close this section of the libraryftp://coral.cs.jcu.edu.au (379)
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The Best Algorithm for Searching a Binary Search Tree David Spuler
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EXTENSIONS TO REGULARISED DISCRIMINANT ANALYSIS Stefan Aeberhard1, Danny Coomans2 and Olivier de Vel1;3 James Cook University Townsville QLD 4811, Australia
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-1- Compiler Optimization for ANSI C 1 David Spuler Department of Computer Science James Cook University of North Queensland
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-1- Binary Split TreeInsertion and Deletion Algorithms David A. Spuler and Gopal K. Gupta Dept. of Computer Science James Cook University of North Queensland
open this document and view contentsftp://coral.cs.jcu.edu.au//pub/JCUCSdocs/techreports/92-2.ps.gz, 19921012
-1- An Empirical Study of Nearly Optimal Binary Search Trees and Split Trees David A. Spuler and Gopal K. Gupta Dept. of Computer Science James Cook University of North Queensland
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-1- Static Detection of Preprocessor MacroErrors in C David A. Spuler and A. Sayed Muhammed Sajeev Computer Science Department James Cook University Townsville, QLD 4811 Australia
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The Optimal Binary Search Tree For Andersson'sSearch Algorithm David Spuler Dept. of Computer Science James Cook University
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-1- Two-Way Comparison Search Trees AGeneralization of Binary Search Trees and Split Trees David Spuler Dept. of Computer Science James Cook University of North Queensland
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Supporting a Rapid Prototyping System for Distributed Algorithms on a Transputer Network 1 Jiannong Cao, Olivier de Vel, Kai Leung Wong Dept of Computer Science, James Cook University Townsville, Qld. 4811, Australia
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Backwards Context Approximation John Farrell James Cook University November 16, 1992
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Implementation of an IPC Rapid Prototyping System on a Transputer Network Jiannong Cao Olivier de Vel Dept of Computer Science. James Cook University Townsville, Qld. 4811 Australia January 18, 1993
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Aspects of Shape-from-X Brendan McCane Department of Computer Science James Cook University of North Queensland To wnsville, QLD, 4811, Australia.
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An Atmospheric Cloud Model for Image Synthesis and Deterministic and Nondeterministic Animation Brendan McCane Peter Stephenson Computer Science Department James Cook University of North Queensland To wnsville Q 4811 Australia
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THE PERFORMANCE OF STATISTICAL PATTERN RECOGNITION METHODS IN HIGH DIMENSIONAL SETTINGS Stefan Aeberhard1, Danny Coomans2 and Olivier de Vel1;3 James Cook University, 4811 Australia
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Check: A Better Checker for C Thesis submitted by: David AndrewSpuler (BSc. JCUNQ) November 1990 in partial ful llment of the requirements for the Degree of Bachelor of Science with Honours in the Department of Computer Science of James Cook University of North Queensland. Supervisor: Professor Gopal K.
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Prolog-D-Linda v2 : A New Embedding of Linda in SICStus Prolog Page 1 Prolog-D-Linda v2 : A New Embedding of Linda in SICStus Prolog Geoff Sutcliffe Dep't of Computer Science, James Cook University, Townsville, Australia, 4811 Email: geoff@cs.jcu.edu.au
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A Context-Free Language Decision Problem B. Litow x
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THE SIMULATION OF THE BEHAVIOUR AND EVOLUTION OF ARTIFICIAL ORGANISMS Ross Milward and Olivier de Vel Department of Computer Science, James Cook University, Townsville Q4811, Australia. olivier@curacoa.cs.jcu.edu.au c 1993 Milward & de Vel A modified version of this report has been submitted to the
open this document and view contentsftp://coral.cs.jcu.edu.au//pub/JCUCSdocs/techreports/93-10.ps.gz, 19930615
New Fast Algorithms for Variable Selection based on Classifier Performance Stefan Aeberhard1, Olivier de Vel1;2 and Danny Coomans3 James Cook University Townsville QLD 4811, Australia
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A Linear Deduction System with Integrated Semantic Guidance by Geoff Sutcliffe (BSc, BSc (Hons), MSc) This thesis is presented for the degree of Doctor of Philosophy at The University of Western Australia, Department of Computer Science, 1992. Page ii A Linear Deduction System with Integrated Semantic
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Context Approximation for Functional Languages by John William Farrell B.A. (Honours) A thesis submitted to The Department of Computer Science University of Queensland for the degree of DOCTOR OF PHILOSOPHY 12 June 1992 Declaration I declare that the work presented in this thesis is, to the best of my
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Performance Characteristics of a Load Balancing Algorithm B. Litow x, S.H. Hosseini, and K. Vairavan July 28, 1993 1 Introduction In this paper we examine and extend some properties of a novel graphcoloring based load balancing algorithm proposed by the authors in . A formal analysis of the
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Compiler Code Generation forMultiway Branch Statements as a Static Search Problem D.A. SPULER Technical Report 94/03 January 1994 Compiler Code Generation for Multiway Branch Statements as a Static Search Problem David Spuler1
open this document and view contentsftp://coral.cs.jcu.edu.au//pub/JCUCSdocs/techreports/94-2.ps.gz, 19940125
Optimal Binary Split Trees Revisited D.A. SPULER Technical Report 94/02 January 1994 Optimal Binary Split Trees Revisited David A. Spuler Dept. of Computer Science James Cook University of North Queensland
open this document and view contentsftp://coral.cs.jcu.edu.au//pub/JCUCSdocs/techreports/94-1.ps.gz, 19940125
Compiler Detection of Function Call Side Effects D.A. SPULER and A.S.M SAJEEV Technical Report 94/01 January 1994 Compiler Detection of Function Call Side Effects David A. Spuler and A. S. M. Sajeev Department of Computer Science James Cook University To wnsville, QLD 4811 Australia
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1 Learning and Using UNIX1 G. K. Gupta and L. G. Siebuhr Department of Computer Science James Cook University Townsville, Qld 4811 Australia
open this document and view contentsftp://coral.cs.jcu.edu.au//web/FP/glasgow/workshop-titles91.ps.gz, 19940302
1991 Glasgow Workshop on Functional Programming, Portree, Isle of Skye, Scotland R. Heldal, C.K. Holst and P.L. Wadler (Editors) Springer-Verlag Series of Workshops in Computing, Series Editor C.J. van Rijsbergen, Springer-Verlag, Berlin, 1992. ISBN 0-387-19760-5/3-540-19760-5. A Parallel Functional
open this document and view contentsftp://coral.cs.jcu.edu.au//web/FP/glasgow/workshop-titles90.ps.gz, 19940302
1990 Glasgow Workshop on Functional Programming, Ullapool, Scotland S.L. Peyton Jones, G. Hutton and C.K. Holst (Editors) Springer-Verlag Series of Workshops in Computing, Series Editor C.J. van Rijsbergen, Springer-Verlag, Berlin, 1991. ISBN 0-387-19667-6/3-540-19667-6. Lifetime Analysis G. Argo : : :
open this document and view contentsftp://coral.cs.jcu.edu.au//web/FP/glasgow/workshop-titles89.ps.gz, 19940302
1989 Glasgow Workshop on Functional Programming, Fraserburgh, Scotland M.K. Davis and R.J.M. Hughes (Editors) Springer-Verlag Series of Workshops in Computing, Series Editor C.J. van Rijsbergen, Springer-Verlag, Berlin, 1990. ISBN 0-387-19609-9/3-540-19609-9. A New Method for Strictness Analysis on
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1992 Glasgow Workshop on Functional Programming J. Launchbury and P.M. Sansom (Editors) Springer-Verlag Series of Workshops in Computing, Series Editor C.J. van Rijsbergen, Springer-Verlag, Berlin, 1993. ISBN 0-387-19820-2/3-540-19820-2. High Level Specification of I/O in Functional Languages P.M.
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The TPTP Problem Library TPTP v1.0.0 - TR Date 8.12.93 Technical Report FKI-184-931 Technical Report 93/112 Christian Suttner1, Geoff Sutcliffe2, Theodor Yemenis1 1Institut f ur Informatik TU M unchen Germany 2Department of Computer Science James Cook University Australia
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Notes on Functional Programming with Gofer Technical Report UMCIS{1994{01 H. Conrad Cunningham cunningham@cs.olemiss.edu Software Methods Research Group Department of Computer and Information Science University of Mississippi 302 Weir Hall University, Mississippi 38677 USA 19 January 1994 Copyright c
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Announcement Workshop Series in Algorithms III. Navigation without a Map Port Stephens, NSW, Australia 5 { 9 December 1994 Organised by Hossam ElGindy and Martin Sutton The University of Newcastle, New South Wales, Australia Those interested in attending the workshop should contact Hossam ElGindy Fax
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Lecture Contents 26/7/94 1. What is a Functional Language 2. Features to Expect 3. Data Types | Integer, Real, Char, Boolean, String 4. Function Definitions 5. Function Application 6. Types Coming Up: ffl Lists ffl Algebraic Data Structures Gofer Interface LIST OF COMMANDS: Any command may be
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Department of Computer Science, James Cook University Tutorial Sheet 1 CP2050 { Computer Science IIB 1/8/94 - 5/8/94 1. Find the errors in the following C program. #include void have_fun(); main { char c; int i; c = 25; have_fun; printf("%c " c); i = 15; int i; scanf("%d", i); square(m);
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Department of Computer Science James Cook University CP3070 Computer Architecture & Communications 1994 Subject Objectives SUBJECT OBJECTIVES: The following is a list of topics a student should use as a guideline to successfully complete and pass the Data Communications and Computer Networks section of
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CP3220: Advanced Artificial Intelligence 1994 Subject Requirements Xindong Wu, Shyam Kapur, Olivier de Vel The following is a list of topics a student should use as a guideline to successfully complete and pass the subject Advanced Artificial Intelligence. 1 Expert Systems ffl Give the general
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CP3220: Statistical Natural Language Processing Language is a means of communication, of information transfer, from one individual or entity to another. It is not surprising that the field of information theory has a significant role to play in the development of good Natural Language Processing
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CP3220:045 SOLUTION TO THE SAMPLE FINAL EXAMINATION: SEMESTER 2, 1994/1/1/3. Section B: Natural Language Processing (33 marks) Question 1 Multiple Choice Questions Correct answers for parts (a) through (f) are (iv), (iii), (iii), (iv), (iii), and (ii), respectively. Question 2 VP H
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AI'94 Tutorial on Intelligent Learning Database Systems Seventh Australian Joint Conference on Artificial Intelligence (AI'94) 21 - 25 November 1994 The University of New England Armidale, NSW 2351 Australia Xindong Wu y Department of Computer Science, James Cook University, Townsville, QLD 4814,
open this document and view contentsftp://coral.cs.jcu.edu.au//pub/JCUCSdocs/acsws95/CFP.ps.gz, 19941118
Call for Participation Australian Winter School in Computer Science Workshop on Artificial Intelligence Magnetic Island 2-4 July 1995 Supported by Australian Computer Society and Department of Computer Science James Cook University The 1995 Australian Winter School in Computer Science will be held at
open this document and view contentsftp://coral.cs.jcu.edu.au//pub/JCUCSdocs/techreports/krisar-masters.ps.gz, 19941205
Noise Handling in Inductive Learning 1995 Johan Kris ar Department of Numerical Analysis and Computer Science Royal Institute of Technology Stockholm Sweden Supervised by Dr Xindong Wu Department of Computer Science James Cook University Townsville, Qld Australia Keywords: inductive learning, HCV,
open this document and view contentsftp://coral.cs.jcu.edu.au//pub/JCUCSdocs/techreports/mahlen-masters.ps.gz, 19941205
Dealing with Continuous Attribute Domains in Inductive Learning Petter M ahl en Supervised by Xindong Wu Department of Computer Science James Cook University Townsville, Qld Australia Copyright c 1994: Petter M ahl en, Xindong Wu Till Mamma Mor, lilla Mor, vem ar v al som hon 1
open this document and view contentsftp://coral.cs.jcu.edu.au//pub/JCUCSdocs/techreports/94-7.ps.gz, 19941207
The Influence of Graph Strucure on Generalized Dimension Exchange B. Litow x
open this document and view contentsftp://coral.cs.jcu.edu.au//pub/JCUCSdocs/techreports/94-5.ps.gz, 19941207
AStereo Matching Algorithm using Curve Segments and Cluster Analysis Brendan McCane Olivier de Vel Department of Computer Science James Cook University of North Queensland To wnsville, QLD, 4811, Australia. mccane@coral.cs.jcu.edu.au olivier@coral.cs.jcu.edu.au
open this document and view contentsftp://coral.cs.jcu.edu.au//pub/JCUCSdocs/techreports/94-6.ps.gz, 19941207
Evaluation of Automatically-Generated Compilers Anthony M. Sloane Department of Computer Science James Cook University Townsville, QLD, 4811 AUSTRALIA Anthony.Sloane@jcu.edu.au Technical Report 94/6 September 9, 1994
open this document and view contentsftp://coral.cs.jcu.edu.au//pub/JCUCSdocs/techreports/94-8.ps.gz, 19941209
Comparative Performance Analysis of Non-Linear Dimensionality Reduction Methods Sofianto Li Dept. of Computer Science James Cook University Townsville, Qld. 4811 Australia Olivier de Vel Dept. of Computer Science James Cook University Townsville, Qld. 4811 Australia olivier@cs.jcu.edu.au Danny Coomans
open this document and view contentsftp://coral.cs.jcu.edu.au//pub/JCUCSdocs/techreports/94-9.ps.gz, 19941214
Optimization in a Hierarchical Distributed Performance Monitoring System Ling Shi, Olivier de Vel Department of Computer Science James Cook University Townsville QLD 4811 Australia Tel: + 61 77 81 5851 Fax: + 61 77 81 4029 @cs.jcu.edu.au Jiannong Cao Michel Cosnard Department of Computer
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Implementation of an Intelligent Document Understanding and Reproduction System1 Michael Sharpe, Geoff Sutcliffe and Nizam Ahmed James Cook University of North Queensland Department of Computer Science Townsville. December 15, 1994
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Australian National University Computer Science AUSTRALIAN NATIONAL UNIVERSITY Computer Sciences Laboratory Canberra ACT 0200 FAX: WWW: Name Title Interests Email Phone (W) Brent R P Prof G.1 G.4 rpb@cslab.anu.edu.au +61 6 249 3329 Burkitt A N Dr F.1.4 I.2.11 J.2 tony@nimbus.anu.edu.au +61 6 249 5097
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1995 Australian Winter School in Computer Science Registration Form If you are interested in attending the Winter School, please complete the registration form below and return to us as soon as possible and no later than June 9, 1995. Surname: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : :
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Call for Attendance Australian Winter School in Computer Science Artificial Intelligence Magnetic Island 2-4 July 1995 Supported by Australian Computer Society Department of Computer Science (James Cook University) Computer Science Association Microsoft Institute The annual Australian Winter School in
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Integer Division is in NC1 A. Chiu, G. Davida x, and B. Litow { April 10, 1995
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The Weighted Finite Automaton Inference Problem B. Litow and O. de Vel x February 27, 1995
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Decidable Cases of the Rational Sequence Problem B. Litow x April 10, 1995
open this document and view contentsftp://coral.cs.jcu.edu.au//pub/JCUCSdocs/techreports/95-2.ps.gz, 19950411
Compact Search Trees for Main Memory Indices James Bell Computer Science Department James Cook University To wnsville, Qld. 4814. Australia. jim@cs.jcu.edu.au
open this document and view contentsftp://coral.cs.jcu.edu.au//pub/JCUCSdocs/techreports/95-5.ps.gz, 19950427
A Size Efficient Polylog Time Context-Free Language Recognition Algorithm B. Litow x keywords: computational complexity, parallel algorithms 1 Introduction Context-free language (CFL) recognition was first shown to be in NC by Ruzzo in . In fact the algorithm there is NC2, however it appears to
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LATEX ffl macro package on top of TeX ffl steps in creating a document 1. edit file.tex vi one.tex 2. run LATEX latex one.tex 3. LATEX creates various files one.dvi { device independent output one.aux { inter-document references one.log { what latex did 4. view output using xdvi xdvi one.dvi & 5. create
open this document and view contentsftp://coral.cs.jcu.edu.au//pub/JCUCSdocs/techreports/95-8.ps.gz, 19950621
Beyond Traditional Program Slicing Anthony M. Sloane Jason Holdsworth Department of Computer Science James Cook University Townsville, QLD, 4811 AUSTRALIA Technical Report 95/8
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Timetable for Ferries to Magnetic Island - departing from the Great Barrier Reef Wonderland and the Townsville Breakwater Marina Days Wonderland Breakwater Daily 6.05am Mon to Fri only 7.00am Daily 7.15am Daily 8.20am 8.35am Daily 9.00am 9.15am Daily 10.05am 10.20am Daily 11.00am 11.15am Daily 12.40pm
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3080/cp/slides/slide_ada/slide_ada_4.ps, 19950719
Advanced Programming Languages - CP3080 Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au July 1995 Goal of the Course "The limits of my language mean the limits of my world", Ludwig Wittgenstein Attempt to set free from the von Neumann mind-set ffl Concurrent programming concepts, not
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Concurrency Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au July 1995 Breaking away from the sequential paradigm Attempt to set free from the von Neumann mind-set : ffl Do not think in a way that has to do with the machine, ffl Think in a way that is appropriate for the problem at hand.
