Costs for correcting errors. Analytical evaluation. Performance measurement. Empirical evaluation GOMS. GOMS model. Let users try out a prototype

Size: px
Start display at page:

Download "Costs for correcting errors. Analytical evaluation. Performance measurement. Empirical evaluation GOMS. GOMS model. Let users try out a prototype"

Transcription

1 Why ad how? Usability aalysis ad ispectio Why? Iterative desig Prototypig Measurig usability Objective/subjective feedback Quick ad dirty Slow ad clea With or without users Evaluatio Questios that might arise durig systems developmet: Will the switch board operator be able to aswer more telephoe calls per hour tha before? Will less customers have problems withdrawig cash from the ATM? If a user eters a wrog umber, what is the cosequece ad ca we recover from it? Evaluatio Evaluatig usability These questios are about usability Usability ca be measured through such questios Evaluatios typically address whether the system meets the requiremets, it seldom explais the reasos behid the problems. ca we chage the curret meu structure without causig problems? how fast ca the users read text from the scree? how log time will it take to fid specific iformatio decreasig the fot size from 12 p to 10 p, what cosequeces will it have? how small ca we make the buttos? Measurig usability No silver bullet Evaluatio durig the developmet,ot afterwards usability is ot oly a measure but a goal for developers ad developmet. Usability builds o aalysis ad experimets. Iexperieced developers ofte igore validatio i preferece of verificatio of a system. 1

2 Costs for correctig errors Costs Time sice project start Aalytical evaluatio A logical simulatio of user behaviour E.g. GOMS, KLM, Cogitive Walkthrough Ca be used to test thigs before they have bee built. Saves time sice we do ot eed to costruct i advace. No user experimets ecessary. Create a sequece of steps ad measure the time per step. Empirical evaluatio Let users try out a prototype Simple ad cheap tests. Performace measuremet Test users perform predefied tasks Defie a set of usability goals Measure errors ad times Laboratory + Results i hard umbers, easy to compare + Objective results - time cosumig, expesive - skilled users for skilled performace GOMS Measurig speed of performace Describes differet possible ways (sequeces) of solvig a problem Goal - a certai objective to be achieved Operator - available actios Methods - Sequeces of operators Selectio rules - whe choosig betwee methods GOMS model GOAL: PHOTOCOPY PAPER GOAL: LOCATE-ARTICLE GOAL: PHOTOCOPY-PAGE repeat util o more pages GOAL: ORIENT-PAGE OPEN-COVER SELECT-PAGE POSITION-PAGE CLOSE-COVER GOAL: VERIFY-COPY LOCATE-OUT-TRAY EXAMINE-COPY GOAL: COLLECT-COPY LOCATE-OUT-TRAY REMOVE-COPY (OBS! Yttre målet fullföljt!) GOAL: RETRIEVE-JOURNAL OPEN-COVER REMOVE-JOURNAL CLOSE-COVER 2

3 Keystroke-Level Aalysis Divide each task-performace ito compoets assig executio times to each compoet e.g. press key s, poit with mouse s + compare differet methods - oly o smaller parts of the work KLM model Operatio Amärkig Tid (i sekuder) K Tryck ed taget Good maskiskrivare (90 ord/mi.) 0,12 Medelmåttig maskiskrivare (40 ord/mi.) 0,28 Icke-maskiskrivare 1,20 B Muskappstryckig er eller upp 0,10 dubbelklick 0,20 P Peka med muse Fitts lag 0,1log(D/S+0,5) Medelrörelse 1,10 H Had till eller frå tagetbord 0,40 D Rita (domäberoede) - M Metalt förbereda sig 1,35 R Respos frå systemet - Tabell X: tider för vissa operatioer som e operatör utför eligt KLM-metode (efter Card, Mora & Newell, 1983) KLM example Exempel. Det fis två olika sätt att utföra kommadot Spara. Atige ka ma välja Ctrl-S som e tagetkombiatio på tagetbordet, eller också ka ma välja alterativet spara i arkivmey. Alterativ 1: Ctrl S T 1 = M + H + K = 1,35 + 0,40 + 0,28 = 2,03 sek Alterativ 2: Meyalterativ T 2 = M + H + P + B + P + B = 1,35 + 0,40 + 1,10 + 0,10 + 1,10 + 0,10 = 4,15 sek Thikig aloud users verbalise their thoughts idetify miscoceptios of the system + direct feedback from the users - ot atural to thik out loud - difficult to verbalise their decisios Questioaires Subjective satisfactio + Ca be distributed to may people - Users aswer what they thik they do, ot what they actually do Cogitive Walkthrough A evaluator walks through the iterface Tries to act as a user Actios based o kowledge about users + Quick - Dirty - Does ot ivolve users, difficult to capture domai specific problems 3

4 Cogitive walkthrough Explaatory learig Costructed for ovice users Suitable for walk-up-ad-use-iterfaces A successive walkthrough of tasks based o questios o differet levels Does the user see the search alterative? Do the user select the right alterative? Do the user uderstad the system respose? Card Sortig Physical paper cards with text ad images The test perso sorts the cards accordig to importace Good for sortig icos, cocepts etc. Ca promote uderstadig ad relevace of cocepts by users Requires real users for good results Heuristic evaluatio Uses sets of guidelies Ispectio of the iterface Documet potetial usability problems + Easy to apply + possible to use early i the desig - Domai problems Heuristic evaluatio Jacob Nielse, Measures usability Iformal walkthrough Cheap ad simple No users required, ca be performed idividually, requires miimal plaig, ca be used early i the process Cocetrates o idetifyig errors i the desig (good ad bad) Heuristic evaluatio method A group of evaluators (more evaluators fid more errors) 3-5 persos Desig help i the ispectio Heuristic evaluatio 1. Simple ad atural dialogue 2. Speak the users laguage 3. Miimise Users Memory Load 4. Cosistecy 5. Feedback 6. Clearly marked exits 7. Shortcuts 8. Good error messages 9. Prevet errors 10. Help ad documetatio 4

