Repository logo

COMPUTER SCIENCE (CN-csci)

Permanent URI for this communityhttps://repo.agh.edu.pl/handle/AGH/102745

...

The main areas of interest of the journal are theoretical aspects of computer science, soft computing, HPC, cloud and distributed processing and simulation, multimedia systems and computer graphics, and natural language processing.

New!   Aktualny numer: 2025 - Vol. 26 - No. 4

Roczniki i numery

Browse

Filters

Advanced Search

Filter by

Settings

Search Results

Now showing 1 - 10 of 565
  • thumbnail.journal.alt
    Item type:Article, Access status: Open Access ,
    Preliminary study on artificial intelligence methods for cybersecurity threat detection in computer networks based on raw data packets
    (Wydawnictwa AGH, 2025) Ogonowski, Aleksander; Żebrowski, Michał; Ćwiek, Arkadiusz; Jarosiewicz, Tobiasz; Klimaszewski, Konrad; Padee, Adam; Wasiuk, Piotr; Wójcik, Michał
    potential of deep learning algorithms to directly extract features and patterns from raw packets. Moreover, it impedes real-time monitoring due to the necessity of waiting for the processing pipeline to complete and introduces dependencies on additional software components. In this paper, we investigate deep learning methodologies capable of detecting attacks in real-time directly from raw packet data within network traffic. Our investigation utilizes the CIC-IDS-2017 dataset, which includes both benign traffic and prevalent real-world attacks, providing a comprehensive foundation for our research.
  • thumbnail.journal.alt
    Item type:Article, Access status: Open Access ,
    Characteristic sky background features around galaxy mergers
    (Wydawnictwa AGH, 2025) Suelves, Luis E.; Pearson, William J.; Pollo, Agnieszka
    In the context of finding galaxy mergers in large-scale surveys, we applied machine-learning algorithms that made use of flux measurements instead of using images (as is the current standard). By training multiple NNs using the Sloan Digital Sky Survey class-balanced data set of mergers and non-mergers, we found that sky-background error parameters could provide a validation accuracy of 92.64 ± 0.15% and a training accuracy of 92.36 ± 0.21%. Moreover, analyzing the NN identifications led us to find that a simple decision diagram using the sky error for two flux filters was enough to gain a 91.59% accuracy. By understanding how the galaxies vary along the diagram and trying to parametrize the methodology in the deeper images of the Hyper Suprime Cam, we are currently trying to define and generalize this sky error-based methodology.
  • thumbnail.journal.alt
    Item type:Article, Access status: Open Access ,
    Reconstruction of muon bundles in KM3NeT detectors using machine learning methods
    (Wydawnictwa AGH, 2025) Kalaczyński, Piotr
    The KM3NeTCollaboration is installing the ARCA and ORCA neutrino detectors at the bottom of the Mediterranean Sea. The focus of ARCA is neutrino astronomy, while ORCA is optimised for neutrino oscillation studies. Both detectors are already operational in their intermediate states and collect valuable data, including the measurements of the muons produced by cosmic ray interactions in the atmosphere. This work explores the potential of machine learning models for the reconstruction of muon bundles, which are multi-muon events. For this, data collected with intermediate detector configurations of ARCA and ORCA was used in addition to simulated data from the envisaged final configurations of those detectors. Prediction of the total number of muons in a bundle as well as their total energy and even the energy of the primary cosmic ray is presented.
  • thumbnail.journal.alt
    Item type:Journal Issue,
    Computer Science
    2025 - Vol. 26 - No. SI
  • thumbnail.journal.alt
    Item type:Article, Access status: Open Access ,
    AQMLATOR – An auto quantum machine learning e-platform
    (Wydawnictwa AGH, 2025) Rybotycki, Tomasz; Gawron, Piotr
    The successful implementation of a machine-learning (ML) model requires three main components: a training data set, a suitable model architecture, and a suit able training procedure. Given the data set and task, finding an appropriate model might be challenging. AutoML, a branch of ML, focuses on an automatic architecture search– a meta method that aims to remove the need for human interaction with the ML system-design process. The success of ML and the development of quantum computing (QC) in recent years has led to the birth of a new fascinating field called quantum machine learning (QML), which incorporates quantum computers into ML models (among other things). In this paper, we present AQMLator, an auto quantum machine-learning platform that aims to automatically propose and train the quantum layers of an ML model with minimal input from the user. In this way, data scientists can bypass the entry barrier for QC and use QML. AQMLator uses standard ML libraries, making it easy to introduce into existing ML pipelines.
  • thumbnail.journal.alt
    Item type:Article, Access status: Open Access ,
    Network-based computational pipeline for studying variability of transcriptome profiles for human diseases
