Search Results for "projects based on ai"

The UnifiedSessionsManager supports the integrated management of user sessions within Private-Clouds, comprising heterogeneous IT landscapes of various physical and virtual machines, hypervisor management, and virtual user sessions with remote desktops. Extracted documents see https://sourceforge.net/projects/ctys-doc.

This is a software agent based computing program that will enable researchers and other users to tap in computing power of machine available by sharing work load on the fly with zero configuration on network & resources A self organizing agent program that will understand network and its resource. where as the only job left to researcher is to split up jobs in several chunks of programs either parallel or sequential jobs and go issue the job (A visual Modeler or Scripting support need to...

Released August 22, 2013 Lubuntu Blends: Biochemistry 13.04 (Raring) v5.44 Linux Kernel Image 3.8.0-29 Lubuntu Blends are pre-installed Wubi disk image remixes of Ubuntu and Debian Science meta packages, A custom boot loader allows installations to be copied and automatically booted from most external or USB flash drives. Once up and running, use earlier Lubuntu Remix README instructions here until documentation is updated....

This Project aims to create a Linux Distribution focused on the IBM zOS system, it includes a set of Mainframe tools, based all in Linux Kernel modules, C and Python. -MainFrame Access Methods -ISPF menus -REXX scripting -RACF security -JCL Batch Job

Spyse is a software framework for building multi-agent systems. It allows Python developers to build distributed intelligent systems of multiple cooperative agents based on FIPA, OWL, SOA and many others. Spyse is designed for ease-of-use and fun.

Cloud Target is a front-end to the python scripts that google uses to create, run and upload projects to its App Server system. This .NET application is based on the OSX front-end provided by Google in the SDK.

Multi Agent based distributed application. The code can be processed over multiple common machines with fault-tolerance. It is designed to distributively run any Python's script, which can be applied to a given input data set.