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UC Berkeley UC Berkeley Electronic Theses and Dissertations
UC Berkeley UC Berkeley Electronic Theses and Dissertations Title Perceptual and Context Aware Interfaces on Mobile Devices Permalink https://escholarship.org/uc/item/7tg54232 Author Wang, Jingtao Publication Date 2010 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California Perceptual and Context Aware Interfaces on Mobile Devices by Jingtao Wang A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Computer Science in the Graduate Division of the University of California, Berkeley Committee in charge: Professor John F. Canny, Chair Professor Maneesh Agrawala Professor Ray R. Larson Spring 2010 Perceptual and Context Aware Interfaces on Mobile Devices Copyright 2010 by Jingtao Wang 1 Abstract Perceptual and Context Aware Interfaces on Mobile Devices by Jingtao Wang Doctor of Philosophy in Computer Science University of California, Berkeley Professor John F. Canny, Chair With an estimated 4.6 billion units in use, mobile phones have already become the most popular computing device in human history. Their portability and communication capabil- ities may revolutionize how people do their daily work and interact with other people in ways PCs have done during the past 30 years. Despite decades of experiences in creating modern WIMP (windows, icons, mouse, pointer) interfaces, our knowledge in building ef- fective mobile interfaces is still limited, especially for emerging interaction modalities that are only available on mobile devices. This dissertation explores how emerging sensors on a mobile phone, such as the built-in camera, the microphone, the touch sensor and the GPS module can be leveraged to make everyday interactions easier and more efficient. -
Topics in Computational Mathematics
Topics in Computational Mathematics Notes for Computational Mathematics (MA1611) Information Technology (AS1054) Dr G Bowtell Contents 1 Curve Sketching 1 1.1 CurveSketching ................................ 1 1.2 IncreasingandDecreasingFunction . .... 1 1.3 StationaryPoints ................................ 2 1.4 ClassificationofStationaryPoints. ...... 3 1.5 PointofInflection-DefinitionandComment . ..... 4 1.6 Asymptotes................................... 5 2 Root Finding 7 2.1 Introduction................................... 7 2.2 Existence of solution of f(x) = 0 ....................... 8 2.3 Iterative method to solve f(x) = 0 byrearrangement . 10 2.4 IterationusingExcel-Method1. ... 11 2.5 Newton’s Method to solve f(x) = 0 ...................... 12 2.6 IterationusingExcel-Method2. ... 14 2.7 SimultaneousEquations- linearand non-linear . ........ 15 2.7.1 Linearsimultaneousequations . 15 2.7.2 MatrixproductandinverseusingExcel . .. 18 2.7.3 Non-linearsimultaneousequations . ... 20 3 Financial Functions in Excel 27 3.1 Introduction................................... 27 3.2 GeometricProgression . 27 3.3 BasicCompoundInterest . 28 3.4 BasicInvestmentProblem. 29 3.5 BasicFinancialWorksheetFunctionsinExcel . ....... 31 3.6 Further Financial Worksheet Functionsin Excel . ........ 34 4 Curvefitting-InterpolationandExtrapolation 39 4.1 Introduction................................... 39 4.2 LinearSpline .................................. 42 4.3 CubicSpline-natural ............................. 45 4.4 LinearLeastSquaresFitting. ... 49 4.4.1 Linear -
Domain-Specific Programming Systems
Lecture 22: Domain-Specific Programming Systems Parallel Computer Architecture and Programming CMU 15-418/15-618, Spring 2020 Slide acknowledgments: Pat Hanrahan, Zach Devito (Stanford University) Jonathan Ragan-Kelley (MIT, Berkeley) Course themes: Designing computer systems that scale (running faster given more resources) Designing computer systems that are efficient (running faster under constraints on resources) Techniques discussed: Exploiting parallelism in applications Exploiting locality in applications Leveraging hardware specialization (earlier lecture) CMU 15-418/618, Spring 2020 Claim: most software uses modern hardware resources inefficiently ▪ Consider a piece of sequential C code - Call the performance of this code our “baseline performance” ▪ Well-written sequential C code: ~ 5-10x faster ▪ Assembly language program: maybe another small constant factor faster ▪ Java, Python, PHP, etc. ?? Credit: Pat Hanrahan CMU 15-418/618, Spring 2020 Code performance: relative to C (single core) GCC -O3 (no manual vector optimizations) 51 40/57/53 47 44/114x 40 = NBody 35 = Mandlebrot = Tree Alloc/Delloc 30 = Power method (compute eigenvalue) 25 20 15 10 5 Slowdown (Compared to C++) Slowdown (Compared no data no 0 data no Java Scala C# Haskell Go Javascript Lua PHP Python 3 Ruby (Mono) (V8) (JRuby) Data from: The Computer Language Benchmarks Game: CMU 15-418/618, http://shootout.alioth.debian.org Spring 2020 Even good C code is inefficient Recall Assignment 1’s Mandelbrot program Consider execution on a high-end laptop: quad-core, Intel Core i7, AVX instructions... Single core, with AVX vector instructions: 5.8x speedup over C implementation Multi-core + hyper-threading + AVX instructions: 21.7x speedup Conclusion: basic C implementation compiled with -O3 leaves a lot of performance on the table CMU 15-418/618, Spring 2020 Making efficient use of modern machines is challenging (proof by assignments 2, 3, and 4) In our assignments, you only programmed homogeneous parallel computers. -
