A novel deep reinforcement learning-based approach for task-offloading in vehicular networks

Kazmi, S. M. Ahsan; Otoum, Safa; Hussain, Rasheed; Mouftah, Hussein T.

doi:10.1109/GLOBECOM46510.2021.9685073

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

All Outputs (24)