Chapter 1: Introduction COMP9313: Big Data Management Lecturer: Xin Cao Course web site: http://www.cse.unsw.edu.au/~cs9313/ 5.‹#› 1 Chapter 5: Graph Data Processing in MapReduce 5.‹#› What’s a Graph? G = (V,E), where V represents the set of vertices (nodes) E represents the set of edges (links) Both vertices and edges may contain additional information Different types […]
Object-Oriented Programming Operating Systems Lecture 5b Dr Ronald Grau School of Engineering and Informatics Spring term 2018 Previously Process Synchronisation Inter-Process Communication Race conditions Communication models Critical section Software vs. Hardware solutions Condition synchronisation 1 Today Process Synchronisation Critical Sections Common application problems: Producer-Consumer Readers-Writers
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MondayAuth2 Humans – The Weakest Link? • Identification by ID document/passport • Authentication by signature In the beginning •Identification •Authentication •Authorisation What’s the difference? Identification On the Internet, nobody knows you’re a dog Enrol Authenticate Close Account Replace Identifier Authenticator Stages Process I am John Claim Identity Authenticator Prove Identity Authorised Access •What you know
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Object-Oriented Programming Operating Systems Lecture 4b Dr Ronald Grau School of Engineering and Informatics Spring term 2018 Previously Scheduling Scheduling policies & performance Multi-level queue scheduling Feedback scheduling Real-time scheduling Java thread scheduling 1 Today Evaluating Scheduling Algorithms Deterministic Evaluation Probabilistic Evaluation Stochastic Evaluation Simulation
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COMP2610 / 6261 – Information Theory – Lecture 14: Source Coding Theorem for Symbol Codes COMP2610 / 6261 – Information Theory Lecture 14: Source Coding Theorem for Symbol Codes Robert C. Williamson Research School of Computer Science The Australian National University 1 L O G O U S E G U I D E L
COMP284 Scripting Languages – Handouts COMP284 Scripting Languages Lecture 1: Overview of COMP284 Handouts Ullrich Hustadt Department of Computer Science School of Electrical Engineering, Electronics, and Computer Science University of Liverpool Contents 1 Introduction Motivation Scripting languages 2 COMP284 Aims Learning outcomes Delivery Assessment COMP284 Scripting Languages Lecture 1 Slide L1 – 1 Introduction Motivation
CO580 Algorithms CO580 Algorithms Dr Timothy Kimber January 2018 Introduction Course Outline The lecturer PhD in Computational Logic (Imperial) 5 years as Teaching Fellow/Senior Teaching Fellow Also teach Prolog to the MAC and Specialism classes The structure 28 hours of interactive lectures (weeks 2–9) Sessions include unassessed group and individual exercises Two assessed exercises (one
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Imperial College London – Department of Computing MSc in Computing Science 580: Algorithms Tutorial 2 1. (Part of a 2015 exam question.) (a) Using either Java or pseudocode, write a recursive procedure Pow(x,N) to compute xN , where N is a positive integer. Use a divide and conquer strategy. Hint: xN = xN/2 × xN/2
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Computational Linguistics Computational Linguistics Copyright © 2017 Suzanne Stevenson , Graeme Hirst and Gerald Penn. All rights reserved. 3 3. Chart parsing Gerald Penn Department of Computer Science, University of Toronto CSC 2501 / 485 Fall 2018 Reading: Jurafsky & Martin: 13.3–4. Allen: 3.4, 3.6. Bird et al: 8.4, online extras 8.2 to end of
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GraRep: Learning Graph Representations with Global Structural Information Shaosheng Cao Xidian University shelsoncao@gmail.com Wei Lu Singapore University of Technology and Design luwei@sutd.edu.sg Qiongkai Xu IBM Research – China Australian National University xuqkai@cn.ibm.com ABSTRACT In this paper, we present GraRep, a novel model for learn- ing vertex representations of weighted graphs. This model learns low dimensional