Outputs (29)

Studying how digital luthiers choose their tools (2022)
Conference Proceeding
Renney, N., Renney, H., Mitchell, T. J., & Gaster, B. R. (2022). Studying how digital luthiers choose their tools. . https://doi.org/10.1145/3491102.3517656

Digital lutherie is a sub-domain of digital craft focused on creating digital musical instruments: high-performance devices for musical expression. It represents a nuanced and challenging area of human-computer interaction that is well established an... Read More about Studying how digital luthiers choose their tools.

Machine in the middle: Exploring dark patterns of emotional human-computer integration through media art (2022)
Conference Proceeding
Dickinson, R., Semertzidis, N., & Mueller, F. F. (2022). Machine in the middle: Exploring dark patterns of emotional human-computer integration through media art. In S. Barbosa, C. Lampe, C. Appert, & D. A. Shamma (Eds.), CHI EA '22: Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3491101.3503555

As our relationship with machines becomes evermore intimate, we observe increasing efforts in the quantification of human emotion, which has historically generated unintended consequences. We acknowledge an amplification of this trend through recent... Read More about Machine in the middle: Exploring dark patterns of emotional human-computer integration through media art.

LSTM for periodic broadcasting in green IoT applications over energy harvesting enabled wireless networks: Case study on ADAPCAST (2022)
Conference Proceeding
Mustapha, K., Djenouri, D., Jianguo, D., & Djenouri, Y. (2022). LSTM for periodic broadcasting in green IoT applications over energy harvesting enabled wireless networks: Case study on ADAPCAST. In 2021 17th International Conference on Mobility, Sensing and Networking (MSN) (694-699). https://doi.org/10.1109/MSN53354.2021.00107

The present paper considers emerging Internet of Things (IoT) applications and proposes a Long Short Term Memory (LSTM) based neural network for predicting the end of the broadcasting period under slotted CSMA (Carrier Sense Multiple Access) based MA... Read More about LSTM for periodic broadcasting in green IoT applications over energy harvesting enabled wireless networks: Case study on ADAPCAST.

Roadmap to overcoming the challenges of cyber security and forensics education in the age of distance learning and the COVID-19 pandemic (2022)
Conference Proceeding
Elliott, G., & Malik, M. (2022). Roadmap to overcoming the challenges of cyber security and forensics education in the age of distance learning and the COVID-19 pandemic. In 2022 Journal of The Colloquium for Information Systems Security Education (1). https://doi.org/10.53735/cisse.v9i1.133

This paper focuses on developing a pedagogic roadmap to overcoming the challenges of delivering cyber security and forensics education in colleges and universities through distance learning during the COVID-19 pandemic. The research in this paper ide... Read More about Roadmap to overcoming the challenges of cyber security and forensics education in the age of distance learning and the COVID-19 pandemic.

A novel deep reinforcement learning-based approach for task-offloading in vehicular networks (2022)
Conference Proceeding
Kazmi, S. M. A., Otoum, S., Hussain, R., & Mouftah, H. T. (2022). A novel deep reinforcement learning-based approach for task-offloading in vehicular networks. In 2021 IEEE Global Communications Conference (GLOBECOM) (1-6). https://doi.org/10.1109/GLOBECOM46510.2021.9685073

Next-generation vehicular networks will impose unprecedented computation demand due to the wide adoption of compute-intensive services with stringent latency requirements. Computational capacity of vehicular networks can be enhanced by integration of... Read More about A novel deep reinforcement learning-based approach for task-offloading in vehicular networks.

HyperModels - A framework for GPU accelerated physical modelling sound synthesis (2022)
Conference Proceeding
Renney, H., Willemsen, S., Gaster, B. R., & Mitchell, T. J. (2022). HyperModels - A framework for GPU accelerated physical modelling sound synthesis. . https://doi.org/10.21428/92fbeb44.98a4210a

Physical modelling sound synthesis methods generate vast and intricate sound spaces that are navigated using meaningful parameters. Numerical based physical modelling nsynthesis methods provide authentic representations of the physics they model. Unf... Read More about HyperModels - A framework for GPU accelerated physical modelling sound synthesis.

Network maintenance planning via multi-agent reinforcement learning (2022)
Conference Proceeding
Thomas, J., Pérez Hernández, M., Parlikad, A. K., & Piechocki, R. (2022). Network maintenance planning via multi-agent reinforcement learning. In 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (2289-2295). https://doi.org/10.1109/SMC52423.2021.9659150

Within this work, the challenge of developing maintenance planning solutions for networked assets is considered. This is challenging due to the very nature of these systems which are often heterogeneous, distributed and have complex co-dependencies b... Read More about Network maintenance planning via multi-agent reinforcement learning.

On the fairness of generative adversarial networks (GANs) (2022)
Conference Proceeding
Kenfack, P. J., Arapov, D. D., Hussain, R., Kazmi, S. A., & Khan, A. (2022). On the fairness of generative adversarial networks (GANs). In 2021 International Conference "Nonlinearity, Information and Robotics" (NIR) (1-7). https://doi.org/10.1109/NIR52917.2021.9666131

Generative adversarial networks (GANs) are one of the greatest advances in AI in recent years. With their ability to directly learn the probability distribution of data and then sample synthetic realistic data. Many applications have emerged, using G... Read More about On the fairness of generative adversarial networks (GANs).

Memory-constrained context-aware reasoning (2022)
Conference Proceeding
Uddin, I., Rakib, A., Ali, M., & Vinh, P. C. (2022). Memory-constrained context-aware reasoning. In P. Cong Vinh, & A. Rakib (Eds.), Context-Aware Systems and Applications (133-146). https://doi.org/10.1007/978-3-030-93179-7_11

The context-aware computing paradigm introduces environments, known as smart spaces, which can unobtrusively and proactively assist their users. These systems are currently mostly implemented on mobile platforms considering various techniques, includ... Read More about Memory-constrained context-aware reasoning.