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arXiv 2025年

作者： Firmino, Paulo Leuştean, Laurenţiu Departamento de Matemática Faculdade de Ciências Universidade de Lisboa Portugal LOS Faculty of Mathematics and Computer Science University of Bucharest Romania Simion Stoilow Institute of Mathematics The Romanian Academy Romania Institute for Logic and Data Science Bucharest Romania

In this paper we use proof mining methods to compute rates of (T-)asymptotic regularity of the generalized Krasnoselskii-Mann-type iteration associated to a nonexpansive mapping T: X → X in a uniformly convex normed space X. For special choices of the parameter sequences, we obtain quadratic *** Codes 47H05, 47H09, 47J25 © 2025, CC BY.

关键词： Iterative methods

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Detail-preserving smoke simulation using an efficient high-order numerical scheme

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science China(Information sciences) 2020年第6期63卷 217-219页

作者： Jian ZHU Zhuo YANG Hanqiu SUN Enhua WU Ruichu CAI Zhifeng HAO School of Computer Science Guangdong University of Technology Institute of Space and Earth Info.Science the Chinese University of Hong Kong State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Faculty of Science and Technology University of Macau School of Mathematics and Big Data Foshan University

The advection step in Eulerian fluid simulation is prone to numerical dissipation [1], resulting in the loss of fluid details. Among the various attempts to develop accurate advection solvers, high-order advection schemes such as back and forth error compensation and correction (BFECC)[2] and MacCormack [3] are effective solutions. Complementary to high-order advection schemes are

关键词： CIP simulation Detail-preserving smoke simulation using an efficient high-order numerical scheme

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Incremental Learning Algorithms for Broad Learning System with Node and Input Addition

Incremental Learning Algorithms for Broad Learning System wi...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Guang-Ze Chen Junwei Jin Hai-Wei Sun C. L. Philip Chen Department of Mathematics Faculty of Science and Technology University of Macau Macau China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou China South China University of Technology and Pazhou Lab School of Computer Science and Engineering Guangzhou China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The Broad Learning System (BLS) has been established as an effective flat network alternative to Deep Neural Networks (DNNs), delivering high efficiency while achieving competitive accuracy. Despite its advantages, the incremental learning methods of BLS face challenges in stability and computation when expanding with new nodes or input. We introduce two novel incremental learning algorithms based on factorization updates for BLS that optimize node and input additions to overcome these limitations. Our node addition algorithm utilizes QR decomposition and Cholesky factorization, using the update of the Cholesky factor instead of pseudo-inverse computations. For input addition, we propose an iterative Cholesky factor update algorithm. Our algorithms demonstrate not only faster computation compared to the existing BLS but also improved testing accuracy on the MNIST or Fashion-MNIST dataset. This work presents a significant step forward in the practical application and scalability of BLS in various data-dense environments.

关键词： Learning systems Incremental learning Accuracy Scalability Parallel processing Stability analysis Matrix decomposition Thermal stability Numerical stability Testing

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Correcting for bias due to categorisation based on cluste analysis using multiple continuous error-prone exposures

arXiv

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arXiv 2023年

作者： Intemann, Timm Pigeot, Iris Biometry and Data Management Leibniz Institute for Prevention Research Epidemiology - BIPS Bremen Germany Faculty of Mathematics and Computer Science University Bremen Bremen Germany

The association between multidimensional exposure patterns and outcomes is commonly investigated by first applying cluster analysis algorithms to derive patterns and then estimating the associations. However, errors in the underlying continuous, possibly skewed, exposure variables lead to misclassified exposure patterns and therefore to biased effect estimates. This is often the case for lifestyle exposures in epidemiology, e.g. for dietary variables measured on daily basis. We introduce three new algorithms for correcting the biased effect estimates, which are based on regression calibration (RC), simulation extrapolation (SIMEX) and multiple imputation (MI). In addition, the naïve method ignoring the measurement error structure is considered for comparison. These methods are combined with the k-means cluster algorithm and the Gaussian mixture model to derive exposure patterns. The performance of the correction methods is compared in a simulation study regarding absolute, maximum and relative bias. The simulated data mimic a typical situation in nutritional epidemiology when diet is assessed using repeated 24-hour dietary recalls. Continuous and binary outcomes are considered. Simulation results show, that the correction method based on RC and MI perform better than the naïve and the SIMEX-based method. Furthermore, the MI-based approach, which can use outcome information in the error model, is superior to the RC-based approach in most scenarios. Therefore, we recommend using the MI-based approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Measurement errors

