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Current

DyMAX - Dynamic malware analysis with explainable AI
Duration:	1. 9. 2024 - 30. 6. 2028
Evidence number:	DyMAX
Program:	APVV
Project leader:	Ing. Budinská Ivana, PhD.
Annotation:	a

Computer simulation of airflows and fire smoke spread in critical structures
Duration:	1. 1. 2024 - 31. 12. 2027
Evidence number:	VEGA 2/0096/24
Program:	VEGA
Project leader:	Mgr. Weisenpacher Peter, PhD.
SAS cosolvers:	Mgr. Baida Olena, RNDr. Glasa Ján, CSc., Mgr. Kubišová Tatiana, Ing. Valášek Lukáš, PhD.
Annotation:	Research in the proposed project is focused on formulation of new scientific knowledge on computer simulation ofairflows and fire smoke spread in critical structures. Motorway tunnels were selected as the main subject ofresearch interest based on discussions with specialists on fire safety in Slovakia. Tunnels belong to inteligentstructures with high safety requirements due to potentially huge losses in case of fire. Natural airflows, airflowscreated by emergency ventilation, airflows induced by fire and fire smoke spread will be analyzed with focus onvelocity fields, velocity profiles and smoke stratification. Computational aspects of efficient parallel realization ofcomputer simulation on HPC systems will be investigated as well. The previous research results, experience andobtained experimental data will be utilized. The research is in line with current research trends and requirementsof the fire researchers’ and simulators developers’ community and has potential to have significant social impact.

SPICE - Smart Data PIpelines for the Cognitive ComputE Continuum (SPICE)
Duration:	1. 4. 2025 - 30. 9. 2027
Evidence number:	09I02-03-V01-00012/2025/VA/PZ
Program:	Plán obnovy EÚ
Project leader:	doc. Ing. Hluchý Ladislav, CSc.
SAS cosolvers:	Ing. Astaloš Ján, Mgr. Bobák Martin, PhD., Ing. Dlugolinský Štefan, PhD., doc. Ing. Forgáč Radoslav, PhD., Ing. Habala Ondrej, Ing. Krammer Peter, doc. Ing. Očkay Miloš, PhD., Ing. Staňo Michal, Mgr. Šeleng Martin, PhD.
Annotation:	The growing scale and complexity of the Cloud led to the emergence of Edge and IoT computing in the "computecontinuum" for applications relying heavily on sensor data and time-critical AI-assisted data processing. Its primarygoal is to enable intelligent decision making and gain deeper insights across the continuum. At the same time,assembling and maintaining well-designed data pipelines involves addressing numerous technical, operational andorganizational challenges. These hurdles and expertise/resource constraints act as a barrier for wider adoption of datapipelines. Our goal is to provide: (1) AI-assisted composition of smart data pipelines to radically simplify complexand distributed data processing, (2) a highly optimized execution environment and resource management that enablesecure and smart data processing for the compute continuum. The results of our project will be made available as aninnovation transformation incubator for others to exploit.

AI4CC - AI-Driven Self-awareness and Cognition for Compute Continuum (AI4CC)
Duration:	1. 7. 2024 - 30. 6. 2027
Evidence number:	APVV-23-0430
Program:	APVV
Project leader:	doc. Ing. Hluchý Ladislav, CSc.
SAS cosolvers:	Ing. Balogh Zoltán, PhD., Mgr. Bobák Martin, PhD., Ing. Dlugolinský Štefan, PhD., Ing. Dora Jean Rosemond, PhD., doc. Ing. Forgáč Radoslav, PhD., Ing. Gatial Emil, PhD., Ing. Habala Ondrej, Ing. Krammer Peter, doc. Ing. Očkay Miloš, PhD., Ing. Staňo Michal, Mgr. Šeleng Martin, PhD.
Annotation:	AI4CC aims to contribute with new AI-based methods and algorithms to the development of modern computingcontinuums, addressing some of the key challenges of this domain. These challenges include enhancing theautonomy and self-adaptation capabilities of compute continuum platforms to optimize decision-making andresponsiveness; adapting to changing conditions, necessitating flexible and adaptive computing models fordynamicity; efficiently managing vast amounts of data, considering factors like decentralization, scalability, andreal-time processing for effective Data Management; ensuring transparency, explainability, and accountability in AIand machine learning models within compute continuums; achieving seamless interoperability between diversedevices and platforms within the continuum, impacting data exchange and communication for Interoperability, anddealing with resource heterogeneity—variability in computing resources across the continuum, including edge andcloud environments, posing challenges in optimizing performance and resource utilization. By systematicallyaddressing these challenges and pioneering advancements in these key areas, AI4CC aims not only to bridgeexisting gaps but to propel compute continuums into a new era of efficiency, adaptability and security. The projectenvisions a future where compute continuums seamlessly integrate into various domains, fostering a holistic andintelligent computing environment that adapts to the ever-evolving demands of the digital landscape.

