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6th International Conference on Networking and Advanced Systems, ICNAS 2023

作者： Sakraoui, Sabrina Derdour, Makhlouf Ahmim, Ahmed Badji Mokhtar Annaba University Networks and Systems Laboratory Department of Computer Science Annaba Algeria Oum El Bouaghi University Networks and Systems Laboratory Department of Computer Science Oum El Bouaghi Algeria Mohamed-Cherif Messaadia University Networks and Systems Laboratory Dpt. of Mathematics and Computer Science Souk Ahras Algeria

ISBN: (纸本)9798350319170

As the development of wireless networks advances towards the deployment of 6G technology, ensuring robust security measures becomes crucial. In this paper, we propose 6G-SECUREIDS, a novel intrusion detection system designed specifically for 6G wireless networks. The system leverages machine learning techniques and blockchain technology to detect and mitigate security threats, safeguard data privacy, and ensure efficient and optimized training. Our experimental results demonstrate the effectiveness and reliability of the system, with the clients achieving high accuracy scores across different model architectures. The promising results highlight the potential of 6G-SECUREIDS as a valuable security solution for future 6G networks, contributing to the overall protection and integrity of the wireless communication ecosystem. © 2023 IEEE.

关键词： Wireless networks

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Leveraging protein-protein interactions in phenotype prediction through graph neural networks

Leveraging protein-protein interactions in phenotype predict...

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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： Riccardo Smeriglio Joana Rosell-Mirmi Petia Radeva Jordi Abante Dpt. of Physics Universitat de Barcelona Spain Control and Computer Engineering Dpt. Politecnico di Torino Turin Italy Headache Research Group Vall d’Hebron Institut de Recerca Barcelona Catalonia Spain Dpt. of Mathematics & Computer Science Institute of Neurosciences Universitat de Barcelona Barcelona Catalonia Spain Laboratory of Stem Cells & Regenerative Medicine Creatio Production & Validation Center of Advanced Therapies Dpt. of Biomedical Sciences Institute of Neurosciences Universitat de Barcelona Dpt. of Mathematics & Computer Science Universitat de Barcelona August Pi i Sunyer Biomedical Research Institute (IDIBAPS) Barcelona Catalonia Spain

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

Current genotype-to-phenotype models, such as polygenic risk scores, only account for linear relationships between genotype and phenotype and ignore epistatic interactions, limiting the complexity of the diseases that can be properly characterized. Protein-protein interaction networks have the potential to improve the performance of the models. Moreover, interactions at the protein level can have profound implications in understanding the genetic etiology of diseases and, in turn, for drug development. In this article, we propose a novel approach for phenotype prediction based on graph neural networks (GNNs) that naturally incorporates existing protein interaction networks into the model. As a result, our approach can naturally discover relevant epistatic interactions. We assess the potential of this approach using simulations and comparing it to linear and other non-linear approaches. We also study the performance of the proposed GNN-based methods in predicting Alzheimer’s disease, one of the most complex neurodegenerative diseases, where our GNN approach outperform state of the art methods. In addition, we show that our proposal is able to discover critical interactions in the Alzheimer’s disease. Our findings highlight the potential of GNNs in predicting phenotypes and discovering the underlying mechanisms of complex diseases.

关键词： Proteins Training Phenotypes Limiting Prevention and mitigation Predictive models Graph neural networks Proposals Alzheimer's disease Genotypes

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To Trust or Not to Trust: Towards a novel approach to measure trust for XAI systems

arXiv

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arXiv 2024年

作者： Miró-Nicolau, Miquel Moyà-Alcover, Gabriel Jaume-I-Capó, Antoni González-Hidalgo, Manuel Campello, Maria Gemma Sempere Sancho, Juan Antonio Palmer UGiVIA Research Group University of the Balearic Islands Dpt. of Mathematics and Computer Science Palma07122 Spain University of the Balearic Islands Dpt. of Mathematics and Computer Science Palma07122 Spain Hospital Universitari Son Espases Palma07010 Spain SCOPIA Research Group Department of Mathematical Sciences and Computer Science University of the Balearic Islands Palma07122 Spain Palma07010 Spain

