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Outputs (3169)

Interaction-Based Human Activity Comparison (2019)
Journal Article
Shen, Y., Yang, L., Ho, E. S., & Shum, H. P. (2020). Interaction-Based Human Activity Comparison. IEEE Transactions on Visualization and Computer Graphics, 26(8), 2620-2633. https://doi.org/10.1109/tvcg.2019.2893247

Traditional methods for motion comparison consider features from individual characters. However, the semantic meaning of many human activities is usually defined by the interaction between them, such as a high-five interaction of two characters. Ther... Read More about Interaction-Based Human Activity Comparison.

Scheduling flows to make the most of WMN resources with guaranteed performance (2019)
Other
Tu, W. (2019). Scheduling flows to make the most of WMN resources with guaranteed performance. [Blog Post]

In the wireless world, we have limited transmission medium resources and bandwidth as compared to wired networks. Multi-hop mesh topologies can quickly use up channels and available bandwidth. This has led to various studies and technological develop... Read More about Scheduling flows to make the most of WMN resources with guaranteed performance.

Can We Assess Mental Health through Social Media and Smart Devices? Addressing Bias in Methodology and Evaluation (2019)
Presentation / Conference Contribution
Tsakalidis, A., Liakata, M., Damoulas, T., & Cristea, A. I. (2018, September). Can We Assess Mental Health through Social Media and Smart Devices? Addressing Bias in Methodology and Evaluation. Presented at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2018 Applied Data Science Track), Dublin

Predicting mental health from smartphone and social media data on a longitudinal basis has recently attracted great interest, with very promising results being reported across many studies. Such approaches have the potential to revolutionise mental h... Read More about Can We Assess Mental Health through Social Media and Smart Devices? Addressing Bias in Methodology and Evaluation.

Tight & Simple Load Balancing (2019)
Presentation / Conference Contribution
Berenbrink, P., Friedetzky, T., Kaaser, D., & Kling, P. (2019, December). Tight & Simple Load Balancing. Presented at IEEE International Parallel & Distributed Processing Symposium (IPDPS), Rio de Janeiro, Brazil

We consider the following load balancing process for m tokens distributed arbitrarily among n nodes connected by a complete graph. In each time step a pair of nodes is selected uniformly at random. Let ℓ 1 and ℓ 2 be their respective number of tokens... Read More about Tight & Simple Load Balancing.

Constraint satisfaction problems for reducts of homogeneous graphs (2019)
Journal Article
Bodirsky, M., Martin, B., Pinsker, M., & Pongracz, A. (2019). Constraint satisfaction problems for reducts of homogeneous graphs. SIAM Journal on Computing, 48(4), 1224-1264. https://doi.org/10.1137/16m1082974

For $n\geq 3$, let $(H_n, E)$ denote the $n$th Henson graph, i.e., the unique countable homogeneous graph with exactly those finite graphs as induced subgraphs that do not embed the complete graph on $n$ vertices. We show that for all structures $\Ga... Read More about Constraint satisfaction problems for reducts of homogeneous graphs.

Collaborative Denoising Autoencoder for High Glycated Haemoglobin Prediction (2019)
Presentation / Conference Contribution
Alhassan, Z., Budgen, D., Alessa, A., Alshammari, R., Daghstani, T., & Al Moubayed, N. (2019, September). Collaborative Denoising Autoencoder for High Glycated Haemoglobin Prediction. Presented at 28th International Conference on Artificial Neural Networks (ICANN2019), Munich, Germany

A pioneering study is presented demonstrating that the presence of high glycated haemoglobin (HbA1c) levels in a patient’s blood can be reliably predicted from routinely collected clinical data. This paves the way for performing early detection of Ty... Read More about Collaborative Denoising Autoencoder for High Glycated Haemoglobin Prediction.

Style Augmentation: Data Augmentation via Style Randomization (2019)
Presentation / Conference Contribution
Jackson, P., Atapour-Abarghouei, A., Bonner, S., Breckon, T., & Obara, B. (2019, June). Style Augmentation: Data Augmentation via Style Randomization. Presented at IEEE/CVF Conference on Computer Vision and Pattern Recognition, Deep Vision, Long Beach, CA, USA

We introduce style augmentation, a new form of data augmentation based on random style transfer, for improving the robustness of Convolutional Neural Networks (CNN) over both classification and regression based tasks. During training, style augmentat... Read More about Style Augmentation: Data Augmentation via Style Randomization.