Techmatters: Further Adventures in the Googleverse: Exploring the Google Labs
LOEX Quarterly Volume 31 TechMatters Further Adventures in the Googleverse: Exploring the Google Labs Krista Graham, Central Michigan University Little did I know when I set out to write my last Tech price as well as compare prices between online retailers. In Matters column that the folks at Google were going to addition, Froogle provides store and product reviews and steal my thunder by announcing not one, but two, signifi- ratings. Overall, Froogle can be a very useful tool for stu- cant development initiatives. Over the last few months, dents and general consumers looking for product informa- these two projects dubbed Google Scholar and Google tion. Print have been a major topic of conversation in libraries, on library discussion lists, and even in the national press. Discussion and debate regarding the implications and Google Deskbar potential impact of these new search tools on libraries, Google deskbar is an application that allows users to search librarians, and library services abound. But where did the web without opening a web browser. Similar in concept they come from? Although it may seem as though these to the Google toolbar, the deskbar program places a search two developments sprang fully formed from the techno- box in the taskbar that appears at the bottom of every Win- logical ooze, they actually started in a lesser known, (but dows screen. Search results appear in a “mini-viewer” that mighty powerful), corner of the Googleverse know as the allows users to preview search results prior to launching a Google Labs. browser session. Using the deskbar, a student writing a re- search paper in Word could quickly use Google’s diction- ary, calculator, or web search features without leaving his/ What is Google Labs? her document to “check the web”. -
20 Years of Four HCI Conferences: a Visual Exploration
INTERNATIONAL JOURNAL OF HUMAN–COMPUTER INTERACTION, 23(3), 239–285 Copyright © 2007, Lawrence Erlbaum Associates, Inc. HIHC1044-73181532-7590International journal of Human–ComputerHuman-Computer Interaction,Interaction Vol. 23, No. 3, Oct 2007: pp. 0–0 20 Years of Four HCI Conferences: A Visual Exploration 20Henry Years et ofal. Four HCI Conferences Nathalie Henry INRIA/LRI, Université Paris-Sud, Orsay, France and the University of Sydney, NSW, Australia Howard Goodell Niklas Elmqvist Jean-Daniel Fekete INRIA/LRI, Université Paris-Sud, Orsay, France We present a visual exploration of the field of human–computer interaction (HCI) through the author and article metadata of four of its major conferences: the ACM conferences on Computer-Human Interaction (CHI), User Interface Software and Technology, and Advanced Visual Interfaces and the IEEE Symposium on Information Visualization. This article describes many global and local patterns we discovered in this data set, together with the exploration process that produced them. Some expected patterns emerged, such as that—like most social networks— coauthorship and citation networks exhibit a power-law degree distribution, with a few widely collaborating authors and highly cited articles. Also, the prestigious and long-established CHI conference has the highest impact (citations by the others). Unexpected insights included that the years when a given conference was most selective are not correlated with those that produced its most highly referenced arti- cles and that influential authors have distinct patterns of collaboration. An interest- ing sidelight is that methods from the HCI field—exploratory data analysis by information visualization and direct-manipulation interaction—proved useful for this analysis. -
Focus+Context Sphere Visualization for Interactive Large Graph Exploration
© ACM, 2017. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in: Du, F., Cao, N., Lin, Y.