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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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HE-DKSAP: Privacy-Preserving Stealth Address Protocol via Additively Homomorphic Encryption

arXiv

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arXiv 2023年

作者： Yan, Yuping Shao, George Song, Dennis Song, Mason Jin, Yaochu Trustworthy and General AI Lab School of Engineering Westlake University Hangzhou310024 China Mind Network Singapore Faculty of Informatics Department of Computeralgebra Budapest1117 Hungary Ethereum Foundation Zug Switzerland Department of Computer Science University of Surrey Surry GuildfordGU2 7XH United Kingdom

Blockchain transactions have gained widespread adoption across various industries, largely attributable to their unparalleled transparency and robust security features. Nevertheless, this technique introduces various privacy concerns, including pseudonymity, Sybil attacks, and potential susceptibilities to quantum computing, to name a few. In response to these challenges, innovative privacy-enhancing solutions like zero-knowledge proofs, homomorphic encryption, and stealth addresses (SA) have been developed. Among the various schemes, SA stands out as it prevents the association of a blockchain transaction's output with the recipient's public address, thereby ensuring transactional anonymity. However, the basic SA schemes have exhibited vulnerabilities to key leakage and quantum computing attacks. To address these shortcomings, we present a pioneering solution ' Homomorphic Encryption-based Dual-Key Stealth Address Protocol (HE-DKSAP), which can be further extended to Fully HE-DKSAP (FHE-DKSAP). By leveraging the power of homomorphic encryption, HE-DKSAP introduces a novel approach to safeguarding transaction privacy and preventing potential quantum computing attacks. This paper delves into the core principles of HE-DKSAP, highlighting its capacity to enhance privacy, scalability, and security in programmable blockchains. Through a comprehensive exploration of its design architecture, security analysis, and practical implementations, this work establishes a privacy-preserving, practical, and efficient stealth address protocol via additively homomorphic encryption. © 2023, CC BY.

关键词： Blockchain

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The stratified K-folds cross-validation and class-balancing methods with high-performance ensemble classifiers for breast cancer classification

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Healthcare Analytics 2023年 4卷 100247-100247页

作者： T R, Mahesh V, Vinoth Kumar V, Dhilip Kumar Geman, Oana Margala, Martin Guduri, Manisha Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore 562112 India School of Computer Science Engineering & Information Systems (SCORE) Vellore Institute of Technology (VIT) 632014 India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai 600062 India Department of Computers Electronics and Automation Faculty of Electrical Engineering and Computer Science Stefan cel Mare University of Suceava Suceava 720229 Romania School of Computing and Informatics University of Louisiana at Lafayette United States

Breast cancer is one of the most common causes of death among women, and early diagnosis is vital for reducing the fatality rate. This study evaluates the most widely used machine-learning breast cancer prediction and diagnosis methods. We use synthetic minority over-sampling to handle imbalanced data in the breast cancer diagnosis dataset obtained from the Wisconsin Machine Learning Repository. We use a variety of machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Classification and Regression Tree (CART), Naive Bayes (NB), and well-known ensembles methods like Majority-Voting, eXtreme Gradient Boosting algorithm (XGBoost), and Random Forest (RF) for the breast cancer classification. The findings show that the Majority-Voting ensemble method, built on the top three classifiers (LR, SVM, and CART), outperforms all other individual classifiers and offers the highest accuracy of 99.3%. © 2023 The Author(s)

关键词： Breast cancer Class-balancing Cross-validation Diagnostic analytics Ensemble method Machine learning

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Effect of lead time prediction accuracy in trust-based resource sharing

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IFAC-PapersOnLine 2021年第1期54卷 1126-1131页

作者： Ádám Szaller Botond Kádár Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control Budapest Hungary Department of Manufacturing Science and Engineering Budapest University of Technology and Economics Budapest Hungary

Nowadays, challenges are changing production facilities: decentralized production networks are replacing centralized organizations to remain competitive. This paper investigates a resource sharing approach where matching resource offers and requests are made by an intermediate platform. One of the main pillars of collaboration is to keep promises, especially about deadlines: in the presented model, facilities could rate each other based on trustfulness and choose from offers based on this setting. Lead time prediction accuracy has a direct effect on the real processing intervals: if the prediction was accurate, the deadline could be met, which results in good ratings and a higher possibility to win more jobs. In the paper, effect of lead time prediction accuracy is investigated in trust-based resource sharing, and the performance of facilities is compared with agent-based simulation.

