Outputs (406)

MMST-LSTM: Leveraging Radar Echo Prediction for Emerging Consumer Applications in Edge Computing (2025)
Journal Article
Wu, M., Xiao, B., Yang, Z., Sun, J., Liu, Q., Zhang, Y., & Liu, X. (in press). MMST-LSTM: Leveraging Radar Echo Prediction for Emerging Consumer Applications in Edge Computing. IEEE Transactions on Consumer Electronics,

With the increasing frequency of extreme weather events, there is a growing demand from the public for rapid and accurate short-term heavy precipitation forecasts. This study proposes a lightweight deep learning model, MMST-LSTM, which integrates Mul... Read More about MMST-LSTM: Leveraging Radar Echo Prediction for Emerging Consumer Applications in Edge Computing.

GSFL: A Privacy-Preserving Grouping-Split Federated Learning Approach in Resource-Constrained Edge Computing Scenarios (2025)
Journal Article
Liu, Q., Wang, Z., Zhou, X., Zhang, Y., Liu, X., & Lin, H. (online). GSFL: A Privacy-Preserving Grouping-Split Federated Learning Approach in Resource-Constrained Edge Computing Scenarios. ACM transactions on autonomous and adaptive systems, https://doi.org/10.1145/3725221

The advancement of mobile multimedia communications, 5G, and Internet of Things (IoT) has led to the widespread use of edge devices, including sensors, smartphones, and wearables. This has generated in a large amount of distributed data, leading to n... Read More about GSFL: A Privacy-Preserving Grouping-Split Federated Learning Approach in Resource-Constrained Edge Computing Scenarios.

Scalable Machine Learning Architectures for IPA-Driven Maintenance Task Allocation in Large-Scale Building Portfolios (2025)
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, UK

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.

Enhancing Automotive Intrusion Detection Systems with Capability Hardware Enhanced RISC Instructions-Based Memory Protection (2025)
Journal Article
Kalutharage, C. S., Mohan, S., Liu, X., & Chrysoulas, C. (2025). Enhancing Automotive Intrusion Detection Systems with Capability Hardware Enhanced RISC Instructions-Based Memory Protection. Electronics, 14(3), 474. https://doi.org/10.3390/electronics14030474

The rapid integration of connected technologies in modern vehicles has introduced significant cybersecurity challenges, particularly in securing critical systems against advanced threats such as IP spoofing and rule manipulation. This study investiga... Read More about Enhancing Automotive Intrusion Detection Systems with Capability Hardware Enhanced RISC Instructions-Based Memory Protection.

Neurosymbolic learning and domain knowledge-driven explainable AI for enhanced IoT network attack detection and response (2025)
Journal Article
Kalutharage, C. S., Liu, X., & Chrysoulas, C. (2025). Neurosymbolic learning and domain knowledge-driven explainable AI for enhanced IoT network attack detection and response. Computers and Security, 151, Article 104318. https://doi.org/10.1016/j.cose.2025.104318

In the dynamic landscape of network security, where cyberattacks continuously evolve, robust and adaptive detection mechanisms are essential, particularly for safeguarding Internet of Things (IoT) networks. This paper introduces an advanced anomaly d... Read More about Neurosymbolic learning and domain knowledge-driven explainable AI for enhanced IoT network attack detection and response.

A Multi-Tier Offloading Optimization Strategy for Consumer Electronics in Vehicular Edge Computing (2025)
Journal Article
Lin, H., Xiao, B., Zhou, X., Zhang, Y., & Liu, X. (2025). A Multi-Tier Offloading Optimization Strategy for Consumer Electronics in Vehicular Edge Computing. IEEE Transactions on Consumer Electronics, https://doi.org/10.1109/tce.2025.3527043

In the domain of consumer electronics, vehicular edge computing (VEC) technology is emerging as a novel data processing paradigm within vehicular networks. By sending tasks related to vehicular applications to the edge, this model makes it easier for... Read More about A Multi-Tier Offloading Optimization Strategy for Consumer Electronics in Vehicular Edge Computing.

FedCST: Federated Learning on Heterogeneous Resource-constrained Devices Using Clustering and Split Training (2024)
Presentation / Conference Contribution
Wang, Z., Lin, H., Liu, Q., Zhang, Y., & Liu, X. (2024, July). FedCST: Federated Learning on Heterogeneous Resource-constrained Devices Using Clustering and Split Training. Presented at The 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C), Cambridge, UK

With the rapid development of 5G and Internet of Things (IoT) technologies, edge devices such as sensors, smartphones, and wearable devices have become increasingly prevalent. The massive amount of distributed data generated by these devices offers u... Read More about FedCST: Federated Learning on Heterogeneous Resource-constrained Devices Using Clustering and Split Training.

A New Improved Method of Recurrent Memory Perception for Radar Echo Extrapolation (2024)
Presentation / Conference Contribution
Ji, R., Liu, Q., Zhang, Y., & Liu, X. (2024, July). A New Improved Method of Recurrent Memory Perception for Radar Echo Extrapolation. Presented at 2024 IEEE 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C), Cambridge, United Kingdom

Precipitation forecasting has long been a prominent topic in meteorology, as accurate predictions of impending rainfall are crucial for daily life and travel planning. Currently, radar echo extrapolation serves as the primary method for precipitation... Read More about A New Improved Method of Recurrent Memory Perception for Radar Echo Extrapolation.

