All Outputs (3178)

Performance of machine learning versus the national early warning score for predicting patient deterioration risk: a single-site study of emergency admissions (2024)
Journal Article
Watson, M., Boulitsakis Logothetis, S., Green, D., Holland, M., Chambers, P., & Al Moubayed, N. (2024). Performance of machine learning versus the national early warning score for predicting patient deterioration risk: a single-site study of emergency admissions. BMJ Health & Care Informatics, 31(1), Article e101088. https://doi.org/10.1136/bmjhci-2024-101088

Objectives: Increasing operational pressures on emergency departments (ED) make it imperative to quickly and accurately identify patients requiring urgent clinical intervention. The widespread adoption of electronic health records (EHR) makes rich fe... Read More about Performance of machine learning versus the national early warning score for predicting patient deterioration risk: a single-site study of emergency admissions.

Intelligent edge–fog interplay for healthcare informatics: A blockchain perspective (2024)
Journal Article
Rathore, N., Gupta, R., Thakkar, N., Gohil, K., Tanwar, S., Aujla, G. S., Alqahtani, F., & Tolba, A. (2025). Intelligent edge–fog interplay for healthcare informatics: A blockchain perspective. Ad Hoc Networks, 169, Article 103727. https://doi.org/10.1016/j.adhoc.2024.103727

This paper explores artificial intelligence (AI) and edge–fog interplay to strengthen healthcare informatics (HCI), while also considering the blockchain perspective for securing HCI to transform cloud-based HCI to edge–fog-based HCI to serve real-ti... Read More about Intelligent edge–fog interplay for healthcare informatics: A blockchain perspective.

From Category to Scenery: An End-to-End Framework for Multi-Person Human-Object Interaction Recognition in Videos (2024)
Presentation / Conference Contribution
Qiao, T., Li, R., Li, F. W. B., & Shum, H. P. H. (2024, December). From Category to Scenery: An End-to-End Framework for Multi-Person Human-Object Interaction Recognition in Videos. Presented at ICPR 2024: International Conference on Pattern Recognition, Kolkata, India

Video-based Human-Object Interaction (HOI) recognition explores the intricate dynamics between humans and objects, which are essential for a comprehensive understanding of human behavior and intentions. While previous work has made significant stride... Read More about From Category to Scenery: An End-to-End Framework for Multi-Person Human-Object Interaction Recognition in Videos.

SK_DU Team: Cross-Encoder based Evidence Retrieval and Question Generation with Improved Prompt for the AVeriTeC Shared Task (2024)
Presentation / Conference Contribution
Malviya, S., & Katsigiannis, S. (2024, November). SK_DU Team: Cross-Encoder based Evidence Retrieval and Question Generation with Improved Prompt for the AVeriTeC Shared Task. Presented at 7th Fact Extraction and VERification Workshop (FEVER), Miami, Florida, USA

As part of the AVeriTeC shared task, we developed a pipelined system comprising robust and finely tuned models. Our system integrates advanced techniques for evidence retrieval and question generation, leveraging cross-encoders and large language mod... Read More about SK_DU Team: Cross-Encoder based Evidence Retrieval and Question Generation with Improved Prompt for the AVeriTeC Shared Task.

6DGS: 6D Pose Estimation from a Single Image and a 3D Gaussian Splatting Model (2024)
Presentation / Conference Contribution
Matteo, B., Tsesmelis, T., James, S., Poiesi, F., & Del Bue, A. (2024, September). 6DGS: 6D Pose Estimation from a Single Image and a 3D Gaussian Splatting Model. Presented at Computer Vision – ECCV 2024 18th European Conference, Milan, Italy

We propose 6DGS to estimate the camera pose of a target RGB image given a 3D Gaussian Splatting (3DGS) model representing the scene. 6DGS avoids the iterative process typical of analysis-by-synthesis methods (e. g.iNeRF) that also require an initiali... Read More about 6DGS: 6D Pose Estimation from a Single Image and a 3D Gaussian Splatting Model.

Exploring rounD Dataset for Domain Generalization in Autonomous Vehicle Trajectory Prediction (2024)
Journal Article
Zhang, Z. (2024). Exploring rounD Dataset for Domain Generalization in Autonomous Vehicle Trajectory Prediction. Sensors, 24(23), Article 7538. https://doi.org/10.3390/s24237538

This paper analyzes the rounD dataset to advance motion forecasting algorithms for autonomous vehicles navigating complex roundabout environments. We develop a trajectory prediction framework inspired by Gated Recurrent Unit (GRU) networks and graph-... Read More about Exploring rounD Dataset for Domain Generalization in Autonomous Vehicle Trajectory Prediction.

