Facebook Instagram Twitter RSS Feed Back to top

The list of international projects SAS

Institute of Informatics
Security of Air Transport Infrastructure of Europe (SATIE)
Bezpečnosť infraštruktúry leteckej dopravy v Európe
Program: Horizon 2020
Project leader: Ing. Rusko Milan PhD.
Annotation:The twenty-first century experiments a digital revolution that simplifies flight and cross-border. Digitalization contributes to leverage information sharing, reduce exploitation costs and improve travel experience, but it also blurs the lines between virtual world and reality with serious security matters. In the meanwhile airports face a daily challenge to ensure business continuity and passengers’ safety. SATIE adopts a holistic approach about threat prevention, detection, response and mitigation in the airports, while guaranteeing the protection of critical systems, sensitive data and passengers. Critical assets are usually protected against individual physical or cyber threats, but not against complex scenarios combining both categories of threats. In order to handle it, SATIE develops an interoperable toolkit which improves cyber-physical correlations, forensics investigations and dynamic impact assessment at airports. Having a shared situational awareness, security practitioners and airport managers collaborate more efficiently to the crisis resolution. Emergency procedures can be triggered simultaneously through an alerting system in order to reschedule airside/landside operations, notify first responders, cybersecurity and maintenance teams towards a fast recovery. Innovative solutions will be integrated on a simulation platform in order to improve their interoperability and to validate their efficiency. Three demonstrations will be conducted at different corners of Europe (Croatia, Italy and Greece) in order to evaluate the solutions in operational conditions (TRL≥7). Results and best practises will be widely disseminated to the scientific community, standardization bodies, security stakeholders and the aeronautic community. Finally, SATIE paves the way to a new generation of Security Operation Centre that will be included in a comprehensive airport security policy.
Duration: 1.5.2019 - 31.7.2021
COnversational BRAins
Program: Horizon 2020
Project leader: Prof.Mgr. Beňuš Štefan PhD.
Annotation:The EU-funded COBRA project will train the next generation of researchers to accurately characterise and model the linguistic and cognitive brain mechanisms that allow conversation to unfold in both human-human and human-machine interactions. The first challenge will be to determine how alignment and prediction may both rely on and contribute to setting up brain-to-brain coupling relationships. The second will relate to the development of computational models of alignment and prediction for more socially-acceptable text-to-speech synthesisers, human-machine dialogue systems, and social robots.
Duration: 1.2.2020 - 31.1.2024
European Open Science Cloud - Expanding Capacities by building Capabilities (EOSC-Synergy)
Európsky cloud pre otvorenú vedu – rozšírenie kapacít budovaním infraštruktúrneho potenciálu
Program: Horizon 2020
Project leader: doc. Ing. Hluchý Ladislav CSc.
Annotation:EOSC-synergy extends the EOSC coordination to nine participating countries by harmonizing policies and federating relevant national research e-Infrastructures, scientific data and thematic services, bridging the gap between national initiatives and EOSC. The project introduces new capabilities by opening national thematic services to European access, thus expanding the EOSC offer in the Environment, Climate Change, Earth Observation and Life Sciences. This will be supported by an expansion of the capacity through the federation of compute, storage and data resources aligned with the EOSC and FAIR policies and practices. EOSC-synergy builds on the expertise of leading research organizations, infrastructure providers, NRENs and user communities from Spain, Portugal, Germany, Poland, Czech Republic, Slovakia, Netherlands, United Kingdom and France, all already committed to the EOSC vision and already involved in related activities at national and international level. Furthermore, we will expand EOSC’s global reach by integrating infrastructure and data providers beyond Europe, fostering international collaboration and open new resources to European researchers. The project will push the EOSC state-of-the-art in software and services life-cycle through a quality-driven approach to services integration that will promote the convergence and alignment towards EOSC standards and best practices. This will be complemented by the expansion of the EOSC training and education capabilities through the introduction of an on-line platform aimed at boosting the development of EOSC skills and competences. EOSC-synergy complements on-going activities in EOSC-hub and other related projects liaising national bodies and infrastructures with other upcoming governance, data and national coordination projects.
Duration: 1.9.2019 - 31.10.2022
Integrating and managing services for the European Open Science Cloud (EOSC-hub)
Integrovanie a manažment služieb pre európsky cloud pre otvorenú vedu
Program: Horizon 2020
Project leader: doc. Ing. Hluchý Ladislav CSc.
Annotation:The EOSC-hub project creates the integration and management system of the future European Open Science Cloud that delivers a catalogue of services, software and data from the EGI Federation, EUDAT CDI, INDIGO-DataCloud and major research e-infrastructures. This integration and management system (the Hub) builds on mature processes, policies and tools from the leading European federated e-Infrastructures to cover the whole life-cycle of services, from planning to delivery. The Hub aggregates services from local, regional and national e-Infrastructures in Europe, Africa, Asia, Canada and South America. The Hub acts as a single contact point for researchers and innovators to discover, access, use and reuse a broad spectrum of resources for advanced data-driven research. Through the virtual access mechanism, more scientific communities and users have access to services supporting their scientific discovery and collaboration across disciplinary and geographical boundaries. The project also improves skills and knowledge among researchers and service operators by delivering specialised trainings and by establishing competence centres to co-create solutions with the users. In the area of engagement with the private sector, the project creates a Joint Digital Innovation Hub that stimulates an ecosystem of industry/SMEs, service providers and researchers to support business pilots, market take-up and commercial boost strategies. EOSC-hub builds on existing technology already at TRL 8 and addresses the need for interoperability by promoting the adoption of open standards and protocols. By mobilizing e-Infrastructures comprising more than 300 data centres worldwide and 18 pan-European infrastructures, this project is a ground-breaking milestone for the implementation of the European Open Science Cloud.
