Download JAR files tagged by specific with all dependencies

The boilerpipe library provides algorithms to detect and remove the surplus "clutter" (boilerplate, templates) around the main textual content of a web page. The library already provides specific strategies for common tasks (for example: news article extraction) and may also be easily extended for individual problem settings. Extracting content is very fast (milliseconds), just needs the input document (no global or site-level information required) and is usually quite accurate. Boilerpipe is a Java library written by Christian Kohlschütter. It is released under the Apache License 2.0. The algorithms used by the library are based on (and extending) some concepts of the paper "Boilerplate Detection using Shallow Text Features" by Christian Kohlschütter et al., presented at WSDM 2010 -- The Third ACM International Conference on Web Search and Data Mining New York City, NY USA.

[](https://drone.io/bitbucket.org/tidalwave/tidalwave-superpom-src/latest) The super POM for all Tidalwave projects. It is not designed for being used by others, as it contains some corporate-specific configurations, but its ancestor [TheseFooolishThings SuperPOM](http://bitbucket.org/tidalwave/thesefoolishthings-superpom-src) has been designed to be reusable. Please have a look at it. This super POM adds to its ancestor: + some Tidalwave variables that refers to the issue tracker, continuous integration system, etc...; + the definitions of versions of a number of commonly used libraries and their dependency management: * [AspectJ](https://www.eclipse.org/aspectj) * [Hamcrest Matchers](http://hamcrest.org/JavaHamcrest) * [JSR 330](https://github.com/google/guice/wiki/JSR330) * [Jakarta XML Binding (JAXB)](https://eclipse-ee4j.github.io/jaxb-ri/) * [Spotbugs annotations](https://spotbugs.readthedocs.io) * [JUnit](https://junit.org/junit5) * [Logback](http://logback.qos.ch) * [Lombok](https://projectlombok.org) * [SLF4J](slf4j.org) * [Spring 5](https://spring.io/projects/spring-framework) * [TestNG](https://testng.org) + the definition for Tidalwave 3rd party repository (stuff that is not available on Maven Central); + a profile for using the [TheseFoolishThings](http://tidalwave.it/projects/thesefoolishthings) Event Bus (```it.tidalwave-spring-messagebus-v1```); + profiles for the [Mycila License plugin](https://github.com/mycila/license-maven-plugin); + configuration of the UMLGraphDoc maven plugin; + the configuration for the TheseFoolishThings TestNG listener (which provides enhanced test logging); + definitions of some custom javadoc tags; + a blacklist for some old artifacts; + some other minor customisations.

Java Prolog Interface (JPI) is an Application Provider Interface (API) for interaction between Java and Prolog programming languages. Is a bidirectional interface that communicate Java applications with Prolog program or database and Prolog procedures with Java class and methods. JPI is an abstraction layer over concrete prolog drivers over Prolog Engines. This API define all mechanism to interact with any Prolog Engine and maintain the application independent to a specific underlying engine. JPI have several connectors to open source prolog engines like SWI, YAP, XSB native engines and tuProlog, jTrolog, jLog Java based prolog engines. JPI study all related Java-Prolog integration libraries and take the betters features from each solution with the propose to achieve a common integration interface. The last feature allows switch the under laying Prolog Engine driver and the application code still be the same. JPI run over any Java Virtual Machine that support Java SE 5 or above. The project was tested over HotSpot, Open J9 and JRockit Virtual Machines over Operating Systems like Windows (7,8,10), Linux (Debian, Ubuntu) and Mac OS X. Can be deployed on Servlets Containers like Jetty, Tomcat or Glassfish Application Server. JPI can be include in any Java Project using the commonest Java Integration Development Enviroment (IDE) like Eclipse, Netbeans, IntelijIDEA and so on. JPI is developed and maintained by Prolobjectlink Project an open source initiative for build logic based applications using Prolog like fundamental Logic Programming Language in the persistence layer and application programming.

