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Pseudorandom number generators | Computer Science | Khan Academy
 
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Random vs. Pseudorandom Number Generators Watch the next lesson: https://www.khanacademy.org/computing/computer-science/cryptography/modern-crypt/v/the-fundamental-theorem-of-arithmetic-1?utm_source=YT&utm_medium=Desc&utm_campaign=computerscience Missed the previous lesson? https://www.khanacademy.org/computing/computer-science/cryptography/crypt/v/perfect-secrecy?utm_source=YT&utm_medium=Desc&utm_campaign=computerscience Computer Science on Khan Academy: Learn select topics from computer science - algorithms (how we solve common problems in computer science and measure the efficiency of our solutions), cryptography (how we protect secret information), and information theory (how we encode and compress information). About Khan Academy: Khan Academy is a nonprofit with a mission to provide a free, world-class education for anyone, anywhere. We believe learners of all ages should have unlimited access to free educational content they can master at their own pace. We use intelligent software, deep data analytics and intuitive user interfaces to help students and teachers around the world. Our resources cover preschool through early college education, including math, biology, chemistry, physics, economics, finance, history, grammar and more. We offer free personalized SAT test prep in partnership with the test developer, the College Board. Khan Academy has been translated into dozens of languages, and 100 million people use our platform worldwide every year. For more information, visit www.khanacademy.org, join us on Facebook or follow us on Twitter at @khanacademy. And remember, you can learn anything. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to Khan Academy’s Computer Science channel: https://www.youtube.com/channel/UC8uHgAVBOy5h1fDsjQghWCw?sub_confirmation=1 Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 155561 Khan Academy Labs
Coding Math: Episode 51 - Pseudo Random Number Generators Part I
 
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Back to School Special. This short series will discuss pseudo random number generators (PRNGs), look at how they work, some algorithms for PRNGs, and how they are used. Support Coding Math: http://patreon.com/codingmath Source Code: https://jsbin.com/nifutup/1/edit?js,output Earlier Source Code: http://github.com/bit101/codingmath
Views: 22908 Coding Math
Pseudo Random Number Generator - Applied Cryptography
 
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This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
Views: 7989 Udacity
Random Numbers (How Software Works)
 
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How Software Works is a book and video series explaining the magic behind software encryption, CGI, video game graphics, and a lot more. The book uses plain language and lots of diagrams, so no technical or programming background is required. Come discover what's really happening inside your computer! This episode is about random numbers--why software needs them, why they can't really make them, and why that's okay. Learn more about the book... - At the Amazon page (http://amzn.to/1mZ276M). - At my publisher (http://www.nostarch.com/howsoftwareworks) - At my site (http://www.vantonspraul.com/HSW). If you'd like to contact me visit my site (http://vantonspraul.com), or just leave a comment below. Suggestions for future topics are welcome!
Views: 10324 V. Anton Spraul
Pseudo Random Number Generators (CSS322, Lecture 7, 2013)
 
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Pseudo random number generators; Linear Congruential Generator. Lecture 7 of CSS322 Security and Cryptography at Sirindhorn International Institute of Technology, Thammasat University. Given on 12 December 2013 at Bangkadi, Pathumthani, Thailand by Steven Gordon. Course material via: http://sandilands.info/sgordon/teaching
Views: 20887 Steven Gordon
Applied Cryptography: Random Numbers (2/2)
 
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Previous video: https://youtu.be/g3iH74XFaT0 Next video:
Views: 1289 Leandro Junes
How to Generate Pseudorandom Numbers | Infinite Series
 
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Viewers like you help make PBS (Thank you 😃) . Support your local PBS Member Station here: https://to.pbs.org/donateinfi What is a the difference between a random and a pseudorandom number? And what can pseudo random numbers allow us to do that random numbers can't? Tweet at us! @pbsinfinite Facebook: facebook.com/pbsinfinite series Email us! pbsinfiniteseries [at] gmail [dot] com Previous Episode How many Cops to catch a Robber? | Infinite Series https://www.youtube.com/watch?v=fXvN-pF76-E Computers need to have access to random numbers. They’re used to encrypt information, deal cards in your game of virtual solitaire, simulate unknown variables -- like in weather prediction and airplane scheduling, and so much more. But How can a computer possibly produce a random number? Written and Hosted by Kelsey Houston-Edwards Produced by Rusty Ward Graphics by Ray Lux Assistant Editing and Sound Design by Mike Petrow Made by Kornhaber Brown (www.kornhaberbrown.com) Special Thanks to Alex Townsend Big thanks to Matthew O'Connor and Yana Chernobilsky who are supporting us on Patreon at the Identity level! And thanks to Nicholas Rose and Mauricio Pacheco who are supporting us at the Lemma level!
Views: 101034 PBS Infinite Series
Applied Cryptography: Random Numbers in Java (1/5)
 
