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Outputs (237)

Neurosymbolic Learning in the XAI Framework for Enhanced Cyberattack Detection with Expert Knowledge Integration (2024)
Presentation / Conference Contribution
Kalutharage, C. S., Liu, X., Chrysoulas, C., & Bamgboye, O. (2024, June). Neurosymbolic Learning in the XAI Framework for Enhanced Cyberattack Detection with Expert Knowledge Integration. Presented at The 39th International Conference on ICT Systems Security and Privacy Protection (SEC 2024), Edinburgh

The perpetual evolution of cyberattacks, especially in the realm of Internet of Things (IoT) networks, necessitates advanced, adaptive, and intelligent defence mechanisms. The integration of expert knowledge can drastically enhance the efficacy of Io... Read More about Neurosymbolic Learning in the XAI Framework for Enhanced Cyberattack Detection with Expert Knowledge Integration.

Investigating Markers and Drivers of Gender Bias in Machine Translations (2024)
Presentation / Conference Contribution
Barclay, P., & Sami, A. (2024, March). Investigating Markers and Drivers of Gender Bias in Machine Translations. Presented at IEEE International Conference on Software Analysis, Evolution and Reengineering, Rovaniemi, Finland

Implicit gender bias in Large Language Models (LLMs) is a well-documented problem, and implications of gender introduced into automatic translations can perpetuate real-world biases. However, some LLMs use heuristics or post-processing to mask such b... Read More about Investigating Markers and Drivers of Gender Bias in Machine Translations.

Scalable Machine Learning Architectures for IPA-Driven Maintenance Task Allocation in Large-Scale Building Portfolios (2024)
Presentation / Conference Contribution
Huang, Z., Liu, X., Romdhani, I., & Shih, C.-S. (2024, August). Scalable Machine Learning Architectures for IPA-Driven Maintenance Task Allocation in Large-Scale Building Portfolios. Presented at The 7th International Conference on Information Science and Systems (ICISS 2024), Edinburgh

This research presents a groundbreaking approach to Building Maintenance Management (BMM) by introducing an Intelligent Process Automation (IPA)-Driven Building Maintenance Management (IBMM) model. This innovative model harnesses the synergies betwee... Read More about Scalable Machine Learning Architectures for IPA-Driven Maintenance Task Allocation in Large-Scale Building Portfolios.

Utilizing the Ensemble Learning and XAI for Performance Improvements in IoT Network Attack Detection (2024)
Presentation / Conference Contribution
Kalutharage, C. S., Liu, X., Chrysoulas, C., & Bamgboye, O. (2023, September). Utilizing the Ensemble Learning and XAI for Performance Improvements in IoT Network Attack Detection. Presented at The 4th International Workshop on Cyber-Physical Security for Critical Infrastructures Protection (CPS4CIP 2023) - in conjunction with ESORICS 2023, The Hague, Netherlands

PFL-LDG: Privacy-preserving Federated Learning via Lightweight Device Grouping (2023)
Presentation / Conference Contribution
Wang, Z., Liu, Q., & Liu, X. (2023, August). PFL-LDG: Privacy-preserving Federated Learning via Lightweight Device Grouping. Presented at The 9th IEEE International Conference on Privacy Computing and Data Security (PCDS 2023) as Part of the IEEE Smart World Congress 2023, Portsmouth, UK

The rapid growth of private data from distributed edge networks, driven by the proliferation of IoT sensors, wearable devices, and smartphones, offers significant opportunities for AI applications. However, traditional distributed machine learning me... Read More about PFL-LDG: Privacy-preserving Federated Learning via Lightweight Device Grouping.

Proceedings of the 18th International Audio Mostly Conference (2023)
Presentation / Conference Contribution
(2023, August). Proceedings of the 18th International Audio Mostly Conference. Presented at AM '23: Audio Mostly 2023, Edinburgh

Audio Mostly is an interdisciplinary conference on design and experience of interaction with sound that prides itself on embracing applied theory and reflective practice. Its annual gatherings bring together thinkers and doers from academia and indus... Read More about Proceedings of the 18th International Audio Mostly Conference.

