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cognitivepersonalizationtaskfingerprinting
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The source code and more information are published in the following repository: https://github.com/dennispaulino/TaskFingerprintingForCognitivePersonalizationOfWebTasksDesignPackage
Source code for the framework built for our study on assessing crowd workers behaviors for task design personalization in crowdsourcing. Beside crowdsourcing, this package can be used to perform cognitive personalization seamlessly in web tasks. If you use our work in your research, please cite the following paper published at Sensors 2023.
Dennis Paulino, Diogo Guimarães, António Correia, José Ribeiro, João Barroso and Hugo Paredes. A Model for Cognitive Personalization of Microtask Design. Sensors 2023, 23, 3571. https://doi.org/10.3390/s23073571
Example of installing and using the package:
pip install cognitivepersonalizationtaskfingerprinting
import cptf.cog_personalization_micro_task_fingerprinting as taskfingerprinting
import cptf.cog_personalization_deep_learning_model as dl
#It is performed the task fingerprinting technique based on prompt user to enter the path of the interaction log files, and then it will be generated a csv file
taskfingerprinting.main()
#Based on the task fingerprintings' csv file, the user will be prompted to select the file and develop a deep learning model
#CAUTION: It is best to look at the source code and adapt the respective parameters to better ajust for each specific dataset
dl.main()
Please feel free to contact Dennis Paulino (dpaulino@utad.pt) for further questions.
FAQs
The source code and more information are published in the following repository: https://github.com/dennispaulino/TaskFingerprintingForCognitivePersonalizationOfWebTasksDesignPackage
We found that cognitivepersonalizationtaskfingerprinting demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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