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DefaultJ is a powerful Java library that provides default values for any class, offering multiple strategies for obtaining those values. It can be used as a dependency injection utility, similar to Guice. With 14 built-in strategies, DefaultJ can obtain default values through various means such as explicit bindings, annotations, enum values, factory methods, and more. It also supports custom annotations and offers annotation processors for validation. Available on Maven Central, DefaultJ can be easily integrated into Gradle and Maven projects. Contributions and selective usage of the library are encouraged.

Simple Validation Parent Project: This is a simple library for quickly adding validation code to Swing user-interfaces. It handles validating user input when the user changes a component's value, showing error messages and decorating components to indicate which component is the source of the problem. It contains a large number of built-in validators to handle most common situations, such as validating numbers, email addresses, urls and so forth.

This is a plugin to Madura Objects. It provides a rules/constraint engine to assist with validation, deriving new values from user inputs (eg total of invoices entered on this customer) an manipulating metadata (eg because the amount is above X we make some field readonly). Note that the Java that is using the monitored objects is quite unaware of the rules layer implemented here. That means you can change rules without having to go back to your Java code, and it also means you don't have to wonder if everything implemented the same rules. Anything using that Java object has the rules (unless you turn them all off).

<b>JCAPTCHA</b>, for <b>J</b>ava <b>C</b>ompletely <b>A</b>utomated <b>P</b>ublic <b>T</b>est to tell <b>C</b>omputers and <b>H</b>umans <b>A</b>part <br/>The open source java framework for captcha definition and integration <br/> A captcha is a simple captcha container that contains a question, a challenge, and a response validation routine.<BR/> A captcha can only be built by a captcha factory that provides methods to build localized captchas.<br/> This jar aims to provide interfaces and implementation of generic and typed captcha and captcha factories.<BR/> It uses the word generator, types sub packages (image and sound) components (word to image, word to sound) to build typed captchas captchas.

EasyConfig provides simple way to overview and apply settings to file or folder based collections of files. Synonyms to "setting" are property, attribute, value while throughout application "setting" is used. The settings are groupped in "configuration" that is collection of settings from various sources. Main design concepts are: * minimalistic way to describe configuration * pluggable support for data types (validation), setting sources, source handlers Sample use case: An application is deployed in multiple locations. We need to quickly check key settings/parameters and optionally modify some of them. These values are located in different places: - in files directly in file structure - in files inside archive files (optionally nested archives) - values in DB tables - values accessible via URLs - other sources (just guessed: SSH/telnet connection+some command(s), UPnP devices, proprietary protocols, etc) We gather info from any supported (extendable) source and can modify and apply changes if supported by source (e.g. we can't update value that is count of rows in DB table, but we can read that value).

Races the cross validation error of competing attribute subsets. Use in conjuction with a ClassifierSubsetEval. RaceSearch has four modes: forward selection races all single attribute additions to a base set (initially no attributes), selects the winner to become the new base set and then iterates until there is no improvement over the base set. Backward elimination is similar but the initial base set has all attributes included and races all single attribute deletions. Schemata search is a bit different. Each iteration a series of races are run in parallel. Each race in a set determines whether a particular attribute should be included or not---ie the race is between the attribute being "in" or "out". The other attributes for this race are included or excluded randomly at each point in the evaluation. As soon as one race has a clear winner (ie it has been decided whether a particular attribute should be inor not) then the next set of races begins, using the result of the winning race from the previous iteration as new base set. Rank race first ranks the attributes using an attribute evaluator and then races the ranking. The race includes no attributes, the top ranked attribute, the top two attributes, the top three attributes, etc. It is also possible to generate a raked list of attributes through the forward racing process. If generateRanking is set to true then a complete forward race will be run---that is, racing continues until all attributes have been selected. The order that they are added in determines a complete ranking of all the attributes. Racing uses paired and unpaired t-tests on cross-validation errors of competing subsets. When there is a significant difference between the means of the errors of two competing subsets then the poorer of the two can be eliminated from the race. Similarly, if there is no significant difference between the mean errors of two competing subsets and they are within some threshold of each other, then one can be eliminated from the race.