Show color codes preview per line in a sidebar area of an editor. <h2>Disable / Enable</h2> Check/Uncheck View > Show Colors <h2>Supported color patterns</h2> <ul> <li>Hex color code (e.g. #ffffff, #000)</li> <li>Css rgb/rgba values (e.g. rgb(0,0,0), rgba(255, 255, 255, 0.8))</li> <li>Css hsl/hsla values (e.g. hsl(0, 100%, 50%), hsla(120, 100%, 50%, 0.5))</li> <li>Named colors (e.g. red, blue)</li> <li>Java Color class (e.g. Color.black, new Color(100, 100, 100))</li> </ul> <h2>Multiple colors</h2> <ul> <li>Show top two colors in a sidebar if there are multiple colors in a line.</li> <li>If you want to check all colors, please click a specific rectangle. They will be shown as a list.</li> </ul> <h2>Change a color using the color chooser</h2> <ul> <li>Click a colored rectangle</li> <li>Click a color value of a list</li> <li>Select a new color in the color chooser</li> <li>An old color value will be changed to new one with the same format</li> </ul> <h2>Generate color codes</h2><p>You can generate color codes via a code generator(<kbd>Alt</kbd> + <kbd>Ins</kbd>).</p> <ol> <li>Run a code generator(Alt + Ins)</li> <li>Choose <code>Color...</code></li> <li>Choose format you expect (e.g. <code>new Color(r, g, b)</code>)</li> <li>Choose a color</li> <li>Click the OK button</li> <li>A color code is generated at the caret position</li> </ol> <h2>Options</h2> Tools > Options > Miscellaneous > Color Codes Preview <h3>Regex for enabled mime-types for Hex and CSS colors</h3> Default value is `^text/(x-)?(css|less|sass|scss)$`. If you would like to disable/enable some mime-types, please change the default regex. This pattern is used when the plugin checks a mime-type. <h3>Named Colors</h3> This option is `false` by default. If you would like to show named colors, please check it. <h2>NOTE</h2> <ul> <li>If you would like to show colors of `Color.decode(<hex>)` e.g. `Color.decode(#000000)`, Please add `java` to "Regex for enabled mime-types" of Hex and CSS e.g. (`^text/(x-)?(css|less|sass|scss|java)$`)</li> <li>Colors may be shown if they are not color codes. e.g. "#feature" contains #fea. This plugin recognizes it as a hex color code.</li> <li>If you use the GTK Look and Feel, you cannot change an alpha value in the color chooser.</li> <li>Hsl or hsla color values may not be changed correctly when you use the color chooser. (There may be 1% errors.)</li> </ul>

JUNG the Java Universal Network/Graph Framework--is a software library that provides a common and extensible language for the modeling, analysis, and visualization of data that can be represented as a graph or network. It is written in Java, which allows JUNG-based applications to make use of the extensive built-in capabilities of the Java API, as well as those of other existing third-party Java libraries. The JUNG architecture is designed to support a variety of representations of entities and their relations, such as directed and undirected graphs, multi-modal graphs, graphs with parallel edges, and hypergraphs. It provides a mechanism for annotating graphs, entities, and relations with metadata. This facilitates the creation of analytic tools for complex data sets that can examine the relations between entities as well as the metadata attached to each entity and relation. The current distribution of JUNG includes implementations of a number of algorithms from graph theory, data mining, and social network analysis, such as routines for clustering, decomposition, optimization, random graph generation, statistical analysis, and calculation of network distances, flows, and importance measures (centrality, PageRank, HITS, etc.). JUNG also provides a visualization framework that makes it easy to construct tools for the interactive exploration of network data. Users can use one of the layout algorithms provided, or use the framework to create their own custom layouts. In addition, filtering mechanisms are provided which allow users to focus their attention, or their algorithms, on specific portions of the graph.

JUNG the Java Universal Network/Graph Framework--is a software library that provides a common and extendible language for the modeling, analysis, and visualization of data that can be represented as a graph or network. It is written in Java, which allows JUNG-based applications to make use of the extensive built-in capabilities of the Java API, as well as those of other existing third-party Java libraries. The JUNG architecture is designed to support a variety of representations of entities and their relations, such as directed and undirected graphs, multi-modal graphs, graphs with parallel edges, and hypergraphs. It provides a mechanism for annotating graphs, entities, and relations with metadata. This facilitates the creation of analytic tools for complex data sets that can examine the relations between entities as well as the metadata attached to each entity and relation. The current distribution of JUNG includes implementations of a number of algorithms from graph theory, data mining, and social network analysis, such as routines for clustering, decomposition, optimization, random graph generation, statistical analysis, and calculation of network distances, flows, and importance measures (centrality, PageRank, HITS, etc.). JUNG also provides a visualization framework that makes it easy to construct tools for the interactive exploration of network data. Users can use one of the layout algorithms provided, or use the framework to create their own custom layouts. In addition, filtering mechanisms are provided which allow users to focus their attention, or their algorithms, on specific portions of the graph.

