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All Outputs (35)

On Independence and Compound and Iterated Conditionals (2024)
Presentation / Conference Contribution
Gilio, A., Over, D., Pfeifer, N., & Sanfilippo, G. (2024). On Independence and Compound and Iterated Conditionals. In Human and Artificial Rationalities: Second International Conference, HAR 2023, Paris, France, September 19–22, 2023, Proceedings (159-177). https://doi.org/10.1007/978-3-031-55245-8_11

Understanding the logic of uncertain conditionals is a key problem in the new paradigm psychology of reasoning and related fields. We investigate conjunctions of conditionals, iterated conditionals, and independence within the theory of logical opera... Read More about On Independence and Compound and Iterated Conditionals.

Evaluating Gaussian Grasp Maps for Generative Grasping Models (2022)
Presentation / Conference Contribution
Prew, W., Breckon, T., Bordewich, M., & Beierholm, U. (2022). Evaluating Gaussian Grasp Maps for Generative Grasping Models.

Generalising robotic grasping to previously unseen objects is a key task in general robotic manipulation. The current method for training many antipodal generative grasping models rely on a binary ground truth grasp map generated from the centre thir... Read More about Evaluating Gaussian Grasp Maps for Generative Grasping Models.

Feeling Colours: Crossmodal Correspondences Between Tangible 3D Objects, Colours and Emotions (2021)
Presentation / Conference Contribution
Lin, A., Scheller, M., Feng, F., Proulx, M. J., & Metatla, O. (2021). Feeling Colours: Crossmodal Correspondences Between Tangible 3D Objects, Colours and Emotions. In CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (1-12). https://doi.org/10.1145/3411764.3445373

With increasing interest in multisensory experiences in HCI there is a need to consider the potential impact of crossmodal correspondences (CCs) between sensory modalities on perception and interpretation. We investigated CCs between active haptic ex... Read More about Feeling Colours: Crossmodal Correspondences Between Tangible 3D Objects, Colours and Emotions.

Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss (2021)
Presentation / Conference Contribution
Prew, W., Breckon, T., Bordewich, M., & Beierholm, U. (2021). Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss. . https://doi.org/10.1109/icpr48806.2021.9413197

In this paper we introduce two methods of improving real-time object grasping performance from monocular colour images in an end-to-end CNN architecture. The first is the addition of an auxiliary task during model training (multi-task learning). Our... Read More about Improving Robotic Grasping on Monocular Images Via Multi-Task Learning and Positional Loss.

Diversity As I Say, Not As I Do: Organizational Authenticity and Diversity Management Effectiveness (2020)
Presentation / Conference Contribution
Marcinko, A. (2020). Diversity As I Say, Not As I Do: Organizational Authenticity and Diversity Management Effectiveness. In G. Atinc (Ed.), . https://doi.org/10.5465/AMBPP.2020.189

This research examined how a misalignment between an organization’s espoused values and its realized practices regarding diversity affected employees’ affective commitment, organizational identification, and turnover intentions. Further, it investiga... Read More about Diversity As I Say, Not As I Do: Organizational Authenticity and Diversity Management Effectiveness.

Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation (2019)
Presentation / Conference Contribution
Aznan, N., Connolly, J., Al Moubayed, N., & Breckon, T. (2019). Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation. In 2019 International Conference on Robotics and Automation (ICRA) ; proceedings (4889-4895). https://doi.org/10.1109/icra.2019.8794060

This paper addresses the challenge of humanoid robot teleoperation in a natural indoor environment via a Brain-Computer Interface (BCI). We leverage deep Convolutional Neural Network (CNN) based image and signal understanding to facilitate both real-... Read More about Using Variable Natural Environment Brain-Computer Interface Stimuli for Real-time Humanoid Robot Navigation.

Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification (2019)
Presentation / Conference Contribution
Aznan, N., Atapour-Abarghouei, A., Bonner, S., Connolly, J., Al Moubayed, N., & Breckon, T. (2019). Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification. In 2019 International Joint Conference on Neural Networks (IJCNN) ; proceedings (1-8). https://doi.org/10.1109/ijcnn.2019.8852227

Despite significant recent progress in the area of Brain-Computer Interface (BCI), there are numerous shortcomings associated with collecting Electroencephalography (EEG) signals in real-world environments. These include, but are not limited to, subj... Read More about Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification.

Subcomponents of visuospatial working memory: Investigating the importance of order in sequential recall and its relationship with mathematics performance (2019)
Presentation / Conference Contribution
Allen, K. (2019). Subcomponents of visuospatial working memory: Investigating the importance of order in sequential recall and its relationship with mathematics performance. In X. Shao, & E. Dobson (Eds.), Imagining Better Education: Conference Proceedings 2018 (1-10)

Visuospatial working memory (VSWM) is responsible for storing and manipulating visual and spatial information. Its predictive relationship with mathematics performance in children is well known, especially in younger children. Research has so far not... Read More about Subcomponents of visuospatial working memory: Investigating the importance of order in sequential recall and its relationship with mathematics performance.

On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks (2018)
Presentation / Conference Contribution
Aznan, N., Bonner, S., Connolly, J., Al Moubayed, N., & Breckon, T. (2018). On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks. In Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC2018): Miyazaki, Japan, 7-10 October 2018 (3726-3731). https://doi.org/10.1109/smc.2018.00631

Electroencephalography (EEG) is a common signal acquisition approach employed for Brain-Computer Interface (BCI) research. Nevertheless, the majority of EEG acquisition devices rely on the cumbersome application of conductive gel (so-called wet-EEG)... Read More about On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks.

Impact of lower alcohol strength labels on consumption of wine and beer: a bar lab randomised controlled trial (2018)
Presentation / Conference Contribution
Vasiljevic, M., Couturier, D., Frings, D., Moss, A., Albery, I., & Marteau, T. (2018). Impact of lower alcohol strength labels on consumption of wine and beer: a bar lab randomised controlled trial. Annals of Behavioral Medicine, 52(suppl_1), S158-S158

Background: Labels indicating low/light versions of tobacco and foods are perceived as less harmful which may increase consumption. The current study tests the hypothesis that labelling wine and beer as lower in alcohol increases their consumption. M... Read More about Impact of lower alcohol strength labels on consumption of wine and beer: a bar lab randomised controlled trial.

A non-parametric Bayesian prior for causal inference of auditory streaming (2017)
Presentation / Conference Contribution
Yates, T., Larigaldie, N., & Beierholm, U. (2017). A non-parametric Bayesian prior for causal inference of auditory streaming. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. . J. Davelaar (Eds.), Proceedings of the 39th Annual Conference of the Cognitive Science Society (1381-1386). https://doi.org/10.1101/139188

traditionally been modeled using a mechanistic approach. The problem however is essentially one of source inference – a problem that has recently been tackled using statistical Bayesian models in visual and auditory-visual modalities. Usually the mod... Read More about A non-parametric Bayesian prior for causal inference of auditory streaming.

How the distorted representation of visual space in our brain constrains the way we move our eyes. (2014)
Presentation / Conference Contribution
(2014). How the distorted representation of visual space in our brain constrains the way we move our eyes. https://doi.org/10.1167/14.10.751

The representation of visual space is distorted in several areas of the brain, including the Superior Colliculus (SC) where population averaging presumably determines the amplitude of saccadic eye movements. We previously showed, based on human behav... Read More about How the distorted representation of visual space in our brain constrains the way we move our eyes..