add-ons in the sidebar by pressing Ctrl+Shift+E.

Small and simple extension: only 2 KB!

Only works in development builds with the new Add-ons Manager!

Dont install this if you have Firefox 1.5.0.x or lower.

Google::Adwords::Data is base class for the Data modules.

This module is not supposed to be used directly. Use the child data modules.

File::Data is a Perl module as a interface to file data.

Wraps all the accessing of a file into a convenient set of calls for reading and writing data, including a simple regex interface.

Note that the file needs to exist prior to using this module!

See new()

SYNOPSIS

use strict;

use File::Data;

my $o_dat = File::Data->new(./t/example);

$o_dat->write("complete file contentsn");

$o_dat->prepend("first linen"); # line 0

$o_dat->append("original second (last) linen");

$o_dat->insert(2, "new second linen"); # inc. zero!

$o_dat->replace(line, LINE);

print $o_dat->READ;

Or, perhaps more seriously :-}

my $o_sgm = File::Data->new(./sgmlfile);

print "new SGML data: ".$o_sgm->REPLACE(
s*((?s).*)s* ,
qq| key="val" |,
) if $o_sgm;

See METHODS and EXAMPLES.

IMPORTANT

lowercase method calls return the object itself, so you can chain calls.

my $o_obj = $o_dat->read; # ! READ; # !<<less