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Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.fr
Intelligent sensorIntelligent sensor
and learning challengesand learning challengesfor context aware appliancesfor context aware appliances
Intelligent sensorIntelligent sensor
and learning challengesand learning challengesfor context aware appliancesfor context aware appliances
>> Stéphane Canu scanu@insa-rouen.fr
asi.insa-rouen.fr/~scanu
INSA Rouen, France - EU
Laboratoire PSI
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.fr1984: La souris et leMacintoch
200X : la nouvelle rupture "break through"
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frLa technologie d'aujourd'hui
• Loi de Moore
• Communication "sans fil"
• L'ère des données
Quelles applications ?
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frIHM
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.
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frWearable
http://www.redwoodhouse.com/wearable/index.htmlhttp://wearables.cs.bris.ac.uk/public/wearables/esleeve.htmhttp://www.ices.cmu.edu/design/streetware/
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frcontext aware appliances
Phone by night
http://mediacup.teco.edu/overview/engl/m_what.html
The mediacup(calm version of the active badge)
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frGeneral Motors and CMUThe car- drives together- informs you- in a parking…
GM/CMU Companion driver interface systemGM/CMU Companion driver interface system
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frOops! Where is my car?• Old fashion software design: process
1.Match the sentence 2.Send the query to the satellite3.Satellite send query to the car on its own frequency4.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 PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frCalm 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 PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.fr
• user- activity (available/meeting)- location, - identity, profile
• environment monitoring- time, day/night, temperature, weather, - resources (networks, services…)
• appliance - proprioception- usage - functionalities- maintenance- resources (energy…)
What is the context?
Abstract representation of the situationKnowledge?
How to find it from data?
+ history…
ExplicitOutput
actuators
Context input
Context output
ExplicitInput
sensors
Context-awareapplication
Adapted From Henry Lieberman and Ted Selker, Out of Context: Computer Systems That Adapt To, and Learn From, Context,IBM Systems Journal 39, 2000.
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frSensing context from the environmentpresentation roadmap
>> Kristof Van Laerhoven, Kofi Aidoo: Teaching Context to Applications In Personal and Ubiquitous Computing, Volume 5 Issue 1 (2001) pp 46-49
1. Data
2. Representation
3. Information retrieval
4. Context evolution
5. User interaction
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frContext from data
• 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
DataRepresentationInformation retrievalContext evolution
>>
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frBiological sensors
http://www.teco.edu/tea/sensors.html
How are you?
DataRepresentationInformation retrievalContext evolution
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Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.fr
http://markov.ucsd.edu/~movellan/mplab/index.html
Machine Perception LabFace Detection and Expression Recognition
Expression recognitionDataRepresentationInformation retrievalContext evolution
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Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frToo much informationkills information
• Critic of the "Data Era"
• Data smog
• Non measurable things
• Ethical consequences- the Orwellian future
Filter data!
DataRepresentationInformation retrievalContext evolution
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"We are drowning in information and starving for knowledge." - Rutherford D. Roger
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frIntelligent sensors• Requirements:
- Data- Accuracy and confidence- Self diagnostic- Self calibration
• How to do it?- Uncertainty management- Learning ability
• Network + database- Adaptation ability- Fault detection mechanism
Associated software sensors
DataRepresentationInformation retrievalContext evolution
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>>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 PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frData validation• Mono sensor validation
- Static validation• Mean, variance
- 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
DataRepresentationInformation retrievalContext evolution
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Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frSoftware sensor
• 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)
learning = Black box modeling
DataRepresentationInformation retrievalContext evolution
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environment
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frTowards proprioceptors
• Learn
• How to learn?- Gaussian mixture + EM- Include prior: Bayesian networks- Deal with uncertainty: Evidence framework
• Use to:- Detect non nominal situations- Replace missing data
vxxx di ,,...,...,Pr 1
d = Curse of dimensionality (Belman)
DataRepresentationInformation retrievalContext evolution
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>> E. Petriu et al., "Sensor based information appliances",
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frWhat is data?
• Individuals or measurements• Associated variables• Data set (matrix)
- line = measurements
- column = variable
• Data: point clouds- Data exploration: recognize patterns
too many data: SUMARIZE
DataRepresentationInformation retrievalContext evolution
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Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frSummarize data
• 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
DataRepresentationInformation retrievalContext evolution
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>> 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.
Kernel representation
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.fr
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.