High Intensity Radar Echo Extrapolation Based on Stacked Generative Structure (2024)
Presentation / Conference Contribution
Tao, S., Liu, Q., Zhang, Y., & Liu, X. (2024, July). High Intensity Radar Echo Extrapolation Based on Stacked Generative Structure. Presented at 2024 IEEE 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C), Cambridge, UK

In recent years, the phenomenon of severe convective disaster weather has increased significantly in the world. Due to the characteristics of small spatial scale, short occurrence period, great destructiveness and drastic changes, severe convective d... Read More about High Intensity Radar Echo Extrapolation Based on Stacked Generative Structure.

Reputation Gaming in Crowd Technical Knowledge Sharing (2024)
Journal Article
Mazloomzadeh, I., Uddin, G., Khomh, F., & Sami, A. (2025). Reputation Gaming in Crowd Technical Knowledge Sharing. ACM transactions on software engineering and methodology, 34(1), Article 10. https://doi.org/10.1145/3691627

Stack Overrow incentive system awards users with reputation scores to ensure quality. The decentralized nature of the forum may make the incentive system prone to manipulation. This paper ooers, for the rst time, a comprehensive study of the reported... Read More about Reputation Gaming in Crowd Technical Knowledge Sharing.

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.

Improved Double Deep Q Network-Based Task Scheduling Algorithm in Edge Computing for Makespan Optimization (2024)
Journal Article
Zeng, L., Liu, Q., Shen, S., & Liu, X. (2024). Improved Double Deep Q Network-Based Task Scheduling Algorithm in Edge Computing for Makespan Optimization. Tsinghua Science and Technology, 29(3), 806 - 817. https://doi.org/10.26599/TST.2023.9010058

Edge computing nodes undertake more and more tasks as business density grows. How to efficiently allocate large-scale and dynamic workloads to edge computing resources has become a critical challenge. An edge task scheduling approach based on an impr... Read More about Improved Double Deep Q Network-Based Task Scheduling Algorithm in Edge Computing for Makespan Optimization.

A spatio-temporal graph convolutional approach to real-time load forecasting in an edge-enabled distributed Internet of Smart Grids energy system (2024)
Journal Article
Liu, Q., Pan, L., Cao, X., Gan, J., Huang, X., & Liu, X. (2024). A spatio-temporal graph convolutional approach to real-time load forecasting in an edge-enabled distributed Internet of Smart Grids energy system. Concurrency and Computation: Practice and Experience, 36(13), Article e8060. https://doi.org/10.1002/cpe.8060

As the edge nodes of the Internet of Smart Grids (IoSG), smart sockets enable all kinds of power load data to be analyzed at the edge, which create conditions for edge calculation and real-time (RT) load forecasting. In this article, an edge-cloud co... Read More about A spatio-temporal graph convolutional approach to real-time load forecasting in an edge-enabled distributed Internet of Smart Grids energy system.

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

An Entity Ontology-Based Knowledge Graph Embedding Approach to News Credibility Assessment (2024)
Journal Article
Liu, Q., Jin, Y., Cao, X., Liu, X., Zhou, X., Zhang, Y., Xu, X., & Qi, L. (2024). An Entity Ontology-Based Knowledge Graph Embedding Approach to News Credibility Assessment. IEEE Transactions on Computational Social Systems, 11(4), 5308 - 5318. https://doi.org/10.1109/TCSS.2023.3342873

Fake news is a prevalent issue in modern society, leading to misinformation and societal harm. News credibility assessment is a crucial approach for evaluating the accuracy and authenticity of news. It plays a significant role in enhancing public awa... Read More about An Entity Ontology-Based Knowledge Graph Embedding Approach to News Credibility Assessment.

DenMerD: a feature enhanced approach to radar beam blockage correction with edge-cloud computing (2024)
Journal Article
Liu, Q., Sun, J., Zhang, Y., & Liu, X. (2024). DenMerD: a feature enhanced approach to radar beam blockage correction with edge-cloud computing. Journal of cloud computing: advances, systems and applications, 13, Article 32. https://doi.org/10.1186/s13677-024-00607-x

In the field of meteorology, the global radar network is indispensable for detecting weather phenomena and offering early warning services. Nevertheless, radar data frequently exhibit anomalies, including gaps and clutter, arising from atmospheric re... Read More about DenMerD: a feature enhanced approach to radar beam blockage correction with edge-cloud computing.

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.

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. 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.

Deep Vision in Analysis and Recognition of Radar Data: Achievements, Advancements, and Challenges (2023)
Journal Article
Liu, Q., Yang, Z., Ji, R., Zhang, Y., Bilal, M., Liu, X., Vimal, S., & Xu, X. (2023). Deep Vision in Analysis and Recognition of Radar Data: Achievements, Advancements, and Challenges. IEEE Systems, Man, and Cybernetics Magazine, 9(4), 4-12. https://doi.org/10.1109/msmc.2022.3216943

Radars are widely used to obtain echo information for effective prediction, such as precipitation nowcasting. In this article, recent relevant scientific investigation and practical efforts using deep learning (DL) models for weather radar data analy... Read More about Deep Vision in Analysis and Recognition of Radar Data: Achievements, Advancements, and Challenges.