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27th International Conference on Pattern Recognition, ICPR 2024

作者： Pani, Alessandro Loddo, Andrea Putzu, Lorenzo Zedda, Luca Di Ruberto, Cecilia Department of Mathematics and Computer Science University of Cagliari Cagliari Italy Department of Electrical and Electronic Engineering University of Cagliari Cagliari Italy

ISBN: (纸本)9783031876592

Multiple sclerosis lesion segmentation is a critical task in medical imaging since it aims to identify and delineate brain lesions. In this study, we propose a new method called Models Integration for Reliable Identification and Accurate Multiple Sclerosis Segmentation (MIRIAMS), which is a robust ensemble approach that integrates three distinct neural network architectures to enhance segmentation performance in 3D volumes, setting it apart from the majority of existing methods which operate in 2D. MIRIAMS consists of three different 3D networks: a traditional 3D U-Net, a 3D UNETR that leverages Vision Transformers, and a 3D Swin-UNETR, which employs Swin Vision Transformers. This combination allows the model to capture features with varying receptive fields, improving the overall lesion segmentation. Unlike previous studies, we conduct a per-patient evaluation, ensuring a more individualised and clinically relevant assessment of the segmentation performance. Our results demonstrate that the ensemble approach outperforms individual models, providing a promising, reliable and effective tool for multiple sclerosis lesion segmentation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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VESPACE: A verifiable blockchain-based data space solution to empower the data economy

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computer Communications 2025年 239卷

作者： Costagliola, Andrea Roberta Mazzocca, Carlo Bujari, Armir Montanari, Rebecca Bellavista, Paolo Department of Computer Science and Engineering University of Bologna Bologna Italy Department of Information and Electrical Engineering and Applied Mathematics University of Salerno Fisciano Italy

In the rapidly evolving data economy, the ability to securely and efficiently share data between organizations has become paramount, unlocking new opportunities for innovation and growth. In this context, different initiatives have worked on conceptual proposals and enabling technological building blocks, addressing design aspects of data spaces. However, the current landscape lacks practical implementations and integration of secure data-sharing primitives supporting a decentralized data ecosystem. To this end, we conduct an analysis of previous efforts and initiatives, identifying gaps. We then introduce VESPACE, a blockchain-based platform for data spaces that enables participants to selectively and securely share verifiable data with authorized users while maintaining control over their access. Our framework incorporates data sovereignty principles implemented through Decentralized Identifiers (DIDs), Verifiable Credentials (VCs), and blockchain technology, qualifying decentralized identity and access control as key features to establish user trust in decentralized data ecosystems. We present a prototype system implementation of VESPACE, evaluating the design choices, showcasing the feasibility of our proposal. © 2025 The Authors

关键词： Authentication

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Transformer Models for Enhanced Calcifications Detection in Mammography 27th

Transformer Models for Enhanced Calcifications Detection in...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Cantone, Marco Marrocco, Claudio Tortorella, Francesco Bria, Alessandro Department of Electrical and Information Engineering University of Cassino and Southern Latium Via Gaetano Di Biasio 43 FR Cassino03043 Italy Department of Information and Electrical Engineering and Applied Mathematics University of Salerno SA Fisciano84084 Italy

ISBN: (纸本)9783031782008

In recent years, the image analysis landscape is witnessing a paradigm shift with the emergence of the vision transformer as a better alternative to Convolutional Neural Networks (CNNs). Transformers process sequences globally with self-attention capturing long-range features, while CNNs extract features locally through convolutional operations. We propose the adoption of Swin Transformer as backbone for calcification cluster detection in mammography, assessing its efficacy through a comprehensive experimental study comparing transformer-based and CNN-based models. Our experiments conducted on the large-scale mammography image database OMI-DB demonstrate a notable superiority of the Swin Transformer architecture. The best-performing Swin backbone obtained a sensitivity of 80.67% at 0.1 false positive per image, with a +3.34% improvement over the best convolutional backbone. Our findings underscore the efficacy of transformer-based architectures for detecting clusters of calcifications in mammography, offering improved diagnostic accuracy in this field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Distribution transformers

