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International Journal of Interactive Mobile Technologies 2025年第3期19卷 170-190页

作者： Al-Nawashi, Malek M. Al-Hazaimeh, Obaida M. Tahat, Nedal M. Gharaibeh, Nasr Abu-Ain, Waleed A. Abu-Ain, Tarik Al-Huson University College Al-Balqa Applied University Irbid Jordan Faculty of Information Technology Department of Information Security and Cybersecurity Philadelphia University Amman Jordan Faculty of Science The Hashemite University Zarqa Jordan College of Science and Computer Engineering Taibah University Yanbu Saudi Arabia College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

The detection of cyberattacks has been increasingly emphasized in recent years, focusing on both infrastructure and people. Conventional security measures such as intrusion detection, firewalls, and encryption are insufficient in protecting cyber systems against growing and changing threats. In order to address this problem, scholars have explored reinforcement learning (i.e., RL) as a potential solution for intricate cybersecurity decision-making difficulties. Nevertheless, the use of RL faces several obstacles, including dynamic attack scenarios, insufficient training data, and the challenge of replicating real-world complexities. This study presents a novel framework that uses deep reinforcement learning (i.e., DRL) to simulate harmful cyberattacks and improve cybersecurity. This study presents an agent-based framework that is capable of ongoing learning and adaptation in a dynamic network security environment. The agent determines the optimal course of action by considering the current state of the network and the rewards it receives for its decisions. The CIC-IDS-2018 database, constructed using Python 3.7 programming, was used. The conducted studies yielded outstanding results, with a detection accuracy of 98.82% achieved for the CIC-IDS-2018 database in cyber-attack classification. © 2025 by the authors of this article. Published under CC-BY.

关键词： Cybersecurity

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Recursive Broadcasting Approach 33

Recursive Broadcasting Approach

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33rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2025

作者： Harutyunyan, Hovhannes A. Hovhannisyan, Narek Concordia University Department of Computer Science and Software Engineering MontrealQCH3G 1M8 Canada Institute for Informatics and Automation Problems NAS RA Yerevan0014 Armenia

ISBN: (纸本)9798331524937

Broadcasting is one of the fundamental information dissemination primitives in interconnection networks, where a message is passed from one node (called originator) to all other nodes in the network. Following the increasing interest in interconnection networks, extensive research was dedicated to broadcasting. Two main research goals of this area are finding inexpensive network structures that maintain efficient broadcasting and finding the broadcast time for well-known and widely used network topologies. In the scope of this study, we will mainly focus on determining the broadcast time and nearoptimal broadcasting schemes in networks. Determination of the broadcast time of any node in an arbitrary network is known to be NP-hard. Polynomial time solutions are known only for a few network topologies. There also exist various heuristic and approximation algorithms for different network topologies. In this study, we consider the broadcast time problem on graphs that comprise some recursive structures. We initiate a novel direction to designing broadcasting algorithms on recursively defined graphs. We provide a theoretical foundation for future broadcasting studies, as well as discuss several practical applications of the approach we introduce. © 2025 IEEE.

关键词： Interconnection networks (circuit switching)

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Leveraging LLM in Genetic Programming Hyper-heuristics for Dynamic Microservice Deployment 37th

Leveraging LLM in Genetic Programming Hyper-heuristics for...

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37th Australasian Joint Conference on Artificial Intelligence, AJCAI 2024

作者： Fang, Zhengxin Ma, Hui Chen, Gang Hartmann, Sven Wang, Chen Centre for Data Science and Artificial Intelligence and School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand Department of Informatics Clausthal University of Technology Clausthal-Zellerfeld Germany National Institute of Water and Atmospheric Research Wellington New Zealand