Data
Dat
a'
Influence map
Kernel representationDistance maps
Analyze data proximity through the kernel map
),(exp),( jidistjiI
i
j
>> B. Scholkopf and A. Smola, "Leaning with Kernels", MIT Press, 2001
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frExample of kernel map
Data clouds in two dimensions Associated kernel map
Class 2
Class 1
Class 2
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 PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.fr
>> Balázs Kégl http://www.iro.umontreal.ca/~kegl/research/pcurves/
Looking for hiden shapes
• Data point = information + noise
• Principal curve- Non linear PCA
• Independent curve - Non linear ICA
DataRepresentationInformation retrievalContext evolution
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Kernel representation + linear analysis
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.fr
>> J. B. Tenenbaum, V. de Silva and J. C. Langford http://isomap.stanford.edu/handfig.html
Navigatein high dimensional space Data
RepresentationInformation retrievalContext evolution
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Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frInformation retrieval
• What for- User profiling- User identification- Battery discharge rate- Sequence induction…
• Classification problem- Decision theory- Example based programming- Learning machine
Select relevant cases
DataRepresentationInformation retrievalContext evolution
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Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frA brief historical perspectiveof machine learning
• Before machines- 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?
DataRepresentationInformation retrievalContext evolution
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>> T. Hastie, R. Tibshirani and J. Friedman, "The elements of statistical learning", Springer, 2001
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.fr
• Data- Training set
- Test point looking for such that
• Learning is balancing
1. Hypothesis set (Neural networks, Kernels)
2. Fitting criterion (least square, absolute value)
3. Compression criterion (penalization, Margin)
4. Balancing mechanism (cross validation, generalization)
What is learning?
Learning is summarizing
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nnii
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yxyxyx
Fit Summarize data data
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frLinear discriminationseparable case
+
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++
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+wx+ b=0
(w,b) ???
Use hyperplane
DataRepresentationInformation retrievalContext evolution
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How to correctlyclassify
all points?
Occam Razor's
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frLinear discriminationseparable case
+
+
+ +
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++
+
+
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+wx+ b=0
Be sparse
DataRepresentationInformation retrievalContext evolution
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How to correctlyclassify
all points?
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.fr
+
+
+ +
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++
+
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The classifier Margin
wx+ b=0Margin
Margin
Be sparse
DataRepresentationInformation retrievalContext evolution
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How to correctlyclassify
all points?
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.fr
+
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++
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Maximize the marginBe sparse
wx+ b=0
wx+ b=-1
wx+ b=1
Margin
Margin
Support Vector Machines: SVM
DataRepresentationInformation retrievalContext evolution
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How to correctlyclassify
all points?
>> V. N. Vapnik, "The nature of statistical learning theory", Springer-Verlag, 1995
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frWhat is learning?
Learning is summarizing
SVM
Fit Summarize data data
• Data- Training set
- Test point looking for such that
• Learning is balancing
1. Hypothesis set (Neural networks, Kernels)
2. Fitting criterion (least square, absolute value)
3. Compression criterion (penalization, Margin)
4. Balancing mechanism (cross validation, generalization)
)(ˆ
,,...,,,...,,
111
11
nnn
nnii
xfyfx
yxyxyx
>> S. Canu, A. Rakotomamonjy, Ozone peak and pollution forecasting using Support Vectors, IFAC workshop, Yokohama, 2001.
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frSummarize Inputadaptive scaling
• Enumerate all combination …and score
• Preprocessing- Information theory - Statistical test
• Wrapper- Use a relevance index - Learn and select together
Global formulation
Example of relevance index for a toy problemwith 2 relevant features and 50 irrelevants
DataRepresentationInformation retrievalContext evolution
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>> Y. Gandvalet and S. Canu, "Adaptive Scaling for Feature Selection in SVMs", accepted for publication at NIPS 2002
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.fr
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
DataRepresentationInformation retrievalContext evolution
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Summarize patterns
"Kernelize" wavelets>> A. Rakotomamonjy and S. Canu, "Frame, Reproducing Kernel, Regularization and Learning", accepted in JMLR 2002
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frLearning machines challenges1. Hypothesis set
Multi scale data representation: wavelets Use context: mixture of experts
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
DataRepresentationInformation retrievalContext evolution
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Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frContext assessment• 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?
DataRepresentationInformation retrievalContext evolution>>
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frContext 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 languageHow to retrieve the context?
Henry Lieberman: http://web.media.mit.edu/~lieber/
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frNew idea to deal with context• Current context: working memory
- Prior knowledge: transition law
• Available information: evidence- 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 contexthttp://htk.eng.cam.ac.uk/
DataRepresentationInformation retrievalContext evolution>>
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frContext: 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
DataRepresentationInformation retrievalContext evolution>>
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frBreak through
• What is information?- Computer science- Coding- Signal
• Mathematics- Statistics & computer science- Pattern recognition- Functional analysis- ??????