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Previous video: https://youtu.be/KuthrX4G1ss Next video: https://youtu.be/FhrsUCICh-Y
Views: 956 Leandro Junes
Coding Math: Episode 52 - Pseudo Random Number Generators, Part II
 
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This time we look at a couple of existing PRNG libraries available in JavaScript, and look at some examples of how PRNGs can be used in cryptography, games, and generative art. Support Coding Math: http://patreon.com/codingmath Source Code: Crypto: http://jsbin.com/kipequk/2/edit?js,console Landscape: http://jsbin.com/zizeje/1/edit?js,output Circles: http://jsbin.com/zizeje/2/edit?js,output
Views: 5349 Coding Math
A Quantum Random Number Generator for cryptographic applications
 
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This project presents a quantum random number generator for a multitude of cryptographic applications based on the alpha decay of a household radioactive source.
Views: 621 BTYoungScientists
Arduino Pseudo Random Non-Consecutive Number Generator
 
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*Click Below to Sign up for the free Arduino Video Course:* http://bit.ly/Arduino_Course *Click Below to Check Out the Premium Arduino Video Course:* http://bit.ly/Premium_Arduino *Click Below to Read About This Topic on Our Website* http://bit.ly/Random_Arduino *Description:* In this video we demonstrate how to create pseudo random numbers with Arduino - with a useful twist. This lesson was inspired by the following viewer question: "How do I create Random Non-Consecutive numbers with Arduino. P.S. These are the best tutorials that a complete idiot like you could ever make, thanks." -Anonymous *Let's overview exactly what we will talk about in todays episode:* Talk about pseudo random numbers. Identify the problem - using an Arduino sketch to demonstrate. Discuss how we might solve the problem. Write an Arduino sketch that solves the problem. Review what we talked about. *Pseudo Random Numbers* Before we answer the viewer’s question it is important to talk about what a pseudo random number is. A purely random number in the mathematical sense can't be predicted. The microcontroller that the Arduino uses (and for that case, most computers in general) can't really create pure random numbers. What they create instead are called pseudo random numbers. These are numbers that appear to be randomly generated, but if studied over time a predictable pattern emerges. The bottom line is that the random numbers we create with Arduino can be predicted. Now there are clever ways to create pseudo random numbers that act like the real deal – you can learn about one method in our video tutorial talking all about random numbers – but for this discussion, let’s return to our viewers inquiry. *Identify the Viewer’s Problem - use an Arduino sketch to demonstrate.* Ok, so let's go back to the viewers question, he wants to generate random numbers, but he never wants the same number generated two times in a row. Let's write an Arduino Sketch to make this clear. //This sketch outputs pseudo random integers. //A variable to hold pseudo random integers. int randomInt = 0; void setup() { //Initiate serial communication. Serial.begin(9600); }//Close setup function void loop() { //Create a random number and assign it to the randomInt variable. randomInt = random(0, 10); //Send randomInt to the serial port for displaying on the serial monitor window. Serial.print(randomInt); }//Close loop function. In the first block of code a variable that will hold the pseudo random integers is declared and initialized. //A variable to hold pseudo random integers. int randomInt = 0; In the setup() function we begin serial communication in order to display the numbers we generate on a computer display. void setup() { //Initiate serial communication. Serial.begin(9600); }//Close setup function In the loop() we create the random number with the Arduino random() function and assign the output to the variable we had just created. The random() function can take two arguments 1) the minimum value of the number we want generated 2) the maximum value we want generated. //Create a random number and assign it to the randomInt variable. randomInt = random(0, 10); I will use 0 for the minimum, and 10 for the maximum. Every time through the loop, a new random number will be assigned the randomInt variable. Finally, the value of randomInt is sent over the serial port to be displayed in the serial monitor window. //Send randomInt to the serial port for displaying on the serial monitor window. Serial.print(randomInt); If you upload this code and open the serial monitor you will see in some cases where the same number shows up two times in a row. This is the problem. The viewer doesn't ever want the same number two times in a row. *Discuss how we might solve the problem.* So let's talk about how we might solve this problem. We know we need to generate a random number. What if we create a variable to track the previous random number? Then we could use a condition that says something like "If the previous random number is equal to the random number that was just generated, toss that number out the window, and create a different one.” The final thing we would need to do is set the previous random number equal to the new random number, that way we keep updating our previous random number every time through the loop(). *Let’s Implement our solution in an Arduino Sketch.* Copy and paste this code into your Arduino IDE. All you need is an Arduino board attached to your computer to make it work. *Get the Code from the below address* http://bit.ly/Random_Arduino *About Us:* This Arduino tutorial was created by Open Source Hardware Group. We are an education company who seek to help people learn about electronics and programming through the ubiquitous Arduino development board.
The Lava Lamps That Help Keep The Internet Secure
 
