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JCQL is a tool allowing to generate boilerplate java code from existing cassandra schema. It is intended to be used with Cassandra 2.1+ due to support of UDTs (User Defined Types)/Tuples/Collections. Cassandra's UDTs/Tuples/Collections and an ability to introspect schema through java driver make it possible to automatically generate POJOs and corresponding mappers between database and java models. Properly generated java code saves development efforts and is less error-prone compared to hand coding. Accompanied with proper CI and deployment it can also guarantee consistency between database and java models at any point of application lifecycle from development to production rollout. JCQL does not rely on java reflection or annotations which means all discrepancies between actual cassandra schema and what client code expects it to be will be identified during compilation not at runtime in the middle of the night right after production release. No need to worry about Cassandra client code performance implications due to use of reflection.

All Gwt jre emulation code goes in this module, as well as any gwt-compiler overrides. xapi-gwt-api.jar must come before gwt-dev.jar on your compile classpath. A plugin is being built to automatically adjust maven runtime dependencies, but users of ant or IDEs will need to ensure the super jar comes before gwt-dev. We will petition gwt to accept our mods, but, until then, if you want bleeding edge features, you gotta do bleeding edge configuration. Code that ties directly into other modules, like java.lang.reflect for the reflection submodule, have their super-source here, and generators or other implementations in their own modules. This is to maintain consistency in what is or isn't whitelisted in XApi GWT. Some modules, like appengine, provide dependency-specific super-source in their own packages. This module is for jre, junit and core XApi services.

Spring LDAP is a Java library for simplifying LDAP operations, based on the pattern of Spring's JdbcTemplate. The framework relieves the user of common chores, such as looking up and closing contexts, looping through results, encoding/decoding values and filters, and more. The LdapTemplate class encapsulates all the plumbing work involved in traditional LDAP programming, such as creating a DirContext, looping through NamingEnumerations, handling exceptions and cleaning up resources. This leaves the programmer to handle the important stuff - where to find data (DNs and Filters) and what do do with it (map to and from domain objects, bind, modify, unbind, etc.), in the same way that JdbcTemplate relieves the programmer of all but the actual SQL and how the data maps to the domain model. In addition to this, Spring LDAP provides transaction support, a pooling library, exception translation from NamingExceptions to a mirrored unchecked Exception hierarchy, as well as several utilities for working with filters, LDAP paths and Attributes.

This plugin provides support for textlint. <h3>What&rsquo;s the textlint?</h3> <p>See <a href="https://github.com/textlint/textlint">https://github.com/textlint/textlint</a></p> <h3>Usage</h3> <h4>Install textlint and rules</h4> <p>Of course, it assumes that nodejs and npm are installed.</p> <p>e.g.</p> <pre><code>$ mkdir txtlint $ cd txtlint $ npm init $ npm install textlint --save-dev $ npm install textlint-rule-max-ten textlint-rule-spellcheck-tech-word textlint-rule-no-mix-dearu-desumasu --save-dev </code></pre> <h4>Create .textlintrc</h4> <pre><code>$ touch .textlintrc </code></pre> <pre><code class="json">{ &quot;rules&quot;: { &quot;max-ten&quot;: { &quot;max&quot;: 3 }, &quot;spellcheck-tech-word&quot;: true, &quot;no-mix-dearu-desumasu&quot;: true } } </code></pre> <p>You can also set parameters to Options (see below).</p> <h4>Set textlint and .textlintrc paths</h4> <p>Set paths to the Options (see below).</p> <p>e.g.</p> <ul> <li>textlint Path: /path/to/txtlint/node_modules/.bin/textlint (textlint.cmd in Windows)</li> <li>.textlintrc Path: /path/to/textlint/.textlintrc</li> </ul> <h4>Open Action Items window</h4> <ul> <li>Click Window > Action Items.</li> <li>Click &ldquo;Show action items for currently edited file only&rdquo; icon.</li> <li>Open your markdown or text file.</li> </ul> <h3>Options</h3> <p>Tools > Options > Editor > textlint</p> <ul> <li>textlint Path: Absolute path to textlint</li> <li>.textlintrc Path: Absolute path to .textlintrc</li> <li>Options : You can set options for the textlint command</li> <li>Enable in HTML files: To use the html plugin, you can check this</li> <li>Refresh on Save: To scan the document on save, you can check this (Checked by default)</li> <li>Show Annotations: To show annotations in the glyph gutter, you can check this (Checked by default)</li> </ul> <h3>Actions</h3> <h4>Fix</h4> <p>You have to save your file before you run this action.<br/> If there is a fixable rule&rsquo;s error, you can fix it. Right-click an item > Click <code>Fix</code>.<br/> To refresh items, your document is saved once.</p> <h4>Fix All</h4> <p>You have to save your file before you run this action.<br/> If there are fixable rule&rsquo;s errors, you can fix them. Right-click an item > Click <code>Fix All</code>.<br/> This action runs <code>textlint --fix</code> command.</p> <h4>Refresh</h4> <p>You can refresh results forcibly by the following action: Right-click your editor > Click "textlint Refresh".</p> <p>You can also set the shortcut key(Tools > Options > Keymap). </p> <h3>NOTE</h3> <ul> <li>The plugin scans only current file.</li> <li>The plugin does not refresh results automatically. Please save your file or run the refresh action.</li> <li>Use <code>UTF-8</code> as file encoding and <code>LF</code> as line endings.</li> <li>This plugin may not work properly in Windows. (Please try to check above.)</li> <li>If you cannot get expected results, just try to run the <code>textlint</code> commands once in your CLI.</li> </ul>