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3080/cp/slides/slide_conc/mutual_excl_4.ps, 19950815
Mutual Exclusion Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au July 1995 Two processes incrementing a variable N: Integer := 0; task body P1 is begin N := N + 1; end P1; task body P2 is begin N := N + 1; end P2; Mutual Exclusion - B. Mans 1 Contention for Registers and Shared-Memory P1:
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Dining Philosophers Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au August 1995 Dining Philosophers task body Philosopher is begin loop Think; Pre_Protocol; Eat; Post_Protocol; end loop; end; ffl A philosopher eats only of he has 2 forks. ffl No two philosophers may hold the same fork
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Distributed Mutual Exclusion Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au August 1995 Synchronous versus Asynchronous Tasking and Rendez-Vous create synchronous communication. Demanding Task --------------------|....|----- -----|..................|...|------ RdV waiting RdV Accepting
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Termination Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au August 1995 Distributed Framework Each node does his own job and a signal processing for administration purposes. procedure distributed is num_nodes : constant INTEGER := 4; task type NODES is entry Message(Data : INTEGER; Id :
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Termination Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au August 1995 Distributed Framework Each node does his own job and a signal processing for administration purposes. procedure distributed is num_nodes : constant INTEGER := 4; task type NODES is entry Message(Data : INTEGER; Id :
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3080/fp/slides/fp.ps, 19950914
Functional Programming Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au September 1995 Introduction to Functional Language ffl What is a Functional Language ffl Features to Expect ffl Data Types Integer, Real, Char, Boolean, String ffl Function Definitions ffl Function Application ffl
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3080/fp/slides/fp_4.ps, 19950914
Functional Programming Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au September 1995 Introduction to Functional Language ffl What is a Functional Language ffl Features to Expect ffl Data Types Integer, Real, Char, Boolean, String ffl Function Definitions ffl Function Application ffl
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3080/fp/slides/fp2_4.ps, 19950918
Functional Programming 2 Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au September 1995 Reduction Evaluation, simplification, normal form, canonical representation... square :: Int -> Int square x = x * x square (3 + 4) => square 7 (+) => 7 X 7 (square) => 49 (X) square (3 + 4) => (3 + 4)
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Department of Computer Science James Cook University of North Queensland Operating Systems (CP2050 B) In-semester Test 1 (1995) Instructions Name: Sample answers Duration: 50 minutes Use the space provided Be precise and to the point Make suitable assumptions if necessary Total Marks: 75 Total
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Functional Programming 3 Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au September 1995 Algebraic Types data Tree a = Leaf | Branch a (Tree a) (Tree a) -- inorder traversal of tree inorder :: Tree a -> inorder Leaf = inorder (Branch e left right) = (inorder left) ++ ++ (inorder
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Department of Computer Science James Cook University of North Queensland Operating Systems (CP2050 B) In-semester Test 1 (1995) Instructions Name: Sample answers Duration: 50 minutes Use the space provided Be precise and to the point Make suitable assumptions if necessary Total Marks: 75 Total
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3080/fp/slides/fp3.ps, 19950928
Functional Programming 3 Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au September 1995 Algebraic Types data Tree a = Leaf | Branch a (Tree a) (Tree a) -- inorder traversal of tree inorder :: Tree a -> inorder Leaf = inorder (Branch e left right) = (inorder left) ++ ++ (inorder
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp2050b/lnotes/s3_4.ps, 19951005
CHAPTER 9: VIRTUAL MEMORY Background Demand Paging Performance of Demand Paging Page Replacement Page-Replacement Algorithms Allocation of Frames Thrashing Other Considerations Demand Segmentation Operating System Concepts, Addison-Wesley 1994 Silberschatz & Galvin 1994 Background Virtual memory -
open this document and view contentsftp://coral.cs.jcu.edu.au//pub/JCUCSdocs/techreports/95-11.ps.gz, 19951011
The Boomerang White Paper: a Page As You Like It Curtis E. Dyreson & Anthony M. Sloane TR 95/11 JAMES COOK UNIVERSITY OF NORTH QUEENSLAND DEPARTMENT OF COMPUTER SCIENCE TOWNSVILLE QUEENSLAND 4811 AUSTRALIA Title The Boomerang White Paper: a Page As You Like It Primary Author(s) Curtis E. Dyreson &
open this document and view contentsftp://coral.cs.jcu.edu.au//pub/JCUCSdocs/techreports/95-7.ps.gz, 19951011
Efficiently Supporting Temporal Granularities in a DBMS Curtis Dyreson, Richard Snodgrass, and Marshall Freiman TR 95/07 JAMES COOK UNIVERSITY OF NORTH QUEENSLAND DEPARTMENT OF COMPUTER SCIENCE TOWNSVILLE QUEENSLAND 4811 AUSTRALIA Title Efficiently Supporting Temporal Granularities in a DBMS Primary
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3080/fp/slides/fp4_4.ps, 19951016
Functional Programming 4 Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au October 1995 Clock Result thedraw t1 vs t2 t3 vs Bye t4 vs t5 t1 vs t3 t4 vs t2 t5 vs Bye t1 vs t4 t5 vs t3 Bye vs t2 t1 vs t5 Bye vs t4 t2 vs t3 t1 vs Bye t2 vs t5 t3 vs t4 Functional Programming 4 - B. Mans 1
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1 ATool for Introducing Persistent Programming Lukito Nugroho Geoff Sutcliffe Department of Electrical Engineering Department of Computer Science Gadjah Mada University James Cook University Jl. Grafika 2, Yogyakarta 55281 Townsville Indonesia Australia, 4811 lukito@ugmgtw.ugm.ac.id geoff@cs.jcu.edu.au
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A Programmers' Tool for Managing Persistent Object Structures Thesis submitted by Lukito Edi Nugroho, Ir (UGM) December 1994 for the research Degree of Master of Science in the Department of Computer Science at James Cook University of North Queensland. Declaration I declare that this thesis is my own
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp4000/archive/95/paul/seminar2.ps.gz, 19951114
Massively Parallel Self-Organising Feature Maps Paul Frantz Department of Computer Science James Cook University Townsville, QLD, 4811 AUSTRALIA 14 November 1995 Overview ffl Self-Organising Feature Maps (SOFMs) revisited Kohonen SOFMs HKFN SOFMs ffl Parallel Kohonen Implementations ffl PHKFN - A
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp2003/1996/javaspec.ps, 19951130
The JavaTM Language Specification Version 1.0 Beta Beta Draft of October 30, 1995 10:33 am Please Recycle 1993, 1994, 1995 Sun Microsystems, Inc. 2550 Garcia Avenue, Mountain View, California 94043-1100 U.S.A. All rights reserved. This Beta quality release and related documentation are protected by
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp4000/archive/95/kal/litreview.ps.gz, 19951214
Literature Review for XPATP Author: Kalvinder Singh Supervisor: Dr Geoff Sutcliffe
open this document and view contentsftp://coral.cs.jcu.edu.au/web/teaching/Subjects/cp4000/archive/95/marianne/seminar1.ps.gz, 19951214
A Framework for Building Parallel ATPs Kalvinder Singh Department of Computer Science James Cook University Townsville, QLD, 4811 AUSTRALIA July 1995 The need for Theorem Provers ffl Inference Engines in Expert Systems ffl Used in software and hardware verification ffl Improving management in Amste
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Image Compression Using Weighted Finite Automata Progress Report Ty Newton September 4, 1995 supervisors: Dr Olivier De Vel Dr Bruce Litow
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Literature Review AGE Project, Jason James Holdsworth BSc. Supervised by Dr. Anthony M. Sloane.
