Artem Yankov yankov

## readme.md

      
              2 files
            
          
              257 forks
            
          
              182 comments
            
          
              741 stars
            
          
                baraldilorenzo
                / readme.md
            
            
              Last active
              November 21, 2023 22:41
            
              
                VGG-16 pre-trained model for Keras
              
          
    ##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

  
## min-char-rnn.py
"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

## requests_from_generator.py
class MySpider(Spider):
	# [...]

	# start requests from generator
	def start_requests(self):
		url = 'http://some.page.tld/%s/category'
		for page in xrange(1, 247):
			link = url % page
			yield Request(url=link)

## waves.js
var AMP = 0.5; // amplitude
var len = 15; // seconds
e = new webkitAudioContext();
var source = e.createBufferSource();
var SR = e.sampleRate;
source.buffer = e.createBuffer(1, len * SR, SR);
var dat = source.buffer.getChannelData(0);
for (var i = 0; i < len * SR; i++) {
  dat[i] = AMP * (Math.random() * 2 - 1);
}

## gist:8172796

      
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                debasishg
                / gist:8172796
            
            
              Last active
              March 15, 2024 15:05
            
              
                A collection of links for streaming algorithms and data structures
              
          
    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&amp;rep=rep1&amp;t


## ch5_streams.scala
// Excercies from chapter 5: Strictness and laziness

def foldRight[A,B](z: => B)(s: Stream[A])(f: (A, => B) => B): B =
  s match {
    case hd #:: tail => f(hd, foldRight(z)(tail)(f))
    case _ => z
  }

def exists[A](s: Stream[A])(p: A => Boolean): Boolean =
	foldRight(false)(s)((x,a) => if (p(x)) true else a)

## Backend Architectures Keywords and References.md

      
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                ragingwind
                / Backend Architectures Keywords and References.md
            
            
              Last active
              April 17, 2024 10:51
            
              
                Backend Architectures Keywords and References
              
          
    Backend Architectures

Twitter

ror, scala, jetty, erlang, thrift, mongrel, comet server, my-sql, memchached, varnish, kestrel(mq), starling, gizzard, cassandra, hadoop, vertica, munin, nagios, awstats

Introduce Gizzard of Twitter - 트위터의 새로운 분산 관리 라이브러리 Gizzard를 소개합니다.
The Architecture Twitter Uses to Deal with 150M Active Users, 300K QPS, a 22 MB/S Firehose, and Send Tweets in Under 5 Seconds
Scaling Twitter: Making Twitter 10000 Percent Faster


## rails_rce.rb
#!/usr/bin/env ruby
#
# Proof-of-Concept exploit for Rails Remote Code Execution (CVE-2013-0156)
#
# ## Advisory
#
#   https://groups.google.com/forum/#!topic/rubyonrails-security/61bkgvnSGTQ/discussion
#
# ## Caveats
#

## gist:4230948
mkdir project1
cd project1

sbt
set name := "project1"
set version := "1.0"
set scalaVersion := "2.9.3"
session save
exit

## gist:4195130

<!DOCTYPE html>
<html>
<head><title>ChamberedTest</title></head>
<script type="text/javascript" src="js/chambered.js"></script>
<style type="text/css">
    canvas, img {
        image-rendering: optimizeSpeed;
        image-rendering: -moz-crisp-edges;
        image-rendering: -webkit-optimize-contrast;
	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)
	class MySpider(Spider):
	# [...]

	# start requests from generator
	def start_requests(self):
	url = 'http://some.page.tld/%s/category'
	for page in xrange(1, 247):
	link = url % page
	yield Request(url=link)
	var AMP = 0.5; // amplitude
	var len = 15; // seconds
	e = new webkitAudioContext();
	var source = e.createBufferSource();
	var SR = e.sampleRate;
	source.buffer = e.createBuffer(1, len * SR, SR);
	var dat = source.buffer.getChannelData(0);
	for (var i = 0; i < len * SR; i++) {
	dat[i] = AMP * (Math.random() * 2 - 1);
	}
	// Excercies from chapter 5: Strictness and laziness

	def foldRight[A,B](z: => B)(s: Stream[A])(f: (A, => B) => B): B =
	s match {
	case hd #:: tail => f(hd, foldRight(z)(tail)(f))
	case _ => z
	}

	def exists[A](s: Stream[A])(p: A => Boolean): Boolean =
	foldRight(false)(s)((x,a) => if (p(x)) true else a)
	#!/usr/bin/env ruby
	#
	# Proof-of-Concept exploit for Rails Remote Code Execution (CVE-2013-0156)
	#
	# ## Advisory
	#
	# https://groups.google.com/forum/#!topic/rubyonrails-security/61bkgvnSGTQ/discussion
	#
	# ## Caveats
	#
	mkdir project1
	cd project1

	sbt
	set name := "project1"
	set version := "1.0"
	set scalaVersion := "2.9.3"
	session save
	exit

	<!DOCTYPE html>
	<html>
	<head><title>ChamberedTest</title></head>
	<script type="text/javascript" src="js/chambered.js"></script>
	<style type="text/css">
	canvas, img {
	image-rendering: optimizeSpeed;
	image-rendering: -moz-crisp-edges;
	image-rendering: -webkit-optimize-contrast;