Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
scanu@insa-rouen.fr
Intelligent sensor
and learning challenges
for context aware appliances
>> Stéphane Canu
scanu@insa-rouen.fr
asi.insa-rouen.fr/~scanu
INSA Rouen, France - EU
Laboratoire PSI
1984: La souris et leMacintoch
200X : la nouvelle rupture "break through"
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
scanu@insa-rouen.fr
La technologie d'aujourd'hui
• Loi de Moore
• Communication "sans fil"
• L'ère des données
Quelles applications ?
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
scanu@insa-rouen.fr
Wearable
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
scanu@insa-rouen.fr
IHM
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
scanu@insa-rouen.fr
Olympus Optical Co., Ltd. is pleased to announce its new
wearable user interface technologies.
Employing gestures and other hand movements for input, the system
is an ideal match for new wearable PCs.
Wearable
http://www.redwoodhouse.com/wearable/index.html
http://wearables.cs.bris.ac.uk/public/wearables/esleeve.htm
http://www.ices.cmu.edu/design/streetware/
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
scanu@insa-rouen.fr
Reasearch on wearable
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
scanu@insa-rouen.fr
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
Wearable
scanu@insa-rouen.fr
context aware appliances
The mediacup
(calm version of the active badge)
Phone by night
http://mediacup.teco.edu/overview/engl/m_what.html
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
scanu@insa-rouen.fr
General Motors and CMU
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
scanu@insa-rouen.fr
The car
- drives together
- informs you
- in a parking…
GM/CMU Companion driver interface system
Oops! Where is my car?
• Old fashion software design: process
1.Match the sentence
2.Send the query to the satellite
3.Satellite send query to the car on its own frequency
4.Car answers…
- Tell the computer what to do (where is the switch)
• Distributed software design: interaction
- Software agents talk together
• Future way: Programming by Example
- Show the computer what to do
• Today's solution: Louis my 3 years old son
Disappearing computer
>> Your Wish is My Command: Programming by Example Henry Lieberman, editor, Published by Morgan Kaufmann, 2001.
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
scanu@insa-rouen.fr
Calm technology
• Ubiquitous computing
- One people - many computer
• Technology at our service
- Reactive to what user do
- Proactive - Prepare what to do next
- Situated – sharing context
(Hans Gellersen, Sensing in Ubiquitous Computing)
• Adapted to our needs
- New functionalities and new behaviors
- New way of communicating
- Learn to adapt
Machines have to know their context
>> M. Weiser "The Computer for the 21st Century." Scientific American, September 1991
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
scanu@insa-rouen.fr
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
What is the context?
scanu@insa-rouen.fr
• user
Context input
- activity (available/meeting)
- location,
- identity, profile
Explicit
Input
sensors
• environment monitoring
- time, day/night, temperature, weather,
- resources (networks, services…)
Context-aware
application
Explicit
Output
actuators
Context output
Adapted From Henry Lieberman and Ted Selker, Out of Context:
Computer Systems That Adapt To, and Learn From, Context,
IBM Systems Journal 39, 2000.
• appliance - proprioception
- usage - functionalities
- maintenance
- resources (energy…)
+ history…
Abstract representation of the situation
Knowledge?
How to find it from data?
Sensing context from the environment
presentation roadmap
1. Data
2. Representation
3. Information retrieval
4. Context evolution
5. User interaction
>> Kristof Van Laerhoven, Kofi Aidoo: Teaching Context to Applications
In Personal and Ubiquitous Computing, Volume 5 Issue 1 (2001) pp 46-49
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
scanu@insa-rouen.fr
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
Context from data
scanu@insa-rouen.fr
>>
• Unbelievable capacity
- Moore’s law
• New sensors
- Artificial nose
- Bio sensor
• “Personal” data
- humor: affective computing
Data Era!
http://www-stat.stanford.edu/~donoho/lectures.html
Data
Representation
Information retrieval
Context evolution
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
Biological sensors
scanu@insa-rouen.fr
>>
Data
Representation
Information retrieval
Context evolution
How are you?
http://www.teco.edu/tea/sensors.html
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
Expression recognition
scanu@insa-rouen.fr
>>
Machine Perception Lab
Face Detection and Expression Recognition
http://markov.ucsd.edu/~movellan/mplab/index.html
Data
Representation
Information retrieval
Context evolution
Too much information
kills information
"We are drowning in information and starving for knowledge."
- Rutherford D. Roger
• Critic of the "Data Era"
• Data smog
• Non measurable things
• Ethical consequences
- the Orwellian future
Filter data!
