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Automatic item generation (AIG), or automated item generation, is a process linking psychometrics with computer programming. It uses a computer algorithm to automatically create test items that are the basic building blocks of a psychological test .
eric is licensed under the GNU General Public License version 3 or later and is thereby Free Software.This means in general terms that the source code of eric can be studied, changed and improved by anyone, that eric can be run for any purpose by anyone and that eric - and any changes or improvements that may have been made to it - can be redistributed by anyone to anyone as long as the ...
The Mobile Robot Programming Toolkit (MRPT) is a cross-platform software C++ library for helping robotics researchers design and implement algorithms related to simultaneous localization and mapping (SLAM), computer vision, and motion planning (obstacle avoidance). Different research groups have employed MRPT to implement projects reported in ...
The simulation model must be sufficiently faithful to the original, so that it behaves in practically the same way as the original brain. [118] Whole brain emulation is a type of brain simulation that is discussed in computational neuroscience and neuroinformatics , and for medical research purposes.
OpenAI Codex is an artificial intelligence model developed by OpenAI. It parses natural language and generates code in response. It powers GitHub Copilot, a programming autocompletion tool for select IDEs, like Visual Studio Code and Neovim. [1] Codex is a descendant of OpenAI's GPT-3 model, fine-tuned for use in programming applications.
Template matching [1] is a technique in digital image processing for finding small parts of an image which match a template image. It can be used for quality control in manufacturing, [ 2 ] navigation of mobile robots , [ 3 ] or edge detection in images.
Retrieval-Augmented Generation (RAG) is a technique that grants generative artificial intelligence models information retrieval capabilities. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to augment information drawn from its own vast, static training data.
For online match moving, SIFT features again are extracted from the current video frame and matched to the features already computed for the world model, resulting in a set of 2D-to-3D correspondences. These correspondences are then used to compute the current camera pose for the virtual projection and final rendering.