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Special Issue "Annotation of User Data for Sensor-Based Systems"
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".
Deadline for manuscript submissions: closed (31 May 2018) | Viewed by 34656
Special Issue Editors
Interests: activity and intention recognition; human behavior models; knowledge elicitation; natural language processing; automatic extraction of behavior models from textual sources
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Topics: Methods for Data Labelling for Intelligent Systems
Interests: computer vision; machine learning; AI-assisted healthcare
Interests: artificial intelligence; computer vision; affective computing; computational social science; Markov decision processes
Special Issue Information
Labelling user data is a central part of the design and evaluation of sensors and sensor-based systems that aim to support the user through situation-aware reasoning. It is essential, both in designing and training a sensor-based system to recognize and reason about the situation, either through the design of new sensors, the definition of suitable observation and situation models 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 sensors and sensor-based applications as well as for selecting the best performing sensor setup and configuration.
With sensor-based systems relying increasingly on large datasets with multiple sensors, the process of data labelling is becoming a major concern for the community.
To address these problems, this Special Issue contains selected papers from the International Workshop on Annotation of useR Data for UbiquitOUs Systems (ARDUOUS)(2017/2018) with focus on:
1) intelligent and interactive tools and automated methods for annotating large sensor datasets.
2) the role and impact of annotations in designing sensor-based applications,
3) the process of labelling, and the requirements to produce high quality annotations, especially in the context of large sensor datasets.
In addition, we are looking for outstanding submissions, which will extend the state-of-the-art in annotation for sensor-based systems. The scope of the issue includes but is not limited to:
- methods and intelligent tools for annotating sensor data
- processes of and best practices in annotating sensor data
- annotation methods and tools for sensor setup and configuration
- sensors and sensor-based methods and practices towards an automation of the annotation
- improving and evaluating the annotation quality for better sensor interpretation
- ethical issues concerning the collection and annotation of sensor data
- beyond the labels: ontologies for semantic annotation of sensor data
- high-quality and re-usable annotation for publicly available sensor datasets
- impact of annotation on a sensor-based 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
Dr.-Ing. Kristina Yordanova
Dr. Adeline Paiement
Prof. Jesse Hoey
Manuscript Submission Information
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