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Outputs (188)

Keeping it Human: A Focus Group Study of Public Attitudes Towards AI in Banking (2020)
Presentation / Conference Contribution
Aitken, M., Ng, M., Toreini, E., van Moorsel, A., Coopamootoo, K. P., & Elliott, K. (2020, December). Keeping it Human: A Focus Group Study of Public Attitudes Towards AI in Banking. Presented at European Symposium on Research in Computer Security 2020, Guildford, England

While there is substantial interest in ethical practice relating to Artificial Intelligence (AI), to date there has been limited consideration of what this means in the banking sector. This study aimed to address this gap in the literature through a... Read More about Keeping it Human: A Focus Group Study of Public Attitudes Towards AI in Banking.

Running Industrial Workflow Applications in a Software-defined Multi-Cloud Environment using Green Energy Aware Scheduling Algorithm (2020)
Journal Article
Wen, Z., Garg, S., Aujla, G. S., Alwasel, K., Puthal, D., Dustdar, S., Zomaya, A. Y., & Rajan, R. (2021). Running Industrial Workflow Applications in a Software-defined Multi-Cloud Environment using Green Energy Aware Scheduling Algorithm. IEEE Transactions on Industrial Informatics, 17(8), 5645-5656. https://doi.org/10.1109/tii.2020.3045690

Industry 4.0 have automated the entire manufacturing sector (including technologies and processes) by adopting Internet of Things and Cloud computing. To handle the work-flows from Industrial Cyber-Physical systems, more and more data centers have be... Read More about Running Industrial Workflow Applications in a Software-defined Multi-Cloud Environment using Green Energy Aware Scheduling Algorithm.

Segmentation of macular edema datasets with small residual 3D U-Net architectures (2020)
Presentation / Conference Contribution
Frawley, J., Willcocks, C. G., Habib, M., Geenen, C., Steel, D. H., & Obara, B. (2020, October). Segmentation of macular edema datasets with small residual 3D U-Net architectures. Presented at 20th IEEE International Conference on BioInformatics and BioEngineering, Cincinnati, OH

This paper investigates the application of deep convolutional neural networks with prohibitively small datasets to the problem of macular edema segmentation. In particular, we investigate several different heavily regularized architectures. We find t... Read More about Segmentation of macular edema datasets with small residual 3D U-Net architectures.

A machine learning driven solution to the problem of perceptual video quality metrics (2020)
Book Chapter
Katsigiannis, S., Rabah, H., & Ramzan, N. (2020). A machine learning driven solution to the problem of perceptual video quality metrics. In M. Z. Shakir, & N. Ramzan (Eds.), AI for Emerging Verticals; Human-robot computing, sensing and networking. IET

The advent of high-speed internet connections, advanced video coding algorithms, and consumer-grade computers with high computational capabilities has led videostreaming-over-the-internet to make up the majority of network traffic. This effect has le... Read More about A machine learning driven solution to the problem of perceptual video quality metrics.

EEG-based biometrics: Effects of template ageing (2020)
Book Chapter
Arnau-González, P., Katsigiannis, S., Arevalillo-Herráez, M., & Ramzan, N. (2020). EEG-based biometrics: Effects of template ageing. In M. Z. Shakir, & N. Ramzan (Eds.), AI for Emerging Verticals; Human-robot computing, sensing and networking. IET

This chapter discusses the effects of template ageing in EEG-based biometrics. The chapter also serves as an introduction to general biometrics and its main tasks: Identification and verification. To do so, we investigate different characterisations... Read More about EEG-based biometrics: Effects of template ageing.

Machine learning-based affect detection within the context of human-horse interaction (2020)
Book Chapter
Althobaiti, T., Katsigiannis, S., West, D., Rabah, H., & Ramzan, N. (2020). Machine learning-based affect detection within the context of human-horse interaction. In M. Z. Shakir, & N. Ramzan (Eds.), AI for Emerging Verticals; Human-robot computing, sensing and networking. IET

This chapter focuses on the use of machine learning techniques within the field of affective computing, and more specifically for the task of emotion recognition within the context of human-horse interaction. Affective computing focuses on the detect... Read More about Machine learning-based affect detection within the context of human-horse interaction.

A Privacy-Preserving Efficient Location-Sharing Scheme for Mobile Online Social Network Applications (2020)
Journal Article
Bhattacharya, M., Roy, S., Mistry, K., Shum, H. P., & Chattopadhyay, S. (2020). A Privacy-Preserving Efficient Location-Sharing Scheme for Mobile Online Social Network Applications. IEEE Access, 8, 221330 - 221351. https://doi.org/10.1109/ACCESS.2020.3043621

The rapid development of mobile internet technology and the better availability of GPS have made mobile online social networks (mOSNs) more popular than traditional online social networks (OSNs) over the last few years. They necessitate fundamental s... Read More about A Privacy-Preserving Efficient Location-Sharing Scheme for Mobile Online Social Network Applications.

A plug-in attribute correction module for generalized zero-shot learning (2020)
Journal Article
Zhang, H., Bai, H., Long, Y., Liu, L., & Shao, L. (2021). A plug-in attribute correction module for generalized zero-shot learning. Pattern Recognition, 112, Article 107767. https://doi.org/10.1016/j.patcog.2020.107767

While Zero Shot Learning models can recognize new classes without training examples, they often fails to incorporate both seen and unseen classes together at the test time, which is known as the Generalized Zero-shot Learning (GZSL) problem. This pap... Read More about A plug-in attribute correction module for generalized zero-shot learning.

Bounding the mim-width of hereditary graph classes (2020)
Presentation / Conference Contribution
Brettell, N., Horsfield, J., Munaro, A., Paesani, G., & Paulusma, D. (2020, December). Bounding the mim-width of hereditary graph classes. Presented at IPEC 2020

A large number of NP-hard graph problems are solvable in XP time when parameterized by some width parameter. Hence, when solving problems on special graph classes, it is helpful to know if the graph class under consideration has bounded width. In thi... Read More about Bounding the mim-width of hereditary graph classes.