The increasing reliance on Deep Learning models, combined with their inherent lack of transparency, has spurred the development of a novel field of study known as eXplainable AI (XAI) methods. These methods seek to enhance the trust of end-users in automated systems by providing insights into the rationale behind their decisions. This paper presents a novel approach for measuring user trust in XAI systems, allowing their refinement. Our proposed metric combines both performance metrics and trust indicators from an objective perspective. To validate this novel methodology, we conducted a case study in a realistic medical scenario: the usage of XAI system for the detection of pneumonia from x-ray images. © 2024, CC BY.

关键词： Deep learning

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FraMark: A Blockchain Marketplace for a 5G Network Management using Fractional NFTs

FraMark: A Blockchain Marketplace for a 5G Network Managemen...

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IEEE World Symposium on Communication Engineering (WSCE)

作者： George Michoulis Kyros Tsourdinis George Niotis Konstantinos Kyranou Eleutherios Fountoukidis Thomas Lagkas Vasileios Argyriou Panagiotis Sarigiannidis Konstantinos E. Psannis Sotirios K. Goudos R&D Dpt. Sidroco Holdings Ltd Thessaloniki Greece R&D Dpt. Sidroco Holdings Ltd Nicosia Cyprus Dpt. of Computer Science Democritus University of Thrace Kavala Greece Dpt. of Electrical & Computer Engineering University of Western Macedonia Kozani Greece Dpt. of Networks and Digital Media Kingston University London Kingston Upon Thames Surrey UK Department of Applied Informatics University of Macedonia Thessaloniki Greece ELEDIA@AUTH School of Physics Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (数字)9798331542825

ISBN: (纸本)9798331542832

This paper presents FraMark, a blockchain-based framework for 5G network management that utilizes Hyperledger Fabric and fractional Non-Fungible Tokens (NFTs) to optimize resource allocation. We introduce a comprehensive system that fractionalizes digital assets to represent 5G network resources, enhancing efficiency and allocation flexibility. Our framework addresses the challenges of 5G network management by ensuring low latency, high transaction throughput, and secure resource trading. We conduct experiments to evaluate the performance of our approach, including CPU and memory utilization, latency analysis, and block size assessment. The results demonstrate the effectiveness of FraMark in improving network resource utilization and transaction efficiency, validating our framework’s claims. By leveraging blockchain technology and innovative resource slicing techniques, FraMark paves the way for dynamic and efficient 5G network management.

关键词： 5G mobile communication Distributed ledger Throughput Dynamic scheduling Fabrics Resource management Nonfungible tokens Low latency communication

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An efficient heuristic for geometric analysis of cell deformations

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computers in Biology and Medicine 2025年 186卷 109709-109709页

作者： Soto, Yaima Paz Garcia, Silena Herold Gual-Arnau, Ximo Jaume-i-Capó, Antoni González-Hidalgo, Manuel Department of Informatics University of Guantánamo Guantánamo Cuba Department of Computer Science University of Oriente Santiago de Cuba Cuba Departament de Matemàtiques Institute of New Imaging Technologies Universitat Jaume I Castelló Spain UGiVIA Research Group University of the Balearic Islands Dpt. of Mathematics and Computer Science Crta. Valldemossa Km 7.5 PalmaE-07122 Spain SCOPIA Research Group University of the Balearic Islands Dpt. of Mathematics and Computer Science Crta. Valldemossa Km 7.5 PalmaE-07122 Spain PalmaE-07122 Spain PalmaE-07122 Spain PalmaE-07122 Spain

