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ARDUOUS 2021 : 5th International Workshop on Annotation of useR Data for UbiquitOUs Systems

Kassel, Germany
Event Date: Mar 22, 2021 - Mar 26, 2021
Submission Deadline: Nov 09, 2020
Notification of Acceptance: Jan 05, 2021
Camera Ready Version Due : Jan 31, 2021

About

Labelling user data is a central part of the design and evaluation of pervasive systems that aim to support the user through situation-aware reasoning. It is essential both in designing and training the system to recognise and reason about the situation, either through the definition of a suitable situation model in knowledge-driven applications, or though the preparation of training data for learning tasks in data-driven models. Hence, the quality of annotations can have a significant impact on the performance of the derived systems. Labelling is also vital for validating and quantifying the performance of applications. With pervasive systems relying increasingly on large datasets for designing and testing models of users’ activities, the process of data labelling is becoming a major concern for the community. Even more, with the increase of pervasive applications relying on annotated data, it becomes important to develop standards and normalisation methodologies for transferability of annotated data across different applications.

Call For Paper

We invite you to submit papers with a maximum of 6 pages that offer new empirical or theoretical insights on the challenges and innovative solutions associated with labelling of user data, as well as on the impact that labeling choices have on the user and the developed system. The topics of interest include, but are not limited to:

  • methods and intelligent tools for annotating user data for pervasive systems;
  • methods for standardisation and normalisation in annotation practices;
  • influence of interface on annotation;
  • processes of and best practices in annotating user data;
  • methods towards automation of the annotation process;
  • improving and evaluating the quality of annotations;
  • ethical and privacy issues concerning the annotation of user data;
  • beyond the labels: ontologies for semantic annotation of user data;
  • high-quality and re-usable annotation for publicly available datasets;
  • impact of annotation on a ubiquitous and intelligent system’s performance;
  • building classifier models that are capable of dealing with multiple (noisy) annotations and/or making use of taxonomies/ontologies;
  • the potential value of incorporating modelling of the annotators into predictive models.

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