ISBN: (纸本)9789819603473

Microservice deployment in cloud computing is a challenging combinatorial optimization problem due to the complex dependencies among microservices and the intricate trade-offs among different QoS requirements, e.g., minimizing Energy Consumption (EC) vs. minimizing Communication Overhead (CO). Recently, some hyper-heuristics methods, particularly Genetic Programming Hyper-Heuristics (GPHH), have been proposed to automatically generate heuristics for solving dynamic microservice deployment problems. Meanwhile, Large Language Models (LLMs) are becoming popular for solving various domain-specific problems thanks to their strong ability to learn problem-related knowledge. However, hybridizing GPHH with LLM by combining their abilities in solving complex optimization problems remains unexplored. In this paper, we propose an LLM-enhanced Genetic Programming Hyper-Heuristic (LLM-GPHH) algorithm to evolve heuristics for the dynamic deployment of applications composed of microservices, to jointly optimize EC and CO. Our experiments on real-world datasets demonstrate the effectiveness of the newly proposed LLM-GPHH. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Genetic programming

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A Transfer Learning-based Framework for Enhanced Classification of Perceived Mental Stress using EEG Spectrograms

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IEEE Access 2025年 13卷 89266-89286页

作者： Khan, Sheharyar Noorani, Sadam Hussain Frnda, Jaroslav Rauf, Usman Arsalan, Aamir Saeed, Sanay Muhammad Umar University of Engineering and Technology Department of Computer Engineering Taxila47050 Pakistan HITEC University Department of Computer Science Taxila47050 Pakistan University of Žilina Faculty of Operation and Economics of Transport and Communications Department of Quantitative Methods and Economic Informatics Žilina01026 Slovakia Fatima Jinnah Women University Department of Software Engineering Rawalpindi46000 Pakistan

Stress is a significant health concern, impacting both physical and mental well-being. Prolonged exposure to stress can lead to numerous physical health issues, including cardiovascular diseases, and a weakened immune system. This study presents a novel methodology for classifying perceived mental stress using electroencephalography (EEG) signals. By utilizing the publicly available Leipzig Study for Mind-Body-Emotion Interactions dataset, we analyze EEG data collected from 53 participants over a 7-minute resting-state duration. Our approach involves transforming EEG signals into spectrograms using the Short-Time Fourier Transform (STFT), resulting in a time-frequency representation of the input *** employ transfer learning to fine-tune three pre-trained deep neural networks i.e., ResNet50, EfficientNetB0, and DenseNet121 for classifying stress into two and three levels. Our findings demonstrate that the ResNet50 model achieves superior classification accuracies of 95.80% and 86.02% for two and three-level stress classification, respectively, outperforming existing state-of-the-art methods. This study is the first to utilize STFT-generated spectrograms and transfer learning for perceived stress classification, highlighting the efficacy of deep learning techniques in quantifying perceived mental stress through non-invasive EEG recordings. Our results indicate that the proposed method can significantly enhance the accuracy of stress classification frameworks, offering potential improvements in mental health assessment and intervention strategies. © 2013 IEEE.

关键词： Noninvasive medical procedures

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A Comprehensive Survey on Federated Learning Applications in Computational Mental Healthcare

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computer Modeling in engineering & Sciences 2025年第1期142卷 49-90页

作者： Vajratiya Vajrobol Geetika Jain Saxena Amit Pundir Sanjeev Singh Akshat Gaurav Savi Bansal Razaz Waheeb Attar Mosiur Rahman Brij B.Gupta Institute of Informatics and Communication University of DelhiDelhi110021India Maharaja Agrasen College University of DelhiDelhi110096India Computer Engineering Ronin InstituteMontclairNJ 07043USA Department of Research and Innovation Insights2TechinfoJaipur302001India University Centre for Research and Development(UCRD) Chandigarh UniversityChandigarh140413India Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia CCRI&Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India

Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing *** aims to alleviate the workload on healthcare professionals and aid *** applications have been developed to support the challenges in intelligent healthcare ***,because mental health data is sensitive,privacy concerns have *** learning has gotten some *** research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare *** explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health *** research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security *** survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.

关键词： Depression emotional recognition intelligent healthcare systems mental health federated learning stress detection sleep behaviour

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Clustering Single-Cell RNA-seq Data: Impact of Data Binarization on Algorithmic Performance

Clustering Single-Cell RNA-seq Data: Impact of Data Binariza...