git-commit-id-plugin is a plugin quite similar to https://fisheye.codehaus.org/browse/mojo/tags/buildnumber-maven-plugin-1.0-beta-4 for example but as buildnumber only supports svn (which is very sad) and cvs (which is even more sad). This plugin makes basic repository information available through maven resources. This can be used to display "what version is this?" or "who has deployed this and when, from which branch?" information at runtime - making it easy to find things like "oh, that isn't deployed yet, I'll test it tomorrow" and making both testers and developers life easier. The data currently exported is like this (that's the end effect from the GitRepositoryState Bean): { "branch" : "testing-maven-git-plugin", "commitTime" : "06.01.1970 @ 16:16:26 CET", "commitId" : "787e39f61f99110e74deed68ab9093088d64b969", "commitUserName" : "Konrad Malawski", "commitUserEmail" : "[email protected]", "commitMessageFull" : "releasing my fun plugin :-) + fixed some typos + cleaned up directory structure + added license etc", "commitMessageShort" : "releasing my fun plugin :-)", "buildTime" : "06.01.1970 @ 16:17:53 CET", "buildUserName" : "Konrad Malawski", "buildUserEmail" : "[email protected]" } Note that the data is exported via maven resource filtering and is really easy to use with spring - which I've explained in detail in this readme https://github.com/ktoso/maven-git-commit-id-plugin

IronPDF Java library offers an extensive compatibility range, making it a go-to solution for a wide array of developers. It fully supports JVM languages like Java, Scala, and Kotlin, making it incredibly versatile. This Java PDF library is also compatible with Java 8 and above, providing optimum performance across multiple platforms. It's been designed with a wide range of users in mind Here's a look at what it supports: JVM Languages: Java, Scala, Kotlin.Platforms: Java 8 and above.Operating Systems: Microsoft Windows, Linux, Docker, Azure, AWS.IDEs: Jetbrains IntelliJ IDEA, Eclipse. You can deploy IronPDF Java across various platforms, including Microsoft Windows, Linux, Docker, Azure, and AWS. It is also fully compatible with popular IDEs like Jetbrains IntelliJ IDEA and Eclipse, facilitating smooth project development and management. Your pom.xml file is essentially the backbone of your project when you're using Maven. It's here where you introduce new dependencies that you wish to include. To make IronPDF Java package a part of your Maven project, you simply need to add the following snippets to your pom.xml: Remember to replace '20xx.xx.xxxx' with the latest version of IronPDF. IronPDF Java simplifies the process of creating PDF files. Convert HTML files, HTML strings, or URLs directly to new PDF documents in a few lines of code. The variety of file formats it handles is vast, as it can even transform images into PDF documents and vice versa. Need to use base 64 encoding, base URLs, or custom file paths? No problem! IronPDF Java has got you coveredFor more detail about installing and using IronPDF Java. When you run your project for the first time post-integration, IronPDF's engine binaries will automatically be downloaded. The engine starts its journey when you call any IronPDF function for the first time and takes a breather when your application is either closed or enters an idle state. It is not an open source java PDF library but here's the best part - IronPDF Java is offering a 30-day free trial. So, why wait? Give it a go and boost your PDF operations today.

Chips-n-Salsa is a Java library of customizable, hybridizable, iterative, parallel, stochastic, and self-adaptive local search algorithms. The library includes implementations of several stochastic local search algorithms, including simulated annealing, hill climbers, as well as constructive search algorithms such as stochastic sampling. Chips-n-Salsa now also includes genetic algorithms as well as evolutionary algorithms more generally. The library very extensively supports simulated annealing. It includes several classes for representing solutions to a variety of optimization problems. For example, the library includes a BitVector class that implements vectors of bits, as well as classes for representing solutions to problems where we are searching for an optimal vector of integers or reals. For each of the built-in representations, the library provides the most common mutation operators for generating random neighbors of candidate solutions, as well as common crossover operators for use with evolutionary algorithms. Additionally, the library provides extensive support for permutation optimization problems, including implementations of many different mutation operators for permutations, and utilizing the efficiently implemented Permutation class of the JavaPermutationTools (JPT) library. Chips-n-Salsa is customizable, making extensive use of Java's generic types, enabling using the library to optimize other types of representations beyond what is provided in the library. It is hybridizable, providing support for integrating multiple forms of local search (e.g., using a hill climber on a solution generated by simulated annealing), creating hybrid mutation operators (e.g., local search using multiple mutation operators), as well as support for running more than one type of search for the same problem concurrently using multiple threads as a form of algorithm portfolio. Chips-n-Salsa is iterative, with support for multistart metaheuristics, including implementations of several restart schedules for varying the run lengths across the restarts. It also supports parallel execution of multiple instances of the same, or different, stochastic local search algorithms for an instance of a problem to accelerate the search process. The library supports self-adaptive search in a variety of ways, such as including implementations of adaptive annealing schedules for simulated annealing, such as the Modified Lam schedule, implementations of the simpler annealing schedules but which self-tune the initial temperature and other parameters, and restart schedules that adapt to run length.

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.