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At the headquarters of Cloudflare, in San Francisco, there's a wall of lava lamps: the Entropy Wall. They're used to generate random numbers and keep a good bit of the internet secure: here's how. Thanks to the team at Cloudflare - this is not a sponsored video, they just had interesting lava lamps! There's a technical rundown of the system on their blog here: https://blog.cloudflare.com/lavarand-in-production-the-nitty-gritty-technical-details Edited by Michelle Martin, @mrsmmartin I'm at http://tomscott.com on Twitter at http://twitter.com/tomscott on Facebook at http://facebook.com/tomscott and on Snapchat and Instagram as tomscottgo
Views: 1237011 Tom Scott
Build a Pseudo Random Number Generator that Follows a Specific Sequence in Ruby
 
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In this coding exercise you'll learn how to build a pseudo random number generator in Ruby that dynamically creates a set of random numbers based on a pre-defined sequence.
Views: 426 edutechional
Linear Congruential Random Number Generators
 
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Random Number Generators (RNGs) are useful in many ways. This video explains how a simple RNG can be made of the 'Linear Congruential Generator' type. This type of generator is not very robust, but it is quick and easy to program with little memory requirement.
Views: 17869 physics qub
Random Number Generation - How does a computer generate random numbers?
 
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~ Be sure to like the video and comment down below over what you would like to see next video. Don't forget to subscribe to the channel to get receive new videos every week! ~ FUN FACTS - Some PRNG's (Pseudo-Random Number Generators) can pass mathematical probability tests. - A common PRNG seed is "Xsub(n+1) = (a * (Xsub(n)) mod m", when "a and b are large integers", and m is the maximum number being generated SOURCES https://www.random.org/ https://en.wikipedia.org/wiki/Random_number_generation
Views: 1940 Computer Central
True Random Number Generators - FST-01 - Well Tempered Hacker
 
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Randomness forms the basis of cryptography but computers are deterministic and therefore terrible for generating true randomness. In this episode we'll look at the FST-01, a $35 USB based true random number generator (TRNG) which harvests randomness from the environment. We'll flash the NeuG random number generator software onto the device using a serial programmer and a few wires. Then we'll plug it in, start it up and look at the random data it generates. Hardware: http://www.seeedstudio.com/wiki/FST-01 http://www.seeedstudio.com/depot/s/fst-01.html Software: http://www.gniibe.org/memo/development/gnuk/rng/neug.html #crypto #cryptography #random #randomnumber #geigercounter #cryptography #mouse #pgp #privatekey #flyingstonetiny #FST-01 #randomnumbergenerator #environment #computing #communication #messaging #mail #email
Views: 12701 Anders Brownworth
PRNG Part 1
 
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Part 1 of a 3 part lesson on Pseudo Random Number Generators (PRNGs)
How Machines Generate Random Numbers with Time
 
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Pseudorandom number generators are explained using John Von Neumann's middle squares method. Machines can't roll dice so they do a trick to generate randomness - they grow randomness. The middle squares method is explained from a computer science perspective using clocks as seeds. This is a clip from Art of the Problem episode #1. This clip features original music from Hannah Addario-Berry
Views: 38225 Art of the Problem
Random Number Generator Seed Source
 
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A random number generator is a complex device which depends on a seed source in order to build a true random number. This video will show how a microcontroller creates a seed internally.
Views: 531 0033mer
Quantum Optics – Quantum random numbers generator QRNG
 
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One-photon based quantum technologies In this lesson, you will discover two quantum technologies based on one photon sources. Quantum technologies allow one to achieve a goal in a way qualitatively different from a classical technology aiming at the same goal. For instance, quantum cryptography is immune to progress in computers power, while many classical cryptography methods can in principle be broken when we have more powerful computers. Similarly, quantum random number generators yield true random numbers, while classical random number generators only produce pseudo-random numbers, which might be guessed by somebody else than the user. This lesson is also an opportunity to learn two important concepts in quantum information: (i) qubits based on photon polarization; (ii) the celebrated no-cloning theorem, at the root of the security of quantum cryptography. Learning Objectives • Apply your knowledge about the behavior of a single photon on a beam splitter to quantum random number generators. • Understand the no-cloning theorem • Understand and remember the properties of q qubit This course gives you access to basic tools and concepts to understand research articles and books on modern quantum optics. You will learn about quantization of light, formalism to describe quantum states of light without any classical analogue, and observables allowing one to demonstrate typical quantum properties of these states. These tools will be applied to the emblematic case of a one-photon wave packet, which behaves both as a particle and a wave. Wave-particle duality is a great quantum mystery in the words of Richard Feynman. You will be able to fully appreciate real experiments demonstrating wave-particle duality for a single photon, and applications to quantum technologies based on single photon sources, which are now commercially available. The tools presented in this course will be widely used in our second quantum optics course, which will present more advanced topics such as entanglement, interaction of quantized light with matter, squeezed light, etc... So if you have a good knowledge in basic quantum mechanics and classical electromagnetism, but always wanted to know: • how to go from classical electromagnetism to quantized radiation, • how the concept of photon emerges, • how a unified formalism is able to describe apparently contradictory behaviors observed in quantum optics labs, • how creative physicists and engineers have invented totally new technologies based on quantum properties of light, then this course is for you. Subscribe at: https://www.coursera.org
Views: 213 intrigano
Math for Game Developers - Making Randomness (Pseudorandom Number Generators)
 
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How random number generators work and how to get good numbers out of them. Find the source code here: https://github.com/BSVino/MathForGameDevelopers/tree/probability-random New video every Thursday. Question? Leave a comment below, or ask me on Twitter: https://twitter.com/VinoBS EXERCISES: 1. Modify the function to pass the current time into the random number seed and verify that a new sequence is always produced. 2. Create a pseudorandom number generator that generates only 1's and 0's, false and true values. 3. How would the difference in probabilities be between outputs if there were only 2^8 input values and 100 output values? What about if there were 2^8 input values and 128 output values? 4. Tricky: How would you design a pseudorandom number generator over arbitrary output ranges where all of the output values are exactly equally likely?
Views: 5574 Jorge Rodriguez
PRNG Part 3
 
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Part 3 of a 3 part lesson on PRNGs.
What is PSEUDORANDOM NUMBER GENERATOR? What does PSEUDORANDOM NUMBER GENERATOR mean?
 
