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All You Need are Random Walks: Fast and Simple Distributed Conductance Testing (2024)
Presentation / Conference Contribution
Batu, T., Trehan, A., & Trehan, C. (2024, May). All You Need are Random Walks: Fast and Simple Distributed Conductance Testing. Presented at SIROCCO 2024: 31st International Colloquium On Structural Information and Communication Complexity, Vietri sul Mare, Salerno, Italy

We propose a simple and time-optimal algorithm for property testing a graph for its conductance in the CONGEST model. Our algorithm takes only O(log n) rounds of communication (which is known to be optimal), and consists of simply running multiple ra... Read More about All You Need are Random Walks: Fast and Simple Distributed Conductance Testing.

IFFNeRF: Initialisation Free and Fast 6DoF pose estimation from a single image and a NeRF model (2024)
Presentation / Conference Contribution
Bortolon, M., Tsesmelis, T., James, S., Poiesi, F., & Bue, A. D. (2024, May). IFFNeRF: Initialisation Free and Fast 6DoF pose estimation from a single image and a NeRF model. Presented at 2024 IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan

We introduce IFFNeRF to estimate the six degrees-of-freedom (6DoF) camera pose of a given image, building on the Neural Radiance Fields (NeRF) formulation. IFFNeRF is specifically designed to operate in real-time and eliminates the need for an initia... Read More about IFFNeRF: Initialisation Free and Fast 6DoF pose estimation from a single image and a NeRF model.

Advanced VR Calibration for Upper Limb Rehabilitation: Making Immersive Environments Accessible (2024)
Presentation / Conference Contribution
Herrera, V., Reyes-Guzmán, A., Vallejo, D., Castro-Schez, J., Monekosso, D., Carlos, G.-M., & Albusac, J. (2024, April). Advanced VR Calibration for Upper Limb Rehabilitation: Making Immersive Environments Accessible. Presented at 26th International Conference on Enterprise Information Systems, Angers, France

The creation of accessible spaces is essential for patients with motor injuries to conduct therapy safely and effectively. Disruptive technologies such as Virtual Reality (VR) are increasingly being used as a complement to traditional therapy, with e... Read More about Advanced VR Calibration for Upper Limb Rehabilitation: Making Immersive Environments Accessible.

CXR-IRGen: An Integrated Vision and Language Model for the Generation of Clinically Accurate Chest X-Ray Image-Report Pairs (2024)
Presentation / Conference Contribution
Shentu, J., & Al Moubayed, N. (2024, January). CXR-IRGen: An Integrated Vision and Language Model for the Generation of Clinically Accurate Chest X-Ray Image-Report Pairs. Presented at 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, Hawaii, USA

Chest X-Ray (CXR) images play a crucial role in clinical practice, providing vital support for diagnosis and treatment. Augmenting the CXR dataset with synthetically generated CXR images annotated with radiology reports can enhance the performance of... Read More about CXR-IRGen: An Integrated Vision and Language Model for the Generation of Clinically Accurate Chest X-Ray Image-Report Pairs.

U3DS3 : Unsupervised 3D Semantic Scene Segmentation (2024)
Presentation / Conference Contribution
Liu, J., Yu, Z., Breckon, T. P., & Shum, H. P. H. (2024, January). U3DS3 : Unsupervised 3D Semantic Scene Segmentation. Presented at 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, Hawaii, USA

Contemporary point cloud segmentation approaches largely rely on richly annotated 3D training data. However , it is both time-consuming and challenging to obtain consistently accurate annotations for such 3D scene data. Moreover, there is still a lac... Read More about U3DS3 : Unsupervised 3D Semantic Scene Segmentation.

Analysing Learner Behaviour in an Ontology-Based E-learning System: A Graph Neural Network Approach (2024)
Presentation / Conference Contribution
Wynn, A., Wang, J., Sun, Z., & Shimada, A. (2024, March). Analysing Learner Behaviour in an Ontology-Based E-learning System: A Graph Neural Network Approach. Paper presented at LAK '24: The 14th Learning Analytics and Knowledge Conference, Kyoto, Japan

Despite the prevalence of e-learning systems, there is a lack of support for learners to identify and compare new knowledge with existing cognitive structures. Therefore, an ontology-based visualization support system was previously introduced which... Read More about Analysing Learner Behaviour in an Ontology-Based E-learning System: A Graph Neural Network Approach.