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Literature Review: Massively Parallel Self-Organising Feature Maps (Neural Networks) Paul Frantz Supervisors: Olivier de Vel and Danny Coomans April 28th, 1995
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Automated Theorem Prover Graphical Interface Kalvinder Singh Supervisor: Geoff Sutcliffe March 17th, 1995 1 Introduction Automated Theorem Provers (ATPs) are used to validate a proposed theorem, corresponding to a given set of axioms. The one major problem with ATPs is that difficult problems cause
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APE: The Amazing Prolog Editor Literature Review Marianne Brown Supervisors: Dr G. Sutcliffe and Dr A. Sloane 28th of April, 1995
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An Overview Of Database Management Systems For The TPTP John Cooney Supervisors: Geoff Sutcliffe and Curtis Dyreson April 28th, 1995
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XPP The X{windows Environment for Parallel Processes Progress Report submitted by Kalvinder Singh September 1995 Supervisor: Dr Geoff Sutcliffe
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp4000/archive/95/bella/seminar1.ps.gz, 19951214
Image Compression using Weighted Finite Automata Bella Robinson Supervisors: Dr Bruce Litow and Dr Olivier de Vel July 1995 Contents ffl Image compression and why we need it ffl Types of image compression ffl WFA and how they can be used to encode an image ffl Bruce and Olivier's WFA Inference Algorithm
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Proposal for an Intelligent Text Recognition System Michael Sharpe March 7, 1994 1 Introduction Information is stored in several forms, books, journals, reviews, video, audio cassette and more recently in electronic form on computer systems. Three broad categories present themselves. They are (1)
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Massively Parallel Self-Organising Feature Maps (Neural Networks) Paul Frantz Supervisors: Olivier de Vel and Danny Coomans March 17th, 1995 1 1 Introduction Neural networks provide a unique computing architecture whose potential has only begun to be tapped . The Kohonen self-organising feature
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The Amazing Prolog Editor (APE) An Intelligent Editor for Prolog Marianne Brown Supervisors: Dr G. Sutcliffe and Dr A. Sloane 1 Introduction Designing a computerised solution to a problem can be a challenging and stimulating task. Eventually, however, the design must be translated into code and typed
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Massively Parallel Self-Organising Feature Maps Paul Frantz Department of Computer Science James Cook University Townsville, QLD, 4811 AUSTRALIA 21 July 1995 Overview ffl What are neural networks ffl The Kohonen Self-Organising Feature Map ffl The HKFN Learning Algorithm ffl A sequential implementation
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Research Proposal - Weighted Finite Automata and Image Compression Bella Robinson Supervisors: Dr Bruce Litow and Dr Olivier de Vel March 17, 1995 1 Introduction Images can take up considerable space when stored as raw data. ie. storing at least one value for each pixel. For example a 512x512 image
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp4000/archive/95/ty/litreview.ps.gz, 19951214
Image Compression Literature Review Ty Newton April 28, 1995 supervisors: Dr Olivier De Vel Dr Bruce Litow
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Literature Review Bella Robinson Supervisors: Dr Bruce Litow and Dr Olivier de Vel April 28, 1995
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Massively Parallel Self-Organising Feature Maps: Progress Report Paul Frantz Supervisor: Dr Olivier de Vel September 4th, 1995
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Department of Computer Science James Cook University Proposed Honours Project Topics (1995) December 14, 1994 Early in first semester Computer Science Honours students are required to select a project that will form a large part of their assessment for the year. The choice of project can be critical to
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Research Proposal - Save the World Wide Web Helen Leong Supervisors: Shyam Kapur and Olivier de Vel March 15, 1996 1 1 Introduction The World Wide Web (W3) is an initiative aimed at providing Internet users with simple and consistent access to a wide variety of information . Recent times have seen an
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AGE The Attribute Grammar Editor Project Proposal By Jason J Holdsworth Supervised by Dr. Anthony Sloane 1 Introduction Almost every program that a programmer writes is described using a high level language. A compiler then takes the specifications for that program and creates something a computer can
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Image Compression Using The Culik And Kari Weighted Finite Automata Inference Technique Ty Philip Newton November 20, 1995 Dissertation submitted by Ty Philip Newton in partial fulfilment of the requirements for the Degree of Bachelor of Information Technology with Honours in the Department of Computer
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Image Encoding Using Weighted Finite Automata Bella Fay Robinson November 20, 1995 Dissertation submitted by Bella Fay Robinson in partial fulfilment of the requirements for the Degree of Bachelor of Information Technology with Honours in the Department of Computer Science at James Cook University of
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Image Compression using Weighted Finite Automata Bella Robinson Supervisors: Dr Bruce Litow and Dr Olivier de Vel November 1995 Contents ffl Introduction to using WFA for encoding images ffl The Litow - de Vel WFA inference algorithm ffl Improvements made to the inference algorithm ffl Implementation
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp4000/archive/95/marianne/thesis.ps.gz, 19960110
Massively Parallel Self-Organising Feature Maps Thesis submitted by Paul Frantz November 1995 in partial fulfilment of the requirements for the Degree of Bachelor of Information Technology with Honours in the Department of Computer Science of James Cook University of North Queensland. Supervisor: Dr
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp4000/archive/95/marianne/seminar2.ps.gz, 19960110
APE An Intelligent Editor for Prolog Marianne Brown Supervisors: Dr. G. Sutcliffe and Dr. A.M. Sloane November 1995 APE Aims to provide the user with a text editing environment which is aware of Prolog syntax and uses this awareness to assist the programmer as they wish. ffl previous systems implemented
open this document and view contentsftp://coral.cs.jcu.edu.au//pub/JCUCSdocs/techreports/95-14.ps.gz, 19960206
Automatic Filtering of Now-centric Data Curtis Dyreson TR 95/14 JAMES COOK UNIVERSITY OF NORTH QUEENSLAND DEPARTMENT OF COMPUTER SCIENCE TOWNSVILLE QUEENSLAND 4811 AUSTRALIA Title Automatic Filtering of Now-centric Data Primary Author(s) Curtis Dyreson Contact Information Department of Computer Science
open this document and view contentsftp://coral.cs.jcu.edu.au//pub/JCUCSdocs/techreports/96-2.ps.gz, 19960206
Bounded Length UCFG Equivalence B. Litow TR 96/02 JAMES COOK UNIVERSITY OF NORTH QUEENSLAND DEPARTMENT OF COMPUTER SCIENCE TOWNSVILLE QUEENSLAND 4811 AUSTRALIA Title Bounded Length UCFG Equivalence Primary Author(s) B. Litow Contact Information Dept. of Computer Science James Cook University
open this document and view contentsftp://coral.cs.jcu.edu.au//pub/JCUCSdocs/techreports/95-12.ps.gz, 19960206
Learning to Recognise 3D Objects from 2D Views Brendan McCaney, Terry Caelliz, Olivier de Vely TR 95/12 JAMES COOK UNIVERSITY OF NORTH QUEENSLAND DEPARTMENT OF COMPUTER SCIENCE TOWNSVILLE QUEENSLAND 4811 AUSTRALIA Title Learning to Recognise 3D Objects from 2D Views Primary Author(s) Brendan McCaney,
open this document and view contentsftp://coral.cs.jcu.edu.au//pub/JCUCSdocs/techreports/95-10.ps.gz, 19960206
Multi-scale Adaptive Segmentation using Edge and Region Based Attributes Brendan McCaney, Terry Caelliz TR 95/10 JAMES COOK UNIVERSITY OF NORTH QUEENSLAND DEPARTMENT OF COMPUTER SCIENCE TOWNSVILLE QUEENSLAND 4811 AUSTRALIA Title Multi-scale Adaptive Segmentation using Edge and Region Based Attributes
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp4000/archive/95/jason/seminar1.ps.gz, 19960209
Introductory concepts of an Attribute Grammar Development Tool Jason J Holdsworth BSc (supervisor: Dr Anthony Sloane) Department of Computer Science James Cook University Townsville, QLD, 4811A USTRALIAJuly 1995 BACKGROUND AND MOTIVATION ffl Problems with current Programming Tools low level aspects of
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Introduction and description of AGE 1 Jason J Holdsworth BSc (supervisor: Dr Anthony Sloane) Department of Computer Science James Cook University Townsville, QLD, 4811 AUSTRALIA July 1995 The task of the project: ffl design/build a specialised editor for LIDO source code ffl editor provides a high level
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp4000/archive/95/john/thesis.ps.gz, 19960209
XPP The X{windows Environment for Parallel Processing Thesis submitted by Kalvinder Singh February 9, 1996 in partial fulfilment of the requirements for the Degree of Bachelor of Science with Honours in the Department of Computer Science of James Cook University of North Queensland. Supervisor: Dr Geoff
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp4000/1996/guide.ps, 19960214
Department of Computer Science James Cook University Guide to the Honours Program Coordinator for 1994: Xindong Wu (xindong@coral.cs.jcu.edu.au) November 1993 1 Introduction Students who have completed 180 points or more of third-year Computer Science subjects with a credit average or above are eligible
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Department of Computer Science James Cook University Proposed Honours Project Topics for 1994 1. Area of Research - Algorithms: Theory and Practice Project Title: Traffic Light Algorithms (David Spuler and Bruce Litow) This project involves the analysis of the algorithms used by traffic lights at road
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5002/1996/pvm-book.ps, 19960219
PVM: Parallel Virtual Machine Scientific and Engineering Computation Janusz Kowalik, Editor Data-Parallel Programming on MIMD Computers by Philip J. Hatcher and Michael J. Quinn, 1991 Unstructured Scientific Computation on Scalable Multiprocessors edited by Piyush Mehrotra, Joel Saltz, and Robert Voigt,
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5002/pvm-book.ps, 19960219
PVM: Parallel Virtual Machine Scientific and Engineering Computation Janusz Kowalik, Editor Data-Parallel Programming on MIMD Computers by Philip J. Hatcher and Michael J. Quinn, 1991 Unstructured Scientific Computation on Scalable Multiprocessors edited by Piyush Mehrotra, Joel Saltz, and Robert Voigt,
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp2001/1996/foils/Olivier/stacksqueues.ps, 19960222
5/ 1 Introduction (Standish Chapter 7) Linear data structure collections of components organised in a straight line that obey certain growth and decay laws. Stack LIFO (new components are added/removed from the same end of the DS) Queue FIFO (new components are added/removed from different ends of the
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2/ 1 Introduction We introduce the basic concept of the struct construct in C: ffl Defining a New Data Type (Typedef ) ffl What Is a Structure ffl Declaring a Structure variable ffl Initializing a Structure Variablex O. de Vel (CP2001) Introduction to Structures in C Data Structures 2/ 2 ffl Using a
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Marking Scheme for CP3110 Concept Exploration Project Name: Component Possible Marks Comments Marks Scored Introduction/Problem Definition 1 System justification 1 User characteristics 1 Features and function 3 Goals for system and project, constraints, development, operation and maintenance 3
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp2001/1996/foils/Olivier/lists.ps, 19960222
6/ 1 Introduction (Standish Chapter 8) We have seen simple single (one-way) linked lists and the associated set of operations. However, lists may have one of several forms: ffl Doubly linked lists (with two pointer fields: prev and next) ffl Sorted and unsorted lists ffl Circular lists ffl etc. . . O.
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3110/1996/markplan.ps, 19960222
Marking Scheme for CP3110 Project Plan Project Name: Component Possible Marks Comments Marks Scored Introduction, overview, terminology 4 Team structure 1 Development schedule 2 Deliverables (documents, code, etc) 2 Manner of demonstration 1 Prog. languages and dev. tools 2 Writing style, clarity,
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp2001/1996/foils/Olivier/algorithms.ps, 19960222
4/ 1 Introduction (Standish Chapter 6) A good algorithm is like a sharp knife it does exactly what it is supposed to do with a minimum of applied effort! O. de Vel Analysis of Algorithms Data Structures 4/ 2 There are often many algorithms to solve a problem how to choose the best algorithm The design
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3110/1996/marksrs.ps, 19960222
Marking Scheme for CP3110 Software Requirements Specification Project Name: Component Possible Marks Comments Marks Scored Overview; development, operation and maintenance envs; conceptual model, 5 glossary User interface reqs. 10 Functional reqs. 10 Non-functional reqs. (incl. ext. interfaces) 5 Error
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp2001/1996/foils/Olivier/adt.ps, 19960222
3/ 1 Introduction (Standish Chapter 4) Consider a washing machine: O. de Vel Abstract Data Types Data Structures 3/ 2 This leads to information hiding (a consequence of abstraction). ) can be used as a basis for implementing abstract data types (ADT). An ADT is defined to be: ffl a collection of objects
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Marking Scheme for CP3110 Design Document Project Name: Component Possible Marks Comments Marks Scored Overview, architecture description (perhaps including data flow diagram, structure 10 chart, data dictionary, uses relationships, design rationale) Module descriptions behaviour (inputs, outputs) 5
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp2001/1996/foils/Olivier/memoryallocation.ps, 19960223
7/ 1 Introduction (Standish Chapter 8.6) There are various memory allocation techniques for programs (depending on the programming language used): ffl static memory allocation (e.g. array) ffl dynamic memory allocation (e.g. linked list) ffl persistent memory allocation (beyond the life of the executing
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5002/1996/complexity.ps.gz, 19960227
Sequential Complexity Notes B. Litow x February 27, 1996 The fundamental sequential computation model is the Turing machine. A Turing machine is a finite automaton equipped with a two way infinite tape and the ability to write as well as read the tape. The automaton part can be thought of as a finite
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5002/complexity.ps.gz, 19960227
Sequential Complexity Notes B. Litow x February 27, 1996 The fundamental sequential computation model is the Turing machine. A Turing machine is a finite automaton equipped with a two way infinite tape and the ability to write as well as read the tape. The automaton part can be thought of as a finite
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5002/max.ps.gz, 19960228
Max of n elements B. Litow x February 28, 1996 We want to compute max(x1; : : : ; xn) under the assumption that max(xi; xj) has O(1) cost. Our model will be the standard CRCW PRAM, and our focus will be the tradeoff between speed and number of processors. The first step is to see whether or not O(1)
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5002/1996/complexity2.ps.gz, 19960228
Sequential Complexity II B. Litow x February 28, 1996 1 Asymptotic complexity We need a few facts about asymptotic rates of growth. These rates are represented by the notation: ffl O(f(n)) ffl (f(n)) ffl o(f(n)) ffl (f(n)) f : N ! N, where N = f0; 1; 2; : : :g. The term cofinitely is short for `holds
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5002/1996/max.ps.gz, 19960228
Max of n elements B. Litow x February 28, 1996 We want to compute max(x1; : : : ; xn) under the assumption that max(xi; xj) has O(1) cost. Our model will be the standard CRCW PRAM, and our focus will be the tradeoff between speed and number of processors. The first step is to see whether or not O(1)
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5002/complexity2.ps.gz, 19960228
Sequential Complexity II B. Litow x February 28, 1996 1 Asymptotic complexity We need a few facts about asymptotic rates of growth. These rates are represented by the notation: ffl O(f(n)) ffl (f(n)) ffl o(f(n)) ffl (f(n)) f : N ! N, where N = f0; 1; 2; : : :g. The term cofinitely is short for `holds
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5002/1996/models2.ps.gz, 19960229
PRAM Models B. Litow x February 29, 1996 The PRAM (Parallel Random Access Machine) model is used widely because it is easy to describe parallel algorithms in terms of a PRAM. The basic PRAM is just a bunch of processors, each with some local storage, and a link to global or shared storage. The
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5002/models2.ps.gz, 19960229
