Why Bloom filters work the way they do
Imagine you’re a programmer who is developing a new web browser. There are many malicious sites on the web, and you want your browser to warn users when they attempt to access dangerous sites. For example, suppose the user attempts to access You’d like a way of checking whether domain is known to be a malicious site. What’s a good way of doing this? An obvious naive way is for your browser to maintain a list or set data structure containing all known malicious domains. In this post I’ll describe a data structure which provides an excellent way of solving this kind of problem. Most explanations of Bloom filters cut to the chase, quickly explaining the detailed mechanics of how Bloom filters work. In this post I take an unusual approach to explaining Bloom filters. Of course, this means that if your goal is just to understand the mechanics of Bloom filters, then this post isn’t for you. A stylistic note: Most of my posts are code-oriented. of objects. is a member of . ?
natural language processing blog
Operational transformation
Operational transformation (OT) is a technology for supporting a range of collaboration functionalities in advanced collaborative software systems. OT was originally invented for consistency maintenance and concurrency control in collaborative editing of plain text documents. Two decades of research has extended its capabilities and expanded its applications to include group undo, locking, conflict resolution, operation notification and compression, group-awareness, HTML/XML and tree-structured document editing, collaborative office productivity tools, application-sharing, and collaborative computer-aided media design tools (see OTFAQ). In 2009 OT was adopted as a core technique behind the collaboration features in Apache Wave and Google Docs. History[edit] Operational Transformation was pioneered by C. System architecture[edit] Basics[edit] The basic idea of OT can be illustrated by using a simple text editing scenario as follows. Consistency models[edit] The CC model[edit] T(ins( ),ins( and
免费的英语语料库汇总 Open English Corpora(1) - jinchangge的日志 - 网易博客
网易 新闻 微博 邮箱 相册 阅读 有道 摄影 爱拍 优惠券 云笔记 闪电邮 手机邮 印像派 网易识字 更多 博客 手机博客 博客搬家 博客VIP服务 LiveWriter写博 word写博 邮件写博 短信写博 群博客 博客油菜地 博客话题 博客热点 博客圈子 找朋友 发现 小组 风格 手机博客 网易真人搭配社区iStyle 下载最文艺的手机博客APP> 收藏级艺术作品，限时售卖>> 创建博客 登录 加关注 显示下一条 | 关闭 温馨提示！ jinchangge的博客 趣味大学英语 导航 日志 jinchang 加博友 关注他 他的网易微博 被推荐日志 最新日志 该作者的其他文章 博主推荐 随机阅读 首页推荐 更多>> 10 Fastest Mammals(哺乳动物)of Our Planet 6 Bars with the Best Views in the World 免费的英语语料库汇总 Open English Corpora(1) 2010-06-28 18:06:45| 分类： 语料库 | 标签： |举报 |字号大中小 订阅 The list is constantly updated. Strictly speaking, some of them are not corpora, but archives, databases or even dictionaries. 1. Corpus of Global Web-Based English (GloWbE): COCA： COHA: Download N-Grams from COCA and COHA: BYU-TIME： Bank of English (BoE): 1 month free trial A. B. C.
Genetic Programming: Evolution of Mona Lisa | Roger Alsing Weblog
[EDIT] Added FAQ here: Gallery here: This weekend I decided to play around a bit with genetic programming and put evolution to the test, the test of fine art :-) I created a small program that keeps a string of DNA for polygon rendering. The procedure of the program is quite simple: 0) Setup a random DNA string (application start) 1) Copy the current DNA sequence and mutate it slightly 2) Use the new DNA to render polygons onto a canvas 3) Compare the canvas to the source image 4) If the new painting looks more like the source image than the previous painting did, then overwrite the current DNA with the new DNA 5) repeat from 1 Now to the interesting part :-) Could you paint a replica of the Mona Lisa using only 50 semi transparent polygons? That is the challenge I decided to put my application up to. So what do you think? Like this: Like Loading...
Languages - Homepage: All you need to start learning a foreign language
Bitap algorithm
The bitap algorithm (also known as the shift-or, shift-and or Baeza-Yates–Gonnet algorithm) is an approximate string matching algorithm. The algorithm tells whether a given text contains a substring which is "approximately equal" to a given pattern, where approximate equality is defined in terms of Levenshtein distance — if the substring and pattern are within a given distance k of each other, then the algorithm considers them equal. The algorithm begins by precomputing a set of bitmasks containing one bit for each element of the pattern. Due to the data structures required by the algorithm, it performs best on patterns less than a constant length (typically the word length of the machine in question), and also prefers inputs over a small alphabet. Exact searching[edit] The bitap algorithm for exact string searching, in full generality, looks like this in pseudocode: Fuzzy searching[edit] External links and references[edit]
