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All Outputs (1380)

Techno-Economic-Environmental Analysis for Net-Zero Sustainable Residential Buildings (2023)
Presentation / Conference Contribution
Garg, A., Aujla, G., & Sun, H. (2023, October). Techno-Economic-Environmental Analysis for Net-Zero Sustainable Residential Buildings. Presented at IEEE PES ISGT Europe 2023, Grenoble, France

Carbon emissions are becoming a global concern responsible for climate change. The renewable energy sources (RESs) such as wind, solar, biomass are gaining importance to reduce emissions in the energy sector. However, these sources depend highly on v... Read More about Techno-Economic-Environmental Analysis for Net-Zero Sustainable Residential Buildings.

Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption (2023)
Presentation / Conference Contribution
Barker, J., Bhowmik, N., Gaus, Y., & Breckon, T. (2023, February). Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption. Presented at VISAPP 2023: 18th International Conference on Computer Vision Theory and Applications, Lisbon, Portugal

Anomaly detection is the task of recognising novel samples which deviate significantly from pre-established normality. Abnormal classes are not present during training meaning that models must learn effective representations solely across normal clas... Read More about Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption.

Tackling Data Bias in Painting Classification with Style Transfer (2023)
Presentation / Conference Contribution
Vijendran, M., Li, F. W., & Shum, H. P. (2023, February). Tackling Data Bias in Painting Classification with Style Transfer. Presented at VISAPP '23: 2023 International Conference on Computer Vision Theory and Applications, Lisbon, Portugal

It is difficult to train classifiers on paintings collections due to model bias from domain gaps and data bias from the uneven distribution of artistic styles. Previous techniques like data distillation, traditional data augmentation and style transf... Read More about Tackling Data Bias in Painting Classification with Style Transfer.

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.

Anomaly Detection with Transformers in Face Anti-spoofing (2023)
Presentation / Conference Contribution
Abduh, L., Omar, L., & Ivrissimtzis, I. (2023, May). Anomaly Detection with Transformers in Face Anti-spoofing. Presented at WSGC 2023: 31. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2023, Plzen, Czech Republic

Transformers are emerging as the new gold standard in various computer vision applications, and have already been used in face anti-spoofing demonstrating competitive performance. In this paper, we propose a network with the ViT transformer and ResNe... Read More about Anomaly Detection with Transformers in Face Anti-spoofing.

Sorting and Hypergraph Orientation under Uncertainty with Predictions (2023)
Presentation / Conference Contribution
Erlebach, T., de Lima, M., Megow, N., & Schlöter, J. (2023, August). Sorting and Hypergraph Orientation under Uncertainty with Predictions. Presented at Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, S.A.R., China

Learning-augmented algorithms have been attracting increasing interest, but have only recently been considered in the setting of explorable uncertainty where precise values of uncertain input elements can be obtained by a query and the goal is to min... Read More about Sorting and Hypergraph Orientation under Uncertainty with Predictions.

Unaligned 2D to 3D Translation with Conditional Vector-Quantized Code Diffusion using Transformers (2023)
Presentation / Conference Contribution
Corona-Figueroa, A., Bond-Taylor, S., Bhowmik, N., Gaus, Y. F. A., Breckon, T. P., Shum, H. P., & Willcocks, C. G. (2023, October). Unaligned 2D to 3D Translation with Conditional Vector-Quantized Code Diffusion using Transformers. Presented at ICCV23: 2023 IEEE/CVF International Conference on Computer Vision, Paris, France

Generating 3D images of complex objects conditionally from a few 2D views is a difficult synthesis problem, compounded by issues such as domain gap and geometric misalignment. For instance, a unified framework such as Generative Adversarial Networks... Read More about Unaligned 2D to 3D Translation with Conditional Vector-Quantized Code Diffusion using Transformers.

An Element-Wise Weights Aggregation Method for Federated Learning (2023)
Presentation / Conference Contribution
Hu, Y., Ren, H., Hu, C., Deng, J., & Xie, X. (2023, December). An Element-Wise Weights Aggregation Method for Federated Learning. Presented at 2023 IEEE International Conference on Data Mining Workshops (ICDMW), Shanghai, China

Federated learning (FL) is a powerful Machine Learning (ML) paradigm that enables distributed clients to collaboratively learn a shared global model while keeping the data on the original device, thereby preserving privacy. A central challenge in FL... Read More about An Element-Wise Weights Aggregation Method for Federated Learning.

Fg-T2M: Fine-Grained Text-Driven Human Motion Generation via Diffusion Model (2023)
Presentation / Conference Contribution
Wang, Y., Leng, Z., Li, F. W. B., Wu, S.-C., & Liang, X. (2023, October). Fg-T2M: Fine-Grained Text-Driven Human Motion Generation via Diffusion Model. Presented at 2023 IEEE/CVF International Conference on Computer Vision (ICCV), Paris

Text-driven human motion generation in computer vision is both significant and challenging. However, current methods are limited to producing either deterministic or imprecise motion sequences, failing to effectively control the temporal and spatial... Read More about Fg-T2M: Fine-Grained Text-Driven Human Motion Generation via Diffusion Model.

DrawGAN: Multi-view Generative Model Inspired By The Artist's Drawing Method (2023)
Presentation / Conference Contribution
Yang, B., Chen, Z., Li, F. W. B., Sun, H., & Cai, J. (2023, August). DrawGAN: Multi-view Generative Model Inspired By The Artist's Drawing Method. Presented at CGI 2023: Advances in Computer Graphics, Shanghai, China

We present a novel approach for modeling artists' drawing processes using an architecture that combines an unconditional generative adversarial network (GAN) with a multi-view generator and multi-discriminator. Our method excels in synthesizing vario... Read More about DrawGAN: Multi-view Generative Model Inspired By The Artist's Drawing Method.

