52 citations · 194 across the 34 of their papers we have counts for
37 papers
PILAR: Personalizing Augmented Reality Interactions with LLM-based Human-Centric and Trustworthy Explanations for Daily Use Cases
Ripan Kumar Kundu, Istiak Ahmed, Khaza Anuarul Hoque
Artificial intelligence (AI)-driven augmented reality (AR) systems are becoming increasingly integrated into daily life, and with this growth comes a greater need for explainabilit…
Adversarial VR: An Open-Source Testbed for Evaluating Adversarial Robustness of VR Cybersickness Detection and Mitigation
Istiak Ahmed, Ripan Kumar Kundu, Khaza Anuarul Hoque
Deep learning (DL)-based automated cybersickness detection methods, along with adaptive mitigation techniques, can enhance user comfort and interaction. However, recent studies sho…
TOGGLE: Temporal Logic-Guided Large Language Model Compression for Edge
Khurram Khalil, Khaza Anuarul Hoque
Large Language Models (LLMs) deliver exceptional performance across natural language tasks but demand substantial computational resources, limiting their deployment on resource-con…
PrivateXR: Defending Privacy Attacks in Extended Reality Through Explainable AI-Guided Differential Privacy
Ripan Kumar Kundu, Istiak Ahmed, Khaza Anuarul Hoque
The convergence of artificial AI and XR technologies (AI XR) promises innovative applications across many domains. However, the sensitive nature of data (e.g., eye-tracking) used i…
FlipLLM: Efficient Bit-Flip Attacks on Multimodal LLMs using Reinforcement Learning
Khurram Khalil, Khaza Anuarul Hoque
Generative Artificial Intelligence models, such as Large Language Models (LLMs) and Large Vision Models (VLMs), exhibit state-of-the-art performance but remain vulnerable to hardwa…
RIFT: A Scalable Methodology for LLM Accelerator Fault Assessment using Reinforcement Learning
Khurram Khalil, Muhammad Mahad Khaliq, Khaza Anuarul Hoque
The massive scale of modern AI accelerators presents critical challenges to traditional fault assessment methodologies, which face prohibitive computational costs and provide poor…
Hyperproperty-Constrained Secure Reinforcement Learning
Ernest Bonnah, Luan Viet Nguyen, Khaza Anuarul Hoque
Hyperproperties for Time Window Temporal Logic (HyperTWTL) is a domain-specific formal specification language known for its effectiveness in compactly representing security, opacit…
ApproXAI: Energy-Efficient Hardware Acceleration of Explainable AI using Approximate Computing
Ayesha Siddique, Khurram Khalil, Khaza Anuarul Hoque
Explainable artificial intelligence (XAI) enhances AI system transparency by framing interpretability as an optimization problem. However, this approach often necessitates numerous…
EPSILON: Adaptive Fault Mitigation in Approximate Deep Neural Network using Statistical Signatures
Khurram Khalil, Khaza Anuarul Hoque
The increasing adoption of approximate computing in deep neural network accelerators (AxDNNs) promises significant energy efficiency gains. However, permanent faults in AxDNNs can…
Explainable AI-Guided Efficient Approximate DNN Generation for Multi-Pod Systolic Arrays
Ayesha Siddique, Khurram Khalil, Khaza Anuarul Hoque
Approximate deep neural networks (AxDNNs) are promising for enhancing energy efficiency in real-world devices. One of the key contributors behind this enhanced energy efficiency in…
Securing Virtual Reality Experiences: Unveiling and Tackling Cybersickness Attacks with Explainable AI
Ripan Kumar Kundu, Matthew Denton, Genova Mongalo +2
The synergy between virtual reality (VR) and artificial intelligence (AI), specifically deep learning (DL)-based cybersickness detection models, has ushered in unprecedented advanc…
Mazed and Confused: A Dataset of Cybersickness, Working Memory, Mental Load, Physical Load, and Attention During a Real Walking Task in VR
Jyotirmay Nag Setu, Joshua M Le, Ripan Kumar Kundu +5
Virtual Reality (VR) is quickly establishing itself in various industries, including training, education, medicine, and entertainment, in which users are frequently required to car…
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…
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…
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…
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…
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,…
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…
Improving Reliability of Spiking Neural Networks through Fault Aware Threshold Voltage Optimization
Ayesha Siddique, Khaza Anuarul Hoque
Spiking neural networks have made breakthroughs in computer vision by lending themselves to neuromorphic hardware. However, the neuromorphic hardware lacks parallelism and hence, l…
Security-Aware Approximate Spiking Neural Networks
Syed Tihaam Ahmad, Ayesha Siddique, Khaza Anuarul Hoque
Deep Neural Networks (DNNs) and Spiking Neural Networks (SNNs) are both known for their susceptibility to adversarial attacks. Therefore, researchers in the recent past have extens…
RobustPdM: Designing Robust Predictive Maintenance against Adversarial Attacks
Ayesha Siddique, Ripan Kumar Kundu, Gautam Raj Mode +1
The state-of-the-art predictive maintenance (PdM) techniques have shown great success in reducing maintenance costs and downtime of complicated machines while increasing overall pr…
TruVR: Trustworthy Cybersickness Detection using Explainable Machine Learning
Ripan Kumar Kundu, Rifatul Islam, Prasad Calyam +1
Cybersickness can be characterized by nausea, vertigo, headache, eye strain, and other discomforts when using virtual reality (VR) systems. The previously reported machine learning…
Is Approximation Universally Defensive Against Adversarial Attacks in Deep Neural Networks?
Ayesha Siddique, Khaza Anuarul Hoque
Approximate computing is known for its effectiveness in improvising the energy efficiency of deep neural network (DNN) accelerators at the cost of slight accuracy loss. Very recent…
Rule-based Adaptations to Control Cybersickness in Social Virtual Reality Learning Environments
Samaikya Valluripally, Vaibhav Akashe, Michael Fisher +3
Social virtual reality learning environments (VRLEs) provide immersive experience to users with increased accessibility to remote learning. Lack of maintaining high-performance and…