5 Simple ad Natural dialogue Obvious how to iteract with the system Groupig of objects Eough iformatio o the scree Overview ad details Avoid cofusig decoratio Speak the users laguage Domai laguage Avoid system or computer laguage Use metaphors familiar to the users Icos, pictures, etc., relevat for the domai Miimise user memory load Eough iformatio o the scree Recogitio istead of recall Highlight importat iformatio Show status iformatio Default values Cosistecy Cosistet layout Cosistet iteractio Cosistet laguage Cosistet fuctioality Feedback Show waitig times Feedback for actios Work related feedback System failure Clearly marked exits Show the way the user has walked through the system How to get back ad forward Orieted i the iformatio space 5

6 Shortcuts Use shortcuts to perform actios Type ahead Jumps to a desired locatio Reuse of iteractio history Good error messages Avoid obscure data code A error No 13 has occurred Use the users laguage Prevet error Dialogues for actios that may lead to serious cosequeces Avoid modes Show status Support udo ad redo Help ad documetatio Mauals should ot be ecessary Give o-lie help o the users iitiative If o other solutio is possible, give help at the iitiative of the system Nielse s ew desig Visibility of system status The system should always keep users iformed about what is goig o, through appropriate feedback withi reasoable time. Match betwee system ad the real world The system should speak the users laguage, with words, phrases ad cocepts familiar to the user rather tha system-orieted terms. Follow real-world covetios, makig iformatio appear i a atural ad logic order Nielse s ew desig User cotrol ad freedom Users ofte choose system fuctios by mistake ad will eed a clearly marked emergecy exit to leave the uwated state without havig to go through a exteded dialogue. Support udo ad redo. Cosistecy ad stadards Users should ot have to woder whether differet words, situatios, or actios mea the same thig. Follow platform covetios. 6

7 Nielse s ew desig Error prevetio Eve better tha good error messages is a careful desig which prevets a problem from occurrig i the first place. Recogitio rather tha recall Make objects, actios ad optios visible. The user should ot have to remember iformatio from oe part of the dialogue to aother. Istructios for use of the system should be visible or easily retrievable wheever appropriate. Nielse s ew desig Flexibility ad efficiecy of use Accelerators usee by the ovice user may ofte speed up the iteractio for the expert user such that the system ca cater to both iexperieced ad experieced users. Allow users to tailor frequet actios. Aesthetic ad miimalist desig Dialogues should ot cotai iformatio which is irrelevat or rarely eeded. Every extra uit of iformatio i a dialogue competes with the relevat uits of iformatio ad dimiishes their relative visibility. Nielse s ew desig Help users recogise, diagose ad recover from errors Error messages should be expressed i plai laguage (o codes), precisely idicate the problem, ad costructively suggest a solutio. Help ad documetatio Eve though it is better if the system ca be used without documetatio, it may be ecessary to provide help ad documetatio. Ay such iformatio should be easy to search, focused o the user s task, list cocrete steps to be carried out, ad ot be too large. Heuristic evaluatio Not suitable for repeated aalysis (with the same perso). Ca be solved by rotatig the ispectio tasks Good results if the evaluators are skilled Good i combiatio with real user testig (the techiques complemet oe aother) Gradig the errors 0 I do ot agree that this is a usability problem at all 1 Cosmetic problem oly 2 Mior usability problem: should be give low priority 3 Major usability problem: importat to fix, so should be give high priority 4 Usability catastrophe: imperative to fix this before product ca be released How to use the results Aalyse the results Idetify solutios to the problems Create a ew prototype Optimisig betwee requiremets 7

8 Formative evaluatios Helps formig the system Iterative testig Summative evaluatio tests the etire system i the ed (whe it is too late to do aythig) Formative evaluatios Experiece teaches you to build prototypes ad to make cocrete decisios Iformal user tests with simple prototypes measures errors early ad simple. Field tests with prototypes with a series of chages to measure differece i preferece, error rate, speed, etc. Cotrolled experimets (all importat parameters are uder cotrol) Evaluatio steps Idetify usability aspects, specify special requiremets o the product Produce a prototype Prepare experimets (Fid users, select parameters to test, documetatio method) Test ad collect data Aalysis Coclusios Evaluate... Which method should you use ad why? Micro wave ove - which is the fastest way of heatig your food, 1.10 or 1.11? System for phoig via your computer. Public service phoe (e.g. Bak, RSV) Web site for fidig courses (e.g. Aske) Digital wrist watch with oe butto 8

Why? Usability analysis and inspection. Evaluation. Evaluation. Measuring usability. Evaluating usability

Why? Usability analysis and inspection. Evaluation. Evaluation. Measuring usability. Evaluating usability Usability analysis and inspection Why and how? Iterative design Prototyping Measuring usability Why? Objective/subjective feedback Quick and dirty Slow and clean With or without users 1MD113 Evaluation

More information

Usability analysis and inspection

Usability analysis and inspection Usability analysis and inspection Why and how? 1MD113 Why? Iterative design Prototyping Measuring usability Objective/subjective feedback Quick and dirty Slow and clean With or without users 1 Evaluation

More information

n Some thoughts on software development n The idea of a calculator n Using a grammar n Expression evaluation n Program organization n Analysis

n Some thoughts on software development n The idea of a calculator n Using a grammar n Expression evaluation n Program organization n Analysis Overview Chapter 6 Writig a Program Bjare Stroustrup Some thoughts o software developmet The idea of a calculator Usig a grammar Expressio evaluatio Program orgaizatio www.stroustrup.com/programmig 3 Buildig

More information

Human-Computer Interaction IS4300

Human-Computer Interaction IS4300 Huma-Computer Iteractio IS4300 1 P4 Desig Sketches Due Iteractio Scearios Expad each of your activity desig scearios (3+) ito full iteractio scearios, thikig about what the user perceives ad the actios

More information

Human-Computer Interaction IS4300

Human-Computer Interaction IS4300 Huma-Computer Iteractio IS4300 1 I5 due ext class Your missio i this exercise is to implemet a very simple Java paitig applicatio. The app must support the followig fuctios: Draw curves, specified by a

More information

How do we evaluate algorithms?

