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Quantifying and Leveraging Uncertain and Imprecise Answers in Multiple Choice Questionnaires for Crowdsourcing: Quantifier et exploiter des réponses incertaines et imprécises dans des questionnaires à choix multiples pour le crowdsourcing.

, , , , , , , and . IHM, page 14:1-14:10. ACM, (2024)

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Modélisation de l'incertitude et de l'imprécision de données de crowdsourcing : MONITOR., , , and . EGC, volume E-36 of RNTI, page 425-432. Éditions RNTI, (2020)Validation of Smets' Hypothesis in the Crowdsourcing Environment., , , and . BELIEF, volume 12915 of Lecture Notes in Computer Science, page 259-268. Springer, (2021)Real Bird Dataset with Imprecise and Uncertain Values., , , , and . BELIEF, volume 13506 of Lecture Notes in Computer Science, page 275-285. Springer, (2022)Évaluation de la qualité des contributions et des contributeurs sur plateformes de crowdsourcing. (Evaluation of the quality of contributions and contributors on crowdsourcing platforms).. University of Rennes 1, France, (2021)Datasets with Rich Labels for Machine Learning., , , , and . FUZZ, page 1-6. IEEE, (2023)Modeling evolutionary responses in crowdsourcing MCQ using belief function theory., , , and . KES, volume 225 of Procedia Computer Science, page 2575-2584. Elsevier, (2023)Modeling Uncertainty and Inaccuracy on Data from Crowdsourcing Platforms: MONITOR., , , and . ICTAI, page 776-783. IEEE, (2019)Automated Hierarchical Conflict Reduction for Crowdsourced Annotation Tasks Using Belief Functions., , , and . BELIEF, volume 14909 of Lecture Notes in Computer Science, page 244-252. Springer, (2024)Contributors profile modelization in crowdsourcing platforms., , , and . CoRR, (2018)A Mean Distance Between Elements of Same Class for Rich Labels., , , and . BELIEF, volume 14909 of Lecture Notes in Computer Science, page 207-215. Springer, (2024)