Open Source Linux System Software - Page 3

Cognitive agent based social simulation toolkit (RBOT+MRS) / production system based on ACT-R (http://act-r.psy.cmu.edu) allows for modelling single actor cognitive experiments (RBOT) and multiple actors in a simulated (semiotic) world (RBOT + MRS).

This is an implementation of the Granular Neural Network architecture defined by S. Dick, A. Tappenden, C. Badke, O. Olarewaju. It is provided for the use of the public, and the convenience of researchers who may wish to develop or use this new system.

An architecture development environment for distributed agent architectures for virtual and robotic agents with focus on human-robot interaction

This project about providing run-time support for developing Decentralized Autonomic Computing systems (Eclipse Innovation Grand). Currently the project contains a automatic guided vehicle (AGV) simulator and an editor and visualisation as eclipse plugin

AHANNS enables the user to create and train artificial neural networks. Training is carried out by an implementation of back propagation learning algorithm. See the documentation for details. This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

The purpose of the project is to make the bash behave intelligent. This includes currently corrrection of mistyped program names, autocorrection while pressing TAB and learning which program are associated with which suffix.

A.I. security app. Development ceased.

The AWS-IoT-Arduino-Yún-SDK allows developers to connect their Arduino Yún compatible Board to AWS IoT. By connecting the device to the AWS IoT, users can securely work with the message broker, rules and the Thing Shadow provided by AWS IoT and with other AWS services like AWS Lambda, Amazon Kinesis, Amazon S3, etc. The AWS-IoT-Arduino-Yún-SDK consists of two parts, which take use of the resources of the two chips on Arduino Yún, one for native Arduino IDE API access and the other for functionality and connections to the AWS IoT built on top of AWS IoT Device SDK for Python. The AWS-IoT-Arduino-Yún-SDK provides APIs to let users publish messages to AWS IoT and subscribe to MQTT topics to receive messages transmitted by other devices or coming from the broker. This allows to interact with the standard MQTT PubSub functionality of AWS IoT.

Easily collect, transform, and transfer vehicle data to the cloud in near-real-time. AWS IoT FleetWise makes it easy and cost-effective for automakers to collect, transform, and transfer vehicle data to the cloud in near-real-time and use it to build applications with analytics and machine learning that improve vehicle quality, safety, and autonomy. Train autonomous vehicles (AVs) and advanced driver assistance systems (ADAS) with camera data collected from a fleet of production vehicles. Improve electric vehicle (EV) battery range estimates with crowdsourced environmental data, such as weather and driving conditions, from nearby vehicles. Collect select data from nearby vehicles and use it to notify drivers of changing road conditions, such as lane closures or construction. Use near real-time data to proactively detect and mitigate fleet-wide quality issues.

The shortest path problem is solved by many methods. Heuristics offer lower complexity in expense of accuracy. There are many use cases where the lower accuracy is acceptable in return of lower consumption of computing resources. The basic idea of the Ant System is that virtual ants are exploited for finding paths with a specific property, e.g., short distance between physical nodes, in the same way nature guides physical ants.

Using this plugin-based framework, you can instantly start working on the *brain* of your bot (irc bot, chatterbot, robot, ...). With support for db, irc, logging and programming-language independent plugins, users can easily enhance the functionality.

Keras implementation of a CNN network for age and gender estimation. This is a Keras implementation of a CNN for estimating age and gender from a face image [1, 2]. In training, the IMDB-WIKI dataset is used. Because the face images in the UTKFace dataset is tightly cropped (there is no margin around the face region), faces should also be cropped in demo.py if weights trained by the UTKFace dataset is used. Please set the margin argument to 0 for tight cropping. You can evaluate a trained model on the APPA-REAL (validation) dataset. We pose the age regression problem as a deep classification problem followed by a softmax expected value refinement and show improvements over direct regression training of CNNs. Our proposed method, Deep EXpectation (DEX) of apparent age, first detects the face in the test image and then extracts the CNN predictions from an ensemble of 20 networks on the cropped face.

The project has moved to github https://github.com/EnFlexIT/AgentWorkbench Agent.GUI is a simulation framework and toolkit based on the JADE framework. It provides functionalities for time aspects, agent-environment interaction, visualization and load balancing, Furthermore, the included application focuses the usability for end users.

Agentopia is a programming framework (API) for Java 5 mobile agents in peer-to-peer networks. Main features: Routing around firewalls, anonymity, and it is extremely easy to write new agents. No RMI, no CORBA, just plain Java bytecode loading.

This project provides a simple kernel for building distributed agent-based applications. It is based on the idea that the principles underlying dataflow programming are well-suited to design a coordination language for multi-agent environments.

System using to verify personality by face's photo.

The goal of this project is to learn about and develop an AI. Current path is using a bot on AIM.

Artificial Intelligence Machine System is an automated secure robot powered by Artificial Intelligence that help datacentre administrator to perform administrative task such as add/remove/edit accounts and resetting of passwords.

In an optical network scenario which consists of multiple nodes (whiteboxes) at its edges and ROADMs in-between, the coherent transceiver average laser configuration time is improved. The process is evaluated according to a testbed setup. This is facilitated in the appropriate lab equipment (or via simulation when required). For that purpose, a software agent (Netconf server) residing at the whiteboxes, is developed receiving input from the Software-Defined Networking (SDN) packet controller (PacketCTL - a Netconf client). Then, configuration of the local transceiver laser frequencies of the controlled pluggable devices takes place, for facilitating the connectivity in-between the ROADM network. Also, the agent records and reports back telemetry data (feedback) which is used by the PacketCTL's resource-allocating mechanism to improve efficiency within the network topology.

This project turns edge devices such as Raspberry Pi into an intelligent gateway with deep learning running on it. No internet connection is required, everything is done locally on the edge device itself. Further, multiple edge devices can create a distributed AIoT network. At DT42, we believe that bringing deep learning to edge devices is the trend towards the future. It not only saves costs of data transmission and storage but also makes devices able to respond according to the events shown in the images or videos without connecting to the cloud. One of the applications of this intelligent gateway is to use the camera to monitor the place you care about. For example, Figure 3 shows the analyzed results from the camera hosted in the DT42 office. The frames were captured by the IP camera and they were submitted into the AI engine. The output from the AI engine will be shown in the dashboard.

It moves by itself inside networks like virus infection & plagues, it is being written to solve computer virus problem drastically and responsibly. It is legal, free and open for public domain to improve W3 ICT Security.

Blackboard implements a lightweight, portable tuple space suitable for multi-agent system and distributed component design. Supports implicit invocation via content-filtered asynchronous events, blocking call semantics, and shared memory messaging.

CAMA (Context-Aware Mobile Agents) is a middleware for supporting mobile agents coordination. It is based on Linda-style coordination and provides numerous extentions for interoperability and fault-tolerance. It supports portable platforms such as PDAs.

CArtAgO is a framework for programming and executing virtual environments in multi-agent programs.

CHAC is a MATLAB toolbox for statistical downscaling.