How do we evaluate algorithms? F2 Readig referece: chapter 2 + slides Algorithm complexity Big O ad big Ω To calculate ruig time Aalysis of recursive Algorithms Next time: Litterature: slides mostly The first Algorithm desig methods:

More information

Python Programming: An Introduction to Computer Science

Python Programming: An Introduction to Computer Science Pytho Programmig: A Itroductio to Computer Sciece Chapter 1 Computers ad Programs 1 Objectives To uderstad the respective roles of hardware ad software i a computig system. To lear what computer scietists

More information

Lecture 28: Data Link Layer

Lecture 28: Data Link Layer Automatic Repeat Request (ARQ) 2. Go ack N ARQ Although the Stop ad Wait ARQ is very simple, you ca easily show that it has very the low efficiecy. The low efficiecy comes from the fact that the trasmittig

More information

What does JFC stand for?

What does JFC stand for? IS4300 HCI No-Quiz What does JFC stad for?! Java Fudametal Classes! Java Foudatio Creator! Java Fried Chicke! Java Foudatio Classes! Java Framework Creator 1 No-Quiz What is pluggable look ad feel?! Swig

More information

Copyright 2016 Ramez Elmasri and Shamkant B. Navathe

Copyright 2016 Ramez Elmasri and Shamkant B. Navathe Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe CHAPTER 22 Database Recovery Techiques Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe Itroductio Recovery algorithms Recovery cocepts Write-ahead

More information

CSC 220: Computer Organization Unit 11 Basic Computer Organization and Design

CSC 220: Computer Organization Unit 11 Basic Computer Organization and Design College of Computer ad Iformatio Scieces Departmet of Computer Sciece CSC 220: Computer Orgaizatio Uit 11 Basic Computer Orgaizatio ad Desig 1 For the rest of the semester, we ll focus o computer architecture:

More information

COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Interface. Chapter 4. The Processor. Single-Cycle Disadvantages & Advantages

COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Interface. Chapter 4. The Processor. Single-Cycle Disadvantages & Advantages COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Iterface 5 th Editio Chapter 4 The Processor Pipeliig Sigle-Cycle Disadvatages & Advatages Clk Uses the clock cycle iefficietly the clock cycle must

More information

MANAGED! PREPARE TO BE FEATURES HANDHELD USER DISPLAYS. Specifications MEASUREMENT STABILIZATION INDICATOR

MANAGED! PREPARE TO BE FEATURES HANDHELD USER DISPLAYS. Specifications MEASUREMENT STABILIZATION INDICATOR FEATURES Trasfers data easily betwee Hadheld & PC via USB cable. Stores up to 3000 temperatures ad 300 meu items. Sets Max / Mi temperature limit idicators. Stores custom meus for easy recall. Exports

More information

Data diverse software fault tolerance techniques

Data diverse software fault tolerance techniques Data diverse software fault tolerace techiques Complemets desig diversity by compesatig for desig diversity s s limitatios Ivolves obtaiig a related set of poits i the program data space, executig the

More information

Term Project Report. This component works to detect gesture from the patient as a sign of emergency message and send it to the emergency manager.

Term Project Report. This component works to detect gesture from the patient as a sign of emergency message and send it to the emergency manager. CS2310 Fial Project Loghao Li Term Project Report Itroductio I this project, I worked o expadig exercise 4. What I focused o is makig the real gesture recogizig sesor ad desig proper gestures ad recogizig

More information

Chapter 1. Introduction to Computers and C++ Programming. Copyright 2015 Pearson Education, Ltd.. All rights reserved.

Chapter 1. Introduction to Computers and C++ Programming. Copyright 2015 Pearson Education, Ltd.. All rights reserved. Chapter 1 Itroductio to Computers ad C++ Programmig Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 1.1 Computer Systems 1.2 Programmig ad Problem Solvig 1.3 Itroductio to C++ 1.4 Testig

More information

Task scenarios Outline. Scenarios in Knowledge Extraction. Proposed Framework for Scenario to Design Diagram Transformation

Task scenarios Outline. Scenarios in Knowledge Extraction. Proposed Framework for Scenario to Design Diagram Transformation 6-0-0 Kowledge Trasformatio from Task Scearios to View-based Desig Diagrams Nima Dezhkam Kamra Sartipi {dezhka, sartipi}@mcmaster.ca Departmet of Computig ad Software McMaster Uiversity CANADA SEKE 08

More information

BAAN IVc/BaanERP. Conversion Guide Oracle7 to Oracle8

BAAN IVc/BaanERP. Conversion Guide Oracle7 to Oracle8 BAAN IVc/BaaERP A publicatio of: Baa Developmet B.V. P.O.Box 143 3770 AC Bareveld The Netherlads Prited i the Netherlads Baa Developmet B.V. 1999. All rights reserved. The iformatio i this documet is subject

More information

GE FUNDAMENTALS OF COMPUTING AND PROGRAMMING UNIT III

GE FUNDAMENTALS OF COMPUTING AND PROGRAMMING UNIT III GE2112 - FUNDAMENTALS OF COMPUTING AND PROGRAMMING UNIT III PROBLEM SOLVING AND OFFICE APPLICATION SOFTWARE Plaig the Computer Program Purpose Algorithm Flow Charts Pseudocode -Applicatio Software Packages-

More information

COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Interface. Chapter 4. The Processor. Part A Datapath Design

COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Interface. Chapter 4. The Processor. Part A Datapath Design COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Iterface 5 th Editio Chapter The Processor Part A path Desig Itroductio CPU performace factors Istructio cout Determied by ISA ad compiler. CPI ad

More information

1 Enterprise Modeler

1 Enterprise Modeler 1 Eterprise Modeler Itroductio I BaaERP, a Busiess Cotrol Model ad a Eterprise Structure Model for multi-site cofiguratios are itroduced. Eterprise Structure Model Busiess Cotrol Models Busiess Fuctio

