Download JAR files tagged by welcome with all dependencies

JARTs are Java-based Asynchronous Real Time sockets. JART uses JNA to build a pure Java TCP/IP stack upon Netmap. It is implemented in imperative style (no TCP state machine) using asynchronous programming with the help of ea-async. JART runs on both Linux (with the proper Netmap kernel module) and FreeBSD with Netmap enabled. FreeBSD 12.1+ has Netmap in the kernel by default so it works "out of the box". While JART has proven fairly robust in limited testing, it is still a work-in-progress and may not be suitable for production use. Pull requests welcome!

Welcome to the Blazegraph/TinkerPop3 project. The TP3 implementation has some significant differences from the TP2 version. The data model has been changed to use RDF*, an RDF reification framework described here: https://wiki.blazegraph.com/wiki/index.php/Reification_Done_Right. The concept behind blazegraph-gremlin is that property graph (PG) data can be loaded and accessed via the TinkerPop3 API, but underneath the hood the data will be stored as RDF using the PG data model described in this document. Once PG data has been loaded you can interact with it just like you would interact with ordinary RDF - you can run SPARQL queries or interact with the data via the SAIL API. It just works. The PG data model is also customizable via a round-tripping interface called the BlazeValueFactory, also described in detail in this document.

Provides the support for <a href="https://tagmycode.com">TagMyCode</a>. This plugin allows you to manage your own snippets.<br/> <br/> Features:<br/> * Add snippets: you can save your code snippets including description, language, and tags<br/> * List snippets (CRUD): snippets are stored locally and you can filter, sort, create, modify, edit or delete them directly from the IDE<br/> * Quick search: you can search your snippets and insert them directly into the document<br/> <br/> CHANGELOG:<br/> <br/> 2.3.0 (released 2020-07-26)<br/> * published plugin into Apache NetBeans Plugin Portal<br/> * filter snippets by languages<br/> <br/> 2.2.1 (released 2018-01-10)<br/> * Quick Search dialog is now resizable</br> * fixed syntax highlight for PHP and HTML</br> * if refresh token is not valid user will be automatically logged out</br> </br> 2.2.0 (released 2017-11-06)<br/> * snippets management works in offline mode<br/> * autodetect language on new snippet<br/> * added settings dialog with editor theme and font size option<br/> * added title and description to snippet view<br/> * changed open browser class<br/> * text can be dragged into table to create a new snippet<br/> * snippets can be dragged directly into editor and the code are copied<br/> * added "save as file" feature<br/> * added "clone snippet" feature<br/> * added "snippet properties" dialog<br/> * detect binary file<br/> <br/> 2.1.0 (released 2017-04-24)<br/> * moved error messages from dialog to Netbeans Notification Log<br/> * added welcome panel<br/> * about dialog shows plugin version and framework version<br/> * moved storage from JSON to SQL<br/> <br/> 2.0 (released 2016-07-11)<br/> * new user interface<br/> * list of snippets stored locally<br/> * syntax highlight powered by <a href="http://bobbylight.github.io/RSyntaxTextArea/">RSyntaxTextArea</a><br/> * snippets are synchronized with server<br/> * filter snippets<br/> * quick search feature<br/> * insert selected snippet at cursor in document<br/> <br/> 1.1.3 (released 2015-12-18)<br/> * Fix for NetBeans 8.1<br/> <br/> 1.1.2 (released 2014-10-03)<br/> * Switched authentication from OAuth 1.0a to OAuth 2<br/> * Console write also snippet title when new snippet is created (thanks to bejoy)<br/> <br/> 1.1 (released 2014-08-19)<br/> * Added "Search snippets" feature<br/> * Fixed some minor bugs<br/> <br/> 1.0 (released 2014-04-14)<br/> * First release with feature "Create snippet"<br/>

Mahout's goal is to build scalable machine learning libraries. With scalable we mean: Scalable to reasonably large data sets. Our core algorithms for clustering, classification and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for non-distributed algorithms. Scalable to support your business case. Mahout is distributed under a commercially friendly Apache Software license. Scalable community. The goal of Mahout is to build a vibrant, responsive, diverse community to facilitate discussions not only on the project itself but also on potential use cases. Come to the mailing lists to find out more. Currently Mahout supports mainly four use cases: Recommendation mining takes users' behavior and from that tries to find items users might like. Clustering takes e.g. text documents and groups them into groups of topically related documents. Classification learns from existing categorized documents what documents of a specific category look like and is able to assign unlabelled documents to the (hopefully) correct category. Frequent itemset mining takes a set of item groups (terms in a query session, shopping cart content) and identifies, which individual items usually appear together.

Mahout's goal is to build scalable machine learning libraries. With scalable we mean: Scalable to reasonably large data sets. Our core algorithms for clustering, classfication and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for non-distributed algorithms. Scalable to support your business case. Mahout is distributed under a commercially friendly Apache Software license. Scalable community. The goal of Mahout is to build a vibrant, responsive, diverse community to facilitate discussions not only on the project itself but also on potential use cases. Come to the mailing lists to find out more. Currently Mahout supports mainly four use cases: Recommendation mining takes users' behavior and from that tries to find items users might like. Clustering takes e.g. text documents and groups them into groups of topically related documents. Classification learns from exisiting categorized documents what documents of a specific category look like and is able to assign unlabelled documents to the (hopefully) correct category. Frequent itemset mining takes a set of item groups (terms in a query session, shopping cart content) and identifies, which individual items usually appear together.