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Hidden Markov Model code developed from scratch. Configured to run on Linux BlueHive Supercomputing Cluster.

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Name		Name	Last commit message	Last commit date
Latest commit History 26 Commits
analysis		analysis
example		example
input_sequences/subset_data		input_sequences/subset_data
sh		sh
README.md		README.md
hmm.py		hmm.py
lstm_hmm.py		lstm_hmm.py
runner.sh		runner.sh
truth_bluff.py		truth_bluff.py

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Hidden Markov Models (HMM)

Hidden Markov Model code developed from scratch to be used to identify deceptive behavior from the ROC-HCI deception dataset.

Authors: Taylan Sen and Matt Levin

Main Files/Folders

hmm.py - Implements and test a hidden Markov model
truth_bluff.py - Dual-HMM classification on a dataset using cross-validation
lstm_hmm.py - Modified HMM to avoid exponential decay of prolonged hidden states
runner.sh - Used to deploy code on a BlueHive computing cluster (SLURM system)
analysis - Jupyter notebooks to analyze results and code to apply confidence-based thresholds to results
sh - Shell scripts for dispatching jobs to obtain the dataset and other utilities

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Hidden Markov Model code developed from scratch. Configured to run on Linux BlueHive Supercomputing Cluster.

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