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Maryland - OTAL - 2001
Searching for Airline Tickets: A Comparison of Tabular and Graphical PresentationsTsz-Chiu Au Kelvin Kam Wing Chu Yu Deng Xue WuExperimentIndependent variable: style of presentationstabular display graphical display Time Error rates Subje
Maryland - OTAL - 2001
The Effectiveness of Online Help Systems: Text Only, Animated Images Only, and Integrated InteractiveAuthorsWendy Adams- wadams@telocity.com Jiraphan Brown- djlb@erols.com Dorothy Rapeepun- drapeep@wam.umd.edu Wayne Williams- wcwill@wam.umd.eduAb
Maryland - OTAL - 2001
Effectiveness of Online Help Systems: Text Only, Animated Images Only, and Integrated InteractiveWendy Adams Jiraphan Brown Dorothy Rapeepun Wayne WilliamsHypothesis Null Hypothesis:The mean of the three treatments will not differ and therefore
Maryland - OTAL - 2001
"In Web We Trust": Establishing Strategic Trust Among Online CustomersIrina Ceaparu Dina Demner Edward Hung Haixia ZhaoDepartment of Computer Science University of Maryland College Park, MD 20742 USA May 2001Theory and Background Goal: establ
Maryland - OTAL - 2001
The "Degree Navigator" Nightmare: Taming an Overly Graphical User InterfaceAuthorsSemion Bezrukov - deltree@rocketmail.com Alec Browne - browne@wam.umd.edu Kanta Jiwnani - kanta@cs.umd.edu Donna Malayeri - donna@wam.umd.eduAbstractThis experimen
Maryland - OTAL - 2001
The "Degree Navigator" Nightmare: Taming an Overly Graphical User InterfaceAuthorsSemion Bezrukov - deltree@rocketmail.com Alec Browne - browne@wam.umd.edu Kanta Jiwnani - kanta@cs.umd.edu Donna Malayeri - donna@wam.umd.eduAbstractThis experimen
Maryland - OTAL - 2001
The Degree Navigator Nightmare: Taming An Overly Graphical InterfaceSemion Bezrukov Alec Browne Kanta Jiwnani Donna MalayeriWhy this project?Redesigning the Interface2 Major Flaws Use of Inappropriate Metaphor Metaphor does not meet the crit
Maryland - OTAL - 2001
Cross-Language Information Retrieval: Layout Strategies for Gloss TranslationEiman M. Elnahrawy Nagia M. Ghanem Moustafa A. YoussefGoalIn University of Maryland TranslingualThe three most common translations for each word are displayed Using thi
Maryland - OTAL - 2001
The Impact of Window Desktop Design on User PerformanceWoei-Jyh Lee Ser Nam Lim Tzu-Ting Chen Yu-Lin Wen University of Maryland Dept. of Computer Science College Park, MD 20742 May 7, 2001Advisor: Dr. Ben ShneidermanExperiments Independent Vari
Maryland - OTAL - 2001
Survey and Questionnaire Please circle the choice that answers the question. NA = Not Applicable BackgroundID:What level do you consider yourself in terms of computer experience? No experience Beginner IntermediateAdvanced Is English your first l
Maryland - OTAL - 2001
CMSC838S-0101 Project Proposal February 14, 2001Title: 3-Dimensional versus 2-Dimensional Window Environment Member:Tzu-Ting Chen jacktar@cs.umd.edu Woei-Jyh Lee adamlee@cs.umd.edu Ser Nam Lim sernam@cs.umd.edu Yu-Lin Wen ylwen@cs.umd.eduDes
Maryland - OTAL - 2001
Experiment OutlineObjectiveThe objective includes understanding the following issues: 1. 2. 3. Compare the effect of spatial arrangement on both users retention and ease of navigation. Compare the amount of users retention for both 2D and 3D window
Maryland - OTAL - 2001
CMSC434/838S-0101 : March 7, 2001Title: The Impact of the Window Desktop Dimension on the User Performance Member:Tzu-Ting Chen jacktar@cs.umd.edu Woei-Jyh Lee adamlee@cs.umd.edu Ser Nam Lim sernam@cs.umd.edu Yu-Lin Wen ylwen@cs.umd.eduRefer
Maryland - OTAL - 2001