FEGR: Feature Enhanced Graph Representation Method for Graph Classification (2024)
Presentation / Conference Contribution
Abushofa, M., Atapour-Abarghouei, A., Forshaw, M., & McGough, A. S. (2023, November). FEGR: Feature Enhanced Graph Representation Method for Graph Classification. Presented at 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Kusadasi, Turkey

Graph representation plays a key role in graph analytics to perform a variety of downstream machine-learning tasks. This paper presents a novel method for extracting expressive graph representation based on a combination of statistics captured from a... Read More about FEGR: Feature Enhanced Graph Representation Method for Graph Classification.

A Virtual Reality Framework for Human-Driver Interaction Research: Safe and Cost-Effective Data Collection (2024)
Presentation / Conference Contribution
Crosato, L., Wei, C., Ho, E. S. L., Shum, H. P. H., & Sun, Y. (2024, March). A Virtual Reality Framework for Human-Driver Interaction Research: Safe and Cost-Effective Data Collection. Presented at 2024 ACM/IEEE International Conference on Human Robot Interaction (HRI '24), Boulder, CO, USA

The advancement of automated driving technology has led to new challenges in the interaction between automated vehicles and human road users. However, there is currently no complete theory that explains how human road users interact with vehicles, an... Read More about A Virtual Reality Framework for Human-Driver Interaction Research: Safe and Cost-Effective Data Collection.

A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (2024)
Presentation / Conference Contribution
Khanam, Z., Achari, V., Boukhennoufa, I., Jindal, A., & Singh, A. K. (2024, January). A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control. Presented at Workshop on Next Generation Real-Time Embedded Systems (NG-RES), Munich, Germany

Traffic congestion is one of the growing urban problem with associated problems like fuel wastage, loss of lives, and slow productivity. The existing traffic system uses programming logic control (PLC) with round-robin scheduling algorithm. Recent wo... Read More about A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control.

A multiscale optimisation algorithm for shape and material reconstruction from a single X-ray image (2024)
Presentation / Conference Contribution
Westmacott, H., Ivrissimtzis, I., & Weinzierl, T. (2024, January). A multiscale optimisation algorithm for shape and material reconstruction from a single X-ray image. Presented at ICIGP 2024: The 7th International Conference on Image and Graphics Processing, Beijing, China

We produce thickness and bone to soft tissue ratio estimations from a single, 2D medical X-ray image. For this, we simulate the scattering of the rays through a model of the object and embed this simulation into an optimiser which iteratively adjusts... Read More about A multiscale optimisation algorithm for shape and material reconstruction from a single X-ray image.

Awake Complexity of Distributed Minimum Spanning Tree (2024)
Presentation / Conference Contribution
Augustine, J., Moses Jr, W. K., & Pandurangan, G. (2024, May). Awake Complexity of Distributed Minimum Spanning Tree. Presented at SIROCCO 2024: 31st International Colloquium On Structural Information and Communication Complexity, Vietri sul Mare, Salerno, Italy

The \emph{awake complexity} of a distributed algorithm measures the number of rounds in which a node is awake. When a node is not awake, it is {\em sleeping} and does not do any computation or communication and spends very little resources.
Reduci... Read More about Awake Complexity of Distributed Minimum Spanning Tree.

Time- and Communication-Efficient Overlay Network Construction via Gossip (2024)
Presentation / Conference Contribution
Dufoulon, F., Moorman, M., Moses Jr., W. K., & Pandurangan, G. (2024, January). Time- and Communication-Efficient Overlay Network Construction via Gossip. Presented at ITCS 2024: Innovations in Theoretical Computer Science (ITCS), Berkeley, California

We focus on the well-studied problem of distributed overlay network construction. We consider a synchronous gossip-based communication model where in each round a node can send a message of small size to another node whose identifier it knows. The ne... Read More about Time- and Communication-Efficient Overlay Network Construction via Gossip.