Towards Improving Accessibility of Web Auditing with Google Lighthouse (2023)
Presentation / Conference Contribution
McGill, T., Bamgboye, O., Liu, X., & Kalutharage, C. S. (2023, June). Towards Improving Accessibility of Web Auditing with Google Lighthouse. Presented at The 47th IEEE Annual Conference on Computers, Software, and Applications (COMPSAC), Turin, Italy

Google Lighthouse is a tool made by Google for auditing web pages performance, accessibility, SEO, and best practices with the intention of improving the quality of the websites. This allows software developers to understand areas of improvement with... Read More about Towards Improving Accessibility of Web Auditing with Google Lighthouse.

DanceGraph: A Complementary Architecture for Synchronous Dancing Online (2023)
Presentation / Conference Contribution
Sinclair, D., Ademola, A. V., Koniaris, B., & Mitchell, K. (2023, May). DanceGraph: A Complementary Architecture for Synchronous Dancing Online. Paper presented at 36th International Computer Animation & Social Agents (CASA) 2023, Limassol, Cyprus

DanceGraph is an architecture for synchronized online dancing overcoming the latency of net-worked body pose sharing. We break down this challenge by developing a real-time bandwidth-efficient architecture to minimize lag and reduce the timeframe of... Read More about DanceGraph: A Complementary Architecture for Synchronous Dancing Online.

Dense-FCN: A Deep Learning Approach for Weather Radar Beam Blockage Correction (2022)
Presentation / Conference Contribution
Sun, J., Wu, H., Liu, Q., Liu, X., & Ma, J. (2022, September). Dense-FCN: A Deep Learning Approach for Weather Radar Beam Blockage Correction. Presented at 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), Falerna, Italy

The weather radar will receive a lot of non-meteorological echo information during the body scan process, such as: object echoes, co-wave interference echoes, airplanes, flocks of birds, etc. These non-meteorological echoes will cause pollution to no... Read More about Dense-FCN: A Deep Learning Approach for Weather Radar Beam Blockage Correction.

High Resolution Remote Sensing Water Image Segmentation Based on Dual Branch Network (2022)
Presentation / Conference Contribution
Zhang, Z., Li, Y., Liu, Q., & Liu, X. (2022, September). High Resolution Remote Sensing Water Image Segmentation Based on Dual Branch Network. Presented at 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), Falerna, Italy

A basic stage of hydrological research is to automatically extract water body information from high-resolution remote sensing images. Various methods based on deep learning convolutional neural networks have been proposed in recent studies to achieve... Read More about High Resolution Remote Sensing Water Image Segmentation Based on Dual Branch Network.

Emerging Simulation Frameworks for Analyzing Smart Grid Cyberattack: A Literature Review (2022)
Presentation / Conference Contribution
Darteh, O. F., Liu, Q., Liu, X., Bah, I., Nakoty, F. M., & Acakpovi, A. (2022, September). Emerging Simulation Frameworks for Analyzing Smart Grid Cyberattack: A Literature Review. Presented at 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), Falerna, Italy

The transition of the conventional power grid into the Smart Grid (SG), a widely distributed energy delivery network characterized by a two-way flow of electricity and information, is key for energy sector stakeholders. Despite the SG’s clear improve... Read More about Emerging Simulation Frameworks for Analyzing Smart Grid Cyberattack: A Literature Review.

An Input Sampling Scheme to Radar Echo Extrapolation For RNN-Based Models (2022)
Presentation / Conference Contribution
Wang, Y., Yang, Z., Liu, Q., & Liu, X. (2022, September). An Input Sampling Scheme to Radar Echo Extrapolation For RNN-Based Models. Presented at 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), Falerna, Italy

Short-term heavy rainfall can have a significant impact on people's production, life and travel. Numerical Weather Prediction (NWP) is complex. It can predict weather conditions for the next week or even two weeks, but cannot predict the weather in t... Read More about An Input Sampling Scheme to Radar Echo Extrapolation For RNN-Based Models.

