All Outputs (10)

Is Unimodal Bias Always Bad for Visual Question Answering? A Medical Domain Study with Dynamic Attention (2022)
Presentation / Conference Contribution

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.

Efficient Uncertainty Quantification for Multilabel Text Classification (2022)
Presentation / Conference Contribution

Despite rapid advances of modern artificial intelligence (AI), there is a growing concern regarding its capacity to be explainable, transparent, and accountable. One crucial step towards such AI systems involves reliable and efficient uncertainty qua... Read More about Efficient Uncertainty Quantification for Multilabel Text Classification.

Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification (2022)
Presentation / Conference Contribution

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.

INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations (2022)
Presentation / Conference Contribution

XAI with natural language processing aims to produce human-readable explanations as evidence for AI decisionmaking, which addresses explainability and transparency. However, from an HCI perspective, the current approaches only focus on delivering a s... Read More about INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations.

Fine-grained Main Ideas Extraction and Clustering of Online Course Reviews (2022)
Book Chapter

Online course reviews have been an essential way in which course providers could get insights into students’ perceptions about the course quality, especially in the context of massive open online courses (MOOCs), where it is hard for both parties to... Read More about Fine-grained Main Ideas Extraction and Clustering of Online Course Reviews.

Balancing Fined-Tuned Machine Learning Models Between Continuous and Discrete Variables - A Comprehensive Analysis Using Educational Data (2022)
Book Chapter

Along with the exponential increase of students enrolling in MOOCs [26] arises the problem of a high student dropout rate. Researchers worldwide are interested in predicting whether students will drop out of MOOCs to prevent it. This study explores a... Read More about Balancing Fined-Tuned Machine Learning Models Between Continuous and Discrete Variables - A Comprehensive Analysis Using Educational Data.

SimStu-Transformer: A Transformer-Based Approach to Simulating Student Behaviour (2022)
Book Chapter

Lacking behavioural data between students and an Intelligent Tutoring System (ITS) has been an obstacle for improving its personalisation capability. One feasible solution is to train “sim students”, who simulate real students’ behaviour in the ITS.... Read More about SimStu-Transformer: A Transformer-Based Approach to Simulating Student Behaviour.

Tailored gamification in education: A literature review and future agenda (2022)
Journal Article

Gamification has been widely used to design better educational systems aiming to increase students’ concentration, motivation, engagement, flow experience, and others positive experiences. With advances in research on gamification in education, over... Read More about Tailored gamification in education: A literature review and future agenda.

Novel Decision Forest Building Techniques by Utilising Correlation Coefficient Methods (2022)
Book Chapter

Decision Forests have attracted the academic community’s interest mainly due to their simplicity and transparency. This paper proposes two novel decision forest building techniques, called Maximal Information Coefficient Forest (MICF) and Pearson’s C... Read More about Novel Decision Forest Building Techniques by Utilising Correlation Coefficient Methods.

AI‐driven user aesthetics preference prediction for UI layouts via deep convolutional neural networks (2022)
Journal Article

Leveraging the power of computational methods, AI can perform effective strategies in intelligent design. Researchers are pushing the boundaries of AI, developing computational systems to solve complex questions. The authors investigate the associati... Read More about AI‐driven user aesthetics preference prediction for UI layouts via deep convolutional neural networks.