About ICIST 2025

15th International Conference on Information Society and Technology will take place on Kopaonik, Serbia on Mar 9-12, 2025.

ICIST Conference series is one of the top IT scientific events in the region, with 60-100 papers presented each year, average of 200 participants per edition, attended also by the number of IT industry representatives in the region. It hosts distinguished scientists keynotes (in past years, we were honored to share the stage with Richard Mark Soley, Orri Erling, Howard Moskowitz, Bran Selić and others), scientific sessions, panel discussions, industry presentations with exciting social programme for motivated and successfull networking. It is a perfect venue for networking, dissemination of recent research results, getting new project ideas, finding new scientific partners and bringing great vibes back home.

The conference is open for all scientific contributions to the different areas of ICT. It also welcomes technical papers with case studies and demonstrations of novel ICT technologies and approaches in industry and society. It hosts loyal research communities dealing with the areas of information systems, model-based software engineering, e-government, big data, biomedical engineering, semantic web research and since recently, Internet of Things.

All accepted papers will be published in ISOS Conference Proceedings Series (ISOS-CPS, ISSN: 2738-1447) book with title Proceedings of the 15th International Conference on Information Society and Technology or Springer's Lecture Notes book. Past editions of the online proceedings can be viewed here: ICIST2014, ICIST2015, ICIST2016, ICIST2017, ICIST2018, ICIST2019, ICIST2020, ICIST2021, ICIST2022, ICIST2023, ICIST2024.

ICIST Keynotes

Pawel Herman, Division of Computational Science and Technology,
School of Electrical Engineering and Computer Science, KTH and
Digital Futures

Pawel Herman is an associate professor of Computational Neuroscience and Neuroinformatics in
the Division of Computational Science and Technology at the KTH Royal Institute of Technology.
He earned his PhD degree in Intelligent Systems/Brain-Computer Interfacing from the University
of Ulster, UK in 2009. He continued his research as a postdoc at the Donders Institute for Brain,
Cognition and Behaviour, the Netherlands, and Stockholm Brain Institute.

His research lies at crossroads between brain science and computer science. The current focus
of his lab istwo-fold: first, on computational modelling of brain’s cognitive function with memory
in the limelight and, second, on developing a neuro-computational framework for brain-like
machine intelligence. The main research methods used in the Herman lab are large-scale spiking
as well as rate based neural network models, and advanced multivariate techniques for analysis
of multimodal brain data recorded from human subjects and animals.

Miroslav Trajković, Zebra Technologies Corporation, USA

Miroslav Trajković is a researcher and leader in Computer Vision and is currently a Director of the Data Capture and Computer Vision Group in Zebra Technologies Corporation, a global leader in enterprise mobility computing and data capture technologies.

He has over 20 years of experience in research and development in Computer Vision, Machine Learning, and Product Development, where he combined his technology innovation with real-world product requirements and led multifunctional teams, spanning disciplines from optics to hardware design. His algorithms are being used in millions of Zebra products used across the globe on a daily basis. In 2003, the AutoTrack Product by Bosch Surveillance Systems, based on his algorithm for tracking objects with a moving (pan-tilt-zoom) camera, developed during his tenure in Philips Research, received three best product of the year awards by three leading video surveillance magazines. He is the author of over 30 papers in numerical mathematics and computer vision and has been granted over 100 US patents.

He received his Dipl. Ing. Degree in Electrical Engineering from the University of Niš, a PhD in Computer Vision from the University of Sydney, and an MBA from the Stern School of Business at NYU.

 

 

 

ICIST Special Tracks

ICT for health, well-being and sport

Organisers:

  • prof. Miroslav Trajanović. Faculty of Mechanical Engineering, University of Niš, Niš, Serbia
  • prof. Nenad Filipović. Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia BIOIRC Bioengineering Research and Development Center, Kragujevac, Serbia
  • prof. dr Anton Kos. Faculty of Electrical Engineering, University of Ljubljana, Slovenia, Ljubljana, Slovenia
  • prof. dr Osiris Canciglieri Jr. Pontifical Catholic University of Parana – PUCPR / PPGEPS, Curitiba, Brasil

One of the greatest social challenges today is addressing many different concerns and issues related to healthcare and well-being. These challenges include, but are not restricted to, improving the understanding of the causes and mechanisms connected to health; ability to monitor, prevent, detect, and treat disease; personalize health and care delivery. For addressing such challenges, a new approach is needed, which will transform the health and care delivery. This approach is proactive rather than reactive. Healthcare should evolve mechanisms for early risk detection, shifting from the acute treatment to long-term health monitoring. Instead of focusing on single disease treatment, healthcare systems should become capable of handling multiple chronic conditions and comorbidities at the same time. Care delivery should become pervasive, not exclusively institutional. Finally, the generic treatment approach should be abandoned, and care should become more personalized.