关键词： Co-operative control/manufacturing Multi-agent simulation Manufacturing System engineering

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Towards Quantum Graph Neural Networks: An Ego-Graph Learning Approach

arXiv

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arXiv 2022年

作者： Ai, Xing Sun, Luzhe Yan, Junchi Zhang, Zhihong Hancock, Edwin R. School of Informatics Xiamen University Fujian Xiamen China Computer Science Department in University of Chicago ChicagoIL United States Department of Computer Science and Engineering MOE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Shanghai200240 China University of York York United Kingdom

Quantum machine learning is a fast-emerging field that aims to tackle machine learning using quantum algorithms and quantum computing. Due to the lack of physical qubits and an effective means to map real-world data from Euclidean space to Hilbert space, most of these methods focus on quantum analogies or process simulations rather than devising concrete architectures based on qubits. In this paper, we propose a novel hybrid quantum-classical algorithm for graph-structured data, which we refer to as the Ego-graph based Quantum Graph Neural Network (egoQGNN). egoQGNN implements the GNN theoretical framework using the tensor product and unity matrix representation, which greatly reduces the number of model parameters required. When controlled by a classical computer, egoQGNN can accommodate arbitrarily sized graphs by processing ego-graphs from the input graph using a modestly-sized quantum device. The architecture is based on a novel mapping from real-world data to Hilbert space. This mapping maintains the distance relations present in the data and reduces information loss. Experimental results show that the proposed method outperforms competitive state-of-the-art models with only 1.68% parameters compared to those models. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Duplicate-sensitivity guided transformation synthesis for DBMS correctness bug detection

arXiv

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arXiv 2021年

作者： Zhang, Yushan Yao, Peisen Wu, Rongxin Zhang, Charles Department of Computer Science and Engineering HKUST Hong Kong Department of Cyber Space Security School of School of Informatics Xiamen University Xiamen China

Database Management System (DBMS) plays a core role in modern software from mobile apps to online banking. It is critical that the DBMS provides correct data to all applications. When the DBMS returns incorrect data, a correctness bug is triggered. Current production-level DBMSs still suffer from insufficient testing due to the limited hand-written test cases. Recently several works proposed to automatically generate many test cases with query transformation, a process of generating an equivalent query pair and testing a DBMS by checking whether the system returns the same result set for both queries. However, all of them still heavily rely on manual work to provide a transformation which largely confines their exploration of the valid input query space. This paper introduces duplicate-sensitivity guided transformation synthesis which automatically finds new transformations by first synthesizing many candidates then filtering the nonequivalent ones. Our automated synthesis is achieved by mutating a query while keeping its duplicate sensitivity, which is a necessary condition for query equivalence. After candidate synthesis, we keep the mutant query which is equivalent to the given one by using a query equivalent checker. Furthermore, we have implemented our idea in a tool Eqsql and used it to test the production-level DBMSs. In two months, we detected in total 30 newly confirmed and unique bugs in MySQL, TiDB and CynosDB. Copyright © 2021, The Authors. All rights reserved.

关键词： Database systems

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Delayed Feedback with Selection Bias in Recommender Systems

Delayed Feedback with Selection Bias in Recommender Systems

引用

Data Analytics, Computing and Artificial Intelligence (ICDACAI), International Conference on

作者： Renjie Mao Chenhao Xu Kaifeng Jin Xiaotong Li Shuning Zhang Xiao Yang School of Informatics The University of Edinburgh Edinburgh UK Department of Computer Science University of Illinois Urbana-Champaign Champaign US School of Computer Science University of Nottingham Ningbo China Ningbo China Department of Mathematics College of Arts of Sciences The University of Chicago US The Grainger College of Engineering University of Illinois Urbana-Champaign Champaign US College of Sciences Virginia Polytechnic Institute and State University Blacksburg US

One challenge for online advertising companies is the lack of time-sensitive data, as users may not buy the product immediately after clicking on the ads. Wanting to improve the performance of their products, advertisements companies can constantly train on the real-time data stream. Without fully generated data, this approach will lead to few biases. In this paper, we discussed non-parametric models regarding delay distributions and three estimators for the training model. In the end, we proposed the Dual Learning Algorithm. While training on 100000 data points and different sets of hyper parameters, we achieved the overall best set of F1 and AUC scores: 0.55 and 0.70.

