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W.M. Campbell, Samuel Barrett, J. Acevedo-Aviles, B. Delaney, and C. Weinstein. Detection and simulation of scenarios with hidden Markov models and event dependency graphs. In Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on, pp. 5434–5437, 2010.
The wide availability of signal processing and language tools to extract structured data from raw content has created a new opportunity for the processing of structured signals. In this work, we explore models for the simulation and recognition of scenarios-i.e., time sequences of structured data. For simulation, we construct two models-hidden Markov models (HMMs) and event dependency graphs. Combined, these two simulation methods allow the specification of dependencies in event ordering, simultaneous execution of multiple scenarios, and evolving networks of data. For scenario recognition, we consider the application of multi-grained HMMs. We explore, in detail, mismatch between training scenarios and simulated test scenarios. The methods are applied to terrorist scenario detection with a simulation coded by a subject matter expert.
@INPROCEEDINGS{ICASSP10-Campbell,
author={Campbell, W.M. and Samuel Barrett and Acevedo-Aviles, J. and Delaney, B. and Weinstein, C.},
booktitle={Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on},
title={Detection and simulation of scenarios with hidden {M}arkov models and event dependency graphs},
year={2010},
pages={5434-5437},
abstract={The wide availability of signal processing and language tools to extract structured data from raw content has created a new opportunity for the processing of structured signals. In this work, we explore models for the simulation and recognition of scenarios-i.e., time sequences of structured data. For simulation, we construct two models-hidden Markov models (HMMs) and event dependency graphs. Combined, these two simulation methods allow the specification of dependencies in event ordering, simultaneous execution of multiple scenarios, and evolving networks of data. For scenario recognition, we consider the application of multi-grained HMMs. We explore, in detail, mismatch between training scenarios and simulated test scenarios. The methods are applied to terrorist scenario detection with a simulation coded by a subject matter expert.},
keywords={data structures;feature extraction;graph theory;hidden Markov models;signal processing;data network;event dependency graph;hidden Markov model;language tool;multigrained HMM;scenario detection;scenario recognition;signal processing;structured data extraction;structured signal;time sequence;Data mining;Discrete event simulation;Event detection;Hidden Markov models;Information systems;Laboratories;Ontologies;Signal processing;Speech;Testing;Goal Recognition;Hidden Markov Models},
doi={10.1109/ICASSP.2010.5494918},
ISSN={1520-6149},
}
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