Free ait trains software for linux

Score Reading Trainer helps you improve your (musical) score reading skills by practicing with random scores.
It works in a very simple way, you choose the clef, the key and the accidentals you want to practice as well as thee range of notes and then, the program generates a on-screen score with that parameters but randomizing the notes and the accidentals (and everything else that is randomizable).
The keys of the keyboard are mapped to the notes like in a piano (it would be nice to support midi in the future) and all you have to do, is press the right key for the first note you see in the score on the screen.
If you pressed the right key, the note will disapear and replaced by the second one, a diferent note, and you have to repeat the process.
As the notes are generated randomly, theres no way one can predict the following note and since several notes (as much as the screen allows) are on the screen, one can also train pre-reading (reading ahead of the current one) of the notes.
The program doesnt emit any sound (at least, yet) and its not inteded to teach rithmic or melodic concepts.
Enhancements:
2004-03-08 Jos? Pablo Ezequiel "Pupeno" Fern?ndez
- Try harder not to leave any extra line floating arround.
2004-03-08 Jos? Pablo Ezequiel "Pupeno" Fern?ndez
- Respect the position (over the third line) where notes are inverted.
2004-03-08 Jos? Pablo Ezequiel "Pupeno" Fern?ndez
- Limits wasnt really respected in other clefs than G2.
2004-03-08 Jos? Pablo Ezequiel "Pupeno" Fern?ndez
- Solved important bug. Extre lines was not being shown correctly (http://sourceforge.net/tracker/index.php?func=detail&aid=909427&group_id=97653&atid=618659).

CTF project is a multi-agent capture-the-flag framework for education.
This project was started by Jason Rohrer during the fall of 2000 and was initially used to teach CS 472, Introduction to AI, at Cornell University.
A homework assignment was given that asked students to design a CTF agent for the framework.
Students in the class responded to the assignment with great enthusiasm, and many of their final agents far exceeded our expectations (one student group went so far as to design a genetic algorithm to evolve a team of agents).
CTF forces students to explore the issues surrounding agents that operate in a limited information environments.
The framework is flexible enough to allow almost any possible implementation of agent control, from the simplest reactive agents, to agents that query powerful knowledge bases, to neural network agents that are trained by back propagation or reinforcement methods.
When using this framework at Cornell, we left the assignment open-ended. However, you can use this framework in your own class to teach a specific agent control concept (by forcing every student to implement a reinforcement learning system, for example).
Main features:
- Runtime loading of agent classes-- plugging new agents into the framework is incredibly easy
- Runtime loading of obstacle maps
- Automatic round-robin tournament system
- Automatic grading-- an email-ready message is generated for each student team at the end of the tournament (a script for sending out the email messages is included); the point system can be completely configured
- Assignment handout-- click here to see an example of the instructions we handed out to students for CS 472 at Cornell
- Licensed under GPL-- if the framework doesnt work for you as is, you can improve it yourself

Fearann Muin is an online role-playing game in a medieval fantasy world.
Fearann Muin is an online role-playing game (MMORPG without the Massive part) with a medieval-fantasy setting and standard rule sets. It will try to be focused on community and roleplaying instead of other aspects.
Main features:
Universe
- The setting of the game is Medieval Fantasy, similar to many other games (computer games or tabletop), but not copying exactly any system in particular. We plan to introduce non-standard creatures and environments more often as the development goes on.
- The initial races for playable characters are Dwarves, Elves and Humans; we plan to add new races later. While this doesnt sound original it has some benefits, like people knowing the race traits, so they can start playing right away without pondering which race is the best suited to their taste (as it happens when all the races in a game are original, unknown to you).
- The Rule Set is the standard for many games, with well-known classes, types of magic, spells, bonus and malus for each race, etc. We would prefer to not have some of the restrictions of the rule set (in example, we would prefer to have a class-less system); but as with the races, this allows us to work with a known standard that many people already know, so its easier to learn for most people, and its known to work (we chose to not make modifications at the moment, to not end up with an unbalanced, broken system).
- Well try to make of the environment a real, living world, when possible; with different behaviors for different creatures, the environment changing a bit with seasons, and so forth.
Community and Roleplay
- People will be able to sell items or resources that they craft and harvest; and maybe also sell other services, like training people with lower levels of knowledge, or being paid by performing tasks. This will provide a full range of social relationships based on economy, that will make the players to directly affect the rest of the world with every action; as well as provide the means to get a complex society running.
- There will be Player Guilds to allow people to form groups of friends or whatever interests they want; and there will be also some kind of Professional Guilds (the name is not decided yet, it might be even Universities or Schools) that will allow players to train and be part of that Institution, which will play a powerful role in the game.
Combat
- PvP is restricted, it will be allowed in the form of Duels, where one player challenges another one, and this one decides whether to engage in combat or not. There might be some special areas (arenas) where this will be allowed without the player having to agree to fight, or maybe complete maps where PvP is unrestricted, we havent decided that yet.
- Fighting with creatures and NPCs is usually allowed (althought it might not always be a wise decision :) ), and were designing it at the moment, probably itll will be present in the next release.
- Guild vs. Guild or similar advanced forms of combat are not yet planned, since we need a significant amount of things working before we get to that, and we have to learn from experience before being able to implement them. This doesnt mean that we dont want to have it, its just that were not ready to implement it in the next few releases yet.

