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The ResourcePool is a generic connection caching and pooling management facility. It might be used in an Apache/mod_perl environment to support connection caching like Apache::DBI for non-DBI resources (e.g. Net::LDAP). It's also useful in a stand alone perl application to handle connection pools. The key benefit of ResourcePool is the generic design which makes it easily extensible to new resource types. The ResourcePool has a simple check mechanism to detect and close broken connections (e.g. if the database server was restarted) and opens new connections if possible. If you are new to ResourcePool you should go to the ResourcePool::BigPicture documentation which provides the best entry point to this module. The ResourcePool itself handles always exactly equivalent connections (e.g. connections to the same server with the same user-name and password) and is therefore not able to do a load balancing. The ResourcePool::LoadBalancer is able to do a advanced load balancing across different servers and increases the overall availability by applying a failover policy if there is a server breakdown.

GraphicsMagick is the swiss army knife of image processing. Comprised of 267K physical lines (according to David A. Wheeler's SLOCCount) of source code in the base package (or 1,225K including 3rd party libraries) it provides a robust and efficient collection of tools and libraries which support reading, writing, and manipulating an image in over 88 major formats including important formats like DPX, GIF, JPEG, JPEG-2000, PNG, PDF, PNM, and TIFF. GraphicsMagick supports huge images and has been tested with gigapixel-size images. GraphicsMagick can create new images on the fly, making it suitable for building dynamic Web applications. GraphicsMagick may be used to resize, rotate, sharpen, color reduce, or add special effects to an image and save the result in the same or different image format. Image processing operations are available from the command line, as well as through C, C++, Lua, Perl, PHP, Python, Tcl, Ruby, Windows .NET, or Windows COM programming interfaces. With some modification, language extensions for ImageMagick may be used.

CenterIM is a fork of CenterICQ. CenterIM is a text mode menu- and window-driven IM interface that supports the ICQ2000, Yahoo!, MSN, AIM, Gadu-Gadu and IRC protocols as well as posting to LiveJournal aggregating RSS feeds. It allows you to send, receive, and forward messages, URLs, SMSes, contacts, and email express messages. It also lets you set your own and fetch others' away messages, and define external handlers for incoming events. You can mass message-send, search for users, view users' details, maintain your contact list directly from the program, view the message history, register a new UIN and update your details, be informed upon receipt of email messages, automatically set away after the defined period of inactivity, and have your own ignore, visible, and invisible lists. It can also associate events with sounds, make log of events, and allows arrangement of contacts into groups. WARNING: This is the development version of centerim. There's no proof that it will build and/or run properly on your system. But we will be happy to get some feedback if you experience any problems. For testing purposes, all available protocols are enabled in this port. If you don't agree to these facts, you should probable use net-im/centerim release version.

LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification. Since version 2.8, it implements an SMO-type algorithm proposed in this paper: R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working set selection using second order information for training SVM. Journal of Machine Learning Research 6, 1889-1918, 2005. You can also find a pseudo code there. Our goal is to help users from other fields to easily use SVM as a tool. LIBSVM provides a simple interface where users can easily link it with their own programs. Main features of LIBSVM include * Different SVM formulations * Efficient multi-class classification * Cross validation for model selection * Probability estimates * Weighted SVM for unbalanced data * Both C++ and Java sources * GUI demonstrating SVM classification and regression * Python, R (also Splus), MATLAB, Perl, Ruby, Weka, Common LISP and LabVIEW interfaces. C# .NET code is available. It's also included in some learning environments: YALE and PCP. * Automatic model selection which can generate contour of cross valiation accuracy.