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18th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Widzisz, Karolina Kania, Mateusz Zyla, Joanna Polański, Andrzej Department of Computer Graphics Vision and Digital Systems Silesian University of Technology Akademicka 16 Gliwice Poland Department of Applied Informatics Silesian University of Technology Akademicka 16 Gliwice Poland Department of Data Science and Engineering Silesian University of Technology Akademicka 16 Gliwice Poland

The primary objective of this study was to test the hypothesis that the binary information on the presence or absence of gene expression can sufficiently capture the inherent heterogeneity within single-cell RNA se quencing (scRNA-seq) data. This hypothesis posits that even without detailed expression levels, valuable insights about cellular diversity can be obtained. Utilizing this method can be particularly advantageous when analyzing large datasets, a common scenario in the field of scRNA-seq. In this paper, we evaluate clustering performance and cluster separability of a variety of model-based algorithms and distance-based methods to an alyze both expression level data and threshold-encoded binarized data. We examined the performance of the Bernoulli-mixture model and Gaussian-mixture model. These were compared against traditional clustering techniques such as hierarchical clustering, K-means, and the Louvain algorithm on a range of scRNA-seq datasets. Our findings reveal that mixture models exhibit a lower dependence on the specific dataset compared to distance-based methods. Mixture models, particularly, demonstrate greater efficacy in accurately estimat ing the number of clusters present within the data. Among analyzed algorithms, the Bernoulli-mixture model stands out, outperforming distance-based approaches in several key aspects. Binary data, presence/absence of gene expression, seem to be indeed adequate to capture the heterogeneity of scRNA-seq data when clus tering with methods specifically designed for binary datasets. The implications of this finding are significant, as it opens up new possibilities for simplifying data analysis in scRNA-seq studies without compromising the accuracy of the results. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Data accuracy

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Evaluating Beam Sweeping for AoA Estimation with an RIS Prototype: Indoor/Outdoor Field Trials

arXiv

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arXiv 2025年

作者： Vordonis, Dimitris Kompostiotis, Dimitris Paliouras, Vassilis Alexandropoulos, George C. Grec, Florin Electrical and Computer Engineering Department University of Patras Greece Department of Informatics and Telecommunications National and Kapodistrian University of Athens Greece Noordwijk Netherlands

Reconfigurable Intelligent Surfaces (RISs) have emerged as a promising technology to enhance wireless communication systems by enabling dynamic control over the propagation environment. However, practical experiments are crucial towards the validation of the theoretical potential of RISs while establishing their real-world applicability, especially since most studies rely on simplified models and lack comprehensive field trials. In this paper, we present an efficient method for configuring a 1-bit RIS prototype at sub-6 GHz, resulting in a codebook oriented for beam sweeping;an essential protocol for initial access and Angle of Arrival (AoA) estimation. The measured radiation patterns of the RIS validate the theoretical model, demonstrating consistency between the experimental results and the predicted beamforming behavior. Furthermore, we experimentally prove that RIS can alter channel properties and by harnessing the diversity it provides, we evaluate beam sweeping as an AoA estimation technique. Finally, we investigate the frequency selectivity of the RIS and propose an approach to address indoor challenges by leveraging the geometry of environment. © 2025, CC BY-NC-ND.

关键词： Frequency estimation

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Innovative methods of signal processing for hydrogen-filled proportional counters

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IEEE Sensors Journal 2025年第11期25卷 19159-19167页

作者： Horna, Aleš Kolář, Ondřej Kubíček, Michal Czakoj, Tomáš Košťál, Michal Mazánková, Věra Petr, Jiří Cvachovec, František Ančář, Aleš Král, Jan Přenosil, Václav Matěj, Zdeněk Masaryk University Faculty of Informatics Department of Computer Systems and Communications Brno602 00 Czech Republic Brno University of Technology Faculty of Electrical Engineering and Communication Department of Radioelectronics Brno616 00 Czech Republic Research Centre Řež Husinec-Řež250 68 Czech Republic University of Defence Faculty of Military Technology Department of Mathematics and Physics Brno602 00 Czech Republic VF a.s. Černá Hora 679 21 Czech Republic

