Semi-Automatic Generation of Three-Dimensional Visual Algorithm Simulations
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CRN What It Was Doing and Why It Was Cognitive Systems Vision Doing It, and to Recover from Mental Continued on Page 8 Expanding the Pipeline
COMPUTING RESEARCH NEWS Computing Research Association, Celebrating 30 Years of Service to the Computing Research Community November 2002 Vol. 14/No. 5 DARPA’s New Cognitive Systems Vision By Ron Brachman and IPTO’s goal is to create a new to cope with systems both keep Zachary Lemnios generation of cognitive systems. growing. In order to make our systems more reliable, more secure, The impact of the Defense Mired in Moore’s Law? and more understandable, and to Advanced Research Projects Agency One benefit of such cognitive continue making substantial contri- (DARPA) on computing over the systems would be their help in butions to society, we need to do past 40 years has been profound. Led extracting us from a corner into something dramatically different. by the visionary J.C.R. Licklider and which our success seems to have his innovative successors in the painted us. The research that has The Promise of Cognitive Information Processing Techniques helped the industry follow Moore’s Systems Office (IPTO), DARPA initiated “Law” has created processors that are IPTO is attacking this problem by work that ultimately put personal remarkably fast and small, and data driving a fundamental change in computers on millions of desktops storage capabilities that are vast and computing systems. By giving systems Ron Brachman and made the global Internet a cheap. Unfortunately, these incred- more cognitive capabilities, we reality. In fact, the original IPTO, ible developments have cut two ways. believe we can make them more which lasted from 1962 to 1985, was While today’s computers are more responsible for their own behavior in large part responsible for estab- powerful than ever, we have been and maintenance. -
Bubble Sort: an Archaeological Algorithmic Analysis
Bubble Sort: An Archaeological Algorithmic Analysis Owen Astrachan 1 Computer Science Department Duke University [email protected] Abstract 1 Introduction Text books, including books for general audiences, in- What do students remember from their first program- variably mention bubble sort in discussions of elemen- ming courses after one, five, and ten years? Most stu- tary sorting algorithms. We trace the history of bub- dents will take only a few memories of what they have ble sort, its popularity, and its endurance in the face studied. As teachers of these students we should ensure of pedagogical assertions that code and algorithmic ex- that what they remember will serve them well. More amples used in early courses should be of high quality specifically, if students take only a few memories about and adhere to established best practices. This paper is sorting from a first course what do we want these mem- more an historical analysis than a philosophical trea- ories to be? Should the phrase Bubble Sort be the first tise for the exclusion of bubble sort from books and that springs to mind at the end of a course or several courses. However, sentiments for exclusion are sup- years later? There are compelling reasons for excluding 1 ported by Knuth [17], “In short, the bubble sort seems discussion of bubble sort , but many texts continue to to have nothing to recommend it, except a catchy name include discussion of the algorithm after years of warn- and the fact that it leads to some interesting theoreti- ings from scientists and educators. -
Data Structures & Algorithms
