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Talend ESB SE bundles Service Factory and Integration Factory, Service Locator, Service Activity Monitoring and a demo application to a stable, production ready distribution based on the industry leading open source frameworks Apache CXF and Apache Camel. It includes Web Service support based on JAX-WS and enables simple and scalable message based systems using well known Enterprise Integration Patterns. The Service Locator maintains a repository of service endpoints which can be used by Service Consumers to support simple failover and load balancing scenarios. Service Activity Monitoring allows to log and monitor service calls. A documented Car Rental demo application illustrates how to use these features.

This artifact provides the implementation of (a blue-fringe version of) the "regular positive negative inference" (RPNI) learning algorithm as presented in the paper "Inferring regular languages in polynomial update time" (https://dx.doi.org/10.1142/9789812797902_0004) by Jose Oncina and Pedro García using the "minimum description length" (MDL) heuristic. More details on this algorithm can be found in the book "Grammatical Inference" (https://doi.org/10.1017/CBO9781139194655) by Colin de la Higuera.

This artifact provides the implementation of (a blue-fringe version of) the "regular positive negative inference" (RPNI) learning algorithm as presented in the paper "Inferring regular languages in polynomial update time" (https://dx.doi.org/10.1142/9789812797902_0004) by Jose Oncina and Pedro García using the "evidence-driven state merging" (EDSM) heuristic. More details on this algorithm can be found in the book "Grammatical Inference" (https://doi.org/10.1017/CBO9781139194655) by Colin de la Higuera.

This artifact provides the implementation of the L* learning algorithm described in the paper "Learning Regular Sets from Queries and Counterexamples" (https://doi.org/10.1016/0890-5401(87)90052-6) by Dana Angluin including variations and optimizations thereof such as the versions based on "On the Learnability of Infinitary Regular Sets" (https://dx.doi.org/10.1006/inco.1995.1070) by Oded Maler and Amir Pnueli or "Inference of finite automata using homing sequences" (http://dx.doi.org/10.1006/inco.1993.1021) by Ronald L. Rivest and Robert E. Schapire.

Provides non-free data, including the EPSG geodetic dataset, in a single read-only JAR file. This 'sis-embedded-data' module contains a copy of EPSG geodetic dataset in an embedded Apache Derby database. Having this module on the classpath avoid the need to set the 'SIS_DATA' environment variable for using the Coordinate Reference Systems (CRS) and Coordinate Operations defined by EPSG. EPSG is maintained by the IOGP Surveying & Positioning Committee and reproduced in this module with same content. See https://www.epsg.org/ for more information.

This package contains several example programs of the usage of the Chips-n-Salsa library. Chips-n-Salsa is a Java library of customizable, hybridizable, iterative, parallel, stochastic, and self-adaptive local search algorithms. Chips-n-Salsa's source code is maintained on GitHub, and the prebuilt jars of the library can be imported from Maven Central using maven or other build tools. The purpose of the package chips-n-salsa-examples is to demonstrate usage of the major functionality of the Chips-n-Salsa library. You can find out more about the Chips-n-Salsa library itself from its website: https://chips-n-salsa.cicirello.org/.

Maximum Likelihood Rule Ensembles (MLRules) is a new rule induction algorithm for solving classification problems via probability estimation. The ensemble is built using boosting, by greedily minimizing the negative loglikelihood which results in estimating the class conditional probability distribution. The main advantage of decision rules is their simplicity and comprehensibility: they are logical statements of the form "if condition then decision", which is probably the easiest form of model to interpret. On the other hand, by exploiting a powerful statistical technique to induce the rules, the final ensemble has very high prediction accuracy. Fork of the original code located at: http://www.cs.put.poznan.pl/wkotlowski/software-mlrules.html

This archetype is creating a full Sling Project composed of a OSGi Bundle and a Content Package which can be deployed to the Sling using specific profiles. It also contains two shadow folders that provide example code / files which are not part of the default build but can be easily copied into the active modules. If the project was created with the **optionAll** property set to **y** (yes) then an **All** package is created with acts as the single deployment unit of all bundles and packages in that project. Otherwise the **ui.apps** package is the deployment unit.

This package contains several example programs of the usage of the JavaPermutationTools (JPT) library. JPT is a Java library that enables representing and generating permutations and sequences, as well as performing computation on permutations and sequences. It includes implementations of a variety of permutation distance metrics as well as distance metrics on sequences (i.e., Strings, arrays, and other ordered data types). In addition to programs demonstrating the usage of the JPT library, the jpt-examples package also contains programs for replicating the experiments from a few published papers that utilized the library or implementations on which the library is based. JPT's source code is maintained on GitHub, and the prebuilt jars of the library can be imported from Maven Central using maven or other build tools. The purpose of the package jpt-examples is to demonstrate usage of the major functionality of the JPT library. You can find out more about the JPT library itself from its website: https://jpt.cicirello.org/.

A simple repository to salt a secret key and verify its authenticity. Developed to be used in mobile applications while communicating with a server side using an Api. Instead of sending an apikey straight on the request, SaltedKey can generate a temporary public key, valid for one time use and during a specific time frame, default time is set to 60 seconds. This public key can be sent on request and on server side SaltedKey can verify its authenticity. The Salt is based on time millis and uses the algorithm SHA-256 to create the temporary public key. The public key base will change every time it exceeds the time frame. This library can increase the API security. Even if the public key used on request is exposed, no one will be able to use it again! As it is a one time use only.