Outputs (3148)

From a Series of (Un)fortunate Events to Global Explainability of Runtime Model-Based Self-Adaptive Systems (2021)
Presentation / Conference Contribution
Ullauri, J. M. P., García-Domínguez, A., & Bencomo, N. (2021, December). From a Series of (Un)fortunate Events to Global Explainability of Runtime Model-Based Self-Adaptive Systems. Presented at ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion, MODELS 2021 Companion, Fukuoka, Japan, October 10-15, 2021

Towards Technology Acceptance: a Bayesian Network of soft requirements, the case of the NHS COVID-19 Test and Trace App (2021)
Presentation / Conference Contribution
Paucar, L. H. G., Bencomo, N., Sutcliffe, A. G., Yue, T., & Mirakhorli, M. (2021, December). Towards Technology Acceptance: a Bayesian Network of soft requirements, the case of the NHS COVID-19 Test and Trace App. Presented at 29th IEEE International Requirements Engineering Conference Workshops, RE 2021 Workshops, Notre Dame, IN, USA, September 20-24, 2021

Gamified and Self-Adaptive Applications for the Common Good: Research Challenges Ahead (2021)
Presentation / Conference Contribution
Bucchiarone, A., Cicchetti, A., Bencomo, N., Loria, E., & Marconi, A. (2021, December). Gamified and Self-Adaptive Applications for the Common Good: Research Challenges Ahead. Presented at 16th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS@ICSE 2021, Madrid, Spain, May 18-24, 2021

A Pose-based Feature Fusion and Classification Framework for the Early Prediction of Cerebral Palsy in Infants (2021)
Journal Article
McCay, K. D., Hu, P., Shum, H. P., Woo, W. L., Marcroft, C., Embleton, N. D., Munteanu, A., & Ho, E. S. (2022). A Pose-based Feature Fusion and Classification Framework for the Early Prediction of Cerebral Palsy in Infants. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30, 8-19. https://doi.org/10.1109/tnsre.2021.3138185

The early diagnosis of cerebral palsy is an area which has recently seen significant multi-disciplinary research. Diagnostic tools such as the General Movements Assessment (GMA), have produced some very promising results. However, the prospect of aut... Read More about A Pose-based Feature Fusion and Classification Framework for the Early Prediction of Cerebral Palsy in Infants.

The Secret to Better AI and Better Software (Is Requirements Engineering) (2021)
Journal Article
Bencomo, N., Guo, J., Harrison, R., Heyn, H.-M., & Menzies, T. (2022). The Secret to Better AI and Better Software (Is Requirements Engineering). IEEE Software, 39(1), 105-110. https://doi.org/10.1109/ms.2021.3118099

Much has been written about the algorithmic role that AI plays for automation in SE. But what about the role of AI, augmented by human knowledge? Can we make a profound advance by combining human and artificial intelligence? Researchers in requiremen... Read More about The Secret to Better AI and Better Software (Is Requirements Engineering).

Computing subset transversals in H-free graphs (2021)
Journal Article
Brettell, N., Johnson, M., Paesani, G., & Paulusma, D. (2022). Computing subset transversals in H-free graphs. Theoretical Computer Science, 902, 76-92. https://doi.org/10.1016/j.tcs.2021.12.010

we study the computational complexity of two well-known graph transversal problems, namely Subset Feedback Vertex Set and Subset Odd Cycle Transversal, by restricting the input to H-free graphs, that is, to graphs that do not contain some fixed graph... Read More about Computing subset transversals in H-free graphs.

Event-driven Temporal Models for Explanations - ETeMoX: Explaining Reinforcement Learning (2021)
Journal Article
Parra-Ullauri, J. M., Garcıa-Domınguez, A., Bencomo, N., Zheng, C., Zhen, C., Boubeta-Puig, J., Ortiz, G., & Yang, S. (2022). Event-driven Temporal Models for Explanations - ETeMoX: Explaining Reinforcement Learning. Software and Systems Modeling, 21(3), 1091-1113. https://doi.org/10.1007/s10270-021-00952-4

Modern software systems are increasingly expected to show higher degrees of autonomy and self-management to cope with uncertain and diverse situations. As a consequence, autonomous systems can exhibit unexpected and surprising behaviours. This is exa... Read More about Event-driven Temporal Models for Explanations - ETeMoX: Explaining Reinforcement Learning.

Identification of Driver Phone Usage Violations via State-of-the-Art Object Detection with Tracking (2021)
Presentation / Conference Contribution
Carrell, S., & Atapour-Abarghouei, A. (2021, December). Identification of Driver Phone Usage Violations via State-of-the-Art Object Detection with Tracking. Presented at 2021 IEEE International Conference on Big Data (IEEE BigData 2021), Orlando, FL, USA

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.

A directed graph convolutional neural network for edge-structured signals in link-fault detection (2021)
Journal Article
Kenning, M., Deng, J., Edwards, M., & Xie, X. (2022). A directed graph convolutional neural network for edge-structured signals in link-fault detection. Pattern Recognition Letters, 153, 100-106. https://doi.org/10.1016/j.patrec.2021.12.003

The growing interest in graph deep learning has led to a surge of research focusing on learning various characteristics of graph-structured data. Directed graphs have generally been treated as incidental to definitions on the more general class of un... Read More about A directed graph convolutional neural network for edge-structured signals in link-fault detection.

Rank over Class: The Untapped Potential of Ranking in Natural Language Processing (2021)
Presentation / Conference Contribution
Atapour-Abarghouei, A., Bonner, S., & McGough, A. S. (2021, December). Rank over Class: The Untapped Potential of Ranking in Natural Language Processing. Presented at 2021 IEEE International Conference on Big Data (IEEE BigData 2021), Orlando, FL, USA

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.