arXiv subjects

Meiyu Li

Publications and source records attributed to Meiyu Li.

3 recordsLinked to original sources

arXiv2025

A Survey of Information Disorder on Video-Sharing Platforms

Video sharing platforms (VSPs) have become central information hubs but also facilitate the spread of information disorder, from misleading narratives to fabricated content. This survey synthesizes research on VSPs' multimedia ecosystems across three dimensions: (1) types of information disorder, (2) methodological approaches, and (3) platform features. We conclude by identifying key challenges and open questions for future research.

cs.MM↗

arXiv2025

Extracting Research Instruments from Educational Literature Using LLMs

Large Language Models (LLMs) are transforming information extraction from academic literature, offering new possibilities for knowledge management. This study presents an LLM-based system designed to extract detailed information about research instruments used in the education field, including their names, types, target respondents, measured constructs, and outcomes. Using multi-step prompting and a domain-specific data schema, it generates structured outputs optimized for educational research. Our evaluation shows that this system significantly outperforms other approaches, particularly in identifying instrument names and detailed information. This demonstrates the potential of LLM-powered information extraction in educational contexts, offering a systematic way to organize research instrument information. The ability to aggregate such information at scale enhances accessibility for researchers and education leaders, facilitating informed decision-making in educational research and policy.

cs.IR↗

arXiv2019

DC-AL GAN: Pseudoprogression and True Tumor Progression of Glioblastoma Multiform Image Classification Based on DCGAN and AlexNet

Pseudoprogression (PsP) occurs in 20-30% of patients with glioblastoma multiforme (GBM) after receiving the standard treatment. In the course of post-treatment magnetic resonance imaging (MRI), PsP exhibits similarities in shape and intensity to the true tumor progression (TTP) of GBM. So, these similarities pose challenges on the differentiation of these types of progression and hence the selection of the appropriate clinical treatment strategy. In this paper, we introduce DC-AL GAN, a novel feature learning method based on deep convolutional generative adversarial network (DCGAN) and AlexNet, to discriminate between PsP and TTP in MRI images. Due to the adversarial relationship between the generator and the discriminator of DCGAN, high-level discriminative features of PsP and TTP can be derived for the discriminator with AlexNet. Also, a feature fusion scheme is used to combine higher-layer features with lower-layer information, leading to more powerful features that are used for effectively discriminating between PsP and TTP. The experimental results show that DC-AL GAN achieves desirable PsP and TTP classification performance that is superior to other state-of-the-art methods.

cs.CV↗