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Society with Trust: A Scientometrics Review of Zero-Knowledge Proof Advanced Applications in Preserving Digital Privacy for Society 5.0 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Dominic, Nicholas Pratama, Naufal Rizqi Cornelius, Kenny Senewe, Shavellin Herliman Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Department of Computer Science Universitas Multimedia Nusantara Tangerang15111 Indonesia Psychology Department Faculty of Humanities Bina Nusantara University Jakarta11480 Indonesia BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Society 5.0 focuses on human productivity in the midst of advanced technological services. While the concept has human trust at its core, technology development is now leading to zero-trust architecture. In this scientometrics review, 107 selected papers were analyzed to obtain the nature of the Zero-knowledge Proof (ZKP) theory and its advanced applications to favor digital privacy in greater society. With the common literature review strategy, it was found that the most citations in this field appeared in 2018, with IEEE, Springer, and Elsevier as the top three publishers. By affiliations, China and United States are both leading countries where the researcher comes from. And as the positive trend emerged, the ZKP mechanism will keep being leveraged as one of the cryptographic toolsets in the cloud services, Internet of Things (IoT), smart contracts, healthcare, electronic systems, and many other industrial needs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Smart contract

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Ocular Cataract Identification Using Deep Convolutional Neural Networks

Ocular Cataract Identification Using Deep Convolutional Neur...

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International Conference on Advances in Big data, Computing and data Communication Systems (icABCD)

作者： Feliciana M. E. Manuel Saide Manuel Saide Felermino D. M. A. Ali Sanae Lotfi Dept. of Computer Engineering Faculty of Engineering Lurio University Pemba Mozambique Center for Data Science New York University New York USA

Ocular cataract is among diseases that result in blindness if not treated in time. It affects people worldwide, primarily in underdeveloped countries. This health problem affects the quality of patients' lives. However, early diagnosis avoids blindness and allows the patient to have appropriate treatment. Developing countries, especially those with low income, have a precarious health system, even in the ophthalmology sector, where equipment is lacking. This research aims to develop a deep learning-based model to detect ocular cataracts based on retinal images. We collect 1000 retinal images from Kaggle, which are then equally divided into two classes: with and without cataracts. We then use several neural architectures to correctly classify these images, including ResNet18, ResNet34, InceptionResNetV2, and InceptionV4. We demonstrate that ResNet18 outperforms the other architectures, reaching 95.5% accuracy score. Our results suggest that deep convolutional neural networks can achieve a significant performance in ocular cataracts classification using retinal images.

关键词：

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Co-evolution-based Metal-binding Residue Prediction with Graph Neural Networks

arXiv

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arXiv 2025年

作者： Rastegari, Sayedmohammadreza Tabakhi, Sina Liu, Xianyuan Sang, Wei Lu, Haiping Faculty of Computer Engineering University of Isfahan Isfahan Iran School of Computer Science Centre for Machine Intelligence University of Sheffield Sheffield United Kingdom School of Basic Medical Sciences Institute of Medical Technology Shanxi Medical University Taiyuan China Institute of Big Data Science and Industry Shanxi University Taiyuan China

In computational structural biology, predicting metal-binding sites and their corresponding metal types is challenging due to the complexity of protein structures and interactions. Conventional sequence- and structure-based prediction approaches cannot capture the complex evolutionary relationships driving these interactions to facilitate understanding, while recent co-evolution-based approaches do not fully consider the entire structure of the co-evolved residue network. In this paper, we introduce MBGNN (Metal-Binding Graph Neural Network) that utilizes the entire co-evolved residue network and effectively captures the complex dependencies within protein structures via graph neural networks to enhance the prediction of co-evolved metal-binding residues and their associated metal types. Experimental results on a public dataset show that MBGNN outperforms existing co-evolution-based metal-binding prediction methods, and it is also competitive against recent sequence-based methods, showing the potential of integrating co-evolutionary insights with advanced machine learning to deepen our understanding of protein-metal interactions. The MBGNN code is publicly available at https://***/SRastegari/MBGNN. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

