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This is a handy tool many developers already use to create better interaction models for their custom Alexa skills. The tool introduces an easy to read grammar for generating hundreds and thousands of variant sample utterances and slot values with just a few written lines. The resulting JSON file can be used to upload the model to your Alexa skill right away either via your web browser into Alexa skill builder interface or with help of SMAPI.

Asynchronous publish-subscribe communication library. Features: 1. Listeners are stored using WeakReferences to prevent memory leaks when they are not unsubscribed. 2. Uses fork-join framework for concurrent event delivery. 3. Publish methods returns collection of Futures that represent event notifications. 4. Supports ordered publishing: guaranteed to notify of the events in order they were published. 5. Provides EventSource and EventService interfaces for better integration with IOC containers and alternative implementations.

The JSP TLD Maven Plugin is used to automate the management of JSP Tag Library Descriptors as part of a Maven build. It is intended to decrease the burden on JSP developers associated with writing custom JSP Tag Libraries. The goal is to encourage JSP developers to write better JSP files by using custom JSP Tag Libraries more extensively. The Plugin can process Java annotations on Tag implementation classes and also discover Tag Files (.tag and .tagx files). The Java 5 Annotations recognized by this Plugin are all part of the <a href="../squeakysand-jsp/apidocs/com/squeakysand/jsp/tagext/annotations/package-summary.html">com.squeakysand.jsp.tagext.annotations</a> package.

This package supports the parsing of AIS messages in Java. AIS, the Automatic Identification System, is a system aiming at improving maritime safety by exchanging messages between ships, other vehicles in particular aircraft involved in search-and-rescue (SAR), and (fixed) base stations. To be precise, this package support the ITU-R M.1371-4 AIS standard. See our extensive javadoc and in particular the class AISParser for more information on how to use this package. The parser was used in the Poseidon project, and is improved in the Metis project to better handle uncertain information. Both projects were led by the Embedded Systems Institute. In both projects Thales Nederlands was the carrying industrial partner, and multiple Dutch universities participated.

Performs a grid search of parameter pairs for the a classifier (Y-axis, default is LinearRegression with the "Ridge" parameter) and the PLSFilter (X-axis, "# of Components") and chooses the best pair found for the actual predicting. The initial grid is worked on with 2-fold CV to determine the values of the parameter pairs for the selected type of evaluation (e.g., accuracy). The best point in the grid is then taken and a 10-fold CV is performed with the adjacent parameter pairs. If a better pair is found, then this will act as new center and another 10-fold CV will be performed (kind of hill-climbing). This process is repeated until no better pair is found or the best pair is on the border of the grid. In case the best pair is on the border, one can let GridSearch automatically extend the grid and continue the search. Check out the properties 'gridIsExtendable' (option '-extend-grid') and 'maxGridExtensions' (option '-max-grid-extensions <num>'). GridSearch can handle doubles, integers (values are just cast to int) and booleans (0 is false, otherwise true). float, char and long are supported as well. The best filter/classifier setup can be accessed after the buildClassifier call via the getBestFilter/getBestClassifier methods. Note on the implementation: after the data has been passed through the filter, a default NumericCleaner filter is applied to the data in order to avoid numbers that are getting too small and might produce NaNs in other schemes.