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Outputs (191)

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.

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.

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.

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.

Innovative haptic-based system for upper limb rehabilitation in visually impaired individuals: a multilayer approach (2023)
Journal Article
Albusac, J., Herrera, V., Schez-Sobrino, S., Grande, R., Vallejo, D., & Monekosso, D. (2024). Innovative haptic-based system for upper limb rehabilitation in visually impaired individuals: a multilayer approach. Multimedia Tools and Applications, 83(21), 60537-60563. https://doi.org/10.1007/s11042-023-17892-4

The integration of technology in healthcare has revolutionized physical rehabilitation of patients affected by neurological conditions, such as spinal cord injuries and strokes. However, a significant gap remains in addressing the needs of the visual... Read More about Innovative haptic-based system for upper limb rehabilitation in visually impaired individuals: a multilayer approach.

Trustworthy IAP: An Intelligent Applications Profiler to Investigate Vulnerabilities of Consumer Electronic Devices (2023)
Journal Article
Su, J., Hong, Z., Ye, L., Liu, T., Liang, S., Ji, S., Aujla, G. S., Beyah, R., & Wen, Z. (online). Trustworthy IAP: An Intelligent Applications Profiler to Investigate Vulnerabilities of Consumer Electronic Devices. IEEE Transactions on Consumer Electronics, 70(1), 4605 - 4616. https://doi.org/10.1109/tce.2023.3347651

As a typical representative of the Internet of Energy (IoE) intelligent era, consumer electronic (CE) devices continue to evolve at a remarkable pace. Computers, as typical and essential CE devices, have been instrumental in enhancing efficiency, com... Read More about Trustworthy IAP: An Intelligent Applications Profiler to Investigate Vulnerabilities of Consumer Electronic Devices.

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.

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.

FedBoosting: Federated learning with gradient protected boosting for text recognition (2023)
Journal Article
Ren, H., Deng, J., Xie, X., Ma, X., & Wang, Y. (2024). FedBoosting: Federated learning with gradient protected boosting for text recognition. Neurocomputing, 569, Article 127126. https://doi.org/10.1016/j.neucom.2023.127126

Conventional machine learning methodologies require the centralization of data for model training, which may be infeasible in situations where data sharing limitations are imposed due to concerns such as privacy and gradient protect... Read More about FedBoosting: Federated learning with gradient protected boosting for text recognition.