Data Mining
TETRIS AI WITH TEMPORAL DYNAMIC LEARNING OF MDP
2.3. Java cracking random_utils
Using network science the understand elections: the South African 2014 national elections on Twitter
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Jack Davis Andrew Henrey FROM N00B TO PRO. PURPOSE Create a simulator from scratch that: Generates data from a variety of distributions Makes a response.
WORM PROPAGATION Terry Griffin Sandeep Pinnamaneni Vandana Gunupudi.
1 The Fortuna PRNG Niels Ferguson. 2 The problem We need to make “random” choices in cryptographic protocols. Computers are deterministic. Standard “random”
Feb. 2015Part V – Malfunctions: Architectural AnomaliesSlide 1 Robust Parallel Processing Resilient Algorithms.
Stochastic Synthesis of Natural Organic Matter Steve Cabaniss, UNM Greg Madey, Patricia Maurice, Yingping Huang, Xiaorong Xiang, UND Laura Leff, Ola Olapade.
Some Recent Results in Secure Pseudorandom Number Generation Berry Schoenmakers Joint work with Andrey Sidorenko and (partly) with Reza Rezaeian Farashahi.
1 Slides by Iddo Tzameret and Gil Shklarski. Adapted from Oded Goldreich’s course lecture notes by Erez Waisbard and Gera Weiss.
