AUT is home to a number of renowned research institutes in engineering, and computer and mathematical sciences. The School of Engineering, Computer and Mathematical Sciences strong industry partnerships and the unique combination of engineering, computer and mathematical sciences within one school stimulates interdisciplinary research beyond traditional boundaries.
Current research interests include:
Artificial Intelligence; Astronomy and Space Research;
Computer Engineering; Computer Vision; Construction Management;
(Institute of Electrical and Electronics Engineers (IEEE), 2024) Zhu, Wenjie; Peng, Bo; Yan, Wei Qi
Unsupervised person re-identification (Re-ID) has made significant progress by leveraging valuable pseudo labels from completely unlabeled data. However, the predominant use of pseudo labels heavily relies on clustering results, which may lead to the accumulation of supervision deviation due to inevitable noise. In this paper, we propose a novel framework, namely Dual Knowledge Distillation on Multiview Pseudo Labels (DKD-MPL), to address this challenge. Specifically, the proposed DKD-MPL framework consists of two modules: Global Knowledge Distillation (GKD) and Self-Knowledge Distillation (SKD). In the GKD module, the pseudo labels obtained from the epoch-wise clustering procedure serve as the logits for the teacher model, while the mini-batch query images' pseudo labels act as the logits for the student model. Within the SKD module, we facilitate self-knowledge distillation by considering the pseudo labels generated by positive anchors and query images as two augmentations of the mini-batch data. As a result, DKD-MPL facilitates the exploitation of both global and local complementary knowledge across different views of pseudo labels, thereby mitigating supervision deviation. To demonstrate the effectiveness of DKD-MPL, we provide a theoretical analysis of the proposed loss and conduct extensive experiments on four popular datasets, e.g., Market-1501, DukeMTMC-reID, MSMT17, and VeRi-776. The results indicate that our method surpasses unsupervised approaches and achieves comparable performance to supervised person Re-ID methods.
Deepfakes are notorious for their unethical and malicious applications to achieve economic, political, and social reputation goals. Recent years have seen widespread facial forgery, which does not require technical skills. Since the development of generative adversarial networks (GANs) and diffusion models (DMs), deepfake generation has been moving toward better quality. Therefore, it is necessary to find an effective method to detect fake media. This contemporary survey provides a comprehensive overview of several typical facial forgery detection methods proposed from 2019 to 2023. We also analyze and group them into four categories in terms of their feature extraction methods and network architectures: traditional convolutional neural network (CNN)-based detection, CNN backbone with semi-supervised detection, transformer-based detection, and biological signal detection. Furthermore, it summarizes several representative deepfake detection datasets with their advantages and disadvantages. Finally, we evaluate the performance of these detection models with respect to different datasets by comparing their evaluating metrics. Across all experimental results on these state-of-the-art detection models, we find that the accuracy is largely degraded if we utilize cross-dataset evaluation. These results will provide a reference for further research to develop more reliable detection algorithms.
(Elsevier BV, 2023-11-01) Ayazi, M; Rasul, MG; Khan, MMK; Hassan, NMS
Biodiesel and bioethanol are two popular biofuels that commonly are used in combination with diesel and gasoline fuels respectively in diesel engines and gasoline engines. Diesel and biodiesel fuels have similar characteristics. However, they have different characteristics compared to bioethanol. Also high proportions of bioethanol cannot be solved in diesel fuel. Then using bioethanol and diesel fuel blends and the effects on diesel engine performance needs further investigation. Biodiesel as a co-solvent is used in bioethanol and diesel fuel mixtures to increase lubricity and cetane number of the blends. In this study, a 4-stroke, 4-cylinder diesel engine coupled with a dynamometer was used for the investigation of diesel engine emission and fuel consumption. Fuels B10E10D80, B10E15D75, B10E20D70, and D100 were used in this study at different speeds (from 1200 to 2400 rpm with 200 rpm increment) on 50% and 100% engine loads were measured. Here, B, E, and D respectively represent biodiesel, bioethanol, and diesel fuel and numbers indicate the percentage of those fuels. Fuel consumption and emissions (CO, CO2, NOx, and C6H14) were measured. Average changes of emissions of CO, CO2, NO, NO2 and C6H14 on different loads and speeds were decreased respectively by 20–38%, 1–6%, 11–14%, 9%, and 3–24%, respectively The average diesel engine fuel consumption using B10E10D80 and B10E20D70 fuels was only higher by 2% and 3% than that of using pure diesel fuel and with using B10E15D75 fuel it was equal to that of using pure diesel fuel. In conclusion, using ternary fuel blends instead of pure diesel fuel significantly decreases diesel engine emissions for CO, CO2, and NO. Fuel consumption negligibly increased by using ternary fuel blends instead of pure diesel fuel. As a result, ternary fuel blends could be considered as alternatives to diesel fuel.
(IOS Press, 2024-01-25) Nazayer, M; Madanian, S; Rasouli Panah, H; Parry, D
The inefficiency of the healthcare system in addressing pandemics is highlighted after COVID-19 which is mostly rooted in data availability and accuracy. As it is believed we might witness more pandemics in future, our research's main objective is to propose an integrated health system to support healthcare preparedness for future infectious outbreaks and pandemics. The system could support managers and authorities in healthcare and disaster management, and policymakers through data collection, sharing, and analysis.
(IOS Press, 2024-01-25) Choi, Adi; Li, Weihua; Warren, Jim
Digital tools for mental health show great promise, but concerns arise when they fail to recognize the user state. We train a classifier to detect the emotional context of dialogs among 6 categories, achieving 78% accuracy on top choice. Importantly greatest areas of confusion (excited-hopeful, angry-sad) are not of the most unsafe kind. Such a classifier could serve as a resource to the dialog managers of future digital mental health agents.