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MOCBOA:Multi-Objective Chef-Based Optimization Algorithm Using Hybrid Dominance Relations for Solving engineering Design Problems

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computer Modeling in engineering & Sciences 2025年第4期143卷 967-1008页

作者： Nour Elhouda Chalabi Abdelouahab Attia Abdulaziz S.Almazyad Ali Wagdy Mohamed Frank Werner Pradeep Jangir Mohammad Shokouhifar Computer Science Department University of M’silaIchebiliaM’sila28000Algeria Computer ScienceDepartment Mohamed El Bachir El IbrahimiUniversity of Bordj BouArreridjBordj BouArreridj34000Algeria Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Faculty of Mathematics Otto-von-Guericke UniversityMagdeburg39016Germany University Centre for Research and Development Chandigarh UniversityMohali140413India Department of Electrical and Computer Engineering Shahid Beheshti UniversityTehran14399Iran DTU AI and Data Science Hub(DAIDASH) Duy Tan UniversityDa Nang550000Vietnam

Multi-objective optimization is critical for problem-solving in engineering,economics,and *** study introduces the Multi-Objective Chef-Based Optimization Algorithm(MOCBOA),an upgraded version of the Chef-Based Optimization Algorithm(CBOA)that addresses distinct *** approach is unique in systematically examining four dominance relations—Pareto,Epsilon,Cone-epsilon,and Strengthened dominance—to evaluate their influence on sustaining solution variety and driving convergence toward the Pareto *** comparison investigation,which was conducted on fifty test problems from the CEC 2021 benchmark and applied to areas such as chemical engineering,mechanical design,and power systems,reveals that the dominance approach used has a considerable impact on the key optimization measures such as the hypervolume *** paper provides a solid foundation for determining themost effective dominance approach and significant insights for both theoretical research and practical applications in multi-objective optimization.

关键词： Multi-objective optimization chef-based optimization algorithm(CBOA) pareto dominance epsilon dominance cone-epsilon dominance strengthened dominance

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A Robust Speech Encryption Method Using Segmentation and Chaotic Keys for Enhanced Audio Security 3rd

A Robust Speech Encryption Method Using Segmentation and Ch...

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3rd International Conference on Advances in Computing Research, ACR 2025

作者： Abu-Faraj, Mua’ad Al-Hyari, Abeer Alqadi, Ziad Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Department of Electrical Engineering Al-Balqa Applied University As Salt19117 Jordan Department of Computers and Networks Engineering Al-Balqa Applied University Amman15008 Jordan

ISBN: (纸本)9783031876462

This paper presents an innovative method for safeguarding audio files. The proposed technique leverages an advanced primary key with a complex structure, offering a vast keyspace resistant to hacking. The private key includes details for segmenting the speech audio file and generating unique chaotic keys for encryption. These keys are extremely sensitive to minor changes in the primary key, flagging any slight alteration during decryption as a hacking attempt. The method is tested and implemented on various speech signals, with the results analyzed to highlight the main features and improvements. The findings demonstrate how the technique improves encryption and decryption by optimizing metrics such as MSE, PSNR, CC, and NSCR. Speed analysis verifies the method’s performance by maximizing the throughput of the speech cryptography process. The results are compared with existing approaches to show the superiority and enhancements provided. This method is straightforward to implement, as it does not require modifying the algorithm when altering the speech audio file or the private key, making it a versatile solution for various speech encryption needs. This research is highly relevant to network security, as it ensures the integrity and confidentiality of transmitted speech signals, preventing unauthorized access and improving overall communication security. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Speech enhancement

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Theoretical framework for chip-scale wavefront shaping with transverse spatial mode modulation

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Optics Express 2025年第8期33卷 18197-18213页

作者： Harrington, Daniel M. Cohen, Andrew H. Davis, John Mohanty, Aseema Department of Physics and Astronomy Tufts University MedfordMA02155 United States Department of Electrical and Computer Engineering Tufts University MedfordMA02155 United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States