BioStimul - Experimental System for Wireless Stimulation and Monitoring of Selected Biological Properties and Cognitive Abilities of Drosophila melanogaster.
Duration:	1. 9. 2024 - 30. 6. 2027
Evidence number:	APVV-23-0173
Program:	APVV
Project leader:	Ing. Mgr. Andok Robert, PhD.
SAS cosolvers:	Ing. Mgr. Andok Robert, PhD., Ing. Benčurová Anna, Mgr. Gáliková Martina, PhD., Ing. Hricko Jaroslav, PhD., Ing. Klarák Jaromír, PhD., Mgr. et Mgr. Klepsatel Peter, PhD., Mgr. Knoblochová Diana, Ing. Konečníková Anna, Ing. Nemec Pavol, Ing. Predanocy Martin, PhD., Ing. Ritomský Mário
Annotation:	The fruit fly Drosophila melanogaster serves as one of the most versatile models for studying human diseases, including neurodegenerative or learning disorders. Thanks to the unique genetic tools available exclusively in the fly model, Drosophila considerably contributed to discoveries of genetic regulations behind processes such as learning and behavior. Nevertheless, progress in this research area is limited by available technologies for the stimulation and tracking fruit flies. This project aims to develop novel equipment for wireless stimulation and monitoring of insect behavior. A wireless micro-heater will be installed onto the Drosophila’s body, designed and developed within this project, together with the microsensor of position / force. The fly will be stimulated individually under certain circumstances by locally controlled heat induced in the micro-heater realized by connecting a nanometric diamond film (as a supporting material bilogically compatible with the examined tiny insects, and at the same time a material with excellent thermal conductivity) with a high density micro coil consisting of a closed LC resonant circuit with a high-k dielectric material (ZrO2, SrTiO3, HfO2) used for the micro-capacitor. By exerting electromagnetic waves of certain resonant frequency related to the size of the coil, electromagnetic induction will occur and the device will heat up stimulating the insect. The project aims to optimize and utilize this technology for the study of olfactory and social learning. The technology will nonetheless have a broad impact in other areas of Drosophila neuroscience, ethology and physiology. The system can be, for example, used also for wireless stimulation of flies in various types of learning experiments, for tracking fly behavior in complex environments, or for studies of behavioral roles of diverse genes and environmental factors. The suggested technology will have broad implications in both primary research and biomedicine.

INFOTICK - Getting the right info on ticks (INFOTICK)
Duration:	1. 7. 2023 - 30. 6. 2027
Evidence number:	APVV-22-0372
Program:	APVV
Project leader:	Ing. Gatial Emil, PhD.
SAS cosolvers:	Ing. Balogh Zoltán, PhD., MVDr. Derdáková Markéta, PhD., MVDr. Didyk Yuliya, PhD., Ing. Gatial Emil, PhD., Mgr. Chvostáč Michal, PhD., RNDr. Kazimírová Mária, CSc., Mgr. Mangová Barbara , PhD., Mgr. Peresh Yevheniy Yuliy, Mgr. Rusňáková Tarageľová Veronika, PhD., Mgr. Selyemová Diana, PhD., Mgr. Špitalská Eva, PhD., Mgr. Šujanová Alžbeta, PhD., Mgr. Václav Radovan, PhD., Mgr. Zhovnerchuk Olha, PhD.
Annotation:	Despite the fact that the castor bean tick, Ixodes ricinus has been studied for a century, many questions regardingits ecology remains unanswered. Several aspects of its basic biology and phenology are still unexplored. Globalchanges, including climate shifts, transformation of the landscape and urbanization, contribute to the switch notonly in tick distribution, but also in bionomics and seasonal activity of ticks. The ornate dog tick, Dermacentorreticulatus adapts quickly to changing conditions and its range is expanding. There is the need for detaileddescription of areas where these ticks are found (natural as well as urban habitats), since their ranges havechanged during the last decades. The main risk factor for tick -exposed people in a given area is the density ofinfected questing ticks. In the proposed project, questing activity of ticks will be monitored using the tick -plotmethodology „tick gardens“ in field plots as well as flagging the vegetation for questing ticks. Using the tick-plotmethodology, we will also follow the tick life cycle and the seasonality of various developmental events (especiallymoulting) as well as the longevity of different life stages. Since these two species of ticks are consideredepidemiologically the most important, we will also identify the prevalence and occurrence of both pathogen infectedquesting ticks and infected ticks feeding on animals. Furthermore, with changing conditions, the invasion andoccurrence of „non-native“ species of ticks in Slovakia will be closely monitored since these emerging tick speciescan introduce new pathogens to our area. The information obtained by the research team during the project as wellas during previous studies will be transferred and used in the development of a mobile application for tickidentification and the creation of a website that will bring benefits to the general public and professionals tounderstand the risk of infection with the tick-borne pathogens.

