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arXiv 2025年

作者： Choi, Young-Pil Kim, Jeongho Tse, Oliver Department of Mathematics Yonsei University Seoul03722 Korea Republic of Department of Applied Mathematics Kyung Hee University 1732 Deogyeong-Daero Giheung-gu Gyeonggi-Do Yongin-si17104 Korea Republic of Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven5600 MB Netherlands

This paper investigates the diffusion limit of a kinetic BGK-type equation, focusing on its relaxation to a nonlinear aggregation-diffusion equation, where the diffusion exhibits a porous-medium-type nonlinearity. Unlike previous studies by Dolbeault et al. [Arch. Ration. Mech. Anal., 186, (2007), 133–158] and Addala and Tayeb [J. Hyperbolic Differ. Equ., 16, (2019), 131–156], which required bounded initial data, our work considers initial data that need not be bounded. We develop new techniques for handling weak entropy solutions that satisfy the natural bounds associated with the kinetic entropy inequality. Our proof employs the relative entropy method and various compactness arguments to establish the convergence and properties of these solutions. © 2025, CC BY.

关键词： Nonlinear equations

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Helmet Detection System for Motorcycle Riders with Explainable Artificial Intelligence Using Convolutional Neural Network and Grad-CAM 1

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15th Multi-disciplinary International Conference on Artificial Intelligence, MIWAI 2022

作者： Intagorn, Suradej Pinitkan, Suriya Panmuang, Mathuros Rodmorn, Chonnikarn Department of Mathematics Statistics and Computer Science Faculty of Liberal Arts and Science Kasetsart University Kamphaeng Saen Campus Nakhon Pathom Thailand Department of Educational Technology and Communications Faculty of Technical Education Rajamangala University of Technology Thanyaburi Pathum Thani Thailand Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9783031209925

ISBN: (纸本)9783031209918

Motorcycle accidents are one of the most common causes of injury and death in road users. This research has applied convolutional neural network (CNN) and explainable AI to detect motorcyclist without helmet and explain why CNN made that decision. The concept is based on deep learning and CNN principles applied to automatically detect motorcycle riders without helmet from images using three baseline classifiers: support vector machine, random forest and logistic regression to compare with deep convolutional neural network and measure accuracy with accuracy and F1-Score. The results revealed that CNN's F1-Score was 0.8326, which was the highest among all predictive models. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Support vector machines

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A Survey on Decentralized Identifiers and Verifiable Credentials

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IEEE Communications Surveys and Tutorials 2025年

作者： Mazzocca, Carlo Acar, Abbas Uluagac, Selcuk Montanari, Rebecca Bellavista, Paolo Conti, Mauro University of Salerno Department of Information and Electrical Engineering and Applied Mathematics Fisciano84084 Italy Florida International University Cyber-Physical Systems Security Lab School of Computing and Information Science MiamiFL33174 United States University of Bologna Department of Computer Science and Engineering Bologna40136 Italy University of Padua Department of Mathematics Padua35131 Italy

Digital identity has always been one of the keystones for implementing secure and trustworthy communications among parties. The ever-evolving digital landscape has undergone numerous technological transformations that have profoundly reshaped digital identity management, leading to a major shift from centralized to decentralized identity models. The latest stage of this evolution is represented by the emerging paradigm of Self-Sovereign Identity (SSI), which gives identity owners full control over their data. SSI leverages Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs), which have been recently standardized by theWorldWideWeb Consortium (W3C). These technologies have the potential to build more secure and decentralized digital identity systems, significantly strengthening communication security in scenarios involving many distributed participants. It is worth noting that use of DIDs and VCs is not limited to individuals but extends to a wide range of entities including cloud, edge, and Internet of Things (IoT) resources. However, due to their novelty, existing literature lacks a comprehensive survey on DIDs and VCs beyond the scope of SSI. This paper fills this gap by providing a comprehensive overview of DIDs and VCs from multiple perspectives. It identifies key security threats and mitigation strategies, analyzes available implementations to guide practitioners in making informed decisions, and reviews the adoption of these technologies across various application domains. Moreover, it also examines related regulations, projects, and consortiums emerging worldwide. Finally, it discusses the primary challenges hindering their real-world adoption and outlines future research directions. © 1998-2012 IEEE.