Sickle cell disease causes erythrocytes to become sickle-shaped, affecting their movement in the bloodstream and reducing oxygen delivery. It has a high global prevalence and places a significant burden on healthcare systems, especially in resource-limited regions. Automated classification of sickle cells in blood images is crucial, allowing the specialist to reduce the effort required and avoid errors when quantifying the deformed cells and assessing the severity of a crisis. Recent studies have proposed various erythrocyte representation and classification methods (Jennifer et al., 2023 [1]). Since classification depends solely on cell shape, a suitable approach models erythrocytes as closed planar curves in shape space (Epifanio et al., 2020). This approach employs elastic distances between shapes, which are invariant under rotations, translations, scaling, and reparameterizations, ensuring consistent distance measurements regardless of the curves’ position, starting point, or traversal speed. While previous methods exploiting shape space distances had achieved high accuracy, we refined the model by considering the geometric characteristics of healthy and sickled erythrocytes. Our method proposes (1) to employ a fixed parameterization based on the major axis of each cell to compute distances and (2) to align each cell with two templates using this parameterization before computing distances. Aligning shapes to templates before distance computation, a concept successfully applied in areas such as molecular dynamics (Richmond et al., 2004 [2]), and using a fixed parameterization, instead of minimizing distances across all possible parameterizations, simplifies calculations. This strategy achieves 96.03% accuracy rate in both supervised classification and unsupervised clustering. Our method ensures efficient erythrocyte classification, maintaining or improving accuracy over shape space models while significantly reducing computational costs. © 2025 Elsevier Ltd

关键词： Template matching

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Software failure time series prediction with RBF, GRNN, and LSTM neural networks 26

Software failure time series prediction with RBF, GRNN, and ...

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26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022

作者： Yakovyna, Vitaliy Shakhovska, Natalya University of Warmia and Mazury in Olsztyn Faculty of Mathematics and Computer Science ul. Oczapowskiego 2 Olsztyn10-719 Poland Lviv Polytechnic National University Artificial Intelligence Dpt. 12 S. Bandery St. Lviv79013 Ukraine

The important task of software quality assurance is failure prediction. Time series forecasting methods can be successfully used for this purpose. This paper aims to study and compare the effectiveness of software failure prediction using different types of neural networks, in particular RBF neural network and recurrent neural networks (GRNN and LSTM). The Chromium browser failure dataset was used in this study. The dataset contains information about 11, 001 bug reports for 759 consecutive days. Predictions were performed for 150-day, 500-day, and 759-day time series. Long time series of software failures cannot be approximated with satisfactory accuracy by an RBF neural network. Both GRNN and LSTM neural networks were able to predict the long software failures time series with RMSE values of 0.321 and 0.241 correspondingly. © 2022 The Authors. Published by Elsevier B.V.

关键词： Forecasting

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6G-SECUREIDS: Blockchain-Enhanced Secure Knowledge Transfer for Distributed Intrusion Detection Systems in Advanced Networks

6G-SECUREIDS: Blockchain-Enhanced Secure Knowledge Transfer ...

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International Conference on Networking and Advanced Systems (ICNAS)

作者： Sabrina Sakraoui Makhlouf Derdour Ahmed Ahmim Department of Computer Science Networks and Systems Laboratory Badji Mokhtar Annaba University Annaba Algeria Department of Computer Science Networks and Systems Laboratory Oum El Bouaghi University Oum El Bouaghi Algeria Dpt. of Mathematics and Computer Science Networks and Systems Laboratory Mohamed-Cherif Messaadia University Souk Ahras Algeria

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Functional Regression Models with Functional Response: A New Approach and a Comparative Study

arXiv

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arXiv 2022年

作者： Febrero–Bande, Manuel Oviedo–de la Fuente, Manuel Darbalaei, Mohammad Amini, Morteza Dpt. of Statistics Mathematical Analysis and Optimization Univ. of Santiago de Compostela Facultade de Matemáticas Campus Vida s/n A Coruña Santiago de Compostela15782 Spain Santiago de Compostela Spain Dpt. of Mathematics CITIC Univ. de A Coruña A Coruña Spain Dpt. of Statistics School of Mathematics Statistics and Computer Science University of Tehran Tehran Iran

