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May 4, 2006 21 st Annual Huggins High School Science Seminar 1 From Mozhart to Moe’s Heart: Computer Science is Everywhere Danny Silver, Acadia University.

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Presentation on theme: "May 4, 2006 21 st Annual Huggins High School Science Seminar 1 From Mozhart to Moe’s Heart: Computer Science is Everywhere Danny Silver, Acadia University."— Presentation transcript:

1 May 4, st Annual Huggins High School Science Seminar 1 From Mozhart to Moe’s Heart: Computer Science is Everywhere Danny Silver, Acadia University

2 2 Imagine A World without Computers … The family car The family car Microwave oven Microwave oven Photocopier Photocopier Cellular phone Cellular phone CD players CD players Digital TV Digital TV Gameboy, X-Box Gameboy, X-Box Wrist watch Wrist watch

3 3 Food for Thought … Bits and bytes that is … Bits and bytes that is … How many of you have written a computer program? How many of you have written a computer program?

4 4 Computer Science – What is it? Computer Science Computer Science Subareas … Subareas … Information Technology Information Technology Informatics Informatics

5 5 Computer Demos Written by Petri Kuittinen (gerippt und leicht abgeändert von Diver^Salva Mea ;) Programming Technique + Art = Demo Computer demos should not be confused with the demo versions of commercial programs. They are "demos" too, but the word "demo" in this text means a program whose purpose is to present the technical and artistic skills of its makers and produce audiovisual pleasure to the viewer. A computer demo usually includes various kind of real-time produced computer graphics effects which have little relation to each other accompanied by music. In a way a demo could be described as a sort of music video or a short computer animation film without a plot or message other than just "hey, I can do this" and "greetings to my friends". Of course there is exception to every rule and some demos have a plot and message. An important distinction between demos and movies or videos is that the visual effects seen in demos are real-time calculated, instead of rendered in beforehand like conventional computer animations (where often hours of computer time are spent to calculate just one frame). Written by Petri Kuittinen (gerippt und leicht abgeändert von Diver^Salva Mea ;) Programming Technique + Art = Demo Computer demos should not be confused with the demo versions of commercial programs. They are "demos" too, but the word "demo" in this text means a program whose purpose is to present the technical and artistic skills of its makers and produce audiovisual pleasure to the viewer. A computer demo usually includes various kind of real-time produced computer graphics effects which have little relation to each other accompanied by music. In a way a demo could be described as a sort of music video or a short computer animation film without a plot or message other than just "hey, I can do this" and "greetings to my friends". Of course there is exception to every rule and some demos have a plot and message. An important distinction between demos and movies or videos is that the visual effects seen in demos are real-time calculated, instead of rendered in beforehand like conventional computer animations (where often hours of computer time are spent to calculate just one frame).Petri KuittinenPetri Kuittinen

6 6 Speech Synthesis and Recognition Speech Synthesis: Speech Synthesis: English: How much wood would a woodchuck chuck, if a woodchuck could chuck wood? French: Bonjour. Mon nom est Alain. Common ca va? Microsoft Microsoft AT&T - AT&T - Speech Recognition: Speech Recognition: Microsoft.. Let’s try it.. Microsoft.. Let’s try it..

7 7 Human Communications / Text Messaging / Text Messaging Chat rooms Chat rooms Web log => we blog => Blogging Web log => we blog => Blogging Webpages Webpages Text/Images/Videos Text/Images/Videos Voice … Let’s Skype someone … Voice … Let’s Skype someone …

8 8 MusicPath Jim Diamond grew up in Boutilier's Point, about 30 km southwest of Halifax on St. Margaret's Bay. He went to high school at Sir John A. MacDonald High School in Five Island Lake. Upon graduation he continued his studies at Acadia, earning an honours degree in computer science, in spite of having met Dan Silver (ha ha, thought I'd put that in to see if you were paying attention). He then went to University of Waterloo for a master's degree; while there he took up flying and got his private pilot's license. He went to the University of Toronto for his Ph.D.; during his time there he fenced on the varsity fencing team and got his commercial pilot's license. Jim Diamond grew up in Boutilier's Point, about 30 km southwest of Halifax on St. Margaret's Bay. He went to high school at Sir John A. MacDonald High School in Five Island Lake. Upon graduation he continued his studies at Acadia, earning an honours degree in computer science, in spite of having met Dan Silver (ha ha, thought I'd put that in to see if you were paying attention). He then went to University of Waterloo for a master's degree; while there he took up flying and got his private pilot's license. He went to the University of Toronto for his Ph.D.; during his time there he fenced on the varsity fencing team and got his commercial pilot's license. Since then Jim has worked in research and development for the Department of National Defence and has also worked for computer consulting companies in the Halifax area. Jim returned to Acadia as a computer science professor in He has been concentrating on two areas of research, data compression and remote music instruction. He will now tell us a bit about the MusicPath system, which allows people to take interactive piano lessons from an instructor who could be thousands of miles away. Since then Jim has worked in research and development for the Department of National Defence and has also worked for computer consulting companies in the Halifax area. Jim returned to Acadia as a computer science professor in He has been concentrating on two areas of research, data compression and remote music instruction. He will now tell us a bit about the MusicPath system, which allows people to take interactive piano lessons from an instructor who could be thousands of miles away