Although health is an important, if not necessary, component of well-being, physical activities such as recreation and sports are a good way to maintain and improve it. With the rapid advancement and affordability of miniature, wearable sensor devices, new approaches to quantifying and qualifying our physical activities are possible. For example, innovative applications based on sensor data will contribute to faster, cheaper and safer physical rehabilitation programs, personalized recreational plans, and in-depth analysis of athletes' performance in different sports.

Advanced ICT technologies will play a crucial role in this transformation. Health-related data will grow and will have accurate and useful digital representations. Predictive computing, used by the care providers and customers, will use health-related data to facilitate early risk detection and self-management of health and disease. Health systems will become interoperable. Robotic systems, driven by artificial intelligence will enable assisted living, in smart, age-friendly homes. Biofeedback applications in healthcare, sport, recreation, and rehabilitation will enable an easier and faster way to achieving the desired results. They will assist individuals, healthcare personnel, and trainers in many ways; from influencing the physiological processes in the human body, to motor learning in sports and rehabilitation.

With this session, we aim to uncover the current research in the above mentioned topics and topics closely related to them. All relevant contributions are strongly welcomed.

 

Digital Agriculture: The Synergy of AI and Cutting-Edge Technologies

Organizers:

  • Msc Željana Grbović, BioSense Institute, University of Novi Sad, Serbia
  • dr Goran Kitić, BioSense Institute, University of Novi Sad, Serbia

The Digital Agriculture Track delves into the transformative power of Artificial Intelligence (AI) in reshaping agricultural practices. As Digital Agriculture undergoes a profound shift driven by advanced Information and Communication Technologies (ICT), the integration of AI algorithms, image processing, and data analytics is the key. These innovations redefine efficiency and precision in agriculture, launching in a new era of technological advancements.

Machine learning, a subset of AI, takes a central role in deciphering complex patterns in agricultural data, enabling informed decision-making and resource optimization. Proximal sensing sensors deliver real-time, high-resolution crop and environmental data, facilitating precision agriculture interventions. Additional in-field data are provided by IoT sensors that are able to monitor soil and microclimate conditions to optimize agricultural production. The hardware revolution in Digital Agriculture introduces Unmanned Ground Vehicles (UGV) and robotic platforms tailored for field and orchard management. These systems navigate challenging terrains with precision, transforming traditional orchard practices and promising increased yields. Elevating precision agriculture, Unmanned Aerial Vehicles (UAVs) equipped with advanced imaging technology provide a bird's eye view for detailed analysis. This aerial perspective enables swift identification of crop health issues and timely intervention.

The synergy of AI algorithms, image processing, machine learning, proximal IoT-based and in-situ sensing sensors, advanced hardware, UGVs, robotics platforms, and UAVs is steering a paradigm shift in agriculture. Digital Agriculture not only promises increased productivity but also advocates for sustainable and resource-efficient practices in this dynamic landscape.

Additionally, in the era marked by climate change concerns, the track also underscores how AI can play a pivotal role in promoting sustainability through the optimized management of vital resources like water and soil.

This specialized track invites contributions spanning a wide spectrum of interdisciplinary research and applications centered around AI, robotics and sensors in agriculture. The track aims to serve researchers, practitioners, and industry experts to engage in discussions about the challenges and opportunities inherent in the fusion of AI and Agriculture. By fostering an environment for informal discussions, this track aims to facilitate knowledge exchange and collaboration, paving the way for advancements at the intersection of AI and Agriculture. We eagerly anticipate your submissions and the opportunity to connect with you.

 

Generative AI and Large Language Models (LLMs)

Organisers:

  • Prof. dr Aleksandar Stanimirović (Faculty of Electronic Engineering, University of Niš, Serbia)
  • Prof. dr Miloš Bogdanović (Faculty of Electronic Engineering, University of Niš, Serbia)

Generative AI, including Large Language Models (LLMs), Generative Adversarial Networks (GANs), Diffusion Models, and Neural Radiance Fields (NeRFs), has revolutionized content creation across domains such as text, images, 3D models, and even video. With the rise of LLMs like GPT, BERT, Llama and Mistral, alongside powerful generative models like DALL-E, Midjourney, and Stable Diffusion, the impact on industries as diverse as entertainment, healthcare, education, and design is profound. But the rapid advancement of these models also brings significant engineering challenges, from optimizing architectures and training methods to ensuring scalability and robustness.