>> L. Devroye, L. Györfi and G. Lugosi, "A Probabilistic Theory of Pattern Recognition", Springer-Verlag 1996.
…remember Albert and relativity
Theoretical models are essentials (Mark Weiser, Computer Science Challenges for the Next Ten Years)
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frMy long bet
Before 2050We will faced a scientific revolutionregarding information definitionComparable with the one induced in physics by the relativity theory
$ 500To greenpeace
Long bet fundation at San Francisco http://www.longbets.org/
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frResearch challenges• create context
- how to define prior contexts: user’s needs- how to represent contexts: stochastic automaton- learn from data: modify, create and destroy context
• decide context - validate data software sensors- select relevant inputs representation + distance- select relevant patterns wavelets- select relevant situations SVM and kernel- make decision using data fusion Dempster-Shafer + EM
• loop with the user- reinforcement learning- user’s needs
Bayesian networks
Integrate: create relevant learning architecture
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frQuestions?• Asia
- Scurry™, Wearable & Virtual Keyboard - Samsung, - K. Doya for reinforcement
• America- 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 PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frFrom macroscopic…DataRepresentationInformation retrievalContext evolution
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Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.fr
http://www.mcell.cnl.salk.edu/
MCell Simulation of miniature endplatecurrent generation at the neuromuscular junction.
Image rendered with Pixar Photorealistic RenderMan.
…to Microscopic dataDataRepresentationInformation retrievalContext evolution
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Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frEmotion detection >> E-Motions Data
RepresentationInformation retrievalContext evolution
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>> R. Picard, Affective Computing, MIT Press, 1997 http://graphics.usc.edu/~dfidaleo/Emotion/ http://www.mis.atr.co.jp/~mlyons/facial_expression.html
Towards affective computing
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frLearning accuracy
• Find (a,b) such that
• Model the model- 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
How to compute error bars?
>> 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/
DataRepresentationInformation retrievalContext evolution
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1ˆˆPr bvvavAccuracy confidence
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.fr
Locally linear representations
Looking for hiden shapes
>> Sam T. Roweis & Lawrence K. Saul http://www.cs.toronto.edu/~roweis/lle/
DataRepresentationInformation retrievalContext evolution
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Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frMovie synthesis from text
Turing proof
>> Tony Ezzat and Tomaso Poggio http://cuneus.ai.mit.edu:8000/research/mary101/mary101.html
…from text to movie
DataRepresentationInformation retrievalContext evolution
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Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frFrom one expression to anotherDataRepresentationInformation retrievalContext evolution
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Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frNon euclidian metrics
http://cs.unm.edu/~joel/NonEuclid/
DataRepresentationInformation retrievalContext evolution
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Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frHyperbolic Self-Organizing MapDataRepresentationInformation retrievalContext evolution
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What is the "distance" between two objects
Séminaire PSIFRE CNRS 254616 Janvier 2003
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http://www.techfak.uni-bielefeld.de/ags/ni/projects/hsom/hsom.html
- 650 documents - from 16000 reviews - Internet Movie Database
Disney's animationMovies are closed
DataRepresentationInformation retrievalContext evolution
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Example on movies - HSOM
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frExample on moviesDataRepresentationInformation retrievalContext evolution
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Séminaire PSIFRE CNRS 254616 Janvier 2003
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DataRepresentationInformation retrievalContext evolution
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Curvlets
Deal with high dimensional spacehttp://www-stat.stanford.edu/~jstarck/
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.fr
DataRepresentationInformation retrievalContext evolution
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Curvlets
Deal with high dimensional space
The original image 64,536 coefficients.
http://www-stat.stanford.edu/~jstarck/
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.fr
-2 0 2-3
-2
-1
0
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0 1
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1
Select relevant situations
• Relevant representation- "Invent" features- Select features
• Relevant "distance"- map- Use kernel
• Summarize the examples- Define a relevant global criterion to be minimized- Support vector machines (SVM)
Be sparse
DataRepresentationInformation retrievalContext evolution
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Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frLearning architecture
• Agent - Data base - Communication• Metadata• Context language• Adaptability: control mechanism• Pre programming: anticipation• Open – modular – distributed
- 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/
DataRepresentationInformation retrievalContext evolution
Séminaire PSIFRE CNRS 254616 Janvier 2003
scanu@insa-rouen.frFuture 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