9 9 Wireless Sensor Networks 1. Determine cluster heads 2. Broadcast advertisement 3. Nodes transmit membership 4. Heads select associates 5. Heads broadcast schedule 6. Nodes transmit data 10. New round begins. 8.Next transmission 7. Heads transmit aggregated data 9. Heads transmit aggregated data

10 10 Simulating Plant Growth An L-system is a way of generating a graphical representation of the growth of a plant through a set of rules. The rules tell us how the plant should grow between each step. All the rules are defined beforehand, and the growth is allowed to proceed automatically based on the rules. An L-system is a way of generating a graphical representation of the growth of a plant through a set of rules. The rules tell us how the plant should grow between each step. All the rules are defined beforehand, and the growth is allowed to proceed automatically based on the rules. By changing the set of rules, we can change the shape of the plant that grows as a result. By changing the set of rules, we can change the shape of the plant that grows as a result apps/lscape.html apps/lscape.html apps/lscape.html apps/lscape.html

11 11 BioInformatics apps/cella.html apps/cella.html apps/cella.html apps/cella.html

12 12 Computer Science, Physics and Engineering apps/spring.html apps/spring.html apps/spring.html apps/spring.html apps/waves.html apps/waves.html apps/waves.html apps/waves.html apps/mdsheet.html apps/mdsheet.html apps/mdsheet.html apps/mdsheet.html Source: Dennis C. Rapaport, Department of Physics, Bar-Ilan University, Israel Source: Dennis C. Rapaport, Department of Physics, Bar-Ilan University, Israel Source: Paul Falstad, Source: Paul Falstad,

13 13 Enviromatics ? Environmental Studies and Informatics Environmental Studies and Informatics Let’s do a search on Let’s do a search on environmental informatics environmental informaticsenvironmental informaticsenvironmental informatics GIS – Geographical Information Systems GIS – Geographical Information Systems It drives Google maps It drives Google mapsGoogle mapsGoogle maps

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15 15 GPS and Server Dust How GPS works How GPS works How GPS works How GPS works Location tracking – amazing applications Location tracking – amazing applications GPS of Snowboarding Park City Utah GPS of Snowboarding Park City UtahSnowboarding The Star Trek lapel pin The Star Trek lapel pin Server dust = GPS + wireless + server software Server dust = GPS + wireless + server software

16 16 What is Learning? The process of acquiring knowledge or skill through study, experience or teaching The process of acquiring knowledge or skill through study, experience or teaching Fundamental to success and survival … Fundamental to success and survival …

17 17 What is Learning? Inductive inference/modeling Inductive inference/modeling Developing a general model/hypothesis from examples Developing a general model/hypothesis from examples Face Recognition … Face Recognition … Happy Face recognition, that is! Happy Face recognition, that is! Happy Face Happy Face It’s like … Fitting a curve to data It’s like … Fitting a curve to dataFitting a curve to dataFitting a curve to data Also considered modeling the data Also considered modeling the data Statistical modeling Statistical modeling

18 18 Machine Learning Problem: We wish to learn to classifying two people (A and B) based on their keyboard typing. Problem: We wish to learn to classifying two people (A and B) based on their keyboard typing. Approach: Approach: Acquire lots of typing examples from each person Acquire lots of typing examples from each person Extract relevant features ?? - representation! Extract relevant features ?? - representation! Transform feature representation as needed Transform feature representation as needed Use an algorithm to fit a model to the data - search! Use an algorithm to fit a model to the data - search! Test the model on an independent set of examples of typing from each person Test the model on an independent set of examples of typing from each person

19 19 Classification A B B B B B B B BB B B B B B B B BB B B A A A A A A A A A A A A A A A A A A A B B B B B B B B B Artificial Neural Network A Mistakes Typing Speed MT Y

20 20 Classification A B B B B B B B BB B B B B B B B BB B B A A A A A A A A A A A A A A A A A A B B B B B B B B B Inductive Decision Tree A A Mistakes Typing Speed M? T? Root Leaf A B Blood Pressure Example

21 21 User Modeling and Adaptive Systems Expertise: Machine Learning Expertise: Machine Learning Sub-area of artificial intelligence Sub-area of artificial intelligence Development of predictive models from examples Development of predictive models from examples Application to User Modeling and Identification Application to User Modeling and Identification User User Interface Application Software Learning System User Model Explicit data – preferences, chosen options Implicit data - keystroke and mouse click traces