Global Assassin is a text-based action game.

The Global Assassin game is a multiplayer, shoot-em-up game set in the first person, and yet its text-based.

The year is 2035, and robots have taken over the Earth. Most humans died during the invasion or were forced to flee the planet. You are part of a small resistance that has managed to survive underground and is now prepared to strike back.

As a trained assassin, your objective is to return to the Earths surface, stealthily move through the worlds major cities, and systematically rid the planet of its invaders.

The details of the game are as follows.

The battlefield consists of 20 cities around the globe.

These cities have been connected for your mission by an ellaborate portal system.

Use the portals to move between cities as you search for and destroy the robots.

You are armed with a gun, bullets, and grenades.

To successfully strike an invader, you and your target must occupy the same city at the precise moment you attack.

You can lay a mine in a given city, and the very next player to enter that city trips the mine.

You remain alive as long as you have health points.

Over time, you automatically earn back health points, bullets, and grenades.

This is a multiplayer game. You may or may not be the only assassin on the planet!

Keen little rabbit ears is a simple ear training program for Linux and Windows. It is free of charge. You can exercise the recogniton of intervalls, chords, scales and chord progressions.
Keen little rabbit ears requires the installation of Perl (Version 5), Perl/Tk, Perl::MIDI and an extermal MIDI player, like timidity (Verion 0.2i). Keen little rabbit ears is written in perl, uses Perl/Tk for the user interface, Perl::MIDI to write MIDI files and timidity to play MIDI files.
This is an early release, which means that there is a number of features which are not implemented. But keen little rabbit ears works pretty fine on my Pentium 90 using Suse Linux (Version 6.1) and under Window 95 too.
I strongly recommend everyone to read a good book on this. There are several ways to train. In general you do it like this. In the first level only select 2 or 3 items you already know or which are
the most common. In the next level you proceed and add the next 2 common items.
Below is are two lists, which can be used as guideline. But it is usually better to ask your teacher which exercises should you train.
Enhancements:
- Quiz mode
- advanced root note selection
- extension to playing direction (both)
- highlighting of selected instrument / root note
- 440 Hz (sing an "a" test)
- bug fix (pitch drill)

netAI comes from Network Traffic based Application Identification and has been developed for identifying the end host applications that are responsible for traffic flows in the network.
Unlike previous solutions that identify the application based on port numbers or packet payload (either through protocol decoding or signatures) netAI computes various payload independent features (e.g. packet length and packet inter-arrival time statistics) for a traffic flow and uses machine learning (ML) techniques.
ML is a discipline of the wider area of Artificial Intelligence (AI). Before netAI can be used to classify a particular application it must be trained on a representative set of traffic flows of that application. netAI can be used offline (reading packet data from tracefiles) and online (live capturing on network interfaces).
Main features:
- Reading packet data from live network interfaces or tracefiles (tcpdump or Endance format)
- Direct creation of WEKA data files (.arff files) from the packet data
- Interim flow information export (while flows are still active), TCP and time-based flow timeouts
- Flexible packet classification and filtering thanks to NetMate
- New features can be easily added and used
- Flexible selection of features to be used for classification
- A large number of machine learning algorithms can be used thanks to WEKA
- Feature extraction and ML based flow classification can be run on different machines - feature extractor supports data export via UDP or TCP