Abstract Machine learning (ML) models are nowadays used in complex applications in
various domains, such as medicine, bioinformatics, and other sciences. Due to their black …

Self-supervised learning has been shown to be very effective in learning useful
representations, and yet much of the success is achieved in data types such as images …

Abstract Internet of Things (IoT) is set to revolutionize all aspects of our lives. The number of
objects connected to IoT is expected to reach 50 billion by 2020, giving rise to an enormous …

Vertical federated learning (VFL), a variant of federated learning, has recently attracted
increasing attention. An active party having the true labels jointly trains a model with other …

Voxel-based soft robots (VSRs) are a type of modular robots composed by interconnected
soft and deformable blocks, ie, voxels. Thanks to the softness of their bodies, VSRs may …

Time series with missing values (incomplete time series) are ubiquitous in real life on
account of noise or malfunctioning sensors. Time-series imputation (replacing missing data) …

The field of neuroevolution has achieved much attention in recent years from both academia
and industry. Numerous papers have reported its successful applications in different fields …

Distributed fuzzy neural networks (DFNNs) have attracted increasing attention recently due
to their learning abilities in handling data uncertainties in distributed scenarios. However, it …

The use of Deep learning in Cyber-Physical Systems (CPSs) is gaining popularity due to its
ability to bring intelligence to CPS behaviors. However, both CPSs and deep learning have …

Fuzzy neural networks (FNNs) have been very successful at handling uncertainty in data
using fuzzy map**s and if-then rules. However, they suffer from generalization and …