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A test dataset is used to evaluate the performance of the classifiers for the given task.

First, primary measures of performance on the given task can be used to infer workload.

The participants were asked to rate 5 questions regarding the given task.

This indicates that, for Bayesian optimization, hyperparameters should be adjusted carefully depending on the given task.

where E indicates whether or not the given task is effective.

An optimization problem is formulated considering the collision avoidance, the approximation of the given task curve and critical optimization criteria.

Then, the processor sends the AST to the interpreter which builds an execution context for the given task.

Task value can be predictive of students' motivation for the given task or, in relation to the current study, science.

To discriminate workload efficiently in the given task, there was a need to include nonlinear classifier with best EEG feature.

The files created and schedule data for the given task are also handled as task context data.

This paper proposes five methods for efficient exact Boolean schedulability tests for PTS, which returns the Boolean result that tells whether the given task is schedulable or not.

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