关键词： Decentralized control

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Global-Local Active Contour for Segmenting Images with Simultaneous Inhomogeneity and Texture

SSRN

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SSRN 2024年

作者： Shao, Pei-Chiang Department of Applied Mathematics Chung Yuan Christian University Zhongli District Taoyuan City320314 Taiwan Undergraduate Program in College of Electrical Engineering and Computer Science Chung Yuan Christian University Zhongli District Taoyuan City320314 Taiwan

Severe intensity inhomogeneity (InH) and complex real-world textures cause great difficulties and become two important issues in image segmentation and object extraction applications. Plenty of methods are proposed to tackle these issues, however, they usually perform poorly on images possess both InH and texture. In this paper, we propose a region-based active contour model for segmenting images with simultaneous inhomogeneity and texture. A new bias field possesses both global and local smoothness properties is proposed to handle the InH issue, and a pixel inhomogeneity factor (PIF) is employed to jointly deal with InH and textured images. We also employ the total variation (TV) regularization to further smooth and denoise the transformed PIF image, making the segmentation process to have the nice properties of global convergence and independence of contour initialization. A large number of numerical experiments and comparisons are conducted to demonstrate the effective performance of the newly proposed method. © 2024, The Authors. All rights reserved.

关键词： Textures

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Revisiting Fuel Subsidies in Indonesia using K-Means, PAM, and CLARA

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2023年第3期50卷 858页

作者： Prasetyo, Fajar Agung Caraka, Rezzy Eko Kim, Yunho Goldameir, Noor Ell Sulistyowati Tyasti, Avia Enggar Gio, Prana Ugiana Anggoro, Faisal Ramadhani, Muthia Pardamean, Bens Faculty of Economics and Business Universitas Indonesia Indonesia Indonesia Department of Mathematics Ulsan National Institute of Science and Technology Korea Republic of Department of Mathematics Statistics Study Program Riau University Riau Pekanbaru Indonesia Institut Teknologi Adhi Tama Surabaya East Java Indonesia International Trade of ASEAN and RRT Region Polytechnic of APP DKI Jakarta Indonesia Department of Mathematics Universitas Sumatera Utara Medan20155 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

Indonesia is one of the countries in the world that still applies subsidies for fuel oil. By the law, the Indonesian government must ensure the supply and distribution of fuel for all Indonesian people. To implement this policy properly, understanding the pattern of fuel consumption is fundamental. In this study, clustering will be used to determine the categories of districts and cities based on subsidized fuel consumption patterns. Our research used several methods to compare which cluster method is the most optimal;the methods include k-means, Partitioning Around Medoids (PAM), and Clustering Large Applications (CLARA). The results show that k-means is the best clustering method with the highest Dunn index and Silhouette coefficient compared to others. The optimum cluster number we get is two and represents fuel consumption from several districts and cities. Some districts and cities had under-average consumption and needed to be monitored. © (2023), (International Association of Engineers). All Rights Reserved.

关键词： K-means clustering

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A Deep Multi-Task Learning Network for Activity Recognition and User Identification Using Smartphone Sensors

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Procedia computer science 2025年 256卷 1350-1357页

作者： Sakorn Mekruksavanich Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao 56000 Thailand Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok 10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok 10800 Thailand