Visible-wavelength applications such as quantum information systems, portable displays, and biological sensing and imaging devices require compact wavefront shaping capabilities. However, current chip-scale methods based on optical phased arrays require complex control of a large number of waveguides, leading to a large device footprint and limited field-of-view. Here, we propose an alternative framework for wavefront shaping using amplitude and phase control of transverse spatial modes within compact multimode waveguides. We show through analytical calculations and numerical simulations that changing the modal superposition of as few as 10 modes allows for dynamic control over the field both inside and outside of the waveguide. Grating-lobe-free beam steering over a 130° field-of-view is demonstrated with peak resolution below 5° and side lobe suppression levels up to −51 dB from a 5 µm aperture. Reconfigurable focusing of points in the waveguide and in the near field, coupling to free-space Hermite-Gaussian modes, and generation of non-diffracting Bessel and Airy beams are also demonstrated, showing the vast capabilities possible within near-term experimental reach. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Wavefronts

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Ultrasound-based AI for COVID-19 detection: a comprehensive review of public and private lung ultrasound datasets and studies

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Multimedia Tools and Applications 2025年 1-55页

作者： Morshed, Abrar Al Shihab, Abdulla Jahin, Md Abrar Al Nahian, Md Jaber Hossain Sarker, Md Murad Ibne Wadud, Md Sharjis Uddin, Mohammad Istiaq Imtiaz Siraji, Muntequa Anjum, Nafisa Shristy, Sumiya Rajjab Rahman, Tanvin Khatun, Mahmuda Rubel Dewan, Md Hossain, Mosaddeq Sultana, Razia Chakma, Ripel Emon, Sonet Barua Islam, Towhidul Hussain, Mohammad Arafat Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka1208 Bangladesh Department of Electrical and Telecommunication Engineering Daffodil International University Dhaka1216 Bangladesh Khulna9203 Bangladesh Okinawa904-0412 Japan Department of Information and Communication Technology Bangladesh University of Professionals Dhaka1216 Bangladesh Department of Information and Communication Technology Comilla University Cumilla3506 Bangladesh Department of Biomedical Physics and Technology University of Dhaka Dhaka1000 Bangladesh Department of Computer Science and Engineering International Islamic University Chittagong Chittagong4318 Bangladesh Department of Electrical and Electronic Engineering Islamic University of Technology Gazipur1704 Bangladesh Department of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Chittagong4349 Bangladesh Department of Computer Science and Engineering Notre Dame University Bangladesh Dhaka1000 Bangladesh Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh Department of Computer Science and Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Electronics and Communication Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Mathematics Jahangirnagar University Dhaka1342 Bangladesh Department of Electrical and Electronic Engineering Daffodil International University Dhaka1216 Bangladesh Department of Electrical and Electronic Engineering Dhaka University of Engineering and Technology Gazipur1707 Bangladesh Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Noakhali3814 Bangladesh Department of Computer Science and Engineering Northern University Bangladesh Dhaka1230 Bangladesh Boston Children’s Hospital Harvard Medical School BostonMA02

The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings for detecting COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as a low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on AI-driven studies utilizing lung ultrasound (LUS) for COVID-19 detection and analysis. We provide a detailed overview of both publicly available and private LUS datasets and categorize the AI studies according to the dataset they used. Additionally, we systematically analyzed and tabulated the studies across various dimensions, including data preprocessing methods, AI models, cross-validation techniques, and evaluation metrics. In total, we reviewed 60 articles, 41 of which utilized public datasets, while the remaining employed private data. Our findings suggest that ultrasound-based AI studies for COVID-19 detection have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： COVID-19

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Prescribing Decision Conservativeness in Two-Stage Power Markets: A Distributionally Robust End-to-End Approach 59

Prescribing Decision Conservativeness in Two-Stage Power Mar...

