Abstract The integration of Artificial Intelligence (AI) with the Internet of Things (IoT), known
as the Artificial Intelligence of Things (AIoT), enhances the devices' processing and analysis …

Artificial Intelligence (AI) methods are powerful tools for various domains, including critical
fields such as avionics, where certification is required to achieve and maintain an …

The remarkable advancements in artificial intel-ligence (AI), primarily driven by deep neural
networks, are facing challenges surrounding unsustainable computational tra-jectories …

Disentangling attributes of various sensory signals is central to human-like perception and
reasoning and a critical task for higher-order cognitive and neuro-symbolic AI systems. An …

Graph convolutional networks (GCNs) have demonstrated superiority in graph-based
learning tasks. However, training GCNs on full graphs is particularly challenging, due to the …

The remarkable advancements in artificial intelligence (AI), primarily driven by deep neural
networks, are facing challenges surrounding unsustainable computational trajectories …

Neural (aka subsymbolic) AI methods, in particular, those based on deep learning, recently
achieved great successes in various application domains, eg,[10, 19]. However, they are …

Growing concerns over the lack of transparency in AI, particularly in high-stakes fields like
healthcare and finance, drive the need for explainable and trustworthy systems. While Large …

Large language models (LLMs) have significantly transformed the landscape of artificial
intelligence, demonstrating exceptional capabilities in natural language understanding and …

This paper explores the intersection of emotional hermeneutics and artificial intelligence
(AI), examining the challenges and potential of integrating deep emotional understanding …