Intelligent sensor systems and data processing
Duration:	1. 1. 2023 - 31. 12. 2026
Evidence number:	VEGA 2/0135/23
Program:	VEGA
Project leader:	Ing. Malík Peter, PhD.
SAS cosolvers:	Mgr. Baida Maryna, Ing. Baláž Marcel, PhD., Ing. Bečková Jana, Ing. Budinská Ivana, PhD., Ing Hassankhani Dolatabadi Sepideh, Ing. Havlík Štefan, DrSc., Ing. Hricko Jaroslav, PhD., Ing. Kachman Ondrej, PhD., Ing. Kasanický Tomáš, Ing. Kašková Nikola, Ing. Kenyeres Martin, PhD., Ing. Klarák Jaromír, PhD., Ing. Krištofík Štefan, PhD., Ing. Lovíšková Jana, PhD., Mgr. Mojžiš Ján, PhD., Ing. Zelenka Ján, PhD.
Annotation:	The central theme of Industry 4.0 and 5.0 is the digitization, intelligence and decentralization of management, so a key research is the new generation of smart sensors, able to cooperate and adapt to environment changes. This will be achieved by researching new methods of aggregating hyperspectral and multimodal data, as well as algorithms using artificial intelligence. The project is focused on intelligent algorithms for non-contact surface sensing in high-noise environments, which are able to learn the nature and noise distribution from data. This results in higher accuracy and greater noise robustness. The emphasis is on the classification and anomaly detection, which will bring more accurate and robust algorithms for use with the high noise content and long-tailed distribution that dominates in the common industrial environment. Research into aggregation algorithms for heterogeneous and multisensor data will bring new compensation mechanisms to suppress the effects of negative factors on sensor systems.

EnviroSens - Environmental sensors based on 2D nanomaterials
Duration:	1. 8. 2024 - 31. 7. 2026
Evidence number:	SK-BG-23-0017
Program:	APVV
Project leader:	RNDr. Kostič Ivan
SAS cosolvers:	Ing. Mgr. Andok Robert, PhD., Ing. Barák Vladislav, Ing. Benčurová Anna, Ing. Hrkút Pavol, CSc., Ing. Klarák Jaromír, PhD., Ing. Konečníková Anna, Ing. Predanocy Martin, PhD., Ing. Ritomský Mário, Ing. Škriniarová Jaroslava, CSc.
Annotation:	The objective of this project proposal is the research of new semiconducting 2D materials for application in environmental sensors. 2-dimensional (2D) materials have been at the forefront of materials research in recent years due to their unic electrical and optical properties and interesting mechanical properties deriving from their atomically thin dimensions. One of promising applications of 2D materials are chemical, environmental, and biological sensor devices based on such 2D materials. In this project, we will focus on the development of environmental sensors based on 2D materials with concentration on gas sensors.
Project website:	https://www.ui.sav.sk/w/odd/senzor/projekty/

DICRIS - Digital Technologies for Critical Infrastructures (DICRIS)
Duration:	1. 8. 2024 - 31. 3. 2026
Evidence number:	09I05-03-V02-00055
Program:	Plán obnovy EÚ
Project leader:	doc. Ing. Hluchý Ladislav, CSc.
Annotation:	To advance foundational scientific knowledge and design methodologies necessary foraccelerating the digital transformation of the Slovak power industry. This comprehensive research anddevelopment effort aims primarily at creating and integrating digital twin technology for criticalinfrastructure components of the Slovak electric transmission system. Additionally, it includes thedevelopment of predictive analytical models and a context-aware decision support empowered with humanvoice processing capabilities in Slovak. The ultimate goal is to enhance the safety, security, and reliability ofthe electric power grid while driving broader digital transformation across various economic sectors.

