Download JAR files tagged by both with all dependencies

The Finch HRIS API provides a unified way to connect to a multitide of HRIS systems. The API requires an access token issued by Finch. By default, Organization and Payroll requests use Finch's [Data Syncs](/development-guides/Data-Syncs). If a request is made before the initial sync has completed, Finch will request data live from the provider. The latency on live requests may range from seconds to minutes depending on the provider and batch size. For automated integrations, Deductions requests (both read and write) are always made live to the provider. Latencies may range from seconds to minutes depending on the provider and batch size. Employer products are specified by the product parameter, a space-separated list of products that your application requests from an employer authenticating through Finch Connect. Valid product names are— - `company`: Read basic company data - `directory`: Read company directory and organization structure - `individual`: Read individual data, excluding income and employment data - `employment`: Read individual employment and income data - `payment`: Read payroll and contractor related payments by the company - `pay_statement`: Read detailed pay statements for each individual - `benefits`: Create and manage deductions and contributions and enrollment for an employer [](https://god.gw.postman.com/run-collection/21027137-08db0929-883d-4094-a9ce-dbf5a9bee4a4?action=collection%2Ffork&collection-url=entityId%3D21027137-08db0929-883d-4094-a9ce-dbf5a9bee4a4%26entityType%3Dcollection%26workspaceId%3D1edf19bc-e0a8-41e9-ac55-481a4b50790b)

[] (https://drone.io/bitbucket.org/tidalwave/thesefoolishthings-superpom-src/latest) A feature-rich SuperPOM for building Java projects. It features: * explicit version configuration for a number of plugins; * easy configurability by means of pre-defined properties to avoid cut & copy of plugin sections. A number of profiles, that can be easily activated, are available for: * Spring-AOP configuration; * different kinds of Continuous Integration tasks, including a full run of QA tools such as JaCoCo, FindBugs, PMD, etc... * deploying WARs and locally running them with Tomcat or Jetty; * creating a Mac OS X bundle for JavaFX applications; * creating .deb packages for both application and services; * a customized release cycle, including all requirements for the Maven Central such as signing, with a 'transactional' behaviour (all artifacts, both the DSCM and the Maven artifacts are prepared on the local disk, so they can be uploaded in a second moment); Remember to customise it ------------------------ If you use it, please remember to change the ```description```,```url```, ```organization```, ```developers```, ```license```, etc... to override those related to the development of this POM.

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.

git-commit-id-plugin is a plugin quite similar to https://fisheye.codehaus.org/browse/mojo/tags/buildnumber-maven-plugin-1.0-beta-4 for example but as buildnumber only supports svn (which is very sad) and cvs (which is even more sad). This plugin makes basic repository information available through maven resources. This can be used to display "what version is this?" or "who has deployed this and when, from which branch?" information at runtime - making it easy to find things like "oh, that isn't deployed yet, I'll test it tomorrow" and making both testers and developers life easier. The data currently exported is like this (that's the end effect from the GitRepositoryState Bean): { "branch" : "testing-maven-git-plugin", "commitTime" : "06.01.1970 @ 16:16:26 CET", "commitId" : "787e39f61f99110e74deed68ab9093088d64b969", "commitUserName" : "Konrad Malawski", "commitUserEmail" : "[email protected]", "commitMessageFull" : "releasing my fun plugin :-) + fixed some typos + cleaned up directory structure + added license etc", "commitMessageShort" : "releasing my fun plugin :-)", "buildTime" : "06.01.1970 @ 16:17:53 CET", "buildUserName" : "Konrad Malawski", "buildUserEmail" : "[email protected]" } Note that the data is exported via maven resource filtering and is really easy to use with spring - which I've explained in detail in this readme https://github.com/ktoso/maven-git-commit-id-plugin

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.