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The rooster crows immediately before sunrise, the rooster causes the sun to rise. Using GraphX to detect possible causes and effects between time related events. We observe a true causation signal by generating random correlations over same events at different time and back propagate these scores to their most connected events. Finally, we extract the most probable causes and effects together with a score of aggressiveness (how likely an event could explain downstream effects) and sensitivity (how likely an event results from an upstream cause).

liveSense WRO4J framework. WRO4J is Free and Open Source Java project which brings together almost all the modern web tools: JsHint, CssLint, JsMin, Google Closure compressor, YUI Compressor, UglifyJs, Dojo Shrinksafe, Css Variables Support, JSON Compression, Less, Sass, CoffeeScript and much more. In the same time, the aim is to keep it as simple as possible and as extensible as possible in order to be easily adapted to application specific needs.

This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels. A rule consists of antecedents "AND"ed together and the consequent (class value) for the classification/regression. In this case, the consequent is the distribution of the available classes (or mean for a numeric value) in the dataset. If the test instance is not covered by this rule, then it's predicted using the default class distributions/value of the data not covered by the rule in the training data.This learner selects an antecedent by computing the Information Gain of each antecendent and prunes the generated rule using Reduced Error Prunning (REP) or simple pre-pruning based on the number of antecedents. For classification, the Information of one antecedent is the weighted average of the entropies of both the data covered and not covered by the rule. For regression, the Information is the weighted average of the mean-squared errors of both the data covered and not covered by the rule. In pruning, weighted average of the accuracy rates on the pruning data is used for classification while the weighted average of the mean-squared errors on the pruning data is used for regression.

The goal of quality-test is to provide a small Java library for basic code quality checks within unit tests. It is discouraged to use quality-test in production code. The checks provided in this library are designed to be used in unit-tests. The checks and utilities provided in this package check static properties of classes, for example whether classes are marked final or constructors are private. Additionally, there are utils to give additional code coverage for private constructors. Quality-Test often can be used together with Google Reflections (http://code.google.com/p/reflections/) to perform checks such as, assure that every class in package *.dto.* is final or make sure that no class in the package *.controller.* contains any non-final static variables.

The JXTA Shell is a sample application that demonstrates key concepts of JXTA JXSE. The JXTA Shell enables users to interact with the JXTA platform through a command-line interpreter. Much like the UNIX® shell, the JXTA Shell is useful for accessing and managing core platform objects (peers, groups, pipes), debugging communications problems, checking the status of peers and/or peer groups, and communicating with other JXTA services and applications. As in UNIX®, the JXTA shell allows users to "pipe" shell commands together connecting commands "on-the-fly." Pipes in Project JXTA are useful for collecting information at one peer and perhaps processing the command at another.

PATRIUS is a core space dynamics Java library that enables to quickly develop high level algorithms such as orbit extrapolator. PATRIUS contains several sub-libraries that work together and cover low level classes (i.e.: such as matrix, vectors, orbits parameters) as well as high level classes and interfaces (i.e.: numerical propagators, attitude laws, manoeuvers sequences). All the main domains of space dynamics are available: Analysis, algebra and geometry core library (quaternions, derivable functions, integrators …) Core objects for space dynamics (dates, orbits, frames...) Orbit propagation: analytical, semi-analytical and numerical propagators, a full set of force models Maneuvers: impulsive or continuous thrust, sequences Attitude: extensible set of attitude laws, sequences and guidance framework Events: event detection (orbital, sensor events, etc.) and post-processing (chronograms) Spacecraft: characteristics of mass, geometry (drag force), sensors field of view, etc.

Iris is an attempt at bringing the simplicity and elegance of cloud computing to the application layer. Consumer clouds provide unlimited virtual machines at the click of a button, but leaves it to developer to wire them together. Iris ensures that you can forget about networking challenges and instead focus on solving your own domain problems. It is a completely decentralized messaging solution for simplifying the design and implementation of cloud services. Among others, Iris features zero-configuration (i.e. start it up and it will do its magic), semantic addressing (i.e. application use textual names to address each other), clusters as units (i.e. automatic load balancing between apps of the same name) and perfect secrecy (i.e. all network traffic is encrypted).