Outputs (6)

Analysing Learner Behaviour in an Ontology-Based E-learning System: A Graph Neural Network Approach (2024)
Presentation / Conference Contribution
Wynn, A., Wang, J., Sun, Z., & Shimada, A. (2024, March). Analysing Learner Behaviour in an Ontology-Based E-learning System: A Graph Neural Network Approach. Paper presented at LAK '24: The 14th Learning Analytics and Knowledge Conference, Kyoto, Japan

Despite the prevalence of e-learning systems, there is a lack of support for learners to identify and compare new knowledge with existing cognitive structures. Therefore, an ontology-based visualization support system was previously introduced which... Read More about Analysing Learner Behaviour in an Ontology-Based E-learning System: A Graph Neural Network Approach.

MONEY: Ensemble learning for stock price movement prediction via a convolutional network with adversarial hypergraph model (2023)
Journal Article
Sun, Z., Harit, A., Cristea, A. I., Wang, J., & Lio, P. (2023). MONEY: Ensemble learning for stock price movement prediction via a convolutional network with adversarial hypergraph model. AI open, 4, 165-174. https://doi.org/10.1016/j.aiopen.2023.10.002

Stock price prediction is challenging in financial investment, with the AI boom leading to increased interest from researchers. Despite these recent advances, many studies are limited to capturing the time series characteristics of price movement via... Read More about MONEY: Ensemble learning for stock price movement prediction via a convolutional network with adversarial hypergraph model.

Language as a latent sequence: Deep latent variable models for semi-supervised paraphrase generation (2023)
Journal Article
Yu, J., Cristea, A. I., Harit, A., Sun, Z., Aduragba, O. T., Shi, L., & Al Moubayed, N. (2023). Language as a latent sequence: Deep latent variable models for semi-supervised paraphrase generation. AI open, 4, 19-32. https://doi.org/10.1016/j.aiopen.2023.05.001

This paper explores deep latent variable models for semi-supervised paraphrase generation, where the missing target pair for unlabelled data is modelled as a latent paraphrase sequence. We present a novel unsupervised model named variational sequence... Read More about Language as a latent sequence: Deep latent variable models for semi-supervised paraphrase generation.

Is Unimodal Bias Always Bad for Visual Question Answering? A Medical Domain Study with Dynamic Attention (2022)
Presentation / Conference Contribution
Sun, Z., Harit, A., Cristea, A. I., Yu, J., Al Moubayed, N., & Shi, L. (2022, December). Is Unimodal Bias Always Bad for Visual Question Answering? A Medical Domain Study with Dynamic Attention. Presented at IEEE Big Data, Osaka, Japan

Medical visual question answering (Med-VQA) is to answer medical questions based on clinical images provided. This field is still in its infancy due to the complexity of the trio formed of questions, multimodal features and expert knowledge. In this... Read More about Is Unimodal Bias Always Bad for Visual Question Answering? A Medical Domain Study with Dynamic Attention.

Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification (2022)
Presentation / Conference Contribution
Sun, Z., Harit, A., Cristea, A. I., Yu, J., Shi, L., & Al Moubayed, N. (2022, July). Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification. Presented at 2022 International Joint Conference on Neural Networks (IJCNN), Padova, Italy

Graph neural networks (GNNs) have attracted extensive interest in text classification tasks due to their expected superior performance in representation learning. However, most existing studies adopted the same semi-supervised learning setting as the... Read More about Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification.

A Generative Bayesian Graph Attention Network for Semi-supervised Classification on Scarce Data (2021)
Presentation / Conference Contribution
Sun, Z., Harit, A., Yu, J., Cristea, A., & Al Moubayed, N. (2021, July). A Generative Bayesian Graph Attention Network for Semi-supervised Classification on Scarce Data. Presented at IEEE International Joint Conference on Neural Network (IJCNN2021), Virtual

This research focuses on semi-supervised classification tasks, specifically for graph-structured data under datascarce situations. It is known that the performance of conventional supervised graph convolutional models is mediocre at classification ta... Read More about A Generative Bayesian Graph Attention Network for Semi-supervised Classification on Scarce Data.