The International Conference on Informatics and Information Technologies is the 16th of the series of conferences organized by the Faculty of Computer Science and Engineering (FCSE).

Date and venue

May 10-12, 2019, Hotel Bistra, Mavrovo, Republic of North Macedonia

Conference Schedule

General schedule for CIIT 2019

Detailed schedule for CIIT 2019


  • Artificial Intelligence, Robotics, Bioinformatics
  • Multimedia, Signal Processing
  • Computer Networks
  • Sensor Networks
  • Distributed Systems
  • Computer Architecture and Parallel Processing
  • Cloud and GRID Computing
  • Wireless and Mobile Computing
  • Security and Cryptography
  • Theoretical Foundations of Informatics
  • Applied Mathematics
  • E-Technologies, E-Commerce and E-Business
  • ICT in education
  • Green ICT

Invited Talks

"Artificial Intelligence: A European Perspective", by Blagoj Delipetrev, PhD

Abstract: We are at the beginning of a rapid period of transformation of our economy and society due to the convergence of many digital technologies. Artificial Intelligence (AI) is central to this change and offers major opportunities to improve our lives. The recent AI developments are the result of increased processing power, algorithms advancements and the exponential growth in the volume and variety of digital data. The EU Member States and the European Commission are developing coordinated national and European strategies, recognizing that only together we can succeed. We can build on our areas of strength including excellent research, leadership in industrial sectors, a solid legal and regulatory framework, and very rich cultural diversity also at regional and sub-regional levels. We should challenge the shortcomings of AI and work towards strong evaluation strategies, transparent and reliable systems, and good human-AI interactions. Ethical and secure-by-design algorithms are crucial to build trust in this disruptive technology, but we also need a broader engagement of civil society on the values to be embedded in AI and the directions for future development.

Short biography: Blagoj Delipetrev is a scientific officer at the B6 Digital Economy Unit, Joint Research Center, European Commission (2017-). He holds two PhDs, one from TU Delft, the Netherlands on machine learning/AI and optimization algorithms (2016) and from Faculty of Electrical Engineering and Information Technologies, University Ss Cyril and Methodius, Skopje on cloud computing applications and platforms (2011). Blagoj was a vice-dean and associate professor (2016-) at the Faculty of Computer Science, University Goce Delcev. He was a management board member of MarNet - Macedonian Academic and Research Network (2015-2017). He is founder and former CEO of BITT Solutions Unlimited, consultancy and software outsourcing company (2015-). His research interests are in AI, machine learning, reinforcement learning and deep learning algorithms.


"Side-Channel Attacks on Cryptographic Implementations and Efficient Countermeasures", by Louiza Papachristodoulou, PhD

Abstract: Recent side-channel attacks (SCA) on elliptic curve algorithms have shown that the security of these crypto-systems is a matter of serious concern. Side-channel attacks exploit various physical leakages of secret information or instructions from cryptographic devices and they constitute a constant threat for cryptographic implementations. In this talk, after introducing some basic principles of side-channel attacks, we are going to present practical SCA on open source cryptographic libraries using elliptic-curve protocols (OpenSSL, mbedTLS, libecc). Our methodology offers a generic attack framework with minimal assumptions for the attacker model, which is applicable to various forms of curves (Weierstrass, Edwards and Montgomery curves) and implementations. As a proof of concept, we attack the doubling operation in the double-and-add-always scalar multiplication algorithm. Finally, we are going to present some traditional and recently proposed countermeasures that would efficiently protect against those attacks. One of this countermeasures, the Residue-Number System, coming from the area of parallel computing to the cryptographic world, is particularly interesting; it shows how vital the sharing of knowledge is, in order to perform advances in research.

Short biography: Louiza Papachristodoulou is a Senior Researcher at NavInfo Europe B.V. performing research in cybersecurity for the automotive industry. She received her PhD from the Digital Security Group at Radboud University Nijmegen under the supervision of Professor Lejla Batina. During her PhD she collaborated with research groups from Telecom-ParisTech in Paris, Rambus (Cryptography Research Inc.) in San Francisco, Riscure B.V in Delft and Patras University Greece. She won the Christinne Mohrmann award in 2016, which she used to perform research on side-channel attacks on cryptocurrency at the Security Group of the University of Edinburgh, UK and the Computer Science Group of the University of Adelaide, Australia. Before that, she worked as Cryptographer at Compumatica Secure Networks, Netherlands. She obtained her M.Sc in Information Security Technology from Eindhoven University of Technology and her M.Sc in Applied Mathematical and Physical Sciences from National Technical University of Athens. Her research interests include elliptic curve cryptography, security of embedded devices, side-channel attacks and countermeasures.


"Featurewise Transformations in Multi-Task Learning for Vision", by Gjorgji Strezoski, PhD Candidate at University of Amsterdam

Abstract: Typical multi-task learning (MTL) methods rely on architectural adjustments and a large trainable parameter set to jointly optimize over several tasks. However, when the number of tasks increases so do the complexity of the architectural adjustments and resource requirements. In this talk, after a brief overview of the core principles of Computer Vision and  MTL, we will introduce two orthogonal MTL approaches we developed to tackle these issues. First, a method which applies conditional feature-wise transformations over the convolutional activations dubbed Task Routing (TR), then a structured gradient-guided method for unsupervised task grouping named Selective Sharing (SS). The TR method is encapsulated in a new transformation layer we call the Task Routing Layer (TRL) and SS is a general MTL paradigm. In both cases, to distinguish from regular MTL, we are going to introduce Many Task Learning (MaTL) as a special case of MTL where more than 20 tasks are performed by a single model. We will conclude the talk with two technical demos in the art domain, showcasing how the learned representations can be used at inference time.

Short biography: Gjorgji Strezoski is a PhD candidate in the Intelligent Sensory Information Systems group at the University of Amsterdam under supervision of Marcel Worring. His research is performed jointly with the Rijksmuseum in Amsterdam, where the focus is developing novel computer vision and multi-modal methods for art collection analysis. He specializes in multi-task deep learning for computer vision where leveraging multiple information sources and optimizing for multiple objectives is key. He applies his research in a generative form as the principal data driven exhibition consultant at Paradox studios in Amsterdam. He is particularly interested in solving the catastrophic forgetting effects in deep nets as learning becomes sequential or iterative. He received his BSc and MSc in Software Engineering at the Faculty for Computer Science and Engineering at the Ss. Cyril and Methodius University in Skopje, Macedonia.