Submitted keynotes abstracts are listed below.
Visualisation and crowdsourcing tools for quantitative studies
Data-driven environmental sustainability should consider not just getting more data, but smarter representation of data. Today's...
Graph-representation and learning framework for Smart Cities big data analytics
Given the increased dynamism and complexity of modern world, researchers struggle to cope to exploit as much as possible...
Machine Learning Enabled Transparent Manufacturing
Benefiting from the advancement of information and communication technology, more and more data related to product manufacturing...
Explainable AI in process of complexity reduction of DL models and in boosting theirs performance
The performance of AI systems based on deep learning models is exceeding the human level on an increasing number of tasks like...
Explaining Visual Classification using Attributes
The performance of deep Convolutional Neural Networks (CNN) has been reaching or even exceeding the human level on large number of...
Causal-AI: Explainability of AI Models through Cause and Effect Reasoning
Interpretability of artificial intelligence (AI) models is one of the most discussed topics in contemporary AI research (Holm...
Scalable Constraint-based Optimisation
Declarative methods for combinatorial optimisation (such as modeling as a CSP) can form the basis of highly scalable solvers...
The Need to Empirically Evaluate Explanation Quality
Organisations face growing legal and social responsibilities to be able to explain decisions they have made using autonomous...
Explanation of Smart 5G Network Intrusion Detection using Attack Trees
Successful Intrusion Detection systems heavily rely on machine learning to detect anomaly. However, particularly in 5G networks...
Explainable artificial intelligence for physical and technical systems
Physical and technical systems are peculiar for machine learning. The solution of a variety of engineering problems necessitates...
From explaining models to explaining decisions and systems
Explainability has been investigated in several ways in the field of machine learning: there are more interpretable models (e.g...
On Combing Deep-Learning and Classical Control Theoretic Approaches
End to end deep learning of control policies have gathered much attention in recent times. Their attractiveness stems from the...