no code implementations • 9 Mar 2024 • Mojtaba Taherisadr, Salma Elmalaki
Reinforcement Learning (RL) has increasingly become a preferred method over traditional rule-based systems in diverse human-in-the-loop (HITL) applications due to its adaptability to the dynamic nature of human interactions.
no code implementations • 6 Nov 2023 • Tianyu Zhao, Salma Elmalaki
Ensuring fairness in decision-making systems within Human-Cyber-Physical-Systems (HCPS) is a pressing concern, particularly when diverse individuals, each with varying behaviors and expectations, coexist within the same application space, influenced by a shared set of control actions in the system.
no code implementations • 12 Jul 2023 • Tianyu Zhao, Mojtaba Taherisadr, Salma Elmalaki
Furthermore, we recognize that fairness-aware policies can sometimes conflict with the application's utility.
no code implementations • 7 Mar 2023 • Mojtaba Taherisadr, Stelios Andrew Stavroulakis, Salma Elmalaki
On the one hand, RL methods enhance the user experience by trying to adapt to the highly dynamic nature of humans.
no code implementations • 7 Mar 2023 • Mojtaba Taherisadr, Mohammad Abdullah Al Faruque, Salma Elmalaki
Thanks to the rapid growth in wearable technologies and recent advancement in machine learning and signal processing, monitoring complex human contexts becomes feasible, paving the way to develop human-in-the-loop IoT systems that naturally evolve to adapt to the human and environment state autonomously.
1 code implementation • 25 Feb 2022 • Hieu Le, Salma Elmalaki, Athina Markopoulou, Zubair Shafiq
AutoFR is effective: it generates filter rules that can block 86% of the ads, as compared to 87% by EasyList, while achieving comparable visual breakage.
no code implementations • 30 Mar 2021 • Salma Elmalaki
Results obtained on these two applications validate the generality of FaiR-IoT and its ability to provide a personalized experience while enhancing the system's performance by 40%-60% compared to non-personalized systems and enhancing the fairness of the multi-human systems by 1. 5 orders of magnitude.