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
scanu@insa-rouen.fr
>>
Data
Representation
Information retrieval
Context evolution
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
Intelligent sensors
• Requirements:
-
Data
Accuracy and confidence
Self diagnostic
Self calibration
scanu@insa-rouen.fr
>>
Data
Representation
Information retrieval
Context evolution
• How to do it?
- Uncertainty management
- Learning ability
• Network + database
- Adaptation ability
- Fault detection mechanism
Associated software sensors
>>S. Canu et al., "Black-box Software Sensor Design for Environmental Monitoring" , in International Conference on
Artificial Neural Networks , Skovde, Sweden. Sep 2-4, 1998 (and related work on data validation within the EM2S project)
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
Data validation
• Mono sensor validation
- Static validation
• Mean, variance
scanu@insa-rouen.fr
>>
Data
Representation
Information retrieval
Context evolution
- Dynamic validation
• Cusum (control charts)
• Trend analysis
• Multisensor validation
- Residual analysis
- Fusion: Joint probability estimation
- Prior knowledge: Balanced relations
• Hierarchical validation
- Multisensor perception
Interactive matrix of smart sensors
>> http://www.accenture.com/xd/xd.asp?it=enWeb&xd=services\technology\research\tech_sensor_matrix.xml
>> K. Van Laerhoven, A. Schmidt and H.-W. Gellersen. "Multi-Sensor Context-Aware Clothing". In Proceedings
of the 6th International Symposium on Wearable Computers, 2002
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
Software sensor
scanu@insa-rouen.fr
>>
• Value + confidence interval + validity domain
• How to build it ?
- From a model: tracking = Kalman filter
- When no model is available: learn it!
Raw data
v(t)
Raw data
x(t)
Raw data
y(t)
Raw data
z(t)
environment
learning = Black box modeling
Data
Representation
Information retrieval
Context evolution
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
Towards proprioceptors
• Learn
Pr  x1,..., xi ,...xd , v 
scanu@insa-rouen.fr
>>
Data
Representation
Information retrieval
Context evolution
• How to learn?
- Gaussian mixture + EM
- Include prior: Bayesian networks
- Deal with uncertainty: Evidence framework
• Use to:
- Detect non nominal situations
- Replace missing data
d = Curse of dimensionality (Belman)
>> E. Petriu et al., "Sensor based information appliances",
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
What is data?
scanu@insa-rouen.fr
>>
• Individuals or measurements
• Associated variables
• Data set (matrix)
- line = measurements
- column = variable
• Data: point clouds
- Data exploration: recognize patterns
too many data: SUMARIZE
Data
Representation
Information retrieval
Context evolution
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
Summarize data
scanu@insa-rouen.fr
>>
• Non linear components analysis
-
Feature space: kernel (PCA or ICA)
Local linear
Quantisation (SOM)
Relevant distance
• Select features
- Local adapted representation
- Feature selection
• Select relevant situations
- Sparse learning
- Kernel learning
Kernel representation
>> J. Mäntyjärvi, J. Himberg, P. Korpipää, H. .Mannila, "Extracting the Context of a Mobile Device User",
8th Symposium on Human-Machine Systems-HMS,Kassel, Germany, 2001.
Data
Representation
Information retrieval
Context evolution
Kernel representation
Distance maps
Data'
j
i
Data
Influence
map
I (i, j )  exp
dist (i , j )
Example in 2 dimension of the influence map of the "black
circle". Red color denotes a high influence while the low
influence zones are in blue.
Analyze data proximity through the kernel map
>> B. Scholkopf and A. Smola, "Leaning with Kernels", MIT Press, 2001
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
scanu@insa-rouen.fr
Example of kernel map
Class 1
Class 2
Class 2
Data clouds in two dimensions
Associated kernel map
Even in d dimensions you can visualize
>> S. Canu and al., "Functionnal learning through kernels", invited lecture at the NATO institute in Leuven, 2002
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
scanu@insa-rouen.fr
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
Looking for hiden shapes
scanu@insa-rouen.fr
>>
• Data point = information + noise
Data
Representation
Information retrieval
Context evolution
• Principal curve
- Non linear PCA
• Independent curve
- Non linear ICA
Kernel representation + linear analysis
>> Balázs Kégl
http://www.iro.umontreal.ca/~kegl/research/pcurves/
Navigate
in high dimensional space
>> J. B. Tenenbaum, V. de Silva and J. C. Langford
http://isomap.stanford.edu/handfig.html
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
scanu@insa-rouen.fr
>>
Data
Representation
Information retrieval
Context evolution
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
Information retrieval
• What for
-
User profiling
User identification
Battery discharge rate
Sequence induction…
• Classification problem
- Decision theory
- Example based programming
- Learning machine
Select relevant cases
scanu@insa-rouen.fr
>>
Data
Representation
Information retrieval
Context evolution
A brief historical perspective
of machine learning
• Before machines
>>
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
scanu@insa-rouen.fr
Data
Representation
Information retrieval
Context evolution
- Statistics: PCA, DA, regression, CART, kNN
• 70's - Learning is logic
- Grammatical inference in expert systems
• 80's - Learning is human
- Neural networks: backprop
• 90's - Learning is a problem: COLT
- Kernel machines: SVM
- Mixture of experts: adaboost
What is the learning problem?
