Outputs (29)

Problem classification for tailored help desk auto replies (2022)
Conference Proceeding
Nicholls, R., Fellows, R., Battle, S., & Ihshaish, H. (2022). Problem classification for tailored help desk auto replies. In E. Pimenidis, P. Angelov, C. Jayne, A. Papaleonidas, & M. Aydin (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2022 (445-454). https://doi.org/10.1007/978-3-031-15937-4_37

IT helpdesks are charged with the task of responding quickly to user queries. To give the user confidence that their query matters, the helpdesk will auto-reply to the user with confirmation that their query has been received and logged. This auto-re... Read More about Problem classification for tailored help desk auto replies.

Deep learning for estimating sleeping sensor’s values in sustainable IoT applications (2022)
Conference Proceeding
Djenouri, D., Laidi, R., & Djenouri, Y. (2022). Deep learning for estimating sleeping sensor’s values in sustainable IoT applications. In 2022 International Balkan Conference on Communications and Networking (BalkanCom) (147-151). https://doi.org/10.1109/BalkanCom55633.2022.9900817

The aim of this work is to develop a deep learning model that uses spatial correlation to enable turning turn off a subset of sensors while predicting their readings. This considerably saves the energy that would be consumed by those sensors both for... Read More about Deep learning for estimating sleeping sensor’s values in sustainable IoT applications.

OGMA: Visualisation for software container security analysis and automated remediation (2022)
Conference Proceeding
Mills, A., White, J., & Legg, P. (2022). OGMA: Visualisation for software container security analysis and automated remediation. In 2022 IEEE International Conference on Cyber Security and Resilience (CSR) (76-81). https://doi.org/10.1109/CSR54599.2022.9850335

The use of software containerisation has rapidly increased in academia and industry which has lead to the production of several container security scanning tools for assessing the security posture and threat of a container image. The variability betw... Read More about OGMA: Visualisation for software container security analysis and automated remediation.

Cellular automata application on chemical computing logic circuits (2022)
Conference Proceeding
Tsompanas, M., Chatzinikolaou, T. P., & Sirakoulis, G. C. (2022). Cellular automata application on chemical computing logic circuits. In B. Chopard, S. Bandini, A. Dennunzio, & M. A. Haddad (Eds.), International Conference on Cellular Automata for Research and Industry: ACRI 2022: Cellular Automata (3-14). https://doi.org/10.1007/978-3-031-14926-9_1

Cellular Automata (CAs) have been proved to be a robust tool for mimicking a plethora of biological, physical and chemical systems. CAs can be used as an alternative to partial differential equations, in order to illustrate the evolution in time of t... Read More about Cellular automata application on chemical computing logic circuits.

Task-oriented dialogue systems: Performance vs. quality-optima, a review (2022)
Conference Proceeding
Fellows, R., Ihshaish, H., Battle, S., Haines, C., Mayhew, P., & Deza, J. I. (2022). Task-oriented dialogue systems: Performance vs. quality-optima, a review. In David C. Wyld et al. (Eds): SIPP, NLPCL, BIGML, SOEN, AISC, NCWMC, CCSIT - 2022 pp. 69-87, 2022. CS & IT - CSCP 2022 (69-87). https://doi.org/10.5121/csit.2022.121306

Task-oriented dialogue systems (TODS) are continuing to rise in popularity as various industries find ways to effectively harness their capabilities, saving both time and money. However, even state-of-the-art TODS are not yet reaching their full pote... Read More about Task-oriented dialogue systems: Performance vs. quality-optima, a review.

Deep learning-based defect inspection in sheet metal stamping parts (2022)
Conference Proceeding
Singh, A. R., Bashford-Rogers, T., Hazra, S., & Debattista, K. (2022). Deep learning-based defect inspection in sheet metal stamping parts. In NUMISHEET 2022 Proceedings of the 12th International Conference and Workshop on Numerical Simulation of 3D Sheet Metal Forming Processes (411-419). https://doi.org/10.1007/978-3-031-06212-4_38

Defect inspection is a crucial step in sheet metal stampingmanufacturing. However, current inspection methods largely consist of visual inspection by trained operatives but are unreliable and prone to error. Computer vision techniques have the potent... Read More about Deep learning-based defect inspection in sheet metal stamping parts.

Bu-Dash: A universal and dynamic graphical password scheme (2022)
Conference Proceeding
Andriotis, P., Kirby, M., & Takasu, A. (2022). Bu-Dash: A universal and dynamic graphical password scheme. In A. Moallem (Ed.), HCI for Cybersecurity, Privacy and Trust: 4th International Conference, HCI-CPT 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Virtual Event, June 26 – July 1, 2022, Proceedings (209-227). https://doi.org/10.1007/978-3-031-05563-8_14

Biometric authentication gradually replaces knowledge-based methods on mobile devices. However, Personal Identification Numbers, passcodes, and graphical password schemes such as the Android Pattern Unlock (APU) are often the primary means for authen... Read More about Bu-Dash: A universal and dynamic graphical password scheme.

Supporting patient nutrition in critical care units (2022)
Conference Proceeding
Soomro, K., Pimenidis, E., & McWilliams, C. (2022). Supporting patient nutrition in critical care units. In Engineering Applications of Neural Networks: EANN 2022: Engineering Applications of Neural Networks (128-136). https://doi.org/10.1007/978-3-031-08223-8_11

Critical Care Unit (CCU) patients often benefit from being referred to dietitians for various reasons. This can help improve recovery time, resulting in more effective utilisation of valuable resources within the NHS (National Health Service) in the... Read More about Supporting patient nutrition in critical care units.

The value of information for dynamic decentralised criticality computation (2022)
Conference Proceeding
Proselkov, Y., Herrera, M., Hernandez, M. P., Kumar Parlikad, A. K., & Brintrup, A. (2022). The value of information for dynamic decentralised criticality computation. In IFAC-PapersOnLine (408-413). https://doi.org/10.1016/j.ifacol.2022.04.228

Smart manufacturing uses advanced data-driven solutions to improve performance and operations resilience requiring large amounts of data delivered quickly, enabled by telecom networks and network elements such as routers or switches. Disruptions can... Read More about The value of information for dynamic decentralised criticality computation.

Ego-graph replay based continual learning for misinformation engagement prediction (2022)
Conference Proceeding
Bo, H., Mcconville, R., Hong, J., & Liu, W. (in press). Ego-graph replay based continual learning for misinformation engagement prediction. . https://doi.org/10.48550/arXiv.2207.12105

Online social network platforms have a problem with misinformation. One popular way of addressing this problem is via the use of machine learning based automated misinfor-mation detection systems to classify if a post is misinformation. Instead of po... Read More about Ego-graph replay based continual learning for misinformation engagement prediction.