Apache XML Graphics Commons is a library that consists of several reusable components used by Apache Batik and Apache FOP. Many of these components can easily be used separately outside the domains of SVG and XSL-FO. You will find components such as a PDF library, an RTF library, Graphics2D implementations that let you generate PDF and PostScript files, and much more.
While the author of BSAX-J has not yet come to a final conclusion about the need for a binary XML format, BSAX is his idea of one possible encoding that leverages other XML prior art (SAX events and UTF-8, in particular). It is complete in that it can be used to perform round-trip conversions from textual XML to SAX events to BSAX binary streams, and back to SAX events and textual XML. The test code in the distribution does exactly that for a simple example XML file, and measures the difference in file size (the file is slightly smaller for the BSAX encoding of the sample file) and the difference in read time (the read time is significantly faster for the sample file).
CollabNet Connector Framework is an Openadaptor-based SDK that allows rapid integrations and migrations dealing with the artifact data shared between different tools in the ALM cycle in combination with the collaborative platforms from CollabNet. It features bidirectional, out-of-the-box tracker integration between HP Quality Center, CollabNet SourceForge Enterprise, CollabNet Enterprise Edition, database tables, and CSV files.
Cypress is an open-source Cascading Style Sheet (CSS) parser that lets you add well-documented, standardized name/value pairs (a.k.a. CSS style properties) to your own XML markup languages. It supports inline styles so you can add style properties to individual XML tags using the style attribute or external style sheets so that you can store style rules for reuse in separate, XML-free text documents. Cypress supports three forms of selectors to match your XML tags and style rules, that is, element selectors, class selectors, and id selectors.
Erudite is an application for training and testing back propogation neural networks using the ANNeML (Artifical Neural Network Markup Language) XML format. It supports testing and training neural nets with CSV files and has support for randomized training sets, optional adapting learning rate, sigmoid or hyperbolic tangent transfer functions, optional bias and weight adjustment locking, and more.