22 22 User Modeling Intelligent Web Filters Form Field Ordering and Completion Smart Handheld Fashion Consultant

23 23 User Identification Key Stroke Biometrics Smart Navigator Handwriting ID Eye-tracking Biometrics

24 24 How do we get a Machine to Learn? Demo - Typist Identification Demo - Typist IdentificationTypist IdentificationTypist Identification Application: user validation - BioPassword Application: user validation - BioPasswordBioPassword

25 25 Autonomous Robots Queue the Lego Mindstorms Video … Queue the Lego Mindstorms Video … Acadia’s Annual Robot Programming Competition Acadia’s Annual Robot Programming Competition Acadia’s Annual Robot Programming Competition Acadia’s Annual Robot Programming Competition

26 26 Stanford Racing Team's leader Sebastian Thrun holds a $2-million dollar check as he catches a ride on top of Stanley No. 03, a tricked-out Volkswagen Touareg R5, after his team was declared the official winner of the DARPA Grand Challenge 2005 in Primm, Nevada. Source: Associated Press – Saturday, Oct 8, 2005 DARPA Grand Challenge 2005

27 27 DARPA Grand Challenge 2005 Stanley the VW Touareg, designed by Stanford University, zipped through the 132-mile Mojave Desert course in six hours and 53 minutes Saturday, using only its computer brain and sensors to navigate rough and twisting desert and mountain trails. Stanley the VW Touareg, designed by Stanford University, zipped through the 132-mile Mojave Desert course in six hours and 53 minutes Saturday, using only its computer brain and sensors to navigate rough and twisting desert and mountain trails. According to Thrun and Mike Montemerlo, a postdoc who was the software guru for the Stanford team, this robot had the ability to learn about the road. Its sensors gathered information about what was underneath its front bumper and used that knowledge to figure out what was road and what was not road for hundreds of feet ahead. Also, when it came to figuring out what should be avoided and what could be ignored, Stanley was trained to emulate the behavior of human drivers. According to Thrun and Mike Montemerlo, a postdoc who was the software guru for the Stanford team, this robot had the ability to learn about the road. Its sensors gathered information about what was underneath its front bumper and used that knowledge to figure out what was road and what was not road for hundreds of feet ahead. Also, when it came to figuring out what should be avoided and what could be ignored, Stanley was trained to emulate the behavior of human drivers.

28 28 Collaboration Over the Internet Often people want to meet at a distance: Often people want to meet at a distance: Hear and see each other Hear and see each other View and modify documents as a group View and modify documents as a group Share applications – run them together Share applications – run them together Solution: Collaborative Virtual Workspace Solution: Collaborative Virtual Workspace Computer Supported Cooperative Work environment Computer Supported Cooperative Work environment

29 29 CVW (Collaborative Virtual Workspace) is a CSCW environment. CVW (Collaborative Virtual Workspace) is a CSCW environment. developed by MITRE developed by MITRE we are extending it with the help students. we are extending it with the help students. Basic ideas: Basic ideas: log in and you enter a ‘building’ containing floors and rooms (text windows interface, not 3D). log in and you enter a ‘building’ containing floors and rooms (text windows interface, not 3D). meet other people in rooms, meet other people in rooms, use rooms for documents, use rooms for documents, use whiteboard for shared work, use whiteboard for shared work, have conferences, have conferences, program environment if you want to change it. program environment if you want to change it.

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31 31 Work on CVW at Acadia Interface for mobile phones and BlackBerry. Chat with others in the room View objects in the room

32 32 Woof! Did you say Collaborate! bstv1.htm bstv1.htm bstv1.htm bstv1.htm

33 33 Morphme.com Perception Laboratory, School of Psychology, University of St Andrews, Scotland and.ac.uk/~morph/Transformer/index.html Perception Laboratory, School of Psychology, University of St Andrews, Scotland and.ac.uk/~morph/Transformer/index.html and.ac.uk/~morph/Transformer/index.html and.ac.uk/~morph/Transformer/index.html

34 34 Ubiquitous &Wearable Computing Ubiquitous computing is the method of enhancing computer use by making many computers available throughout the physical environment, but making them effectively invisible to the user. Ubiquitous computing is the method of enhancing computer use by making many computers available throughout the physical environment, but making them effectively invisible to the user. Headsup display: Headsup display: html html html html myvu_macworld.wmv myvu_macworld.wmv myvu_macworld.wmv Need a keyboard.. How about a virtual one. Need a keyboard.. How about a virtual one.

