Canumb aims to crunch data on various forms and turn them into something meaningful. A variety of inputs are currently supported, such as different number base conversions (decimal to hexadecimal, binary to hexadecimal, octal to decimal, etc.) and various encoded formats (a base64 encoder decoder and a URI encoder decoder). It's built to allow for extension when the future demands other input formats be crunched.
360-FAAR (Firewall Analysis Audit and Repair) is an offline, command line, Perl firewall policy manipulation tool to filter, compare to logs, merge, translate, and output firewall commands for new policies, in Checkpoint dbedit, Cisco ASA, or ScreenOS commands. It is all contained in one file. It can read policy and logs for: Checkpoint FW1 (in odumper.csv / logexport format), Netscreen ScreenOS (in get config / syslog format), and Cisco ASA (show run / syslog format). It uses both inclusive and exclusive CIDR and text filters, permitting you to split large policies into smaller ones for virutalization at the same time as removing unused connectivity. It supports policy to log association, object translation, rulebase reordering and simplification, rule moves, and duplicate matching automatically. It allows you to seamlessly move rules to where you need them. 'print' mode creates a spreadsheet for your audit needs with one command.
Eero is a binary-compatible variant of Objective-C 2.0, implemented with a patched version of the Clang/LLVM compiler. It features a streamlined syntax with improved readability and reduced code clutter, as well as new features such as Python-like indentation and a limited form of operator overloading. It is inspired by languages such as Smalltalk and Ruby.
The Graphical Models Toolkit (GMTK) is a toolkit for rapidly prototyping statistical models using dynamic graphical models (DGMs) and dynamic Bayesian networks (DBNs). It can be used for speech and language processing, bioinformatics, activity recognition, and any time series application. It features exact and approximate inference, many built-in factors including dense, sparse, and deterministic conditional probability tables, native support for ARPA backoff-based factors and factored language models, parameter sharing, gamma and beta distributions, dense and sparse Gaussian factors, heterogeneous mixtures, deep neural network factors, and time-inhomogeneous trellis factors, arbitrary order embedded Markov chains, a GUI graph viewer, and much more.