The Seven Stages of Expertise in Software Engineering. © Copyright 1998, Wayland Systems Inc.
All rights reserved. (In this article, an indefinite “he” means “he or she”.) For almost two decades we at Wayland Systems Inc. have toiled to transfer the latest developments in software engineering to practitioners at large and small companies around the world. However, until recently we didn’t do a very good job of quantifying the long-term field results of our efforts. So we decided to ask a question reminiscent of the rhetoric of Ed Koch (the former mayor of ): “How’re we doin’?” From Altair to iPad: 35 years of personal computer market share.
Back in 2005, we charted 30 years of personal computer market share to show graphically how the industry had developed, who succeeded and when, and how some iconic names eventually faded away completely.
With the rise of whole new classes of "personal computers"—tablets and smartphones—it's worth updating all the numbers once more. And when we do so, we see something surprising: the adoption rates for our beloved mobile devices absolutely blow away the last few decades of desktop computer growth. People are adopting new technology faster than ever before. XSS (Cross Site Scripting) Cheat Sheet. Last revision (mm/dd/yy): 04/7/2014 This cheat sheet is for people who already understand the basics of XSS attacks but want a deep understanding of the nuances regarding filter evasion.
Please note that most of these cross site scripting vectors have been tested in the browsers listed at the bottom of the scripts. Recommender systems. Good Overview Papers Empirical Analysis of Predictive Algorithms for Collaborative Filtering Breese, Heckerman and Kadie Online References Berkeley Collaborative Filtering Not up to date, but still has many good pointers Collaborative Filtering mailing list archive Six years of discussions on collaborative filtering ACM Collaborative Filtering not maintained Recommender Systems Workshop at SIGIR 99 Has a nice summary and has several papers online.
MultiAgent.com. A Guide to Recommender Systems. We're running a special series on recommendation technologies and in this post we look at the different approaches - including a look at how Amazon and Google use recommendations.
The Wikipedia entry defines "recommender systems" as "a specific type of information filtering (IF) technique that attempts to present information items (movies, music, books, news, images, web pages, etc.) that are likely of interest to the user. " That entry goes on to note that recommendations are generally based on an "information item (the content-based approach) or the user's social environment (the collaborative filtering approach).
" We think there's also a personalization approach, which Google in particular is focused on. We explore some of these concepts below. Briancarper.net (λ) Sketch of The Analytical Engine. By L.
F. MENABREAof Turin, Officer of the Military Engineers from the Bibliothèque Universelle de Genève, October, 1842, No. 82.
Teorema dell'impilamento. Da Wikipedia, l'enciclopedia libera.
In crittanalisi il teorema dell'impilamento (piling-up lemma) è un principio utilizzato nella crittanalisi lineare per costruire le approssimazioni lineari dei cifrari a blocchi. È stato introdotto da Mitsuru Matsui nel 1993 come uno strumento analitico della crittanalisi lineare. Teoria[modifica | modifica sorgente] AI. Databases. Programming.