PRAM Models B. Litow x February 29, 1996 The PRAM (Parallel Random Access Machine) model is used widely because it is easy to describe parallel algorithms in terms of a PRAM. The basic PRAM is just a bunch of processors, each with some local storage, and a link to global or shared storage. The
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CP2710 1996 Syllabus Introduction to the Theory of Computation Formal Languages and Automata Instructor: Bruce Litow, rm 102, x5844, bruce@reef.cs.jcu.edu.au Text: Introduction to Languages and the Theory of Computation, John Martin, McGrawHill. Reserve collection: (until June 28, 1996) ffl 1 Topics
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5002/work_time.ps.gz, 19960305
Optimality and the Work-Time Principle B. Litow x March 5, 1996 The work done by an algorithm is the total number of operations it performs. The crucial issue is the allocation of processors to carry out operations at each parallel time step 1 <= i <= T (n), where T (n) is the parallel time. If W (n) is
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5002/models.ps.gz, 19960305
Parallel Computation Models B. Litow x March 5, 1996 Three types of parallel computing models are described. This does not exhaust the possibilities, nor is each of the models fully characterised. Circuits The circuit or DAG (directed acyclic graph) model is probably the easiest model to work with
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5002/1996/work_time.ps.gz, 19960305
Optimality and the Work-Time Principle B. Litow x March 5, 1996 The work done by an algorithm is the total number of operations it performs. The crucial issue is the allocation of processors to carry out operations at each parallel time step 1 <= i <= T (n), where T (n) is the parallel time. If W (n) is
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5002/csa.ps.gz, 19960305
Multiplication in Logdepth in the DAG Model B. Litow x March 5, 1996 It is not difficult to see that two n-bit integers can be added in O(logn) time in the DAG model. It is not so clear that the same holds for multiplication. The key to achieving this result is the Carry-Save-Adder devised by Chris
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5002/1996/models.ps.gz, 19960305
Parallel Computation Models B. Litow x March 5, 1996 Three types of parallel computing models are described. This does not exhaust the possibilities, nor is each of the models fully characterised. Circuits The circuit or DAG (directed acyclic graph) model is probably the easiest model to work with
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5002/1996/csa.ps.gz, 19960305
Multiplication in Logdepth in the DAG Model B. Litow x March 5, 1996 It is not difficult to see that two n-bit integers can be added in O(logn) time in the DAG model. It is not so clear that the same holds for multiplication. The key to achieving this result is the Carry-Save-Adder devised by Chris
open this document and view contentsftp://coral.cs.jcu.edu.au//pub/JCUCSdocs/techreports/95-9.ps.gz, 19960306
Portable Distributed Priority Queues with MPI Bernard Mans TR 95/09 JAMES COOK UNIVERSITY OF NORTH QUEENSLAND DEPARTMENT OF COMPUTER SCIENCE TOWNSVILLE QUEENSLAND 4811 AUSTRALIA Title Portable Distributed Priority Queues with MPI Primary Author(s) Bernard Mans Contact Information Department of Computer
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5002/1996/power_set.ps.gz, 19960312
The Power Set B. Litow x March 12, 1996 The power set 2S of a set S is the set of all its subsets. Note that ; 2 2S since always ; S. A set T is said to have smaller size (cardinality) than a set S iff there is no surjection (onto) mapping from T to S. Theorem 1 S is smaller than 2S . Proof : The proof
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5002/power_set.ps.gz, 19960312
The Power Set B. Litow x March 12, 1996 The power set 2S of a set S is the set of all its subsets. Note that ; 2 2S since always ; S. A set T is said to have smaller size (cardinality) than a set S iff there is no surjection (onto) mapping from T to S. Theorem 1 S is smaller than 2S . Proof : The proof
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5002/kruskal.ps.gz, 19960313
A complexity theory of efficient parallel algorithms C. Kruskal, L. Rudolph and M. Snir, TCS 71 1990 p. 95-132 Proposed Algorithm Classes: ENC polylog time, O(1) inefficiency ANC polylog time, polylog inefficiency SNC polylog time, poly inefficiency (many NC algorithms are here) EP sublinear time,
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5002/1996/kruskal.ps.gz, 19960313
A complexity theory of efficient parallel algorithms C. Kruskal, L. Rudolph and M. Snir, TCS 71 1990 p. 95-132 Proposed Algorithm Classes: ENC polylog time, O(1) inefficiency ANC polylog time, polylog inefficiency SNC polylog time, poly inefficiency (many NC algorithms are here) EP sublinear time,
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5002/hypercube.ps.gz, 19960314
Hypercube B. Litow x March 14, 1996 The Binary Hypercube is a network computer family. We will designate this family by fBng, where Bn is the cube of degree n. Bn is a graph with 2n nodes. Nodes are regarded as processors. The nodes have a canonical labeling. For <= i < 2n, the label of node i is the
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3060/1996/exams/cp3060_93.exam.ps, 19960314
Primitives---------- 1. A volume is de ned by a three dimensional grid where each volume element is de ned by a tuple (xi, yi, zi). Givenathree dimensional line z = ax + by + c convert the two-dimensional Digital Difference Analyzer (DDA) algorithm to de ne which volume elements are intersected by the
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5002/1996/hypercube.ps.gz, 19960314
Hypercube B. Litow x March 14, 1996 The Binary Hypercube is a network computer family. We will designate this family by fBng, where Bn is the cube of degree n. Bn is a graph with 2n nodes. Nodes are regarded as processors. The nodes have a canonical labeling. For <= i < 2n, the label of node i is the
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3060/1996/exams/cp3060_94.exam.ps, 19960314
1.(a) The polygon mesh givenonthe right of gure 1 comprises the three two-dimensional triangles on the left of the gure. Construct the polygon tables for the polygon mesh. Include reverse referencing in the edge and vertextables. P1 P2 P3 P5 P4 P2 P3 P4 P P 1 5 Figure1: Tw o-dimensional polygon mesh. (5
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp4000/archive/96/angela/proposal.ps.gz, 19960317
Parallel and Distributed Priority Queues Regan Russell Supervisor: Bernard Mans. Introduction Priority queues are abstract data structures containing a set of items with associated priorities (keys). The two defining operations on priority queues are Delmin and Insert. Delmin operations delete and
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Optimal Ranking B. Litow x March 19, 1996 Let A = (a1; : : : ; an); B = (b1; : : : ; bm) be two sorted (increasing order) lists. Rank(x; A) is the number of y 2 A such that y <= x. Rank(A; B) = (Rank(a1; B); : : : ; Rank(an; B)). Note that Rank(x; A
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5002/ranking.ps.gz, 19960319
Optimal Ranking B. Litow x March 19, 1996 Let A = (a1; : : : ; an); B = (b1; : : : ; bm) be two sorted (increasing order) lists. Rank(x; A) is the number of y 2 A such that y <= x. Rank(A; B) = (Rank(a1; B); : : : ; Rank(an; B)). Note that Rank(x; A
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp2001/1996/foils/Olivier/io.ps, 19960320
1/ 1 Introduction The C language does not include I/O constructs need to use standard input/output functions defined in C system libraries. The I/O library (
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3050/1996/Tutorials/tut4.ps, 19960322
CP3050 1996 - B. Mans - Tutorial 4 1. Give an O(jXj+ jY j) time algorithm to construct an LCS(X;Y ) from just X;Y and the c table. Show that your algorithm satisfies the time bound. 2. Give an O(n2) time algorithm to find the longest monotonically increasing subsequence in a sequence of n numbers. Is
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CP3050 1996 - B. Mans - Tutorial 1 Solutions 1. log n; p n; n; n log n; n p n; n2; n3; 2n; n! 2. We have c0g(n) < f(n) < c1g(n)8n > n0 and c2h(n) < g(n) < c3h(n) for n > n1. Let N = maxfn0; n1g. Note that if n > N , then c0c2h(n) < c0g(n) < f(n), and f(n) < c1g(n) < c1c3h(n). This shows that f 2 (h). 3.
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CP3050 1996 Tutorial 2 Solutions 1. 1. False: let f(n) = n; g(n) = n2. 2. False: let f(n) = 1; g(n) = n. 3. False: let f(n) = 2n; g(n) = n. 4. False: let f(n) = 1=n. 5. True: f <= c g implies that g >= f=c. 6. False: let f(n) = 2n. 7. True: For n sufficiently large o(f)(n) < f(n)=2, so f(n) <= f(n) +
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3050/1996/Lectures/notes_2_4.ps, 19960322
Algorithms and Complexity Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au March 1996 Advanced Design and Analysis Techniques ffl Iteration Methods ffl Divide-and-Conquer ffl Dynamic Programming ffl Greedy Algorithms ffl Amortized Analysis ffl Randomized Algorithms 1 Optimization Problems
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CP3050 1996 - B. Mans - Solution Tutorial 3 1. If n is the length, the algorithm takes (n2) steps: The most executed instruction is 4: (n 1) + (n 2) + + 1, that is (n 1)(n 2)=2 = (n2). 2. Merge(A,p,q,r) will need O(r p) steps. Both functions will be called 2n 1 times (the number of nodes in a
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3050/1996/Lectures/notes_00_4.ps, 19960322
Algorithms and Complexity Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au Feb. 1996 Introduction to Algorithms and Complexity ffl Define complexity measures. ffl Identify complexity measures with actual computing resources. ffl Establish the correctness of algorithms. ffl Determine
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3050/1996/Tutorials/tut1.ps, 19960322
CP3050 1996 - B. Mans - Tutorial 1 1. Order the following functions of n by increasing asymptotic growth of rate: n!; n2; n log n; pn; n; n3; 2n; log n; npn 2 Prove Transitivity for (): if f(n) = (g(n)) and g(n) = (h(n)) then f(n) = (h(n)) 3 Prove that T (n) = nO(1) iff 9c; T (n) = O(nc). 4. Prove that
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3050/1996/Assignment/ass1.ps, 19960322
Department of Computer Science James Cook University of North Queensland Algorithms and Complexity (CP3050) Assignment 1 (1996) - bernard@cs.jcu.edu.au Instructions ffl Due date: Thursday 28th March (6 p.m.). ffl Assessment: 10% ffl Plagiarism and copying will be harshly treated. 1. Rank the 15
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Algorithms and Complexity Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au Feb. 1996 Properties of Asymtotic Growth Claim 1: If f = (g), then g = (f). proof We have c1 <= f(n)=g(n) <= c2. We want to find constants d1; d2 such that d1 <= g(n)=f(n) <= d2. Choose d2 = 1=c1 and d1 = 1=c2. 2
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Using Artificial Neural Networks for Meteor-Burst Communications Trail Prediction Stuart Melville Geoff Sutcliffe David Fraser Dep t of Computer Studies Dep t of Computer Science Dep t of Electronic Engineering ML Sultan Technikon James Cook University University of Natal Durban, South Africa
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CP3050 1996 - B. Mans - Tutorial 3 1. Give the complexity of the following Algorithm: Swapping-Sort(A) 1 for i:=1 to (length-1) 2 do for j:=i to length 3 -- swap larger element if required 4 do if (A > A) 5 then temp:=A 6 A:=A 7 A:=temp 2. Write the algorithm of the Merge
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CP3050 1996 - B. Mans - Tutorial 2 1. Prove or give a counterexample: 1. f = O(g) implies g = O(f) 2. f + g = (min(f; g)) 3. f = O(g) implies 2f = O(2g) 4. f = O(f2) 5. f = O(g) implies g = (f) 6. f(n) = (f(n=2)) 7. f + o(f) = (f) 2. Find an upper bound for blog nc X k=0 dn=2ke 3. Solve T (n) = 2T (pn)
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The Practice of Clausification in Automatic Theorem Proving Page 1 The Practice of Clausification in Automatic Theorem Proving Geoff Sutcliffe Stuart Melville Department of Computer Science Department of Computer Studies James Cook University, Australia M.L. Sultan Technikon, South Africa Email:
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CP3050 1996 - B. Mans - Tutorial 4 Solutions 1. At an entry c(i; j), look at its nearest neighbours, c(i 1; j); c(i; j 1); c(i 1; j 1). If Xi = Yj , then move to i 1; j 1, else move to i 1; j or i; j 1, based on whichever of c(i 1; j); c(i; j 1) is smaller. The maximum number of moves is clearly bounded
open this document and view contentsftp://coral.cs.jcu.edu.au//pub/JCUCSdocs/techreports/96-3.ps.gz, 19960322
Weakly Bounded Probabilistic Polytime is Contained in POLYSIZE B. Litow TR 96/03 JAMES COOK UNIVERSITY OF NORTH QUEENSLAND DEPARTMENT OF COMPUTER SCIENCE TOWNSVILLE QUEENSLAND 4811 AUSTRALIA Title Weakly Bounded Probabilistic Polytime is Contained in POLYSIZE Primary Author(s) B. Litow Contact
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CP3050 1996 - B. Mans - Tutorial 2 1. Prove or give a counterexample: 1. f = O(g) implies g = O(f) 2. f + g = (min(f; g)) 3. f = O(g) implies 2f = O(2g) 4. f = O(f2) 5. f = O(g) implies g = (f) 6. f(n) = (f(n=2)) 7. f + o(f) = (f) 2. Find an upper bound for blog nc X k=0 dn=2ke 3. Solve T (n) = 2T (pn)
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3050/1996/Lectures/graphs_4.ps, 19960328
Algorithms and Complexity Graphs Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au March 1996 Graphs Algorithms (5,23-27) Graph G is a pair (V; E) V is the vertex set (vertices) and E is the set of edges ffl Directed Graph (digraph) G if E is a binary relation on V: (u; v) 2 E a self-loop
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3210/tute1.ps, 19960329
CP3210 - Prolog Tutorial Questions Reference: Geoff's Prolog Lecture Notes, 1996. 1. Page 15 skip last two questions 2. Page 17 - first question only. 3. Page 18 - degugging. 4. Page 19 5. Page 24 - first question only. 6. Page 26 - first question only. 1
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An Experiment in Class Management Using the World-Wide Web Curtis Dyreson TR 96/08 JAMES COOK UNIVERSITY OF NORTH QUEENSLAND DEPARTMENT OF COMPUTER SCIENCE TOWNSVILLE QUEENSLAND 4811 AUSTRALIA Title An Experiment in Class Management Using the World-Wide Web Primary Author(s) Curtis Dyreson Contact
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Information Retrieval from an Incomplete Data Cube Curtis Dyreson TR 96/07 JAMES COOK UNIVERSITY OF NORTH QUEENSLAND DEPARTMENT OF COMPUTER SCIENCE TOWNSVILLE QUEENSLAND 4811 AUSTRALIA Title Information Retrieval from an Incomplete Data Cube Primary Author(s) Curtis Dyreson Contact Information
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3210/1996/tute1.ps.gz, 19960329
CP3210 - Prolog Tutorial Questions Reference: Geoff's Prolog Lecture Notes, 1996. 1. Page 15 skip last two questions 2. Page 17 - first question only. 3. Page 18 - degugging. 4. Page 19 5. Page 24 - first question only. 6. Page 26 - first question only. 1
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3210/slide4.ps, 19960401
The final resolvent should be: True ) S(A) _ s(A). There is a separate inference rule called factoring but it is simpler here just to make it be part of the resolution rule. CP3210: Introduction to Artificial Intelligence Slides Chapter 9 1 One complete inference procedure using resolution is
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3210/1996/slide4.ps.gz, 19960401
The final resolvent should be: True ) S(A) _ s(A). There is a separate inference rule called factoring but it is simpler here just to make it be part of the resolution rule. CP3210: Introduction to Artificial Intelligence Slides Chapter 9 1 One complete inference procedure using resolution is
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3210/s1.ps, 19960401
Knowledge-based Agents 1. Begin with some knowledge of the world and of its own actions. 2. Use logical reasoning to maintain a new representation of the world as new percepts arrive. 3. Use these new representations to deduce what course of action to take. CP3210: Introduction to Artificial
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3210/s3.ps, 19960401
Recall, that an inference procedure that generates only entailed sentences is called sound or truth-preserving. CP3210: Introduction to Artificial Intelligence Slides Chapter 9 1 In Chapter 9: 1. extend these results to first-order logic 2. provide some additional inference rules to deal with
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3210/slide3.ps, 19960401
Recall, that an inference procedure that generates only entailed sentences is called sound or truth-preserving. CP3210: Introduction to Artificial Intelligence Slides Chapter 9 1 In Chapter 9: 1. extend these results to first-order logic 2. provide some additional inference rules to deal with
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3210/1996/slide3.ps.gz, 19960401
Recall, that an inference procedure that generates only entailed sentences is called sound or truth-preserving. CP3210: Introduction to Artificial Intelligence Slides Chapter 9 1 In Chapter 9: 1. extend these results to first-order logic 2. provide some additional inference rules to deal with
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3210/1996/s2.ps.gz, 19960401
1 First-Order Logic In first-order logic, the world consists of: objects, things with individual identities and properties that distinguish them from other objects. There are various relations between these objects. Some of the relations are functions, in which there is only one value" for a given
