Open Source Linux Library Management Software - Page 12

DBNL is a cross-platform library that offers a variety of implementations of Bayesian networks and machine learning algorithms. It is a flexible library that covers all aspects of Bayesian netwoks from representation to reasoning and learning. It allows you to create simple static networks as well as complex temporal models with changing structure. It can handle highly non-linear dependencies between multivariate random variables. The particle based inference can answer arbitrary questions given the provided evidence and can even cope with multimodal densities. The library supports the most common types of densities and conditional densities, like uniform or normal densities and facilitates user defined density functions. To enable easy use the library is taking account of modern development techniques like policy based design and template programming. All these properties make it applicaple for a wide range of applications.

DIASER, Geo-data duplication long-term archive system & WAN vault. Manage mixed data archives generated by existing backup software. Ensure availability using commodity hardware. Retain administrative and financial control.

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.

Egyszerű digitális technika függvénykönyvtár, egyenlőre grafikus felület nélkül.

DILPS is a library for media database management. DILPS/W is a frontend for this library, which is implemented as a mediawiki extension with the aim of replacing the mediawiki file management subsystem with a much more elaborated media library

Deep Recurrent Neural Networks for Source Separation.

DLF Aquifer Development for Interoperability Across Scholarly Repositories: American Social History Online will implement schemas, data models and technologies to enable scholars to use distributed digital collections in a variety of local environments.

Digital Publishing System (DPubS) is electronic publishing software for organizing, presenting, and delivering both open access and subscription-controlled scholarly communications in multiple formats (journals, books, conference proceedings, etc.).

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Professional digital video archiving system solution, developed and used by the Austrian national audio/video archive, designed to handle vast amounts of video content from ingest to long-term storage, including analysis, transcoding and metadata.

DaNNet is a C++ deep neural network library using the Armadillo library as a base. It is intended to be a small and easy to use framework with no other dependencies than Armadillo. It uses independent layer-wise optimization giving you full flexibility to train your network.

Rich user interface for the DAISY Pipeline. The DAISY Pipeline is a cross-platform, open source framework for DTB-related document transformations.

Damon is a Distributed AOP Middleware on top of a p2p Overlay Network. Its main aim is to offer the necessary abstractions and services to develop distributed aspects, and to provide scalability to existent or new applications transparently.

Command Line by Jeroen Janssens, published by O’Reilly Media in October 2021. Obtain, scrub, explore, and model data with Unix Power Tools. This repository contains the full text, data, and scripts used in the second edition of the book Data Science at the Command Line by Jeroen Janssens. This thoroughly revised guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You’ll learn how to combine small yet powerful command-line tools to quickly obtain, scrub, explore, and model your data. To get you started, author Jeroen Janssens provides a Docker image packed with over 100 Unix power tools, useful whether you work with Windows, macOS, or Linux. You’ll quickly discover why the command line is an agile, scalable, and extensible technology. Even if you’re comfortable processing data with Python or R, you’ll learn how to greatly improve your data science workflow by leveraging the command line’s power.

This project provide data of Esperanto-Vietnamese for all dictionary software. Example: Lingoes, Stardict and many online dictionaries on Internet. Dự án này cung cấp dữ liệu từ điển Quốc tế ngữ cho các từ điển số.

The Dataverse Network is a Java EE5 app that enables researchers to share data on-line. It provides data citation standards, facilitates preservation, distribution and replication of data, and includes statistical analysis. More at: http://thedata.org

Dbus Cairo Pango drawing board, is a Dbus server for drawing on a board using Cairo and Pango

Deep-Learning-for-Medical-Applications is a repository that compiles deep learning methods, code implementations, and examples applied to medical imaging and healthcare data. The project addresses domain-specific challenges like segmentation, classification, detection, and multimodal data (e.g. MRI, CT, X-ray) using state-of-the-art architectures (e.g. U-Net, ResNet, GAN variants) tailored to medical constraints (small datasets, annotation costs, class imbalance). It includes Jupyter notebooks, model architectures, data preprocessing pipelines, and evaluation scripts specific to medical imaging tasks. The repository may also contain domain-specific modules: loss functions like Dice, focal loss, metrics such as sensitivity/recall/IoU, and visualization utilities for overlaying segmentation masks.

DeepMind Lab is a 3D learning environment based on id Software's Quake III Arena via ioquake3 and other open source software. DeepMind Lab provides a suite of challenging 3D navigation and puzzle-solving tasks for learning agents. Its primary purpose is to act as a testbed for research in artificial intelligence, especially deep reinforcement learning. If you use DeepMind Lab in your research and would like to cite the DeepMind Lab environment, we suggest you cite the DeepMind Lab paper. To enable compiler optimizations, pass the flag --compilation_mode=opt, or -c opt for short, to each bazel build, bazel test and bazel run command. The flag is omitted from the examples here for brevity, but it should be used for real training and evaluation where performance matters. DeepMind Lab ships with an example random agent in python/random_agent.py which can be used as a starting point for implementing a learning agent.

Catalog software for a personal library. Integrates with ISBNdb for lookup of metadata. Data-driven application using SWT and JFace Data Binding; EJB3-based data access layer to allow alternative servlet implementation.

The Dependency Discovery Tool searches through binary office files (.doc, .xls and .ppt) and tries to find any documents or files that are linked to the document. It is written in java, using the Apache POI libraries ( http://poi.apache.org ) This project was part of a summer scholarship from the School of Engineering and Computer Science at Victoria University, Wellington (http://ecs.vuw.ac.nz) in conjunction with Archives New Zealand ( http://archives.govt.nz ). At the moment it requires Java 6 to build and run, but this should change soon, and it will run on Java 5 upwards.

NO LONGER MAINTAINED, NO LONGER SUPPORTED Digital Preservation Software Platform (DPSP) consists of a number of open source products such as Xena and Digital Preservation Recorder. This installs and configures the complete platform for preserving digital records - just install and use!

Digitization Metadata Editor is a Content Management System for Digital Libraries. It's a complex web-based system supporting all essential steps in the development of the digital library.

Digrary is Open Source Digital Library System that enables services for creating electronic archives.

A framework for solving large systems of linear equations (Ax=b) in a distributed network using different iterative methods. The solver is written on Java, can be run on Windows or Linux. The solver is compatible with either MPICH2 or mpj-express.