More information

IMP: Superposer Integrated Morphometrics Package Superposition Tool

IMP: Superposer Integrated Morphometrics Package Superposition Tool IMP: Superposer Itegrated Morphometrics Package Superpositio Tool Programmig by: David Lieber ( 03) Caisius College 200 Mai St. Buffalo, NY 4208 Cocept by: H. David Sheets, Dept. of Physics, Caisius College

More information

HAFOD MAKING A COMPLAINT [NEW]_Layout 1 21/03/ :06 Page 1 MAKING A COMPLAINT

HAFOD MAKING A COMPLAINT [NEW]_Layout 1 21/03/ :06 Page 1 MAKING A COMPLAINT HAFOD MAKING A COMPLAINT [NEW]_Layout 1 21/03/2013 17:06 Page 1 MAKING A COMPLAINT FEBRUARY 2010 HAFOD MAKING A COMPLAINT [NEW]_Layout 1 21/03/2013 17:06 Page 2 ABOUT THIS BOOKLET This booklet outlies

More information

Computers and Scientific Thinking

Computers and Scientific Thinking Computers ad Scietific Thikig David Reed, Creighto Uiversity Chapter 15 JavaScript Strigs 1 Strigs as Objects so far, your iteractive Web pages have maipulated strigs i simple ways use text box to iput

More information

Web OS Switch Software

Web OS Switch Software Web OS Switch Software BBI Quick Guide Nortel Networks Part Number: 213164, Revisio A, July 2000 50 Great Oaks Boulevard Sa Jose, Califoria 95119 408-360-5500 Mai 408-360-5501 Fax www.orteletworks.com

More information

Global Support Guide. Verizon WIreless. For the BlackBerry 8830 World Edition Smartphone and the Motorola Z6c

Global Support Guide. Verizon WIreless. For the BlackBerry 8830 World Edition Smartphone and the Motorola Z6c Verizo WIreless Global Support Guide For the BlackBerry 8830 World Editio Smartphoe ad the Motorola Z6c For complete iformatio o global services, please refer to verizowireless.com/vzglobal. Whether i

More information

Τεχνολογία Λογισμικού

Τεχνολογία Λογισμικού ΕΘΝΙΚΟ ΜΕΤΣΟΒΙΟ ΠΟΛΥΤΕΧΝΕΙΟ Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών Τεχνολογία Λογισμικού, 7ο/9ο εξάμηνο 2018-2019 Τεχνολογία Λογισμικού Ν.Παπασπύρου, Αν.Καθ. ΣΗΜΜΥ, ickie@softlab.tua,gr

More information

Lecture Notes 6 Introduction to algorithm analysis CSS 501 Data Structures and Object-Oriented Programming

Lecture Notes 6 Introduction to algorithm analysis CSS 501 Data Structures and Object-Oriented Programming Lecture Notes 6 Itroductio to algorithm aalysis CSS 501 Data Structures ad Object-Orieted Programmig Readig for this lecture: Carrao, Chapter 10 To be covered i this lecture: Itroductio to algorithm aalysis

More information

Data Warehousing. Paper

Data Warehousing. Paper Data Warehousig Paper 28-25 Implemetig a fiacial balace scorecard o top of SAP R/3, usig CFO Visio as iterface. Ida Carapelle & Sophie De Baets, SOLID Parters, Brussels, Belgium (EUROPE) ABSTRACT Fiacial

More information

What are Information Systems?

What are Information Systems? Iformatio Systems Cocepts What are Iformatio Systems? Roma Kotchakov Birkbeck, Uiversity of Lodo Based o Chapter 1 of Beett, McRobb ad Farmer: Object Orieted Systems Aalysis ad Desig Usig UML, (4th Editio),

More information

System and Software Architecture Description (SSAD)

System and Software Architecture Description (SSAD) System ad Software Architecture Descriptio (SSAD) Diabetes Health Platform Team #6 Jasmie Berry (Cliet) Veerav Naidu (Project Maager) Mukai Nog (Architect) Steve South (IV&V) Vijaya Prabhakara (Quality

More information

Python Programming: An Introduction to Computer Science

Python Programming: An Introduction to Computer Science Pytho Programmig: A Itroductio to Computer Sciece Chapter 6 Defiig Fuctios Pytho Programmig, 2/e 1 Objectives To uderstad why programmers divide programs up ito sets of cooperatig fuctios. To be able to

More information

12-5A. Equivalent Fractions and Decimals. 1 Daily Common Core Review. Common Core. Lesson. Lesson Overview. Math Background

12-5A. Equivalent Fractions and Decimals. 1 Daily Common Core Review. Common Core. Lesson. Lesson Overview. Math Background Lesso -A Equivalet Fractios ad Decimals Commo Core Lesso Overview Domai Number ad Operatios Fractios Cluster Uderstad decimal otatio for fractios, ad compare decimal fractios. Stadards.NF. Use decimal

More information

9.1. Sequences and Series. Sequences. What you should learn. Why you should learn it. Definition of Sequence

9.1. Sequences and Series. Sequences. What you should learn. Why you should learn it. Definition of Sequence _9.qxd // : AM Page Chapter 9 Sequeces, Series, ad Probability 9. Sequeces ad Series What you should lear Use sequece otatio to write the terms of sequeces. Use factorial otatio. Use summatio otatio to

More information

One advantage that SONAR has over any other music-sequencing product I ve worked

One advantage that SONAR has over any other music-sequencing product I ve worked *gajedra* D:/Thomso_Learig_Projects/Garrigus_163132/z_productio/z_3B2_3D_files/Garrigus_163132_ch17.3d, 14/11/08/16:26:39, 16:26, page: 647 17 CAL 101 Oe advatage that SONAR has over ay other music-sequecig

