8 December 2021

Tutorial Sessions–1: 08.30 – 10.30



Spiking Neural Networks for Deep Learning and Knowledge Representation: Methods, Systems, Applications


by Prof. Nikola Kasabov, Dr. Maryam Doborjeh, Dr. Zohreh Doborjeh


The 2-hour tutorial demonstrates that the third generation of artificial neural networks, the spiking neural networks (SNN) are not only capable of deep, incremental learning of temporal or spatio-temporal data, but also enabling the extraction of knowledge representation from the learned data and tracing the knowledge evolution over time from the incoming data. Similarly to how the brain learns, these SNN models do not need to be restricted in number of layers, neurons in each layer, etc. as they adopt self-organising learning principles of the brain. The tutorial consists of 3 parts:

1)         Brain-inspired SNN for deep learning. NeuCube.

2)        Design of SNN systems in NeuCube

3)        Applications in brain data modelling

The material is illustrated on an exemplar SNN architecture NeuCube (free software and open source along with a cloud-based version available from www.kedri.aut.ac.nz/neucube and www.neucube.io). Case studies are presented including: predictive modelling of EEG and fMRI data measuring cognitive processes and response to treatment; AD prediction; understanding depression; predicting environmental hazards and extreme events.

It is also demonstrated that brain-inspired SNN architectures, such as the NeuCube, allow for knowledge transfer between humans and machines through building brain-inspired Brain-Computer Interfaces (BI-BCI). These are used to understand human-to-human knowledge transfer through hyper-scanning and also to create brain-like neuro-rehabilitation robots. This opens the way to build a new type of AI systems – the open and transparent AI.



Exact vs Metaheuristics for Solving Combinatorial Problems


by Dr. Malek Mouhoub


Combinatorial problems refer to those applications where we either look for the existence of a consistent scenario satisfying a set of constraints (decision problem), or for one or more good/best solutions meeting a set of requirements while optimizing some objectives (optimization problem). These latter objectives include user’s preferences that reflect desires and choices that need to be satisfied as much as possible. Moreover, constraints and objectives (in the case of an optimization problem) often come with uncertainty due to lack of knowledge, missing information, or variability caused by events, which are under nature’s control. Finally, in some applications such as timetabling, urban planning and robot motion planning, these constraints and objectives can be temporal, spatial or both. In this latter case, we are dealing with entities occupying a given position in time and space.

Because of the importance of these problems in so many fields, a wide variety of techniques and programming languages from artificial intelligence, computational logic, operations research and discrete mathematics, are being developed to tackle problems of this kind. While these tools have provided very promising results at both the representation and the reasoning levels, they are still impractical to dealing with many real-world applications, especially given the challenges we listed above.

In this tutorial, we will show how to overcome the above limitation, when solving a combinatorial problem. The approach that we will adopt is based on the Constraint Satisfaction Problem (CSP) paradigm and its variants. Solving techniques will include both exact methods and metaheuristics.

Tutorial Sessions–2: 10.45 – 12.45



Ethical Considerations in the Development and Use of Neural Information Processing Systems


by Prof. Jim Tørresen


Ethical considerations are getting increased attention with regards to providing responsible personalization for robots and autonomous systems. This is partly as a result of the currently limited deployment of such systems in human support and interaction settings. The tutorial will give an overview of the most commonly expressed ethical challenges and ways being undertaken to reduce their impact using the findings in an earlier undertaken review supplemented with recent work and initiatives. The tutorial will exemplify the challenges related to privacy, security, and safety through several examples from own and others’ work.



AI Techniques to Combat COVID-19


by Dr. Sonali Agarwal








The rampant outbreak of the novel coronavirus (COVID-19, SARS-Cov-2), during early December 2019 in Wuhan, China, has created a staggering worldwide crisis along with the widespread loss of lives. The scarcity of resources and lack of experiences to endure the COVID-19 pandemic, combined with the fear of future consequences has established the need for adoption of Artificial Intelligence (AI) techniques to address the challenges. Motivated by the need to highlight the need for employing AI in combating the COVID-19 pandemic, this tutorial aims to help the audience to gain comprehensive understanding of the current state of AI applications in developing the computer-assisted (controlling, monitoring, discovery, diagnosing and treatment) systems to battle the COVID-19 crisis along with the AI assisted spread containment measures. This tutorial is suitable for academic and industrial researchers, graduate students, and practitioners, and anyone with keen interest.