filippovitale

import pefile
import argparse


def decrypt_one_byte(b):
    key = 0x58  # The xor key hasn’t changed
    xor = (b ^ key) & 0xff
    return (xor + key) & 0xff

filippovitale

import scalanative.native._
import SDL._
import SDLExtra._

@extern
@link("SDL2")
object SDL {
  type Window   = CStruct0
  type Renderer = CStruct0

filippovitale

#!/bin/bash

# Get temp dir
tmpdir=$(mktemp -d)

# Generate test
cat > ${tmpdir}/ListenToSignal.java <<EOF
import sun.misc.Signal;
import sun.misc.SignalHandler;
public class ListenToSignal {

filippovitale

trait MyOption[A] {
  def fold[B](n: => B, s: A => B): B

  // Define the usual Option API.
  //
  // * Constructors (on the object)
  // some
  // none
  // * methods
  // map

filippovitale

1. General Background and Overview

• 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