>> T. Hastie, R. Tibshirani and J. Friedman, "The elements of statistical learning", Springer, 2001
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
What is learning?
• Data
- Training set
- Test point
scanu@insa-rouen.fr
 x1, y1 ,...,  xi , yi ,...,  xn , yn 

xn1
looking for f such that yˆ n1  f ( xn1 )
• Learning is balancing
Fit
data
Summarize
data
1. Hypothesis set
2. Fitting criterion
3. Compression criterion
4. Balancing mechanism
(Neural networks,
Kernels)
(least square,
absolute value)
(penalization,
Margin)
(cross validation,
Learning is summarizing
generalization)
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
Linear discrimination
separable case
scanu@insa-rouen.fr
>>
Data
Representation
Information retrieval
Context evolution
How to
correctly
classify
all points?
Occam
Razor's
wx+ b=0
(w,b) ???
+
+
+
+
+
+
+
+
+
+
+
+
Use hyperplane
+
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
Linear discrimination
separable case
scanu@insa-rouen.fr
>>
Data
Representation
Information retrieval
Context evolution
How to
correctly
classify
all points?
wx+ b=0
+
+
+
+
+
+
+
+
+
+
+
+
Be sparse
+
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
The classifier Margin
scanu@insa-rouen.fr
>>
Data
Representation
Information retrieval
Context evolution
How to
correctly
Margin classify
all points?
wx+ b=0
Margin
+
+
+
+
+
+
+
+
+
+
+
+
Be sparse
+
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
Maximize the margin
Be sparse
scanu@insa-rouen.fr
>>
Data
Representation
Information retrieval
Context evolution
How to
correctly
Margin classify
all points?
wx+ b=1
wx+ b=0
Margin
+
wx+ b=-1
+
+
+
+
+
+
+
+
+
+
+
+
Support Vector Machines: SVM
>> V. N. Vapnik, "The nature of statistical learning theory", Springer-Verlag, 1995
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
What is learning?
• Data
- Training set
- Test point
scanu@insa-rouen.fr
 x1, y1 ,...,  xi , yi ,...,  xn , yn 

xn1
looking for f such that yˆ n1  f ( xn1 )
• Learning is balancing
Fit
data
Summarize
data
SVM
1. Hypothesis set
2. Fitting criterion
3. Compression criterion
4. Balancing mechanism
(Neural networks,
Kernels)
(least square,
absolute value)
(penalization,
Margin)
(cross validation,
generalization)
Learning is summarizing
>> S. Canu, A. Rakotomamonjy, Ozone peak and pollution forecasting using Support Vectors, IFAC workshop, Yokohama, 2001.
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
Summarize Input
adaptive scaling
scanu@insa-rouen.fr
>>
• Enumerate all combination
Data
Representation
Information retrieval
Context evolution
…and score
• Preprocessing
- Information theory
- Statistical test
• Wrapper
- Use a relevance index
- Learn and select together
Example of relevance index for a toy problem
with 2 relevant features and 50 irrelevants
Global formulation
>> Y. Gandvalet and S. Canu, "Adaptive Scaling for Feature Selection in SVMs", accepted for publication at NIPS 2002
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
Summarize patterns
scanu@insa-rouen.fr
>>
Data
Representation
Information retrieval
Context evolution
Dimension reduction by
>> multi-resolution analysis
(just like in your eyes…)
Learn at the relevant scale
>> multi scale representation
Efficient implementation
- ridgelets, curvelets
- wavelets’ kernel
"Kernelize" wavelets
>> A. Rakotomamonjy and S. Canu, "Frame, Reproducing Kernel, Regularization and Learning", accepted in JMLR 2002
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
Learning machines challenges
1. Hypothesis set
- Multi scale data representation: wavelets
- Use context: mixture of experts
>>
scanu@insa-rouen.fr
Data
Representation
Information retrieval
Context evolution
2. Fitting criterion
- Sparse distance criterion
- Select relevant input (adaptive scaling)
- Relevant distance: adapt the kernel
3. Compression criterion
- Information issues
- Global optimization
4. Balancing mechanism
- Efficient direct algorithm (one shot learning)
Towards Context based learning
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
Context assessment
scanu@insa-rouen.fr
• Deal with uncertainty
- plausibility / credibility
- unknown states / ability to evolve
- data fusion: evidence theory
>>
• Take into account prior knowledge: transitions
- temporal representation
- uncertain transitions
- learn probabilities or possibilities
• Learn the model
- don't start from scratch
- create and delete contexts
• Adapt context determination to user
- from a global imprecise context to specific context
How to implement context?
