activity
20212024
most citedExposing Reliability Degradation and Mitigation in Approximate DNNs under Permanent Faults

20 citations · 26 across the 9 of their papers we have counts for

collaborators

9 papers

cs.CR20241 cited

Formal Verification for Blockchain-based Insurance Claims Processing

Roshan Lal Neupane, Ernest Bonnah, Bishnu Bhusal +3

Insurance claims processing involves multi-domain entities and multi-source data, along with a number of human-agent interactions. Use of Blockchain technology-based platform can s…

cs.LO2023

QTWTL: Quality Aware Time Window Temporal Logic for Performance Monitoring

Ernest Bonnah, Khaza Anuarul Hoque

In various service-oriented applications such as distributed autonomous delivery, healthcare, tourism, transportation, and many others, where service agents need to perform serial…

cs.LO20233 cited

Model Checking Time Window Temporal Logic for Hyperproperties

Ernest Bonnah, Luan Viet Nguyen, Khaza Anuarul Hoque

Hyperproperties extend trace properties to express properties of sets of traces, and they are increasingly popular in specifying various security and performance-related properties…

cs.HC2023

LiteVR: Interpretable and Lightweight Cybersickness Detection using Explainable AI

Ripan Kumar Kundu, Rifatul Islam, John Quarles +1

Cybersickness is a common ailment associated with virtual reality (VR) user experiences. Several automated methods exist based on machine learning (ML) and deep learning (DL) to de…

cs.LG20231 cited

VR-LENS: Super Learning-based Cybersickness Detection and Explainable AI-Guided Deployment in Virtual Reality

Ripan Kumar Kundu, Osama Yahia Elsaid, Prasad Calyam +1

A plethora of recent research has proposed several automated methods based on machine learning (ML) and deep learning (DL) to detect cybersickness in Virtual reality (VR). However,…

cs.AR202320 cited

Exposing Reliability Degradation and Mitigation in Approximate DNNs under Permanent Faults

Ayesha Siddique, Khaza Anuarul Hoque

Approximate computing is known for enhancing deep neural network accelerators' energy efficiency by introducing inexactness with a tolerable accuracy loss. However, small accuracy…