This special track aims to explore recent technical innovations, practical applications, and unresolved challenges in the field of Generative AI. We invite researchers and engineers to submit their work related to the design, optimization, and deployment of these models, with a particular focus on enhancing efficiency and reliability of these systems.

Topics of Interest

Techniques
  • Innovative architectures for generative models, such as advancements in Transformer-based models or diffusion models for image and video generation.
  • Optimization algorithms that enhance the performance, efficiency, and scalability of Generative AI models.
  • Model compression and transfer learning techniques for reducing the computational load of Generative AI.
  • Efficient training techniques for large-scale models, including distributed training, parallelism, and memory optimization for better resource utilization.
  • Zero-shot and few-shot learning for generative tasks, enabling models to generate high-quality content from minimal data.
  • Multimodal AI techniques that integrate text, images, audio, and video for comprehensive content generation.
Applications
  • Innovative applications of Generative AI in various domains, such as medicine, education, business, finance, etc.
  • Advances in models that convert natural language into high-fidelity images and videos, improving content creation for entertainment, design, and marketing.
  • Educational technologies leveraging Generative AI, such as chat-bots, content generation, feedback systems, etc.
  • Techniques for generating synthetic datasets for training other machine learning models, especially in healthcare, autonomous driving, and robotics.
  • Generating synthetic medical images, simulations, and data for training diagnostic systems and aiding in drug discovery.
Challenges
  • Ensuring that generative models can withstand adversarial inputs and produce reliable outputs in high-stakes applications.
  • Developing techniques that give users control over the behavior and outcomes of generative models, particularly in ensuring adherence to constraints like style or factuality.
  • Addressing issues where LLMs generate incorrect or fabricated information, with a focus on improving the reliability of outputs in sensitive fields.
  • Addressing the challenge of limited high-quality data to enable LLMs to learn and perform effectively in languages with fewer available digital or labeled resources.
  • Overcoming the high computational costs of training and deploying large generative models, with a focus on improving energy efficiency and scaling across distributed systems.
  • Engineering solutions to enable real-time output from generative models, especially in applications such as gaming, VR, and live content creation.
  • Developing standardized metrics and tools to assess the performance, efficiency, and scalability of generative models across diverse tasks and domains.

 

Digital Water

Organisers:

  • Nikola Milivojević, Jaroslav Černi Institute, Belgrade, Serbia
  • Boban Stojanović, Department of Mathematics and Informatics, Faculty of Science, University of Kragujevac, Kragujevac, Serbia
  • Milan Stojković, The Institute for Artificial Intelligence Research and Development of Serbia, Novi Sad, Serbia

Water is one of our most critical resources, and its management today requires innovative approaches to address mounting challenges such as climate change, urban growth, and resource sustainability. In recent years, the concept of "Digital Water" has gained increasing significance, aiming to boost the transformative impact of advanced digital technologies on water management, thereby ensuring greater resilience and efficiency in increasingly complex conditions.

This session aims to highlight how technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), and Digital Twins are driving the digital revolution in the water sector. These technologies enable the collection of vast amounts of data, allowing for a deeper understanding of complex water systems, faster and more informed decision-making, and proactive management. Instead of relying on reactive approaches, water utilities are now moving towards predictive and condition-based strategies, which lead to improved efficiency, reduced water losses, and more reliable service delivery for customers.

Participants in the session will gain new insights into how digital transformation can be applied to the water sector from both practical and strategic perspectives. Through insights into successful implementations, including examples from domestic and international projects, participants will understand how these technologies can support their work in research, engineering, or water utility management.

The session will cover the role of IoT sensor networks in water systems, with a focus on their integration within distribution networks for continuous monitoring of key parameters. The session will also address the application of Artificial Intelligence and Machine Learning in tackling issues related to water resource management. In addition to these topics, there will be a focus on the implications of cybersecurity in the context of digital water infrastructure. As the water sector increasingly integrates IoT and other digital solutions, it becomes more vulnerable to cyber threats, which means ensuring the resilience of digital water systems involves addressing these security risks directly.