35 35 Challenges for Computer Science Connections with Other Sciences Connections with Other Sciences Biology Biology Genome Sequencing, Understanding Evolution, Understanding the DNA Programming Language, Understanding the Brain, Bioinformatics Genome Sequencing, Understanding Evolution, Understanding the DNA Programming Language, Understanding the Brain, Bioinformatics Physics Physics Understanding Quantum Mechanics, Quantum Computing, Computational Statistical Mechanics, Understanding Quantum Mechanics, Quantum Computing, Computational Statistical Mechanics, Astronomy Astronomy Discovering Astronomical Structure, Simulation Discovering Astronomical Structure, Simulation Political Science and Sociology Political Science and Sociology Deducing Social Influence Deducing Social Influence Economics Economics Exploring the Impact of Bounded Rationality, Computational Finance Exploring the Impact of Bounded Rationality, Computational Finance

36 36 Challenges for Computer Science Connections with the Web Connections with the Web Searching and Data Mining Searching and Data Mining Electronic Commerce and other Web Applications Electronic Commerce and other Web Applications Security and Privacy Security and Privacy How big is the web? How big is the web?

37 37 Challenges for Computer Science Connections with the Rest of Computer Science Connections with the Rest of Computer Science Software Engineering Software Engineering Computer Aided Verification Computer Aided Verification Networking Networking Database Systems Database Systems Operating Systems Operating Systems Computer Architecture Computer Architecture Artificial Intelligence Artificial Intelligence Scientific Computing Scientific Computing

38 38 Challenges for Computer Science Central Issues for Theoretical Computer Science Central Issues for Theoretical Computer Science Cryptography and Security Cryptography and Security Combinatorial Algorithms Combinatorial Algorithms Computational Geometry Computational Geometry Parallel and Distributed Computing Parallel and Distributed Computing Complexity Classes Complexity Classes Lower Bounds Lower Bounds Logic, Semantics, and Programming Methodology Logic, Semantics, and Programming Methodology Learning Theory and Statistical Inference Learning Theory and Statistical Inference

39 39 Computer Science and Law Computer related crime: Computer related crime: “Lawbreakers have integrated highly technical methods with traditional crimes and developed creative new types of crime, as well. They use computers to cross state and national boundaries electronically, thus complicating investigations. Moreover, the evidence of these crimes is neither physical nor human but, if it exists, is little more than electronic impulses and programming codes.” “Lawbreakers have integrated highly technical methods with traditional crimes and developed creative new types of crime, as well. They use computers to cross state and national boundaries electronically, thus complicating investigations. Moreover, the evidence of these crimes is neither physical nor human but, if it exists, is little more than electronic impulses and programming codes.” Now a global multi-billion dollar problem Now a global multi-billion dollar problem New laws? New laws? New method of law enforcement? New method of law enforcement? Privacy and Security of information Privacy and Security of information Source: Computer Crime: An Emerging Challenge for Law Enforcement David L. Carter, Ph.D. and Andra J. Katz, Ph.D., Feb, 1997

40 40 Computers and Chemistry Computational chemistry has infiltrated just about every subdiscipline of chemistry Computational chemistry has infiltrated just about every subdiscipline of chemistry High performance computing clusters are essential High performance computing clusters are essential Utilize parallel programming methods Utilize parallel programming methods

41 41 E-Business and E-Commerce The Driving Force  The Internet Economy To reach 50,000,000 users To reach 50,000,000 users Radio took 38 years Radio took 38 years Computers took over 16 years Computers took over 16 years TV took 13 years TV took 13 years The Internet took 4 years! The Internet took 4 years! Over 75% of (12.3M)Canadian homes now have PCs Over 75% of (12.3M)Canadian homes now have PCs Over 55% (6.7M) of those are Internet connected, up 51% over 2002 Over 55% (6.7M) of those are Internet connected, up 51% over 2002 Sources: CBC, Forrester

42 42 The Internet Economy Global Forecast $3.2T $1,000 $2,000 $3,000 0 Sales (billions) $80$170$390$970$2,000$3,200 Source: Forrester Hyper-growth begins $150B 1998 – 111M users 2000 – 320M users 2005 – 720M users By 2004: Half of all Internet transactions were made by non-PC devices.

43 43 See is Believing 3D 3D ml ml ml ml Virtual Reality Virtual Reality

44 44 Moe’s Heart Magnetic resonance imaging (MRI) and computed tomography Magnetic resonance imaging (MRI) and computed tomography And Moe’s brain And Moe’s brain Animation of muscle fibers of the heart wall, the mitral valve (purple) and the aortic valve (yellow). The animation shows the heart beating while the viewpoint rotates. Animation of muscle fibers of the heart wall, the mitral valve (purple) and the aortic valve (yellow). The animation shows the heart beating while the viewpoint rotates. Java applet 3D dynamic model of the heart Java applet 3D dynamic model of the heartdynamic model of the heartdynamic model of the heart

45 45 The Power of the Pen: Tablet PCs TabletPC.ppt TabletPC.ppt TabletPC.ppt


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