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59th Annual Conference on Information Sciences and Systems, CISS 2025

作者： Liang, Zhirui Li, Qi Liu, Anqi Dvorkin, Yury Johns Hopkins University Department of Applied Mathematics and Statistics BaltimoreMD United States Johns Hopkins University Department of Computer Science BaltimoreMD United States Johns Hopkins University Department of Electrical and Computer Engineering BaltimoreMD United States Johns Hopkins University Department of Civil and System Engineering BaltimoreMD United States

ISBN: (纸本)9798331513269

This paper presents an end-to-end framework for calibrating wind power forecast models to minimize operational costs in two-stage power markets, where the first stage involves a distributionally robust optimal power flow (DR-OPF) model. Unlike traditional methods that adjust forecast parameters and uncertainty quantification (UQ) separately, this framework jointly optimizes both the forecast model parameters and the decision conservativeness, which determines the size of the ambiguity set in the DR-OPF model. The framework aligns UQ with actual uncertainty realizations by directly optimizing downstream operational costs, a process referred to as cost-oriented calibration. The calibration is achieved using a gradient descent approach. To enable efficient differentiation, the DR-OPF problem is reformulated into a convex form, and the Envelope Theorem is leveraged to simplify gradient derivation in the two-stage setting. Additionally, the framework supports distributed implementation, effectively reducing computational overhead and enhancing efficiency. By proactively calibrating forecast parameters and prescribing optimal decision conservativeness, the framework significantly enhances cost efficiency and reliability in power system operations. Numerical experiments on an IEEE 5-bus system demonstrate the effectiveness and efficiency of the proposed approach. © 2025 IEEE.

关键词： Wind forecasting

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A Dynamical Equation Approach For Quasi-Periodic Gaussian Processes

A Dynamical Equation Approach For Quasi-Periodic Gaussian Pr...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Nigam, Unnati Srivastava, Radhendushka Burke, Michael Marzbanrad, Faezeh IITB-Monash Research Academy India Department of Mathematics IIT Bombay Mumbai India Department of Electrical and Computer Systems Engineering Monash University Clayton Australia

ISBN: (纸本)9798350368741

Quasi (pseudo/approximate) periodic signals often occur in natural settings, particularly when a periodic signal is recorded with noise. In this work, we develop a new dynamical equation system to construct a family of Quasi-periodic Gaussian Processes (QPGP). We provide a computationally inexpensive algorithm based on a newly derived likelihood function for parameter estimation. Our approach also simplifies the signal forecasting. We illustrate via a simulation study that the proposed QPGP estimation strategy is faster as well and more accurate than existing constructions. Unlike these existing models, the proposed approach extends to multiple families of kernels, which we illustrate by modeling sunspot data with both periodic Matérn and MacKay's covariance kernel. This shows the exclusive advantage of the new QPGP family proposed in this work. © 2025 IEEE.

关键词： Covariance kernels Dynamical system Forecasting Likelihood Quasi-Periodic Gaussian Process

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Analysing User Engagement in e-Learning Platforms: A Machine Learning Approach to Web Analytics 27th

Analysing User Engagement in e-Learning Platforms: A Machine...

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27th International Conference on Interactive Collaborative Learning, ICL 2024

作者： Christina, Gouvatsou Pantazatos, Dimitris Grammatikou, Mary Maglaris, Vasilis Stefaneas, Petros School of Applied Mathematics and Physical Sciences National Technical University of Athens Athens15780 Greece School of Electrical and Computer Engineering National Technical University of Athens Athens15780 Greece

ISBN: (纸本)9783031856518

The scope of this paper is to analyze the web analytics of a Next Generation Digital Learning Environment (NGDLE), which was part of a European initiative, specifically focusing on how platform engagement was affected by the COVID-19 pandemic waves and the mandatory e-schooling in Europe. Emphasis was given to the Bounce Rate (%) variable, a metric to estimate the platform’s effectiveness in retaining its users. This was achieved by applying a classification problem, comparing different machine learning models, and identifying the most contributing features. The study revealed patterns in the data, providing an overview of user engagement in the e-service while unfolding the complex relationships among the features and the need for robust analysis. Finally, it highlighted features contributing to the target variable Bounce Rate (%) that could be further explored to enhance the VLE. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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