More information

WYSE Academic Challenge Sectional Computer Science 2005 SOLUTION SET

WYSE Academic Challenge Sectional Computer Science 2005 SOLUTION SET WYSE Academic Challege Sectioal Computer Sciece 2005 SOLUTION SET 1. Correct aswer: a. Hz = cycle / secod. CPI = 2, therefore, CPI*I = 2 * 28 X 10 8 istructios = 56 X 10 8 cycles. The clock rate is 56

More information

Copyright 2016 Ramez Elmasri and Shamkant B. Navathe

Copyright 2016 Ramez Elmasri and Shamkant B. Navathe Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe CHAPTER 18 Strategies for Query Processig Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe Itroductio DBMS techiques to process a query Scaer idetifies

More information

A SOFTWARE MODEL FOR THE MULTILAYER PERCEPTRON

A SOFTWARE MODEL FOR THE MULTILAYER PERCEPTRON A SOFTWARE MODEL FOR THE MULTILAYER PERCEPTRON Roberto Lopez ad Eugeio Oñate Iteratioal Ceter for Numerical Methods i Egieerig (CIMNE) Edificio C1, Gra Capitá s/, 08034 Barceloa, Spai ABSTRACT I this work

More information

Baan Tools User Management

Baan Tools User Management Baa Tools User Maagemet Module Procedure UP008A US Documetiformatio Documet Documet code : UP008A US Documet group : User Documetatio Documet title : User Maagemet Applicatio/Package : Baa Tools Editio

More information

Code Review Defects. Authors: Mika V. Mäntylä and Casper Lassenius Original version: 4 Sep, 2007 Made available online: 24 April, 2013

Code Review Defects. Authors: Mika V. Mäntylä and Casper Lassenius Original version: 4 Sep, 2007 Made available online: 24 April, 2013 Code Review s Authors: Mika V. Mätylä ad Casper Lasseius Origial versio: 4 Sep, 2007 Made available olie: 24 April, 2013 This documet cotais further details of the code review defects preseted i [1]. of

More information

UNIVERSITY OF MORATUWA

UNIVERSITY OF MORATUWA UNIVERSITY OF MORATUWA FACULTY OF ENGINEERING DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING B.Sc. Egieerig 2014 Itake Semester 2 Examiatio CS2052 COMPUTER ARCHITECTURE Time allowed: 2 Hours Jauary 2016

More information

Ones Assignment Method for Solving Traveling Salesman Problem

Ones Assignment Method for Solving Traveling Salesman Problem Joural of mathematics ad computer sciece 0 (0), 58-65 Oes Assigmet Method for Solvig Travelig Salesma Problem Hadi Basirzadeh Departmet of Mathematics, Shahid Chamra Uiversity, Ahvaz, Ira Article history:

More information

Appendix D. Controller Implementation

Appendix D. Controller Implementation COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Iterface 5 th Editio Appedix D Cotroller Implemetatio Cotroller Implemetatios Combiatioal logic (sigle-cycle); Fiite state machie (multi-cycle, pipelied);

More information

Bayesian approach to reliability modelling for a probability of failure on demand parameter

Bayesian approach to reliability modelling for a probability of failure on demand parameter Bayesia approach to reliability modellig for a probability of failure o demad parameter BÖRCSÖK J., SCHAEFER S. Departmet of Computer Architecture ad System Programmig Uiversity Kassel, Wilhelmshöher Allee

More information

Fire Recognition in Video. Walter Phillips III Mubarak Shah Niels da Vitoria Lobo.

Fire Recognition in Video. Walter Phillips III Mubarak Shah Niels da Vitoria Lobo. Fire Recogitio i Video Walter Phillips III Mubarak Shah Niels da Vitoria Lobo {wrp65547,shah,iels}@cs.ucf.edu Computer Visio Laboratory Departmet of Computer Sciece Uiversity of Cetral Florida Orlado,

More information

Pseudocode ( 1.1) Analysis of Algorithms. Primitive Operations. Pseudocode Details. Running Time ( 1.1) Estimating performance

Pseudocode ( 1.1) Analysis of Algorithms. Primitive Operations. Pseudocode Details. Running Time ( 1.1) Estimating performance Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Pseudocode ( 1.1) High-level descriptio of a algorithm More structured

More information

. Written in factored form it is easy to see that the roots are 2, 2, i,

. Written in factored form it is easy to see that the roots are 2, 2, i, CMPS A Itroductio to Programmig Programmig Assigmet 4 I this assigmet you will write a java program that determies the real roots of a polyomial that lie withi a specified rage. Recall that the roots (or

More information

Panel for Adobe Premiere Pro CC Partner Solution

Panel for Adobe Premiere Pro CC Partner Solution Pael for Adobe Premiere Pro CC Itegratio for more efficiecy The makes video editig simple, fast ad coveiet. The itegrated pael gives users immediate access to all medialoopster features iside Adobe Premiere

More information

An Improved Shuffled Frog-Leaping Algorithm for Knapsack Problem

An Improved Shuffled Frog-Leaping Algorithm for Knapsack Problem A Improved Shuffled Frog-Leapig Algorithm for Kapsack Problem Zhoufag Li, Ya Zhou, ad Peg Cheg School of Iformatio Sciece ad Egieerig Hea Uiversity of Techology ZhegZhou, Chia lzhf1978@126.com Abstract.

More information

Designing a learning system

Designing a learning system CS 75 Itro to Machie Learig Lecture Desigig a learig system Milos Hauskrecht milos@pitt.edu 539 Seott Square, -5 people.cs.pitt.edu/~milos/courses/cs75/ Admiistrivia No homework assigmet this week Please

More information

In this chapter, you learn the concepts and terminology of databases and

In this chapter, you learn the concepts and terminology of databases and A Itroductio to Database Developmet I this chapter, you lear the cocepts ad termiology of databases ad how to desig the tables that your forms ad reports will use. Fially, you build the actual tables used

More information

Morgan Kaufmann Publishers 26 February, COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Interface. Chapter 5.

