ARDUOUS 2017 - 1st International Workshop on Annotation of useR Data for UbiquitOUs Systems

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. In particular, comparative evaluations require the production of benchmark datasets based on high-quality and consistent annotations. With ubiquitous systems relying more and more on large datasets for designing and testing models of users’ activities, the process of data labelling is becoming a major concern for the community.
To address the problem, this workshop focuses on 1) the role and impact of annotations in designing ubiquitous applications, 2) the process of labelling, and the requirements to produce high quality annotations, and 3) tools and automated methods for annotating user data. The goal of the workshop is to bring these two topics to the attention of researchers from interdisciplinary backgrounds, and to initiate a reflection on possible resolutions of the related problems.
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 labeling 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;
processes of and best practices in annotating user data;
methods towards an automation of the annotation;
improving and evaluating the annotation quality;
ethical 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.