    (Wydawnictwa AGH, 2025) Cakir, Eda; Hütt, Marc-Thorsten
    Machine learning applications to high-throughput data in medicine– one of the biggest resources for understanding complex diseases– have been limited thus far. Here, we present a computational approach for assessing the intrinsic variability in the most prominent data type, transcriptomics data for diseasecohorts. Our study looks at situations where multiple data sets for the same disease are available. We leverage concepts of network medicine to assess how the match between a biological network and a set of differentially expressed genes varies across different networks and experiments. Our results showed that different biological networks yielded markedly different results; also, the clustering of diseases depended strongly on the choice of the parameters that were contained in the data analysis and network processing.
  • thumbnail.journal.alt
    Item type:Article, Access status: Open Access ,
    Developing artificial intelligence in the cloud: the AI_INFN Platform
    (Wydawnictwa AGH, 2025) Anderlini, Lucio; Barbetti, Matteo; Bianchini, Giulio; Ciangottini, Diego; Dal Pra, Stefano; Michelotto, Diego; Petrini, Rosa; Spiga, Daniele
    The INFN CSN5-funded project AI_INFN (“artificial intelligence at INFN”) aims to promote ML and AI adoption within INFN by providing comprehensive support, including state of-the-art hardware and cloud-native solutions within INFN Cloud. This facilitates efficient sharing of hardware accelerators with out hindering the institute’s diverse research activities. AI_INFN advances from a Virtual-Machine-based model to a flexible Kubernetes-based platform, offering features such as JWT-based authentication, JupyterHub multitenant interface, distributed file system, customizable conda environments, and specialized monitoring and accounting systems. It also enables virtual nodes in the cluster, offloading computing payloads to remote resources through the Virtual Kubelet technology, with InterLink as provider. This setup can manage workflows across various providers and hardware types, which is crucial for scientific use cases that require dedicated infrastructures for different parts of the workload. Results of initial tests to validate its production applicability, emerging case studies and integration scenarios are presented.
  • thumbnail.journal.alt
    Item type:Article, Access status: Open Access ,
    Improving PET scanner time-of-flight resolution using additional prompt photon
    (Wydawnictwa AGH, 2025) Raczyński, Lech; Krzemień, Wojciech; Klimaszewski, Konrad
    Positronium imaging requires two classes of events: double-coincidences originated from pair of back-to-back annihilation photons and triple-coincidences comprised with two annihilation photons and one additional prompt photon. The standard reconstruction of the emission position along the line-of-response of a triple-coincidence event is the same as in the case of double-coincidence event; an information introduced by the high-energetic prompt photon is ignored. In this study, we propose to extend the reconstruction of the position of the triple-coincidence event by taking into account the time and position of the prompt photon. We incorporate knowledge about the positronium lifetime distribution and discuss the limitations of the method based on the simulation data. We highlight that the uncertainty of the estimate provided by prompt photon alone is much higher than the standard deviation estimated based on two annihilation photons. We finally demonstrate the extent of resolution improvement that can be obtained when estimated using three photons.
  • thumbnail.journal.alt
    Item type:Article, Access status: Open Access ,
    Preface: 2nd International workshop on machine learning and quantum computing applications in medicine and physics
    (Wydawnictwa AGH, 2025) Krzemień, Wojciech; Klimaszewski, Konrad; Raczyńnski, Lech
  • thumbnail.journal.alt
    Item type:Article, Access status: Open Access ,
    FL-MEC: Federated learning for network traffic classification on the network edge
    (Wydawnictwa AGH, 2025) Paszko, Patryk; Konieczny, Marek; Zieliński, Sławomir; Kwolek, Bartosz
    Nowadays, two technological trends, Federated Learning (FL) and Edge Computing (EC), are increasingly important and influential. FL is a decentralized machine learning strategy that allows learning on distributed data. It primarily allows performing learning operations close to the user, where the data is gathered. This approach belongs to the EC domain, where the main goal is to move computation closer to the end user (e.g., from the centralized cloud). In our work, we apply the FL and EC in the context of network flow classification. We achieved an accuracy of 0.957 with the FL model, compared to 0.924 for the best local model. We achieved these results thanks to the federated averaging performed on neural network layers. To verify our approach, we executed all our experiments on a virtualized environment that emulates existing mid-scale EC network infrastructure, including limitations related to resource constraints on edge nodes.