CMSC434/838S-0101 : March 28, 2001Title: The Impact of the Window Desktop Dimension on the User Performance Pilot Results1. Two subjects performed the pilot tests on March 22. 2. One pilot subject firstly tested the 2D environment and then the 3D
Maryland - OTAL - 2001
Background surveyThank you for participating in our experiment. Before we begin the experiment, please take a minute to fill out this background survey so we can get a better idea of your computer experiment. Name : Gender : Male Age : 18 ~ 25 Ma
Maryland - OTAL - 2001
The Impact of the Window Desktop Dimension on the User PerformanceThank you very much for participating in our experiment. The agenda listed below entails what you will be doing in the experiment. We will be guiding you through the agenda.Experime
Maryland - OTAL - 2001
Subject Satisfaction QuestionaireThank you for participating in our experiment. Now that you have completed the test, please take a moment to fill out this comparison survey so that we may get a better idea of your impression of both desktop interf
Maryland - OTAL - 2001
Subject Satisfaction QuestionaireThank you for participating in our experiment. Now that you have completed the test, please take a moment to fill out this comparison survey so that we may get a better idea of your impression of both desktop interf
Maryland - OTAL - 2001
User Retention Test(1) Which city(s) did the Internet folder provide weather information? ( ( ( ( ( ( ( ( ( ( ) Atlanta, GA ) Baltimore, MD ) Boston, MA ) Chicago, IL ) Dallas, TX ) Denver, CO ) Houston, TX ) Las Vegas, NV ) Los Angeles, CA ) Miami,
Maryland - OTAL - 2001
User Retention Test(1) Which city(s) did the Internet room provide weather information? ( ( ( ( ( ( ( ( ( ( ) Atlanta, GA ) Baltimore, MD ) Boston, MA ) Chicago, IL ) Dalles, TX ) Denver, CO ) Houston, TX ) Las Vegas, NV ) Los Angeles, CA ) Miami, F
Maryland - OTAL - 2001
The Impact of the Window Desktop Dimension on the User Performance Task ListTask Please find and open the Microsoft Excel application. Please find and open the CD Player application. Please find and open the Solitaire, a windows game. Please find th
Maryland - OTAL - 2001
2D 1 2 3 4 5 6 7 8 9 10 11 12 3D 13 14 15 16 17 18 19 20 21 22 23 24Gender Male Male Male Male Male Male Male Male Male Male Male Male Male Female Male Male Male Male Female Male Male Male Male MaleAge 31 ~ 40 26 ~ 30 26 ~ 30 26 ~ 30 18 ~ 25 18 ~
Maryland - OTAL - 2001
Experiment Consent Agreement1. I have freely volunteered to participate in this experiment. 2. I have been informed in advance as to what my task(s) would be and what procedures would be followed. 3. I have been given the opportunity to ask question
Maryland - OTAL - 2001
Human Subjects Application Department of Computer Science, University of Maryland CMSC434 Experimental Study Permission Form Title of Project: TO HELP OR NOT TO HELP Student Team Members: Wendy Adams wadams@telocity.com Jiraphan Brown djlb@erols.com
Maryland - OTAL - 2001
Sign-up SheetName Available E-mail Phone Number Days
Maryland - OTAL - 2001
0) Log in to the program in the lower right corner. Have the tester show you where the log in box is.1) Locate the photo of the University of Maryland College ParksPresident Dan Mote in PhotoFinders database.2) Annotate the picture of Testudo in
Maryland - OTAL - 2001
S#PF#START experiment timeT1 timeT2 timeT3 timeT4 timeT5 timeSTOP experiment timeRead/use help at allNOTES# = Subject# (Please enter number) P# = PhotoFinder#: 1 = Text Only, 2 = Animated Only, 3 = Integrated Interactive (Please
Maryland - OTAL - 2001
Navigation Bar Experiment SurveySubject ID: _ Part 1: Background and experience Please circle your answer for each of the following questions. 1. Age: 2. Major: 18-21 22-25 26-29 30 +_ Freshman Sophomore 1-3 years Junior 4-6 years < 7 hours Senior
