2 citations · 2 across the 4 of their papers we have counts for
4 papers
The Easy Trap: Why LLMs Underestimate Misconception-Driven Difficulty
Amanda La Hadi, Muhammad Johan Alibasa, Guanliang Chen +1
Large language models (LLMs) are increasingly used for estimating item difficulty in educational assessment. However, it remains unclear whether such estimates reflect how learners…
New Money: A Systematic Review of Synthetic Data Generation for Finance
James Meldrum, Basem Suleiman, Fethi Rabhi +1
Synthetic data generation has emerged as a promising approach to address the challenges of using sensitive financial data in machine learning applications. By leveraging generative…
Automated Unit Test Case Generation: A Systematic Literature Review
Jason Wang, Basem Suleiman, Muhammad Johan Alibasa
Software is omnipresent within all factors of society. It is thus important to ensure that software are well tested to mitigate bad user experiences as well as the potential for se…
Optimisation of federated learning settings under statistical heterogeneity variations
Basem Suleiman, Muhammad Johan Alibasa, Rizka Widyarini Purwanto +3
Federated Learning (FL) enables local devices to collaboratively learn a shared predictive model by only periodically sharing model parameters with a central aggregator. However, F…