- Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
- Models and Issues in Data Stream Systems
- Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
- Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
- [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t
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//To compile: | |
//cc vlcsms.c -o vlcsms -lvlc | |
//This source is by Tim Sheerman-Chase and it is released as public domain. | |
#include <stdio.h> | |
#include <stdint.h> | |
#include <stdlib.h> | |
#include <unistd.h> | |
#include <inttypes.h> | |
#include <vlc/vlc.h> |
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setup.sh | |
token |
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#proxy { | |
position: absolute; | |
top: 0; | |
right: 0; | |
display: none; | |
} | |
#notebook { | |
position: absolute; | |
width: 600px; |
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node_modules |
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library(shiny) | |
shinyServer(function(input, output, session) { | |
output$shinyVer <- reactive({ | |
packageVersion("shiny") | |
}) | |
output$rmarkdownVer <- reactive({ | |
packageVersion("rmarkdown") | |
}) |
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.vagrant | |
pkg | |
src | |
bin |
Putting cryptographic primitives together is a lot like putting a jigsaw puzzle together, where all the pieces are cut exactly the same way, but there is only one correct solution. Thankfully, there are some projects out there that are working hard to make sure developers are getting it right.
The following advice comes from years of research from leading security researchers, developers, and cryptographers. This Gist was [forked from Thomas Ptacek's Gist][1] to be more readable. Additions have been added from