Explainable AI and Deep Autoencoders Based Security Framework for IoT Network Attack Certainty (Extended Abstract) (2022)
Presentation / Conference Contribution
Kalutharage, C. S., Liu, X., & Chrysoulas, C. (2022, September). Explainable AI and Deep Autoencoders Based Security Framework for IoT Network Attack Certainty (Extended Abstract). Presented at 27th European Symposium on Research in Computer Security (ESORICS) 2022, Copenhagen, Denmark

Over the past few decades, Machine Learning (ML)-based intrusion detection systems (IDS) have become increasingly popular and continue to show remarkable performance in detecting attacks. However, the lack of transparency in their decision-making pro... Read More about Explainable AI and Deep Autoencoders Based Security Framework for IoT Network Attack Certainty (Extended Abstract).

Which bugs are missed in code reviews: an empirical study on SmartSHARK dataset (2022)
Presentation / Conference Contribution
Khoshnoud, F., Nasab, A. R., Toudeji, Z., & Sami, A. (2022, May). Which bugs are missed in code reviews: an empirical study on SmartSHARK dataset. Presented at MSR '22: 19th International Conference on Mining Software Repositories, Pittsburgh, US

In pull-based development systems, code reviews and pull request comments play important roles in improving code quality. In such systems, reviewers attempt to carefully check a piece of code by different unit tests. Unfortunately, sometimes they mis... Read More about Which bugs are missed in code reviews: an empirical study on SmartSHARK dataset.

Intelligent Question Answering System Based on Knowledge Graph (2022)
Presentation / Conference Contribution
Feng, X., Liu, Q., & Liu, X. (2021, December). Intelligent Question Answering System Based on Knowledge Graph. Presented at IEEE SmartCity-2021, Hainan, China

In order to build a smart city and pursue more efficient city management, various industries have introduced intelligent question answering into process management. The intelligent question answering system based on the knowledge graph is dedicated t... Read More about Intelligent Question Answering System Based on Knowledge Graph.

An Intelligent Method for Upper Limb Posture Recognition Based on Limited MEMS Data (2022)
Presentation / Conference Contribution
Wu, Z., Wu, X., Liu, Q., & Liu, X. (2021, October). An Intelligent Method for Upper Limb Posture Recognition Based on Limited MEMS Data. Presented at The 6th IEEE Cyber Science and Technology Congress (2021) (CyberSciTech 2021), AB, Canada [Online]

There are more than 10 million new stroke cases worldwide every year, and stroke has become one of the main causes of death and disability. In recent years, with the rapid development of computer science and technology, through the combination of Int... Read More about An Intelligent Method for Upper Limb Posture Recognition Based on Limited MEMS Data.

Improving Domestic NILM Using An Attention- Enabled Seq2Point Learning Approach (2022)
Presentation / Conference Contribution
Zhang, J., Sun, J., Gan, J., Liu, Q., & Liu, X. (2021, October). Improving Domestic NILM Using An Attention- Enabled Seq2Point Learning Approach. Presented at The 6th IEEE Cyber Science and Technology Congress (2021) (CyberSciTech 2021), AB, Canada [Online]

The past decade have seen a growth in Internet technology, the overlap of cyberspace and social space provides great convenience for people's life. The in-depth study of non-intrusive load management (NILM) promotes the development of multi-integrati... Read More about Improving Domestic NILM Using An Attention- Enabled Seq2Point Learning Approach.

Using Semantic Technology to Model Persona for Adaptable Agents (2021)
Presentation / Conference Contribution
Nguyen, J., Farrenkopf, T., Guckert, M., Powers, S., & Urquhart, N. (2021, June). Using Semantic Technology to Model Persona for Adaptable Agents. Presented at ECMS 2021

In state of the art research a growing interest in the application of agent models for the simulation of road traffic can be observed. Software agents are particularly suitable for the representation of travellers and their goal-oriented behaviour. A... Read More about Using Semantic Technology to Model Persona for Adaptable Agents.

Characterization and Prediction of Questions without Accepted Answers on Stack Overflow (2021)
Presentation / Conference Contribution
Yazdaninia, M., Lo, D., & Sami, A. (2021, May). Characterization and Prediction of Questions without Accepted Answers on Stack Overflow. Presented at 2021 IEEE/ACM 29th International Conference on Program Comprehension (ICPC), Madrid, Spain

A fast and effective approach to obtain information regarding software development problems is to search them to find similar solved problems or post questions on community question answering (CQA) websites. Solving coding problems in a short time is... Read More about Characterization and Prediction of Questions without Accepted Answers on Stack Overflow.