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From:
Stephan Sigg <[log in to unmask]>
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Date:
Mon, 5 Mar 2012 09:11:08 +0900
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Extended submission deadline: 

14.03.2012

==================================================================
CALL FOR PAPERS

Workshop on recent advances in behavior prediction and pro-active pervasive 
computing (AwareCast)

(http://www.ibr.cs.tu-bs.de/dus/Awarecast/)

in conjunction with
the 10th International Conference on Pervasive Computing (Pervasive 2012)
(http://pervasiveconference.org/2012/) 

in Newcastle, UK, June 19th, 2012
==================================================================


Important dates

Paper submission:			March 14, 2012 [Extended]
Notification of acceptance:	April 06, 2012 
Camera Ready submission:	April 20, 2012
Workshop:				June 19, 2012


Scope

Behavior and context prediction breaks the border from reaction on past and 
present stimuli to proactive anticipation of actions. 
Researchers have for about one decade now considered the prediction of such 
stimuli to enable pro-active context computing. 
Research directions spread from applications for behavior and context 
prediction over event prediction, information retrieval, machine learning, 
architectures for context prediction, data formats and algorithms. 

Even though a great diversity of applications for behavior and context 
prediction has been proposed, a common methodology or platform has not yet 
crystallised. 
Application developers are forced to start from scratch since previous authors 
seldom provided usable sources of their applications that could be extended. 
To foster the integration into applications, support for application 
developers has to be improved. We require a widely accepted architecture and 
toolkit - easy to use; with an open API; comprising common algorithms, 
accepted data sets and benchmarks. It should enable researchers to test 
prediction algorithms in a common environment on accepted data sets as well as 
to extend and import it by own algorithms and data sets.

When it comes to algorithms for context prediction, a comprehensive comparison 
of strengths and weaknesses on benchmark data sets is yet missing. The 
motivation for choosing an algorithm for a specific application is not seldom 
driven by the experience and education of the researcher. Therefore, inherent 
properties such as the structure and requirements of the data as well as the 
application regarding accuracy and processing load are ignored. To raise the 
field to a level at which it might be integrated in commercial applications, 
common, widely accepted data sets need to be established as well as accepted 
benchmarks.

Analytic studies mainly consider the computational complexity of time-series 
forecasting methods. They are required to establish a theoretically sound 
background for applications. Data formats or impacts of the restriction to few 
symbolical formats might foster comprehension of basic issues for prediction. 
Additionally, the computational complexity and the ability to distribute 
computational load among nodes in a network are promising research directions 
in order to enable prediction in systems of distributed, resource limited 
nodes.

Promising ideas, broadening the field have been mentioned but are addressed 
superficially. A prominent example is prediction sharing among nodes in 
proximity. Related questions regard privacy and trust, service quality, 
communication and storage cost and accuracy amplification through redundancy. 
Likewise, the sharing of time series might be utilised for correction of 
measurement errors or predictions. Also, authors seldom address the prediction 
of rare events. In particular, for disaster or accident prevention, we would 
like to prevent extremely unlikely events for which possibly no training data 
exists. 

After about one decade of activities in the field of behavior and context 
prediction, the workshop will bring together researchers of this field and 
reveal important open issues. 

Among these are
- Accurate prediction of seldom events
- Continuous learning
- User behaviour and habit changes over time
- Prediction in public spaces
- Machine learning
- Information Retrieval
- Creation and dissemination of data sets and benchmarks for pro-active 
computing and context prediction
- Sharing of prediction and time series
- Privacy and trust
- Context prediction
- Pattern matching and statistical approaches
- Prediction of low level vs. high level context
- User routine
- Location, presence, availability, situation and action prediction
- Proactive resource management
- Algorithms for context prediction and time series forecasting
- Event prediction
- Accuracy of prediction methods
- Computational complexity of prediction algorithms
- Novel application areas, including applications for sustainability
- Privacy preserving prediction approaches
- Crowd sourcing for collaborative context prediction

We are seeking unpublished and original submissions in PDF format. 
Papers must not exceed 12 pages formatted according to the LNCS format. 
Papers will be rigorously reviewed by an international technical program 
committee. 
Submissions will be evaluated on the basis of originality, significance of the 
contribution to the field, technical correctness and presentation. 
If a submitted paper overlaps in content with previously published or 
simultaneously reviewed work, the paper should make explicit how the work 
offers unique and substantial contribution beyond what has already been 
published or submitted. Accepted submissions will be published in the 
Pervasive 2012 Workshop Proceedings.  


Workshop organizers

Niklas Klein, Kassel University, Kassel, Germany
Stephan Sigg, National Institute of Informatics, Tokyo, Japan
Brian Ziebart, Carnegie Mellon University, Pittsburgh, US


Program Committee

Christos Anagnostopoulos, Ionian University, Greece
Martin Atzmueller, Kassel University, Germany
Sebastian Bader, Rostock University, Germany
Christian Becker, University of Mannheim, Germany
Oliver Brdiczka, PARC, US
Michael Beigl, Karlsruhe Institute of Technology, Germany
Licia Capra, University College of London, UK
Diane J. Cook, Washington State University, US
Klaus David, Kassel University, Germany
Anind Dey, Carnegie Mellon University, US
Haym Hirsh, Rutgers University, US
Winfried Lamersdorf, University of Hamburg, Germany
Kun Chang Lee, Sungkyunkwan University, Korea
Teddy Mantoro, KICT-IIUM, Malaysia
Mirco Musolesi, University of Birmingham, UK
Andrei Popleteev, Create-Net, Italy
Andreas Riener, Johannes Kepler University Linz, Austria
Nirmalya Roy, Institute for Infocomm Research (I2R), Singapore
Kristof van Laerhoven, TU Darmstadt, Germany
Sang Min Yoon, Yonsei University, Korea
Arkady Zaslavsky, CSIRO ICT Centre, Australia 
Mi Zhang, University of Southern California, US

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