Morgan Kaufmann Publishers 26 February, COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Interface. Chapter 5. Morga Kaufma Publishers 26 February, 208 COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Iterface 5 th Editio Chapter 5 Virtual Memory Review: The Memory Hierarchy Take advatage of the priciple

More information

Review: The ACID properties

Review: The ACID properties Recovery Review: The ACID properties A tomicity: All actios i the Xactio happe, or oe happe. C osistecy: If each Xactio is cosistet, ad the DB starts cosistet, it eds up cosistet. I solatio: Executio of

More information

Informed Search. Russell and Norvig Chap. 3

Informed Search. Russell and Norvig Chap. 3 Iformed Search Russell ad Norvig Chap. 3 Not all search directios are equally promisig Outlie Iformed: use problem-specific kowledge Add a sese of directio to search: work toward the goal Heuristic fuctios:

More information

Software development of components for complex signal analysis on the example of adaptive recursive estimation methods.

Software development of components for complex signal analysis on the example of adaptive recursive estimation methods. Software developmet of compoets for complex sigal aalysis o the example of adaptive recursive estimatio methods. SIMON BOYMANN, RALPH MASCHOTTA, SILKE LEHMANN, DUNJA STEUER Istitute of Biomedical Egieerig

More information

The number n of subintervals times the length h of subintervals gives length of interval (b-a).

The number n of subintervals times the length h of subintervals gives length of interval (b-a). Simulator with MadMath Kit: Riema Sums (Teacher s pages) I your kit: 1. GeoGebra file: Ready-to-use projector sized simulator: RiemaSumMM.ggb 2. RiemaSumMM.pdf (this file) ad RiemaSumMMEd.pdf (educator's

More information

Computer Science Foundation Exam. August 12, Computer Science. Section 1A. No Calculators! KEY. Solutions and Grading Criteria.

Computer Science Foundation Exam. August 12, Computer Science. Section 1A. No Calculators! KEY. Solutions and Grading Criteria. Computer Sciece Foudatio Exam August, 005 Computer Sciece Sectio A No Calculators! Name: SSN: KEY Solutios ad Gradig Criteria Score: 50 I this sectio of the exam, there are four (4) problems. You must

More information

G r a d e. 5 M a t h e M a t i c s. shape and space

G r a d e. 5 M a t h e M a t i c s. shape and space G r a d e 5 M a t h e M a t i c s shape ad space Grade 5: Shape ad Space (Measuremet) (5.SS.1) Edurig Uderstadigs: there is o direct relatioship betwee perimeter ad area. Geeral Outcome: Use direct or

More information

Data Structures and Algorithms. Analysis of Algorithms

Data Structures and Algorithms. Analysis of Algorithms Data Structures ad Algorithms Aalysis of Algorithms Outlie Ruig time Pseudo-code Big-oh otatio Big-theta otatio Big-omega otatio Asymptotic algorithm aalysis Aalysis of Algorithms Iput Algorithm Output

More information

Guide to Applying Online

Guide to Applying Online Guide to Applyig Olie Itroductio Respodig to requests for additioal iformatio Reportig: submittig your moitorig or ed of grat Pledges: submittig your Itroductio This guide is to help charities submit their

More information

Customer Portal Quick Reference User Guide

Customer Portal Quick Reference User Guide Customer Portal Quick Referece User Guide Overview This user guide is iteded for FM Approvals customers usig the Approval Iformatio Maagemet (AIM) customer portal to track their active projects. AIM is

More information

Copyright 2016 Ramez Elmasri and Shamkant B. Navathe

Copyright 2016 Ramez Elmasri and Shamkant B. Navathe Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe CHAPTER 26 Ehaced Data Models: Itroductio to Active, Temporal, Spatial, Multimedia, ad Deductive Databases Copyright 2016 Ramez Elmasri ad Shamkat B.

More information

Sectio 4, a prototype project of settig field weight with AHP method is developed ad the experimetal results are aalyzed. Fially, we coclude our work

Sectio 4, a prototype project of settig field weight with AHP method is developed ad the experimetal results are aalyzed. Fially, we coclude our work 200 2d Iteratioal Coferece o Iformatio ad Multimedia Techology (ICIMT 200) IPCSIT vol. 42 (202) (202) IACSIT Press, Sigapore DOI: 0.7763/IPCSIT.202.V42.0 Idex Weight Decisio Based o AHP for Iformatio Retrieval

More information

Analysis Metrics. Intro to Algorithm Analysis. Slides. 12. Alg Analysis. 12. Alg Analysis

Analysis Metrics. Intro to Algorithm Analysis. Slides. 12. Alg Analysis. 12. Alg Analysis Itro to Algorithm Aalysis Aalysis Metrics Slides. Table of Cotets. Aalysis Metrics 3. Exact Aalysis Rules 4. Simple Summatio 5. Summatio Formulas 6. Order of Magitude 7. Big-O otatio 8. Big-O Theorems

More information

Chapter 4 The Datapath

Chapter 4 The Datapath The Ageda Chapter 4 The Datapath Based o slides McGraw-Hill Additioal material 24/25/26 Lewis/Marti Additioal material 28 Roth Additioal material 2 Taylor Additioal material 2 Farmer Tae the elemets that

More information

Τεχνολογία Λογισμικού

Τεχνολογία Λογισμικού ΕΘΝΙΚΟ ΜΕΤΣΟΒΙΟ ΠΟΛΥΤΕΧΝΕΙΟ Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών Τεχνολογία Λογισμικού, 7ο/9ο εξάμηνο 2018-2019 Τεχνολογία Λογισμικού Ν.Παπασπύρου, Αν.Καθ. ΣΗΜΜΥ, ickie@softlab.tua,gr

More information

CMPT 125 Assignment 2 Solutions

CMPT 125 Assignment 2 Solutions CMPT 25 Assigmet 2 Solutios Questio (20 marks total) a) Let s cosider a iteger array of size 0. (0 marks, each part is 2 marks) it a[0]; I. How would you assig a poiter, called pa, to store the address

More information

Copyright 2016 Ramez Elmasri and Shamkant B. Navathe

Copyright 2016 Ramez Elmasri and Shamkant B. Navathe Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe CHAPTER 19 Query Optimizatio Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe Itroductio Query optimizatio Coducted by a query optimizer i a DBMS Goal:

More information

A Taste of Maya. Character Setup

A Taste of Maya. Character Setup This tutorial goes through the steps to add aimatio cotrols to a previously modeled character. The character i the scee below is wearig clothes made with Cloth ad the sceery has bee created with Pait Effects.

