Intent Classification for Bank Chatbots through LLM Fine-Tuning12 Sep-Tento článok hodnotí použitie veľkých jazykových modelov na klasifikáciu intentov v chatbote s preddefinovanými odpoveďami, určenom pre webové stránky bankového sektora. Zameriavame sa na efektivitu modelu SlovakBERT a porovnávame ho s použitím multilingválnych generatívnych modelov, ako sú Llama 8b instruct a Gemma 7b instruct, v ich predtrénovaných aj fine-tunovaných verziách. Výsledky naznačujú, že SlovakBERT dosahuje lepšie výsledky než ostatné modely, a to v presnosti klasifikácie ako aj v miere falošne pozitívnych predikcií.
Leveraging LLMs for Efficient Religious Text Analysis5 Aug-The analysis and research of texts with religious themes have historically been the domain of philosophers, theologians, and other social sciences specialists. With the advent of artificial intelligence, such as the large language models (LLMs), this task takes on new dimensions. These technologies can be leveraged to reveal various insights and nuances contained in religious texts — interpreting their symbolism and uncovering their meanings. This acceleration of the analytical process allows researchers to focus on specific aspects of texts relevant to their studies.
Mapping Tree Positions and Heights Using PointCloud Data Obtained Using LiDAR Technology25 Jul-Cieľom spolupráce medzi Národným superpočítačovým centrom (NSCC) a firmou SKYMOVE, v rámci projektu Národného kompetenčného centra pre HPC, bol návrh a implementácia pilotného softvérového riešenia pre spracovanie dát získaných technológiou LiDAR (Light Detection and Ranging) umiestnených na dronoch.
Semi-Supervised Learning in Aerial Imagery: Implementing Uni-Match with Frame Field learning for Building Extraction21 Jun-Extrakcia budov v Geografických informačných systémoch (GIS) je kľúčová pre urbanistické plánovanie, environmentálne štúdie a riadenie infraštruktúry, pretože umožňuje presné mapovanie stavieb, vrátane odhaľovania nelegálnych stavieb za účelom dodržiavania právnych predpisov, alebo efektívnejšieho vyberania daní. Integrácia extrahovaných údajov o budovách s inými geopriestorovými vrstvami zlepšuje pochopenie dynamiky miest a priestorových vzťahov.
Named Entity Recognition for Address Extraction in Speech-to-Text Transcriptions Using Synthetic Data22 Dec-Many businesses spend large amounts of resources for communicating with clients. Usually, the goal is
to provide clients with information, but sometimes there is also a need to request specific information
from them.
In addressing this need, there has been a significant effort put into the development of chatbots
and voicebots, which on one hand serve the purpose of providing information to clients, but they can
also be utilized to contact a client with a request to provide some information.
A specific real-world example is to contact a client, via text or via phone, to update their postal address. The address may have possibly changed over time, so a business needs to update this information
in its internal client database.
Anomaly Detection in Time Series Data:
Gambling prevention using Deep Learning28 Jun-Gambling prevention of online casino players is a challenging ambition with positive impacts both on player’s well-being, and for casino providers aiming for responsible gambling. To facilitate this, we propose an unsupervised deep learning method with an objective to identify players showing signs of problem gambling based on available data in a form of time series. We compare the transformer-based autoencoder architecture for anomaly detection proposed by us with recurrent neural network and convolutional neural network autoencoder architectures and highlight its advantages. Due to the fact that the players’ clinical diagnosis was not part of the data at hand, we evaluated the outcome of our study by analyzing correlation of anomaly scores obtained from the autoencoder and several proxy indicators associated with the problem gambling reported in the literature.
Measurement of microcapsule structural parameters using artificial intelligence (AI) and machine learning (ML)30 Jun-The main aim of collaboration between the National Competence Centre for HPC (NCC HPC) and the Institute of Polymers of SAV (IP SAV) was design and implementation of a pilot software solution for automatic processing of polymer microcapsules images using artificial intelligence (AI) and machine learning (ML) approach. The microcapsules consist of semi-permeable polymeric membrane which was developed at the IP SAV.
Use case: Transfer and optimization of CFD calculations workflow in HPC environment15 May-Shark Aero company designs and manufactures ultralight sport aircrafts with two-seat tandem cockpit. For design development they use popular open-source software package openFOAM [1]. The CFD (Computational Fluid Dynamics) simulations use the Finite Elements Method (FEM). After the model is created, using a Computer-Aided Design (CAD) software, it is divided into discrete cells, so called “mesh”. The simulation accuracy depends strongly on mesh density with the computational and memory requirements rising with the 3rd power of the number of mesh vertices.
For some simulations the computational demands can be a limiting factor. Workflow transfer into High-Performance Computing (HPC) environment was thus undertaken, with a special focus on the investigation of computational tasks parallelization efficiency for a given model type.
MEMO9812 Apr-MEMO98 is a non-profit non-government organisation that has been monitoring the media in context of elections and other events for more than 20 years, and has carried out its activities in more than 50 countries. Recently, the organisation has also been dealing with the impact of social media on the integrity of electoral processes.