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What is PSEUDORANDOM NUMBER GENERATOR? What does PSEUDORANDOM NUMBER GENERATOR mean? PSEUDORANDOM NUMBER GENERATOR meaning - PSEUDORANDOM NUMBER GENERATOR definition - PSEUDORANDOM NUMBER GENERATOR explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. A pseudorandom number generator (PRNG), also known as a deterministic random bit generator (DRBG), is an algorithm for generating a sequence of numbers whose properties approximate the properties of sequences of random numbers. The PRNG-generated sequence is not truly random, because it is completely determined by a relatively small set of initial values, called the PRNG's seed (which may include truly random values). Although sequences that are closer to truly random can be generated using hardware random number generators, pseudorandom number generators are important in practice for their speed in number generation and their reproducibility. PRNGs are central in applications such as simulations (e.g. for the Monte Carlo method), electronic games (e.g. for procedural generation), and cryptography. Cryptographic applications require the output not to be predictable from earlier outputs, and more elaborate algorithms, which do not inherit the linearity of simpler PRNGs, are needed. Good statistical properties are a central requirement for the output of a PRNG. In general, careful mathematical analysis is required to have any confidence that a PRNG generates numbers that are sufficiently close to random to suit the intended use. John von Neumann cautioned about the misinterpretation of a PRNG as a truly random generator, and joked that "Anyone who considers arithmetical methods of producing random digits is, of course, in a state of sin." A PRNG can be started from an arbitrary initial state using a seed state. It will always produce the same sequence when initialized with that state. The period of a PRNG is defined thus: the maximum, over all starting states, of the length of the repetition-free prefix of the sequence. The period is bounded by the number of the states, usually measured in bits. However, since the length of the period potentially doubles with each bit of "state" added, it is easy to build PRNGs with periods long enough for many practical applications. If a PRNG's internal state contains n bits, its period can be no longer than 2n results, and may be much shorter. For some PRNGs, the period length can be calculated without walking through the whole period. Linear Feedback Shift Registers (LFSRs) are usually chosen to have periods of exactly 2n-1. Linear congruential generators have periods that can be calculated by factoring. Although PRNGs will repeat their results after they reach the end of their period, a repeated result does not imply that the end of the period has been reached, since its internal state may be larger than its output; this is particularly obvious with PRNGs with a one-bit output. Most PRNG algorithms produce sequences which are uniformly distributed by any of several tests. It is an open question, and one central to the theory and practice of cryptography, whether there is any way to distinguish the output of a high-quality PRNG from a truly random sequence, knowing the algorithms used, but not the state with which it was initialized. The security of most cryptographic algorithms and protocols using PRNGs is based on the assumption that it is infeasible to distinguish use of a suitable PRNG from use of a truly random sequence. The simplest examples of this dependency are stream ciphers, which (most often) work by exclusive or-ing the plaintext of a message with the output of a PRNG, producing ciphertext. The design of cryptographically adequate PRNGs is extremely difficult, because they must meet additional criteria (see below). The size of its period is an important factor in the cryptographic suitability of a PRNG, but not the only one. A PRNG suitable for cryptographic applications is called a cryptographically secure PRNG (CSPRNG). A requirement for a CSPRNG is that an adversary not knowing the seed has only negligible advantage in distinguishing the generator's output sequence from a random sequence. In other words, while a PRNG is only required to pass certain statistical tests, a CSPRNG must pass all statistical tests that are restricted to polynomial time in the size of the seed. Though a proof of this property is beyond the current state of the art of computational complexity theory, strong evidence may be provided by reducing the CSPRNG to a problem that is assumed to be hard, such as integer factorization. In general, years of review may be required before an algorithm can be certified as a CSPRNG.
Views: 2738 The Audiopedia
OneRNG - An Open and Verifiable hardware random number generator
 
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Jim Cheetham http://lca2015.linux.org.au/schedule/30079/view_talk Announced at LCA2014, the NZ-based OneRNG project has created an affordable and verifiable Open Hardware and Open Source entropy generator and RNG, presented as a USB device. In this presentation the creators of the project will demo the device, and discuss the hardware, firmware and OS software. We will also cover trust, distrust and paranoia.
Generate your own Pseudo-Random Numbers / Gere seus próprios Números Pseudo-Aleatórios
 
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Generate pseudo-random numbers using the Linear Congruential Generator. Then simulate the U(0,1), die roll and exponential distribution. Obviously, the rand() "function" could be used instead. This is just to show a version of the mechanism that generate the pseudo-random numbers you get when you use rand() or similar commands in most softwares that generate pseudo-random numbers.
Views: 5126 André Loureiro
Cryptographically secure pseudorandom number generator Top # 7 Facts
 
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Cryptographically secure pseudorandom number generator Top # 7 Facts
Views: 78 Duryodhan Trivedi
Create a Random Number in C++
 
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Demonstrates how to use rand() and srand() to generate random numbers. The software used in this tutorial is Xcode, but the code can be applied to any C++ compiler. Table of Contents: 00:26 - rand() function 01:11 - cstdlib header file 01:49 - Pseudorandom Number 02:39 - time() function 03:18 - ctime header file 03:32 - srand() function
Views: 14442 profgustin
random number generator شرح
 
