Cyber-physical systems (CPS) are increasingly relying on machine learning (ML)
techniques to reduce labor costs and improve efficiency. However, the adoption of ML also …

Emotion recognition from speech has its fair share of applications and consequently
extensive research has been done over the past few years in this interesting field. However …

Power quality disturbance (PQD) can significantly affect the normal operation of the power
system. Deep neural network (DNN) can classify PQD with extremely high accuracy …

Deep learning plays a significant role in develo** a robust and constructive framework for
tackling complex learning tasks. Consequently, it is widely utilized in many security-critical …

The correct classification of power quality (PQ) is the key step to ensure the normal
operation of smart grid. Deep neural networks have been widely used for PQ classification …

Randomized smoothing is the current state-of-the-art method for producing provably robust
classifiers. While randomized smoothing typically yields robust $\ell_2 $-ball certificates …

Identification of burn depth with sufficient accuracy is a challenging problem. This paper
presents a deep convolutional neural network to classify burn depth based on altered tissue …

Hyperdimensional (HD) Computing leverages random high dimensional vectors (> 10000
dimensions) known as hypervectors for data representation. This high dimensional feature …

Recent work has advocated for the use of deep learning to perform power allocation in the
downlink of massive MIMO (maMIMO) networks. Yet, such deep learning models are …

In the last years, Deep Learning technology has been proposed in different fields, bringing
many advances in each of them, but raising new threats in these solutions regarding …