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From:
Stephan Sigg <[log in to unmask]>
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Date:
Wed, 10 Apr 2013 13:00:47 +0200
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[Our apologies if you receive multiple copies of this CFP]

 =======================================================================

                                        CALL FOR PAPERS
 AwareCast 2013: 2nd Workshop on recent advances in behavior prediction 
                         and pro-active pervasive computing
             (http://www.comtec.eecs.uni-kassel.de/awarecast/)

  In conjunction with 2013 ACM International Joint Conference on Pervasive
                  and Ubiquitous Computing (UbiComp 2013)
                  September 8-9, 2013, in Zurich, Switzerland

 =======================================================================

Context prediction breaks the border from reaction on past and present 
stimuli to proactive anticipation of actions. Research directions spread
from applications for context prediction over event prediction, 
architectures for context prediction, data formats, and algorithms. 
Recent work focuses on three main challenges:
1.  Prediction beyond location
2.  Benchmarks and common data sets
3.  Common development frameworks

While there have been contributions targeting some of these challenges, 
we still see them as unsolved. Thus we invite unique contribution 
addressing these challenges and provide a forum to facilitate 
collaboration among research groups focusing on context prediction.

TOPICS OF INTEREST INCLUDE, BUT ARE NOT LIMITED TO:
 
* ACCURATE PREDICTION OF SELDOM EVENTS: Important events are frequently 
  also seldom events. How can we train a system on events which are not 
  likely covered by training data sets? 
* IDENTIFICATION OF ACTIONS AND SITUATIONS SUITABLE FOR CONTEXT 
  PREDICTION: User behaviour is noisy and not necessarily contains 
  patterns which can be predicted. In particular, predictable patterns
  are frequently interleaved with non-predictable patterns. Inherently,
  the underlying (stochastic?) process has to feature some regularity or 
  trends.
* CONTINUOUS LEARNING: User behaviour and habit changes over time. To
  guarantee constant accuracy, the approach must be able to 'forget'
  patterns which grow unimportant.
* DEVELOPMENT FRAMEWORKS: To pave the way for a broader use of context
  prediction in applications, robust and easy to use frameworks are in
  need. These frameworks should simplify the development of context
  prediction applications and preferably be available as open source.
* NOVEL APPLICATIONS: As discussed above, research on context prediction
  used to focus heavily on location prediction. While contributions
  dealing with location prediction are welcome, when they address at
  least one of the other topics, we like to see novel application of
  context prediction.
* MULTI-USER AND MULTI-SENSOR PREDICTION: Since humans tend to behave
  similar, the context time series of other users may be helpful to
  increase the accuracy of context prediction for similar users.
  Additionally the utilization of multiple sensors may affect the
  robustness of the prediction approaches. 
* DATA SETS AND BENCHMARKS: Currently, comprehensive data-sets are
  created for context-computing. However, these data-sets are hardly
  sufficient to be applied for context prediction applications. In
  particular, data has to be sampled over longer time-spans and cover
  stochastic processes which are inherently predictable.
* PRIVACY AND TRUST: Shared time series but also the fact that context
  time series might cover events and actions of remote entities rises
  questions of privacy and trust.
 
IMPORTANT DATES:
Paper Submission Deadline:  May 17, 2013
Author Notification:  June 7, 2013 
Camera-ready version due: June 23, 2013
Workshop: September 8-9, 2013

PROGRAM COMMITTEE, SUBMISSION INSTRUCTIONS:
See the workshop website:
http://www.comtec.eecs.uni-kassel.de/awarecast/

CHAIRS: 
Klaus David, University of Kassel, Germany 
Bernd N. Klein, Institute decentralised Energy Technologies, Germany
Sian Lun Lau, Sunway University, Malysia
Stephan Sigg, National Institute of Informatics, Japan
Brian Ziebart, University of Illinoi, USA

CONTACT INFO:
Email: [log in to unmask]

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