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Yuan Liang

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EAAI Journal 2024 Journal Article

Crowdsourcing incentive mechanisms for cross-platform tasks: A weighted average maximization approach

Yuan Liang

Crowdsourcing refers to the practice of outsourcing tasks previously performed by internal employees of an enterprise or organization to the general public through the internet in a free and voluntary manner, resulting in a mutually beneficial outcome. The incentive mechanism is a critical aspect of crowdsourcing computing. However, existing research mainly focuses on the incentive mechanism of crowdsourcing tasks on a single platform, while crowdsourcing tasks have complex and cross-domain attributes. In actual task requests, multiple crowdsourcing platforms participate in task execution due to geographic, capability, and task attribute constraints, each with its own unique characteristics and attributes. To address the collaboration problem between different platforms in crowdsourcing task allocation, we propose a Multi-Unit and Multi-Platform (MUMP) incentive mechanism based on task interactions, where we first model the problem, design an optimization goal of maximizing the weighted average for cross-platform crowdsourcing tasks, and then propose a feasible budget algorithm with platform weight based on greedy ordering, which achieves an approximation rate. Finally, experimental results demonstrate that the proposed incentive mechanism algorithm outperforms the latest algorithm.

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NeurIPS Conference 2021 Conference Paper

Exploring Forensic Dental Identification with Deep Learning

Yuan Liang
Weikun Han
Liang Qiu
Chen Wu
Yiting Shao
Kun Wang
Lei He

Dental forensic identification targets to identify persons with dental traces. The task is vital for the investigation of criminal scenes and mass disasters because of the resistance of dental structures and the wide-existence of dental imaging. However, no widely accepted automated solution is available for this labour-costly task. In this work, we pioneer to study deep learning for dental forensic identification based on panoramic radiographs. We construct a comprehensive benchmark with various dental variations that can adequately reflect the difficulties of the task. By considering the task's unique challenges, we propose FoID, a deep learning method featured by: (\textit{i}) clinical-inspired attention localization, (\textit{ii}) domain-specific augmentations that enable instance discriminative learning, and (\textit{iii}) transformer-based self-attention mechanism that dynamically reasons the relative importance of attentions. We show that FoID can outperform traditional approaches by at least \textbf{22. 98\%} in terms of Rank-1 accuracy, and outperform strong CNN baselines by at least \textbf{10. 50\%} in terms of mean Average Precision (mAP). Moreover, extensive ablation studies verify the effectiveness of each building blocks of FoID. Our work can be a first step towards the automated system for forensic identification among large-scale multi-site databases. Also, the proposed techniques, \textit{e. g. }, self-attention mechanism, can also be meaningful for other identification tasks, \textit{e. g. }, pedestrian re-identification. Related data and codes can be found at \href{https: //github. com/liangyuandg/FoID}{https: //github. com/liangyuandg/FoID}.

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AAAI Conference 2021 Conference Paper

Oral-3D: Reconstructing the 3D Structure of Oral Cavity from Panoramic X-ray

Weinan Song
Yuan Liang
Jiawei Yang
Kun Wang
Lei He

Panoramic X-ray (PX) provides a 2D picture of the patient’s mouth in a panoramic view to help dentists observe the invisible disease inside the gum. However, it provides limited 2D information compared with cone-beam computed tomography (CBCT), another dental imaging method that generates a 3D picture of the oral cavity but with more radiation dose and a higher price. Consequently, it is of great interest to reconstruct the 3D structure from a 2D X-ray image, which can greatly explore the application of X-ray imaging in dental surgeries. In this paper, we propose a framework, named Oral-3D, to reconstruct the 3D oral cavity from a single PX image and prior information of the dental arch. Specifically, we first train a generative model to learn the cross-dimension transformation from 2D to 3D. Then we restore the shape of the oral cavity with a deformation module with the dental arch curve, which can be obtained simply by taking a photo of the patient’s mouth. To be noted, Oral-3D can restore both the density of bony tissues and the curved mandible surface. Experimental results show that Oral-3D can efficiently and effectively reconstruct the 3D oral structure and show critical information in clinical applications, e. g. , tooth pulling and dental implants. To the best of our knowledge, we are the first to explore this domain transformation problem between these two imaging methods.

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