DATA STRUCTURES & ALGORITHMS Tutorial 6 Questions SORTING ALGORITHMS Required Questions Question 1. Many operations can be performed faster on sorted than on unsorted data. For which of the following operations is this the case? a. checking whether one word is an anagram of another word, e.g., plum and lump b. findin the minimum value. c. computing an average of values d. finding the middle value (the median) e. finding the value that appears most frequently in the data Question 2. In which case, the following sorting algorithm is fastest/slowest and what is the complexity in that case? Explain. a. insertion sort b. selection sort c. bubble sort d. quick sort Question 3. Consider the sequence of integers S = {5, 8, 2, 4, 3, 6, 1, 7} For each of the following sorting algorithms, indicate the sequence S after executing each step of the algorithm as it sorts this sequence: a. insertion sort b. selection sort c. heap sort d. bubble sort e. merge sort Question 4. Consider the sequence of integers 1 T = {1, 9, 2, 6, 4, 8, 0, 7} Indicate the sequence T after executing each step of the Cocktail sort algorithm (see Appendix) as it sorts this sequence. Advanced Questions Question 5. A variant of the bubble sorting algorithm is the so-called odd-even transposition sort . Like bubble sort, this algorithm a total of n-1 passes through the array. Each pass consists of two phases: The first phase compares array[i] with array[i+1] and swaps them if necessary for all the odd values of of i. -
The AWK Programming Language
The Programming ~" ·. Language PolyAWK- The Toolbox Language· Auru:o V. AHo BRIAN W.I<ERNIGHAN PETER J. WEINBERGER TheAWK4 Programming~ Language TheAWI(. Programming~ Language ALFRED V. AHo BRIAN w. KERNIGHAN PETER J. WEINBERGER AT& T Bell Laboratories Murray Hill, New Jersey A ADDISON-WESLEY•• PUBLISHING COMPANY Reading, Massachusetts • Menlo Park, California • New York Don Mills, Ontario • Wokingham, England • Amsterdam • Bonn Sydney • Singapore • Tokyo • Madrid • Bogota Santiago • San Juan This book is in the Addison-Wesley Series in Computer Science Michael A. Harrison Consulting Editor Library of Congress Cataloging-in-Publication Data Aho, Alfred V. The AWK programming language. Includes index. I. AWK (Computer program language) I. Kernighan, Brian W. II. Weinberger, Peter J. III. Title. QA76.73.A95A35 1988 005.13'3 87-17566 ISBN 0-201-07981-X This book was typeset in Times Roman and Courier by the authors, using an Autologic APS-5 phototypesetter and a DEC VAX 8550 running the 9th Edition of the UNIX~ operating system. -~- ATs.T Copyright c 1988 by Bell Telephone Laboratories, Incorporated. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopy ing, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America. Published simultaneously in Canada. UNIX is a registered trademark of AT&T. DEFGHIJ-AL-898 PREFACE Computer users spend a lot of time doing simple, mechanical data manipula tion - changing the format of data, checking its validity, finding items with some property, adding up numbers, printing reports, and the like. -
13 Basic Sorting Algorithms
Concise Notes on Data Structures and Algorithms Basic Sorting Algorithms 13 Basic Sorting Algorithms 13.1 Introduction Sorting is one of the most fundamental and important data processing tasks. Sorting algorithm: An algorithm that rearranges records in lists so that they follow some well-defined ordering relation on values of keys in each record. An internal sorting algorithm works on lists in main memory, while an external sorting algorithm works on lists stored in files. Some sorting algorithms work much better as internal sorts than external sorts, but some work well in both contexts. A sorting algorithm is stable if it preserves the original order of records with equal keys. Many sorting algorithms have been invented; in this chapter we will consider the simplest sorting algorithms. In our discussion in this chapter, all measures of input size are the length of the sorted lists (arrays in the sample code), and the basic operation counted is comparison of list elements (also called keys). 13.2 Bubble Sort One of the oldest sorting algorithms is bubble sort. The idea behind it is to make repeated passes through the list from beginning to end, comparing adjacent elements and swapping any that are out of order. After the first pass, the largest element will have been moved to the end of the list; after the second pass, the second largest will have been moved to the penultimate position; and so forth. The idea is that large values “bubble up” to the top of the list on each pass. A Ruby implementation of bubble sort appears in Figure 1. -
