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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.

The IntelligentGraph SAIL offers an extended capability for embedded calculation support within any RDF graph. When enabled as an RDF4J SAIL, it offers calculation functionality as part of the RDF4J engine, on top of any RDF4J repository, using a variety of script engines including JavaScript, Jython, and Groovy. It preserves the SPARQL capability of RDF4J, but with additional capabilities for calculation debugging and tracing. IntelligentGraph includes the PathQL query language. Just as a spreadsheet cell calculation needs to access other cells, an IntelligentGraph calculation needs to access other nodes within the graph. Although full access to the underlying graph is available to any of the scripts, PathQL provides a succinct, and efficient method to access directly or indirectly related nodes. PathQL can either return just the contents of the referenced nodes, or the contents and the path to the referenced nodes. PathQL can also be used standalone to query the IntelligentGraph-enabled RDF database. This supplements, rather than replaces, SPARQL and GraphQL, as it provides graph-path querying rather than graph-pattern querying capabilities to any IntelligentGraph-enabled RDF database. The principles of IntelligentGraph are described here: https://inova8.com/bg_inova8.com/intelligent-graph-knowledge-graph-embedded-analysis/ The full PathQL syntax is described here: https://inova8.com/bg_inova8.com/pathpatternql-intelligently-finding-knowledge-as-a-path-through-a-maze-of-facts/ Using Jupyter as an IDE to IntelligentGraph and RDF4J, shown here: https://inova8.com/bg_inova8.com/intelligentgraph-getting-started/ IntelligentGraph source is here in GitHub: https://github.com/peterjohnlawrence/com.inova8.intelligentgraph IntelligentGraph Docker containers are available here: https://hub.docker.com/repository/docker/inova8/intelligentgraph

GroupDocs.Editor for Java is a powerful document editing API using HTML. API can be used with any external, opensource or paid HTML editor. Editor API will process to load documents, convert it to HTML, provide HTML to external UI and then save HTML to original document after manipulation. It can also be used to generate different PDF files, Microsoft Word (DOC, DOCX), Excel spreadsheets (XLS, XSLSX), PowerPoint presentations (PPT, PPTX) and TXT documents. Manipulate Using HTML: Load Document Edit content using HTML Edit styles Perform Editor operations Convert back to supported file Document Editor is a computer program for editing HTML, the markup of a webpage. Although the HTML markup of a web page can be written with any text editor, specialized HTML editors can offer convenience and added functionality. For example, many HTML editors handle not only HTML, but also related technologies such as CSS, XML and JavaScript or ECMAScript. In some cases they also manage communication with remote web servers via FTP and WebDAV, and version control systems such as Subversion or Git. Many word processing, graphic design and page layout programs that are not dedicated to web design, such as Microsoft Word or Quark XPress, also have the ability to function as HTML editors.

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.

Introducing "PerScope" Library: Simplifying Privacy Policy Event Handling for Android Apps "PerScope" is a cutting-edge library designed to streamline the processing of privacy policy events within regions where compliance with local legislation is crucial. Specifically crafted for Android applications, this library addresses the intricate task of managing privacy policy-related events while adhering to the legal requirements of the country in which the app is deployed. In today's digital landscape, ensuring user privacy and data protection is of paramount importance. Different countries have varying legal frameworks dictating how user data should be handled, necessitating robust mechanisms to accommodate these differences seamlessly. This is where the "PerScope" library shines. The key feature that sets "PerScope" apart is its incredible simplicity. With just a single function call, developers can integrate the library into their Android applications and gain immediate access to a comprehensive suite of tools for managing privacy policy events. Whether it's presenting privacy-related notifications, tracking user consents, or adapting the app's behavior based on regional requirements, "PerScope" handles it all efficiently and effectively. Here's a glimpse of what "PerScope" brings to the table: Localized Compliance: "PerScope" empowers developers to align their apps with the privacy laws of each region. By intelligently detecting the user's location, the library ensures that the app's behavior remains compliant with the specific privacy regulations of that area. Event Handling Made Easy: Instead of grappling with complex event management code, developers can integrate the "PerScope" function, drastically reducing development time and effort. The library takes care of the intricate event handling process seamlessly. Dynamic Adaptation: With the ability to dynamically adapt the app's features based on the user's consent and the local legal requirements, "PerScope" ensures a personalized and compliant user experience. Notification Presentation: "PerScope" assists in presenting privacy-related notifications to users, making it easier to inform them about data collection practices and obtain necessary consents. Smooth Integration: The library is designed to be easily integrated into existing Android applications, minimizing disruptions to the development process. In a nutshell, "PerScope" is a developer's go-to solution for managing privacy policy events within Android apps. Its single-function approach, combined with its capacity to handle a complex and critical aspect of app development, makes it an indispensable tool for app creators aiming to provide a user-centric, privacy-respecting experience while complying with regional legislation. Stay on the right side of the law and prioritize user privacy with the power of "PerScope."