AI-Driven Feedback for Enhancing Students' Mathematical Problem-Solving: The ScaffoldiaMyMaths System (2024)
Presentation / Conference Contribution
Sun, D., Wang, J., Yang, L., Chou, K.-L., Song, Z., & Zheng, Z. (2024, November). AI-Driven Feedback for Enhancing Students' Mathematical Problem-Solving: The ScaffoldiaMyMaths System. Poster presented at International Conference on Computers in Education, Philippines

As online learning becomes increasingly prevalent, it is essential to understand students' perspectives and address the challenges they encounter. The developing system, ScaffoldiaMyMaths, aims to support the mathematics learning of underprivileged a... Read More about AI-Driven Feedback for Enhancing Students' Mathematical Problem-Solving: The ScaffoldiaMyMaths System.

Enhanced cross-domain lithology classification in imbalanced datasets using an unsupervised domain Adversarial Network (2024)
Journal Article
Xie, Y., Jin, L., Zhu, C., Luo, W., & Wang, Q. (2024). Enhanced cross-domain lithology classification in imbalanced datasets using an unsupervised domain Adversarial Network. Engineering Applications of Artificial Intelligence, 139(Part B), Article 109668. https://doi.org/10.1016/j.engappai.2024.109668

Recent advancements in Artificial Intelligence (AI), particularly deep learning, have significantly improved lithology identification in reservoir exploration by leveraging micrographic rock imagery. Deep neural networks excel in feature extraction,... Read More about Enhanced cross-domain lithology classification in imbalanced datasets using an unsupervised domain Adversarial Network.

Premature mortality analysis of 52,000 deceased cats and dogs exposes socioeconomic disparities (2024)
Journal Article
Farrell, S., Anderson, K., Noble, P.-J. M., & Al Moubayed, N. (2024). Premature mortality analysis of 52,000 deceased cats and dogs exposes socioeconomic disparities. Scientific Reports, 14(1), Article 28763. https://doi.org/10.1038/s41598-024-77385-8

Monitoring mortality rates offers crucial insights into public health by uncovering the hidden impacts of diseases, identifying emerging trends, optimising resource allocation, and informing effective policy decisions. Here, we present a novel approa... Read More about Premature mortality analysis of 52,000 deceased cats and dogs exposes socioeconomic disparities.

A topic map based learning management system to facilitate meaningful grammar learning: the case of Japanese grammar learning (2024)
Journal Article
Wang, J., Wynn, A., Mendori, T., & Hwang, G.-J. (2024). A topic map based learning management system to facilitate meaningful grammar learning: the case of Japanese grammar learning. Smart Learning Environments, 11(1), Article 53. https://doi.org/10.1186/s40561-024-00338-1

This study investigates the effect of studying with topic maps provided by a self-developed language learning support system on (a) learning perception, (b) learning achievement and (c) variation in learning attitude and motivation, from the perspect... Read More about A topic map based learning management system to facilitate meaningful grammar learning: the case of Japanese grammar learning.

ExaGRyPE: Numerical general relativity solvers based upon the hyperbolic PDEs solver engine ExaHyPE (2024)
Journal Article
Zhang, H., Li, B., Weinzierl, T., & Barrera-Hinojosa, C. (2025). ExaGRyPE: Numerical general relativity solvers based upon the hyperbolic PDEs solver engine ExaHyPE. Computer Physics Communications, 307, Article 109435. https://doi.org/10.1016/j.cpc.2024.109435

ExaGRyPE describes a suite of solvers and solver ingredients for numerical relativity that are based upon ExaHyPE 2, the second generation of our Exascale Hyperbolic PDE Engine. Numerical relativity simulations are crucial in resolv... Read More about ExaGRyPE: Numerical general relativity solvers based upon the hyperbolic PDEs solver engine ExaHyPE.

GANzzle + + : Generative approaches for jigsaw puzzle solving as local to global assignment in latent spatial representations (2024)
Journal Article
Talon, D., Del Bue, A., & James, S. (2025). GANzzle + + : Generative approaches for jigsaw puzzle solving as local to global assignment in latent spatial representations. Pattern Recognition Letters, 187, 35-41. https://doi.org/10.1016/j.patrec.2024.11.010

Jigsaw puzzles are a popular and enjoyable pastime that humans can easily solve, even with many pieces. However, solving a jigsaw is a combinatorial problem, and the space of possible solutions is exponential in the number of pieces, intractable for... Read More about GANzzle + + : Generative approaches for jigsaw puzzle solving as local to global assignment in latent spatial representations.