Fall, 2016 Lab #3
Texas Christian University CoSc 20803 - Fall, 2016 Lab #3 Points: 100 points Language: Java Due Date: Midnight, Thursday, Nov 17, 2016 (complete Java project and manually-generated Excel spreadsheet ZIPped together and submitted with TURNIN). Purpose: The purpose of this assignment is to allow you to evaluate the performance of several different sorting algorithms on data in various degrees of disorder. Processing: You should implement the following sorting algorithms and gather statistics as noted below: Method #1 Method #2 Method #3 Method #4 Bubblesort Cocktail Shaker Sort Shell Sort Quick Sort (pp. 230-232) (discussed in class) (discussed in class) (pp. 232-236) Input: Three input data sets will be provided (each of which contains 5000, 4-byte alphanumeric keys). The files are: Ascending.dat, Descending.dat, and Random.dat (obtainable from the CoSc Department’s class website: GUI: For this lab you should develop a manual data collection program that employs a VERY SIMPLE graphical user interface. Your program should accommodate input from the user specifying: a) the integer number of records to be sorted, b) the name of the input file to be used as the source of data (via a JFileChooser()), and c) the sorting method to use. Your program should allow the sorting methods listed above to be performed. Statistics: You should sort, ascendingly, alphanumeric keys in each of the three data files provided, keeping statistics for each file and for each sort method. Keep track of: (i) the number of key compares (Ki:Kj) and (ii) the number of key moves (e.g., Ki <--- Kj). -
Sensory Information Processing (1 January 1976
Im plem enting Functional Program s Using M utable Abstract Data Types Ganesh C. Gopalakrishnan, Department of Computer Science, Univ. of Utah, Salt Lake City, Utah 84112 and Mandayam K. Srivas, Department of Computer Science, SUNY, Stony Brook, N Y 11794 W e study the following problem in this paper. Suppose we have a purely functional program that uses a set of abstract data types by invoking their operations. Is there an order of evaluation of the operations in the program that preserves the applicative order of evaluation semantics of the program even when the abstract data types behave as mutable modules. An abstract data type is mutable if one of its operations destructively updates the object rather than returning a new object as a result. This problem is important for several reasons. It can help eliminate unnecessary copying of data structure states. It supports a methodology in which one can program in a purely functional notation for purposes of verification and clarity, and then automatically transform the program into one in an object oriented, imperative language, such as CLU, A D A , Smalltalk, etc., that supports abstract data types. It allows accruing both the benefits of using abstract data types in programming, and allows modularity and verifiability. Keywords: Functional Program Implementation, Mutable Modules, Abstract Data Types, Syntactic Conditions. C o n t e n t s 1 Introduction 1 1.1 Related W o r k ........................................................................................................................... 2 1.2 Terminology, Assumptions, and Problem Statement............................................... 2 2 Syntactic Characterization of In Situ Evaluability 3 2.1 Syntactic Conditions for Straight-line Expressions.................................................. -
Understanding Programming Languages