More information

Lecture 1: Introduction and Fundamental Concepts 1

Lecture 1: Introduction and Fundamental Concepts 1 Uderstadig Performace Lecture : Fudametal Cocepts ad Performace Aalysis CENG 332 Algorithm Determies umber of operatios executed Programmig laguage, compiler, architecture Determie umber of machie istructios

More information

The VSS CCD photometry spreadsheet

The VSS CCD photometry spreadsheet The VSS CCD photometry spreadsheet Itroductio This Excel spreadsheet has bee developed ad tested by the BAA VSS for aalysig results files produced by the multi-image CCD photometry procedure i AIP4Wi v2.

More information

CMSC Computer Architecture Lecture 12: Virtual Memory. Prof. Yanjing Li University of Chicago

CMSC Computer Architecture Lecture 12: Virtual Memory. Prof. Yanjing Li University of Chicago CMSC 22200 Computer Architecture Lecture 12: Virtual Memory Prof. Yajig Li Uiversity of Chicago A System with Physical Memory Oly Examples: most Cray machies early PCs Memory early all embedded systems

More information

Running Time. Analysis of Algorithms. Experimental Studies. Limitations of Experiments

Running Time. Analysis of Algorithms. Experimental Studies. Limitations of Experiments Ruig Time Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Most algorithms trasform iput objects ito output objects. The

More information

MOTIF XF Extension Owner s Manual

MOTIF XF Extension Owner s Manual MOTIF XF Extesio Ower s Maual Table of Cotets About MOTIF XF Extesio...2 What Extesio ca do...2 Auto settig of Audio Driver... 2 Auto settigs of Remote Device... 2 Project templates with Iput/ Output Bus

More information

Running Time ( 3.1) Analysis of Algorithms. Experimental Studies. Limitations of Experiments

Running Time ( 3.1) Analysis of Algorithms. Experimental Studies. Limitations of Experiments Ruig Time ( 3.1) Aalysis of Algorithms Iput Algorithm Output A algorithm is a step- by- step procedure for solvig a problem i a fiite amout of time. Most algorithms trasform iput objects ito output objects.

More information

Analysis of Algorithms

Analysis of Algorithms Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Ruig Time Most algorithms trasform iput objects ito output objects. The

More information

Getting Started. Getting Started - 1

Getting Started. Getting Started - 1 Gettig Started Gettig Started - 1 Issue 1 Overview of Gettig Started Overview of Gettig Started This sectio explais the basic operatios of the AUDIX system. It describes how to: Log i ad log out of the

More information

Using the Keyboard. Using the Wireless Keyboard. > Using the Keyboard

Using the Keyboard. Using the Wireless Keyboard. > Using the Keyboard 1 A wireless keyboard is supplied with your computer. The wireless keyboard uses a stadard key arragemet with additioal keys that perform specific fuctios. Usig the Wireless Keyboard Two AA alkalie batteries

More information

Abstract. Chapter 4 Computation. Overview 8/13/18. Bjarne Stroustrup Note:

Abstract. Chapter 4 Computation. Overview 8/13/18. Bjarne Stroustrup   Note: Chapter 4 Computatio Bjare Stroustrup www.stroustrup.com/programmig Abstract Today, I ll preset the basics of computatio. I particular, we ll discuss expressios, how to iterate over a series of values

More information

Security of Bluetooth: An overview of Bluetooth Security

Security of Bluetooth: An overview of Bluetooth Security Versio 2 Security of Bluetooth: A overview of Bluetooth Security Marjaaa Träskbäck Departmet of Electrical ad Commuicatios Egieerig mtraskba@cc.hut.fi 52655H ABSTRACT The purpose of this paper is to give

More information

TruVu 360 User Community. SpectroCare. Enterprise Fluid Intelligence for Predictive Maintenance. TruVu 360 Product Information

TruVu 360 User Community. SpectroCare. Enterprise Fluid Intelligence for Predictive Maintenance. TruVu 360 Product Information TruVu 360 User Commuity Cotiuous educatio is importat for a successful o-site lubricat program. With ever growig articles, videos, ad structured learig modules, TruVu 360 user commuity is a digital commuity

More information

Mindmapping: A General Purpose (Test) Planning Tool

Mindmapping: A General Purpose (Test) Planning Tool W8 Test Strategy, Plaig, Metrics Wedesday, May 2d, 2018 1:45 PM Midmappig: A Geeral Purpose (Test) Plaig Tool Preseted by: Bob Gale Zeergy Techologies Brought to you by: 350 Corporate Way, Suite 400, Orage

More information

Arithmetic Sequences

Arithmetic Sequences . Arithmetic Sequeces COMMON CORE Learig Stadards HSF-IF.A. HSF-BF.A.1a HSF-BF.A. HSF-LE.A. Essetial Questio How ca you use a arithmetic sequece to describe a patter? A arithmetic sequece is a ordered

More information

Chapter 9. Pointers and Dynamic Arrays. Copyright 2015 Pearson Education, Ltd.. All rights reserved.