-R., Xu, P., Tong, H. (2017). iSphere: Focus+Context Sphere Visualization for Interactive Large Graph Exploration. In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI 2017) http://doi.org/10.1145/3025453.3025628 iSphere: Focus+Context Sphere Visualization for Interactive Large Graph Exploration Fan Du Nan Cao Yu-Ru Lin University of Maryland Tongji University University of Pittsburgh [email protected] [email protected] [email protected] Panpan Xu Hanghang Tong Hong Kong University of Arizona State University Science and Technology [email protected] [email protected] a b c Figure 1. The node-link diagram shown in (a) the zoomable plane and the focus+context displays, (b) hyperbolic display and (c) iSphere display. ABSTRACT Author Keywords Interactive exploration plays a critical role in large graph visu- Graph visualization; graph exploration; focus+context. alization. Existing techniques, such as zoom-and-pan on a 2D plane and hyperbolic browser facilitate large graph exploration ACM Classification Keywords by showing both the details of a focal area and its surrounding H.5.2 User Interfaces: Graphical user interfaces (GUI) context that guides the exploration process. However, existing techniques for large graph exploration are limited in either INTRODUCTION providing too little context or presenting graphs with too much Interactive exploration is one of the major approaches for nav- distortion. -
The Fourth Paradigm
ABOUT THE FOURTH PARADIGM This book presents the first broad look at the rapidly emerging field of data- THE FOUR intensive science, with the goal of influencing the worldwide scientific and com- puting research communities and inspiring the next generation of scientists. Increasingly, scientific breakthroughs will be powered by advanced computing capabilities that help researchers manipulate and explore massive datasets. The speed at which any given scientific discipline advances will depend on how well its researchers collaborate with one another, and with technologists, in areas of eScience such as databases, workflow management, visualization, and cloud- computing technologies. This collection of essays expands on the vision of pio- T neering computer scientist Jim Gray for a new, fourth paradigm of discovery based H PARADIGM on data-intensive science and offers insights into how it can be fully realized. “The impact of Jim Gray’s thinking is continuing to get people to think in a new way about how data and software are redefining what it means to do science.” —Bill GaTES “I often tell people working in eScience that they aren’t in this field because they are visionaries or super-intelligent—it’s because they care about science The and they are alive now. It is about technology changing the world, and science taking advantage of it, to do more and do better.” —RhyS FRANCIS, AUSTRALIAN eRESEARCH INFRASTRUCTURE COUNCIL F OURTH “One of the greatest challenges for 21st-century science is how we respond to this new era of data-intensive -
Why Does This Suck? Information Visualization
why does this suck? Information Visualization Jeffrey Heer UC Berkeley | PARC, Inc. CS160 – 2004.11.22 (includes numerous slides from Marti Hearst, Ed Chi, Stuart Card, and Peter Pirolli) Basic Problem We live in a new ecology. Scientific Journals Journals/personJournals/person increasesincreases 10X10X everyevery 5050 yearsyears 1000000 100000 10000 Journals 1000 Journals/People x106 100 10 1 0.1 Darwin V. Bush You 0.01 Darwin V. Bush You 1750 1800 1850 1900 1950 2000 Year Web Ecologies 10000000 1000000 100000 10000 1000 1 new server every 2 seconds Servers 7.5 new pages per second 100 10 1 Aug-92 Feb-93 Aug-93 Feb-94 Aug-94 Feb-95 Aug-95 Feb-96 Aug-96 Feb-97 Aug-97 Feb-98 Aug-98 Source: World Wide Web Consortium, Mark Gray, Netcraft Server Survey Human Capacity 1000000 100000 10000 1000 100 10 1 0.1 Darwin V. Bush You 0.01 Darwin V. Bush You 1750 1800 1850 1900 1950 2000 Attentional Processes “What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention, and a need to allocate that attention efficiently among the overabundance of information sources that might consume it.” ~Herb Simon as quoted by Hal Varian Scientific American September 1995 Human-Information Interaction z The real design problem is not increased access to information, but greater efficiency in finding useful information. z Increasing the rate at which people can find and use relevant information improves human intelligence. Amount of Accessible Knowledge Cost [Time] Information Visualization z Leverage highly-developed human visual system to achieve rapid understanding of abstract information. -
No-Scale Conditions
S A J _ 2016 _ 8 _ original scientific article approval date 16 12 2016 UDK BROJEVI: 72.013 COBISS.SR-ID 236418828 RELATIONAL LOGICS AND DIAGRAMS: NO-SCALE CONDITIONS A B S T R A C T The paper investigates logics of relational thinking and connectivity, rendering particular correspondences between the elements of representation and the things represented in drawings, diagrams, maps, or notations, which either deny notions of scale, or work at all scales without belonging to any specific one of them. They include ratios and proportions (static and dynamic, geometric, arithmetic and harmonic progressions) expressing symmetry and self-similarity principles in spatial-metric terms, but also principles of nonlinearity and complexity by symmetry-breakings within non-metric systems. The first part explains geometric and numeric relational figures/sets as taken for “principles of beauty and primary aesthetic quality of all things” in classical philosophy, science, and architecture. These progressions are guided by certain rules or their combinations (codes and algorithms) based on principles of regularity, usually directly spatially reflected. Conversely, configurations representing the main subject of the following sections, could be spatially independent, transformable, and unpredictable, escaping regular extensive definitions. Their forms are presented through transitions from scalable to no-scale conditions showing initial symmetry breakings and abstractions, through complex forms of dynamic modulations and variations of matter, ending with -
