10 citations · 14 across the 9 of their papers we have counts for
10 papers
Can We Trust Video Hallucination Detectors? VidHalLoc for Evaluating the Evaluators
Xinyu Chen, Adnan Mahmood, Mark Dras
Video-language models and video agents can produce hallucinations that conflict with spatiotemporal evidence. Existing benchmarks mainly evaluate model hallucinations, and heteroge…
When Confidence Fails: Overconfidence in LLMs under Uncertainty and Missing Clinical Information
Maryam Tahermazandarani, Adnan Mahmood, Fahmida Islam +1
Large Language Models (LLMs) have achieved strong performance in medical question answering and clinical reasoning tasks. However, their reliability under uncertainty remains poorl…
AgenticAI-DialogGen: Topic-Guided Conversation Generation for Fine-Tuning and Evaluating Short- and Long-Term Memories of LLMs
Manoj Madushanka Perera, Adnan Mahmood, Kasun Eranda Wijethilake +1
Recent advancements in Large Language Models (LLMs) have improved their ability to process extended conversational contexts, yet fine-tuning and evaluating short- and long-term mem…
Federated Learning at the Forefront of Fairness: A Multifaceted Perspective
Noorain Mukhtiar, Adnan Mahmood, Yipeng Zhou +3
Fairness in Federated Learning (FL) is emerging as a critical factor driven by heterogeneous clients' constraints and balanced model performance across various scenarios. In this s…
CoRe-Fed: Bridging Collaborative and Representation Fairness via Federated Embedding Distillation
Noorain Mukhtiar, Adnan Mahmood, Quan Z. Sheng
With the proliferation of distributed data sources, Federated Learning (FL) has emerged as a key approach to enable collaborative intelligence through decentralized model training…
FedCLF -- Towards Efficient Participant Selection for Federated Learning in Heterogeneous IoV Networks
Kasun Eranda Wijethilake, Adnan Mahmood, Quan Z. Sheng
Federated Learning (FL) is a distributed machine learning technique that preserves data privacy by sharing only the trained parameters instead of the client data. This makes FL ide…
FairEquityFL -- A Fair and Equitable Client Selection in Federated Learning for Heterogeneous IoV Networks
Fahmida Islam, Adnan Mahmood, Noorain Mukhtiar +2
Federated Learning (FL) has been extensively employed for a number of applications in machine learning, i.e., primarily owing to its privacy preserving nature and efficiency in mit…
Towards Adaptive Context Management for Intelligent Conversational Question Answering
Manoj Madushanka Perera, Adnan Mahmood, Kasun Eranda Wijethilake +1
This particular paper introduces an Adaptive Context Management (ACM) framework for the Conversational Question Answering (ConvQA) systems. The key objective of the ACM framework i…
A Survey of the State-of-the-Art in Conversational Question Answering Systems
Manoj Madushanka Perera, Adnan Mahmood, Kasun Eranda Wijethilake +3
Conversational Question Answering (ConvQA) systems have emerged as a pivotal area within Natural Language Processing (NLP) by driving advancements that enable machines to engage in…
Fairness in Federated Learning: Trends, Challenges, and Opportunities
Noorain Mukhtiar, Adnan Mahmood, Quan Z. Sheng
At the intersection of the cutting-edge technologies and privacy concerns, Federated Learning (FL) with its distributed architecture, stands at the forefront in a bid to facilitate…