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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.

Xerces2 provides high performance, fully compliant XML parsers in the Apache Xerces family. This new version of Xerces continues to build upon 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 provides fully conforming XML Schema 1.0 and 1.1 processors. An experimental implementation of the "XML Schema Definition Language (XSD): Component Designators (SCD) Candidate Recommendation (January 2010)" is also 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.

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.

DICTIONARY-BASED COMPOUND SPLITTER FOR GERMAN BananaSplit is a compound splitter for German that uses a dictionary resource. The dictionary can be either a simple word list, or a word list equipped with POS values, or an XML based dictionary. The original version was able to use GermaNet as a dictionary. This is useful in applications that rely on GermaNet anyway: no additional lexicon needs to be generated and held in memory. This was also the original purpose of BananaSplit. It served as a compound splitter for a tool called BananaRelation. BananaRelation cannot be published here as it makes heavy use of unpublished code by EML Research, Heidelberg. BananaSplit can either be used as a standalone application or it can be integrated into other Java programs (as a library). This program emerged from the seminar Lexical Semantic Processing in NLP (winter term 2005/2006) taught by Iryna Gurevych at the Seminar für Sprachwissenschaft, Tübingen. Both BananaSplit and BananaRelation were introduced to the seminar participants on 17th of December, 2005. The key algorithm for compound splitting is based on Langer (1998). The program came to use in Müller and Gurevych (2006). Please note that the program splits compounds into two parts only. Details are given in the documents linked below.

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.

# Pact Spring/JUnit5 Support This module extends the base [Pact JUnit5 module](../pact-jvm-provider-junit5). See that for more details. For writing Spring Pact verification tests with JUnit 5, there is an JUnit 5 Invocation Context Provider that you can use with the `@TestTemplate` annotation. This will generate a test for each interaction found for the pact files for the provider. To use it, add the `@Provider` and `@ExtendWith(SpringExtension.class)` and one of the pact source annotations to your test class (as per a JUnit 5 test), then add a method annotated with `@TestTemplate` and `@ExtendWith(PactVerificationSpringProvider.class)` that takes a `PactVerificationContext` parameter. You will need to call `verifyInteraction()` on the context parameter in your test template method. For example: ```java @ExtendWith(SpringExtension.class) @SpringBootTest(webEnvironment = SpringBootTest.WebEnvironment.DEFINED_PORT) @Provider("Animal Profile Service") @PactBroker public class ContractVerificationTest { @TestTemplate @ExtendWith(PactVerificationSpringProvider.class) void pactVerificationTestTemplate(PactVerificationContext context) { context.verifyInteraction(); } } ``` You will now be able to setup all the required properties using the Spring context, e.g. creating an application YAML file in the test resources: ```yaml pactbroker: host: your.broker.host auth: username: broker-user password: broker.password ``` You can also run pact tests against `MockMvc` without need to spin up the whole application context which takes time and often requires more additional setup (e.g. database). In order to run lightweight tests just use `@WebMvcTest` from Spring and `MockMvcTestTarget` as a test target before each test. For example: ```java @WebMvcTest @Provider("myAwesomeService") @PactBroker class ContractVerificationTest { @Autowired private MockMvc mockMvc; @TestTemplate @ExtendWith(PactVerificationInvocationContextProvider.class) void pactVerificationTestTemplate(PactVerificationContext context) { context.verifyInteraction(); } @BeforeEach void before(PactVerificationContext context) { context.setTarget(new MockMvcTestTarget(mockMvc)); } } ``` You can also use `MockMvcTestTarget` for tests without spring context by providing the controllers manually. For example: ```java @Provider("myAwesomeService") @PactFolder("pacts") class MockMvcTestTargetStandaloneMockMvcTestJava { @TestTemplate @ExtendWith(PactVerificationInvocationContextProvider.class) void pactVerificationTestTemplate(PactVerificationContext context) { context.verifyInteraction(); } @BeforeEach void before(PactVerificationContext context) { MockMvcTestTarget testTarget = new MockMvcTestTarget(); testTarget.setControllers(new DataResource()); context.setTarget(testTarget); } @RestController static class DataResource { @GetMapping("/data") @ResponseStatus(HttpStatus.NO_CONTENT) void getData(@RequestParam("ticketId") String ticketId) { } } } ``` **Important:** Since `@WebMvcTest` starts only Spring MVC components you can't use `PactVerificationSpringProvider` and need to fallback to `PactVerificationInvocationContextProvider`

The OODT grid services (product and profile services) use CORBA or RMI as their underlying network transport. However, limitations of CORBA and RMI make them inappropriate for large-scale deployments. For one, both are procedural mechanisms, providing a remote interface that resembles a method call. This makes streaming of data from a service impossible, because there are limitations to the sizes of data structures that can be passed over a remote method call. Instead, repeated calls must be made to retrieve each block of a product, making transfer speeds horribly slow compared to HTTP or FTP. (Block-based retrieval of profiles was never implemented, resulting in out of memory conditions for large profile results, which is another problem.) Second, both CORBA and RMI rely on a central name registry. The registry makes an object independent of its network location, enabling a client to call it by name (looking up its last known location in the registry). However, this requires that server objects be able to make outbound network calls to the registry (through any outbound firewall), and that the registry accept those registrations (through any inbound firewall). This required administrative action at institutions hosting server objects and at the institution hosting the registry. Often, these firewall exceptions would change without notice as system adminstrators changed at each location (apparently firewall exceptions are poorly documented everywhere). Further, in the two major deployments of OODT (PDS and EDRN), server objects have almost never moved, nullifying any benefit of the registry. This project, OODT Web Grid Services, avoids the prolems of CORBA and RMI by using HTTP as the transport mechanism for products and profiles. Further, it provides a password-protected mechanism to add new sets of product and profile query handlers, enabling seamless activation of additional capabilities.

BEhavioural Agents Simple Testing Tool - BEAST Tool The aim of this project is the development of a system which allows Behavior Driven Development (BDD) in Multi-Agent Systems (MAS), to make testing practices more accessible and intuitive to everybody. In one hand, in order to let tests be writable by newcomers and experts alike, system must allow the redaction of tests in plain text, because client does not need to have knowledge of our code. This plain text will be traduced to software later. The definition of test will be realized with the terminology Given-When-Then, which allows trace an easy guide of the behavior of a given scenario when something happened. In the other hand, due to the complexity of MAS, making unit testing of an agent that needs the interaction with others is almost impossible until the whole system is finished. This implies to leave testing issues to the end of the project, generating big troubles in case of malfunction. Consequently, its necessary to carry out a tool to allow the creation of mock agents and to perform tests during the whole development process. Therefore another objective of our systems is to include a mocking tool which permits testing continuously. Definitively, our tool allows the testing of any MAS in the development process, increasing its modularity and decreasing its elaboration and testing cost. These tests will be written in plain text so that anyone would be able to understand them. For further reading, a paper published in ITMAS2012 workshop can be found in: http://scholar.google.es/citations?view_op=view_citation&hl=es&user=mT3KgXUAAAAJ&citation_for_view=mT3KgXUAAAAJ:Tyk-4Ss8FVUC

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.