Relevant topics include (but not limited to):

  • Digital Technologies in Water Monitoring and Management (IoT and ML Technologies for Water System Monitoring, AI in Real-Time Monitoring and Prediction of Water Quality, ML Models for Flood Prediction, Digital Twins in Water Management)
  • Advanced Analytics and Optimization in Hydrological Systems (Hybrid Physically-Based and ML-Based Models, AI and ML Supported Reservoir Operation Optimization, Integrative Data Analytics)
  • Nature-Based Solutions and Sustainability (AI-Informed Nature-Based Solutions, Digital Tools for Improving Aquatic Environments)
  • Decision Support, Policy, and Governance (Decision-Support Systems for Water Resource Planning, Policy and Governance in Digital Water Management, Case Studies on Successful Digital Transformations in the Water Sector)
  • Security and Innovative Solutions (Cybersecurity in Digital Water Systems, Innovative Digital Solutions for Water Systems)

 

AI & IoT for Smart Industry

Organisers:

  • Milovan Medojević, The Institute for Artificial Intelligence R&D of Serbia, EnergyPulse DOO Novi Sad
  • Aleksandar Rikalović, Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia
  • Milan Zdravković, Faculty of Mechanical Engineering, University of Niš, Niš, Serbia

The mutually beneficial relationship between the internet of things (IoT) and Artificial Intelligence (AI) enables disruptive innovations in industry worldwide where the fusion of AI and IoT enables the systems to be predictive, prescriptive, and autonomous. This convergence of AI and IoT is evolving while its continuum impacts variety of industries ranging from manufacturing, retail, healthcare, telecommunication, and transportation, etc. In this context, IoT solutions and real-time data processing can empower a massive amount of data captured from interconnected devices, while Big Data Analytics and AI-based solutions can help tackle many concerns and achieve intelligent prediction, evaluation, optimization, and decisionmaking. However, various challenges like interoperability, decentralization, distributed control, real-time process control, service orientation, and maintenance optimization are being addressed also.

Additionaly, numerous challenges exist in implementing such systems that include algorithmic and design innovations to meet Quality of Service requirements (latency, bandwidth, delay, etc); mechanisms to preserve IoT data privacy and provide secure services for interconnected users; achieving high performance systems that can process both high volume and fast speed IoT data leveraging Edge AI.

For the aforementioned reasons, this research topic aims to solicit the submissions of original and unpublished research articles that present in-depth fundamental research contributions either from theoretical or methodological/application perspective containing novel architectures, algorithms, systems, techniques or applications offering new insights and findings in the field of AI-powered IoT for smart industry of tomorrow.

We seek high-quality submissions related to (but not limited to) one or more of the following topics:

  • Big Data Analytics for Industry 4.0
  • Artificial Intelligence and Evolutionary Techniques for Industry 4.0
  • AIoT for Industry 5.0
  • Intelligent Monitoring & Control Systems
  • Data-driven Modelling and Optimization
  • Machine & Deep Learning for Intelligent Systems
  • Data Analytics for Scheduling Industry Processes
  • Data Analytics for Monitoring and Control of Industry Operations
  • Computational Intelligence Technologies for Industry
  • Cyber-physical systems
  • Real-time condition monitoring in manufacturing
  • Machine learning and deep for fault diagnosis
  • Planning and scheduling in Industry 4.0
  • Advances in smart manufacturing
  • IoT systems for smart manufacturing
  • Digital twin for smart manufacturing
  • Industrial big data and data analytics
  • Manufacturing cyber security
  • IoT and AI techniques
  • Potential of AI-Based Automation for IoT-Enabled Smart industries
  • Machine learning and AI for IoT data processing and analysis
  • Edge AI for industry-centric IoT systems and human-machine interaction
  • New Applications for smart IoT Services
  • Exploring the Role of AIoT in Smart Energy Management
  • Small to Large-scale AIoT pilots
  • IoT testbeds and testing tools
  • Predictive Maintenance and AIoT
  • Smart Manufacturing, Energy, Environment & Water Management and IoT
  • Smart Security and AIoT
  • Smart Metering and AIoT
  • Smart Analytics and AIoT
  • AI for Financial systems
  • Smart Logistics and AIoT

 

Autonomous Driving

Organisers:

  • Prof. Jianxun Cui, School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin, China
  • Prof. Jing Jin, School of Astronoutics, Harbin Institute of Technology, Harbin, China
  • Doc. Dragan Stamenković, Faculty of Mechanical Engineering, University of Belgrade, Serbia
  • Prof. Miroslav Trajanović. Faculty of Mechanical Engineering, University of Niš, Niš, Serbia

Autonomous driving represents a paradigm shift in transportation, promising to revolutionize the way we move people and goods. It holds the potential to significantly reduce accidents, improve traffic flow, and increase energy efficiency. The integration of advanced technologies such as sensor fusion, artificial intelligence, and machine learning is at the core of this transformation.