Chapter 9. Pointers and Dynamic Arrays. Copyright 2015 Pearson Education, Ltd.. All rights reserved. Chapter 9 Poiters ad Dyamic Arrays Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 9.1 Poiters 9.2 Dyamic Arrays Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Slide 9-3

More information

CIS 121 Data Structures and Algorithms with Java Spring Stacks and Queues Monday, February 12 / Tuesday, February 13

CIS 121 Data Structures and Algorithms with Java Spring Stacks and Queues Monday, February 12 / Tuesday, February 13 CIS Data Structures ad Algorithms with Java Sprig 08 Stacks ad Queues Moday, February / Tuesday, February Learig Goals Durig this lab, you will: Review stacks ad queues. Lear amortized ruig time aalysis

More information

Designing a learning system

Designing a learning system CS 75 Machie Learig Lecture Desigig a learig system Milos Hauskrecht milos@cs.pitt.edu 539 Seott Square, x-5 people.cs.pitt.edu/~milos/courses/cs75/ Admiistrivia No homework assigmet this week Please try

More information

1.2 Binomial Coefficients and Subsets

1.2 Binomial Coefficients and Subsets 1.2. BINOMIAL COEFFICIENTS AND SUBSETS 13 1.2 Biomial Coefficiets ad Subsets 1.2-1 The loop below is part of a program to determie the umber of triagles formed by poits i the plae. for i =1 to for j =

More information

CS 111 Green: Program Design I Lecture 27: Speed (cont.); parting thoughts

CS 111 Green: Program Design I Lecture 27: Speed (cont.); parting thoughts CS 111 Gree: Program Desig I Lecture 27: Speed (cot.); partig thoughts By Nascarkig - Ow work, CC BY-SA 4.0, https://commos.wikimedia.org/w/idex.php?curid=38671041 Robert H. Sloa (CS) & Rachel Poretsky

More information

2016 LEARNING SYSTEM FOR CSCP CERTIFICATION EXAM PREPARATION. learncscp.com

2016 LEARNING SYSTEM FOR CSCP CERTIFICATION EXAM PREPARATION. learncscp.com 2016 LEARNING SYSTEM FOR CSCP CERTIFICATION EXAM PREPARATION APICS CSCP Learig System users cosistetly surpass the average CSCP exam pass rate. learcscp.com 2016_APICS_A4_Brochure_parter.idd 1 WHY SEEK

More information

User Interface Evaluation

User Interface Evaluation User Interface Evaluation Heuristic Evaluation Lecture #17 Agenda Evaluation through Expert Analysis Cognitive walkthrough Heuristic evaluation Model-based evaluation Cognitive dimension of notations 2

More information

BEA WebLogic Process Integrator

BEA WebLogic Process Integrator BEA WebLogic Process Itegrator A Compoet of BEA WebLogic Itegratio BEA WebLogic Process Itegrator Studio Olie Help BEA WebLogic Process Itegrator Release 2.0 Documet Editio 2.0 July 2001 Copyright Copyright

More information

Session Initiated Protocol (SIP) and Message-based Load Balancing (MBLB)

Session Initiated Protocol (SIP) and Message-based Load Balancing (MBLB) F5 White Paper Sessio Iitiated Protocol (SIP) ad Message-based Load Balacig (MBLB) The ability to provide ew ad creative methods of commuicatios has esured a SIP presece i almost every orgaizatio. The

More information

Octahedral Graph Scaling

Octahedral Graph Scaling Octahedral Graph Scalig Peter Russell Jauary 1, 2015 Abstract There is presetly o strog iterpretatio for the otio of -vertex graph scalig. This paper presets a ew defiitio for the term i the cotext of

More information

Descriptive Statistics Summary Lists

Descriptive Statistics Summary Lists Chapter 209 Descriptive Statistics Summary Lists Itroductio This procedure is used to summarize cotiuous data. Large volumes of such data may be easily summarized i statistical lists of meas, couts, stadard

More information

Generation of a New Complexity Dimension Scheme for Complexity Measure of Procedural Program

Generation of a New Complexity Dimension Scheme for Complexity Measure of Procedural Program Geeratio of a New Complexity Dimesio Scheme for Complexity Measure of Procedural Program Nikhar Tak, Dr. Navee Hemrajai Departmet of Computer Sciece & Egg, Suresh Gya Vihar Uiversity,Jaipur Abstract -

More information

Workflow model GM AR. Gumpy. Dynagump. At a very high level, this is what gump does. We ll be looking at each of the items described here seperately.

Workflow model GM AR. Gumpy. Dynagump. At a very high level, this is what gump does. We ll be looking at each of the items described here seperately. Workflow model GM AR Gumpy RM Dyagump At a very high level, this is what gump does. We ll be lookig at each of the items described here seperately. User edits project descriptor ad commits s maitai their

More information

Outline. Research Definition. Motivation. Foundation of Reverse Engineering. Dynamic Analysis and Design Pattern Detection in Java Programs

Outline. Research Definition. Motivation. Foundation of Reverse Engineering. Dynamic Analysis and Design Pattern Detection in Java Programs Dyamic Aalysis ad Desig Patter Detectio i Java Programs Outlie Lei Hu Kamra Sartipi {hul4, sartipi}@mcmasterca Departmet of Computig ad Software McMaster Uiversity Caada Motivatio Research Problem Defiitio

More information

Innovative. Pharma-Tech Process Services is a high-energy, schedule-driven, technical service provider. Our innovative consulting services cover

Innovative. Pharma-Tech Process Services is a high-energy, schedule-driven, technical service provider. Our innovative consulting services cover Iovative S o l u t i o s Pharma-Tech Process Services is a high-eergy, schedule-drive, techical service provider. Our iovative cosultig services cover Commissioig, Validatio ad Costructio Period Services

More information

Bezier curves. Figure 2 shows cubic Bezier curves for various control points. In a Bezier curve, only

Bezier curves. Figure 2 shows cubic Bezier curves for various control points. In a Bezier curve, only Edited: Yeh-Liag Hsu (998--; recommeded: Yeh-Liag Hsu (--9; last updated: Yeh-Liag Hsu (9--7. Note: This is the course material for ME55 Geometric modelig ad computer graphics, Yua Ze Uiversity. art of

More information

Operating System Concepts. Operating System Concepts

Operating System Concepts. Operating System Concepts Chapter 4: Mass-Storage Systems Logical Disk Structure Logical Disk Structure Disk Schedulig Disk Maagemet RAID Structure Disk drives are addressed as large -dimesioal arrays of logical blocks, where the

More information