Natural Language Generation for Low-resource Domains

A Novel Continual Learning and Adaptive Sensing State Response‐Based Target Recognition and Long‐Term Tracking Framework for Smart Industrial Applications (2025)
Journal Article
Chen, L., Li, G., Tan, J., Li, Y., Fu, S., Ma, H., Liu, Y., Yang, Y., Qian, W., Zhu, Q., & Hussain, A. (2025). A Novel Continual Learning and Adaptive Sensing State Response‐Based Target Recognition and Long‐Term Tracking Framework for Smart Industrial Applications. Expert Systems, 42(5), Article e70037. https://doi.org/10.1111/exsy.70037

Purpose
With the rapid development of artificial intelligence technology, highly intelligent and unmanned factories have become an important trend. In the complex environments of smart factories, the long-term tracking and inspection of specified ta... Read More about A Novel Continual Learning and Adaptive Sensing State Response‐Based Target Recognition and Long‐Term Tracking Framework for Smart Industrial Applications.

Evaluating Language Model Vulnerability to Poisoning Attacks in Low-Resource Settings (2024)
Journal Article
Plant, R., Giuffrida, M. V., Pitropakis, N., & Gkatzia, D. (2024). Evaluating Language Model Vulnerability to Poisoning Attacks in Low-Resource Settings. IEEE/ACM Transactions on Audio, Speech and Language Processing, 33, 54-67. https://doi.org/10.1109/taslp.2024.3507565

Pre-trained language models are a highly effective source of knowledge transfer for natural language processing tasks, as their development represents an investment of resources beyond the reach of most researchers and end users. The widespread avail... Read More about Evaluating Language Model Vulnerability to Poisoning Attacks in Low-Resource Settings.

MTFDN: An image copy‐move forgery detection method based on multi‐task learning (2024)
Journal Article
Liang, P., Tu, H., Hussain, A., & Li, Z. (2025). MTFDN: An image copy‐move forgery detection method based on multi‐task learning. Expert Systems, 42(2), Article e13729. https://doi.org/10.1111/exsy.13729

Image copy-move forgery, where an image region is copied and pasted within the same image, is a simple yet widely employed manipulation. In this paper, we rethink copy-move forgery detection from the perspective of multi-task learning and summarize t... Read More about MTFDN: An image copy‐move forgery detection method based on multi‐task learning.

Unveiling machine learning strategies and considerations in intrusion detection systems: a comprehensive survey (2024)
Journal Article
Ali, A. H., Charfeddine, M., Ammar, B., Hamed, B. B., Albalwy, F., Alqarafi, A., & Hussain, A. (2024). Unveiling machine learning strategies and considerations in intrusion detection systems: a comprehensive survey. Frontiers in Computer Science, 6, Article 1387354. https://doi.org/10.3389/fcomp.2024.1387354

The advancement of communication and internet technology has brought risks to network security. Thus, Intrusion Detection Systems (IDS) was developed to combat malicious network attacks. However, IDSs still struggle with accuracy, false alarms, and d... Read More about Unveiling machine learning strategies and considerations in intrusion detection systems: a comprehensive survey.

SAR Ship Instance Segmentation With Dynamic Key Points Information Enhancement (2024)
Journal Article
Gao, F., Han, X., Wang, J., Sun, J., Hussain, A., & Zhou, H. (2024). SAR Ship Instance Segmentation With Dynamic Key Points Information Enhancement. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 11365-11385. https://doi.org/10.1109/jstars.2024.3383779

There are several unresolved issues in the field of ship instance segmentation in synthetic aperture radar (SAR) images. First, in inshore dense ship area, the problems of missed detections and mask overlap frequently occur. Second, in inshore scenes... Read More about SAR Ship Instance Segmentation With Dynamic Key Points Information Enhancement.

Wireless Power Transfer Technologies, Applications, and Future Trends: A Review (2024)
Journal Article
Alabsi, A., Hawbani, A., Wang, X., Al-Dubai, A., Hu, J., Aziz, S. A., Kumar, S., Zhao, L., Shvetsov, A. V., & Alsamhi, S. H. (2025). Wireless Power Transfer Technologies, Applications, and Future Trends: A Review. IEEE Transactions on Sustainable Computing, 10(1), 1-17. https://doi.org/10.1109/TSUSC.2024.3380607

Wireless Power Transfer (WPT) is a disruptive technology that allows wireless energy provisioning for energy- limited IoT devices, thus decreasing the over-reliance on batteries and wires. WPT could replace conventional energy provisioning (e.g., ene... Read More about Wireless Power Transfer Technologies, Applications, and Future Trends: A Review.

A novel generative adversarial network‐based super‐resolution approach for face recognition (2024)
Journal Article
Chougule, A., Kolte, S., Chamola, V., & Hussain, A. (2024). A novel generative adversarial network‐based super‐resolution approach for face recognition. Expert Systems, 41(8), Article e13564. https://doi.org/10.1111/exsy.13564

Face recognition is an essential feature required for a range of computer vision applications such as security, attendance systems, emotion detection, airport check-in, and many others. The super-resolution of subject images is an important and chall... Read More about A novel generative adversarial network‐based super‐resolution approach for face recognition.

Novel Category Discovery without Forgetting for Automatic Target Recognition (2024)
Journal Article
Huang, H., Gao, F., Sun, J., Wang, J., Hussain, A., & Zhou, H. (2024). Novel Category Discovery without Forgetting for Automatic Target Recognition. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 4408-4420. https://doi.org/10.1109/jstars.2024.3358449

We explore a cutting-edge concept known as C lass Incremental Learning in N ovel Category Discovery for Synthetic Aperture Radar T argets (CNT). This innovative task involves the challenge of identifying categories within unlabeled datasets by utiliz... Read More about Novel Category Discovery without Forgetting for Automatic Target Recognition.

SAR Target Incremental Recognition Based on Features With Strong Separability (2024)
Journal Article
Gao, F., Kong, L., Lang, R., Sun, J., Wang, J., Hussain, A., & Zhou, H. (2024). SAR Target Incremental Recognition Based on Features With Strong Separability. IEEE Transactions on Geoscience and Remote Sensing, 62, 1-13. https://doi.org/10.1109/tgrs.2024.3351636

With the rapid development of deep learning technology, many synthetic aperture radar (SAR) target recognition algorithms based on convolutional neural networks have achieved exceptional performance on various datasets. However, conventional neural n... Read More about SAR Target Incremental Recognition Based on Features With Strong Separability.

Unveiling NLG Human-Evaluation Reproducibility: Lessons Learned and Key Insights from Participating in the ReproNLP Challenge (2023)
Presentation / Conference Contribution
Watson, L., & Gkatzia, D. (2023, September). Unveiling NLG Human-Evaluation Reproducibility: Lessons Learned and Key Insights from Participating in the ReproNLP Challenge. Presented at 3rd Workshop on Human Evaluation of NLP Systems (HumEval), Varna, Bulgaria

Human evaluation is crucial for NLG systems as it provides a reliable assessment of the quality, effectiveness, and utility of generated language outputs. However, concerns about the reproducibility of such evaluations have emerged, casting doubt on... Read More about Unveiling NLG Human-Evaluation Reproducibility: Lessons Learned and Key Insights from Participating in the ReproNLP Challenge.