Human activity recognition (HAR) and user identification utilizing smartphone sensors are crucial in domains such as wellness tracking, tailored services, and security. Conventional approaches handle these activities as distinct entities, leading to less-than-ideal performance and restricted applicability. This research presents a novel deep multi-task learning network that simultaneously performs activity recognition and user identification using smartphone sensor data. By leveraging shared representations and task-specific information, the network enhances accuracy. It combines a shared convolutional neural network (CNN) with bidirectional long short-term memory (BiLSTM) layers to extract features from raw sensor data. The CNN identifies patterns, while the BiLSTM captures sequential dependencies and contextual information. These features are processed by fully connected layers for both activity recognition and user identification. A multi-task loss function is proposed to optimize both tasks based on their complexity and importance. The approach is tested on the UCI-HAR dataset, containing diverse activities from multiple users. Experimental results show that the proposed CNN-BiLSTM model achieves 97.64% accuracy in activity recognition and 82.59% in user identification, surpassing current state-of-the-art methods. This demonstrates the effectiveness of the multi-task learning framework and the advantage of using the shared CNN-BiLSTM architecture for improving both tasks.

关键词： human activity recognition user identification multi-task learning deep learning smartphone sensors

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Causal machine learning methods and use of sample splitting in settings with high-dimensional confounding

arXiv

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arXiv 2024年

作者： Ellul, Susan Carlin, John B. Vansteelandt, Stijn Moreno-Betancur, Margarita Murdoch Children's Research Institute VIC Australia Department of Paediatrics University of Melbourne VIC Australia Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium

Observational epidemiological studies commonly seek to estimate the causal effect of an exposure on an outcome. Adjustment for potential confounding bias in modern studies is challenging due to the presence of high-dimensional confounding, which occurs when there are many confounders relative to sample size or complex relationships between continuous confounders and exposure and outcome. Despite recent advances, limited evaluation, and guidance are available on the implementation of doubly robust methods, Augmented Inverse Probability Weighting (AIPW) and Targeted Maximum Likelihood Estimation (TMLE), with dataadaptive approaches and cross-fitting in realistic settings where high-dimensional confounding is present. Motivated by an early-life cohort study, we conducted an extensive simulation study to compare the relative performance of AIPW and TMLE using data-adaptive approaches in estimating the average causal effect (ACE). We evaluated the benefits of using cross-fitting with a varying number of folds, as well as the impact of using a reduced versus full (larger, more diverse) library in the Super Learner ensemble learning approach used for implementation. We found that AIPW and TMLE performed similarly in most cases for estimating the ACE, but TMLE was more stable. Cross-fitting improved the performance of both methods, but was more important for estimation of standard error and coverage than for point estimates, with the number of folds a less important consideration. Using a full Super Learner library was important to reduce bias and variance in complex scenarios typical of modern health research studies. © 2024, CC BY.

关键词： Contrastive Learning

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Visual password scheme using bag context shape grammars 19th

Visual password scheme using bag context shape grammars

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19th International Conference on Intelligent Systems Design and Applications, ISDA 2019

作者： Ogbuokiri, Blessing Raborife, Mpho School of Computer Science and Applied Mathematics University of the Witwatersrand Johannesburg South Africa Department of Applied Information Systems University of Johannesburg Johannesburg South Africa

ISBN: (纸本)9783030493417

In this paper, we implemented the similar images generated by bag context shape grammars as distractors in a prototype visual password scheme. A bag context shape grammar is a shape grammar that uses spatial rules to generate images in a regulated manner. That is, during the generative process, bag context is used as a technique to control when a shape grammar rule should be applied. The prototype visual password scheme is used to measure user experience as to whether users can remember their passwords immediately after enrolment and one week after enrolment. This is to ascertain whether the similar images generated using bag context shape grammars are good as a distractor for visual password scheme. The prototype visual password scheme is also built for shoulder surfing and guessing attacks resistance. The outcome of this study shows that bag context shape grammars are good for the generation of similar images as distractors for visual password schemes. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Formal languages

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Insight into the Hamilton and Crosser model for ternary hybrid nanofluid flow over a Riga wedge with heterogeneous catalytic reaction