This paper proposes a new nonlinear approach for additive functional regression with functional response based on kernel methods along with some slight reformulation and implementation of the linear regression and the spectral additive model. The latter methods have in common that the covariates and the response are represented in a basis and so, can only be applied when the response and the covariates belong to a Hilbert space, while the proposed method only uses the distances among data and thus can be applied to those situations where any of the covariates or the response is not Hilbert, typically normed or even metric spaces with a real vector structure. A comparison of these methods with other procedures readily available in R is perfomed in a simulaton study and in real datasets showing the results the advantages of the nonlinear proposals and the small loss of efficiency when the simulation scenario is truly linear. The comparison is done in the Hilbert case as it is the only scenario where all the procedures can be compared. Finally, the supplementary material provides a visualization tool for checking the linearity of the relationship between a single covariate and the response, another real data example and a link to a GitHub repository where the code and data is available. © 2022, CC BY-NC-ND.

关键词： Support vector regression

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Mean first exit times of Ornstein-Uhlenbeck processes in high-dimensional spaces

arXiv

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arXiv 2022年

作者： Kersting, Hans Orvieto, Antonio Proske, Frank Lucchi, Aurelien Inria Ecole Normale Supérieure PSL Research University Paris France Department of Computer Science ETH Zürich Zürich Switzerland Department of Mathematics University of Oslo Oslo Norway Dpt. of Mathematics & Computer Science University of Basel Basel Switzerland

The d-dimensional Ornstein–Uhlenbeck process (OUP) describes the trajectory of a particle in a d-dimensional, spherically symmetric, quadratic potential. The OUP is composed of a drift term weighted by a constant θ ≥ 0 and a diffusion coefficient weighted by σ > 0. In the absence of drift (i.e. θ = 0), the OUP simply becomes a standard Brownian motion (BM). This paper is concerned with estimating the mean first-exit time (MFET) of the OUP from a ball of finite radius L for large d >> 0. We prove that, asymptotically for d → ∞, the OUP takes (on average) no longer to exit than BM. In other words, the mean-reverting drift of the OUP (scaled by θ ≥ 0) has asymptotically no effect on its MFET. This finding might be surprising because, for small d ∈ N, the OUP exit time is significantly larger than BM by a margin that depends on θ. As it allows for the drift to be ignored, it might simplify the analysis of high-dimensional exit-time problems in numerous areas. Finally, our short proof for the non-asymptotic MFET of OUP, using the Andronov–Vitt–Pontryagin formula, might be of independent interest. © 2022, CC BY.

关键词： Brownian movement

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Use of QUIC Protocol for Efficient Data Transmission Over Satellite in Emergency Scenario

Use of QUIC Protocol for Efficient Data Transmission Over Sa...

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International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)

作者： Armir Bujari Mirko Franco Claudio E. Palazzi Mattia Quadrini Cesare Roseti Francesco Zampognaro Department of Computer Science and Engineering University of Bologna Bologna Italy Department of Mathematics University of Padua Padua Italy Research and Development RomARS S.r.l. Rome Italy Electronics Engineering Dpt. University of Rome “Tor Vergata” Rome Italy

During emergency situations, reliable and fast communication is crucial for an effective response and coordination of recovery and rescue operations. However, regular communication means may not be always available nor reliable. In this scenario, constellations of Low Earth Orbit satellites can offer high bandwidth capabilities with (relatively) low latency, making them ideal for supporting disaster management teams on the ground and complementing (or replacing) terrestrial telecommunication infrastructures. In this context, the use of the recently defined QUIC protocol can provide the possibility to seamlessly switch from ground communication to satellite ones when needed. In this paper, we discuss the case study of satellite communication in an emergency scenario, assessing via a simulation study the benefits achievable by QUIC under varying queuing parameters and congestion control algorithm behavior.

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