In this post-covid year when we strive to fully normalize our lives, we are organizing the 15th ICT Innovations conference onsite with the title “Learning: Humans, Theory, Machines, and Data”. In the last years there have been great breakthroughs in the field of AI and machine learning, and also a great shift in human education forced by the necessity to use ICT, and wide understanding of the power of it. We hope to discuss how human learning theory can inform and improve machine learning, and how machine learning can in turn shed light on human learning processes. The title also implies that data can be a key component in making these connections, potentially through the use of data-driven methods in both fields to better understand and model the ways in which humans and machines learn. Can we design Machine Learning models that can learn from humans in the most natural way possible, and also from the perspective of Machine Learning to interpret how humans learn and to enhance learning outcome?

We invite scholars with different ideas and solutions that apply tools and techniques of information, communication and computer science and technologies to reshape the future. We expect to bring fresh insights in artificial intelligence, e-learning, blockchain technologies, e-commerce, healthcare, networks and communications, security, software systems, transportation and delivery and all other relevant sectors. Formal methods in computer science are base tools for development of the practical applications, so this year’s conference encourages papers in all fields of theory of computation.

Topics of interest include, but are not limited to, the following topic areas: 
  • Human-centered Artificial Intelligence
  • Information Engineering
  • Human-Machine Interaction
  • Data science
  • Business Intelligence
  • Human-AI interaction and explainability
  • Cognitive computing
  • Education in engineering
  • Education technology and e-learning
  • Parallel and distributed processing
  • Human-Machine Collaborative Learning
  • Formal methods in CS
  • ICT in Medicine and Healthcare
  • Intelligent systems
  • Machine learning technologies
  • Software Engineering
  • Data-Driven Modeling of Human Learning
  • Dew computing