Search Results for "data capture framework"

...Built with simplicity and extensibility in mind, the project includes REST APIs, database models, and a user interface that can be extended or embedded into existing internal systems. Because it prioritizes lean data structures and predictable code paths, developers can customize pipelines, fields, and behaviors to align with their unique business logic.

Megatron-LM is a GPU-optimized deep learning framework from NVIDIA designed to train extremely large transformer-based language models efficiently at scale. The repository provides both a reference training implementation and Megatron Core, a composable library of high-performance building blocks for custom large-model pipelines. It supports advanced parallelism strategies including tensor, pipeline, data, expert, and context parallelism, enabling training across massive multi-GPU and multi-node clusters. ...

Fuzzy machine learning framework is a library and a GUI front-end for machine learning using intuitionistic fuzzy data. The approach is based on the intuitionistic fuzzy sets and the possibility theory. Further characteristics are fuzzy features and classes; numeric, enumeration features and features based on linguistic variables; user-defined features; derived and evaluated features; classifiers as features for building hierarchical systems; automatic refinement in case of dependent features; incremental learning; fuzzy control language support; object-oriented software design with extensible objects and automatic garbage collection; generic data base support through ODBC or SQLite; text I/O and HTML output; an advanced graphical user interface based on GTK+; and examples of use.

The key difference with current graph deep learning libraries, such as PyTorch Geometric (PyG) and Deep Graph Library (DGL), is that, while PyG and DGL support basic graph deep learning operations, DIG provides a unified testbed for higher level, research-oriented graph deep learning tasks, such as graph generation, self-supervised learning, explainability, 3D graphs, and graph out-of-distribution. If you are working or plan to work on research in graph deep learning, DIG enables you to develop your own methods within our extensible framework, and compare with current baseline methods using common datasets and evaluation metrics without extra efforts. It includes unified implementations of data interfaces, common algorithms, and evaluation metrics for several advanced tasks. Our goal is to enable researchers to easily implement and benchmark algorithms.

AllenNLP makes it easy to design and evaluate new deep learning models for nearly any NLP problem, along with the infrastructure to easily run them in the cloud or on your laptop. AllenNLP includes reference implementations of high quality models for both core NLP problems (e.g. semantic role labeling) and NLP applications (e.g. textual entailment). AllenNLP supports loading "plugins" dynamically. A plugin is just a Python package that provides custom registered classes or additional...

Catalyst is a PyTorch framework for accelerated Deep Learning research and development. It allows you to write compact but full-featured Deep Learning pipelines with just a few lines of code. With Catalyst you get a full set of features including a training loop with metrics, model checkpointing and more, all without the boilerplate. Catalyst is focused on reproducibility, rapid experimentation, and codebase reuse so you can break the cycle of writing another regular train loop and make...

panFMP is a generic framework suitable for harvested XML metadata that is searchable through Apache Lucene without any additional RDBMS. Fields can be defined by XPath allowing for full text queries on all types of fields including numerical ranges. The code was moved to Github: https://github.com/pangaea-data-publisher/panfmp

NO LONGER MAINTAINED, NO LONGER SUPPORTED The Digital Preservation Recorder (DPR) has been developed by the National Archives of Australia to manage a digital preservation workflow. It features antivirus integration and makes use of the Xena framework for preservation conversions of data objects

Bibliograph is a powerful and intuitive open source web application for the collaborative collection, editing and publishing of bibliographic data such as a bibliographies, small library collections or shared research data. It features a modern user interface similar to a desktop application.

mediaTUM is free software written in Python for archiving and retrieval of images, documents and other research data. It was originally developed in the framework of the DFG project IntegraTUM and is continuously expanded with new functionalities as required.

OCL stands for Open Cezeri Library (yet another linear algebra and matrix library). This library provides rapid coding as matlab ease of use. To learn for library please try to use test examples at OpenCezeriLibrary\test\test. It is originally developed at el-cezeri laboratory of Siirt University, in order to establish generic framework of reusable components and software tools for machine vision, machine learning, AI and robotic applications. Currently, it holds following main concepts 1-...

SITools2 is a new CNES generic tool performed by a joint effort between CNES and scientific laboratories. The aim of SITools is to provide a self-manageable data access layer deployed on already existing scientific laboratory databases.

S-Match is a semantic matching framework. S-Match takes any two tree like structures (such as database schemas, classifications, lightweight ontologies) and returns a set of correspondences between those tree nodes which semantically correspond to one another. S-Match contains implementations of the semantic matching, minimal semantic matching and structure preserving semantic matching algorithms. S-Match applies as a solution in many fields, including: information integration,...

Semtinel is an application framework for the semi-automatic creation, maintenance and analysis of hierarchical concept schemes (thesaurus, classification or ontology). Semtinel supports the development of new analysis methods and visualization techniques

Biomedical Information Management System (BIMS) is a modular multilayer object - oriented software architecture, which provides a flexible computational solution to capture and manage a heterogeneous biomedical data: textual and visual information.

This research project seeks to use compiler techniques to perform an attribute-based data-flow analysis on (MOF/UML)models allowing for a syntax-driven validation of a model's static semantics as well as an abstract interpretation of i dynamic behavior.

Facilitates data mining/natural language processing experiments to be executed on weblogs, such as classification, clustering and rating. As part of these experiments, it is possible to apply Latent Semantic Analysis.

.Net Generic Data Structures Provides parametric polymorphic data structores (the first is an AVL Binary tree, always balanced), for the .Net framework.

A self-sufficient java framework for numerical solution of ODEs (diff eqs) and visualization. Samples are aeronautics/astronautics/physics/rocketry themed. Focus on clean design, modularity, reusability and documentation. Educational project.

The aDORe Federation is a standards-based federated repository framework and reference implementation which aims to address many of the scalability issues experienced by large scale digital object repositories.