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one time pad #2
Views: 981 missra mansour
Truly Random Number Generator Is Bringing Encryption To Every Device
 
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So long pseudo-random numbers. Quantum mechanics is making encryption much stronger.
Views: 44 Sara Peters
Digital Logic - Linear Feedback Shift Register
 
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This is another video in my series of videos where I talk about Digital Logic. In this video, I show how you can make a Linear Feedback Shift Register, which is a circuit that allows you to generate pseudo-random numbers.
Views: 37543 Robot Brigade
Quantum Random Number Generator by the Qubit Lab
 
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What's a quantum random number generator and why use one? Simple explanations by the Qubit Lab brought to you by ID Quantique.
Views: 1229 ID Quantique
C# Beginners Tutorial - 54 - Generating Random Numbers
 
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Facebook - https://www.facebook.com/TheNewBoston-464114846956315/ GitHub - https://github.com/buckyroberts Google+ - https://plus.google.com/+BuckyRoberts LinkedIn - https://www.linkedin.com/in/buckyroberts reddit - https://www.reddit.com/r/thenewboston/ Support - https://www.patreon.com/thenewboston thenewboston - https://thenewboston.com/ Twitter - https://twitter.com/bucky_roberts
Views: 55053 thenewboston
Fast random number generation in Python and NumPy
 
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Bernardt Duvenhage https://za.pycon.org/talks/53-fast-random-number-generation-in-python-and-numpy/ A fast Random Number Generator (RNG) is key to doing Monte Carlo simulations, efficiently initialising machine learning models, shuffling long sequences of numbers and many tasks in scientific computing. CPython and NumPy use implementations of the Mersenne Twister RNG and rejection sampling to generate random numbers in an interval. The NumPy implementation trades more samples for cheaper division by a power of two. The implementation is very efficient when the random interval is a power of two, but on average generate many more samples compared to the GNU C or Java algorithms. The Python RNG uses an algorithm similar to GNU C. A recent paper by Daniel Lemire (https://arxiv.org/abs/1805.10941) discusses an efficient division light method to generate uniform random numbers in an interval. This method is apparently used in the Go language. On 64-bit platforms there are also fast alternative RNGs that perform comparatively on statistical examinations passing tests like BigCrush. The splitmix64 (part of the standard Java API) and lehmer64 RNGs, for example, require approximately 1.5 cycles to generate 32 random bits (without using SIMD) while the 32-bit Mersenne Twister requires approximately 10 cycles per 32 bits. This talk will discuss the inclusion of Lemire's method in NumPy as an alternative to the current rejection sampling implementation. My implementation results in a 2x - 3x improvement in the performance of generating a sequence of random numbers. Using splitmix64 or lehmer64 RNGs in NumPy instead of the Mersenne Twister results in a further 2x performance improvement. The random module in Python does not do the rejection sampling in C like NumPy does. Much of the time to get a random number is therefore spent in the Python code. This talk will also discuss a fast random Python module that implements Lemire's method instead of the current rejection sampling, provides alternative RNGs and moves more of the code into C. I'm considering doing pull requests for both the NumPy modification and the Python fast random module and will present a detailed analysis of the proposed modifications. pyconza2018 python
Views: 133 PyCon South Africa
Generating Random Numbers (random.c)
 
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Demonstration of generating random numbers every time you press RB0. NDSU / ECE 376 Embedded Systems. Lecture notes located at www.BisonAcademy.com
Views: 94 Jacob Glower
Openwest 2015 - Robert Stone - "Pseudo-Random Number Generation" (91)
 
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As advanced as computers have become they are still deterministic creatures at heart. With revelations by Edward Snowden surrounding Ellipitic Curve Cryptography and the discovery that the NSA and CIA were involved in the development of one of the RSA's psuedo-random number generators questions abound as to "What do those three letter agencies actually know and what can they do with this information?" This presentation introduces the concept of Psuedo and Truly Random number generation, provides an overview of the different types of algorithms used in their generation, and then dives into a discussion about the Math and Theory behind how Prime Numbers and Elliptic Curves factor into the generation of psuedo-random numbers. An analysis of Dual_EC_DRBG is presented making it clear what the problem actually was and just how naughty the government has been! Best practices and gotchas are also outlined, a discussion regarding where randomness comes from in Perl as well as a few case studies are presented so that developers can protect themselves from common mistakes. A background in Perl is not required and you are sure to find this presentation fun, entertaining, and just a bit random! Friday, May 8th, 10:30am-11:15am Room SB 073 (Security)
Views: 407 Utah Open Source
Random Numbers with Block Ciphers (CSS441, L09, Y15)
 
