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ECAI
2004
Springer
14 years 1 months ago
Learning Complex and Sparse Events in Long Sequences
The Hierarchical Hidden Markov Model (HHMM) is a well formalized tool suitable to model complex patterns in long temporal or spatial sequences. Even if effective algorithms are ava...
Marco Botta, Ugo Galassi, Attilio Giordana
IUI
2003
ACM
14 years 1 months ago
Lessons learned in modeling schizophrenic and depressed responsive virtual humans for training
This paper describes lessons learned in developing the linguistic, cognitive, emotional, and gestural models underlying virtual human behavior in a training application designed t...
Robert C. Hubal, Geoffrey A. Frank, Curry I. Guinn
EUROSYS
2011
ACM
12 years 11 months ago
Striking a new balance between program instrumentation and debugging time
Although they are helpful in many cases, state-of-the-art bug reporting systems may impose excessive overhead on users, leak private information, or provide little help to the dev...
Olivier Crameri, Ricardo Bianchini, Willy Zwaenepo...
BMCBI
2010
141views more  BMCBI 2010»
13 years 8 months ago
BioPhysConnectoR: Connecting Sequence Information and Biophysical Models
Background: One of the most challenging aspects of biomolecular systems is the understanding of the coevolution in and among the molecule(s). A complete, theoretical picture of th...
Franziska Hoffgaard, Philipp Weil, Kay Hamacher
RTAS
2008
IEEE
14 years 2 months ago
Schedulability Analysis of MSC-based System Models
Message Sequence Charts (MSCs) are widely used for describing interaction scenarios between the components of a distributed system. Consequently, worst-case response time estimati...
Lei Ju, Abhik Roychoudhury, Samarjit Chakraborty