Understanding Programming Languages M. Ben-Ari Weizmann Institute of Science Originally published by John Wiley & Sons, Chichester, 1996. Copyright °c 2006 by M. Ben-Ari. You may download, display and print one copy for your personal use in non-commercial academic research and teaching. Instructors in non-commerical academic institutions may make one copy for each student in his/her class. All other rights reserved. In particular, posting this document on web sites is prohibited without the express permission of the author. Contents Preface xi I Introduction to Programming Languages 1 1 What Are Programming Languages? 2 1.1 The wrong question . 2 1.2 Imperative languages . 4 1.3 Data-oriented languages . 7 1.4 Object-oriented languages . 11 1.5 Non-imperative languages . 12 1.6 Standardization . 13 1.7 Computer architecture . 13 1.8 * Computability . 16 1.9 Exercises . 17 2 Elements of Programming Languages 18 2.1 Syntax . 18 2.2 * Semantics . 20 2.3 Data . 21 2.4 The assignment statement . 22 2.5 Type checking . 23 2.6 Control statements . 24 2.7 Subprograms . 24 2.8 Modules . 25 2.9 Exercises . 26 v Contents vi 3 Programming Environments 27 3.1 Editor . 28 3.2 Compiler . 28 3.3 Librarian . 30 3.4 Linker . 31 3.5 Loader . 32 3.6 Debugger . 32 3.7 Profiler . 33 3.8 Testing tools . 33 3.9 Configuration tools . 34 3.10 Interpreters . 34 3.11 The Java model . 35 3.12 Exercises . 37 II Essential Concepts 38 4 Elementary Data Types 39 4.1 Integer types . -
BRIDGE Linking Engin Ee Ring and Soci E T Y
Summer 2009 ENERGY EFFICIENCY The BRIDGE LINKING ENGIN ee RING AND SOCI E TY The Potential of Energy Efficiency: An Overview Lester B. Lave Improving Energy Efficiency in the Chemical Industry Jeremy J. Patt and William F. Banholzer Energy Efficiency in Passenger Transportation Daniel Sperling and Nic Lutsey Building Materials, Energy Efficiency, and the American Recovery and Reinvestment Act Robin Roy and Brandon Tinianov Coming of Age in New York: The Maturation of Energy Efficiency as a Resource Paul A. DeCotis The Greening of the Middle Kingdom: The Story of Energy Efficiency in China Mark D. Levine, Nan Zhou, and Lynn Price Promoting the technological welfare of the nation by marshalling the knowledge and insights of eminent members of the engineering profession. The BRIDGE NationaL AcaDemy OF Engineering Irwin M. Jacobs, Chair Charles M. Vest, President Maxine L. Savitz, Vice President Thomas F. Budinger, Home Secretary George Bugliarello, Foreign Secretary William L. Friend, Treasurer Editor in Chief (interim): George Bugliarello Managing Editor: Carol R. Arenberg Production Assistant: Penelope Gibbs The Bridge (USPS 551-240) is published quarterly by the National Academy of Engineering, 2101 Constitution Avenue, N.W., Washington, DC 20418. Periodicals postage paid at Washington, DC. Vol. 39, No. 2, Summer 2009 Postmaster: Send address changes to The Bridge, 2101 Constitution Avenue, N.W., Washington, DC 20418. Papers are presented in The Bridge on the basis of general interest and time- liness. They reflect the views of the authors and not necessarily the position of the National Academy of Engineering. The Bridge is printed on recycled paper. © 2009 by the National Academy of Sciences. -
Second International Computer Programming Education Conference
Second International Computer Programming Education Conference ICPEC 2021, May 27–28, 2021, University of Minho, Braga, Portugal Edited by Pedro Rangel Henriques Filipe Portela Ricardo Queirós Alberto Simões OA S I c s – Vo l . 91 – ICPEC 2021 www.dagstuhl.de/oasics Editors Pedro Rangel Henriques Universidade do Minho, Portugal [email protected] Filipe Portela Universidade do Minho, Portugal [email protected] Ricardo Queirós Politécnico do Porto, Portugal [email protected] Alberto Simões Politécnico do Cávado e Ave, Portugal [email protected] ACM Classifcation 2012 Applied computing → Education ISBN 978-3-95977-194-8 Published online and open access by Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH, Dagstuhl Publishing, Saarbrücken/Wadern, Germany. Online available at https://www.dagstuhl.de/dagpub/978-3-95977-194-8. Publication date July, 2021 Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografe; detailed bibliographic data are available in the Internet at https://portal.dnb.de. License This work is licensed under a Creative Commons