Data
Representation
Information retrieval
Context evolution
Context implementation
• Context = state
- List of variables
- Petri's nets
• State = stochastic
- Markov model
- Bayesian networks
• Identify = decision theory (data fusion)
- Information retrieval
• Learn context
- Knowledge discovery
- Create / delete
- Context hierarchy (time granularity)
Context is a language
How to retrieve the context?
Henry Lieberman: http://web.media.mit.edu/~lieber/
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
scanu@insa-rouen.fr
New idea to deal with context
• Current context: working memory
- Prior knowledge: transition law
>>
- Data fusion
• Learn context
- Transition law
- Context retrieval from data
• Context is a language
• Speech recognition
Markovian model
Evidence
Language + previous state
Locator's adaptation
Adapt speech recognition ideas to context
http://htk.eng.cam.ac.uk/
scanu@insa-rouen.fr
Data
Representation
Information retrieval
Context evolution
• Available information: evidence
-
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
Context: Research chalenges
• Inputs
- Deal with uncertainty (and missing data)
- Representation
- Data fusion (multimedia fusion)
• Context
- Define a language
- Represent previous state
- Learn transition
• Feed Back to inputs
• Adapt transition to the user
- Loop the user: reinforcement
- Control mechanism (stability/plasticity dilemma)
Challenging research issues
http://cslu.cse.ogi.edu/tutordemos/nnet_training/tutorial.html
>>
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
scanu@insa-rouen.fr
Data
Representation
Information retrieval
Context evolution
Break through
Theoretical models are essentials
(Mark Weiser, Computer Science Challenges for the Next Ten Years)
• What is information?
- Computer science
- Coding
- Signal
• Mathematics
-
Statistics & computer science
Pattern recognition
Functional analysis
??????
…remember Albert and relativity
>> L. Devroye, L. Györfi and G. Lugosi, "A Probabilistic Theory of Pattern Recognition", Springer-Verlag 1996.
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
scanu@insa-rouen.fr
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
My long bet
scanu@insa-rouen.fr
Before 2050
We will faced a scientific revolution
regarding information definition
Comparable with the one induced in physics
by the relativity theory
$ 500
To greenpeace
Long bet fundation at San Francisco
http://www.longbets.org/
Research challenges
• create context
- how to define prior contexts: user’s needs
- how to represent contexts: stochastic automaton
Bayesian
- learn from data: modify, create and destroy context networks
• decide context
- validate data
- select relevant inputs
- select relevant patterns
- select relevant situations
- make decision using data fusion
software sensors
representation + distance
wavelets
SVM and kernel
Dempster-Shafer + EM
• loop with the user
- reinforcement learning
- user’s needs
Integrate: create relevant learning architecture
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
scanu@insa-rouen.fr
Questions?
•
Asia
•
America
- Scurry™, Wearable & Virtual Keyboard - Samsung,
- K. Doya for reinforcement
-
•
Context Aware Computing group - Media lab MIT
CMU, Stanford
Georgia tech: Future Computing Environments
Smart Matter Integrated Systems (Xerox PARC)
Montreal – learning lab
Australia
- ANU for learning
- University of South Australia - wearable computer lab
•
Europe
-
Telecooperation Office (TecO) at the University of Karlsruhe
The disappearing computer, a EU-funded proactive initiative
The Smart-Its project
Equator project focuses on the integration of physical and digital interaction
Perceptual Computing in general and Computer Vision in ETH Zurich
IDIAP for machine learning and speech recognition
PSI, France for learning
Some context aware references
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
scanu@insa-rouen.fr
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
scanu@insa-rouen.fr
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
From macroscopic…
scanu@insa-rouen.fr
>>
Data
Representation
Information retrieval
Context evolution
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
…to Microscopic data
scanu@insa-rouen.fr
>>
Data
Representation
Information retrieval
Context evolution
MCell Simulation of miniature endplate
current generation at the neuromuscular junction.
