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FUTURE MACHINE: Making Myths & Designing Technology for a Responsible Future: Making Myths and Entanglement: Community engagement at the edge of participatory design and user experience (2023)
Presentation / Conference Contribution
Jacobs, R., Spence, J., Abbott, F., Chamberlain, A., Heim, W., Yemaoua Dayo, A., …King, J. (2023). FUTURE MACHINE: Making Myths & Designing Technology for a Responsible Future: Making Myths and Entanglement: Community engagement at the edge of participatory design and user experience. . https://doi.org/10.1145/3616961.3616979

This paper explores the unique methods and strategies employed by a team of artists, in collaboration with engineers, programmers, a climate scientist, researchers and members of the public, who have come together to create the Future Machine, with t... Read More about FUTURE MACHINE: Making Myths & Designing Technology for a Responsible Future: Making Myths and Entanglement: Community engagement at the edge of participatory design and user experience.

Push-to-See: Learning Non-Prehensile Manipulation to Enhance Instance Segmentation via Deep Q-Learning (2022)
Presentation / Conference Contribution
Serhan, B., Pandya, H., Kucukyilmaz, A., & Neumann, G. (2022). Push-to-See: Learning Non-Prehensile Manipulation to Enhance Instance Segmentation via Deep Q-Learning. In IEEE International Conference on Robotics and Automation (ICRA 2022) (1513-1519). https://doi.org/10.1109/ICRA46639.2022.9811645

Efficient robotic manipulation of objects for sorting and searching often rely upon how well the objects are perceived and the available grasp poses. The challenge arises when the objects are irregular, have similar visual features (e.g., textureless... Read More about Push-to-See: Learning Non-Prehensile Manipulation to Enhance Instance Segmentation via Deep Q-Learning.

ALTCAI: Enabling the Use of Embodied Conversational Agents to Deliver Informal Health Advice during Wizard of Oz Studies (2021)
Presentation / Conference Contribution
Galvez Trigo, M. J., Porcheron, M., Egede, J., Fischer, J. E., Hazzard, A., Greenhalgh, C., …Valstar, M. (2021). ALTCAI: Enabling the Use of Embodied Conversational Agents to Deliver Informal Health Advice during Wizard of Oz Studies. In Proceedings of CUI 2021 : Conversational User Interfaces. https://doi.org/10.1145/3469595.3469621

We present ALTCAI, a Wizard of Oz Embodied Conversational Agent that has been developed to explore the use of interactive agents as an effective and engaging tool for delivering health and well-being advice to expectant and nursing mothers in Nigeria... Read More about ALTCAI: Enabling the Use of Embodied Conversational Agents to Deliver Informal Health Advice during Wizard of Oz Studies.

Teachers’ Perspectives on the Adoption of an Adaptive Learning System Based on Multimodal Affect Recognition for Students with Learning Disabilities and Autism (2021)
Presentation / Conference Contribution
Standen, P. J., Brown, D. J., Kwiatkowska, G. M., Belmonte, M. K., Galvez Trigo, M. J., Boulton, H., …Traversi, M. (2021). Teachers’ Perspectives on the Adoption of an Adaptive Learning System Based on Multimodal Affect Recognition for Students with Learning Disabilities and Autism. In R. A. Sottilare, & J. Schwarz (Eds.), Adaptive Instructional Systems. Design and Evaluation : Third International Conference, AIS 2021, Held as Part of the 23rd HCI International Conference, HCII 2021, Virtual Event, July 24–29, 2021, Proceedings, Part I. https://doi.org/10.1007/978-3-030-77857-6

Adoption of e-learning for those with special needs lags that for mainstream learners. Not much is known about barriers and facilitators that drive this disparity. The present study used focus groups and interviews to collect the views of 21 teachers... Read More about Teachers’ Perspectives on the Adoption of an Adaptive Learning System Based on Multimodal Affect Recognition for Students with Learning Disabilities and Autism.

Spectral Representation of Behaviour Primitives for Depression Analysis (2020)
Journal Article
Song, S., Jaiswal, S., Shen, L., & Valstar, M. (2020). Spectral Representation of Behaviour Primitives for Depression Analysis. IEEE Transactions on Affective Computing, https://doi.org/10.1109/taffc.2020.2970712

Depression is a serious mental disorder affecting millions of people. Traditional clinical diagnosis methods are subjective, complicated and require extensive participation of clinicians. Recent advances in automatic depression analysis systems promi... Read More about Spectral Representation of Behaviour Primitives for Depression Analysis.

Deep learning approaches to aircraft maintenance, repair and overhaul: a review (2018)
Presentation / Conference Contribution
Rengasami, D., Morvan, H., & Patrocinio Figueredo, G. (2018). Deep learning approaches to aircraft maintenance, repair and overhaul: a review. In 21st IEEE International Conference on Intelligent Transportation Systems. https://doi.org/10.1109/ITSC.2018.8569502

The use of sensor technology constantly gathering aircrafts' status data has promoted the rapid development of data-driven solutions in aerospace engineering. These methods assist, for instance, with determining appropriate actions for aircraft maint... Read More about Deep learning approaches to aircraft maintenance, repair and overhaul: a review.

Model checking for Coalition Announcement Logic (2018)
Presentation / Conference Contribution
Galimullin, R., Alechina, N., & van Ditmarsch, H. (2018). Model checking for Coalition Announcement Logic. In F. Trollmann, & A.-Y. Turhan (Eds.), KI 2018: Advances in Artificial Intelligence, 41st German Conference on AI, Berlin, Germany, September 24–28, 2018, Proceedings (11-23). https://doi.org/10.1007/978-3-030-00111-7_2

Coalition Announcement Logic (CAL) studies how a group of agents can enforce a certain outcome by making a joint announcement, regardless of any announcements made simultaneously by the opponents. The logic is useful to model imperfect information ga... Read More about Model checking for Coalition Announcement Logic.