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Outputs (120)

Doubt and Redundancy Kill Soft Errors---Towards Detection and Correction of Silent Data Corruption in Task-based Numerical Software (2021)
Conference Proceeding
Samfass, P., Weinzierl, T., Reinarz, A., & Bader, M. (2021). Doubt and Redundancy Kill Soft Errors---Towards Detection and Correction of Silent Data Corruption in Task-based Numerical Software. . https://doi.org/10.1109/ftxs54580.2021.00005

Resilient algorithms in high-performance computing are subject to rigorous non-functional constraints. Resiliency must not increase the runtime, memory footprint or I/O demands too significantly. We propose a task-based soft error detection scheme th... Read More about Doubt and Redundancy Kill Soft Errors---Towards Detection and Correction of Silent Data Corruption in Task-based Numerical Software.

Identification of Driver Phone Usage Violations via State-of-the-Art Object Detection with Tracking (2021)
Conference Proceeding
Carrell, S., & Atapour-Abarghouei, A. (2021). Identification of Driver Phone Usage Violations via State-of-the-Art Object Detection with Tracking. . https://doi.org/10.1109/bigdata52589.2021.9671378

The use of mobiles phones when driving has been a major factor when it comes to road traffic incidents and the process of capturing such violations can be a laborious task. Advancements in both modern object detection frameworks and high-performance... Read More about Identification of Driver Phone Usage Violations via State-of-the-Art Object Detection with Tracking.

Rank over Class: The Untapped Potential of Ranking in Natural Language Processing (2021)
Conference Proceeding
Atapour-Abarghouei, A., Bonner, S., & McGough, A. S. (2021). Rank over Class: The Untapped Potential of Ranking in Natural Language Processing. . https://doi.org/10.1109/bigdata52589.2021.9671386

Text classification has long been a staple within Natural Language Processing (NLP) with applications spanning across diverse areas such as sentiment analysis, recommender systems and spam detection. With such a powerful solution, it is often temptin... Read More about Rank over Class: The Untapped Potential of Ranking in Natural Language Processing.

Transforming Fake News: Robust Generalisable News Classification Using Transformers (2021)
Conference Proceeding
Blackledge, C., & Atapour-Abarghouei, A. (2021). Transforming Fake News: Robust Generalisable News Classification Using Transformers. . https://doi.org/10.1109/bigdata52589.2021.9671970

As online news has become increasingly popular and fake news increasingly prevalent, the ability to audit the veracity of online news content has become more important than ever. Such a task represents a binary classification challenge, for which tra... Read More about Transforming Fake News: Robust Generalisable News Classification Using Transformers.

Robust 3D U-Net Segmentation of Macular Holes (2021)
Conference Proceeding
Frawley, J., Willcocks, C. G., Habib, M., Geenen, C., Steel, D. H., & Obara, B. (2021). Robust 3D U-Net Segmentation of Macular Holes. In A. Pakrashi, E. Rushe, M. H. Z. Bazargani, & B. Mac Namee (Eds.),

Macular holes are a common eye condition which result in visual impairment. We look at the application of deep convolutional neural networks to the problem of macular hole segmentation. We use the 3D U-Net architecture as a basis and experiment with... Read More about Robust 3D U-Net Segmentation of Macular Holes.