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arXiv 2022年

作者： Zhang, Yichi Zhang, Chao Zhang, Cai Fan, Lixin Zeng, Bei Yang, Qiang Department of Physics The Hong Kong University of Science and Technology Hong Kong College of Mathematics and Informatics South China Agricultural University China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong WeBank China

This article illustrates a novel Quantum Secure Aggregation (QSA) scheme that is designed to provide highly secure and efficient aggregation of local model parameters for federated learning. The scheme is secure in protecting private model parameters from being disclosed to semi-honest attackers by utilizing quantum bits i.e. qubits to represent model parameters. The proposed security mechanism ensures that any attempts to eavesdrop private model parameters can be immediately detected and stopped. The scheme is also efficient in terms of the low computational complexity of transmitting and aggregating model parameters through entangled qubits. Benefits of the proposed QSA scheme are showcased in a horizontal federated learning setting in which both a centralized and decentralized architectures are taken into account. It was empirically demonstrated that the proposed QSA can be readily applied to aggregate different types of local models including logistic regression (LR), convolutional neural networks (CNN) as well as quantum neural network (QNN), indicating the versatility of the QSA scheme. Performances of global models are improved to various extents with respect to local models obtained by individual participants, while no private model parameters are disclosed to semi-honest adversaries. Copyright © 2022, The Authors. All rights reserved.

关键词： Qubits

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Predicting Off-Design Performance of Axial Compressors Using Multilayer Neural Network Model for Surge Control

SSRN

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SSRN 2023年

作者： Elmelegy, Mohamed Ali Elhosseini, Mostafa A. Ali, Hesham Arafat Haikal, Amira Y. Instrument maintenance support engineer DLNG Damietta LNG Egypt College of Computer Science and Engineering Taibah University Yanbu Saudi Arabia Department of Computers and Control Systems Engineering Faculty of Engineering Mansoura University Mansoura Egypt

The incidence of surge within axial compressors profoundly influences the efficacy and reliability of aero-engines. Conventional methodologies in engine design have predominantly concentrated on the precise and efficient forecasting of critical characteristics during such occurrences. This paper presents a novel approach for predicting the surge phenomenon in axial compressors. Surge is a major issue in the operation of axial compressors, causing reduced performance, increased wear and tear, and in some cases, complete machine failure. The proposed approach utilizes a neural network model based on the compressor's design data to forecast the behavior of the compressor's working map under off-design conditions. The model is verified using real data collected over five years during compressor operation, including the opening angle of the Inlet Guided Van (IGV), an important parameter often ignored or assumed to be fixed in previous studies. The results show that the neural network-based approach effectively predicts the compressor's behavior under different operating conditions, providing valuable insights into the onset of surge and offering a real-time surge prediction and control tool. The ability to predict the compressor's working map enables optimizing its performance and controlling instability. This model was validated by comparing its predictions with real operating conditions observed over five years across various compressor operating modes. As a result, this predictive model enables the safe operation of axial compressors by mitigating instability risks during operation. This study's findings highlight the model's effectiveness, as evidenced by the minimal error observed between the predicted and actual data collected across various compressor operating modes. The model exhibits a strong performance, characterized by a high regression factor of 0.92667 and a low mean squared error of 3.04465 x 10^-3. These numerical values underscore the reliability and pr

关键词： Forecasting

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Reconfigurable Intelligent Computational Surfaces for MEC-Assisted Autonomous Driving Networks

Reconfigurable Intelligent Computational Surfaces for MEC-As...

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IEEE Conference on Wireless Communications and Networking

作者： Bo Yang Xueyao Zhang Zhiwen Yu Xuelin Cao Chongwen Huang George C. Alexandropoulos Yan Zhang Mérouane Debbah Chau Yuen School of Computer Science Northwestern Polytechnical University Xi'an Shaanxi China School of Cyber Engineering Xidian University Xi'an Shaanxi China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Informatics and Telecommunications National and Kapodistrian University of Athens Greece Department of Informatics University of Oslo Oslo Norway Center for 6G Technology the Khalifa University of Science and Technology Abu Dhabi UAE School of Electrical and Electronics Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

In this paper, we focus on improving autonomous driving safety via task offloading from cellular vehicles (CVs), using vehicle-to-infrastructure (V2I) links, to an multi-access edge computing (MEC) server. Considering that the frequencies used for V2I links can be reused for vehicle-to-vehicle (V2V) communications to improve spectrum utilization, the receiver of each V2I link may suffer from severe interference, causing outages in the task offloading process. To tackle this issue, we propose the deployment of a reconfigurable intelligent computational surface (RICS) to enable, not only V2I reflective links, but also interference cancellation at the V2V links exploiting the computational capability of its metamaterials. We devise a joint optimization formulation for the task offloading ratio between the CVs and the MEC server, the spectrum sharing strategy between V2V and V2I communications, as well as the RICS reflection and refraction matrices, with the objective to maximize a safety-based autonomous driving task. Due to the non-convexity of the problem and the coupling among its free variables, we transform it into a more tractable equivalent form, which is then decomposed into three sub-problems and solved via an alternate approximation method. Our simulation results demonstrate the effectiveness of the proposed RICS optimization in improving the safety in autonomous driving networks.

关键词： Simulation Vehicle-to-infrastructure Vehicular ad hoc networks Transforms Safety Servers Task analysis

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Ranking matters: Does the new format select the best teams for the knockout phase in the UEFA Champions League?

arXiv

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arXiv 2025年

作者： Csató, László Devriesere, Karel Goossens, Dries Gyimesi, András Lambers, Roel Spieksma, Frits Laboratory on Engineering and Management Intelligence Research Group of Operations Research and Decision Systems Budapest Hungary Institute of Operations and Decision Sciences Department of Operations Research and Actuarial Sciences Hungary Ghent University Department of Business Informatics and Operations Management Belgium FlandersMake@UGent – core lab CVAMO Ghent Belgium University of Pécs Hungary Eindhoven University of Technology Department of Mathematics and Computer Science Netherlands

Starting in the 2024/25 season, the Union of European Football Associations (UEFA) has fundamentally changed the format of its club competitions: the group stage has been replaced by a league phase played by 36 teams in an incomplete round robin format. This makes ranking the teams based on their results challenging because teams play against different sets of opponents, whose strengths vary. In this research note, we apply several well-known ranking methods for incomplete round robin tournaments to the 2024/25 UEFA Champions League league phase. Our results show that it is doubtful whether the currently used point-based system provides the best ranking of the *** Codes 15A06, 91B14 Copyright © 2025, The Authors. All rights reserved.

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SmartCHANGE: AI-based long-term health risk evaluation for driving behaviour change strategies in children and youth 3

SmartCHANGE: AI-based long-term health risk evaluation for d...

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3rd International Conference on Applied Mathematics and computer science, ICAMCS 2023

作者： Rescic, Nina Alberts, Janna Altenburg, Teatske M. Chinapaw, Mai J. M. De Nigro, Antonio Fenoglio, Dario Gjoreski, Martin Gradisek, Anton Jurak, Gregor Kiourtis, Athanasios Kyriazis, Dimosthenis Langheinrich, Marc Mancuso, Elena Mavrogiorgou, Argyro Pechenizkiy, Mykola Pratola, Roberto Ribeiro, Jose Soric, Maroje Taj, Fawad Tammelin, Tuija H. Vastenburg, Martijn Vilanova, Anna Vrijkotte, Tanja G. M. Lustrek, Mitja Jožef Stefan Institute Department of Intelligent Systems Ljubljana Slovenia ConnectedCare Research & Development Nijmegen Netherlands Vrije Universiteit Amsterdam Amsterdam Public Health Research Institute Department of Public and Occupational Health Amsterdam Umc Amsterdam Netherlands Informatica S.p.A. Engineering Ingegneria Rome Italy Università della Svizzera Italiana Faculty of Informatics Lugano Switzerland University of Ljubljana Faculty of Sports Ljubljana Slovenia University of Piraeus Department of Digital Systems Piraeus Greece Eindhoven University of Technology Department of Mathematics and Computer Science Eindhoven Netherlands University of Porto Faculty of Sport Porto Portugal Jamk University of Applied Sciences Likes School of Health and Social Studies Jyväskylä Finland

ISBN: (纸本)9798350324266

The SmartCHANGE project, a Horizon-Europe Research & Innovation project that has been ongoing since May 2023 and is scheduled to conclude in April 2027, aims to develop AI-driven decision-support tools to identify and reduce the long-term risk of non-communicable chronic diseases (NCDs) in children and youth. NCDs have become a significant public health burden in developed countries, with common risk factors including obesity, low physical fitness, and unhealthy lifestyle habits. Childhood and adolescence are critical periods for establishing healthy habits, and early intervention can prevent or delay the onset of NCDs later in life. However, current tools for identifying high-risk individuals mainly focus on adults, leading to missed opportunities for early detection. The SmartCHANGE project aims to address this gap by creating trustworthy AI tools that accurately assess risk factors in children and youth and promote optimised strategies for risk reduction. The paper outlines the plan for the project from the project's proposed methodologies, including dataset utilisation, data cleaning, risk prediction models (basic and complex), and the implementation of federated learning. Additionally, it discusses user experience aspects such as participatory design, suggested applications, and the evaluation of project outcomes. © 2023 IEEE.

关键词： Health risks

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Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

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Procedia computer science 2023年 218卷 24-34页

作者： Seifedine Kadry Rubén González Crespo Enrique Herrera-Viedma Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Andalusian Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai 602105 Tamil Nadu India

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual Lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores.

