Outputs (395)

Reputation Gaming in Crowd Technical Knowledge Sharing (2024)
Journal Article
Mazloomzadeh, I., Uddin, G., Khomh, F., & Sami, A. (online). Reputation Gaming in Crowd Technical Knowledge Sharing. ACM transactions on software engineering and methodology, 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.

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.

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.

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.

DenMerD: A Feature Propagation Enhanced Approach to Beam Blockage Correction in Weather Radar (2023)
Journal Article
Liu, Q., Sun, J., & Liu, X. (in press). DenMerD: A Feature Propagation Enhanced Approach to Beam Blockage Correction in Weather Radar. Journal on Artificial Intelligence,

In the realm of meteorological research, extensive global radar networks serve to detect and provide early warnings for a diverse array of weather phenomena. However, the inherently discontinuous nature of radar observations often results in the pres... Read More about DenMerD: A Feature Propagation Enhanced Approach to Beam Blockage Correction in Weather Radar.

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.

A cloud-based Bi-directional LSTM approach to grid-connected solar PV energy forecasting for multi-energy systems (2023)
Journal Article
Liu, Q., Darteh, O. F., Bilal, M., Huang, X., Attique, M., Liu, X., & Acakpovi, A. (2023). A cloud-based Bi-directional LSTM approach to grid-connected solar PV energy forecasting for multi-energy systems. Sustainable Computing, 40, Article 100892. https://doi.org/10.1016/j.suscom.2023.100892

The drive for smarter, greener, and more livable cities has led to research towards more effective solar energy forecasting techniques and their integration into traditional power systems. However, the availability of real-time data, data storage, an... Read More about A cloud-based Bi-directional LSTM approach to grid-connected solar PV energy forecasting for multi-energy systems.

PMNet: A Multi-branch and Multi-scale Fusion Convolutional Neural Network for Water Body Extraction of High-resolution Remote Sensing Images (2023)
Journal Article
Liu, Q., Zhang, Z., Liu, X., Zhang, Y., & Du, Z. (in press). PMNet: A Multi-branch and Multi-scale Fusion Convolutional Neural Network for Water Body Extraction of High-resolution Remote Sensing Images. Intelligent Automation and Soft Computing,

Automatic extraction of water body information from high-resolution remote sensing images is one of the core tasks of remote sensing image interpretation. Since the complex multi-scale characteristics of high-resolution remote sensing images, it is d... Read More about PMNet: A Multi-branch and Multi-scale Fusion Convolutional Neural Network for Water Body Extraction of High-resolution Remote Sensing Images.

A Multi-Swarm PSO Approach to Large-Scale Task Scheduling in a Sustainable Supply Chain Datacenter (2023)
Journal Article
Liu, Q., Zeng, L., Bilal, M., Song, H., Liu, X., Zhang, Y., & Cao, X. (2023). A Multi-Swarm PSO Approach to Large-Scale Task Scheduling in a Sustainable Supply Chain Datacenter. IEEE Transactions on Green Communications and Networking, 7(4), 1667 - 1677. https://doi.org/10.1109/tgcn.2023.3283509

Supply chain management is a vital part of ensuring service quality and production efficiency in industrial applications. With the development of cloud computing and data intelligence in modern industries, datacenters have become an important basic s... Read More about A Multi-Swarm PSO Approach to Large-Scale Task Scheduling in a Sustainable Supply Chain Datacenter.

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.

CoBRA without experts: New paradigm for software development effort estimation using COCOMO metrics (2023)
Journal Article
Feizpour, E., Tahayori, H., & Sami, A. (2023). CoBRA without experts: New paradigm for software development effort estimation using COCOMO metrics. Journal of Software: Evolution and Process, 35(12), Article e2569. https://doi.org/10.1002/smr.2569

Software development effort estimation (SDEE) is a critical activity in developing software. Accurate effort estimation in the early phases of software design life cycle has important effects on the success of software projects. COCOMO (Constructive... Read More about CoBRA without experts: New paradigm for software development effort estimation using COCOMO metrics.

CFNet: An Eigenvalue Preserved Approach to Multiscale Building Segmentation in High-Resolution Remote Sensing Images (2023)
Journal Article
Liu, Q., Li, Y., Bilal, M., Liu, X., Zhang, Y., Wang, H., & Xu, X. (2023). CFNet: An Eigenvalue Preserved Approach to Multiscale Building Segmentation in High-Resolution Remote Sensing Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 2481-2491. https://doi.org/10.1109/jstars.2023.3244336

In recent years, AI and Deep Learning (DL) methods have been widely used for object classification, recognition, and segmentation of high-resolution multispectral remote sensing images. These DL-based solutions perform better compare to traditional s... Read More about CFNet: An Eigenvalue Preserved Approach to Multiscale Building Segmentation in High-Resolution Remote Sensing Images.