ARDUOUS 2019

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. This also reflects the increasing need of (semi-)automated annotation tools and knowledge transfer methodologies, which can reduce the manual annotation effort and to improve the annotation performance in large datasets.

To address the problem, this year’s workshop focuses on tools and methods for annotation of data for diverse tasks and settings and such for (semi-)automated annotation of large user datasets and annotation reusability across datasets.

Furthermore, we aim to address the general problems of

1.  the role and impact of annotations in designing ubiquitous applications,
2. the process of labelling, and the requirements to produce high quality annotations especially in the context of large datasets.

The goal of the workshop is to bring these topics to the attention of researchers from interdisciplinary backgrounds, and to initiate a reflection on possible resolutions of the related problems.