11 citations · 34 across the 12 of their papers we have counts for
12 papers
Large language models for generating rules, yay or nay?
Shangeetha Sivasothy, Scott Barnett, Rena Logothetis +4
Engineering safety-critical systems such as medical devices and digital health intervention systems is complex, where long-term engagement with subject-matter experts (SMEs) is nee…
Quantifying Manifolds: Do the manifolds learned by Generative Adversarial Networks converge to the real data manifold
Anupam Chaudhuri, Anj Simmons, Mohamed Abdelrazek
This paper presents our experiments to quantify the manifolds learned by ML models (in our experiment, we use a GAN model) as they train. We compare the manifolds learned at each e…
LLMs for Test Input Generation for Semantic Caches
Zafaryab Rasool, Scott Barnett, David Willie +4
Large language models (LLMs) enable state-of-the-art semantic capabilities to be added to software systems such as semantic search of unstructured documents and text generation. Ho…
ML-On-Rails: Safeguarding Machine Learning Models in Software Systems A Case Study
Hala Abdelkader, Mohamed Abdelrazek, Scott Barnett +3
Machine learning (ML), especially with the emergence of large language models (LLMs), has significantly transformed various industries. However, the transition from ML model protot…
Seven Failure Points When Engineering a Retrieval Augmented Generation System
Scott Barnett, Stefanus Kurniawan, Srikanth Thudumu +2
Software engineers are increasingly adding semantic search capabilities to applications using a strategy known as Retrieval Augmented Generation (RAG). A RAG system involves findin…
6DVF: Data Visualisation Framework for mHealth Apps
Yasmeen Anjeer Alshehhi, Khlood Ahmad, Mohamed Abdelrazek +1
The widespread of data visualisation tools on smartphones has provided end users an easy way to track their health data, leading designers to put more effort into delivering suitab…