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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

3 3 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

4 4 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..

5 5 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 …

6 6 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

7 7 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

8 8 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

9 9 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

10 10 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 …

11 11 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

12 12 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

13 13 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

14 14 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

15 15 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

16 16 User Modeling Intelligent Web Filters Form Field Ordering and Completion Smart Handheld Fashion Consultant

17 17 User Identification Key Stroke Biometrics Smart Navigator Handwriting ID Eye-tracking Biometrics

18 18 How do we get a Machine to Learn? Demo - Typist Identification Demo - Typist IdentificationTypist IdentificationTypist Identification Application: user validation - BioPassword Application: user validation - BioPasswordBioPassword

19 19 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

20 20 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

21 21 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.

22 22 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

23 23 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.

24 24

25 25 Work on CVW at Acadia Interface for mobile phones and BlackBerry. Chat with others in the room View objects in the room

26 26 Woof! Did you say Collaborate! bstv1.htm bstv1.htm bstv1.htm bstv1.htm

27 27 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

28 28 Ubiquitous 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.

29 29 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

30 30 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?

31 31 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

32 32 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

33 33 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

34 34 3D 3D ml ml ml ml Virtual Reality Virtual Reality

35 35 The Power of the Pen: Tablet PCs TabletPC.ppt TabletPC.ppt TabletPC.ppt


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