Outputs (405)

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.

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.

Explainable AI-Based DDOS Attack Identification Method for IoT Networks (2023)
Journal Article
Kalutharage, C. S., Liu, X., Chrysoulas, C., Pitropakis, N., & Papadopoulos, P. (2023). Explainable AI-Based DDOS Attack Identification Method for IoT Networks. Computers, 12(2), Article 32. https://doi.org/10.3390/computers12020032

The modern digitized world is mainly dependent on online services. The availability of online systems continues to be seriously challenged by distributed denial of service (DDoS) attacks. The challenge in mitigating attacks is not limited to identify... Read More about Explainable AI-Based DDOS Attack Identification Method for IoT Networks.

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.

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.

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.

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.

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).

Machine learning-enabled quantitative ultrasound techniques for tissue differentiation (2022)
Journal Article
Thomson, H., Yang, S., & Cochran, S. (2022). Machine learning-enabled quantitative ultrasound techniques for tissue differentiation. Journal of Medical Ultrasonics, 49, 517-528. https://doi.org/10.1007/s10396-022-01230-6

Purpose: Quantitative ultrasound (QUS) infers properties about tissue microstructure from backscattered radio-frequency ultrasound data. This paper describes how to implement the most practical QUS parameters using an ultrasound research system for t... Read More about Machine learning-enabled quantitative ultrasound techniques for tissue differentiation.

A self-attention integrated spatiotemporal LSTM approach to edge-radar echo extrapolation in the Internet of Radars (2022)
Journal Article
Yang, Z., Wu, H., Liu, Q., Liu, X., Zhang, Y., & Cao, X. (2023). A self-attention integrated spatiotemporal LSTM approach to edge-radar echo extrapolation in the Internet of Radars. ISA Transactions, 132, 155-166. https://doi.org/10.1016/j.isatra.2022.06.046

In recent years, the number of weather-related disasters significantly increases across the world. As a typical example, short-range extreme precipitation can cause severe flooding and other secondary disasters, which therefore requires accurate pred... Read More about A self-attention integrated spatiotemporal LSTM approach to edge-radar echo extrapolation in the Internet of Radars.

CEMA-LSTM: Enhancing Contextual Feature Correlation for Radar Extrapolation Using Fine-Grained Echo Datasets (2022)
Journal Article
Yang, Z., Liu, Q., Wu, H., Liu, X., & Zhang, Y. (2023). CEMA-LSTM: Enhancing Contextual Feature Correlation for Radar Extrapolation Using Fine-Grained Echo Datasets. Computer Modeling in Engineering and Sciences, 135(1), 45-64. https://doi.org/10.32604/cmes.2022.022045

Accurate precipitation nowcasting can provide great convenience to the public so they can conduct corresponding arrangements in advance to deal with the possible impact of upcoming heavy rain. Recent relevant research activities have shown their conc... Read More about CEMA-LSTM: Enhancing Contextual Feature Correlation for Radar Extrapolation Using Fine-Grained Echo Datasets.

Near-Data Prediction Based Speculative Optimization in a Distribution Environment (2022)
Journal Article
Liu, Q., Wu, X., Liu, X., Zhang, Y., & Hu, Y. (2022). Near-Data Prediction Based Speculative Optimization in a Distribution Environment. Mobile Networks and Applications, 27(6), 2339-2347. https://doi.org/10.1007/s11036-021-01793-7

Hadoop is an open source from Apache with a distributed file system and MapReduce distributed computing framework. The current Apache 2.0 license agreement supports on-demand payment by consumers for cloud platform services, helping users leverage th... Read More about Near-Data Prediction Based Speculative Optimization in a Distribution Environment.

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.

EfficientMask-Net for face authentication in the era of COVID-19 pandemic (2022)
Journal Article
Azouji, N., Sami, A., & Taheri, M. (2022). EfficientMask-Net for face authentication in the era of COVID-19 pandemic. Signal, Image and Video Processing, 16(7), 1991-1999. https://doi.org/10.1007/s11760-022-02160-z

Today, we are facing the COVID-19 pandemic. Accordingly, properly wearing face masks has become vital as an effective way to prevent the rapid spread of COVID-19. This research develops an Efficient Mask-Net method for low-power devices, such as mobi... Read More about EfficientMask-Net for face authentication in the era of COVID-19 pandemic.