This special track is designed to explore the cutting-edge advancements in autonomous driving technologies and their implications for the future of mobility. We will delve into how these technologies are shaping the automotive industry and the broader implications for society, including safety, accessibility, and environmental impact.

Participants will gain insights into the latest developments in autonomous vehicle technology from both a technical and strategic perspective. Through case studies of successful implementations and ongoing projects, attendees will learn how these technologies are being integrated into real-world applications, from self-driving cars to autonomous freight transport.

The track will cover the role of sensor technology in autonomous vehicles, focusing on the integration of LIDAR, radar, and camera systems for precise environmental perception. We will also address the application of artificial intelligence and machine learning in areas such as decision-making, path planning, and control systems. Additionally, there will be a discussion on the ethical and legal considerations surrounding autonomous driving, including liability, privacy, and the impact on employment in the transportation sector. Relevant topics include (but are not limited to):

  • Autonomous Vehicle Navigation Systems: Innovations in GPS and inertial navigation systems for precision driving.
  • Deep Learning for Autonomous Vehicles: The application of deep learning in perception, decision-making, and control for autonomous cars.
  • Sensor Fusion in Autonomous Driving: Techniques and challenges in combining data from various sensors for improved vehicle autonomy.
  • Autonomous Driving in Complex Urban Environments: Strategies for navigating dense traffic, pedestrians, and dynamic urban settings.
  • Cyber-security for Autonomous Vehicles: Protecting autonomous systems from hacking and data breaches.
  • Ethical Frameworks for Autonomous Vehicles: Developing ethical guidelines for AI decision-making in autonomous vehicles.
  • Legal and Regulatory Challenges in Autonomous Driving: Navigating the legal landscape for the deployment of autonomous vehicles.
  • Human Factors in Autonomous Driving: Understanding driver behavior, trust, and interaction with autonomous systems.
  • Energy Efficiency and Environmental Impact of Autonomous Vehicles: Assessing the ecological footprint and energy consumption of autonomous driving technologies.
  • Autonomous Vehicle Fleet Management: Optimizing logistics and operations for fleets of autonomous trucks and delivery vehicles.
  • Advanced Driver Assistance Systems (ADAS): The evolution of ADAS and their role in the transition to fully autonomous vehicles.
  • Simulation and Testing for Autonomous Vehicles: Virtual testing environments and methodologies for validating autonomous driving systems.
  • Infrastructure Integration for Autonomous Vehicles: Smart roads, traffic management systems, and the role of infrastructure in supporting autonomous driving.
  • Autonomous Vehicle User Experience Design: Creating intuitive and safe interfaces for passengers and operators of autonomous vehicles.
  • Data-Driven Autonomous Vehicle Development: Utilizing big data analytics to enhance autonomous vehicle performance and safety.
  • Autonomous Vehicle Policy and Society: The societal implications of autonomous vehicles and the development of public policy.
  • Autonomous Driving in Special Conditions: Challenges and solutions for autonomous driving in adverse weather, low-visibility, and off-road conditions.

 

 

Important Dates

1 Dec 16

Track Proposals

2 Feb 03

Abstract Submission

3 Feb 17

Notification on abstract acceptance

4 Jul 01

Deadline for submission of full papers

5 Mar 01

Conference Registration

6 Mar 09

Conference is Opened

Call for contributors

This year, we welcome both extended abstracts and full paper submissions.

Contributions submitted as full papers before the deadline (strict deadline of 3.2.2025) will undergo single blind review and be considered for publication in the Springer's Lecture Notes book.

Contributions submitted as extended abstracts will undergo double blind review and be considered for publication in ISOS Conference Proceedings Series (ISOS-CPS, ISSN: 2738-1447) online book with title Proceedings of the 15th International Conference on Information Society and Technology.

We moved to using a new submission platform: Microsoft's Conference Management Toolkit (CMT). You can submit both extended abstract and full paper, here, after registering to the platform.

