Download JAR files tagged by using with all dependencies

This module creates two things: (1) a text file that contains a list of GAV coordinates needed for the 2nd stage boot loader (2) jar files mentioned above, in the Maven repository directory layout. Using Maven repository directory layout, as opposed to more simpler "flat directory with full of jars", allow us to share the jar files between fallback bees-driver dependencies and boot2, which saves disk space a lot. We do this in a separate module outside boot2, so that the list includes the bees-boot2 jar itself.

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 contains a classifier that can be used to train a two-class kernel logistic regression model with the kernel functions that are available in WEKA. It optimises the negative log-likelihood with a quadratic penalty. Both, BFGS and conjugate gradient descent, are available as optimisation methods, but the former is normally faster. It is possible to use multiple threads, but the speed-up is generally very marginal when used with BFGS optimisation. With conjugate gradient descent optimisation, greater speed-ups can be achieved when using multiple threads. With the default kernel, the dot product kernel, this method produces results that are close to identical to those obtained using standard logistic regression in WEKA, provided a sufficiently large value for the parameter determining the size of the quadratic penalty is used in both cases.

Sass makes CSS fun again. Sass is an extension of CSS3, adding nested rules, variables, mixins, selector inheritance, and more. It's translated to well-formatted, standard CSS using the command line tool or a web-framework plugin. This is a repackaged GEM in a JAR format of the sass-lang.gem package. The sass-gems package version follows the sass-lang.gem versions located http://rubyforge.org/frs/?group_id=9702. Simply change the version of this package to download and repackage the same GEM version.

This plugin gives SQuirreLSQL client the ability to configure alternate LaFs provided by the plugin as well as others that the user can provide. All dependencies listed with a groupId of net.sf.squirrel-sql and artifactId starting with squirrelsql-lf- are externally available open-source look and feels whose project maintainers have decided to not use maven, or those which were built and maintained only before the introduction of maven. Maven's central repository policy requires any pom that is deployed there to reference only artifacts which are also hosted in central. And a project may only use the groupId that is related to that project. So, this only leaves us with one option which is to host these external third-party artifacts using our groupId. The hope is that someday these projects will start using maven and the need for this will no longer be.

Siena is a persistence abstraction layer made in Java with various implementations: * siena-jdbc: persists objects into a relational database using JDBC (status: deeply tested) * siena-gae: persists objects into the Google App Engine's datastore using its low-level API (status: beta) * siena-sdb: persists objects using Amazon's SimpleDB (very experimental) * siena-remote: persists objects remotely using a remote web application that uses siena. (planning) Siena is also: * Intrusive. You make a class persistent by extending a certain class. Relationships are defined also in an intrusive way. * Simple. Simplicity is the most important thing. * Limited. Due to the simplicity there are several unsupported things such as JOINs. * Extensible. There are two interfaces you can implement on your own way.

This metaclassifier makes its base classifier cost-sensitive using the method specified in Pedro Domingos: MetaCost: A general method for making classifiers cost-sensitive. In: Fifth International Conference on Knowledge Discovery and Data Mining, 155-164, 1999. This classifier should produce similar results to one created by passing the base learner to Bagging, which is in turn passed to a CostSensitiveClassifier operating on minimum expected cost. The difference is that MetaCost produces a single cost-sensitive classifier of the base learner, giving the benefits of fast classification and interpretable output (if the base learner itself is interpretable). This implementation uses all bagging iterations when reclassifying training data (the MetaCost paper reports a marginal improvement when only those iterations containing each training instance are used in reclassifying that instance).

The ServiceMix Drools component provides JBI integration to the Drools Rules Engine. It can be used to deploy a rules set that will implement a router or an actual service. A router will mostly act as a transparent proxy between the consumer and the target service provider mad will mostly be implemented by the jbi.route(uri) method below. This method creates a new exchange identical to the one received by the component and will send it to the specified destination. You can also send back a Fault if needed. A router can also be implemented by using directly the JBI Apis (available with the jbi helper) by using the provided client.