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All Outputs (302)

Digital twins in industry 4.0 cyber security (2024)
Conference Proceeding
Lo, C., Win, T. Y., Rezaeifar, Z., Khan, Z., & Legg, P. (2024). Digital twins in industry 4.0 cyber security. In Proceedings of the IEEE Smart World Congress 2023. https://doi.org/10.1109/swc57546.2023.10449147

The increased adoption of sophisticated Cyber Physical Systems (CPS) in critical infrastructure and various aspects of Industry 4.0 has exposed vulnerabilities stemming from legacy CPS and Industrial Internet of Things (IIoT) devices. The interconnec... Read More about Digital twins in industry 4.0 cyber security.

Towards digital-twin solutions for the 15 minute city (2023)
Conference Proceeding
Ludlow, D., Khan, Z., Chrysoulakis, N., & Mitraka, Z. (2023). Towards digital-twin solutions for the 15 minute city. In 2023 Joint Urban Remote Sensing Event (JURSE). https://doi.org/10.1109/jurse57346.2023.10144161

The Covid-19 pandemic and the climate emergency have re-emphasised the need to re-evaluate urban governance and planning process in cities. The "new-normal"and digital transformations offer catalysts to drive and deliver climate mitigation in cities.... Read More about Towards digital-twin solutions for the 15 minute city.

Hear here: Sonification as a design strategy for robot teleoperation using virtual reality (2023)
Conference Proceeding
Simmons, J., Bown, A., Bremner, P., McIntosh, V., & Mitchell, T. J. (2023). Hear here: Sonification as a design strategy for robot teleoperation using virtual reality.

This paper introduces a novel methodology for the sonification of data, and shares the results of a usability study, putting the method- ology into practice within an industrial use case. Working with partners at Sellafield nuclear facility, we explo... Read More about Hear here: Sonification as a design strategy for robot teleoperation using virtual reality.

Participatory conceptual design of accessible digital musical instruments using generative AI (2023)
Conference Proceeding
Aynsley, H., Mitchell, T. J., & Meckin, D. (in press). Participatory conceptual design of accessible digital musical instruments using generative AI.

This paper explores the potential of AI text-to-image diffusion models (e.g. DALLE-2 and Midjourney) to support the early phase design of new digital musical instruments in collaboration with Disabled musicians. The paper presents initial findings fr... Read More about Participatory conceptual design of accessible digital musical instruments using generative AI.

Integrity auditing for secure cloud storage on sensitive data protection (2023)
Conference Proceeding
Sivakumar, J., Malik, M., & Rajasekaran, A. S. (2023). Integrity auditing for secure cloud storage on sensitive data protection. In 2022 IEEE 2nd International Conference on Mobile Networks and Wireless Communications (ICMNWC). https://doi.org/10.1109/ICMNWC56175.2022.10031918

Users can interchange data with others and remotely store their data on the cloud using cloud storage services. The integrity of data saved in the cloud should be ensured through remote data integrity audits. An electronic health record system is one... Read More about Integrity auditing for secure cloud storage on sensitive data protection.

Chatbot in E-learning (2023)
Conference Proceeding
Hussain, S., Al-Hashmi, S. H., Malik, M. H., & Ali Kazmi, S. I. (2023). Chatbot in E-learning. In SHS Web of Conferences: International Conference on Teaching and Learning – Digital Transformation of Education and Employability (ICTL 2022). https://doi.org/10.1051/shsconf/202315601002

In many modern apps, especially those that provide the user intelligence help, the usage of chatbots is quite common. In reality, these systems frequently have chatbots that can read user inquiries and give the appropriate replies quickly and accurat... Read More about Chatbot in E-learning.

Vision-based approach for autism diagnosis using transfer learning and eye-tracking (2022)
Conference Proceeding
Elbattah, M., Guérin, J., Carette, R., Cilia, F., & Dequen, G. (2022). Vision-based approach for autism diagnosis using transfer learning and eye-tracking. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF (256-263). https://doi.org/10.5220/0010975500003123

The potentials of Transfer Learning (TL) have been well-researched in areas such as Computer Vision and Natural Language Processing. This study aims to explore a novel application of TL to detect Autism Spectrum Disorder. We seek to develop an approa... Read More about Vision-based approach for autism diagnosis using transfer learning and eye-tracking.

Learning embeddings from free-text triage notes using pretrained transformer models (2022)
Conference Proceeding
Arnaud, É., Elbattah, M., Gignon, M., & Dequen, G. (2022). Learning embeddings from free-text triage notes using pretrained transformer models. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (835-841). https://doi.org/10.5220/0011012800003123

The advent of transformer models has allowed for tremendous progress in the Natural Language Processing (NLP) domain. Pretrained transformers could successfully deliver the state-of-the-art performance in a myriad of NLP tasks. This study presents an... Read More about Learning embeddings from free-text triage notes using pretrained transformer models.

