Download JAR files tagged by extremely with all dependencies

The Jadex micro kernel provides a lightweight and very efficient kernel for minimal agents with an extremely low memory footprint. Micro agents have a simple internal architecture that is similar to active objects. They are programmed as Java objects overriding a framework class, which has predefined methods for the setup, termination, execution logic as well as message receipt.

Class implementing a HyperPipe classifier. For each category a HyperPipe is constructed that contains all points of that category (essentially records the attribute bounds observed for each category). Test instances are classified according to the category that "most contains the instance". Does not handle numeric class, or missing values in test cases. Extremely simple algorithm, but has the advantage of being extremely fast, and works quite well when you have "smegloads" of attributes.

IGRPWeb is a platform developed by the Operational Nucleus for the Information Society (NOSi) to create web applications, based on business steps, processes, automatic code generation and incorporation of the Once-Only-Principle, written in Java. The IGRPWeb core is extremely lightweight and based on simple concepts. It's open-source and distributed under the Apache 2.0 license. Allows you to build unlimited number of e-government applications by yourself ensuring sustainability with an affordable and scalable solution on your own data.

In brewing, a fermenter is a vessel in which unfinished ingredients become nearly finished beer. In Model Driven Architecture, Fermenter is a project that converts functional concepts into nearly finished applications. This approach allows for the quick definition and assembly of applications with the focus on functional concepts rather than technical underpinnings. Fermenter is an MDA engine that allows common concepts to be modeled and spawn any number of generated source files. In turn, this allows developers to focus more time on business logic, less time on technical underpinnings, and achieve extremely high levels of architectural conformity.

In brewing, a fermenter is a vessel in which unfinished ingredients become nearly finished beer. In Model Driven Architecture, Fermenter is a project that converts functional concepts into nearly finished applications. This approach allows for the quick definition and assembly of applications with the focus on functional concepts rather than technical underpinnings. Fermenter is an MDA engine that allows common concepts to be modeled and spawn any number of generated source files. In turn, this allows developers to focus more time on business logic, less time on technical underpinnings, and achieve extremely high levels of architectural conformity.

RuSH is an efficient, reliable, and easy adaptable rule-based sentence segmentation solution. It is specifically designed to handle the telegraphic written text in clinical note. It leverages a nested hash table to execute simultaneous rule processing, which reduces the impact of the rule-base growth on execution time and eliminates the effect of rule order on accuracy. If you wish to cite RuSH in a publication, please use: Jianlin Shi ; Danielle Mowery ; Kristina M. Doing-Harris ; John F. Hurdle.RuSH: a Rule-based Segmentation Tool Using Hashing for Extremely Accurate Sentence Segmentation of Clinical Text. AMIA Annu Symp Proc. 2016: 1587. The full text can be found at: https://knowledge.amia.org/amia-63300-1.3360278/t005-1.3362920/f005-1.3362921/2495498-1.3363244/2495498-1.3363247?timeStamp=1479743941616 This version allows defining section scopes for sentence segmentation.

RuSH is an efficient, reliable, and easy adaptable rule-based sentence segmentation solution. It is specifically designed to handle the telegraphic written text in clinical note. It leverages a nested hash table to execute simultaneous rule processing, which reduces the impact of the rule-base growth on execution time and eliminates the effect of rule order on accuracy. If you wish to cite RuSH in a publication, please use: Jianlin Shi ; Danielle Mowery ; Kristina M. Doing-Harris ; John F. Hurdle.RuSH: a Rule-based Segmentation Tool Using Hashing for Extremely Accurate Sentence Segmentation of Clinical Text. AMIA Annu Symp Proc. 2016: 1587. The full text can be found at: https://knowledge.amia.org/amia-63300-1.3360278/t005-1.3362920/f005-1.3362921/2495498-1.3363244/2495498-1.3363247?timeStamp=1479743941616

Xerces2 is the next generation of high performance, fully compliant XML parsers in the Apache Xerces family. This new version of Xerces introduces the Xerces Native Interface (XNI), a complete framework for building parser components and configurations that is extremely modular and easy to program. The Apache Xerces2 parser is the reference implementation of XNI but other parser components, configurations, and parsers can be written using the Xerces Native Interface. For complete design and implementation documents, refer to the XNI Manual. Xerces2 is a fully conforming XML Schema 1.0 processor. A partial experimental implementation of the XML Schema 1.1 Structures and Datatypes Working Drafts (December 2009) and an experimental implementation of the XML Schema Definition Language (XSD): Component Designators (SCD) Candidate Recommendation (January 2010) are provided for evaluation. For more information, refer to the XML Schema page. Xerces2 also provides a complete implementation of the Document Object Model Level 3 Core and Load/Save W3C Recommendations and provides a complete implementation of the XML Inclusions (XInclude) W3C Recommendation. It also provides support for OASIS XML Catalogs v1.1. Xerces2 is able to parse documents written according to the XML 1.1 Recommendation, except that it does not yet provide an option to enable normalization checking as described in section 2.13 of this specification. It also handles namespaces according to the XML Namespaces 1.1 Recommendation, and will correctly serialize XML 1.1 documents if the DOM level 3 load/save APIs are in use.