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GroupDocs.Signature for Java allows developers to write applications with ability to significantly sign electronic docs and verify them. Signatures can be of different types. API supports multitude of file formats and includes Pdf, Microsoft Office documents, Open Office formats, PowerPoint presentations, Images etc. The quite captivating fact about the API is, its UI less and independent calls can be made. It also provides a useful features to verify signed documents with stored signatures. Signature Types: - Text Signature - Image Signature - Digital Signature - Barcode Signature - QR-Code Signature - Stamp Signature Verification Types: - Text Signature - Digital Signature - Barcode Signature - QR-Code Signature For more details on the library, please visit GroupDocs website at: http://groupdocs.com/products/signature/java Note: The library comes up with some limitations in the evaluation mode. In order to test full features of GroupDocs.Signature for Java library, please request a free 30-day temporary license.

Janet Sudoku is a powerful Sudoku Generator and Sudoku Solver library for Java (since jdk 1.5) Android .NET (since 2.0) MONO CLS compliant. Additionally library delivers Janet Sudoku Demo App implementation in a simple form of command line tool, yet highly functional. 1. Sudoku Generator - generate Sudoku puzzles using variety of different methods. 2. Sudoku Solver - Solve any Sudoku puzzle, get the path leading to the correct solution. Check whether unique solution exists. Find all solutions if more than one present. Analyse many built-in examples of Sudoku puzzles or load any other. 3. Manipulate the board by applying transformations not affecting solution existence (i.e. rotation, reflection, permutation, swapping segments, and others). Interact with the library via consisted and well documented API. 4. Janet Sudoku Demo App - use simple, yet highly functional, command line tool to generate, solve evaluate different puzzles.

POJava DateTime is a simple, light-weight Java-based API for parsing and manipulating dates. It parses dates from most languages and formats out of the box without having to specify which format is expected. Defaults such as time zones, and whether to interpret an internationally ambiguous date like "03/06/2014" as DMY order or MDY order are inferred by system time zone and locale and stored in a default config object that can be replaced or overridden. Multiple languages for month names are supported without any additional configuration needed. The net effect the default parser for a server in Paris would have a different automatic configuration from a server in New York. Throw a random local date at either, and it'll parse it as expected. If your server supports customers from multiple locales and time zones, then each can be specified when parsing a date/time to resolve any ambiguities.

[] (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 is the brand new, re-written from scratch plugin for ANTLR v3. Previous valiant efforts all suffered from being unable to modify the ANTLR Tool itself to provide support not just for Maven oriented things but any other tool that might wish to invoke ANTLR without resorting to the command line interface. Rather than try to shoe-horn new code into the existing Mojo (in fact I think that by incorporating a patch supplied by someone I ended up with tow versions of the Mojo, I elected to rewrite everything from scratch, including the documentation, so that we might end up with a perfect Mojo that can do everything that ANTLR v3 supports such as imported grammar processing, proper support for library directories and locating token files from generated sources, and so on. In the end I decided to also change the the ANTLR Tool.java code so that it would be the provider of all the things that a build tool needs, rather than delegating things to 5 different tools. So, things like dependencies, dependency sorting, option tracking, generating sources and so on are all folded back in to ANTLR's Tool.java code, where they belong, and they now provide a public interface to anyone that might want to interface with them. One other goal of this rewrite was to completely document the whole thing to death. Hence even this pom has more comments than funcitonal elements, in case I get run over by a bus or fall off a cliff while skiing. Jim Idle - March 2009

MassBank of America (MoNA), is an auto curating repository for storing, comparing and querying mass spectra of chemical compounds. It is metadata centric and it was designed to allow easy integration into other tools by utilize its REST based application programming interface. At the current time it contains over 200k predicted and 40k unique experimental mass spectra and their associated metadata. The predicted spectra were obtained by utilizing the lipid blast library and the experimental spectra were acquired from 30 different facilities all over the world, including several major research facilities in the United States and Japan. MoNA is utilizing the InChI Key as unique identifier for chemicals and is designed for easy scalability and expendability. This is realized by utilizing common applications like nginx, grails, AngularJS, postgresSQL and tomcat. MoNA is currently integrated in applications like MSDial, BinBase, MZMine and the statistics package R. This was accomplished by utilizing its REST based API, which is also utilized by its main AngularJS based web interface. We consider MoNA to be highly useful for crosslinking mass spectra in publications, identification of unknowns and integration in data acquisition software. This package provides you with access to the REST api of the main MoNA installation.

GroupDocs.Editor for Java is a powerful document editing API using HTML. API can be used with any external, opensource or paid HTML editor. Editor API will process to load documents, convert it to HTML, provide HTML to external UI and then save HTML to original document after manipulation. It can also be used to generate different PDF files, Microsoft Word (DOC, DOCX), Excel spreadsheets (XLS, XSLSX), PowerPoint presentations (PPT, PPTX) and TXT documents. Manipulate Using HTML: Load Document Edit content using HTML Edit styles Perform Editor operations Convert back to supported file Document Editor is a computer program for editing HTML, the markup of a webpage. Although the HTML markup of a web page can be written with any text editor, specialized HTML editors can offer convenience and added functionality. For example, many HTML editors handle not only HTML, but also related technologies such as CSS, XML and JavaScript or ECMAScript. In some cases they also manage communication with remote web servers via FTP and WebDAV, and version control systems such as Subversion or Git. Many word processing, graphic design and page layout programs that are not dedicated to web design, such as Microsoft Word or Quark XPress, also have the ability to function as HTML editors.

MassBank of America (MoNA), is an auto curating repository for storing, comparing and querying mass spectra of chemical compounds. It is metadata centric and it was designed to allow easy integration into other tools by utilize its REST based application programming interface. At the current time it contains over 200k predicted and 40k unique experimental mass spectra and their associated metadata. The predicted spectra were obtained by utilizing the lipid blast library and the experimental spectra were acquired from 30 different facilities all over the world, including several major research facilities in the United States and Japan. MoNA is utilizing the InChI Key as unique identifier for chemicals and is designed for easy scalability and expendability. This is realized by utilizing common applications like nginx, grails, AngularJS, postgresSQL and tomcat. MoNA is currently integrated in applications like MSDial, BinBase, MZMine and the statistics package R. This was accomplished by utilizing its REST based API, which is also utilized by its main AngularJS based web interface. We consider MoNA to be highly useful for crosslinking mass spectra in publications, identification of unknowns and integration in data acquisition software. This package provides you with access to the REST api of the main MoNA installation.

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.