Open Source Libraries - Page 67
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Atan is an interface to the RoboCup Soccer Server for the 2D simulation league. It should allow you to concentrate on the job of controlling your clients without having to worry about the communication syntax with SServer or creating the UDP connections.
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A multi-SO framework to develop graphical applications, physical simulations or videogames running on wxWidgets or SDL
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AtomicFU
The idiomatic way to use atomic operations in Kotlin
Atomicfu is a multiplatform library that provides an idiomatic and efficient way of using atomic operations in Kotlin. -
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AtomicServer
An open source headless CMS / real-time database
Create, share, fetch, and model Atomic Data! AtomicServer is a lightweight, yet powerful CMS / Graph Database. Atomic Data is a modular specification for sharing, modifying, and modeling graph data. It combines the ease of use of JSON, the connectivity of RDF (linked data), and the reliability of type-safety. Atomic Data uses links to connect pieces of data, and therefore makes it easier to connect datasets to each other - even when these datasets exist on separate machines. -
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Atta
Build tool in pure Python. http://boguslawski-piotr.github.com/atta/
Atta is a FREE build tool, targets-tasks driven, developed in pure Python. Similar in philosophy to the Ant, NAnt, etc. but without the use of XML syntax nightmare. http://boguslawski-piotr.github.com/atta/ -
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Attitude Estimator
A C++ implementation of a nonlinear 3D IMU fusion algorithm.
Attitude Estimator is a generic platform-independent C++ library that implements an IMU sensor fusion algorithm. Up to 3-axis gyroscope, accelerometer and magnetometer data can be processed into a full 3D quaternion orientation estimate, with the use of a nonlinear Passive Complementary Filter. The library is targeted at robotic applications, but is by no means limited to this. Features of the estimator include gyro bias estimation, transient quick learning, multiple estimation algorithms, tuneable estimator parameters, and near-global stability backed by theoretical analysis. Great emphasis has been placed on having a very efficient, yet totally numerically and algorithmically robust implementation of the filter. The code size has also been kept to a minimum, and has been extremely well-commented. The programmatic interface has also been made as easy as possible. Please refer to the extensive documentation of the library for more information on its capabilities and usage caveats. -
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AugLy
A data augmentations library for audio, image, text, and video
AugLy is a data augmentations library that currently supports four modalities (audio, image, text & video) and over 100 augmentations. Each modality’s augmentations are contained within its own sub-library. These sub-libraries include both function-based and class-based transforms, composition operators, and have the option to provide metadata about the transform applied, including its intensity. AugLy is a great library to utilize for augmenting your data in model training, or to evaluate the robustness gaps of your model! We designed AugLy to include many specific data augmentations that users perform in real life on internet platforms like Facebook's -- for example making an image into a meme, overlaying text/emojis on images/videos, reposting a screenshot from social media. While AugLy contains more generic data augmentations as well, it will be particularly useful to you if you're working on a problem like copy detection, hate speech detection, etc. -
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Augmentor.jl
A fast image augmentation library in Julia for machine learning
A fast library for increasing the number of training images by applying various transformations. Augmentor is a real-time image augmentation library designed to render the process of artificial dataset enlargement more convenient, less error prone, and easier to reproduce. It offers the user the ability to build a stochastic image-processing pipeline (or simply augmentation pipeline) using image operations as building blocks. In other words, an augmentation pipeline is little more but a sequence of operations for which the parameters can (but need not) be random variables, as the following code snippet demonstrates. -
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A java based neural network framework. The Auratus network is built around an XML messaging system, allowing for a complete MVC design. Additionally, Auratus networks are constructed and at the node/edge level, allowing for advanced topologies.
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Aurora libraries
Building modern, elegant and fast desktop Compose applications
Aurora is a collection of libraries for writing modern, elegant, and fast desktop Compose applications based on the Ephemeral design system. It is built with Java 11 and runs on Java 11 and later. -
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Australian National Corpus
An ongoing project to collate and provide access to language data
Includes • Scripts for the program/ code developed • High level architecture diagrams • Install guides for developers • Links to end user documentation on the AusNC website Note: The BSD license applies to customised plug-ins, scripts and ingest programs developed by the AusNC project team. Additional open source, 3rd party software products used by the AusNC solution are referenced on our SF wiki space. -
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Authen::Simple::WebForm - Perl library for simple authentication against existing web based forms using Authen::Simple framework.