Image rendered with Pixar Photorealistic RenderMan.
http://www.mcell.cnl.salk.edu/
Emotion detection
>> E-Motions
Towards affective computing
>> R. Picard, Affective Computing, MIT Press, 1997
http://graphics.usc.edu/~dfidaleo/Emotion/
http://www.mis.atr.co.jp/~mlyons/facial_expression.html
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
scanu@insa-rouen.fr
>>
Data
Representation
Information retrieval
Context evolution
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
Learning accuracy
How to compute error bars?
• Find (a,b) such that
• Model the model
scanu@insa-rouen.fr
>>
Data
Representation
Information retrieval
Context evolution
Pr vˆ  a  v  vˆ  b   1  
Accuracy confidence
- The sandwich estimator, (Tibshirani, 1996)
• Likelihood Based on the Hessian matrix
- Confidence machine (Gammerman RHC, 1999)
• Confidence: 73.11% - Credibility: 51.37%
• Sample the models
- Bootstrap (Heskes 1997)
• Learn the error
- Train using absolute error
>> R. Tibshirani, "A comparison of some error estimates for neural network models," Neural Computation, 8, 152-163, 1996.
>> Tom Heskes, "Practical confidence and prediction intervals", Advances in Neural Information Processing 9,
eds. Mozer, M., Jordan, M. and Petsche. T., pp. 176-182, 1997.
http://nostradamus.cs.rhul.ac.uk/~leo/pCoMa/
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
Looking for hiden shapes
scanu@insa-rouen.fr
>>
Locally linear
representations
>> Sam T. Roweis & Lawrence K. Saul
http://www.cs.toronto.edu/~roweis/lle/
Data
Representation
Information retrieval
Context evolution
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
Movie synthesis from text
scanu@insa-rouen.fr
>>
…from text to movie
Turing proof
>> Tony Ezzat and Tomaso Poggio
http://cuneus.ai.mit.edu:8000/research/mary101/mary101.html
Data
Representation
Information retrieval
Context evolution
From one expression to another
>>
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
scanu@insa-rouen.fr
Data
Representation
Information retrieval
Context evolution
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
Non euclidian metrics
scanu@insa-rouen.fr
>>
http://cs.unm.edu/~joel/NonEuclid/
Data
Representation
Information retrieval
Context evolution
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
Hyperbolic Self-Organizing Map
scanu@insa-rouen.fr
>>
What is the "distance" between two objects
Data
Representation
Information retrieval
Context evolution
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
Example on movies - HSOM
scanu@insa-rouen.fr
>>
Data
Representation
Information retrieval
Context evolution
- 650 documents
- from 16000 reviews
- Internet Movie Database
http://www.techfak.uni-bielefeld.de/ags/ni/projects/hsom/hsom.html
Disney's animation
Movies are closed
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
Example on movies
scanu@insa-rouen.fr
>>
Data
Representation
Information retrieval
Context evolution
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
Curvlets
scanu@insa-rouen.fr
>>
Deal with high dimensional space
http://www-stat.stanford.edu/~jstarck/
Data
Representation
Information retrieval
Context evolution
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
Curvlets
scanu@insa-rouen.fr
>>
Data
Representation
Information retrieval
Context evolution
The original image 64,536 coefficients.
Deal with high dimensional space
http://www-stat.stanford.edu/~jstarck/
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
Select relevant situations
scanu@insa-rouen.fr
• Relevant representation
3
2
1
- "Invent" features
- Select features
>>
Data
Representation
Information retrieval
Context evolution
1
-1
0
-1
- map
- Use kernel
1
0
0
• Relevant "distance"
1
-2
-3
-2
0
• Summarize the examples
- Define a relevant global criterion to be minimized
- Support vector machines (SVM)
Be sparse
2
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
Learning architecture
•
•
•
•
•
•
Agent - Data base - Communication
Metadata
Context language
Adaptability: control mechanism
Pre programming: anticipation
Open – modular – distributed
scanu@insa-rouen.fr
Data
Representation
Information retrieval
Context evolution
- The Ektara Architecture (MIT for wearable)
- Nexus - A Platform for Context-Aware Systems
- The Context-Toolkit (Geargia Tech)
How to debug such software?
http://web.media.mit.edu/~rich/
Future appliances?
• Deal with the context
- Recognize
- Adapt
- Create
• Inference, Learning, discovery,
- Represent
- Decide
- Deal with time
• From user interface to user interaction
- Reinforcement learning
- Human factors
• How to know what we need?
Human factors: cool technology is at our service
Séminaire PSI
FRE CNRS 2546
16 Janvier 2003
scanu@insa-rouen.fr