We don't specify ca-certificates (/etc/ssl/cert.pem) to be able to add
our owns under /etc/ssl when needed. Files under /etc/ssl/* will be parsed
and the certificates found will be added to the keyring list on startup.
* New events and missions
* New outfits
* House Soromid now has a logo
* More ways of mapping the universe
* Disabling damage leaks through shields
* conf.lua-tweakable font sizes for accessibility
* Bug fixes
OK from sthen@
Point to new HOMEPAGE and MASTER_SITES.
Include new license marker (revised 3 clause BSD license) and update
PERMIT*_CDROM lines.
Include a space before "=" (which will be a separate diff next time.
ok sthen@
#@---------------------------------------------------
#@ Date : 20120405
#@ Author : Phil Randal (phil.randal@gmail.com)
#@ Reason : Fix lookup of warranty info for Dell
#@---------------------------------------------------
ok sthen@
- sync/re-sort WANTLIBs
- enable GIO support since we have Glib in the dependency path anyway
- simplify one of the Makefile patches a bit
- invert the logic for enabling/disabling the sndio backend
- more appropriate fix for some of the autoconf checks as the LIBS
variable was being polluted very early on breaking most of the
checks using AC_CHECK_LIB as well as remove/fix some improper
use of the AC_CHECK_LIB macro
From Brad
This module provides a utility method, "to_identifier" for converting
an arbitrary string into a readable representation using the ASCII
subset of "\w" for use as an identifier in a computer program. The
intent is to make unique identifier names from which the content
of the original string can be easily inferred by a human just by
reading the identifier.
If you need the full set of "\w" including Unicode, see the subclass
String::ToIdentifier::EN::Unicode.
Currently, this process is one way only, and will likely remain
this way.
The default is to create camelCase identifiers, or you may pass in
a separator char of your choice such as "_".
Binary char groups will be separated by "_" even in camelCase
identifiers to make them easier to read, e.g.: "foo_2_0xFF_Bar".
The module is a probability based, corpus-trained tagger that assigns
POS tags to English text based on a lookup dictionary and a set of
probability values. The tagger assigns appropriate tags based on
conditional probabilities - it examines the preceding tag to determine
the appropriate tag for the current word. Unknown words are classified
according to word morphology or can be set to be treated as nouns
or other parts of speech. The tagger also extracts as many nouns
and noun phrases as it can, using a set of regular expressions.
