[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
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
* 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
* 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.
Paper Submission Deadline (last extension): May 31, 2013
Author Notification: June 14, 2013
Camera-ready version due: June 23, 2013
Workshop: September 8, 2013
PROGRAM COMMITTEE, SUBMISSION INSTRUCTIONS:
See the workshop website:
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 at Chicago, USA
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