Introduction to Information Visualization.Pdf
Introduction to Information Visualization Riccardo Mazza Introduction to Information Visualization 123 Riccardo Mazza University of Lugano Switzerland ISBN: 978-1-84800-218-0 e-ISBN: 978-1-84800-219-7 DOI: 10.1007/978-1-84800-219-7 British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Control Number: 2008942431 c Springer-Verlag London Limited 2009 Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publish- ers, or in the case of reprographic reproduction in accordance with the terms of licences issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. The use of registered names, trademarks, etc., in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant laws and regulations and therefore free for general use. The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made. Printed on acid-free paper Springer Science+Business Media springer.com To Vincenzo and Giulia Preface Imagine having to make a car journey. Perhaps you’re going to a holiday resort that you’re not familiar with. -
The Diversity of Data and Tasks in Event Analytics
The Diversity of Data and Tasks in Event Analytics Catherine Plaisant, Ben Shneiderman Abstract— The growing interest in event analytics has resulted in an array of tools and applications using visual analytics techniques. As we start to compare and contrast approaches, tools and applications it will be essential to develop a common language to describe the data characteristics and diverse tasks. We propose a characterisation of event data along 3 dimensions (temporal characteristics, attributes and scale) and propose 8 high-level user tasks. We look forward to refining the lists based on the feedback of workshop attendees. KEYWORDS having the same time stamp, medical data are often Temporal analysis, pattern analysis, task analysis, taxonomy, big recorded in batches after the fact. data, temporal visualization o The relevant time scale may vary (from milliseconds to years), and may be homogeneous or not. INTRODUCTION o Data may represent changes over time of a status The growing interest in event analytics (e.g. Aigner et al, 2011; indicator, e.g. changes of cancer stages, student status or Shneiderman and Plaisant, 2016) has resulted in an array of novel physical presence in various hospital services, or may tools and applications using visual analytics techniques. As represent a set of events or actions that are not exclusive researchers compare and contrast approaches it will be helpful to from one another, e.g. actions in a computer log or series develop a common language to describe the diverse tasks and data of symptoms and medical tests. characteristics analysts encounter. o Patterns may be very cyclical or not, and this may vary The methodology of design studies - which primarily focus on over time. -
The Golden Age of Statistical Graphics
The Golden Age of Statistical Graphics Michael Friendly Psychology Department and Statistical Consulting Service York University 4700 Keele Street, Toronto, ON, Canada M3J 1P3 in: Statistical Science. See also BIBTEX entry below. BIBTEX: @Article{Friendly:2008:golden, author = {Michael Friendly}, title = {The {Golden Age} of Statistical Graphics}, journal = {Statistical Science}, year = {2008}, volume = {}, number = {}, pages = {}, url = {http://www.math.yorku.ca/SCS/Papers/golden.pdf}, note = {in press (accepted 8-Sep-08)}, } © copyright by the author(s) document created on: September 8, 2008 created from file: golden.tex cover page automatically created with CoverPage.sty (available at your favourite CTAN mirror) In press, Statistical Science The Golden Age of Statistical Graphics Michael Friendly∗ September 8, 2008 Abstract Statistical graphics and data visualization have long histories, but their modern forms began only in the early 1800s. Between roughly 1850 to 1900 ( 10), an explosive oc- curred growth in both the general use of graphic methods and the range± of topics to which they were applied. Innovations were prodigious and some of the most exquisite graphics ever produced appeared, resulting in what may be called the “Golden Age of Statistical Graphics.” In this article I trace the origins of this period in terms of the infrastructure required to produce this explosive growth: recognition of the importance of systematic data collection by the state; the rise of statistical theory and statistical thinking; enabling developments