Aerial robots with cameras on board can be used in surface inspection to observe areas that are difficult to reach by other means. In this type of problem, it is desirable for aerial robots to have a high degree of autonomy. A way to provide more autonomy would be to use computer vision techniques to automatically detect anomalies on the surface. However, the performance of automated visual recognition methods is limited in uncontrolled environments, so that in practice it is not possible to perform a fully automatic inspection. This paper presents a solution for visual inspection that increases the degree of autonomy of aerial robots following a semi-automatic approach. The solution is based on human-robot collaboration in which the operator delegates tasks to the drone for exploration and visual recognition and the drone requests assistance in the presence of uncertainty. We validate this proposal with the development of an experimental robotic system using the software framework Aerostack. The paper describes technical challenges that we had to solve to develop such a system and the impact on this solution on the degree of autonomy to detect anomalies on the surface.

This work presents a longitudinal study of diversity among the Affective Computing research community members. We explore several dimensions of diversity, including gender, geography, institutional types of affiliations and selected combinations of dimensions. We cover the last 10 years of the IEEE Transactions on Affective Computing (TAFFC) journal and the International Conference on Affective Computing and Intelligent Interaction (ACII), the primary sources of publications in Affective Computing. We also present an analysis of diversity among the members of the Association for the Advancement of Affective Computing (AAAC). Our findings reveal a "leaky pipeline" in the field, with a low -albeit slowly increasing over the years-representation of women. They also show that academic institutions clearly dominate publications, ahead of industry and governmental centres. In terms of geography, most publications come from the USA, contributions from Latin America or Africa being almost non-existent. Lastly, we find that diversity in the characteristics of researchers (gender and geographic location) influences diversity in the topics. To conclude, we analyse initiatives that have been undertaken in other AI-related research communities to foster diversity, and recommend a set of initiatives that could be applied to the Affective Computing field to increase diversity in its different facets. The diversity data collected in this work are publicly available, ensuring strict personal data protection and governance rules.

<p>This work presents a longitudinal study of diversity among the Affective Computing research community members. We explore several dimensions of diversity, including gender, geography, institutional types of affiliations and selected combinations of dimensions. We cover the last 10 years of the IEEE Transactions on Affective Computing (TAFFC) journal and the International Conference on Affective Computing and Intelligent Interaction (ACII), the primary sources of publications in Affective Computing. Our findings reveal notable correlations between different types of diversity, such as gender and institutional type, geography and topics, as well as topics and first author’s gender. We also present an analysis of diversity among the members of the Association for the Advancement of Affective Computing (AAAC). Finally, we analyse diversity initiatives that have been undertaken in other AI-related research communities to foster diversity, and conclude on a set of initiatives that could be applied to the Affective Computing field to increase diversity in its different facets. The data collected in this work will be publicly available, ensuring strict personal data protection and governance rules.</p>

<p>This work presents a longitudinal study of diversity among the Affective Computing research community members. We explore several dimensions of diversity, including gender, geography, institutional types of affiliations and selected combinations of dimensions. We cover the last 10 years of the IEEE Transactions on Affective Computing (TAFFC) journal and the International Conference on Affective Computing and Intelligent Interaction (ACII), the primary sources of publications in Affective Computing. Our findings reveal notable correlations between different types of diversity, such as gender and institutional type, geography and topics, as well as topics and first author’s gender. We also present an analysis of diversity among the members of the Association for the Advancement of Affective Computing (AAAC). Finally, we analyse diversity initiatives that have been undertaken in other AI-related research communities to foster diversity, and conclude on a set of initiatives that could be applied to the Affective Computing field to increase diversity in its different facets. The data collected in this work will be publicly available, ensuring strict personal data protection and governance rules.</p>