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’?” We surveyed some of our clients to whom we’d provided education and consulting over the years and discovered an interesting but very disturbing fact. Over 50% of the shops into which we’d introduced Software Engineering techniques had either abandoned the use of the techniques or had let their band of active practitioners shrink to a dwindling core of diehards.
We turned to self-examination and reviewed our courses and consulting practices. “Very universal,” I answered. 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. Humans are naturally competitive creatures. Not only do we compete with each other for money and power, but we form strong allegiances to various tribes. Well—there's certainly plenty of cheerleading, but tracking the rise of fall of market share over time has more serious uses, too. Certain lessons from the past can also be applied today, and may even foreshadow what the future holds. So what is market share? XSS (Cross Site Scripting) Cheat Sheet.
Last revision (mm/dd/yy): 07/4/2018 This cheat sheet lists a series of XSS attacks that can be used to bypass certain XSS defensive filters. Please note that input filtering is an incomplete defense for XSS which these tests can be used to illustrate. Basic XSS Test Without Filter Evasion This is a normal XSS JavaScript injection, and most likely to get caught but I suggest trying it first (the quotes are not required in any modern browser so they are omitted here): XSS Locator (Polygot) The following is a "polygot test XSS payload.
" javascript:/*--></title></style></textarea></script></xmp><svg/onload='+/"/+/onmouseover=1/+/[*/[]/+alert(1)//'> Image XSS using the JavaScript directive Image XSS using the JavaScript directive (IE7.0 doesn't support the JavaScript directive in context of an image, but it does in other contexts, but the following show the principles that would work in other tags as well: No quotes and no semicolon Case insensitive XSS attack vector HTML entities Malformed A tags <! <! Recommender systems. 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. In a recent post, Xavier Vespa of the blog HyveUp analyzed 3 different approaches to recommendation engines on the Web. A couple of years ago, Alex Iskold outlined what he saw as the 4 main approaches to recommendations:
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 With notes upon the Memoir by the Translator ADA AUGUSTA, COUNTESS OF LOVELACE Those labours which belong to the various branches of the mathematical sciences, although on first consideration they seem to be the exclusive province of intellect, may, nevertheless, be divided into two distinct sections; one of which may be called the mechanical, because it is subjected to precise and invariable laws, that are capable of being expressed by means of the operations of matter; while the other, demanding the intervention of reasoning, belongs more specially to the domain of the understanding.
Struck with similar reflections, Mr. I must first premise that this engine is entirely different from that of which there is a notice in the ‘Treatise on the Economy of Machinery,’ by the same author. Such is the nature of the first machine which Mr. We deduce Note A F(x, y, z, &c.
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] Il teorema dell'impilamento permette al crittanalista di determinare la probabilità che la seguente equazione sia valida: dove le x indicano variabili binarie (vale a dire con valore 0 o 1).
Poniamo P(A) come indice della "probabilità che A sia vera": se è uguale a 1, A è certo che si verifichi; se è uguale a 0, A non si può verificare. Adesso consideriamo: A causa delle proprietà dell'operazione di XOR, questa equazione è equivalente a Adesso esprimiamo le probabilità p1 e p2 come ½ + ε1 e ½ + ε2, dove le ε indicano la quantità dello scostamento dalla probabilità rapportata a ½. Questa formula può essere estesa a più X come segue: abbiamo.
AI.
Programming.