Evidence Retrieval for Fact Verification using Multi-stage Reranking (2024)
Presentation / Conference Contribution
Malviya, S., & Katsigiannis, S. (2024, November). Evidence Retrieval for Fact Verification using Multi-stage Reranking. Presented at 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), Miami, FL, USA

Toward a framework for Responsible AI in storytelling for nonprofit fundraising (2024)
Book Chapter
Herrero, M., & Concannon, S. (2024). Toward a framework for Responsible AI in storytelling for nonprofit fundraising. In G. Ugazio, & M. Maricic (Eds.), . Routledge. https://doi.org/10.4324/9781003468615-11

AI techniques offer novel and compelling possibilities for nonprofit fundraising (e.g., data science applications can provide a deeper understanding of audiences and donors, and generative methods can create more personalized and persuasive communica... Read More about Toward a framework for Responsible AI in storytelling for nonprofit fundraising.

MAFin: Maximizing Accuracy in FinFET based Approximated Real-Time Computing (2024)
Presentation / Conference Contribution
Chakraborty, S., Saha, S., Sjalander, M., & Mcdonald-Maier, K. (2024, June). MAFin: Maximizing Accuracy in FinFET based Approximated Real-Time Computing. Presented at DAC '24: 61st ACM/IEEE Design Automation Conference, San Francisco CA USA

We propose MAFin that exploits the unique temperature effect inversion (TEI) property of a FinFET based multicore platform, where processing speed increases with temperature, in the context of approximate real-time computing. In approximate real-time... Read More about MAFin: Maximizing Accuracy in FinFET based Approximated Real-Time Computing.

Democratizing Uncertainty Quantification (2024)
Journal Article
Seelinger, L., Reinarz, A., Lykkegaard, M. B., Akers, R., Alghamdi, A. M., Aristoff, D., Bangerth, W., Bénézech, J., Diez, M., Frey, K., Jakeman, J. D., Jørgensen, J. S., Kim, K.-T., Kent, B. M., Martinelli, M., Parno, M., Pellegrini, R., Petra, N., Riis, N. A., Rosenfeld, K., …Scheichl, R. (2025). Democratizing Uncertainty Quantification. Journal of Computational Physics, 521(1), Article 113542. https://doi.org/10.1016/j.jcp.2024.113542

Uncertainty Quantification (UQ) is vital to safety-critical model-based analyses, but the widespread adoption of sophisticated UQ methods is limited by technical complexity. In this paper, we introduce UM-Bridge (the UQ and Modeling... Read More about Democratizing Uncertainty Quantification.

Maps from Motion (MfM): Generating 2D Semantic Maps from Sparse Multi-view Images (2024)
Presentation / Conference Contribution
Toso, M., Fiorini, S., James, S., & Del Bue, A. (2025, March). Maps from Motion (MfM): Generating 2D Semantic Maps from Sparse Multi-view Images. Presented at International Conference on 3D Vision (3DV), Singapore

World-wide detailed 2D maps require enormous collective efforts. OpenStreetMap is the result of 11 million registered users manually annotating the GPS location of over 1.75 billion entries, including distinctive landmarks and common urban objects. A... Read More about Maps from Motion (MfM): Generating 2D Semantic Maps from Sparse Multi-view Images.

Tensorial multiview low-rank high-order graph learning for context-enhanced domain adaptation (2024)
Journal Article
Zhu, C., Zhang, L., Luo, W., Jiang, G., & Wang, Q. (2025). Tensorial multiview low-rank high-order graph learning for context-enhanced domain adaptation. Neural Networks, 181, Article 106859. https://doi.org/10.1016/j.neunet.2024.106859

Unsupervised Domain Adaptation (UDA) is a machine learning technique that facilitates knowledge transfer from a labeled source domain to an unlabeled target domain, addressing distributional discrepancies between these domains. Existing UDA methods o... Read More about Tensorial multiview low-rank high-order graph learning for context-enhanced domain adaptation.

From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers (2024)
Journal Article
Watson, M., Chambers, P., Steventon, L., Harmsworth King, J., Ercia, A., Shaw, H., & Al Moubayed, N. (2024). From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers. BMJ Oncology, 3(1), Article e000430. https://doi.org/10.1136/bmjonc-2024-000430

Objectives: Routine monitoring of renal and hepatic function during chemotherapy ensures that treatment-related organ damage has not occurred and clearance of subsequent treatment is not hindered; however, frequency and timing are not optimal. Model... Read More about From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers.

MAGR: Manifold-Aligned Graph Regularization for Continual Action Quality Assessment (2024)
Presentation / Conference Contribution
Zhou, K., Wang, L., Zhang, X., Shum, H. P., Li, F. W. B., Li, J., & Liang, X. (2024, September). MAGR: Manifold-Aligned Graph Regularization for Continual Action Quality Assessment. Presented at Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Milan, Italy

Action Quality Assessment (AQA) evaluates diverse skills but models struggle with non-stationary data. We propose Continual AQA (CAQA) to refine models using sparse new data. Feature replay preserves memory without storing raw inputs. However, the mi... Read More about MAGR: Manifold-Aligned Graph Regularization for Continual Action Quality Assessment.