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The European Physical Journal Special Topics 2024年 1-22页

作者： Obalalu, A. M. Shah, S. H. A. M. Darvesh, Adil Khan, Umair Ishak, Anuar Adegbite, Peter Ojewola, O. B. Muhammad, Taseer Galal, Ahmed M. Department of Mathematics and Statistics Kwara State University Malete Nigeria Department of Mathematics and Social Sciences Sukkur IBA University Sukkur Pakistan Department of Mathematics and Statistics Hazara University Mansehra Pakistan Department of Mathematical Sciences Faculty of Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Department of Mathematics Faculty of Science Sakarya University Serdivan/Sakarya Turkey Department of Computer Science and Mathematics Lebanese American University Byblos Lebanon Department of Pure and Applied Mathematics LAUTECH Ogbomosho Nigeria Department of Mathematics College of Science King Khalid University Abha Saudi Arabia Department of Mechanical Engineering College of Engineering in Wadi Alddawasir Prince Sattam bin Abdulaziz University Wadi Alddawasir Saudi Arabia Production Engineering and Mechanical Design Department Faculty of Engineering Mansoura University Mansoura Egypt

The present article is designed to study the Hamilton and Crosser model applied to the flow of ternary hybrid nanofluids over a Riga wedge, incorporating the effects of heterogeneous catalytic reactions. The complex interactions within the ternary hybrid nanofluids, comprising three distinct nanoparticles suspended in a base fluid, present significant challenges in accurately predicting flow and thermal characteristics. The Hamilton and Crosser model, known for its efficacy in determining the thermal conductivity of composite materials, is employed to analyze this intricate system. The analysis reveals the model's potential in offering a comprehensive understanding of the thermal and fluid dynamics involved, highlighting its suitability for predicting the behavior of ternary hybrid nanofluids in the presence of catalytic reactions. The governing model equations and boundary conditions are non-dimensionalized by introducing suitable similarity transformations. Thereafter, the computational Chebyshev collocation spectral technique implemented in the MATHEMATICA 11.3 software is used to calculate the numerical solution. The study reveals that the Casson parameter has a negative influence on the velocity distribution, causing it to reduce as the Casson parameter rises. This research contributes to the advancement of modeling techniques for complex fluid systems, with implications for enhanced design and optimization in various industrial and engineering applications.

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Network-based methods with heterogeneous data to identify severe COVID immune-related genes 26

Network-based methods with heterogeneous data to identify se...

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26th International computer science and Engineering Conference, ICSEC 2022

作者： Sagulkoo, Pakorn Suratanee, Apichat Plaimas, Kitiporn Chiang Mai University Center of Biomedical Informatics Department of Family Medicine Faculty of Medicine Chiang Mai50200 Thailand King Mongkut's University of Technology North Bangkok Department of Mathematics Faculty of Applied Science Bangkok10800 Thailand Faculty of Science Chulalongkorn University Advance Virtual and Intelligent Computing Center Department of Mathematics and Computer Science Bangkok10330 Thailand

ISBN: (纸本)9781665491983

Bioinformatics and systems biology play a vital role in the computational prediction of disease-associated genes using multi-omics data. The network-based approach is one of the most potent tools in disease-associated gene prediction. The two commonly used methods are neighborhood-based and network diffusion techniques. However, there is still a lack of studies comparing the performance of these methods, especially in terms of functional pathway discovery. Thus, this study demonstrated the performance comparison of these two techniques in both numerical accuracies based on the area under the receiver operating characteristic curve (AUROC) and biological meaning efficiency based on functional pathway enrichment. In this study, we analyzed data of severe COVID-19 immune-related genes using heterogeneous data. The prediction results of the COVID-19 immune-related genes in the human protein-protein interaction (PPI) network showed that the network diffusion had better performance in both AUROC and pathway enrichment even though it provided a longer computational time than the neighborhood method. © 2022 IEEE.

关键词： COVID-19

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