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Machine-Learning Enabled Multidimensional Data Utilization Through Multi-Resonance Architecture: A Pathway to Enhanced Accuracy in Biosensing

arXiv

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arXiv 2024年

作者： Aalizadeh, Majid Afshar, Morteza Azmoudeh Fan, Xudong Department of Biomedical Engineering University of Michigan Ann ArborMI48109 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States University of Michigan Ann ArborMI48109 United States Max Harry Weil Institute for Critical Care Research and Innovation University of Michigan Ann ArborMI48109 United States Informatics Institute Istanbul Technical University Istanbul34485 Turkey

A novel framework is proposed that combines multi-resonance biosensors with machine learning (ML) to significantly enhance the accuracy of parameter prediction in biosensing. Unlike traditional single-resonance systems, which are limited to one-dimensional datasets, this approach leverages multi-dimensional data generated by a custom-designed nanostructure—a periodic array of silicon nanorods with a triangular cross-section over an aluminum reflector. High bulk sensitivity values are achieved for this multi-resonant structure, with certain resonant peaks reaching up to 1706 nm/RIU. The field analysis reveals Mie resonances as the physical reason behind the peaks. The predictive power of multiple resonant peaks from transverse magnetic (TM) and transverse electric (TE) polarizations is evaluated using Ridge Regression modeling. Systematic analysis reveals that incorporating multiple resonances yields up to three orders of magnitude improvement in refractive index detection precision compared to single-peak analyses. This precision enhancement is achieved without modifications to the biosensor hardware, highlighting the potential of data-centric strategies in biosensing. The findings establish a new paradigm in biosensing, demonstrating that the synergy between multi-resonance data acquisition and ML-based analysis can significantly enhance detection accuracy. This study provides a scalable pathway for advancing high-precision biosensing technologies. Copyright © 2024, The Authors. All rights reserved.

关键词： Nanorods

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Generative Adversarial Networks for Heterogeneous Unsupervised Domain Adaptation Detection

Generative Adversarial Networks for Heterogeneous Unsupervis...

引用

IEEE International Conference on Intelligent engineering Systems (INES)

作者： Homayoun Safarpour Motealegh Mahalegi Amirfarhad Farhadi György Molnár Enikő Nagy Institute of Mechatronics and Vehicle Engineering Óbuda University Budapest Hungary Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Kandó Kálmán Faculty of Electrical Engineering Óbuda University Budapest Hungary János Csere Apáczai Faculty Széchenyi University Győr Hungary Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty of Informatics Óbuda University Budapest Hungary

ISBN: (数字)9798350367591

ISBN: (纸本)9798350367607

Domain adaptation, a subset of transfer learning, involves generating examples from two related but differently distributed source and target domains. This paper introduces an incremental adversarial learning method for unsupervised domain adaptation, where the source data is labeled, and the target data remains unlabeled. We employ a discriminative adversarial strategy as the primary technique for unsupervised domain adaptation to minimize the significant distributional variance between the source and target domains. Traditional domain adaptation methods typically train the source domain to align with a fixed target domain distribution. However, in many real-world scenarios, the target domain's distribution continuously evolves, leading to a lack of generalizability in the trained model for subsequent domains. To tackle this issue, we suggest using a continuous sequential domain adaptation method. This involves including instances from several related target domains in the training process in a sequential and continuous manner. This process ensures that all receiving domains are correctly aligned with their respective sources, and to maintain sequence continuity, the source domain is incrementally updated by incorporating the most reliable data from the current target domain. This enhancement aims to improve the network's training for forthcoming domain data. Leveraging the effectiveness of deep neural networks, our method utilizes deep GANs for domain adaptation. The approach enhances domain matching by augmenting the source with selected target data and introduces a novel strategy for continuous sequential domain matching. This involves a progressive update of source domain samples with reliable data from the current target domain, facilitating seamless adaptation to successive domain shifts. The proposed method demonstrated a high classification accuracy, achieving an impressive 99% and 95% in different settings. Furthermore, it outperforms previous studies rega

关键词： Training Adaptation models Machine learning algorithms Accuracy Transfer learning Clustering algorithms Sensitivity and specificity

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Evaluation of augmentation methods in classifying autism spectrum disorders from fMRI data with 3D convolutional neural networks

arXiv

引用

arXiv 2021年

作者： Jönemo, Johan Abramian, David Eklund, Anders Division of Medical Informatics Department of Biomedical Engineering Division of Statistics & Machine Learning Department of Computer and Information Science Linköping University Sweden

Classifying subjects as healthy or diseased using neuroimaging data has gained a lot of attention during the last 10 years. Here we apply deep learning to derivatives from resting state fMRI data, and investigate how different 3D augmentation techniques affect the test accuracy. Specifically, we use resting state derivatives from 1,112 subjects in ABIDE preprocessed to train a 3D convolutional neural network (CNN) to perform the classification. Our results show that augmentation only provide minor improvements to the test accuracy. © 2021, CC BY.

关键词： Convolutional neural networks

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