Invited Speakers
Dr. Sergey V. Shushardzhan, Professor

Dr. Sergey V. Shushardzhan, Professor

Academy of Rehabilitation Medicine, Clinical Psychology and Music Therapy, Russia
Speech Title: Scientific Music Therapy High Technologies in the Global Medical and Psychological Care

Abstract: Our paper analyses those global problems that negatively affect the world's population's health, duration, and quality of life. Pandemic, socio-economic issues cause long-term stress among millions of people nowadays, why 80% of all diseases, including Cancer and Cardiovascular Disease (CVD). Stress is a global problem because only CVD and oncology take the first two positions in mortality among the world's population.
Therefore, dealing with stress is critical in the overall health care system, where advanced music therapy methods can play a vital role. Why is music therapy? The language of music is universal, and the achievements of Scientific Music Therapy (SMT), which is the new interdisciplinary direction, are so significant that they allow improving mood and optimizing the function of vital systems.
Our team discovered unique musical-acoustic algorithms, which cause characteristic changes in the neurohormonal system. Found algorithms are the key to organism regulation. In these basics, we have developed innovative technologies with hardware and software for music therapy, widely used in medical and wellness centers of different countries, for rehabilitation, treating various psychosomatic disorders, and anti-aging.
As a concept response to the challenges of the COVID 19 pandemic, our team has developed аn autonomous multifunctional robot “HELPER” for Medical Services, Rehabilitation, and Music Therapy.
The conclusive idea of the paper is that the Synthesis of Medicine, High Technology, and Art integrated with the Internet, Telemedicine, and Artificial Intelligence can reach millions of people. Moreover, that approach can be the basement of the modern Global Medical and Psychological Care system.



Dr. Md. Haider Ali Biswas, Professor

Dr. Md. Haider Ali Biswas, Professor

Mathematics Discipline, Science Engineering and Technology School, Khulna University, Bangladesh
Speech Title: To be updated

Abstract: To be updated



Dr. Abbas Khosravi, Associate Professor

Dr. Abbas Khosravi, Associate Professor

Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds Campus, Australia
Speech Title: Uncertainty quantification for artificial intelligence (UQ4AI): why and how

Abstract: As artificial intelligence (AI) technologies translate into real-world decision tools, many experts are questioning how much subject matter experts could trust decisions and predictions generated by these systems. Trust is the key mechanism that shapes how experts use and adopt AI. Currently, the lack of trust in AI systems is a significant drawback in the adoption of this technology in safety-critical and health-related applications. Confidence of an AI model, in particular deep learning models, about its output has been always a critical point to its performance and reliability. How can one develop, for example, a neural network that knows when it does not know? Answering this type of questions is a prerequisite for widespread deployment of neural networks in safety critical applications. The field of AI-based uncertainty quantification has gained an overwhelming attention among researchers in recent years resulting in an arsenal of different methods. Several methods and frameworks have been proposed in literature to generate predictive uncertainty estimates using neural networks.
In this workshop, we will provide a comprehensive overview on recent advances on deep learning-based uncertainty quantification techniques. This will include ensemble, Bayesian, and direct methods for deep uncertainty quantification. Applications of these algorithms for developing uncertainty-aware decision-making tools will be then reviewed and discussed.



Dr. Konstantin Ryabinin, Associate Professor

Dr. Konstantin Ryabinin, Associate Professor

Perm State University (Computer Science department; Laboratory of Sociocognitive and Computational Linguistics) and Saint Petersburg University (Institute of Cognitive Studies), Russia
Speech Title: Ontology-Driven Visual Analytics Platform for Semantic Data Mining and Fuzzy Classification

Abstract: Visualization is claimed as one of the essential “V’s” of Big Data since it allows presenting the data in a human-friendly way and is, therefore, a steppingstone for the Big Data mining process. Visual analytics, in turn, ensures knowledge discovery out of the data through cognitive graphics and filtering capabilities. But to be efficient, visualization and analytics tools have to consider other Big Data “V’s” by handling the large data volumes, keeping up with the data growth and changing velocity, and adapting to the variety of the data representation formats. We propose using ontology engineering methods to create a visual analytics platform controlled by an ontological knowledge base that describes supported data types, input formats, data filters, visual objects, and visualization algorithms, as well as available communication protocols and computing nodes, the platform modules can run on. This allows introducing new functions and distributed computation scenarios to the platform on the fly just by extending the underlying domain ontologies without changing the source code of the platform’s core. The analytics flow inside this platform is described by task ontologies enabling semantic data mining process. As a result, seamless integration with different data sources is achieved, including plain files, databases, and even third-party soft- and hardware solvers. We demonstrate the viability of the approach proposed by solving several data mining and fuzzy classification problems, including the assessment of the citizens' regional identity according to the mental maps they draw and the reconstruction of ontogenesis of extinct synapsid Titanophoneus potens Efremov, 1938.

Keywords: Visual Analytics, Ontology Engineering, Semantic Data Mining, Fuzzy Classification, Sketch Maps, Paleontology



Dr. Gianni D'Angelo, Tenured Adjunct Professor (RTD.B)

Dr. Gianni D'Angelo, Tenured Adjunct Professor (RTD.B)

Department of Computer Sciences, University of Salerno, Italy
Speech Title: Networking Cognitive Security

Abstract: The talk addresses the changing world of security systems, and the possible approaches to their improvement through the usage of Artificial Intelligence and Machine Learning-based techniques. Although many security aspects are addressed, the talk will be focused on addressing networking security issues. The concept of "Networking Cognitive Security" is mainly explored from three different perspectives and implementation levels, namely:
a) Network-level, by considering only data flowing in a network in order to perform Traffic Classification and Anomaly Detection;
b) Application-level, by modeling the behavior of apps in order to detect suspect behaviors.
c) Social-level, by modeling the behavior of entities involved in social communities in order to detect unfair users of social networks.
For each of these levels, theoretical aspects and implementation details will be shown. In particular, solutions based on Deep Neural Network architectures and ad-hoc intelligent algorithms will be shown.
Particular attention is given to the inner behavior of Deep Neural Networks. In this regard, a formal mathematical exploration of inner processes behind neural network architectures are shown in order to provide a useful understanding of how each neural component affects network performance. This allows improving the skill of a neural network designer to provide improvements in reliability and performance of security systems. These aspects are presented with reference to many security systems developed in our research group making use of Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and their combinations as well as specific algorithms also based on evolutionary approaches to solve constrained optimization problems, and more.



Dr. Dmitri E. Kvasov, Assistant Professor

Dr. Dmitri E. Kvasov, Assistant Professor

University of Calabria, Italy
Speech Title: Global optimization in control theory and machine learning

Abstract: Many problems in the design of systems with parametric uncertainty can be formulated as global optimization problems. Parameters of such systems can be unknown or not uniquely defined, while their functional dependencies can be multiextremal and with no analytical representation (the so-called black-box problems). Due to the high computational cost involved in this decision-making process, the main goal is to develop efficient global optimization algorithms that produce reasonably good and guaranteed solutions with a limited budget of function evaluations. Derivative-free methods can be therefore particularly suitable for addressing these challenging global optimization problems and can be of a deterministic or stochastic (in particular, metaheuristic) nature. Some of the methods of these two groups are briefly surveyed and their application in the fields of control theory and machine learning is discussed.

Keywords: Expensive global optimization, deterministic methods, metaheuristics, comparison, control theory, machine learning.



Dr. Anand Nayyar, Professor

Dr. Anand Nayyar, Professor

Scientist, Vice-Chairman (Research) and Director (IoT and Intelligent Systems Lab), School of Computer Science, Duy Tan University, Viet Nam
Speech Title: To be updated

Abstract: To be updated



Dr. Feng Feng, Professor

Dr. Feng Feng, Professor

Department of Applied Mathematics, Xi'an University of Posts and Telecommunications, China
Speech Title: To be updated

Abstract: To be updated



Dr. Sugiyarto Surono, Associate Professor

Dr. Sugiyarto Surono, Associate Professor

Mathematics Department, Ahmad Dahlan University, Indonesia
Speech Title: To be updated

Abstract: To be updated



Dr. Tanzila Saba, Research Professor

Dr. Tanzila Saba, Research Professor

Artificial Intelligence & Data Analytics (AIDA) Lab, College of Computer and Information Sciences, Prince Sultan University, Saudi Arabia
Speech Title: To be updated

Abstract: To be updated



More speakers will be updated soon...