Outputs (20)

Robots in Pain, Humans in Play: Soma as a Qualitative Method for Investigating Intelligent Human-Robot Configurations (2024)
Presentation / Conference Contribution
Chamberlain, A., Ngo, V., McGarry, G., Kucukyilmaz, A., Benford, S., & Higgins, A. (2025, February). Robots in Pain, Humans in Play: Soma as a Qualitative Method for Investigating Intelligent Human-Robot Configurations. Presented at Designing for Bodies: Practices, Imaginaries and Discourses, University of Southern Denmark, Kolding

In this piece we start to explore the ways in which we somatise our interaction with robots as a symbiotic system - both as human and robot, and as human-robot. We also consider the ways that we might want to take our physical nature (existence), for... Read More about Robots in Pain, Humans in Play: Soma as a Qualitative Method for Investigating Intelligent Human-Robot Configurations.

Making of an Adaptive Podcast that Engenders Trust through Data Negotiability (2024)
Presentation / Conference Contribution
Sailaja, N., Crabtree, A., Lodge, T., Chamberlain, A., Coulton, P., Pilling, M., & Forrester, I. (2024, June). Making of an Adaptive Podcast that Engenders Trust through Data Negotiability. Presented at IMX 2024 - Proceedings of the 2024 ACM International Conference on Interactive Media Experiences, Stockholm, Sweden

As novel media experiences turn towards audience personal data for enhancement, a range of socio-technical challenges that confront their adoption into the everyday lives of audiences are simultaneously surfaced. Podcasts, being the latest member of... Read More about Making of an Adaptive Podcast that Engenders Trust through Data Negotiability.

Communication, Collaboration, and Coordination in a Co-located Shared Augmented Reality Game: Perspectives From Deaf and Hard of Hearing People (2024)
Presentation / Conference Contribution
Luna, S. M., Xu, J., Papangelis, K., Tigwell, G. W., Lalone, N., Saker, M., Chamberlain, A., Laato, S., Dunham, J., & Wang, Y. (2024, May). Communication, Collaboration, and Coordination in a Co-located Shared Augmented Reality Game: Perspectives From Deaf and Hard of Hearing People. Presented at CHI Conference on Human Factors in Computing Systems (CHI ’24), Honolulu, HI, USA

Co-located collaborative shared augmented reality (CS-AR) environments have gained considerable research attention, mainly focusing on design, implementation, accuracy, and usability. Yet, a gap persists in our understanding regarding the accessibili... Read More about Communication, Collaboration, and Coordination in a Co-located Shared Augmented Reality Game: Perspectives From Deaf and Hard of Hearing People.

Adaptive XAI: Towards Intelligent Interfaces for Tailored AI Explanations (2024)
Presentation / Conference Contribution
Turchi, T., Malizia, A., Paternò, F., Borsci, S., & Chamberlain, A. (2024, March). Adaptive XAI: Towards Intelligent Interfaces for Tailored AI Explanations. Presented at 29th International Conference on Intelligent User Interfaces, Greenville, South Carolina, USA

As the integration of Artificial Intelligence into daily decision-making processes intensifies, the need for clear communication between humans and AI systems becomes crucial. The Adaptive XAI (AXAI) workshop focuses on the design and development of... Read More about Adaptive XAI: Towards Intelligent Interfaces for Tailored AI Explanations.

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., Kemp, D., Benford, S., Price, D., Shackford, R., Robson, J., Locke, C., & King, J. (2023, October). 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. Presented at Mindtrek '23: 26th International Academic Mindtrek Conference, Tampere, Finland

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.

LABERT: A Combination of Local Aggregation and Self-Supervised Speech Representation Learning for Detecting Informative Hidden Units in Low-Resource ASR Systems (2023)
Presentation / Conference Contribution
Fatehi, K., & Kucukyilmaz, A. (2023, August). LABERT: A Combination of Local Aggregation and Self-Supervised Speech Representation Learning for Detecting Informative Hidden Units in Low-Resource ASR Systems. Presented at Interspeech 2023, Dublin, Ireland

With advances in deep learning methodologies, Automatic Speech Recognition (ASR) systems have seen impressive results. However, ASR in Low-Resource Environments (LREs) are challenged by a lack of training data for the specific target domain. We propo... Read More about LABERT: A Combination of Local Aggregation and Self-Supervised Speech Representation Learning for Detecting Informative Hidden Units in Low-Resource ASR Systems.