Maryland - OTAL - 2001
Navigation Bar Experiment DebriefingThank you for your participation in the Navigation Bar Experiment. As you saw, we are trying to compare three different styles of navigation bars. The Simple style would only let you return to the home page. The S
Maryland - OTAL - 2001
Experimenter InstructionsPreliminary Setup Instructions 1. Create a directory for all of the experiment files. 2. Put the VB program quiz1.exe into the experiment directory. 3. Create 5 subdirectories in the experiment directory: treatment1, treatme
Maryland - OTAL - 2001
User Satisfaction Questionnaire 1. Have you used the Degree Navigator application, available on Testudo, before? Y N If so, how many times have you used it? _ 2. Overall reactions to the system: terrible 12 3 4 4 4 5 5 5 6 6 6 7 7 7 wonderful 8 9 sat
Maryland - OTAL - 2001
Retention Data 1 2 3 4 5 6 7 8 Q1 4.63 8.10 34.72 5.77 8.10 26.62 5.79 9.26 Q2 6.94 10.42 6.94 6.94 9.26 5.78 6.94 13.89 Q3 31.25 9.26 42.82 38.19 9.26 47.45 31.25 20.83 Q4 13.89 10.42 20.83 16.02 6.94 26.62 8.10 23.15 Q5 10.42 9.25 16.20 11.57 17.36
Maryland - CSCAMM - 03
University of Maryland, College ParkA Program on Nonequilibrium Interface Dynamics: Theory and Simulation from Atomistic to Continuum Scales October 13 - 31, 2003Organizers: T. Einstein, B. Li, J-G. Liu, E. Tadmor, J. Warren, J. Weeks & E. William
Maryland - CSCAMM - 03
Ellen D. WilliamsFluctuations and Nanoscale Structures*Crystalline Nanostructures Not molecules, not macroscopic solids A large fraction of the material is on the surface Edge boundary fluctuations determine structural evolution Deterministic vs.
Maryland - CSCAMM - 03
October 22-24, 2003 CSCAMM Workshop: Fundamental Physical Issues in Nonequilibrium Interface DynamiDrift-Induced Pattern Formation on Si(001) Vicinal SurfacesEffect of Alternating AnisotropyMakio UwahaDept. of Phys., Nagoya Universityuwaha@phys
Maryland - CSCAMM - 03
Coarsening dynamics during the growth of one-dimensional interfacesPaolo Politi IFAC - Istituto di Fisica Applicata N. Carrara CNR - Consiglio Nazionale delle Ricerche (Florence, Italy) Chaouqi Misbah Laboratoire de Spectromtrie Physique, CNRS e (Gr
Maryland - CSCAMM - 03
PREDICTIVE MODELING of EPITAXIAL THIN FILM GROWTH: ATOMISTIC and CONTINUUM APPROACHES CSCAMM 10/03Theory & Modeling: Maozhi Li, Kyle Caspersen1, Maria Bartelt2, Da-Jiang Liu, Jim Evans Experiment: Conrad Stoldt3, Tony Layson4, Vincent Fournee5, Cynt
Maryland - ENEE - 739
Schedule of Lectures for ENEE 739Q and CMSC 858CDate 09/02Topic Overview PR as a decision theoretic problem Bayes decision theory09/04Bayes classifiers for Gaussian case Minimax and Neyman-Pearson Error probabilities, integrals and bounds Max
Maryland - ENEE - 739
Face Recognition: A Literature SurveyW. ZHAOSarnoff CorporationR. CHELLAPPAUniversity of MarylandP. J. PHILLIPSNational Institute of Standards and TechnologyAND A. ROSENFELDUniversity of MarylandAs one of the most successful applications
Maryland - ENEE - 739
c, , 1{43 () Kluwer Academic Publishers, Boston. Manufactured in The Netherlands.A Tutorial on Support Vector Machines for Pattern RecognitionBell Laboratories, Lucent TechnologiesCHRISTOPHER J.C. BURGESburges@lucent.comEditor: Usama Fayya
Maryland - ENEE - 739
1Rayleighs QuotientWe want to prove that if A is a real symmetric n n matrix (more generally complex Hermitian matrix, in which case we need to put proper complex conjugates for scalar product) then the Rayleighs quotient Pn Pn (y, Ay) k=1 Aik y
Maryland - ENEE - 739