SILVANUS-SK - (SILVANUS-SK)
Duration:	1. 1. 2025 - 31. 3. 2026
Evidence number:	09I01-03-V04 -00107
Program:	Plán obnovy EÚ
Project leader:	Ing. Balogh Zoltán, PhD.

Progressive methods of the transfer of nanostructured semiconductive 2D materials based on transition metal dichalcogenides onto microelectronic elements
Duration:	1. 1. 2022 - 31. 12. 2025
Evidence number:	2/0099/22
Program:	VEGA
Project leader:	Ing. Mgr. Andok Robert, PhD.
SAS cosolvers:	Ing. Barák Vladislav, RNDr. Bardošová Mária, CSc., Ing. Benčurová Anna, Ing. Čaplovič Igor, prof. Ing. Hotový Ivan, DrSc., Ing. Hrkút Pavol, CSc., Ing. Konečníková Anna, RNDr. Kostič Ivan, Ing. Nemec Pavol, Ing. Predanocy Martin, PhD., Ing. Ritomský Adrian, Ing. Ritomský Mário, Ing. Škriniarová Jaroslava, CSc.
Annotation:	The aim of this project is to carry out basic research in the field of new progressive nanostruct. semiconductive materials based on dichalcogenides of transition metals with the focus on nanostructured disulfides. The properties of selected nanostructured disulfides will be examined in terms of their use in microlelectronics and expected advantages of nanostructured disulfides in comparison with bulk semiconductor materials will be shown.We will design model microelectronic devices based on specific nanostructured disulfides such as WS2, MoS2, MSe, and develop technological methods for their preparation. We will master mechanical exfoliation of nanostructured disulfide layers and transfer of these nanostructured layers to the microelectronic device on the substrate. We will also focus on the analysis of these layers and their structural properties by physical methods (SEM, AFM, EDX, Raman spectroscopy...) and on the characterization of electrical and transport properties of the model microel. structure.

Semantic distributed computing continuum for extreme data processing
Duration:	1. 1. 2023 - 31. 12. 2025
Evidence number:	2/0131/23
Program:	VEGA
Project leader:	doc. Ing. Hluchý Ladislav, CSc.
SAS cosolvers:	Ing. Astaloš Ján, Ing. Balogh Zoltán, PhD., Mgr. Bobák Martin, PhD., Ing. Dlugolinský Štefan, PhD., Ing. Dobrucký Miroslav, Ing. Dora Jean Rosemond, PhD., doc. Ing. Forgáč Radoslav, PhD., Ing. Gatial Emil, PhD., Ing. Habala Ondrej, Ing. Hucko Michal, Ing. Javurek Martin, PhD., Ing. Krammer Peter, Ing. Kvassay Marcel, PhD., doc. Ing. NGUYEN Giang, PhD., doc. Ing. Očkay Miloš, PhD., Mgr. Pajorová Eva, Ing. Skovajsová Lenka, PhD., Ing. Staňo Michal, Mgr. Šeleng Martin, PhD., Prof.Ing. Štich Ivan, DrSc., Ing. Tran Viet, PhD.

SCDCANS - Stopping criteria to bound distributed consensus algorithms with asymptotic convergence for network size estimation
Duration:	1. 4. 2024 - 31. 12. 2025
Evidence number:	SK-SRB-23-0038
Program:	APVV
Project leader:	Ing. Kenyeres Martin, PhD.
SAS cosolvers:	Ing. Budinská Ivana, PhD., Ing Hassankhani Dolatabadi Sepideh
Other cosolvers:	Hrabovská Nikola Ing. (interná doktorandka, aktuálne na MD)
Annotation:	Knowing the network size (or at least its precise estimate) beforehand is crucial for many modern distributedalgorithms executed in multi-agent systems. As seen in the literature, widespread consensus-based algorithms fordistributed averaging can be easily applicable for this purpose. However, they have not been too frequently used toestimate the network size since their definition. The research of the researchers involved in this project is plannedto be focused on how to efficiently stop the execution of these algorithms for network size estimation in adistributed way. The efficient operation of algorithms is one of the most crucial design requirements placed ontopical multi-agent systems, as identified in many related manuscripts. The primary goals of this project are toanalyze the mentioned algorithms for network size estimation from numerous aspects (e.g., estimation precision,convergence rate, robustness, etc.), examine and optimize the performance of the existing stopping criteria, and topropose novel stopping criteria that optimize consensus-based data aggregation in multi-agent systems. Multiagent systems in this project will be modeled as graphs with random topologies, ensuring a credible representationof real-world systems. An analysis of the used methods\' reliability using probabilistic tools such as large deviationsis also planned to be done.