Responsible Research and Innovation (RRI) Prompts and Practice Cards: a Tool to Support Responsible Practice (2023)
Presentation / Conference Contribution
Portillo, V., Greenhalgh, C., Craigon, P. J., & Ten Holter, C. (2023, July). Responsible Research and Innovation (RRI) Prompts and Practice Cards: a Tool to Support Responsible Practice. Presented at First International Symposium on Trustworthy Autonomous Systems, Edinburgh, UK

Researchers often find it hard to know where, when and how to start when applying Responsible Innovation approaches to their own research projects and proposals. Based on experience supporting a range of researchers and projects, we have developed a... Read More about Responsible Research and Innovation (RRI) Prompts and Practice Cards: a Tool to Support Responsible Practice.

Five Provocations for a More Creative TAS (2023)
Presentation / Conference Contribution
Benford, S., Hazzard, A., Vear, C., Webb, H., Chamberlain, A., Greenhalgh, C., Ramchurn, R., & Marshall, J. (2023, July). Five Provocations for a More Creative TAS. Presented at First International Symposium on Trustworthy Autonomous Systems (TAS 23), Edinburgh, UK

Conventional wisdom has it that trustworthy autonomous systems (AS) should be explainable, dependable, controllable and safe tools for humans to use. Reflecting on a portfolio of artistic applications of TAS leads us adopt an alternative stance and t... Read More about Five Provocations for a More Creative TAS.

TAS for Cats: An Artist-led Exploration of Trustworthy Autonomous Systems for Companion Animals (2023)
Presentation / Conference Contribution
Schneiders, E., Chamberlain, A., Fischer, J. E., Benford, S., Castle-Green, S., Ngo, V., Kucukyilmaz, A., Barnard, P., Row Farr, J., Adams, M., Tandavanitj, N., Devlin, K., Mancini, C., & Mills, D. (2023, July). TAS for Cats: An Artist-led Exploration of Trustworthy Autonomous Systems for Companion Animals. Presented at First International Symposium on Trustworthy Autonomous Systems (TAS 23), Edinburgh, UK

Cat Royale is an artist-led exploration of trustworthy autonomous systems (TAS) created by the TAS Hub's creative ambassadors Blast Theory. A small community of cats inhabits a purpose built 'cat utopia' at the centre of which a robot arm tries to en... Read More about TAS for Cats: An Artist-led Exploration of Trustworthy Autonomous Systems for Companion Animals.

A Practical Taxonomy of TAS-related Usecase Scenarios (2023)
Presentation / Conference Contribution
Masters, P., Young, V., Chamberlain, A., Weerawardhana, S., McKenna, P. E., Lu, Y., Dowthwaite, L., Luff, P., & Moreau, L. (2023, July). A Practical Taxonomy of TAS-related Usecase Scenarios. Presented at Proceedings of the First International Symposium on Trustworthy Autonomous Systems, Edinburgh, UK

This paper proposes a taxonomy of experimental usecase scenarios to facilitate research into trustworthy autonomous systems (TAS). Unable to identify an open-access repository of usecases to support our research, the project team embarked on developm... Read More about A Practical Taxonomy of TAS-related Usecase Scenarios.

Explainable AI for the Arts: XAIxArts (2023)
Presentation / Conference Contribution
Bryan-Kinns, N., Ford, C., Chamberlain, A., Benford, S. D., Kennedy, H., Li, Z., Qiong, W., Xia, G. G., & Rezwana, J. (2023, June). Explainable AI for the Arts: XAIxArts. Presented at 15th Conference on Creativity and Cognition (C&C 23), New York, NY

This first workshop on explainable AI for the Arts (XAIxArts) brings together a community of researchers and creative practitioners in Human-Computer Interaction (HCI), Interaction Design, AI, explainable AI (XAI), and Digital Arts to explore the rol... Read More about Explainable AI for the Arts: XAIxArts.

Socio-Technical Trust For Multi-Modal Hearing Assistive Technology (2023)
Presentation / Conference Contribution
Williams, J., Azim, T., Piskopani, A. M., Chamberlain, A., & Zhang, S. (2023, June). Socio-Technical Trust For Multi-Modal Hearing Assistive Technology. Presented at ICASSPW 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing Workshops, Proceedings, Rhodes Island, Greece

The landscape of opportunity is rapidly changing for audio-visual (AV) hearing assistive technology. While hearing assistive devices, such as hearing aids, have traditionally been developed for populations of deaf and hard of hearing (DHH) communitie... Read More about Socio-Technical Trust For Multi-Modal Hearing Assistive Technology.