Proportional counters filled with hydrogen are suitable for fast neutron detection and spectrometry. As their sensitivity is not limited only to this type of ionizing radiation as desired, an approach suitable for event discrimination was investigated. Several counters of the given construction were subjected to various radiation fields and the effort was directed to the examination of pulse-shape discrimination method intended to distinguish neutrons from gamma rays and parasitic events (microdischarges) if these also occurred. Rising edge duration of the output pulses and their amplitudes were mapped into diagrams enabling visual discrimination while the criteria for further computational processes are to be determined. Pulse waveforms from charge-sensitive preamplifier with the proportional counters connected were recorded using an FPGA-based custom-build acquisition unit and then post-processed. Moreover, the possibility of operation verification and energy calibration based on the introduction of helium-3 admixture into the gas filling was studied, as well as the energy resolution abilities. Also, the theoretical basis for particle discrimination is presented in the form of description of events and principles involved. © 2001-2012 IEEE.

关键词： Gamma rays

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Curious Administrator’s Misbehaviors in Message Layer Security (MLS)

Curious Administrator’s Misbehaviors in Message Layer Secur...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Hyoung-Kee Choi Songhui Kwon College of Computing and Informatics Sungkyunkwan University Suwon Korea Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

A key factor of the MLS group protocol is that it provides asynchronously efficient group key establishment with forward secrecy and post-compromise security for many groups in a standardized way. End-to-end encryption (E2EE) is essential to fulfilling the correctness of security in a messenger application. The correctness heavily depends on the trustworthiness of a middleman server in the center where a curious administrator may break E2EE. If this happened the administrator as a nonmember could read messages and impersonate a member by intervening with group-key management. This paper presents an in-depth explanation of the MLS protocol and demonstrates a proof-of-concept of curious administrator’s misbehaviors by analyzing the protocol. Our research outputs suggest that unconditionally trustworthy relationships on the central server in designing network protocols for security needs to prepare for any incidents caused by misbehaving administrator of the central server.

关键词： Protocols Oral communication Message services Encryption Security Servers

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Brain Magnetic Resonance Imaging Segmentation System in MS with Deep Neural Networks 18th

Brain Magnetic Resonance Imaging Segmentation System in MS w...

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18th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2024

作者： Adamides, Giorgos Panayides, Andreas Loizou, Christos P. Nicolaou, Andria Pantzaris, Marios Pattichis, Constantinos Department of Computer Science University of Cyprus Nicosia Cyprus 3AE HEALTH LTD Nicosia Cyprus CYENS CoE Nicosia Cyprus Department of Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus School of Molecular Medicine The Cyprus Institute of Neurology and Genetics University of Cyprus Nicosia Cyprus

ISBN: (纸本)9783031855719

Multiple Sclerosis (MS) is a chronic autoimmune disease that affects the central nervous system. Nerves are covered by a layer called myelin, which is responsible for protecting and maintaining their functionality. In the case of MS, the myelin becomes damaged, resulting in the nerves functioning unpredictably. MS is a disease characterized by relapses, which can cause permanent and irreversible damage to the patient's mobility, vision, and sensations, unless appropriate medical treatment is administered in time. This highlights the importance of the early detection of the presence or differentiation of MS using computer assisted diagnosis (CAD) systems. This study proposes an automatic segmentation approach of MS damaged regions using deep neural networks that can be integrated with any 3rd party CAD system. The data utilized comprised of a total of 1838 T2-type Magnetic Resonance Imaging (MRI) images collected from a cohort of 38 patients who underwent imaging at two distinct time points. Each MRI image was accompanied by meta-data, detailing the specific locations of the observed damage attributed to MS. The experimental setup investigated and optimized a comprehensive combination of different data preprocessing and augmentation steps, using several variations of the U-Net convolutional neural network (CNN), like U-Net++, Attention U-Net, ResUNet-a and TransUNet. The proposed methods achieved a segmentation accuracy of 0.70 based on the Dice Similarity Coefficient (DSC), a performance comparable to 2D and 3D state-of-the-art approaches in literature. A software framework encapsulating the automated model was also developed to facilitate the clinical practice workflow and underpin adoption by medical practitioners. © ICST Institute for computer Sciences, Social informatics and Telecommunications engineering 2025.

关键词： Image segmentation

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