Computer Science
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ISSN 1508-2806
e-ISSN: 2300-7036
Issue Date
2025
Volume
Vol. 26
Number
No. SI
Description
Journal Volume
Computer Science
Vol. 26 (2025)
Projects
Pages
Articles
Preface: 2nd International workshop on machine learning and quantum computing applications in medicine and physics
(Wydawnictwa AGH, 2025) Krzemień, Wojciech; Klimaszewski, Konrad; Raczyńnski, Lech
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.
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.
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.
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.