Study of critical airflow velocity in tunnel using Fire Dynamics Simulator
Duration:	17. 10. 2024 - 16. 10. 2025
Evidence number:	p851-24-3
Program:	Iné projekty
Project leader:	Ing. Valášek Lukáš, PhD.
SAS cosolvers:	Mgr. Baida Olena, RNDr. Glasa Ján, CSc., Mgr. Kubišová Tatiana, Mgr. Weisenpacher Peter, PhD.

ALOIS - Diagnosis of Alzheimer\'s disease from speech using artificial intelligence and social robotics
Duration:	1. 7. 2022 - 30. 6. 2025
Evidence number:	APVV-21-0373
Program:	APVV
Project leader:	Ing. Rusko Milan, PhD.

RFMEMS - Microelectromechanical sensors with radio frequency data transmission
Duration:	1. 7. 2021 - 30. 6. 2025
Evidence number:	APVV-20-0042
Program:	APVV
Project leader:	Ing. Havlík Štefan, DrSc.
SAS cosolvers:	Ing. Mgr. Andok Robert, PhD., Ing. Havlík Štefan, DrSc., Ing. Hricko Jaroslav, PhD., Ing. Klarák Jaromír, PhD., Ing. Predanocy Martin, PhD., Ing. Ritomský Mário
Other cosolvers:	Slovenská legálna metrológia n.o.
Annotation:	The project elaborates the method proposed by the authors, especially of mechanical quantities with wirelesssignal / energy transmission via electromagnetic field, solution of sensors as well as (micro) electro-mechanisms(MEMS). The task is a logical continuation of the results achieved within the successful solution of the previousproject APVV 14-0076, where the principle of scanning and conception of the sensor solution according to theoriginal design (utility model 8653, published patent applications PP 121-2018) was designed and verified. Thepresented project represents further theoretical and methodological-experimental processing in order to meetspecific requirements for the solution of specific sensors including compliant - deformation members and electronicevaluation circuits as well as other electro-mechanisms with respect to selected applications. Part of the solution isto create tools for modeling, simulation and optimization of properties using available MEMS technologies. Theproject aims to follow the latest global trend in MEMS solutions.
Project website:	-

2DQMC - -
Duration:	1. 7. 2022 - 30. 6. 2025
Evidence number:	APVV-21-0272
Program:	APVV
Project leader:	prof. Ing. Štich Ivan, DrSc.

AIPOLOGY - Artificial Intelligence for Personalised Oncology: from Single-Sample Assessment to Real-time Monitoring of Solid Tumours (AIPOLOGY)
Duration:	1. 7. 2022 - 30. 6. 2025
Evidence number:	APVV-21-0448
Program:	APVV
Project leader:	doc. Ing. Hluchý Ladislav, CSc.
SAS cosolvers:	Mgr. Bobák Martin, PhD., Ing. Dlugolinský Štefan, PhD., Mgr. Šeleng Martin, PhD.
Annotation:	The methodologies that oncologists use to decide on a patient\'s treatment are ever changing. It seems to us that 21st century cancer medicine is much about analysing big data and using mathematical modelling to extract information that can help predict how tumours will evolve and react to potential therapies. The sad fact is, however, that despite ever increasing knowledge on cancer we still lack the proper tools to translate this knowledge to an impactful “bedside” practice that would overcome the limitation from cancer heterogeneity and allow real-time monitoring of disease progression. Here, we propose the AIpology project that aims at the development of novel artificial intelligence strategies to identify molecular traits (individual mutations, mutation signatures and genomic scars) in heterogeneous cancer genomes for which therapeutic targets exist. Based on target clonal mapping and ordering, the system will then outline possible courses of treatment and will intelligently adapt as more data from real time monitoring approaches (such as liquid biopsy) will become available. The system will help us to track each target at the finer time scale than it is possible today and predict future (i.e how the tumour will evolve after being treated with a specific drug) and past (i.e. how long the tumour existed prior to detection) cancer evolutionary trajectories from existing data. Finally, we will understand better why certain cancers become (chemo)therapy-resistant and derive clinically relevant recommendations when they do.