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Authorize.net is by far one of the most used payment gateways around and the AIM (Advanced Integration Method) is one of the most used methods for online shopping. This class will create a wrapper for the AIM method and all of the API call and respons
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Authorize.net-SIM-Wrapper
A simple integration of Authorize.net SIM API in PHP
If you want to integrate Authorize.net SIM API in your web applications, this is the easiest solution for you. I am using an Authorize.net API and written a wrapper class in PHP to make the integration easier for you. -
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AutoDiscovery Library
A tiny library to add auto discovery capability into your applications
A tiny and simple 100% Java library (without any dependency) to easily enable your application to automatically discover the presence of other instances of itself running on the other computers connected to the same LAN network, without having to deal with protocols and network configurations. The protocol used by the library is peer-to-peer, so you don't need a central server. The API is simple, straightforward and there are examples and full documentation to learn it in minutes. -
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AutoKeras
AutoML library for deep learning
AutoKeras: An AutoML system based on Keras. It is developed by DATA Lab at Texas A&M University. The goal of AutoKeras is to make machine learning accessible to everyone. AutoKeras only support Python 3. If you followed previous steps to use virtualenv to install tensorflow, you can just activate the virtualenv. Currently, AutoKeras is only compatible with Python >= 3.7 and TensorFlow >= 2.8.0. AutoKeras supports several tasks with extremely simple interface. AutoKeras would search for the best detailed configuration for you. Moreover, you can override the base classes to create your own block. -
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AutoNumeric
Standalone library that provides live as-you-type formatting
AutoNumeric is a standalone library that provides live-as-you-type formatting for international numbers and currencies. All in all, AutoNumeric supports most international numeric formats and currencies including those used in Europe, Asia, and North and South America. AutoNumeric focuses on efficiency and speed making all your input interactions blazing fast. No dependencies are needed to use AutoNumeric. Written in ES6, AutoNumeric uses the latest technologies and is prepared for the future. While the Android platform makes input management hard to play with, AutoNumeric supports it. AutoNumeric strives to be framework-agnostic, which means you can easily integrate it with third-party frameworks. Checkout the official Vue-AutoNumeric component. -
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Autobahn|Java
WebSocket & WAMP in Java for Android and Java 8
Client library providing WAMP on Java 8 (Netty) and Android, plus (secure) WebSocket for Android. The WebSocket layer is using a callback-based user API, and is specifically written for Android. Eg it does not run any network stuff on the main (UI) thread. The WAMP layer is using Java 8 CompletableFuture for WAMP actions (call, register, publish, and subscribe) and the Observer pattern for WAMP session, subscription and registration lifecycle events. The library is MIT licensed, maintained by the Crossbar.io Project, tested using the AutobahnTestsuite and published as a JAR to Maven and as a Docker toolchain image to Dockerhub. -
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Autologging
Easier logging and tracing of Python functions and class methods.
Autologging eliminates boilerplate logging setup code and tracing code, and provides a means to separate application logging from program flow and data tracing. Autologging provides two decorators and a custom log level: "autologging.logged" decorates a class to create a __log member. By default, the logger is named for the class's containing module and name (e.g. "my.module.ClassName"). "autologging.traced" decorates a class to provide automatic CALL/RETURN tracing for all class, static, and instance methods, as well as the special __init__ method (by default) "autologging.TRACE" is a custom log level (lower than logging.DEBUG) that is registered with the Python logging module when autologging is imported -
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Automatic Report Generator
Generate reports from Java applications directly.
Automatic Report Generator is a mini-ETL API which allows to retrieve data through an SQL query towards a structured file, would it be a CSV, XLSX, or XML. This API also supports BIRT reports, and, in this case, its respective template is required. The project is available on Maven: https://mvnrepository.com/artifact/net.sf.automatic-report-generator Version 3: https://mvnrepository.com/artifact/net.sf.ennahdi.automatic-report-generator Checkout the documentation for both version 2 and version 3: https://sourceforge.net/p/automatic-report-generator/wiki/Home/ -
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Avalanche
End-to-End Library for Continual Learning based on PyTorch
Avalanche is an end-to-end Continual Learning library based on Pytorch, born within ContinualAI with the unique goal of providing a shared and collaborative open-source (MIT licensed) codebase for fast prototyping, training and reproducible evaluation of continual learning algorithms. Avalanche can help Continual Learning researchers in several ways. This module maintains a uniform API for data handling: mostly generating a stream of data from one or more datasets. It contains all the major CL benchmarks (similar to what has been done for torchvision). Provides all the necessary utilities concerning model training. This includes simple and efficient ways of implementing new continual learning strategies as well as a set of pre-implemented CL baselines and state-of-the-art algorithms you will be able to use for comparison! Avalanche the first experiment of an End-to-end Library for reproducible continual learning research & development where you can find benchmarks, algorithms, etc. -
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Awake File
Easy file uploads & downloads through HTTP in Java
Awake File is now KRemote Files : http://www.kremote-files.com -
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Awake SQL
Remote JDBC access through HTTP
Awake SQL is discontinued and replaced by AceQL. See http://www.aceql.com -
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Awesome AWS
A curated list of awesome Amazon Web Services libraries
A curated list of awesome Amazon Web Services (AWS) libraries, open source repos, guides, blogs, and other resources. Featuring the Fiery Meter of AWSome. Each repo listed meets at least one of the following requirements, community-authored repo with 100+ stars, community-vouched repo with < 100 stars, official repo from aws or awslabs. 100+ stars for community repos is not a strict requirement, it only serves as a guideline for the initial compilation. If you can vouch for the awesomeness of a repo with < 100 stars and you can explain why it should be listed, please submit a pull request. Pull requests might be left open for a period of time to let the community chime in and vouch for it. An official repo from aws or awslabs can be removed if the community wishes. The Python module awesome-aws regularly scans repos on Awesome AWS to maintain the accuracy of the Fiery Meter of AWSome. -
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Awesome Conformal Prediction
A professionally curated list of awesome Conformal Prediction videos
awesome-conformal-prediction is a curated “awesome list” repository on GitHub collecting high-quality resources related to conformal prediction: tutorials, books, papers, theses, open-source libraries, videos, and other educational material. It is not a software library itself but a directory of resources for those wanting to learn or work with conformal prediction and uncertainty quantification. This exceptional resource is the culmination of my PhD journey in Machine Learning, specializing in Conformal Prediction under the supervision of its creator, Prof. Vladimir Vovk. Since 2015, I have painstakingly gathered these invaluable resources, and upon completing my PhD (my thesis, "Machine Learning for Probabilistic Prediction," can be found in the "Theses" section), I am thrilled to share my expertise with the global machine learning community. Immerse yourself in a professionally curated collection that has been honed through years of dedication and experience.
