Outputs (224)

Use of Machine Learning for Rate Adaptation in MPEG-DASH for Quality of Experience Improvement (2018)
Presentation / Conference Contribution
Alzahrani, I. R., Ramzan, N., Katsigiannis, S., & Amira, A. (2018, December). Use of Machine Learning for Rate Adaptation in MPEG-DASH for Quality of Experience Improvement. Presented at 5th International Symposium on Data Mining Applications (SDMA), Riyadh, Saudi Arabia

Towards a Unified Model for Harmony and Voice-Leading (2018)
Other
Lieck, R., Harasim, D., & Rohrmeier, M. (2018). Towards a Unified Model for Harmony and Voice-Leading

2D Multi-Band PCA and its Application for Ear Recognition (2018)
Presentation / Conference Contribution
Zarachoff, M., Sheikh-Akbari, A., & Monekosso, D. (2018, December). 2D Multi-Band PCA and its Application for Ear Recognition. Presented at 2018 IEEE INTERNATIONAL CONFERENCE ON IMAGING SYSTEMS AND TECHNIQUES (IST) IEEE Instrumentat \& Measurement; IEEE Advancing Technol Human

Adaptive Skip Graph Framework for Peer-to-Peer Networks: Search Time Complexity Analysis (2018)
Presentation / Conference Contribution
Goyal, A., Batra, S., Kumar, N., Aujla, G. S., & Obaidat, M. S. (2018, December). Adaptive Skip Graph Framework for Peer-to-Peer Networks: Search Time Complexity Analysis. Presented at 2018 IEEE Global Communications Conference (GLOBECOM)

Visual Graphs from Motion (VGfM): Scene understanding with object geometry reasoning (2018)
Presentation / Conference Contribution
Gay, P., James, S., & Del Bue, A. (2018, December). Visual Graphs from Motion (VGfM): Scene understanding with object geometry reasoning. Presented at ACCV 2018: Computer Vision – ACCV 2018, Perth, Australia

Recent approaches on visual scene understanding attempt to build a scene graph – a computational representation of objects and their pairwise relationships. Such rich semantic representation is very appealing, yet difficult to obtain from a single im... Read More about Visual Graphs from Motion (VGfM): Scene understanding with object geometry reasoning.

Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments (2018)
Presentation / Conference Contribution
McGough, S., Forshaw, M., Brennan, J., Al Moubayed, N., & Bonner, S. (2018, October). Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments. Presented at 9th International Green and Sustainable Computing Conference., Pittsburgh, PA, US

High Throughput Computing (HTC) provides a convenient mechanism for running thousands of tasks. Many HTC systems exploit computers which are provisioned for other purposes by utilising their idle time - volunteer computing. This has great advantages... Read More about Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments.

Estimating the accuracy of a reduced-order model for the calculation of fractional flow reserve (FFR) (2018)
Journal Article
Boileau, E., Pant, S., Roobottom, C., Sazonov, I., Deng, J., Xie, X., & Nithiarasu, P. (2018). Estimating the accuracy of a reduced-order model for the calculation of fractional flow reserve (FFR). International Journal for Numerical Methods in Biomedical Engineering, 34(1), https://doi.org/10.1002/cnm.2908

DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals From Wireless Low-cost Off-the-Shelf Devices (2018)
Journal Article
Katsigiannis, S., & Ramzan, N. (2018). DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals From Wireless Low-cost Off-the-Shelf Devices. IEEE Journal of Biomedical and Health Informatics, 22(1), 98-107. https://doi.org/10.1109/jbhi.2017.2688239

Superframes, A Temporal Video Segmentation (2018)
Presentation / Conference Contribution
Sokeh, H. S., Argyriou, V., Monekosso, D., & Remagnino, P. (2018, December). Superframes, A Temporal Video Segmentation. Presented at 2018 24TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) Int Assoc Pattern Recognit; Chinese Assoc Automat

Applying Computational Analysis to Textual Data from the Wild (2018)
Presentation / Conference Contribution
Concannon, S. J., Balaam, M., Simpson, E., & Comber, R. (2018, December). Applying Computational Analysis to Textual Data from the Wild. Presented at Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems