ICAI

Due to the numerous inquiries and the interest in the International IEEE Conference Automatics and Informatics 2024 (ICAI'24), the National Organizing Committee of the conference is pleased to inform you that the deadline for paper submission is extended until 18 August 2024, and the rest of the terms are also changed along with this ( http://icai-conf.org/index.php/en/deadlines ).

INVITATION

The International Conference Automatics and Informatics (ICAI) traditionally is held under the patronage of the President of the Republic of Bulgaria during the John Atanasoff days in October every year. The conference has over 55 years of history and has significantly contributed to the development of automation and computer technology in Bulgaria.
Since 2020 conference organizers are the John Atanasoff Society of Automatics and Informatics, Technical University of Varna, IEEE by Bulgarian section and Federation of the Scientific Engineering Unions in Bulgaria. The conference program for ICAI’24 includes: Plenary session, Invited symposia and Workshops, Scientific sessions, Young researchers school and Company presentations and Exhibitions.The purpose of the conference is to bring together international researchers and industrial practitioners interested in the development and implementation of modern technologies for automation, information, computer science, artificial intelligence and others.
The organizing committee of the conference is inviting interested researchers and professionals to submit papers describing significant scientific achievements and innovations in all scientific areas of the conference.

DOWNLOAD CALL FOR PAPERS

Letter from the Presidency

in Bulgarian

in English

PLENARY SESSION

The Inception of Electronic Digital Computing

Vladimir Getov

Abstract

Discovering and developing new ways to speed up computation, especially the more complex and laborious mathematical problems, has occupied human thought for millennia. Among the many great minds who have worked to overcome this challenge are such geniuses as Leonardo da Vinci, Blaise Pascal, and, more recently, Charles Babbage, who conceived and tirelessly constructed his mechanical computing machines as early as the first half of the 19th century. Of all the brilliant scientists who shaped the automatic computing efforts during the 1930s and 1940s, John Vincent Atanasoff was the first to use digital electronics for arithmetic operations and his invention marked the beginning of the modern information revolution.

The timeline of the early projects presented in the paper shows that 85 years ago – in October 1939 – the first breadboard proof-of-concept prototype of an electronic digital computer became operational at Iowa State College. Using fresh insights and some nearly forgotten facts, the paper also reviews the origins of the IEEE Computer Society (CS) and the role of organized professional activities, as well as the customer demand for spreading new ideas and design solutions for the fast-developing computer industry. It recognizes and explains the contribution of John Vincent Atanasoff to the invention and early development of electronic digital computing that changed the world. Indeed, his design principles have propagated to most of the commercially available modern computers and remain at the core of electronic digital computing technologies to the present day.

Machine Learning Methods for Trustworthy Autonomous Systems

Lyudmila Mihaylova

Abstract

There is a fast development of different machine learning methods – for object classification, tracking, action recognition and other tasks with multiple types of data – from images and videos to data from wireless sensor networks. Autonomous image and video analytics faces a number of challenges due to the huge volumes of data that sensors provide, the changeable environmental conditions and other factors. However, it is important to know when the methods work well and when they are not reliable, e.g. how much could we trust the obtained results? How could we characterise trust is a related question. How could we quantify the impact of uncertainties on the developed solutions? This talk will discuss current trends in the area of machine learning and show results for image and video analytics for autonomous systems.

This talk will present recent results on automated behaviour analysis for decision making. Recent results for automated video analytics will be presented with Gaussian process methods, deep learning and other methods. Their pros and cons will be discussed. Some of these results are part of Digital twins, recently developed new tools that incorporate machine learning and artificial intelligence methods. This talk will discuss the big potential of Digital Twins, the opportunities and challenges that they bring.

Interpretable Machine Learning for Explainable Artificial Intelligence

Alexander Gegov

Abstract

Machine Learning (ML) has recently established itself as the main approach to designing and implementing Artificial Intelligence (AI). However, in spite of the significant improvement in terms of classification and prediction accuracy, most ML models suffer from lack of interpretability and the recommendations made by most AI systems suffer from lack of explainability. This is a significant problem especially for safety critical applications where wrong recommendation may have serious consequences.

The lecture will highlight recent developments and future perspective in making ML models interpretable and AI systems explainable. In particular, it will cover the following related topics:

- Interpretable and black box ML models

- Model specific and agnostic explanation methods for ML models

- Global and local explanation methods for ML models

- Making AI systems more reliable and trustworthy

- Improving AI systems performance in classification and prediction

- Using AI systems for decision and control

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