Modeling Women's Elective Choices in Computing (2023)
Presentation / Conference Contribution
Bradley, S., Parker, M. C., Altin, R., Barker, L., Hooshangi, S., Kunkeler, T., Lennon, R. G., McNeill, F., Minguillón, J., Parkinson, J., Peltsverger, S., & Sibia, N. (2023, July). Modeling Women's Elective Choices in Computing. Presented at ITiCSE 2023: Innovation and Technology in Computer Science Education, Turku Finland

Evidence-based strategies suggest ways to reduce the gender gap in computing. For example, elective classes are valuable in enabling students to choose in which directions to expand their computing knowledge in areas aligned with their interests. T... Read More about Modeling Women's Elective Choices in Computing.

Automated Provenance Collection at Runtime as a Cross-Cutting Concern (2023)
Presentation / Conference Contribution
James Reynolds, O., García-Domínguez, A., & Bencomo, N. (2023, October). Automated Provenance Collection at Runtime as a Cross-Cutting Concern. Presented at 2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C), Västerås, Sweden

Autonomous decision-making is increasingly applied to handle highly dynamic, uncertain environments: as incorrect decisions can cause serious harm to individuals or society, there is a need for accountability. For systems that use runtime models to r... Read More about Automated Provenance Collection at Runtime as a Cross-Cutting Concern.

List 3-Coloring on Comb-Convex and Caterpillar-Convex Bipartite Graphs (2023)
Presentation / Conference Contribution
Baklan Sen, B., Diner, Ö. Y., & Erlebach, T. (2023, December). List 3-Coloring on Comb-Convex and Caterpillar-Convex Bipartite Graphs. Presented at 29th International Computing and Combinatorics Conference (COCOON 2023), Honolulu, Hawaii, USA

Given a graph G = (V, E) and a list of available colors L(v) for each vertex v ∈ V, where L(v) ⊆ {1, 2, . . . , k}, LIST k-COLORING refers to the problem of assigning colors to the vertices of G so that each vertex receives a color from its own list... Read More about List 3-Coloring on Comb-Convex and Caterpillar-Convex Bipartite Graphs.

Analyzing Impact of Data Uncertainty in Distributed Energy Resources using Bayesian Networks (2023)
Presentation / Conference Contribution
Garg;, A., Aujla, G., & Sun, H. (2023, October). Analyzing Impact of Data Uncertainty in Distributed Energy Resources using Bayesian Networks. Presented at 2023 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Glasgow, UK

With the high penetration of distributed energy resources (DERs), distribution networks have become more prone to uncertainties associated with renewable energy sources (RESs). If not handled judiciously, these uncertainties may lead to interruption... Read More about Analyzing Impact of Data Uncertainty in Distributed Energy Resources using Bayesian Networks.

A Mixed Reality Training System for Hand-Object Interaction in Simulated Microgravity Environments (2023)
Presentation / Conference Contribution
Zhou, K., Chen, C., Ma, Y., Leng, Z., Shum, H. P., Li, F. W., & Liang, X. (2023, October). A Mixed Reality Training System for Hand-Object Interaction in Simulated Microgravity Environments. Presented at ISMAR 23: International Symposium on Mixed and Augmented Reality, Sydney, Australia

As human exploration of space continues to progress, the use of Mixed Reality (MR) for simulating microgravity environments and facilitating training in hand-object interaction holds immense practical significance. However, hand-object interaction in... Read More about A Mixed Reality Training System for Hand-Object Interaction in Simulated Microgravity Environments.

An Adaptive Learning Support System based on Ontology of Multiple Programming Languages (2023)
Presentation / Conference Contribution
Nongkhai, L. N. A., Wang, J., & Mendori, T. (2023, December). An Adaptive Learning Support System based on Ontology of Multiple Programming Languages. Presented at ICCE 2023: The 31st International Conference on Computers in Education, Matsue, Shimane, Japan

This research proposes to develop an adaptive ontology-based learning support system for computer programming learning. Firstly, the system adopts a previously developed ontology called CONTINUOUS, which represents programming concepts and their rela... Read More about An Adaptive Learning Support System based on Ontology of Multiple Programming Languages.

Enhancing Perception and Immersion in Pre-Captured Environments through Learning-Based Eye Height Adaptation (2023)
Presentation / Conference Contribution
Feng, Q., Shum, H. P., & Morishima, S. (2023, October). Enhancing Perception and Immersion in Pre-Captured Environments through Learning-Based Eye Height Adaptation. Presented at ISMAR 23: International Symposium on Mixed and Augmented Reality, Sydney, Australia

Pre-captured immersive environments using omnidirectional cameras provide a wide range of virtual reality applications. Previous research has shown that manipulating the eye height in egocentric virtual environments can significantly affect distance... Read More about Enhancing Perception and Immersion in Pre-Captured Environments through Learning-Based Eye Height Adaptation.

Temporal Reachability Dominating Sets: contagion in temporal graphs (2023)
Presentation / Conference Contribution
Kutner, D. C., & Larios-Jones, L. (2023, September). Temporal Reachability Dominating Sets: contagion in temporal graphs. Presented at ALGOWIN 2023: International Symposium on Algorithmics of Wireless Networks, Amsterdam, The Netherlands

SARS-CoV-2 was independently introduced to the UK at least 1300 times by June 2020. Given a population with dynamic pairwise connections, we ask if the entire population could be (indirectly) infected by a small group of k initially infected individu... Read More about Temporal Reachability Dominating Sets: contagion in temporal graphs.