Today's cyber attacks have become more severe and frequent, which calls for a new line of
security defenses to protect against them. The dynamic nature of new-generation threats …

Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

During COVID-19, social media has played an important role for public health agencies and
government stakeholders (ie actors) to disseminate information regarding situations, risks …

With the widespread popularization of social network platforms, user-generated content and
other social network data are growing rapidly. It is difficult for social users to select interested …

This research article presents a novel approach to examining the utilization of artificial
intelligence (AI) in sustainable supply chains (SSCs) by applying social network analysis …

Big Data (BD) has shifted the paradigm of conventional data analysis with the exploitation of
emerging technologies. Analysis using BD contributes to foreseeing and pulling out value …

Graph convolutional network based methods that model the body-joints' relations, have
recently shown great promise in 3D skeleton-based human motion prediction. However …

We propose a multiscale spatio-temporal graph neural network (MST-GNN) to predict the
future 3D skeleton-based human poses in an action-category-agnostic manner. The core of …

Optimal Transport (OT) has recently emerged as a central tool in data sciences to compare
in a geometrically faithful way point clouds and more generally probability distributions. The …

Environmental planning is considered an important process to positively encourage the
inclusion of nature-based solutions (NbS) in urban areas. The success of their …