Duration: 1.1.2018 - 31.3.2021
Development of machine learning models for high-performance computing
Návrh modelov strojového učenia pre vysoko-výkonné počítanie.
Program: Inter-academic agreement
Project leader: doc. Ing. Hluchý Ladislav CSc.
Annotation:Nowadays, Machine learning (ML) is important and relevant trend in the development of modern computer science. ML is not new concept it was part of the field of artificial intelligence for a long time. The main idea focuses on building algorithms that can learn on their own, but recent advances in computing systems and the availability of big data create impressive progress from checkers solving program to self-moved cars and programs that recognize single face from thousands of photos. One of the applications of ML is to improve the efficiency of computations by tuning parameters of environment. Here we deal with complex computational program as with black box, which dynamic depends on parameters. In this project we propose to investigate process of auto tuning of parallel program on heterogeneous multiprocessor system. This problem is important and has some features, that makes it hard for solving. First of all, data and environment can influence performance of program in crucial way. Also, in some setups algorithm can control scheduling making strategy time-dependant. Secondly, an important feature is the presence of competing users. It is necessary to ensure fair and equal access to resources. Because each user is a rational agent and seeks to increase his or her share of the resource at the expense of others, an unbalanced distribution algorithm can shift the system to an inefficient equilibrium. This is problem of game theory. There are notable connections of ML methods with game theory. For example we can mention GANs (generative Adversarial Nets) corresponding to a two-player game, SVM (support vector machine) connected with zero-sum two-player game, and others. This project proposes the following approaches to address the problem. The first is model-free reinforcement machine learning, which is trying to find the best possible control strategy. Secondly, it is the application of the game-theoretic approach, in which the formalized user behavior is involved in the system. Each user has their own learning algorithm and is a rational agent who wants to get some of the computing resource and maximize its utility function. The project continues previous collaboration between institutes in the design of adaptive programming methods for high-performance computing in heterogeneous multiprocessor environments. Project objectives: The purpose of the project is to analyze and synthesize new machine learning algorithms in concurrent high-performance computing. Existing machine learning algorithms are usually heuristic solutions, the quality of which depends on the data and training parameters. One of the promising areas of better understanding of the work is the application of game-theoretical approaches to modeling the learning process of competing algorithms. The results of the game analysis will help to describe the optimal behavior of users at the point of equilibrium and to calculate the characteristics of schedulers and translation algorithms.
Duration: 1.4.2020 - 31.12.2022
Advanced Computing for EOSC (EGI-ACE)
Pokročilé počítanie pre EOSC
Program: Horizon 2020
Project leader: doc. Ing. Hluchý Ladislav CSc.
Annotation:The mission of the EGI-ACE project of EGI is to implement the Compute Platform of the European Open Science Cloud, by delivering a secure federation of Cloud compute and storage facilities in collaboration with providers of the EGI Federation, commercial providers, data providers and international research infrastructures of pan-European relevance.
Duration: 1.1.2021 - 30.6.2023
Fire in Earth System: Science & Society (FIRElinks)
Požiar v systéme Zeme: veda a spoločnosť
Program: COST
Project leader: RNDr. Glasa Ján CSc.
Duration: 1.6.2019 - 1.6.2023
Social Network of Machines (SOON)
Sociálna sieť strojov
Program: ERANET
Project leader: Ing. Balogh Zoltán PhD.
Annotation:This project proposes to investigate the impact of the use of autonomous social agents to optimise manufacturing process in the framework of Industry 4.0. "Social" means that cyber-physical entities will act autonomously in order to optimize an industrial process following behaviour models inspired by human social networks. Currently, in Industry 4.0, smart entities do exist. However, intelligence is localised and intelligent heterogeneous entities cannot communicate together even inside the same shop-floor. Our motivation comes from the observation that, if we want to create a real Internet of Everything that brings together processes, data, things, and people, all these entities have to be connected and follow a shared, easy to understand paradigm. In this project, we propose a holistic multi-agent paradigm that encompasses machines and humans. The presence of human operators is therefore crucial both to teach to and to learn from software agents, via deep learning and data mining algorithms. Agents will take decisions merging and analysing big and heterogeneous data produced by sensors (vibration, temperature, etc.), automation and information systems (such as enterprise resource planning and manufacturing execution system), and humans in real-time. The design and evaluation of the SOON system will be performed through predictive maintenance scenarios in collaboration with three different industrial companies (in Slovakia, Spain and Switzerland). Such collaboration will enable the project consortium to assess the concrete improvement on specific industrial processes. As application scenario, the project will focus on the predictive maintenance tasks. We believe that the arrival of Industry 4.0 revolution combined with recent improvements in machine learning, and the application of autonomous multi-agent architecture can finally bring disruptive innovation in industrial process optimization and modelling.
Duration: 1.3.2019 - 30.4.2022

The total number of projects: 8