Dispersion, Capacitated Nodes, and the Power of a Trusted Shepherd (2024)
Presentation / Conference Contribution
Moses Jr., W. K., & Redlich, A. (2024, January). Dispersion, Capacitated Nodes, and the Power of a Trusted Shepherd. Presented at 25th International Conference on Distributed Computing and Networking, Chennai, India

In this paper, we look at and expand the problems of dispersion and Byzantine dispersion of mobile robots on a graph, introduced by Augustine and Moses Jr. [ICDCN 2018] and by Molla, Mondal, and Moses Jr. [ALGOSENSORS 2020], respectively, to graphs w... Read More about Dispersion, Capacitated Nodes, and the Power of a Trusted Shepherd.

Comparative Study of Face Tracking Algorithms for Remote Photoplethysmography (2024)
Presentation / Conference Contribution
Jayasinghe, J., Katsigiannis, S., & Malasinghe, L. (2023, November). Comparative Study of Face Tracking Algorithms for Remote Photoplethysmography. Presented at International Conference on Electrical, Computer and Energy Technologies (ICECET 2023), Cape Town, South Africa

Remote Photoplethysmography (rPPG) is a non-invasive approach for monitoring Heart Rate (HR) that can be used in various applications in healthcare and biometrics. rPPG measurements acquired using facial videos have become very popular and one of the... Read More about Comparative Study of Face Tracking Algorithms for Remote Photoplethysmography.

∞-Diff: Infinite Resolution Diffusion with Subsampled Mollified States (2024)
Presentation / Conference Contribution
Bond-Taylor, S., & Willcocks, C. G. (2024, May). ∞-Diff: Infinite Resolution Diffusion with Subsampled Mollified States. Presented at The International Conference on Learning Representations (ICLR), Vienna Austria

This paper introduces ∞-Diff, a generative diffusion model defined in an infinite-dimensional Hilbert space, which can model infinite resolution data. By training on randomly sampled subsets of coordinates and denoising content only at those location... Read More about ∞-Diff: Infinite Resolution Diffusion with Subsampled Mollified States.

HSE: Hybrid Species Embedding for Deep Metric Learning (2024)
Presentation / Conference Contribution
Yang, B., Sun, H., Li, F. W. B., Chen, Z., Cai, J., & Song, C. (2023, October). HSE: Hybrid Species Embedding for Deep Metric Learning. Presented at 2023 IEEE/CVF International Conference on Computer Vision (ICCV), Paris

Deep metric learning is crucial for finding an embedding function that can generalize to training and testing data, including unknown test classes. However, limited training samples restrict the model's generalization to downstream tasks. While addin... Read More about HSE: Hybrid Species Embedding for Deep Metric Learning.

PRIMM and Proper: Authentic Investigation in HE Introductory Programming with PeerWise and GitHub (2024)
Presentation / Conference Contribution
Bradley, S., & Ramezani, A. (2024, January). PRIMM and Proper: Authentic Investigation in HE Introductory Programming with PeerWise and GitHub. Presented at CEP '24: Computing Education Practice, Durham, United Kingdom

We explore the use of the PRIMM methodology (Predict, Run, Investigate, Modify, Make) within a higher education introductory programming setting, particularly focusing on the three first three steps. Formative prediction questions on the effects of c... Read More about PRIMM and Proper: Authentic Investigation in HE Introductory Programming with PeerWise and GitHub.

Unifying Human Motion Synthesis and Style Transfer with Denoising Diffusion Probabilistic Models (2023)
Presentation / Conference Contribution
Chang, Z., Findlay, E. J., Zhang, H., & Shum, H. P. (2023, February). Unifying Human Motion Synthesis and Style Transfer with Denoising Diffusion Probabilistic Models. Presented at GRAPP 2023: 2023 International Conference on Computer Graphics Theory and Applications, Lisbon, Portugal

Generating realistic motions for digital humans is a core but challenging part of computer animations and games, as human motions are both diverse in content and rich in styles. While the latest deep learning approaches have made significant advancem... Read More about Unifying Human Motion Synthesis and Style Transfer with Denoising Diffusion Probabilistic Models.