Intent Classification for Bank Chatbots through LLM Fine-Tuning 12 Sep - Tento článok hodnotí použitie veľkých jazykových modelov na klasifikáciu intentov v chatbote s preddefinovanými odpoveďami, určenom pre webové stránky bankového sektora. Zameriavame sa na efektivitu modelu SlovakBERT a porovnávame ho s použitím multilingválnych generatívnych modelov, ako sú Llama 8b instruct a Gemma 7b instruct, v ich predtrénovaných aj fine-tunovaných verziách. Výsledky naznačujú, že SlovakBERT dosahuje lepšie výsledky než ostatné modely, a to v presnosti klasifikácie ako aj v miere falošne pozitívnych predikcií.
Leveraging LLMs for Efficient Religious Text Analysis 5 Aug - The analysis and research of texts with religious themes have historically been the domain of philosophers, theologians, and other social sciences specialists. With the advent of artificial intelligence, such as the large language models (LLMs), this task takes on new dimensions. These technologies can be leveraged to reveal various insights and nuances contained in religious texts — interpreting their symbolism and uncovering their meanings. This acceleration of the analytical process allows researchers to focus on specific aspects of texts relevant to their studies.
Mapping Tree Positions and Heights Using PointCloud Data Obtained Using LiDAR Technology 25 Jul - Cieľom spolupráce medzi Národným superpočítačovým centrom (NSCC) a firmou SKYMOVE, v rámci projektu Národného kompetenčného centra pre HPC, bol návrh a implementácia pilotného softvérového riešenia pre spracovanie dát získaných technológiou LiDAR (Light Detection and Ranging) umiestnených na dronoch.
Semi-Supervised Learning in Aerial Imagery: Implementing Uni-Match with Frame Field learning for Building Extraction 21 Jun - Extrakcia budov v Geografických informačných systémoch (GIS) je kľúčová pre urbanistické plánovanie, environmentálne štúdie a riadenie infraštruktúry, pretože umožňuje presné mapovanie stavieb, vrátane odhaľovania nelegálnych stavieb za účelom dodržiavania právnych predpisov, alebo efektívnejšieho vyberania daní. Integrácia extrahovaných údajov o budovách s inými geopriestorovými vrstvami zlepšuje pochopenie dynamiky miest a priestorových vzťahov.
Named Entity Recognition for Address Extraction in Speech-to-Text Transcriptions Using Synthetic Data 22 Dec - Many businesses spend large amounts of resources for communicating with clients. Usually, the goal is
to provide clients with information, but sometimes there is also a need to request specific information
from them.
In addressing this need, there has been a significant effort put into the development of chatbots
and voicebots, which on one hand serve the purpose of providing information to clients, but they can
also be utilized to contact a client with a request to provide some information.
A specific real-world example is to contact a client, via text or via phone, to update their postal address. The address may have possibly changed over time, so a business needs to update this information
in its internal client database. 
Anomaly Detection in Time Series Data:
Gambling prevention using Deep Learning 28 Jun - Gambling prevention of online casino players is a challenging ambition with positive impacts both on player’s well-being, and for casino providers aiming for responsible gambling. To facilitate this, we propose an unsupervised deep learning method with an objective to identify players showing signs of problem gambling based on available data in a form of time series. We compare the transformer-based autoencoder architecture for anomaly detection proposed by us with recurrent neural network and convolutional neural network autoencoder architectures and highlight its advantages. Due to the fact that the players’ clinical diagnosis was not part of the data at hand, we evaluated the outcome of our study by analyzing correlation of anomaly scores obtained from the autoencoder and several proxy indicators associated with the problem gambling reported in the literature.
Measurement of microcapsule structural parameters using artificial intelligence (AI) and machine learning (ML) 30 Jun - The main aim of collaboration between the National Competence Centre for HPC (NCC HPC) and the Institute of Polymers of SAV (IP SAV) was design and implementation of a pilot software solution for automatic processing of polymer microcapsules images using artificial intelligence (AI) and machine learning (ML) approach. The microcapsules consist of semi-permeable polymeric membrane which was developed at the IP SAV.
Use case: Transfer and optimization of CFD calculations workflow in HPC environment 15 May - Shark Aero company designs and manufactures ultralight sport aircrafts with two-seat tandem cockpit. For design development they use popular open-source software package openFOAM [1]. The CFD (Computational Fluid Dynamics) simulations use the Finite Elements Method (FEM). After the model is created, using a Computer-Aided Design (CAD) software, it is divided into discrete cells, so called “mesh”. The simulation accuracy depends strongly on mesh density with the computational and memory requirements rising with the 3rd power of the number of mesh vertices.
For some simulations the computational demands can be a limiting factor. Workflow transfer into High-Performance Computing (HPC) environment was thus undertaken, with a special focus on the investigation of computational tasks parallelization efficiency for a given model type.
MEMO98 12 Apr - MEMO98 is a non-profit non-government organisation that has been monitoring the media in context of elections and other events for more than 20 years, and has carried out its activities in more than 50 countries. Recently, the organisation has also been dealing with the impact of social media on the integrity of electoral processes.