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3210/s2.ps, 19960401
1 First-Order Logic In first-order logic, the world consists of: objects, things with individual identities and properties that distinguish them from other objects. There are various relations between these objects. Some of the relations are functions, in which there is only one value" for a given
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3210/1996/slide2.ps.gz, 19960401
1 First-Order Logic In first-order logic, the world consists of: objects, things with individual identities and properties that distinguish them from other objects. There are various relations between these objects. Some of the relations are functions, in which there is only one value" for a given
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3050/1996/Tutorials/tut5.ps, 19960401
CP3050 1996 - B. Mans - Tutorial 5 1. What is an optimal Huffman code for the following set of frequencies based on the first 8 Fibonacci numbers a:1 b:1 c:2 d:3 e:5 f:8 g:13 h:21 Can you generalize your answer to find the optimal code when the frequencies are the first n Fibonacci numbers 2. You are
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3210/1996/s3.ps.gz, 19960401
Recall, that an inference procedure that generates only entailed sentences is called sound or truth-preserving. CP3210: Introduction to Artificial Intelligence Slides Chapter 9 1 In Chapter 9: 1. extend these results to first-order logic 2. provide some additional inference rules to deal with
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3210/s4.ps, 19960401
The final resolvent should be: True ) S(A) _ s(A). There is a separate inference rule called factoring but it is simpler here just to make it be part of the resolution rule. CP3210: Introduction to Artificial Intelligence Slides Chapter 9 1 One complete inference procedure using resolution is
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3210/1996/s1.ps.gz, 19960401
Knowledge-based Agents 1. Begin with some knowledge of the world and of its own actions. 2. Use logical reasoning to maintain a new representation of the world as new percepts arrive. 3. Use these new representations to deduce what course of action to take. CP3210: Introduction to Artificial
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3210/slide2.ps, 19960401
1 First-Order Logic In first-order logic, the world consists of: objects, things with individual identities and properties that distinguish them from other objects. There are various relations between these objects. Some of the relations are functions, in which there is only one value" for a given
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3210/1996/s4.ps.gz, 19960401
The final resolvent should be: True ) S(A) _ s(A). There is a separate inference rule called factoring but it is simpler here just to make it be part of the resolution rule. CP3210: Introduction to Artificial Intelligence Slides Chapter 9 1 One complete inference procedure using resolution is
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5002/list.ps.gz, 19960404
List Ranking B. Litow x April 4, 1996 Pointer jumping can be used for list ranking. Let R(i) be the rank of node i (distance from end of list), and S(i) be the successor of node i. For 1 <= i <= n pardo if S(i) != then R(i) := 1 else R(i) := For 1 <= i <= n pardo Q(i) := S(i) while Q(i) != and Q(Q(i) !=
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5002/1996/list.ps.gz, 19960404
List Ranking B. Litow x April 4, 1996 Pointer jumping can be used for list ranking. Let R(i) be the rank of node i (distance from end of list), and S(i) be the successor of node i. For 1 <= i <= n pardo if S(i) != then R(i) := 1 else R(i) := For 1 <= i <= n pardo Q(i) := S(i) while Q(i) != and Q(Q(i) !=
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3050/1996/Assignment/ass1_sol.ps, 19960408
Algorithms and Complexity (CP3050) Solutions Assignment 1 (1996) - bernard@cs.jcu.edu.au 1. Ranking by order of growth ()-notation: g(n) as an asymptotic LOWER bound for f(n) (g(n)) = ff(n) : 9c > (constant); n0 s:t: <= cg(n) <= f(n); 8n >= n0g In each case, to prove they exist, we MUST give a constant
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3050/1996/Assignment/ass2.ps, 19960408
Algorithms and Complexity (CP3050) Assignment 2 (1996) - bernard@cs.jcu.edu.au Instructions ffl Due date: Thursday 9th May (6 p.m.). ffl Assessment: 10%, (2.5% each question) ffl Plagiarism and copying will be harshly treated. 1. Regarding Huffman Codes, prove that a non-full binary tree cannot
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3050/1996/Tutorials/tut6.ps, 19960411
CP3050 1996 - B. Mans - Tutorial 6 1. What is the time complexity of BFS if the adjacency matrix representation is used 2. On the following Directed Graph (where a + b means a to b) 1+---4 2--+3 5--+8 6--+7 A. Show the resulting BFS tree when the BFS algorithm is applied from node 1 B. Show the
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3050/1996/Tutorials/tut6_sol.ps, 19960411
CP3050 1996 - B. Mans - Tutorial 6 1. It is necessary to scan each row in order to find the nodes adjacent to a given node, using the adj. matrix representation. BFS takes O(V 2) time. 2. A. The resulting BFS tree when the BFS algorithm is applied from node 1 1 2 3 B. The resulting DFS forest when the
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CP3050 1996 - B. Mans - Tutorial 5 Solutions 1. An optimal Huffman code solution is h * g * f * e * d * c * b a a:1 b:1 c:2 d:3 e:5 f:8 g:13 h:21 The solution can be generalized to a tree of depth (n 1), since any new term has a frequency larger or equal one of the two remaining items in the queue. 2.
open this document and view contentsftp://coral.cs.jcu.edu.au//pub/JCUCSdocs/techreports/96-4.ps.gz, 19960412
Optimal Distributed Algorithms in Unlabelled Tori and Chordal Rings Bernard Mans TR 96/04 JAMES COOK UNIVERSITY OF NORTH QUEENSLAND DEPARTMENT OF COMPUTER SCIENCE TOWNSVILLE QUEENSLAND 4811 AUSTRALIA Title Optimal Distributed Algorithms in Unlabelled Tori and Chordal Rings Primary Author(s) Bernard Mans
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5002/1996/pvm_4.ps, 19960416
CP5002-Parallel and Distributed Computing PVM Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au April 1996 PVM (Parallel Virtual Machine) PVM (Parallel Virtual Machine) is an integrated set of software tools and libraries that emulates a general-purpose, flexible, heterogeneous concurrent
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5002/distributed_4.ps, 19960416
CP5002-Parallel and Distributed Computing Distributed Computing Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au April 1996 Distributed Systems Distributed system: all computer applications where several computers or processors cooperate in some way. ffl Wide-area computer communication
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5002/1996/pvm_cs003.ps, 19960416
Using PVM at JCU-CS - CP5002 - 1996 - Bernard Mans All the necessary files and examples are in ~ftp/web/teaching/Subjects/cp5002/PVM/ 1 Machines PVM is available on all Unix Machines of the Department of Computer Science (DEC-Ultrix, DEC-OSF/1, and SUN-OS) however since some of these machines are
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5002/pvm_4.ps, 19960416
CP5002-Parallel and Distributed Computing PVM Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au April 1996 PVM (Parallel Virtual Machine) PVM (Parallel Virtual Machine) is an integrated set of software tools and libraries that emulates a general-purpose, flexible, heterogeneous concurrent
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5002/pvm_cs003.ps, 19960416
Using PVM at JCU-CS - CP5002 - 1996 - Bernard Mans All the necessary files and examples are in ~ftp/web/teaching/Subjects/cp5002/PVM/ 1 Machines PVM is available on all Unix Machines of the Department of Computer Science (DEC-Ultrix, DEC-OSF/1, and SUN-OS) however since some of these machines are
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5002/1996/distributed_4.ps, 19960416
CP5002-Parallel and Distributed Computing Distributed Computing Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au April 1996 Distributed Systems Distributed system: all computer applications where several computers or processors cooperate in some way. ffl Wide-area computer communication
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Parallel and Distributed Computing (CP5002) Assignment 2 (1996) - bernard@cs.jcu.edu.au Instructions ffl Due date: Tuesday 21th May (9 a.m.). ffl Assessment: 15% ffl Plagiarism and copying will be harshly treated. Rotating Information around a Torus Write PVM programs for the following problems. Include
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Parallel and Distributed Computing (CP5002) Assignment 2 (1996) - bernard@cs.jcu.edu.au Instructions ffl Due date: Tuesday 21th May (9 a.m.). ffl Assessment: 15% ffl Plagiarism and copying will be harshly treated. Rotating Information around a Torus Write PVM programs for the following problems. Include
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Department of Computer Science HANDBOOK James Cook University 1996 1997 For further information please contact: The Head of Department, Department of Computer Science, James Cook University, Townsville, Queensland 4811, Australia. Tel.: (077) 814622 (International: +61 77 814622) Facsimile: (077) 814029
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CP3050:3/1/1/5. Midsemester Test CP3050 Solutions - Bernard Mans Direction: Midsemester Test of 50 minutes. The exam is open notes (yours only) and the Cormen, Leiserson, Rivest textbook. You must reply on the form. Your answer must be as concise and precise as possible. There are four (4) questions for
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CP3050 1996 - B. Mans - Tutorial 7 1. Give a simple example of a graph such that the set of all light edges crossing some cut does not form a MST. 2. Prove that if edge weights are all positive, then any subset of edges that connects all nodes and has mimimum total weight must be a tree. What happens if
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CP5002 1996 Assignment 1 Solutions B. Litow x April 22, 1996 1.5a Each processor gets n=p consecutive locations to work on. The time to compute AND of n=p values sequentially is O(n=p). Processor Pi writes its result to location i. This is phase 1. In phase 2 a binary tree with p leaves, one for each
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CP5002 1996 Assignment 1 Solutions B. Litow x April 22, 1996 1.5a Each processor gets n=p consecutive locations to work on. The time to compute AND of n=p values sequentially is O(n=p). Processor Pi writes its result to location i. This is phase 1. In phase 2 a binary tree with p leaves, one for each
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3
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2
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Orange Yellow Blue Green Red Black White ColourQuit Edit Pattern
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50 75 150 50 25 100 50
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Circle (white) Circle (black) Rectangle (unfilled) Circles (white) Rectangle (black)
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Algorithms and Complexity Graphs2 Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au March 1996 All-Pair Shortest Paths (26) A weighted graph is an directed graph G(V; E) where a weight w(u; v) is associated to each edge, with w : E ! R Problem: Finding the shortest path between every pair
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CP3050 1996 - B. Mans - Tutorial 7 - Solutions 1. Give a simple example of a graph such that the set of all light edges crossing some cut does not form a MST. A triangle with edge costs 2,2,1. One cut will return both cost 2 edges which is not a MST. 2. Prove that if edge weights are all positive, then
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Lecture Notes for Course #236357 Distributed Algorithms (Spring 1993) Hagit Attiya Department of Computer Science The Technion Haifa 32000, ISRAEL January 30, 1994 Preface These lecture notes describe a course on distributed algorithms I taught in the department of computer science at the Technion
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Lecture Notes for Course #236357 Distributed Algorithms (Spring 1993) Hagit Attiya Department of Computer Science The Technion Haifa 32000, ISRAEL January 30, 1994 Preface These lecture notes describe a course on distributed algorithms I taught in the department of computer science at the Technion
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CP5002-Parallel and Distributed Computing Distributed Algorithms Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au April 1996 Interconnection Networks and Distributed Algorithms MODEL: COMMUNICATION NETWORK N asynchronous processors and E bidirectional communication links Graph G = (V; E)
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CP3050 1996 - B. Mans - Tutorial 8 1. Give 2 shortest-paths trees for the Digraph of figure 25.2 , p.518, other than the two other shown. 2. Run the appropriate SSSP algorithm from node z with the Digraph of figure 25.7, p.533, in which every edge weight is changed to its opposite value (positive to
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CP5002-Parallel and Distributed Computing Distributed Algorithms Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au April 1996 Interconnection Networks and Distributed Algorithms MODEL: COMMUNICATION NETWORK N asynchronous processors and E bidirectional communication links Graph G = (V; E)
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Full Name : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : Student ID : : : : : : : : : : : : : : : : : : : : CP2001:03 Data Structures Mid-Semester Test : 1996 Time allowed : 45 minutes Candidates must answer ALL questions on this paper. There is a total of FIFTEEN
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Full Name : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : Student ID : : : : : : : : : : : : : : : : : : : : Department of Computer Science James Cook University CP3210 Fundamentals of Artificial Intelligence Quiz 1:96 Time Allowed : 45 minutes There are THREE
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Algorithms and Complexity NP-Completeness Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au March 1996 NP-Completeness (36) The Tyranny of the Growth Rate: n = 100, CPU-cycle of 1ms: algo. in n3 takes 15 mn, in 2n takes 4:1017 centuries... Polynomial-time algorithms (running in O(nk) for
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CP3060 Theory Tutorial 1 1. The polygon mesh on the right of figure 1 comprises the 5 2D triangles on the left of the figure. Construct the polygon tables for the polygon mesh. P1 P2 P3 P4 P5 P3 P1 P4 P5 P2 + +
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CP3050 1996 - B. Mans - Tutorial 9 1. For the APSP problem, what does the matrix D(0) with 0s along the diagonal and 1 everywhere else correspond to in terms of matrix multiplication 2. Run the appropriate algorithm to compute the maximum flow of the following capacity graph: 3 a----+c 3 ++ 4 s 1| 2
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CP3060 Theory Tutorial 1 1. Derive the incremental formula for evaluating the depth at point (x1 + ffi x; y1) given the depth at (x1; y1) for a plane polygon (the general formula for a plane is Ax +By +Cz + D = 0). 2. Describe how the scan-line algorithm works for the scan-line shown in figure 1. Show
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Scanline I1 I2 I3 I4
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CP3050 1996 - B. Mans - Solution Tutorial 8 1. Give 2 shortest-paths trees for the Digraph of figure 25.2 , p.518, other than the two other shown. s -> u s->u->x->v -> x -> -> v -> y ->y 2. Run the appropriate SSSP algorithm from node z with the Digraph of figure 25.7, p.533, in which every edge weight
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Literature Review - SAMURAI: An Intelligent Tool for the Discovery of World Wide Web Resources Helen Leong Supervisors: Shyam Kapur and Olivier de Vel May 3rd, 1996
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Algorithms and Complexity (CP3050) Assignment 2 (1996) - Solution - bernard@cs.jcu.edu.au 1. Regarding Huffman Codes, prove that a non-full binary tree cannot correspond to an optimal prefix code. Proof: By contradiction. Assume that a non-full binary tree corresponds to an optimal prefix code. By
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CP3050 1996 - B. Mans - Tutorial 10 1. Give a formal definition for the problem of finding the longest simple cycle in a undirected graph. Give a related decision problem. Give the language corresponding to the decision problem. 2. Give a formal encoding of the directed graphs as binary strings using an
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CP3050 1996 - B. Mans - Solution - Tutorial 9 1. For the APSP problem, what does the matrix D(0) with 0s along the diagonal and 1 everywhere else correspond to in terms of matrix multiplication The identity matrix; 1 is the identity element for MIN and is the identity element for ADDITION. 2. Run the
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Department of Computer Science James Cook University CP5002 Parallel & Distributed Computing Final Examination 1995: Part 2 B. Litow x June 4, 1996 Time allowed: 1.5 Hours There are Two questions in Part 2, each worth 25 marks. Answer ALL parts of each question, and show all of your work. In Part 2, you
