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Found 12 papers, 3 papers with code
LiteVR: Interpretable and Lightweight Cybersickness Detection using Explainable AI
However, most of these cybersickness detection methods are perceived as computationally intensive and black-box methods.
Dimensionality Reduction
Explainable artificial intelligence
+1
VR-LENS: Super Learning-based Cybersickness Detection and Explainable AI-Guided Deployment in Virtual Reality
Our proposed method identified eye tracking, player position, and galvanic skin/heart rate response as the most dominant features for the integrated sensor, gameplay, and bio-physiological datasets.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
RobustPdM: Designing Robust Predictive Maintenance against Adversarial Attacks
We observe that approximate adversarial training can significantly improve the robustness of PdM models (up to 54X) and outperforms the state-of-the-art PdM defense methods by offering 3X more robustness.
Security-Aware Approximate Spiking Neural Networks
Therefore, researchers in the recent past have extensively studied the robustness and defense of DNNs and SNNs under adversarial attacks.
Improving Reliability of Spiking Neural Networks through Fault Aware Threshold Voltage Optimization
Our proposed FalVolt mitigation method improves the performance of systolicSNNs by enabling them to operate at fault rates of up to 60\%, with a negligible drop in classification accuracy (as low as 0. 1\%).
TruVR: Trustworthy Cybersickness Detection using Explainable Machine Learning
The results show that the EBM can detect cybersickness with an accuracy of 99. 75% and 94. 10% for the physiological and gameplay datasets, respectively.
Is Approximation Universally Defensive Against Adversarial Attacks in Deep Neural Networks?
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.
Exploring Fault-Energy Trade-offs in Approximate DNN Hardware Accelerators
Our quantitative analysis shows that the permanent faults exacerbate the accuracy loss in AxDNNs when compared to the accurate DNN accelerators.
Adversarial Examples in Deep Learning for Multivariate Time Series Regression
Due to the tremendous success of deep learning (DL) algorithms in various domains including image recognition and computer vision, researchers started adopting these techniques for solving MTS data mining problems, many of which are targeted for safety-critical and cost-critical applications.
Crafting Adversarial Examples for Deep Learning Based Prognostics (Extended Version)
The obtained results show that all the evaluated PHM models are vulnerable to adversarial attacks and can cause a serious defect in the remaining useful life estimation.
High-level Modeling of Manufacturing Faults in Deep Neural Network Accelerators
The advent of data-driven real-time applications requires the implementation of Deep Neural Networks (DNNs) on Machine Learning accelerators.
False Data Injection Attacks in Internet of Things and Deep Learning enabled Predictive Analytics
In this paper, we demonstrate the effect of IoT sensor attacks on a PdM system.