Eye-tracking dataset to support the research on autism spectrum disorder (2022)
Conference Proceeding
Cilia, F., Carette, R., Elbattah, M., Guérin, J., & Dequen, G. (2022). Eye-tracking dataset to support the research on autism spectrum disorder. In Proceedings of the 1st Workshop on Scarce Data in Artificial Intelligence for Healthcare (59-64). https://doi.org/10.5220/0011540900003523

The availability of data is a key enabler for researchers across different disciplines. However, domains, such as healthcare, are still fundamentally challenged by the paucity and imbalance of datasets. Health data could be inaccessible due to a vari... Read More about Eye-tracking dataset to support the research on autism spectrum disorder.

Fast and accurate evaluation of collaborative filtering recommendation algorithms (2022)
Conference Proceeding
Polatidis, N., Kapetanakis, S., Pimenidis, E., & Manolopoulos, Y. (2022). Fast and accurate evaluation of collaborative filtering recommendation algorithms. In N. Thanh Nguyen, T. Khoa Tran, U. Tukayev, T. Hong, B. Trawiński, & E. Szczerbicki (Eds.), ACIIDS 2022: Intelligent Information and Database Systems (623-634). https://doi.org/10.1007/978-3-031-21743-2_50

Collaborative filtering are recommender systems algorithms that provide personalized recommendations to users in various online environments such as movies, music, books, jokes and others. There are many such recommendation algorithms and, regarding... Read More about Fast and accurate evaluation of collaborative filtering recommendation algorithms.

Towards idea mining: Problem-solution phrases extraction from text (2022)
Conference Proceeding
Liu, H., Brailsford, T., Goulding, J., Maul, T., Tan, T., & Chaudhuri, D. (2022). Towards idea mining: Problem-solution phrases extraction from text. In W. Chen, L. Yao, T. Cai, S. Pan, T. Shen, & X. Li (Eds.), ADMA 2022: Advanced Data Mining and Applications (3–14). https://doi.org/10.1007/978-3-031-22137-8_1

This paper investigates the feasibility of problem-solution phrases extraction from scientific publications using neural network approaches. Bidirectional Long Short-Term Memory with Conditional Random Fields (Bi-LSTM-CRFs) and Bidirectional Encoder... Read More about Towards idea mining: Problem-solution phrases extraction from text.

Towards idea mining: Problem-solution phrase extraction from text (2022)
Conference Proceeding
Liu, H., Brailsford, T., Goulding, J., Maul, T., Tan, T., & Chaudhuri, D. (2023). Towards idea mining: Problem-solution phrase extraction from text. In W. Chen, L. Yao, T. Cai, S. Pan, T. Shen, & X. Li (Eds.), Advanced Data Mining and Applications 18th International Conference, ADMA 2022, Brisbane, QLD, Australia, November 28–30, 2022, Proceedings, Part II (3-14). https://doi.org/10.1007/978-3-031-22137-8_1

This paper investigates the feasibility of problem-solution phrases extraction from scientific publications using neural network approaches. Bidirectional Long Short-Term Memory with Conditional Random Fields (Bi-LSTM-CRFs) and Bidirectional Encoder... Read More about Towards idea mining: Problem-solution phrase extraction from text.

Ego-graph replay based continual learning for misinformation engagement prediction (2022)
Conference Proceeding
Bo, H., McConville, R., Hong, J., & Liu, W. (2022). Ego-graph replay based continual learning for misinformation engagement prediction. In 2022 International Joint Conference on Neural Networks (IJCNN) (01-08). https://doi.org/10.1109/IJCNN55064.2022.9892557

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 misinformation detection systems to classify if a post is misinformation. Instead of pos... Read More about Ego-graph replay based continual learning for misinformation engagement prediction.

Maintenance strategies for networked assets* (2022)
Conference Proceeding
Perez Hernandez, M., Puchkova, A., & Kumar Parlikad, A. K. (2022). Maintenance strategies for networked assets*. . https://doi.org/10.1016/j.ifacol.2022.09.199

The purpose of this paper is to analyse the effect of different maintenance strategies for a network of assets whose condition deteriorates progressively along the time. We propose both an agent-based model that considers the dynamics of data traffic... Read More about Maintenance strategies for networked assets*.

Risk of disclosure when reporting commonly used univariate statistics (2022)
Conference Proceeding
Derrick, B., Green, E., Ritchie, F., & White, P. (2022). Risk of disclosure when reporting commonly used univariate statistics. In Lecture Notes in Computer Science (119-129). https://doi.org/10.1007/978-3-031-13945-1_9

When basic or descriptive summary statistics are reported, it may be possible that the entire sample of observations is inadvertently disclosed, or that members within a sample will be able to work out responses of others. Three sets of univariate su... Read More about Risk of disclosure when reporting commonly used univariate statistics.

Analysing the predictivity of features to characterise the search space (2022)
Conference Proceeding
Durgut, R., Aydin, M. E., Ihshaish, H., & Rakib, A. (2022). Analysing the predictivity of features to characterise the search space. In E. Pimenidis, P. Angelov, C. Jayne, A. Papaleonidas, & M. Aydin (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2022 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings; Part IV (1-13). https://doi.org/10.1007/978-3-031-15937-4_1

Exploring search spaces is one of the most unpredictable challenges that has attracted the interest of researchers for decades. One way to handle unpredictability is to characterise the search spaces and take actions accordingly. A well-characterised... Read More about Analysing the predictivity of features to characterise the search space.

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.