关键词： Breast cancer Thermal imaging Firefly algorithm Feature optimization Classification

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Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical Data

arXiv

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arXiv 2022年

作者： Huo, Zepeng Qian, Xiaoning Huang, Shuai Wang, Zhangyang Mortazavi, Bobak J. Computer Science & Engineering Department Texas A&M University College StationTX United States Electrical & Computer Engineering Department Texas A&M University College StationTX United States Industrial & Systems Engineering Department University of Washington SeattleWA United States Electrical & Computer Engineering Department University of Texas AustinTX United States

Medical events of interest, such as mortality, often happen at a low rate in electronic medical records, as most admitted patients survive. Training models with this imbalance rate (class density discrepancy) may lead to suboptimal prediction. Traditionally this problem is addressed through ad-hoc methods such as resampling or reweighting but performance in many cases is still limited. We propose a framework for training models for this imbalance issue: 1) we first decouple the feature extraction and classification process, adjusting training batches separately for each component to mitigate bias caused by class density discrepancy;2) we train the network with both a density-aware loss and a learnable cost matrix for misclassifications. We demonstrate our model's improved performance in real-world medical datasets (TOPCAT and MIMIC-III) to show improved AUC-ROC, AUC-PRC, Brier Skill Score compared with the baselines in the domain. © 2022, CC BY.

关键词： Medical computing

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SBM: A Time and Resource Efficient Single Backward Match Exact String Searching Algorithm

SBM: A Time and Resource Efficient Single Backward Match Exa...

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Business Analytics for Technology and Security (ICBATS), International Conference on

作者： Ayman Bassam Nassoura Muhammad Ibrahim Atif Ali Sania Safdar Shujah Ur Rehman Amer Ibrahim Hamza Alrababah Management Information Systems Department City University Ajman School of Computer Science NCBA&E Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan PMAS Arid Agriculture University Rawalpindi National University of Science & Technology Abasyn University Islamabad College of Computer and Information Technology American University in the Emirates Dubai United Arab Emirates School of Information Technology Skyline University College University City Sharjah Sharjah UAE

As in the current era, data is expanding daily on the web, where tremendous data banks with big data need to be searched by different users according to their data needs. This is when normal users cannot afford more waiting time searching the required data and indulge themselves in uncomfortable, lengthy, and complex computational processes. Moreover, search results will also play a vital role in extracting the desired output from the existing data bank. By keeping this common and consistent need of the normal user, the effort has been made to formulate a solution in the exact string searching algorithm. The algorithm's simplicity will add efficiency and speed up the processing time compared to the existing exact string-searching algorithms in terms of worst-case complexity. The proposed algorithm is compared with Boyer Moore Hoorspool, Knuth Morris Pratt (KMP), and Back and Forth Matching Algorithms to understand the results better.

关键词： Big Data Computational efficiency Complexity theory Security Indexes Data mining Business

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The Role of Internet of Things (IoT) to Fight against Covid-19 1

The Role of Internet of Things (IoT) to Fight against Covid-...

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1st International Conference on Data science, Machine Learning and Artificial Intelligence, DSMLAI 2021

作者： Haque, Md Alimul Sonal, Deepa Haque, Shameemul Kumar, Kailash Rahman, Moidur Department of Computer Science Veer Kunwar Singh University Bihar Ara India Al - Hafeez College Ara Bihar India College of Computing and Informatics Saudi Electronic University Riyadh Kingdom Saudi Arabia College of Computing and Information Technology Jazan University Jizan Kingdom Saudi Arabia

ISBN: (纸本)9781450387637

The Internet of Things (IoT) has become a new topic of research in various scientific and corporate fields, particularly in healthcare, in recent decades. The IoT revolution transforms new healthcare services by the inclusion of scientific, political and environmental prospects. It progresses from traditional to specialized healthcare systems, making it easier to identify, treat and manage patients. COVID - 19 has developed into a pandemic that spread around the globe. Day and night, scientists and engineers are working to produce a vaccine, develop additional research equipment and improve surveillance. Using contactless equipment is one of the critical factors to reduce the spread. To prevent the transmission of the coronavirus IoT can be used. That is an interconnection between the Internet and electronic devices. Devices are sensuous and recordable as well as can watch and respond. In this article, we discussed Covid-19 literature, monitoring techniques and proposed an IoT model to help mitigate the transmission of Covid-19. © 2021 ACM.

关键词： COVID-19

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DECISION PROBLEMS FOR LINEAR RECURRENCES INVOLVING ARBITRARY REAL NUMBERS

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Logical Methods in computer science 2021年第3期17卷

作者： Neumann, Eike Max Planck Institute for Software Systems Saarland Informatics Campus Saarbrücken Germany Department of Computer Science Oxford University United Kingdom

We study the decidability of the Skolem Problem, the Positivity Problem, and the Ultimate Positivity Problem for linear recurrences with real number initial values and real number coefficients in the bit-model of real computation. We show that for each problem there exists a correct partial algorithm which halts for all problem instances for which the answer is locally constant, thus establishing that all three problems are as close to decidable as one can expect them to be in this setting. We further show that the algorithms for the Positivity Problem and the Ultimate Positivity Problem halt on almost every instance with respect to the usual Lebesgue measure on Euclidean space. In comparison, the analogous problems for exact rational or real algebraic coefficients are known to be decidable only for linear recurrences of fairly low order. © E. Neumann.

关键词： Computability and decidability

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