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Design of a novel sensory neuromorphic circuit 6

Design of a novel sensory neuromorphic circuit

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6th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2023

作者： Chen, Ni Peng, Kangbai Su, Jing Qin, Yurong Guangxi Medical University Department of Biomedical Engineering Nanning China Guangxi University College of Electrical Engineering Nanning China Guangxi University College of Computer and Electronic Information Nanning China Guangxi Medical University College of Information and Management Nanning China City University of Macau Faculty of Data Science China

ISBN: (纸本)9781665460033

To simulate the firing properties of sensory neurons, a sensory neuromorphic circuit was designed using generalized memristors and Mott memristors, and was tested under both DC and AC input conditions, respectively. The test results show that the effective input voltage range of the circuit is 3.5 V ~ 11 V, and the oscillation frequency range is 1.37 KHz ~ 6 KHz. When the input voltage is low ( 8.4 V), the spiking frequency tends to reduce, which faithfully emulates the properties of biological neurons. These research findings provide a new circuit solution scheme for realizing the artificial sensory system. © 2023 IEEE.

关键词： Memristors

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Impact of the Intelligent Control System in Assisting Humans Regarding Complex Information Processing 4

Impact of the Intelligent Control System in Assisting Humans...

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4th International Conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Ambika, R. Lahane, Prashant Kumar, G. Rabbi, Fazle Maarif, Muhammad Anas Qureshi, Mohmad Kashif Bms Institute of Technology and Management Department of Ece India Mit World Peace University Faculty of Engineering India Srm Institute of Science and Technology Department of Management India Australian Computer Society Australia Institut Pesantren Kh. Abdul Chalim Pacet Mojokerto Indonesia Sanskriti University Computer Science & Engineering Professor Cum Data Scientist India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

Intelligent control system is a control technique that utilises different computing approaches such as fuzzy logic, Bayesian probability, neural networks and machine learning. Information processing is change of information within a manner detectable through an observer. It describes everything that happens in universe from falling of rocks to printing text files from a digital computer system. This paper aimed to analyse the impact of intelligent control systems (ICS) to assist humans with complex information systems. In order to achieve this aim, the paper has conducted a literature review to understand other authors' perspectives. It has discussed concept of ICS and its role in assisting information systems. Additionally, the paper has analysed information processing and other information about it. In order to collect relevant data on selected research topic, paper has conducted a secondary data collection method. It has selected data from secondary resources and analysed data with help of thematic analysis. This data analysis method helped to understand data patterns on selected research topic. Based on data analysis, results found that key methods in intelligence control are fuzzy control, neural network control, and expert control and these controls offer useful and relevant programming regarding complex information systems. Additionally, intelligent control can adapt, learn and plan under a huge uncertainty as well as cope with a huge amount of information. Finally, based on robust discussion, paper has formulated an appropriate conclusion for this research. © 2022 IEEE.

关键词： Fuzzy control

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A Three-Way Knot: Privacy, Fairness, and Predictive Performance Dynamics

arXiv

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arXiv 2023年

作者： Carvalho, Tânia Moniz, Nuno Antunes, Luís Faculty of Computer Science University of Porto Portugal Lucy Family Institute for Data & Society University of Notre Dame IN United States TekPrivacy Porto Portugal

As the frontier of machine learning applications moves further into human interaction, multiple concerns arise regarding automated decision-making. Two of the most critical issues are fairness and data privacy. On the one hand, one must guarantee that automated decisions are not biased against certain groups, especially those unprotected or marginalized. On the other hand, one must ensure that the use of personal information fully abides by privacy regulations and that user identities are kept safe. The balance between privacy, fairness, and predictive performance is complex. However, despite their potential societal impact, we still demonstrate a poor understanding of the dynamics between these optimization vectors. In this paper, we study this three-way tension and how the optimization of each vector impacts others, aiming to inform the future development of safe applications. In light of claims that predictive performance and fairness can be jointly optimized, we find this is only possible at the expense of data privacy. Overall, experimental results show that one of the vectors will be penalized regardless of which of the three we optimize. Nonetheless, we find promising avenues for future work in joint optimization solutions, where smaller trade-offs are observed between the three vectors. © 2023, CC BY-NC-SA.

关键词： Vectors

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