JUNG the Java Universal Network/Graph Framework--is a software library that provides a common and extendible language for the modeling, analysis, and visualization of data that can be represented as a graph or network. It is written in Java, which allows JUNG-based applications to make use of the extensive built-in capabilities of the Java API, as well as those of other existing third-party Java libraries. The JUNG architecture is designed to support a variety of representations of entities and their relations, such as directed and undirected graphs, multi-modal graphs, graphs with parallel edges, and hypergraphs. It provides a mechanism for annotating graphs, entities, and relations with metadata. This facilitates the creation of analytic tools for complex data sets that can examine the relations between entities as well as the metadata attached to each entity and relation. The current distribution of JUNG includes implementations of a number of algorithms from graph theory, data mining, and social network analysis, such as routines for clustering, decomposition, optimization, random graph generation, statistical analysis, and calculation of network distances, flows, and importance measures (centrality, PageRank, HITS, etc.). JUNG also provides a visualization framework that makes it easy to construct tools for the interactive exploration of network data. Users can use one of the layout algorithms provided, or use the framework to create their own custom layouts. In addition, filtering mechanisms are provided which allow users to focus their attention, or their algorithms, on specific portions of the graph.

JUNG the Java Universal Network/Graph Framework--is a software library that provides a common and extendible language for the modeling, analysis, and visualization of data that can be represented as a graph or network. It is written in Java, which allows JUNG-based applications to make use of the extensive built-in capabilities of the Java API, as well as those of other existing third-party Java libraries. The JUNG architecture is designed to support a variety of representations of entities and their relations, such as directed and undirected graphs, multi-modal graphs, graphs with parallel edges, and hypergraphs. It provides a mechanism for annotating graphs, entities, and relations with metadata. This facilitates the creation of analytic tools for complex data sets that can examine the relations between entities as well as the metadata attached to each entity and relation. The current distribution of JUNG includes implementations of a number of algorithms from graph theory, data mining, and social network analysis, such as routines for clustering, decomposition, optimization, random graph generation, statistical analysis, and calculation of network distances, flows, and importance measures (centrality, PageRank, HITS, etc.). JUNG also provides a visualization framework that makes it easy to construct tools for the interactive exploration of network data. Users can use one of the layout algorithms provided, or use the framework to create their own custom layouts. In addition, filtering mechanisms are provided which allow users to focus their attention, or their algorithms, on specific portions of the graph.

The Source Analyst library is a powerful tool designed to streamline and expedite the tracking of traffic sources for mobile applications. This versatile library is aptly named "Source Analyst" and is an invaluable asset for app developers and marketers seeking to gain deeper insights into the performance of their advertising campaigns. With just one simple function call, Source Analyst empowers you to efficiently investigate the effectiveness of various advertising sources. Key Features: Effortless Tracking: Source Analyst simplifies the complex task of tracking the origins of traffic for your mobile app. No need for convoluted setups or extensive coding – one function is all it takes. Comprehensive Insights: Gain a comprehensive understanding of where your app's users are coming from. Whether it's through social media, search engines, referral links, or other channels, Source Analyst provides you with clear data on traffic sources. Performance Evaluation: Evaluate the performance of your advertising campaigns with precision. Discover which sources are driving the most valuable users to your app, helping you optimize your marketing efforts effectively. Time-Saving: Say goodbye to hours spent on manual data collection and analysis. Source Analyst automates the tracking process, freeing up your time to focus on making data-driven decisions. Customization: Tailor Source Analyst to your specific needs. Customize the library to track the metrics that matter most to your app's success. Real-time Data: Access real-time data, ensuring that you always have up-to-date insights into your traffic sources. Integration-Friendly: Seamlessly integrate Source Analyst into your existing mobile app infrastructure, whether you're developing for Android or iOS. User-Friendly: Source Analyst is designed with user-friendliness in mind. Its intuitive interface and straightforward documentation make it accessible to developers of all levels of expertise. How It Works: Using Source Analyst is as easy as calling a single function within your code. Simply integrate the library into your app, and you can begin tracking traffic sources immediately. From there, Source Analyst compiles and presents the data in a clear and organized manner, allowing you to make data-driven decisions with ease. In a world where understanding the origins of your app's traffic is essential for marketing success, Source Analyst is the go-to solution. Say goodbye to the complexity of tracking sources and embrace the simplicity and effectiveness of Source Analyst for your mobile app. Harness the power of Source Analyst and unlock a new level of insight into your app's performance today!

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.