The first and most important thing about Levenshtein distance is it’s actually a metric distance. That is, it obeys the triangle inequality. For most other string distance measurements this property doesn’t hold.

To honor Alan Turing, we built a simple LEGO Turing Machine, to show everyone how simple a computer actually is. Primary goals were to make every operation as visible as possible and to make it using the automatic components of just a single LEGO MINDSTORMS NXT set, to make it easy to reproduce for those interested.

Why is there almost no means for correction of blurring and defocusing (except unsharp mask) - maybe it is impossible to do this at all? In fact, it is possible - development of a respective mathematical theory started approximately 70 years ago, but like other algorithms of image processing, deblurring algorithms became wide-used just recently.

AgreementMaker aims at being a user friendly, powerful, and flexible ontology and schema matching system.

After the creation of programming languages themselves, parsing was one of the first major areas tackled by theoretical computer science and, in many peoples eyes, one of its greatest successes. The 1960s saw a concerted effort to uncover good theories and algorithms for parsing. Parsing in the early days seems to have shot off in many directions before, largely, converging. Context Free Grammars (CFGs) eventually "won", because they are fairly expressive and easy to reason about, both for practitioners and theorists.

In one of his talks at QCon, John Allspaw mentioned using Holt-Winter exponential smoothing on various monitoring instances. Wikipedia has a good entry on the subject, of course, but the basic idea is to take a noisy/spikey time series and smooth it out, so that unexpected changes will stand out even more. That's often initially done by taking a moving average, so say averaging the last 7 days of data and using that as the current day's value. More complicated schemes weight that average, so that the older data contributes less.

Members of the modern workforce might be surprised to learn that if they use the word “weekend” in a workplace email, chances are they’re sending the message up the org chart. Likewise the words “voicemail,” “driving,” “okay”—and even a choice four-letter word that rhymes with “hit.” However a new study by Georgia Tech’s Eric Gilbert shows that certain words and phrases indeed are reliable indicators of whether workplace emails are sent to someone higher or lower in the corporate hierarchy.

We describe a novel algorithm for extracting a resolution-independent vector representation from pixel art images, which enables magnifying the results by an arbitrary amount without image degradation. Our algorithm resolves pixel-scale features in the input and converts them into regions with smoothly varying shading that are crisply separated by piecewise-smooth contour curves.

With the emergence of HTML5, WebGL, and other browser-based 3D technologies, the way we think about browsers is quickly shifting. To me, making badass graphics in a browser is a rad prospect, but a hard one. Unless you have a pretty deep understanding of shaders, vertex buffers, matrix transformations and the like, it can be overwhelming just to approach it.

A lot of interesting techniques involve taking statistical samples, and using those to predict what we'll see in the future. Usually this works pretty well, but when we're dealing with a lot of options or if we have some options that are very rare that approach can go pretty wrong.