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

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

Kassel, Germany
Event Date: March 22, 2021 - March 26, 2021
Submission Deadline: November 09, 2020
Notification of Acceptance: January 05, 2021
Camera Ready Version Due: January 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 Papers

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.



Best Deals



Summary

ARDUOUS 2021 : 5th International Workshop on Annotation of useR Data for UbiquitOUs Systems will take place in Kassel, Germany. It’s a 5 days event starting on Mar 22, 2021 (Monday) and will be winded up on Mar 26, 2021 (Friday).

ARDUOUS 2021 falls under the following areas: ARTIFICIAL INTELLIGENCE, UBIQUITOUS COMPUTING, DATA SCIENCE, ANNOTATION, etc. Submissions for this Workshop can be made by Nov 09, 2020. Authors can expect the result of submission by Jan 05, 2021. Upon acceptance, authors should submit the final version of the manuscript on or before Jan 31, 2021 to the official website of the Workshop.

Please check the official event website for possible changes before you make any travelling arrangements. Generally, events are strict with their deadlines. It is advisable to check the official website for all the deadlines.

Other Details of the ARDUOUS 2021

  • Short Name: ARDUOUS 2021
  • Full Name: 5th International Workshop on Annotation of useR Data for UbiquitOUs Systems
  • Timing: 09:00 AM-06:00 PM (expected)
  • Fees: Check the official website of ARDUOUS 2021
  • Event Type: Workshop
  • Website Link: https://text2hbm.org/arduous
  • Location/Address: Kassel, Germany


Credits and Sources

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


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