This simple script will take a picture of a whiteboard and use parts of the ImageMagick library with sane defaults to clean it up tremendously.
The script is here:
#!/bin/bash
convert "$1" -morphology Convolve DoG:15,100,0 -negate -normalize -blur 0x1 -channel RBG -level 60%,91%,0.1 "$2"
One of the best ways to reduce complexity (read: stress) in web development is to minimize the differences between your development and production environments. After being frustrated by attempts to unify the approach to SSL on my local machine and in production, I searched for a workflow that would make the protocol invisible to me between all environments.
Most workflows make the following compromises:
Use HTTPS in production but HTTP locally. This is annoying because it makes the environments inconsistent, and the protocol choices leak up into the stack. For example, your web application needs to understand the underlying protocol when using the secure flag for cookies. If you don't get this right, your HTTP development server won't be able to read the cookies it writes, or worse, your HTTPS production server could pass sensitive cookies over an insecure connection.
Use production SSL certificates locally. This is annoying
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This is a proof-of-concept of a couple of concurrent data structures written in Ruby.
The implementations are heavily commented for those interested. There are benchmarks (with results) included below. The results are interesting, but, as always, take with a grain of salt.
AtomicLinkedQueue is a lock-free queue, built on atomic CAS operations.
Here are the areas I've been researching, some things I've read and some open source packages...
Nearly all text processing starts by transforming text into vectors: http://en.wikipedia.org/wiki/Vector_space_model
Often it uses transforms such as TFIDF to normalise the data and control for outliers (words that are too frequent or too rare confuse the algorithms): http://en.wikipedia.org/wiki/Tf%E2%80%93idf
Collocations is a technique to detect when two or more words occur more commonly together than separately (e.g. "wishy-washy" in English) - I use this to group words into n-gram tokens because many NLP techniques consider each word as if it's independent of all the others in a document, ignoring order: http://matpalm.com/blog/2011/10/22/collocations_1/