Attribution 4.0 International license (CC-BY 4.0): https://creativecommons.org/licenses/by/4.0/legalcode. In brief, this license authorizes each and everybody to share (to copy, distribute and transmit) the work under the following conditions, without impairing or restricting the authors’ moral rights: Attribution: The work must be attributed to its authors. The copyright is retained by the corresponding authors. Digital Object Identifer: 10.4230/OASIcs.ICPEC.2021.0 ISBN 978-3-95977-194-8 ISSN 1868-8969 https://www.dagstuhl.de/oasics 0:iii OASIcs – OpenAccess Series in Informatics OASIcs is a series of high-quality conference proceedings across all felds in informatics. -
A Auxiliary Definitions
A Auxiliary Definitions This appendix contains auxiliary definitions omitted from the main text. Variables fun lvars :: com ⇒ vname set where lvars SKIP = {} lvars (x ::= e)={x} lvars (c1;; c2)=lvars c1 ∪ lvars c2 lvars (IF b THEN c1 ELSE c2)=lvars c1 ∪ lvars c2 lvars (WHILE b DO c)=lvars c fun rvars :: com ⇒ vname set where rvars SKIP = {} rvars (x ::= e)=vars e rvars (c1;; c2)=rvars c1 ∪ rvars c2 rvars (IF b THEN c1 ELSE c2)=vars b ∪ rvars c1 ∪ rvars c2 rvars (WHILE b DO c)=vars b ∪ rvars c definition vars :: com ⇒ vname set where vars c = lvars c ∪ rvars c Abstract Interpretation fun strip :: aacom⇒ com where strip (SKIP {P})=SKIP strip (x ::= e {P})=x ::= e © Springer International Publishing Switzerland 2014 281 T. Nipkow and G. Klein, Concrete Semantics, DOI 10.1007/978-3-319-10542-0 282 A Auxiliary Definitions strip (C 1;;C 2)=strip C 1;; strip C 2 strip (IF b THEN {P 1} C 1 ELSE {P 2} C 2 {P})= IF b THEN strip C 1 ELSE strip C 2 strip ({I } WHILE b DO {P} C {Q})=WHILE b DO strip C fun annos :: aacom⇒ a list where annos (SKIP {P})=[P] annos (x ::= e {P})=[P] annos (C 1;;C 2)=annos C 1 @ annos C 2 annos (IF b THEN {P 1} C 1 ELSE {P 2} C 2 {Q})= P 1 # annos C 1 @ P 2 # annos C 2 @ [Q] annos ({I } WHILE b DO {P} C {Q})=I # P # annos C @ [Q] fun asize :: com ⇒ nat where asize SKIP = 1 asize (x ::= e)=1 asize (C 1;;C 2)=asize C 1 + asize C 2 asize (IF b THEN C 1 ELSE C 2)=asize C 1 + asize C 2 + 3 asize (WHILE b DO C )=asize C + 3 definition shift :: (nat ⇒ a) ⇒ nat ⇒ nat ⇒ a where shift f n =(λp. -
Sorting Algorithms
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 03 Issue: 02 | Feb-2016 www.irjet.net p-ISSN: 2395-0072 SORTING ALGORITHMS Neelam Yadav*, Sangeeta Kumari** *HOD in CSE Dept, DAV college, Kanina **Lecture in CSE Dept, DAV College, Kanina --------------------------------------------------------------------------------------------------------------------------------------------------------------- Abstract: This paper presents different type of sorting that Memory usage are present in data structure for example quick, insertion, Stability: stable sorting algorithms maintains the relative heap and merge. Each algorithm tries to solve sorting order if records with equal keys. problem using different formats. These four algorithms have their own pros and cons. This pa per presents a detailed study Sorting algorithms are sometimes characterized by big O of how these algorithm works and compares them on the notation. This notation indicates performances of basis of various parameters to reach a conclusion. algorithms and the amount of time that the algorithms take. The different cases that are popular in sorting Keywords: Quick Sort, Selection Sort, Insertion Sort, Bubble algorithms are:- Sort, Shell Sort, Cocktail Sort, Comparison, Other Performance Parameters. - O(n) is fair, graph increases in the smooth path. 1. INTRODUCTION - O(n^2): this is inefficient because a small increase in input increases the graph tremendously. A sorting algorithm is an algorithm that arranges the elements of a list in a certain order; the order can be - O(n log n): this is considered as efficient, because it increasing or decreasing. Till now many sorting algorithm shows the slower pace increase in the graph as the size of has been discovered. Some of them were comparison based array or data is increased.