Explainable Artificial Intelligence (XAI) has emerged as a crucial research area to address
the interpretability challenges posed by complex machine learning models. In this survey …

The ability to understand causality from data is one of the major milestones of human-level
intelligence. Causal Discovery (CD) algorithms can identify the cause-effect relationships …

Causal Discovery (CD) is the process of identifying the cause-effect relationships among the
variables from data. Over the years, several methods have been developed primarily based …

Challenges to reproducibility and replicability have gained widespread attention, driven by
large replication projects with lukewarm success rates. A nascent work has emerged …

The literature addressing bias and fairness in AI models (fair-AI) is growing at a fast pace,
making it difficult for novel researchers and practitioners to have a bird's-eye view picture of …

Computing Continuum (CC) systems are challenged to ensure the intricate requirements of
each computational tier. Given the system's scale, the Service Level Objectives (SLOs) …

In the realm of Parkinson's Disease (PD) research, the integration of wearable sensor data
with personal health records (PHR) has emerged as a pivotal avenue for patient alerting and …

Recent studies have exploited the vital role of microblogging platforms, such as Twitter, in
crisis situations. Various machine-learning approaches have been proposed to identify and …

Robots operating in real-world environments must reason about possible outcomes of
stochastic actions and make decisions based on partial observations of the true world state …

Neutron and electron irradiation experimental studies conducted on body-centered cubic Fe
and Fe–Cr alloys have established two prismatic dislocation loop populations, which have …