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PRNGs with block ciphers in counter and OFB mode; ANSI X9.17; RC4. Course material via: http://sandilands.info/sgordon/teaching
Views: 1097 Steven Gordon
LFSR 1
 
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Views: 24179 Jeff Suzuki
EYL - Micro Quantum Random Number Generator
 
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EVERYWHERE IN YOUR LIFE, EYL Lately, as the frequency of threats to data and personal information has been increasing, the security of encryption keys has become crucially important for the perfect security in all areas of information and communication industry. Encryption keys are composed of random numbers that should be impossible to decipher nor predict. Existing Pseudo-random number imitates perfect random number with its generated values from an algorithm that is predictable and vulnerable to hacking. However, EYL will provide perfect random numbers with the world's first encryption technology that utilizes Quantum-random number generator. Since Quantum-random number generator has a mechanism of producing random numbers from detecting the particles emitted randomly and naturally from the radioactive isotopes. EYL provides the perfect encryption keys that even the best hacker cannot even break. As the number of IoT devices is growing exponentially with threatening security risks in reality EYL will provide the perfect security through the encryption technology utilizing quantum-random numbers. In the future, EYL's QRNG, smaller in size with stronger security, will protect your daily lives. QUANTUM SECURITY WILL BE RIGHT IN YOUR POCKET … … … EYL If you have a question, please email to [email protected]
algorithm : get a random number in certain interval in all languages
 
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the best online bank to get a widely used Visa Card : https://goo.gl/6Xsw6F the best bitcoin trading place : https://goo.gl/kF67ti in this video you will learn how to get a bounded random number (integer) in all languages
Views: 944 NadjibSoft
Introduction to Random Numbers in Security (CSS322, L8, Y14)
 
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Short introduction to challenges of generating random numbers for cryptography. Course material via: http://sandilands.info/sgordon/teaching
Views: 365 Steven Gordon
EchRan The First Truly Random Number Generator
 
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Processing echos in rust to create random numbers.
Views: 144 Chad Baxter
Dice Eliminator. Analog Random Number Generator.
 
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Need a Science Fair project? Here is how to make your own Dice Eliminator. In Memoriam of Reuben Rowan (ElectronTeacher) whose human clock stopped recently, I present this video in his honor. Reuben gave me a gift a couple of years back of a Weller Soldering station. I use it quite often and used it to make my own design, simple Analog Random Number generator. It can be used to Randomly pick numbers from 1 to 12, just like dice. For simple dice games, this can be played faster and is a lot of fun. Using 2 different switches, the user can choose how he wants the unit to pick the number. Great for Monopoly and other Dice controlled games. I think building something like this is good for the mind and may get you an A on your next science project! Good luck to you all and Happy Trails to you, Reuben. Thanks for watching and subscribing. Dave Herbert AMA 8221 Certified Contest Director Leader Member Scientific Division. Here are some handy links. Reuben's You Tube Channel. http://www.youtube.com/electronteacher
Views: 6542 NightFlyyer
Windows Random Number Generator Test
 
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Testing different random number generator API's in Visual Studio The Test-Application is generating 13 random numbers (from 0 to 12) 10000 times VBMath.RND has a problem where the minimum number (0) only shows up about half of the times the other numbers show up, otherwise a nice even appearance of all other numbers System.Random has the problem that the maximum number (12) does not show up at all, chance of occurrence differs greatly between all numbers System.Cryptography does produce a nice even appearance of all 13 numbers
Views: 121 realdragnet
Applied Cryptography: Random Numbers in Java (3/5)
 
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Previous video: https://youtu.be/FhrsUCICh-Y Next video: https://youtu.be/KnHp1uSm6k0
Views: 467 Leandro Junes
Random Number generation in C  [ Hindi ] [ हिंदी ]
 
06:42
In This video I have explained that how can you generate - 1. A random number 2. A random with Maximum Limit 3. A random number within a Specific range
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