Introduction to Computer Security – G6077 This lab covers • Understand caesar, Xor and Affine ciphers • Implement and test ciphers in a programming language • Compare and analyse different ciphers implementations Contents Tasks Caesar cipher 3 Task 1 3 Task 2 3 Task 3 3 Affine Cipher 3 Task 1 3 Task 2 3 […]
Distributed Network Algorithms (Lecture Notes for GIAN Course) Assoc.-Prof. Dr. Stefan Schmid Assoc-Prof. Dr. Partha Sarathi Mandal Thanks to Prof. Dr. Roger Wattenhofer and Prof. Dr. Christian Scheideler for basis of manuscript! Summer 2016 Contents 1 Topology & Routing 3 2 Vertex Coloring 9 2.1 Introduction………………………… 9 2.2 ColoringTrees ………………………. 11 3 Leader Election 17
TRUE/FALSE QUESTIONS: • T • T • F • T • F • T • F • T • F • T • T • F • F • T • T MULTIPLE CHOICE QUESTIONS: • D • A • B • B • C • A • C • D • B • A
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Candidate Number THE UNIVERSITY OF SUSSEX BSc FINAL YEAR EXAMINATION MComp THIRD YEAR EXAMINATION January 2019 (A1) Introduction to Computer Security Assessment Period: January 2019 (A1) G6077 DO NOT TURN OVER UNTIL INSTRUCTED TO BY THE LEAD INVIGILATOR Candidates should answer TWO questions out of THREE. If all three questions are attempted only the first
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1. (18 points) Let c be a real constant and the joint pdf of X and Y be given as cxy if0
UCSD DEPARTMENT OF COMPUTER SCIENCE CS 123 Computer Networks: Route Computation In earlier lectures, we broke routing up into four pieces. The first was addressing by which we defined the structure of IP addresses and prefixes and NATs. The second piece of the puzzle is neighbor routing by which endnodes talk to routers (e.g, things
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Introduction to Computer Security Module – G6077 – Introduction to OpenSSL Contents Client-Server architecture 2 OpenSSL Tutorial: An Introduction to Internet Security 2 Abbreviations Key 2 Task 1 install OpenSSL 3 Task 2 start the openssl 3 Task 3 version of openssl 4 Task 4 standard commands 4 Task 5 inspect ciphers 4 Task 6
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Sequence-to-sequence models Sequence-to-sequence models I Sequence goes in, sequence comes out I Sequence-to-sequence models are a powerful learning framework that have found success in a wide range of applications I Automatic Speech Recognition (ASR): sound stream goes in, I text comes out Machine Translation (MT): source language sentence goes in, I target language sentence comes
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Linear models: Recap Linear models: I Perceptron score(y, x; ✓) = ✓ · f (x, y) I Na ̈ıve Bayes: log P(y|x; ✓) = log P(x|y; ) + log P(y; u) = log B(x) + ✓ · f (x, y) I Logistic Regression log P(y|x; ✓) = ✓ · f (x, y) log X exp
Linear models: Recap Linear models: I Perceptron score(y, x; ✓) = ✓ · f (x, y) I Na ̈ıve Bayes: log P(y|x; ✓) = log P(x|y; ) + log P(y; u) = log B(x) + ✓ · f (x, y) I Logistic Regression log P(y|x; ✓) = ✓ · f (x, y) log X exp
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