Designing for Trust: Autonomous Animal-Centric Robotic & AI Systems (2022)
Presentation / Conference Contribution
Chamberlain, A., Benford, S., Fischer, J., Barnard, P., Greenhalgh, C., Row Farr, J., Tandavanitj, N., & Adams, M. (2022, December). Designing for Trust: Autonomous Animal-Centric Robotic & AI Systems. Presented at Proceedings of the Ninth International Conference on Animal-Computer Interaction, Newcastle, UK

From cat feeders and cat flaps to robot toys, humans are deploying increasingly autonomous systems to look after their pets. In parallel, industry is developing the next generation of autonomous systems to look after humans in the home – most notably... Read More about Designing for Trust: Autonomous Animal-Centric Robotic & AI Systems.

ScoutWav: Two-Step Fine-Tuning on Self-Supervised Automatic Speech Recognition for Low-Resource Environments (2022)
Presentation / Conference Contribution
Fatehi, K., Torres, M. T., & Kucukyilmaz, A. (2022, September). ScoutWav: Two-Step Fine-Tuning on Self-Supervised Automatic Speech Recognition for Low-Resource Environments. Presented at Interspeech 2022, Incheon, Korea

Recent improvements in Automatic Speech Recognition (ASR) systems obtain extraordinary results. However, there are specific domains where training data can be either limited or not representative enough, which are known as Low-Resource Environments (... Read More about ScoutWav: Two-Step Fine-Tuning on Self-Supervised Automatic Speech Recognition for Low-Resource Environments.

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, May). Push-to-See: Learning Non-Prehensile Manipulation to Enhance Instance Segmentation via Deep Q-Learning. Presented at 2022 IEEE International Conference on Robotics and Automation (ICRA 2022), Philadelphia, USA

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.

Metaverse: The Vision for the Future (2022)
Presentation / Conference Contribution
Xu, J., Papangelis, K., Dunham, J., Goncalves, J., LaLone, N. J., Chamberlain, A., Lykourentzou, I., Vinella, F. L., & Schwartz, D. I. (2022, April). Metaverse: The Vision for the Future. Presented at CHI '22: CHI Conference on Human Factors in Computing Systems, New Orleans, USA

In recent years, the notion of the Metaverse has become the focus of a growing body of work in the industry. However, there is no consensus on the conceptualization in academia. To date, much of this attention has revolved around technological challe... Read More about Metaverse: The Vision for the Future.

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., Bodiaj, E., & Valstar, M. (2021, July). ALTCAI: Enabling the Use of Embodied Conversational Agents to Deliver Informal Health Advice during Wizard of Oz Studies. Presented at CUI 2021 : Third Conference on Conversational User Interfaces, Bilbao (online), Spain

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., Burton, A., Hallewell, M. J., Shopland, N., Blanco Gonzalez, M. A., Milli, E., Cobello, S., Mazzucato, A., & Traversi, M. (2021, July). Teachers’ Perspectives on the Adoption of an Adaptive Learning System Based on Multimodal Affect Recognition for Students with Learning Disabilities and Autism. Presented at International Conference on Adaptive Instructional Systems (AIS 2021), Virtual Event

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.

Intelligent control of exoskeletons through a novel learning-from-demonstration method (2020)
Presentation / Conference Contribution
Ugur, E., Samur, E., Ugurlu, B., Erol Barkana, D., Kucukyilmaz, A., & Bebek, O. (2020, September). Intelligent control of exoskeletons through a novel learning-from-demonstration method. Poster presented at Cybathlon Symposium 2020, Zurich, Switzerland

We present a novel concept that enables the intelligent and adaptive control of exoskeletons through exploiting our state-of-the-art learning from demonstration (LfD) method, namely Conditional Neural Movement Primitives (CNMPs) [1], on our integrate... Read More about Intelligent control of exoskeletons through a novel learning-from-demonstration method.

Model checking for Coalition Announcement Logic (2018)
Presentation / Conference Contribution
Galimullin, R., Alechina, N., & van Ditmarsch, H. (2018, September). Model checking for Coalition Announcement Logic. Presented at KI 2018: Advances in Artificial Intelligence, Berlin, Germany

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.