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A user may now build a list of rules that will be used to determine if unsafe images (that are linked to remote sites) will be shown in HTML messages. If a message matches any of the rules and contains images that would normally be initially hidden, then they are now shown by default. The user may choose to always show unsafe images, for all message. This is obviously not recommended by the core SquirrelMail Project Team - or they wouldn't have built this functionality to begin with ( See the following: http://www.squirrelmail.org/wiki/UnsafeImages ). A new section is added to the options page titled, 'Unsafe Image Rules'. Within this page the user may define a number of rules to determine when messages are from a trusted source. These options are very similar to the core message filters plugin. A message field (To, From, CC, Subject) can be matched either against a regular expression, or simply searched to see if the given string is within the field. If a match is found then unsafe images are always shown for this source.

OSSP val is a flexible name to value mapping library for C variables. It is a companion library to OSSP var. It allows one to access C variables through name strings, although the C language does neither provide such a dedicated facility nor an evaluation construct (which could be used to implement such a facility easily).

SVMlight is an implementation of Vapnik's Support Vector Machine [Vapnik, 1995] for the problem of pattern recognition, for the problem of regression, and for the problem of learning a ranking function. The optimization algorithms used in SVMlight are described in [Joachims, 2002a ]. [Joachims, 1999a]. The algorithm has scalable memory requirements and can handle problems with many thousands of support vectors efficiently. The software also provides methods for assessing the generalization performance efficiently. It includes two efficient estimation methods for both error rate and precision/recall. XiAlpha-estimates [Joachims, 2002a, Joachims, 2000b] can be computed at essentially no computational expense, but they are conservatively biased. Almost unbiased estimates provides leave-one-out testing. SVMlight exploits that the results of most leave-one-outs (often more than 99%) are predetermined and need not be computed [Joachims, 2002a].

An efficient C++ WSDL library which parses a WSDL file & provides APIs to access WSDL elements. It has a library for parsing xml schemas and validating instances. It uses xml pull parsing methodology, and is meant to be semantically equivalent to WSDL4J.

Quick Spam Filter (qsf) is a small, fast spam filter that works by learning to recognise the words that are more likely to appear in spam than non-spam. It is intended to be used in a procmail recipe to mark email as being possible spam.

CKEditor is a text editor to be used inside web pages. It's a WYSIWYG editor, which means that the text being edited on it looks as similar as possible to the results users have when publishing it. It brings to the web common editing features found on desktop editing applications like Microsoft Word and OpenOffice.

Buici Clock is an attractive X-Window System clock. As clocks go, Buici satisfies the basic need of representing the time accuratel and attractively.

Development tools and base libraries for linux_base-f10. Required by some Linux applications such as Matlab, which allows the user to integrate custom C, C++, and Fortran code via the MEX compiler.

TinyXML-2 is a simple, small, efficient, C++ XML parser that can be easily integrated into other programs. It attempts to be flexible, but correct. It does not rely on exceptions or RTTI. It has UTF-8 support, but does not parse or use DTDs or XSL. It doesn't have the STL support of TinyXML-1, but uses less memory, has a proper namespace, and is faster.

The Vowpal Wabbit (VW) project is a fast out-of-core learning system sponsored by Microsoft Research and (previously) Yahoo! Research. There are two ways to have a fast learning algorithm: (a) start with a slow algorithm and speed it up, or (b) build an intrinsically fast learning algorithm. This project is about approach (b), and it's reached a state where it may be useful to others as a platform for research and experimentation. There are several optimization algorithms available with the baseline being sparse gradient descent (GD) on a loss function (several are available).