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Multiple loosely coupled modules which extend ZK framework, originally created by DataLite company. ZKComposer - controll the MVC design with annotations @ZkEvent?, @ZkComponent?, @ZkBinding?, ... (similar to zkspring or zkcdi projects, however without any framework dependencies). ZKDLComponents splits ZK data driven components (listbox, combobox) to MVC design, adds support for easy large data manipulation and contains many pre-built user controls (filter manager, sort manager, visible listbox columns manager, export to excel, etc.) HibernateDAO & ZKDLComponentsHibernateExtensions - Hibernate generic DAO design with extensions for ZKDLComponents to replace all your plumbing code (filter, sort, lazy loading, CRUD). SpringService? - service layer with spring - generic service which speaks to generic DAO. This is the way we use our components at DataLite?, but you can use any integration or DAO at your will.

Multiple loosely coupled modules which extend ZK framework, originally created by DataLite company. ZKComposer - controll the MVC design with annotations @ZkEvent?, @ZkComponent?, @ZkBinding?, ... (similar to zkspring or zkcdi projects, however without any framework dependencies). ZKDLComponents splits ZK data driven components (listbox, combobox) to MVC design, adds support for easy large data manipulation and contains many pre-built user controls (filter manager, sort manager, visible listbox columns manager, export to excel, etc.) HibernateDAO & ZKDLComponentsHibernateExtensions - Hibernate generic DAO design with extensions for ZKDLComponents to replace all your plumbing code (filter, sort, lazy loading, CRUD). SpringService? - service layer with spring - generic service which speaks to generic DAO. This is the way we use our components at DataLite?, but you can use any integration or DAO at your will.

Iris is an attempt at bringing the simplicity and elegance of cloud computing to the application layer. Consumer clouds provide unlimited virtual machines at the click of a button, but leaves it to developer to wire them together. Iris ensures that you can forget about networking challenges and instead focus on solving your own domain problems. It is a completely decentralized messaging solution for simplifying the design and implementation of cloud services. Among others, Iris features zero-configuration (i.e. start it up and it will do its magic), semantic addressing (i.e. application use textual names to address each other), clusters as units (i.e. automatic load balancing between apps of the same name) and perfect secrecy (i.e. all network traffic is encrypted).

The core of the Struts framework is a flexible control layer based on standard technologies like Java Servlets, JavaBeans, ResourceBundles, and Extensible Markup Language (XML), as well as various Jakarta Commons packages. Struts encourages application architectures based on the Model 2 approach, a variation of the classic Model-View-Controller (MVC) design paradigm. Struts provides its own Controller component and integrates with other technologies to provide the Model and the View. For the Model, Struts can interact with any standard data access technology, including Enterprise Java Beans, JDBC, and Object Relational Bridge. For the View, Struts works well with JavaServer Pages, including JSTL and JSF, as well as Velocity Templates, XSLT, and other presentation systems. The Struts framework provides the invisible underpinnings every professional web application needs to survive. Struts helps you create an extensible development environment for your application, based on published standards and proven design patterns.

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.

This package currently contains classes for training multilayer perceptrons with one hidden layer, where the number of hidden units is user specified. MLPClassifier can be used for classification problems and MLPRegressor is the corresponding class for numeric prediction tasks. The former has as many output units as there are classes, the latter only one output unit. Both minimise a penalised squared error with a quadratic penalty on the (non-bias) weights, i.e., they implement "weight decay", where this penalised error is averaged over all training instances. The size of the penalty can be determined by the user by modifying the "ridge" parameter to control overfitting. The sum of squared weights is multiplied by this parameter before added to the squared error. Both classes use BFGS optimisation by default to find parameters that correspond to a local minimum of the error function. but optionally conjugated gradient descent is available, which can be faster for problems with many parameters. Logistic functions are used as the activation functions for all units apart from the output unit in MLPRegressor, which employs the identity function. Input attributes are standardised to zero mean and unit variance. MLPRegressor also rescales the target attribute (i.e., "class") using standardisation. All network parameters are initialised with small normally distributed random values.

Axis2 is an effort to re-design and totally re-implement both Axis/Java and (eventually) Axis/C++ on a new architecture. Evolving from the now standard "handler chain" model which Axis1 pioneered, Axis2 is developing a more flexible pipeline architecture which can yet be managed and packaged in a more organized manner. This new design acknowledges the maturing of the Web services space in terms of new protocols such as WS-ReliableMessaging, WS-Security and WS-Addressing that are built on top of the base SOAP system. At the time Axis1 was designed, while it was fully expected that other protocols such as WS-ReliableMessaging would be built on top of it, there was not a proper extension architecture defined to enable clean composition of such layers. Thus, one of the key motivations for Axis2 is to provide a clean and simple environment for like Apache Sandesha and Apache WSS4J to layer on top of the base SOAP system. Another driving force for Axis2 as well as the move away from RPC oriented Web services towards more document-oriented, message style asynchronous service interactions. The Axis2 project is centered on a new representation for SOAP messages called AXIOM (AXIs Object Model). AXIOM consists of two parts: a complete XML Infoset representation and a SOAP Infoset representation on top of that. The XML Infoset representation provides a JDOM-like simple API but is built on a deferred model via a StAX-based (Streaming API for XML) pull parsing API. A key feature of AXIOM is that it allows one to stop building the XML tree and just access the pull stream directly; thus enabling both maximum flexibility and maximum performance. This approach allows us to support multiple levels of abstraction for consuming and offering Web services: using plain AXIOM, using generated code and statically data-bound data types and so on. At the time of Axis1's design, RPC-style, synchronous, request-response interactions were the order of the day for Web services. Today service interactions are much more message -oriented and exploit many different message exchange patterns. The Axis2 engine architecture is careful to not build in any assumptions of request-response patterns to ensure that it can be used easily to support arbitrary message exchange patterns.

This is the main aggregator for all gwt submodules. All gwt-specific code resides here. Submodules should avoid inheriting from each other unless necessary. This goes for maven structure and gwt.xml structure. The super module is where our jre emulation layer and super-source live; all modules should inherit super, and a minimum of other modules. Some modules, like injection, are fulfilling an api in the core module, and should be accessed only through core service interfaces. Other modules, like reflection, are capable of being standalone inherits, but can benefit from core utilities like injection, so, two (or more) .gwt.xml modules may be provided. As XApi nears 1.0, all submodules will be routinely stitched together into an uber-jar, in order to have a single jar with a single gwt module that can provide all of the services at once. Internal projects will never use the uber jar, to help maintain modularity, but external projects that want to use more than one service will certainly prefer inheriting one artifact, instead of twelve. When distributed in uber-jar format, it will likely be necessary for either the uber jar, or just xapi-gwt-api.jar to appear before gwt-dev on your compile-time classpath. If using gwt-maven-plugin, the gwtFirstOnClasspath option may become problematic. If so, we will provide a forked gwt-plugin to make sure our compiler enhancements are included in the build process. There is also work going on to make a super-source-everything plugin, which will use maven to find source files, and generate synthetic .gwt.xml for you, as part of an effort to create a wholly unified programming environment. In addition to java-to-javascript, we intend to compile java-to-java and possibly other languages, like go; imagine implementing gwt deferred binding to eliminate cross-platform differences between server environments, or operating systems, or versions of a platform, or anywhere else a core api needs to bind to multiple implementations, depending on the runtime environment.