Context Future Technology

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Published on June 15, 2007

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Intelligent sensorand learning challengesfor context aware appliances:  Intelligent sensor and learning challenges for context aware appliances Intelligent sensor and learning challenges for context aware appliances andgt;andgt; Stéphane Canu scanu@insa-rouen.fr asi.insa-rouen.fr/~scanu INSA Rouen, France - EU Laboratoire PSI 1984: La souris et leMacintoch:  1984: La souris et leMacintoch 200X : la nouvelle rupture 'break through' La technologie d'aujourd'hui:  La technologie d'aujourd'hui Loi de Moore Communication 'sans fil' L'ère des données Quelles applications ? Wearable:  Wearable IHM:  IHM 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:  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/ Reasearch on wearable:  Reasearch on wearable Wearable:  Wearable context aware appliances:  context aware appliances Phone by night http://mediacup.teco.edu/overview/engl/m_what.html The mediacup (calm version of the active badge) General Motors and CMU:  General Motors and CMU The car - drives together - informs you - in a parking… GM/CMU Companion driver interface system Oops! Where is my car?:  Oops! Where is my car? Old fashion software design: process Match the sentence Send the query to the satellite Satellite send query to the car on its own frequency 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 andgt;andgt; Your Wish is My Command: Programming by Example Henry Lieberman, editor, Published by Morgan Kaufmann, 2001. Calm technology:  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 andgt;andgt; M. Weiser 'The Computer for the 21st Century.' Scientific American, September 1991 What is the context?:  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 situation Knowledge? How to find it from data? + history… Adapted From Henry Lieberman and Ted Selker, Out of Context: Computer Systems That Adapt To, and Learn From, Context, IBM Systems Journal 39, 2000. Sensing context from the environmentpresentation roadmap:  Sensing context from the environment presentation roadmap andgt;andgt; Kristof Van Laerhoven, Kofi Aidoo: Teaching Context to Applications In Personal and Ubiquitous Computing, Volume 5 Issue 1 (2001) pp 46-49 Data Representation Information retrieval Context evolution User interaction Context from data:  Context 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 Data Representation Information retrieval Context evolution andgt;andgt; Biological sensors:  Biological sensors http://www.teco.edu/tea/sensors.html How are you? Data Representation Information retrieval Context evolution andgt;andgt; Expression recognition:  http://markov.ucsd.edu/~movellan/mplab/index.html Machine Perception Lab Face Detection and Expression Recognition Expression recognition Data Representation Information retrieval Context evolution andgt;andgt; Too much informationkills information:  Too much information kills information Critic of the 'Data Era' Data smog Non measurable things Ethical consequences the Orwellian future Filter data! Data Representation Information retrieval Context evolution andgt;andgt; 'We are drowning in information and starving for knowledge.' - Rutherford D. Roger Intelligent sensors:  Intelligent 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 Data Representation Information retrieval Context evolution andgt;andgt; andgt;andgt;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) Data validation:  Data 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 andgt;andgt; http://www.accenture.com/xd/xd.asp?it=enWebandamp;xd=services\technology\research\tech_sensor_matrix.xml andgt;andgt; 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 Data Representation Information retrieval Context evolution andgt;andgt; Software sensor:  Software 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 Data Representation Information retrieval Context evolution andgt;andgt; environment Towards proprioceptors:  Towards 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 d = Curse of dimensionality (Belman) Data Representation Information retrieval Context evolution andgt;andgt; andgt;andgt; E. Petriu et al., 'Sensor based information appliances', What is data?:  What 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 Data Representation Information retrieval Context evolution andgt;andgt; Summarize data:  Summarize 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 Data Representation Information retrieval Context evolution andgt;andgt; andgt;andgt; 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 Kernel representationDistance maps:  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. Kernel representation Distance maps Analyze data proximity through the kernel map i j andgt;andgt; B. Scholkopf and A. Smola, 'Leaning with Kernels', MIT Press, 2001 Example of kernel map:  Example of kernel map Data clouds in two dimensions Associated kernel map Class 2 Class 1 Class 2 Even in d dimensions you can visualize andgt;andgt; S. Canu and al., 'Functionnal learning through kernels', invited lecture at the NATO institute in Leuven, 2002 Looking for hiden shapes:  andgt;andgt; 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 Data Representation Information retrieval Context evolution andgt;andgt; Kernel representation + linear analysis Navigatein high dimensional space:  andgt;andgt; J. B. Tenenbaum, V. de Silva and J. C. Langford http://isomap.stanford.edu/handfig.html Navigate in high dimensional space Data Representation Information retrieval Context evolution andgt;andgt; Information retrieval:  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 Data Representation Information retrieval Context evolution andgt;andgt; A brief historical perspectiveof machine learning:  A brief historical perspective of 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? Data Representation Information retrieval Context evolution andgt;andgt; andgt;andgt; T. Hastie, R. Tibshirani and J. Friedman, 'The elements of statistical learning', Springer, 2001 What is learning?:  Data Training set Test point looking for such that Learning is balancing Hypothesis set (Neural networks, Kernels) Fitting criterion (least square, absolute value) Compression criterion (penalization, Margin) Balancing mechanism (cross validation, generalization) What is learning? Learning is summarizing Linear discriminationseparable case:  Linear discrimination separable case + + + + + + + wx+ b=0 (w,b) ??? Use hyperplane Data Representation Information retrieval Context evolution andgt;andgt; How to correctly classify all points? Occam Razor's Linear discriminationseparable case:  Linear