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Department of Computer Science James Cook University CP5002 Parallel & Distributed Computing Final Examination 1995: Part 2 B. Litow x June 4, 1996 Time allowed: 1.5 Hours There are Two questions in Part 2, each worth 25 marks. Answer ALL parts of each question, and show all of your work. In Part 2, you
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CP3060:045/1/1/3. Question 1 (a) Given a line Ax+By+Cz = in 3D, express the value of z for the line at the point (x + ffix; y; z) in terms of the point (x; y; z0). (b) Explain how the above expression can be used to efficiently implement the Z-Buffer algorithm. Question 2 Construct
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The TPTP Problem Library TPTP v1.2.1 { TR Date 18.6.96 Technical Report AR-96-021 Technical Report 96/092 Christian B. Suttner 1Institut f ur Informatik TU M unchen, Germany Phone: +49-89-521098 Fax: +49-89-526502 Email: suttner@informatik.tu-muenchen.de Geoff Sutcliffe 2Department of Computer Science
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OPERATING SYSTEM CONCEPTS Avi Silberschatz Department of Computer Sciences University of Texas at Austin Peter Galvin Department of Computer Science Brown University Copyright 1994 Avi Silberschatz & Peter Galvin CHAPTER 1: INTRODUCTION What is an operating system Early Systems Simple Batch Systems
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CHAPTER 16: DISTRIBUTED-SYSTEM STRUCTURES Network-Operating Systems Distributed-Operating Systems Remote Services Robustness Design Issues Operating System Concepts, Addison-Wesley 1994 Silberschatz & Galvin 1994 Network-Operating Systems - users are aware of multiplicity of machines. Access to
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CHAPTER 6: PROCESS SYNCHRONIZATION Background The Critical-Section Problem Synchronization Hardware Semaphores Classical Problems of Synchronization Critical Regions Monitors Synchronization in Solaris 2 Atomic Transactions Operating System Concepts, Addison-Wesley 1994 Silberschatz & Galvin 1994
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Unix Tools Bernard Mans CP3120 Advanced Software Engineering Semester 2 1996 1 Contents ffl Editors ffl Browsers ffl String matching ffl File comparators ffl Code Indenting ffl Code Checking ffl Function hierarchy ffl Profiling ffl Debuggers 2 ffl Archiving ffl Languages Refer 1. Chapter 7, Frakes, Fox
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Unix Tools CP3120 Advanced Software Engineering - Tutorial 1 Week 2 - Semester 2 1996 Question 1: diff Assume that the file diff2.c contains the information given below. char getdata(); int dosomething( char ); char c; main() { c = getdata(); processData(c); c = getdata(); char data() { processData(c);
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Unix Tools CP3120 Advanced Software Engineering - Answers Tutorial 1 Week 2 - Semester 2 1996 Question 1: diff char getdata(); int dosomething( char ); char c; main() { c = getdata(); dosomething(c); c = getdata(); char getdata() { dosomething(c); Answer to Question 2&3: a. Simplest answer: simulate:
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Teaching Computer Science as the Science of Information Gopal K. Gupta TR 96/10 JAMES COOK UNIVERSITY OF NORTH QUEENSLAND DEPARTMENT OF COMPUTER SCIENCE TOWNSVILLE QUEENSLAND 4811 AUSTRALIA Title Teaching Computer Science as the Science of Information Primary Author(s) Gopal K. Gupta Contact Information
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Segment of a Plane P Normal to P A Plane in 3-D can be specified by: 1. A normal N N and a point U on the plane. OR 2. An Equation: Ax + By + Cz + D = 0.
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Lines and the Two-Dimensional Lattice Peter Stephenson and Bruce Litow TR 96/01 JAMES COOK UNIVERSITY OF NORTH QUEENSLAND DEPARTMENT OF COMPUTER SCIENCE TOWNSVILLE QUEENSLAND 4811 AUSTRALIA Title Lines and the Two-Dimensional Lattice Primary Author(s) Peter Stephenson and Bruce Litow Contact Information
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CP1010: SEM II, 1996 Lecture 1: Why use visualization How does one convey the gist of a large amount of data Simply displaying hundreds, thousands, or even millions of numbers in tabular form is useless. People cannot digest data in that raw form. Even worse, a number may correspond to several inputs,
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CP1010: SEM II, 1996 Lecture 2 The simplest quantitative data is categorized. For example, categories run through a discrete set, i.e. labels, and for each label we measure a number. Bar graphs (histograms) are the classical way to picture this kind of data. This is generally quite useful provided that
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x, y : real; c : char; : integer; i begin i := myconstant; SUBROUTINE BODY end; const myconstant = 17 ; var x, y : real; c : char; i : integer; SUBROUTINE NAME var LOCAL CONSTANT DECLARATIONS GLOBAL VARIABLE DECLARATIONS LOCAL VARIABLE DECLARATIONS mysubprocedure (parameters) SUB- SUBROUTINES PROGRAM
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CP3120 Advanced Software Engineering Project Guidelines Bernard Mans -bernard@cs.jcu.edu.au The group project is an important part of the CP3120 subject. It accounts for 50% of the assessment. All students are required to participate in each aspect of the project; in particular, each member must
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sin(Pi/2) = sin(90) sin(Pi/4) = sin(45) sin(Pi/6) = sin(30)
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CP1010: SEM II, 1996 Introduction to Visualisation for Science Instructor Bruce Litow, TESAG 150, x5844, bruce@cs.jcu.edu.au Text The Student Edition of MatLab Version 4, Users Guide Tutorial Tutorial is weekly in HA012, T 13:00 - 16:00 Lectures Tuesday, 12:00, MP1010, Wednesday, 13:00 MP101 Web All
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Image Compression using Weighted Finite Automata Progress Report Bella Robinson Supervisors: Dr Bruce Litow and Dr Olivier de Vel September 4, 1995
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CP1010: SEM II, 1996 Lecture 3 Sometimes a curve can be made to look simpler by a change of variable. Usually this is of interest when an hypothesis about the function corresponding to the curve is being tested. For example, the ratio of the mass of a radioactive element at time t = to its current mass
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CP1010: SEM II, 1996 Lecture 4 The equation of a plane P in space is ax + by + cz + d = That is P consists of all points (x; y; z) satisfying the above equation. When d = 0, then P includes (passes through) the origin (0; ; 0). If we have another plane P corresponding to a0x + b0y + c0z + d0 = then what
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y = ax y = ax + d (0,0) (0,d) (x,ax) (x,ax+d) L L' L' is L translated by the vector $V = (0,d) V
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Y = 2X Y' = -3X Pi/4 Rotation through Pi/4: Effect on L L L'
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CP1010: SEM II, 1996 Lecture 5 A rotation R about angle in 2-D can be represented by a matrix R = cos sin sin cos R takes the vector (x; y) to the vector (x cos y sin ; x sin +y cos ). For example, if = ss=2, then (x; 0) goes over to (0; x), that is rotation in the counterclockwise direction, through
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d a c b A Brief Summary of LATEX for Document Preparation Olivier de Vel Department of Computer Science, James Cook University, Townsville, Q4811. Internet : olivier@curacoa.cs.jcu.edu.au January 1993 c 1993 { Olivier de Vel Contents 1 Introduction 1 2 Some Preliminaries 3 3 Typesetting Mathematics 12 4
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CP1010: SEM II, 1996 Lecture 4a First, a remark about translates of the plane P : ax+by+cz = 0. The plane given by ax+ by + cz + d = is obtained from P by translating each point (x; y; z) 2 P by the vector (0; ; d=c) to (x; y; z d=c). This point satisfies ax + by + c(z d=c) + d = ax + by + c = Note that
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CP1010: SEM II, 1996 Lecture 6 Let's look at lines and planes in space. The equation of a line L in space going through points (a0; b0; c0) and (a; b; c) is (a0; b0; c0) + t (a a0; b b0; c c0) Notice that when t = 0, we recover (a0; b0; c0) and when t = 1, we get (a; b; c). Let P be a plane given by dx
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Department of Computer Science James Cook University CP5035 { Special Topics II (Machine Learning) Tutorial 1 { Learning Agents and Decision Trees 1) Study pages 525{527 in Artificial Intelligence: A Modern Approach by S. Russell and P. Norvig (Prentice-Hall, 1995) on the general model of learning
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Appendix A CP3100 Project Documentation Obr is a simple imperative language modelled on the Oberon family of languages designed by Niklaus Wirth. This year in CP3100 we will be building an Obr compiler for the Alpha architecture. This document provides information needed to complete the compiler as
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Department of Computer Science James Cook University CP5035 Special Topics II (Machine Learning) Tutorial 2 Maximally Discriminating Attributes Suppose we are constructing a decision tree, and have reached a node corresponding to a subset S of our entire domain (the domain we are going to
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Marking Scheme for CP3120 Project Legacy Project Name: Component Possible Marks Comments Marks Scored Project description 5 Initial expectations 5 Current status of the project 5 Remaining areas of concern 5 Activities/time log(s) 5 Technical lessons learned 5 Managerial lessons learned 5
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Marking Scheme for the CP3120 Project Solution Project Name: Component Possible Marks Comments Marks Scored Quality of solution 20 - Is the solution appropriate to the problem - Is the program well structured - Is the program well commented ... etc. Quality of testing 20 - Is the testing adequate - Is
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Marking Scheme for CP3120 User Manual Project Name: Component Possible Marks Comments Marks Scored Introduction: - Product rationale and overview 3 Introduction: - Terminology 2 Introduction: - Basic features 5 Introduction: - Summary of display and report formats 5 Introduction: - Outline of the manual
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Object-Or iented Analysis Object-Oriented Analysis Contents Managing Complexity De nitions and Notation Analysis Techniques The Procedure Finding Classes and Objects Identifying Structures Identifying Subjects Identifying Attributes Identifying Services Refer Coad, Yourdon, Object-Or iented
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Object-Or iented Analysis Object-Oriented Analysis Contents Managing Complexity De nitions and Notation Analysis Techniques The Procedure Finding Classes and Objects Identifying Structures Identifying Subjects Identifying Attributes Identifying Services Refer Coad, Yourdon, Object-Or iented
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On the Ad am Property for Circulant Graphs Bernard Mans and Bruce Litow TR 96/11 JAMES COOK UNIVERSITY OF NORTH QUEENSLAND DEPARTMENT OF COMPUTER SCIENCE TOWNSVILLE QUEENSLAND 4811 AUSTRALIA Title On the Ad am Property for Circulant Graphs Primary Author(s) Bernard Mans and Bruce Litow Contact
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CP1010: SEM II, 1996 Lecture 9 The ellipse (ax)2 + (by)2 = 1 can be specified parametrically by using the identity (sin )2 + (cos )2 = 1 If we let x = cos a and y = sin b , then (ax)2 + (by)2 = (cos )2 + (sin )2 = 1 If we think of as elapsed time, then at = 0, we get (1=a; 0), at time ss=2 we get (0;
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CP1010: SEM II, 1996 Lecture 8 An ellipse with centre (0; 0) is given by ax2 + by2 = c2 and a; b > When a = b > 0, we get a circle. We can make things a bit simpler by always writing the ellipse equation as (ax)2 + (bx)2 = 1 Now we don't have to restrict a and b to be positive; nonzero is enough. Now
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CP1010: SEM II, 1996 Lecture 7 The equation of a circle C of radius r, with centre (a; b) is (x a)2 + (y b)2 = r2 If L is the line y = cx + d, then (x; y) 2 C L when (x a)2 + (cx + d b)2 r2 = This is a quadratic equation in x, so there can be 0, 1, 2 solutions. Instead of worrying about the parameters
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Marking Scheme for CP3120 Test Plan Specification Project Name: Component Possible Marks Comments Marks Scored Introduction, etc 5 Description of Functional Tests 5 Completeness of Functional Tests 5 Description of Performance Tests 5 Completeness of Performance Tests 5 Description of Stress Tests 5
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Object-Oriented Analysis CP3120 Advanced Software Engineering Tutorial 2 Semester 2 1996 Ecological Simulation Use Object-Oriented Analysis procedures to develop an analysis of the following system. Your answer should follow the Coad and Yourdon notation discussed in lectures. This should include the
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CP1010: SEM II, 1996 Lecture 11 A rotation in the plane in the counterclockwise direction about the origin through an angle can be represented by matrix multiplication: cos sin sin cos x y = x cos y sin x sin + y cos If we normalise x2 + y2 = 1, then x = cos OE and y = sin OE, where OE is the angle
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CP1010: SEM II, 1996 Lecture 10 Let A; B;C;D be four points in 3-D. When are they not all in a single plane Note that any three of them must be in a plane. Let's take A; B;C and determine the plane P containing them. A = (x1; y1; z1); B = (x2; y2; z2); C = (x3; y3; z3). If P is given by ax + by + cz + d
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CP1010: SEM II, 1996 lecture 12 Take the line y = 3x in 2-D. What is the rotation taking it into the x-axis We need to find the angle it makes with the x-axis, and then rotate by . Actually, to build the rotation matrix, we really need cos and sin . Any point on this line has the form (x; 3x). We want a
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5035/1996/tutorials/tut3.ps, 19960904
Department of Computer Science James Cook University CP5035 { Special Topics II (Machine Learning) Tutorial 3 { The k-NN algorithm The k-NN algorithm is based on the idea that samples that are `close' in feature space are likely to belong to the same class". Suppose we are given the following training
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Introduction to Learning Theory Chpt 3 Page ' Introduction We have seen several inductive learning algorithms. However, we may still have some fundamental questions: 1) How can one know that one's learning algorithm has produced a theory that will correctly predict the future How reliable is this theory
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Object-Or iented Design Object-Oriented Design Contents Design models Compar ision of OOAand OOD The components of OOD Problem Domain Component Human Interaction Component Task Management Component Data Management Component Refer Coad, Yourdon, Object-Or iented Design,Second Edition. Peter Stephenson
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3120/1996/Lectures/ood_4.ps, 19960905
Object-Or iented Design Object-Oriented Design Contents Design models Compar ision of OOAand OOD The components of OOD Problem Domain Component Human Interaction Component Task Management Component Data Management Component Refer Coad, Yourdon, Object-Or iented Design,Second Edition. Peter Stephenson
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Literature Review: FPGAs - Programming in the small Robyn Cheyne Supervisor David Wessels May 3rd, 1996
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Intelligent Document Understanding and Reproduction Candidate : Michael McLean Supervisor : Nizam Ahmed Department of Computer Science James Cook University of North Queensland Townsville, QLD 4811, Australia
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Literature Review ETAITAL: Evaluation of Technological Applications for Innovative Teaching and Learning Angela Bennetts Supervisors: Shyam Kapur and Gloria Stillman May 3rd, 1996
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WEIPA - a tool for call site partitioning Dale Richardson 3/5/96
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Which Line Best Fits the Data (centres of the circles ) The radius is the uncertainty in measurement/observation Interpolating: the Linear Case
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CP1010: SEM II, 1996 Lecture 13 A surveyor makes measurements of the heights of three mountains, A = 1236m:; B = 1941m:; C = 2417m:. She also measures the heights of B;C against A, and also that of C against B. The results are, B A = 711m:; C A = 1177m:; C B = 475m: Clearly, these measurements are
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Object-Or iented Analysis Principles of Managing Complexity Methods 1a 1b 2 3 4 5 6a 6b 6c 7 8a 8b 8c Class-Object X XX Gen-Spec X X Whole-Par t X Attr ibute X XX X X Ser vice XXXX X XXX Instance X Message X XX Subject X X 1. Abstraction a. Procedural b. Data 2. Encapsulation 3. Inheritance 4.