Call for extended abstracts

We welcome submission of extended abstracts presenting original unpublished work which is not been submitted for publication elsewhere. Initial submissions must comply to the specific requirements, as it follows.

Download the template for extended abstract, here. The extended abstract must consicely and clearly highlight all following aspects: research motivation and the problem, the research question(s), methodology, description of the solution and/or the discussion. The evaluation of the extended abstracts will be double-blind. Thus, author names, addresses, emails and affiliations MUST NOT be included in the extended abstract. The extended abstract must be created by using the template below, submitted in PDF format, and it MUST NOT exceed 4 A4 pages including figures. Only key references should be included.

The authors of the extended abstracts will be invited to submit full papers after the conference. Those papers will be published in ISOS Conference Proceedings Series (ISOS-CPS, ISSN: 2738-1447) online book with title Proceedings of the 15th International Conference on Information Society and Technology.

Submit the extended abstracts, here.

Call for full papers

The authors are invited to prepare the full papers by using this template. Full papers must not be shorter than 8 pages and no longer than 16 pages, after using the above template. The use of template is mandatory and the authors are invited to fully comply to formatting instructions. The authors are invited to strictly follow instructions for authors.

Accepted full papers presented in the regular sessions and special tracks will be published in Springer's Lecture Notes book. If review results with the reject decision, the paper will be considered for publishing in ISOS Conference Proceedings Series book. 

In order to have the submitted full paper published in the Proceedings books, at least one author or co-author must have been registered at the conference and presented the paper.

Submit the full paper, here.

  Submit abstract

ICIST 2025 Committees

Organizing Committee (OC) is in charge for the organization of the conference. All decisions related to the scientific programme are made by the International Programme Committee (IPC), based on the peer review process, coordinated by the IPC co-chairs. Track chairs are in charge of setting up a scientific programme in the specific topics, which are decided upon based on the submitted track proposals.

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Zora Konjović

Novi Sad, Serbia
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Milan Zdravković

Niš, Serbia
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Miroslav Trajanović

Niš, Serbia
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Nenad Filipović

Kragujevac, Serbia
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Dražen Drašković

Belgrade, Serbia
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Miodrag Ivković

Novi Sad, Serbia
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Alexis Aubry

Nancy, France
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Zorica Bogdanović

Belgrade, Serbia
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Osiris Canciglieri Jr.

Curitiba, Brasil
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Jianxun Cui

Harbin, China
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Tijana Geroski

Kragujevac, Serbia
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Stevan Gostojić

Novi Sad, Serbia
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Željana Grbović

Novi Sad, Serbia
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Luka Humski

Zagreb, Croatia
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Dragan Ivanović

Novi Sad, Serbia
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Valentina Janev

Belgrade, Serbia
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Jing Jin

Harbin, China
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Dorina Kabakchieva

Sofia, Bulgaria
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Goran Kitić

Novi Sad, Serbia
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Anton Kos

Ljubljana, Slovenia
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Aleksandar Kovačević

Novi Sad, Serbia
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Eduardo R. Loures

Curitiba, Brazil
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Ivan Luković

Novi Sad, Serbia
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Oskar Marko

Novi Sad, Serbia
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Nikola Milivojević

Belgrade, Serbia
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Néjib Moalla

Lyon, France
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Hervé Panetto

Nancy, France
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David Romero

Mexico
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Milan Segedinac

Novi Sad, Serbia
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Jean Marcelo Simão

Curitiba, Brazil
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Dubravka Sladić

Novi Sad, Serbia
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Goran Sladić

Novi Sad, Serbia
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Jaka Sodnik

Ljubljana, Slovenia
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Dragan Stamenković

Belgrade, Serbia
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Aleksandar Stanimirović

Niš, Serbia
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Milan Stojkov

Novi Sad, Serbia
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Milan Stojković

Novi Sad, Serbia
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Anderson Luis Szejka

Curitiba, Brasil
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Smiljana Tomašević

Kragujevac, Serbia
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Milan Trifunović

Niš, Serbia
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Milan Vidaković

Novi Sad, Serbia
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Aleksandra Vulović

Kragujevac, Serbia
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Georg Weichhart

Austria
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Miroslav Zarić

Novi Sad, Serbia
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Nemanja Zdravković

Belgrade, Serbia

Contacts

Registration and conference logistics: office AT yuinfo.org

Scientific programme: Milan Zdravković, milan.zdravkovic AT gmail.com