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In the experiments of the first stage, we repeated the same testing procedure 20 times with randomly and independently generated testing data sets.
The parameters of the HDTLM are calibrated by a design of experiments (DOE) on randomly generated testing instances.
To make fair comparison with MetaCluster 5.0 on clustering, we generated testing dataset B with reads sampled from genomes with different coverages.
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Items in the organizational culture questionnaire were generated, tested and validated according to a rigorous process.
Each individual GARP model is a set of 50 rules that are randomly generated, tested and modified.
We randomly place two, three or four irrelevant objects onto the target object to generate testing images with various occlusions.
Using model checking and execution generated tests to find bugs in real code (postscript, PDF, PPT).
Distributions for the other random variables were obtained using previously generated test data.
Non-linear finite element models were developed and validated against the generated test data.
The performance of the developed models was compared among themselves for 4 randomly generated test cases.
The performance of all developed algorithms are then analysed using randomly generated test instances.
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