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Department of Computer Science James Cook University CP5035 { Special Topics II (Machine Learning) Tutorial 4 { Machine Learning (Decision Trees) 1) Consider the following data (training samples) used for assessing an individual's credit risk for a loan application based on credit history, current debt,
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CP1010: SEM II, 1996 Assignment 1. Out: Aug. 13, In: Sept. 10 There are two problems. Each has equal total weight. Show all work, it will be counted. You CAN use MatLab to check your answers. A. Given in 3-D the plane P : 3x 2y + z 4 = 1. determine whether or not (1; 2; 5) is in P . 2. determine the
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Ocean Simulation Tutorial Solution Problem Domain Component PrintState Initialise Iterate Die 1,m 11,m OceanSimulation Move Reproduce Animal 1 Preditor Move Feed Size Species Nof Prey Nof Predators Nof Iterations Chance Of Reproduction Time Of Last Feed Life Span Figure 1: Class-&-Object Diagram of the
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp1010/1996/lect15.ps.gz, 19960918
CP1010 1996 Lecture 15 If the experimental data is hypothesised to obey a `Power Law', y = bxa, then finding a and b can be accomplished by using a Log-Log plot and the method of least squares. The reason is that ln(y) = a ln(x) + ln(b) So if we plot ln y against ln x, we should get a line with slope a
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CP1010 1996 Lecture 14 Having looked at the line of best fit, let's look in detail at the next dimension; the plane of best fit. We have data points that we think should (nearly) lie in a plane in 3-D. That is, each data point (x; y; z) reflects the fact that z depends on two independent variables, x
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Department of Computer Science James Cook University CP5035 { Special Topics II (Machine Learning) Tutorial 5 { Machine Learning (Version Space) Consider the problem of learning a set of playing cards. Each card has attributes fNumber, Suit, FaceCardg with hierarchy: odd even 1 3 5 13 2 4 12 yes no red
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Learning to Recognise 3D Objects from 2D Intensity Images Thesis submitted by Brendan James McCANE BSc(Hons) in March 1996 for the degree of Doctor of Philosophy in the Department of Computer Science at James Cook University of North Queensland i I, the undersigned, the author of this thesis, understand
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Introduction to Object-Oriented Programming Bernard Mans CP3120 Advanced Software Engineering Semester 2 1996 1 ffl Most OOPLs directly support the OOA and OOD results developer empowered environments class libraries ffl Some systems are not implemented using OOPLs need to use: style guides strict
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VICTORIA UNIVERSITY OF WELLINGTON Te Whare Wananga o te Upoko o te Ika a Maui VUW Department of Computer Science PO Box 600 Wellington New Zealand Tel: +64 4 471 5328 Fax: +64 4 495 5232 Internet: Tech.Reports@comp.vuw.ac.nz A Computer Scientist's Guide to Writing and Publishing Technical Articles Paul
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Introduction to Object-Oriented Programming Bernard Mans CP3120 Advanced Software Engineering Semester 2 1996 1 ffl Most OOPLs directly support the OOA and OOD results developer empowered environments class libraries ffl Some systems are not implemented using OOPLs need to use: style guides strict
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5035/1996/tutorials/tut6.ps, 19960926
Department of Computer Science James Cook University CP5035 { Special Topics II (Machine Learning) Tutorial 6 { Machine Learning (Neural Networks { Perceptrons) 1) Consider the Boolean function AND: AND(X; Y ) = ae 1 if X = Y = 1, otherwise. Can the AND function be represented by a Perceptron If not,
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C++ CP3120 Advanced Software Engineering Tutorial 3 Semester 2 1995 Question 1 a. Implement the Complex class from the lecture notes using 3 constructors (the default constructor, a 1-argument constructor and a 2-argument constructor) and a destructor. { Add distinguishing output statements in the
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C++ versus C ffl C++ is an Object-Oriented extension to C ffl C++ is a separate language to C, not a library function suite ffl C++ is (almost) a proper subset of C | most ANSI C programs will compile and run with a C++ compiler. ffl C++ includes the ANSI C libraries (e.g. printf, scanf from )
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3120/1996/Lectures/c++.ps, 19960926
C++ versus C ffl C++ is an Object-Oriented extension to C ffl C++ is a separate language to C, not a library function suite ffl C++ is (almost) a proper subset of C | most ANSI C programs will compile and run with a C++ compiler. ffl C++ includes the ANSI C libraries (e.g. printf, scanf from )
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Development of A Structured and Distributed Performance Monitoring System Thesis submitted by: Ling SHI, BE (Nanjing) in November 1995 for the research Degree of Master of Science in the Department of Computer Science at James Cook University of North Queensland. Supervisors: Dr. Olivier de Vel and Dr.
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C++ Bernard Mans CP3120 Advanced Software Engineering Semester 2 1996 1 C++ versus C ffl C++ is a separate language to C, not a library function suite ffl C++ is an Object Oriented extension to C ffl C is (almost) a proper subset of C++ most ANSI C programs will compile and run with a C++ compiler. ffl
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3120/1996/Tutorials/tutoo2.ps, 19961003
Ocean Simulation Tutorial Solution Problem Domain Component PrintState Initialise Iterate Die 1,m 11,m OceanSimulation Move Reproduce Animal 1 Preditor Move Feed Size Species Nof Prey Nof Predators Nof Iterations Chance Of Reproduction Time Of Last Feed Life Span Figure 1: Class-&-Object Diagram of the
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp3120/1996/Tutorials/c++.tutorial.solution.ps, 19961003
C++ CP3120 Advanced Software Engineering Tutorial 3 Solution Semester 2 1995 Question 1 a. Implement the Complex class from the lecture notes using 3 constructors (the default constructor, a 1-argument constructor and a 2-argument constructor) and a destructor. { Add distinguishing output statements in
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CP3120:045/1/1/14. Section A. Question 1 The following files contain an example of a C++ program implementing and using a Complex number class. Describe each of the syntactic or semantic errors contained in this example. To answer this question, give the name of the file that contains the error, the
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Mathematics in the TPTP Problem Library (v1.2.1) Geoff Sutcliffe Department of Computer Science James Cook University, Australia Phone: +61 77 814622 Fax: +61-77-814029 Email: geoff@cs.jcu.edu.au Christian B. Suttner Institut f ur Informatik TU M unchen, Germany Phone: +49-89-521098 Fax: +49-89-526502
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Optimising project planning through feasible path analysis David Wessels TR 96/14 JAMES COOK UNIVERSITY OF NORTH QUEENSLAND DEPARTMENT OF COMPUTER SCIENCE TOWNSVILLE QUEENSLAND 4811 AUSTRALIA Title Optimising project planning through feasible path analysis Primary Author(s) David Wessels Contact
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Nano-mechanical computing using multiple-valued logic programmable logic arrays David Wessels TR 96/13 JAMES COOK UNIVERSITY OF NORTH QUEENSLAND DEPARTMENT OF COMPUTER SCIENCE TOWNSVILLE QUEENSLAND 4811 AUSTRALIA Title Nano-mechanical computing using multiple-valued logic programmable logic arrays
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/cp5035/1996/tutorials/tut7.ps, 19961020
Department of Computer Science James Cook University CP5035 { Special Topics II (Machine Learning) Tutorial 7 { Machine Learning (Reinforcement Learning) Consider the state-space drawn below; states are represented as nodes and actions by directed arcs. The number on each arc is the current estimate of
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CP2050b:030/1/1/8. Direction: Exam of 2 hours without document. You must reply on the form. Your answer must be as concise and precise as possible. There are 21 questions for a total of 250 marks. Assessment: 25 percent. Question 1 In the table given below, terms in Column 1 and Column 2 are related but
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Randomised techniques to efficiently approximate spectral coefficients and autocorrelation coefficients David Wessels TR 96/12 JAMES COOK UNIVERSITY OF NORTH QUEENSLAND DEPARTMENT OF COMPUTER SCIENCE TOWNSVILLE QUEENSLAND 4811 AUSTRALIA Title Randomised techniques to efficiently approximate spectral
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Department of Computer Science James Cook University CP5035 { Special Topics II (Machine Learning) Essay Marks (Optional Essay) Only those students who handed-in the optional essay are listed below. The maximum mark for the essay is 18 and its loading towards the final mark is 10% (maximum mark,
open this document and view contentsftp://coral.cs.jcu.edu.au//pub/JCUCSdocs/techreports/96-18.ps.gz, 19961029
Text Summarisation for Knowledge Filtering Agents in Distributed Heterogeneous Environments Helen Leong and Shyam Kapur and Olivier de Vel TR 96/18 JAMES COOK UNIVERSITY OF NORTH QUEENSLAND DEPARTMENT OF COMPUTER SCIENCE TOWNSVILLE QUEENSLAND 4811 AUSTRALIA Title Text Summarisation for Knowledge
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Department of Computer Science James Cook University CP5035 { Special Topics II (Machine Learning) Seminar Marks N.B.: The total mark (in right-hand column) has been normalised for all students. The algorithm is as follows: ffl For students who did NOT hand-in the optional essay: The mark in the final
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Department of Computer Science James Cook University CP5035 { Special Topics II (Machine Learning) Project Marks Maximum total of marks is 30. A maximum bonus mark of 6 (20%) has been added for those students who attempted Section 2.4 of the project. Student ID Total Student ID Total (Max. 30) (Max. 30)
open this document and view contentsftp://coral.cs.jcu.edu.au//pub/JCUCSdocs/techreports/96-19.ps.gz, 19961106
Generalised Stochastic Automaton Image Compression B. Litow and O. de Vel TR 96/19 JAMES COOK UNIVERSITY OF NORTH QUEENSLAND DEPARTMENT OF COMPUTER SCIENCE TOWNSVILLE QUEENSLAND 4811 AUSTRALIA Title Generalised Stochastic Automaton Image Compression Primary Author(s) B. Litow and O. de Vel Contact
open this document and view contentsftp://coral.cs.jcu.edu.au//pub/JCUCSdocs/techreports/96-16.ps.gz, 19961118
Knowledge Structuring, Navigation and Retrieval in a Virtual World Wide Web S. Kapur and O. de Vel TR 96/16 JAMES COOK UNIVERSITY OF NORTH QUEENSLAND DEPARTMENT OF COMPUTER SCIENCE TOWNSVILLE QUEENSLAND 4811 AUSTRALIA Title Knowledge Structuring, Navigation and Retrieval in a Virtual World Wide Web
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IDUR Intelligent Document Understanding and Reproduction Thesis submitted by Michael McLean August 1996 in partial fulfillment of the requirements for the Degree of Bachelor of Science with Honours in the Department of Computer Science of James Cook University of North Queensland. Supervisor: Nizam
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Supporting Valid-time Indeterminacy Curtis E. Dyreson and Richard T. Snodgrass TR 96/20 JAMES COOK UNIVERSITY OF NORTH QUEENSLAND DEPARTMENT OF COMPUTER SCIENCE TOWNSVILLE QUEENSLAND 4811 AUSTRALIA Title Supporting Valid-time Indeterminacy Primary Author(s) Curtis E. Dyreson and Richard T. Snodgrass
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Computable Non-computable HardEasy PROBLEMS Analysis and Design ALGORITHMS Pseudo-code Structure charts Flow charts Abstract data types Programming RELOCATABLE EXECUTABLES a.out.exe .com Fortran PROGRAMS Compilation, linking Loading COMPUTER (see architecture) Ada C++ PrologSchemeLisp CBasic
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Department of Computer Science James Cook University Proposed Honours Project Topics (1996) February 15, 1996 Early in first semester Computer Science Honours students are required to select a project that will form a large part of their assessment for the year. The choice of project can be critical to
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Department of Computer Science James Cook University Guide to the Honours Program (1996)1 1 Introduction Students who have completed 12 units or more of third-year Computer Science subjects with a credit average or above are eligible for admission to the Honours program in the Department of Computer
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On the structure of the pixel pattern of a line in the plane B. Litow and Peter Stephenson TR 97/01 JAMES COOK UNIVERSITY OF NORTH QUEENSLAND DEPARTMENT OF COMPUTER SCIENCE TOWNSVILLE QUEENSLAND 4811 AUSTRALIA Title On the structure of the pixel pattern of a line in the plane Primary Author(s) B. Litow
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Department of Computer Science James Cook University Guide to the Master of Applied Science Course (1997)1 1 Introduction The Master of Applied Science course in Computer Science (MAppSc(CompSc)) provides a high quality, professional training at an advanced level for people who either have a background
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Department of Computer Science James Cook University Guide to the Honours Course (1997)1 1 Introduction Students who have completed 12 units or more of third-year Computer Science subjects with a credit average or above are eligible for admission to the Honours program in the Department of Computer
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Research Projects in Computer Science at James Cook University This list will be enlarged and a final, more detailed list will be made available in early February, 1997. If you have any questions about the projects, please direct them to: Dr Geoff Sutcliffe TG141, Tel. 077 815085 email:
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Marking Scheme for CP3110 Design Document Project Name: Component Possible Marks Comments Marks Scored Overview, architecture description (perhaps including data flow diagram, structure 10 chart, data dictionary, uses relationships, design rationale) Module descriptions behaviour (inputs, outputs) 5
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Marking Scheme for CP3110 Software Requirements Specification Project Name: Component Possible Marks Comments Marks Scored Overview; development, operation and maintenance envs; conceptual model, 5 glossary User interface reqs. 10 Functional reqs. 10 Non-functional reqs. (incl. ext. interfaces) 5 Error
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Marking Scheme for CP3110 Concept Exploration Project Name: Component Possible Marks Comments Marks Scored Introduction/Problem Definition 1 System justification 1 User characteristics 1 Features and function 3 Goals for system and project, constraints, development, operation and maintenance 3
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Marking Scheme for CP3110 Project Plan Project Name: Component Possible Marks Comments Marks Scored Introduction, overview, terminology 4 Team structure 1 Development schedule 2 Deliverables (documents, code, etc) 2 Manner of demonstration 1 Prog. languages and dev. tools 2 Writing style, clarity,
open this document and view contentsftp://coral.cs.jcu.edu.au//web/teaching/Subjects/Defunct/cp3080/fp/slides/fp4_4.ps, 19970213
Functional Programming 4 Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au October 1995 Clock Result thedraw t1 vs t2 t3 vs Bye t4 vs t5 t1 vs t3 t4 vs t2 t5 vs Bye t1 vs t4 t5 vs t3 Bye vs t2 t1 vs t5 Bye vs t4 t2 vs t3 t1 vs Bye t2 vs t5 t3 vs t4 Functional Programming 4 - B. Mans 1
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Functional Programming 3 Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au September 1995 Algebraic Types data Tree a = Leaf | Branch a (Tree a) (Tree a) -- inorder traversal of tree inorder :: Tree a -> inorder Leaf = inorder (Branch e left right) = (inorder left) ++ ++ (inorder
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Lecture Contents 26/7/94 1. What is a Functional Language 2. Features to Expect 3. Data Types | Integer, Real, Char, Boolean, String 4. Function Definitions 5. Function Application 6. Types Coming Up: ffl Lists ffl Algebraic Data Structures Gofer Interface LIST OF COMMANDS: Any command may be
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Notes on Functional Programming with Gofer Technical Report UMCIS{1994{01 H. Conrad Cunningham cunningham@cs.olemiss.edu Software Methods Research Group Department of Computer and Information Science University of Mississippi 302 Weir Hall University, Mississippi 38677 USA 19 January 1994 Copyright c
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Functional Programming Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au September 1995 Introduction to Functional Language ffl What is a Functional Language ffl Features to Expect ffl Data Types Integer, Real, Char, Boolean, String ffl Function Definitions ffl Function Application ffl
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Mutual Exclusion Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au July 1995 Two processes incrementing a variable N: Integer := 0; task body P1 is begin N := N + 1; end P1; task body P2 is begin N := N + 1; end P2; Mutual Exclusion - B. Mans 1 Contention for Registers and Shared-Memory P1:
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Functional Programming 3 Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au September 1995 Algebraic Types data Tree a = Leaf | Branch a (Tree a) (Tree a) -- inorder traversal of tree inorder :: Tree a -> inorder Leaf = inorder (Branch e left right) = (inorder left) ++ ++ (inorder
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Functional Programming 2 Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au September 1995 Reduction Evaluation, simplification, normal form, canonical representation... square :: Int -> Int square x = x * x square (3 + 4) => square 7 (+) => 7 X 7 (square) => 49 (X) square (3 + 4) => (3 + 4)
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Dining Philosophers Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au August 1995 Dining Philosophers task body Philosopher is begin loop Think; Pre_Protocol; Eat; Post_Protocol; end loop; end; ffl A philosopher eats only of he has 2 forks. ffl No two philosophers may hold the same fork
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Distributed Mutual Exclusion Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au August 1995 Synchronous versus Asynchronous Tasking and Rendez-Vous create synchronous communication. Demanding Task --------------------|....|----- -----|..................|...|------ RdV waiting RdV Accepting
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Advanced Programming Languages - CP3080 Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au July 1995 Goal of the Course "The limits of my language mean the limits of my world", Ludwig Wittgenstein Attempt to set free from the von Neumann mind-set ffl Concurrent programming concepts, not
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Termination Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au August 1995 Distributed Framework Each node does his own job and a signal processing for administration purposes. procedure distributed is num_nodes : constant INTEGER := 4; task type NODES is entry Message(Data : INTEGER; Id :
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Functional Programming Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au September 1995 Introduction to Functional Language ffl What is a Functional Language ffl Features to Expect ffl Data Types Integer, Real, Char, Boolean, String ffl Function Definitions ffl Function Application ffl
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Termination Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au August 1995 Distributed Framework Each node does his own job and a signal processing for administration purposes. procedure distributed is num_nodes : constant INTEGER := 4; task type NODES is entry Message(Data : INTEGER; Id :
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Concurrency Bernard Mans Room 002A - Phone: 815280 Bernard.Mans@jcu.edu.au July 1995 Breaking away from the sequential paradigm Attempt to set free from the von Neumann mind-set : ffl Do not think in a way that has to do with the machine, ffl Think in a way that is appropriate for the problem at hand.
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Department of Computer Science James Cook University of North Queensland Operating Systems (CP2050 B) In-semester Test 1 (1995) Instructions Name: Sample answers Duration: 50 minutes Use the space provided Be precise and to the point Make suitable assumptions if necessary Total Marks: 75 Total
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Department of Computer Science, James Cook University Tutorial Sheet 1 CP2050 { Computer Science IIB 1/8/94 - 5/8/94 1. Find the errors in the following C program. #include void have_fun(); main { char c; int i; c = 25; have_fun; printf("%c " c); i = 15; int i; scanf("%d", i); square(m);
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Department of Computer Science James Cook University of North Queensland Operating Systems (CP2050 B) In-semester Test 1 (1995) Instructions Name: Sample answers Duration: 50 minutes Use the space provided Be precise and to the point Make suitable assumptions if necessary Total Marks: 75 Total
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CHAPTER 9: VIRTUAL MEMORY Background Demand Paging Performance of Demand Paging Page Replacement Page-Replacement Algorithms Allocation of Frames Thrashing Other Considerations Demand Segmentation Operating System Concepts, Addison-Wesley 1994 Silberschatz & Galvin 1994 Background Virtual memory -
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P1 P2
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CHAPTER 6: PROCESS SYNCHRONIZATION Background The Critical-Section Problem Synchronization Hardware Semaphores Classical Problems of Synchronization Critical Regions Monitors Synchronization in Solaris 2 Atomic Transactions Operating System Concepts, Addison-Wesley 1994 Silberschatz & Galvin 1994
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OPERATING SYSTEM CONCEPTS Avi Silberschatz Department of Computer Sciences University of Texas at Austin Peter Galvin Department of Computer Science Brown University Copyright 1994 Avi Silberschatz & Peter Galvin CHAPTER 1: INTRODUCTION What is an operating system Early Systems Simple Batch Systems
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X Y SUM CARRY HALF ADDER - Adds two bits (X, Y) S C x y HALF ADDER S C x y HALF ADDER PARALLEL ADDER FOR TWO 64-BIT BINARY NUMBERS SUM CARRY FULL ADDER - Adds three bits C Y X FULL ADDER x y c Carry Sum FULL ADDER x y c Carry Sum FULL ADDER x y c Carry Sum SS S S X XY Y X Y1 1 122 2 64 64 6465
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DISK DRIVE Program foo 00000000 93A76B20 274A0C4D 29384760 A3B02470 FFFFFFFF 00000072 PC IR R0 R1 ALU Control Logic Address Contents MEMORY 00000112 C.P.U. DECODING NOTES 00000000 = Start 274A0C4D = Get value (Y) from end of 93A76B20 = Get value (X) from Memory location 10 and store in reg 1 FFFFFFFF =
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DISK DRIVE Program foo 00000000 93A76B20 274A0C4D 29384760 A3B02470 FFFFFFFF 00000072 PC IR R0 R1 ALU Control Logic Address Contents MEMORY 203 C.P.U. DECODING NOTES 00000000 = Start 274A0C4D = Get value (Y) from end of 93A76B20 = Get value (X) from Memory location 10 and store in reg 1 FFFFFFFF = Stop
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Computable Non-computable HardEasy PROBLEMS Analysis and Design ALGORITHMS Pseudo-code Structure charts Flow charts Abstract data types Programming RELOCATABLE EXECUTABLES a.out.exe .com Fortran PROGRAMS Compilation, linking Loading COMPUTER (see architecture) Ada C++ PrologSchemeLisp CBasic
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DISK DRIVE Program foo 00000000 93A76B20 274A0C4D 29384760 A3B02470 FFFFFFFF 00000072 PC IR R0 R1 ALU Control Logic Address Contents MEMORY 93A76B20 C.P.U. DECODING NOTES 00000000 = Start 274A0C4D = Get value (Y) from end of 93A76B20 = Get value (X) from Memory location 10 and store in reg 1 FFFFFFFF =
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X Y SUM CARRY HALF ADDER - Adds two bits (X, Y) S C x y HALF ADDER S C x y HALF ADDER PARALLEL ADDER FOR TWO 64-BIT BINARY NUMBERS SUM CARRY FULL ADDER - Adds three bits C Y X FULL ADDER x y c Carry Sum FULL ADDER x y c Carry Sum FULL ADDER x y c Carry Sum SS S S X XY Y X Y1 1 122 2 64 64 6465
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DISK DRIVE Program foo 00000000 93A76B20 274A0C4D 29384760 A3B02470 FFFFFFFF 00000072 PC IR R0 R1 ALU Control Logic Address Contents MEMORY A3B02470 C.P.U. DECODING NOTES 00000000 = Start 274A0C4D = Get value (Y) from end of 93A76B20 = Get value (X) from Memory location 10 and store in reg 1 FFFFFFFF =
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Disk addr 0001 Disk addr 0002 Disk addr 0003 Disk addr 0004 Disk addr 0005 Disk addr 0006 Disk addr 0007 Disk addr 0008 Disk addr 0009 E Disk addr 0010 Disk addr 0011 Disk addr 0012 Disk addr 0013 Disk addr 0014 Disk addr 0015 Disk addr 0016 D I S K D R I V
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Data: address _______________ Place the data below at SYSTEM BUS MESSAGES Give me the data located at address _______________ Data:
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Ram addr 0013 Ram addr 0008 Ram addr 0015 Ram addr 0011 Ram addr 0009 Ram addr 0007 Ram addr 0005 Rom addr 0003 Rom addr 0001 Rom addr 0002 Rom addr 0004 Ram addr 0006 0 0 0 7 Ram addr 0010 Ram addr 0012 Ram addr 0014 Ram addr 0016 0 1 0 0 0 0 1 0 7 3 0 1 S Y S T E M M E M O R Y
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Part Track Sector 1 0 3 2 0 4 3 1 6 4 4 4 1 File One Circular tracks are numbered 0 .. 6 (from outside in) sector labels are shown around outside of track 0 sector 3 sector 2 sector 0 sector 7 sector 6 sector 4 sector 5 sector 1 1 2 3
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DISK DRIVE Program foo 00000000 93A76B20 274A0C4D 29384760 A3B02470 FFFFFFFF 00000072 PC IR R0 R1 ALU Control Logic Address Contents MEMORY program and store in reg 0 C.P.U. DECODING NOTES 00000000 = Start 274A0C4D = Get value (Y) from end of 93A76B20 = Get value (X) from Memory location 10 and store in
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DISK DRIVE Program foo 00000000 93A76B20 274A0C4D 29384760 A3B02470 FFFFFFFF 00000072 Address Contents MEMORY PC A3B02470 = Print Reg 0 to monitor IR R0 R1 ALU Control Logic C.P.U. DECODING NOTES 00000000 = Start 274A0C4D = Get value (Y) from end of 93A76B20 = Get value (X) from Memory location 10 and
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Disk cache Disk Drives hard drive CD ROM floppy drive Main Memory ROM (volatile) RAM Registers Memory cache (volatile) (non-volatile) Small Large COST SIZE Cheap Expensive THE DATA PYRAMID Slow Fast SPEED (non-volatile) (nonvolatile) (volatile)
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Registers PC R1 IR R2 Control Logic Instruction Code 0000 What it means Beginning of program 20E4 Store register 2 contents into disk location 0012 7C31 Add register 1 contents into register 2 contents A2F0 Copy memory location CENTRAL PROCESSING UNIT (CPU) 0003 into register 2 A371 Copy memory location
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DISK DRIVE Program foo 00000000 93A76B20 274A0C4D 29384760 A3B02470 FFFFFFFF 00000072 PC IR R0 R1 ALU Control Logic Address Contents MEMORY A3B02470 = Print Reg 0 to monitor C.P.U. DECODING NOTES 00000000 = Start 274A0C4D = Get value (Y) from end of 93A76B20 = Get value (X) from Memory location 10 and
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SOFTWARE LAYERING: from applications to hardware The users Microcode and device drivers HARDWARE Operating System Routines Interpreters, tools and libraries High level applications
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FULL ADDER x y c Carry Sum FULL ADDER x y c Carry Sum Control SS S S12 6465 0=Add, 1=Subtract FULL ADDER x y c Carry Sum x x x y y y11 2 2 64 64 ADDER/SUBTRACTOR FOR TWO 64-BIT BINARY NUMBERS (X +/- Y)