The list of national projects SAS

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Institute: 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 - 30.4.2021

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 - 28.2.2022

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 - 31.12.2020

An individual stimulating system with 3D nano-structure carbon/graphene based transducer and wireless heater for automated tiny insects behavior monitoring
Monitorovací a stimulačný systém s 3D snímačom a mikro-ohrievačom na báze uhlíka/grafénu s bezdrôtovým ovládaním pre automatizované individuálne monitorovanie a stimuláciu drobného hmyzu
Program: JRP
Project leader: Ing. Mgr. Andok Robert PhD.
Annotation:This project is a collaboration project with the Department of Power Mechanical Engineering, National Tsing Hua University in Taiwan (NTHU). In this project, a system that could monitor and analyze insect behavior will be developed. Such system can detect the position of the insect and stimulate insects individually in real-time. Generally, this project is divided into three parts: 1. Designing and manufacturing a 3D micro transducer. 2. Designing and manufacturing an individual stimulator made by the wireless heater. 3. Designing a monitoring and analysis system that could observe the behavior of tiny insects systematically. The parameters of the experimental setup are designed based on the characteristics of the Drosophila organism model (made at NTHU), which can also be used on other tiny insects. The nearly negligible weight and size of the insects is what makes them hard to locate in real-time. By using the electron beam lithography and reactive-ion etching equipment that II SAS provides, a micro bridge structure will be developed. After that, 3D nano carbon/graphene material will be grown onto the structure and the resulting device is a highly sensitive micro transducer that can measure small increments in weight. By placing such transducers all around the experimental platform, the precise position of the insects can be monitored. A wireless heater is installed on to the body of the insects to stimulate them individually under certain circumstances. This heater is made by connecting a nanometric diamond film with a high density micro coil produced at II SAS. By exerting electromagnetic waves of certain frequency (this frequency is related to the size of the coil), electromagnetic induction will occur and the device will heat up stimulating the insect. By using the two devices mentioned above, together with a camera, image processing algorithms, and other hardware equipment, such as a camera stand and a container to place the flies, a system that is used to observe the behavior of small insects, will be developed. The capability of stimulating individual insects and tracking them simultaneously brings up new possibilities of designing more complicating experiments regarding the social behavior of insects. It is the experience of dealing with living organisms and the techniques of manufacturing nano-scale structures that both parties will exchang with each other that can make the development of this system such successful. From this collaboration between II SAS and NTHU more novel nanoscale devices are expected in near future. Drosophilas are commonly used in this project as a model organism. The hierarchical structure of their brains resembles the brain of a mammal, which constitutes to their complicated social behavior. As a broader impact of the results of this bilateral cooperation, by observing the social behavior of these flies, insight on typical human brain disorders (such as the Parkinson’s, the Alzheimer’s, and the Huntington’s disease), neural networks, and biological evolution could be gained. Therefore, the results of this project may also affect wider areas of research, including life and medical sciences.
Duration: 1.1.2018 - 31.12.2020

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

PROviding Computing solutions for ExaScale challengeS (PROCESS)
Poskytovanie výpočtových riešení pre výzvy v oblasti ExaScale
Program: Horizon 2020
Project leader: doc. Ing. Hluchý Ladislav CSc.
Annotation:The PROCESS demonstrators will pave the way towards exascale data services that will accelerate innovation and maximise the benefits of these emerging data solutions. The main tangible outputs of PROCESS are five very large data service prototypes, implemented using a mature, modular, generalizable open source solution for user friendly exascale data. The services will be thoroughly validated in real-world settings, both in scientific research and in industry pilot deployments. To achieve these ambitious objectives, the project consortium brings together the key players in the new data-driven ecosystem: top-level HPC and big data centres, communities – such as Square Kilometre Array (SKA) project – with unique data challenges that the current solutions are unable to meet and experienced e-Infrastructure solution providers with an extensive track record of rapid application development. In addition to providing the service prototypes that can cope with very large data, PROCESS addresses the work programme goals by using the tools and services with heterogeneous use cases, including medical informatics, airline revenue management and open data for global disaster risk reduction. This diversity of user communities ensures that in addition to supporting communities that push the envelope, the solutions will also ease the learning curve for broadest possible range of user communities. In addition, the chosen open source strategy maximises the potential for uptake and reuse, together with mature software engineering practices that minimise the efforts needed to set up and maintain services based on the PROCESS software releases.
Duration: 1.11.2017 - 30.10.2020

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
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 - 28.2.2022

Wider Impacts and Scenario Evaluation of Autonomous and Connected Transport (WISE-ACT)
Širšie dopady a vyhodnocovanie scenárov nasadenia autonómnej a prepojenej dopravy
Program: COST
Project leader: RNDr. Glasa Ján CSc.
Duration: 1.3.2019 - 1.3.2021

The total number of projects: 9