4 papers
Explaining Matters: Leveraging Definitions and Semantic Expansion for Sexism Detection
Sahrish Khan, Arshad Jhumka, Gabriele Pergola
The detection of sexism in online content remains an open problem, as harmful language disproportionately affects women and marginalized groups. While automated systems for sexism…
FlexFed: Mitigating Catastrophic Forgetting in Heterogeneous Federated Learning in Pervasive Computing Environments
Sara Alosaime, Arshad Jhumka
Federated Learning (FL) enables collaborative model training while preserving privacy by allowing clients to share model updates instead of raw data. Pervasive computing environmen…
RIFLES: Resource-effIcient Federated LEarning via Scheduling
Sara Alosaime, Arshad Jhumka
Federated Learning (FL) is a privacy-preserving machine learning technique that allows decentralized collaborative model training across a set of distributed clients, by avoiding r…
SafeSpeech: A Comprehensive and Interactive Tool for Analysing Sexist and Abusive Language in Conversations
Xingwei Tan, Chen Lyu, Hafiz Muhammad Umer +7
Detecting toxic language including sexism, harassment and abusive behaviour, remains a critical challenge, particularly in its subtle and context-dependent forms. Existing approach…