IEEE TRANSACTIONS COMMUNICATIOPI TECHNOLOGY ONVOL.COM-15,NO.1FEBRUARY1967The Diver,gence and Bhattacharyya Distance Measures in Signal Selectionfor-we shall only be ableto find weaker relations between distance measures and the probabi
Maryland - ENEE - 739
CORRESPONDENCE485polynomial of degree 2n. Once theserootsarefoundfor a specific value of z the density is determined by the addition of -1 and 1. The simplefunctions of the roots that are within techniqueshownrequires no graphicalsolutionandthere
Maryland - ENEE - 698
Logistic RegressionLaura HeathWhat is linear regression?Begin with a series of measurements Often have a training set (supervised learning) Measurements may be noisy Use them to estimate a linear, stochastic function to model the realworl
Maryland - ENEE - 698
Independent Component Analysis (ICA) and Factor Analysis (FA)Amit AgrawalSep 10, 2003ENEE 698A Seminar1Outline Motivation for ICA Definitions, restrictions and ambiguities Comparison of ICA and FA with PCA Estimation Techniques Applica
Maryland - ENEE - 698
Smoothing SplinesNaresh P. CuntoorIntroduction Problem: Wantyi=f(xi) + i ; i ~N(0,1) fto estimate the underlying expectation function f from n observations M f : non-linear. Write f ( x ) = hm ( x ) m m =1 linear basis expansions Prop
Maryland - ENEE - 698
Basis Expansions and RegularizationSelection of Smoothing Parameters Non-parametric Logistic Regression Multi-dimensional Splines- Nagaraj PrasanthSmoothing ParametersRegression Splines Degree Number of knots Placement of knotsSmoo
Maryland - ENEE - 698
Reproducing Kernel Hilbert Space (RKHS), Regularization Theory, and Kernel MethodsShaohua (Kevin) Zhou Center for Automation Research Department of Electrical and Computer Engineering University of Maryland, College ParkENEE698A Graduate Seminar
Maryland - ENEE - 698
Wavelet SmoothingYinian Mao ECE, Univ. of MarylandBasis ExpansionsRegression Problem: Y = f (X ) f ( X ) = E(Y | X ) is usually non-linear Basis Expansion: Replace input vector X with transformations hm ( X )Use linear models in the new space h
Maryland - ENEE - 698
Bias-Variance TradeoffPresented by Yang Yu yyu3@glue.umd.eduOutline Generalization Performance of Learning Methods Bias, Variance and Model Complexity The Bias-Variance Decomposition Example: Bias-Variance Tradeoff Summary02/16/092Gener
Maryland - ENEE - 698
ENEE698C: Elements of Statistical LearningModel Selection and AssessmentAravind Sundaresanaravinds@cfar.umd.eduModel Selection and Assessment p.1/18Which is the best algorithm?Occams Razor : Given two classiers that perform equally well on
Maryland - ENEE - 698
Model Assessment and Selection, Akaike Information Criterion (AIC)Elements of Statistical Learning Graduate Seminar (ENEE698A) Presented by Zhanfeng Yue Oct. 7, 2003Talk overviewIntroduction to model selection and assessment Problem formulatio
Maryland - ENEE - 698
Bayesian Information CriterionENEE698A: Elements of Statistical Learning Joshua Broadwater 10/08/2003Motivation Need way to select the appropriate dimension of a statistical model (e.g. polynomial regression, multistep Markov chains, etc.) Si
Maryland - ENEE - 698
VC dimension and Bootstrap methodElements of Statistical Learning Graduate Seminar (ENEE698A) Presented by Xue Mei Oct. 15, 2003VC dimensionWhy we use VC dimension? 1. One difficulty in using estimates of insample error is the need to specify t
Maryland - ENEE - 698
Expectation MaximizationAn Approach to Parameter Estimation Yang Ran ENEE 698aOutline Basic ideas of Expectation Maximization An example: problem of Gaussian mixture General EM algorithm Further readings and conclusion02/16/092EM-Backgro