discrimination separable case + + + + + + + wx+ b=0 Be sparse Data Representation Information retrieval Context evolution andgt;andgt; How to correctly classify all points? The classifier Margin:  The classifier Margin wx+ b=0 Margin Margin Be sparse Data Representation Information retrieval Context evolution andgt;andgt; How to correctly classify all points? Maximize the marginBe sparse:  Maximize the margin Be sparse wx+ b=0 wx+ b=-1 wx+ b=1 Margin Margin Support Vector Machines: SVM Data Representation Information retrieval Context evolution andgt;andgt; How to correctly classify all points? andgt;andgt; V. N. Vapnik, 'The nature of statistical learning theory', Springer-Verlag, 1995 What is learning?:  What is learning? Learning is summarizing SVM Data Training set Test point looking for such that Learning is balancing Hypothesis set (Neural networks, Kernels) Fitting criterion (least square, absolute value) Compression criterion (penalization, Margin) Balancing mechanism (cross validation, generalization) andgt;andgt; S. Canu, A. Rakotomamonjy, Ozone peak and pollution forecasting using Support Vectors, IFAC workshop, Yokohama, 2001. Summarize Inputadaptive scaling:  Summarize Input adaptive 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 problem with 2 relevant features and 50 irrelevants Data Representation Information retrieval Context evolution andgt;andgt; andgt;andgt; Y. Gandvalet and S. Canu, 'Adaptive Scaling for Feature Selection in SVMs', accepted for publication at NIPS 2002 Summarize patterns:  Dimension reduction by andgt;andgt; multi-resolution analysis (just like in your eyes…) Learn at the relevant scale andgt;andgt; multi scale representation Efficient implementation ridgelets, curvelets wavelets’ kernel Data Representation Information retrieval Context evolution andgt;andgt; Summarize patterns 'Kernelize' wavelets andgt;andgt; A. Rakotomamonjy and S. Canu, 'Frame, Reproducing Kernel, Regularization and Learning', accepted in JMLR 2002 Learning machines challenges:  Learning machines challenges Hypothesis set Multi scale data representation: wavelets Use context: mixture of experts Fitting criterion Sparse distance criterion Select relevant input (adaptive scaling) Relevant distance: adapt the kernel Compression criterion Information issues Global optimization Balancing mechanism Efficient direct algorithm (one shot learning) Towards Context based learning Data Representation Information retrieval Context evolution andgt;andgt; Context assessment:  Context 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? Data Representation Information retrieval Context evolution andgt;andgt; Context implementation:  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/ New idea to deal with context:  New 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 context http://htk.eng.cam.ac.uk/ Data Representation Information retrieval Context evolution andgt;andgt; Context: Research chalenges:  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 Data Representation Information retrieval Context evolution andgt;andgt; Break through:  Break through What is information? Computer science Coding Signal Mathematics Statistics andamp; computer science Pattern recognition Functional analysis ?????? andgt;andgt; 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) My long bet:  My long bet 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:  Research 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 Questions?:  Questions? Asia Scurry™, Wearable andamp; 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 Slide48:  From macroscopic…:  From macroscopic… Data Representation Information retrieval Context evolution andgt;andgt; …to Microscopic data:  http://www.mcell.cnl.salk.edu/ MCell Simulation of miniature endplate current generation at the neuromuscular junction. Image rendered with Pixar Photorealistic RenderMan. …to Microscopic data Data Representation Information retrieval Context evolution andgt;andgt; Emotion detection >> E-Motions:  Emotion detection andgt;andgt; E-Motions Data Representation Information retrieval Context evolution andgt;andgt; andgt;andgt; 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 Learning accuracy:  Learning 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? andgt;andgt; R. Tibshirani, 'A comparison of some error estimates for neural network models,' Neural Computation, 8, 152-163, 1996. andgt;andgt; 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/ Data Representation Information retrieval Context evolution andgt;andgt; Accuracy confidence Looking for hiden shapes:  Locally linear representations Looking for hiden shapes andgt;andgt; Sam T. Roweis andamp; Lawrence K. Saul http://www.cs.toronto.edu/~roweis/lle/ Data Representation Information retrieval Context evolution andgt;andgt; Movie synthesis from text:  Movie synthesis from text Turing proof andgt;andgt; Tony Ezzat and Tomaso Poggio http://cuneus.ai.mit.edu:8000/research/mary101/mary101.html …from text to movie Data Representation Information retrieval Context evolution andgt;andgt; From one expression to another:  From one expression to another Data Representation Information retrieval Context evolution andgt;andgt; Non euclidian metrics:  Non euclidian metrics http://cs.unm.edu/~joel/NonEuclid/ Data Representation Information retrieval Context evolution andgt;andgt; Hyperbolic Self-Organizing Map:  Hyperbolic Self-Organizing Map Data Representation Information retrieval Context evolution andgt;andgt; What is the 'distance' between two objects Example on movies - HSOM:  http://www.techfak.uni-bielefeld.de/ags/ni/projects/hsom/hsom.html 650 documents from 16000 reviews Internet Movie Database Disney's animation Movies are closed Data Representation Information retrieval Context evolution andgt;andgt; Example on movies - HSOM Example on movies:  Example on movies Data Representation Information retrieval Context evolution andgt;andgt; Curvlets:  Data Representation Information retrieval Context evolution andgt;andgt; Curvlets Deal with high dimensional space http://www-stat.stanford.edu/~jstarck/ Curvlets:  Data Representation Information retrieval Context evolution andgt;andgt; Curvlets Deal with high dimensional space The original image 64,536 coefficients. http://www-stat.stanford.edu/~jstarck/ Select relevant situations:  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 Data Representation Information retrieval Context evolution andgt;andgt; Learning architecture:  Learning 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/ Data Representation Information retrieval Context evolution Future appliances?:  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

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