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SSRN 2023年

作者： Gao, Penglei Yang, Xi Zhang, Rui Huang, Kaizhu Department of Computer Science University of Liverpool United Kingdom School of Advanced Technology Canada Department of Foundational Mathematics Xi’an Jiaotong-Liverpool University China Institute of Applied Physical Sciences and Engineering Duke Kunshan University China

Generalised image outpainting is an important and active research topic in computer vision and pattern recognition, which aims to extend appealing content all-side around a given image. Existing state-of-the-art outpainting methods often rely on discrete extrapolation to extend the feature map in the bottleneck. They thus suffer from content unsmoothness, especially in circumstances where the outlines of objects in the extrapolated regions are incoherent with the input sub-images. To mitigate this issue, we design a novel bottleneck with Neural ODEs to make continuous extrapolation in latent space, which could be a plug-in for many deep learning frameworks. Our ODE-based network continuously transforms the state and makes accurate predictions by learning the incremental relationship among latent points, leading to both smooth and structured feature representation. Experimental results on three real-world datasets both applied on transformer-based and CNN-based frameworks show that our methods could generate more realistic and coherent images against the state-of-the-art image outpainting approaches. © 2023, The Authors. All rights reserved.

关键词： Extrapolation

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Multi-bounce resonances in the interaction of walking droplets

arXiv

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arXiv 2023年

作者： Zhang, George Christov, Ivan C. Rahman, Aminur Department of Applied Mathematics University of Washington United States School of Mechanical Engineering Purdue University United States Department of Computer Science University of Nicosia Cyprus AI Institute in Dynamic Systems University of Washington United States

Discrete dynamical models of walking droplets ("walkers") have allowed swift numerical experiments revealing heretofore unobserved quantum statistics and related behaviors in a classical hydrodynamic system. We present evidence that one such model of walking droplets exhibits the empirically elusive n-bounce resonances that are traditionally seen in the scattering of solitary waves governed by covariant nonlinear field theories with polynomial self-interaction. A numerical investigation of the chosen model of interacting walking droplets reveals a fractal structure of resonances in the velocity in–velocity out diagram, much like the usual maps constructed for collisions of solitary waves. We suggest avenues for further theoretical analysis of walker collisions, which may connect this discrete model to the field-theoretic setting, as well as directions towards new experimental realizations n-bounce *** Codes 37xx, 76B15, 70Kxx Copyright © 2023, The Authors. All rights reserved.

关键词： Drops

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DeceFL:a principled fully decentralized federated learning framework

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National science Open 2023年第1期2卷 35-51页

作者： Ye Yuan Jun Liu Dou Jin Zuogong Yue Tao Yang Ruijuan Chen Maolin Wang Chuan Sun Lei Xu Feng Hua Yuqi Guo Xiuchuan Tang Xin He Xinlei Yi Dong Li Guanghui Wen Wenwu Yu Hai-Tao Zhang Tianyou Chai Shaochun Sui Han Ding School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China Department of Applied Mathematics University of WaterlooWaterloo N2L 3G1Canada State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China School of Mathematical and Physical Sciences Wuhan Textile UniversityWuhan 430200China School of Electrical Engineering and Computer Science and Digital FuturesKTH Royal Institute of TechnologyStockholm 10044Sweden Shenyang Institute of Automation(SIA) Chinese Academy of SciencesShenyang 110169China School of Mathematics Frontiers Science Center for Mobile Information Communication and SecuritySoutheast UniversityNanjing 211189China Purple Mountain Laboratories Nanjing 211111China AVIC Chengdu Aircraft Industrial(Group)Co. Ltd.Chengdu 610091China

Traditional machine learning relies on a centralized data pipeline for model training in various applications;however,data are inherently *** a decentralized nature of databases presents the serious challenge for collaboration:sending all decentralized datasets to a central server raises serious privacy *** there has been a joint effort in tackling such a critical issue by proposing privacy-preserving machine learning frameworks,such as federated learning,most state-of-the-art frameworks are built still in a centralized way,in which a central client is needed for collecting and distributing model information(instead of data itself)from every other client,leading to high communication burden and high vulnerability when there exists a failure at or an attack on the central *** we propose a principled decentralized federated learning algorithm(DeceFL),which does not require a central client and relies only on local information transmission between clients and their neighbors,representing a fully decentralized learning *** has been further proven that every client reaches the global minimum with zero performance gap and achieves the same convergence rate O(1=T)(where T is the number of iterations in gradient descent)as centralized federated learning when the loss function is smooth and strongly ***,the proposed algorithm has been applied to a number of applications to illustrate its effectiveness for both convex and nonconvex loss functions,time-invariant and time-varying topologies,as well as IID and Non-IID of datasets,demonstrating its applicability to a wide range of real-world medical and industrial applications.

关键词： decentralized federated learning smart manufacturing control systems privacy

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Symbolic computation of solitary wave solutions and solitons through homogenization of degree

arXiv

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arXiv 2023年

作者： Hereman, Willy Göktaş, Ünal Department of Applied Mathematics and Statistics Colorado School of Mines GoldenCO80401-1887 United States Department of Computer Science and Engineering Texas A&M University College StationTX77843-3112 United States

A simplified Hirota method for the computation of solitary waves and solitons of nonlinear partial differential equations (PDEs) is presented. A change of dependent variable transforms the PDE into an equation that is homogeneous of degree. Solitons are then computed using a perturbation-like scheme involving linear and nonlinear operators in a finite number of steps. The method is applied to fifth-order Korteweg-de Vries (KdV) equations due to Lax, Sawada-Kotera, and Kaup-Kupershmidt. The method works for non-quadratic homogeneous equations for which the bilinear form might be unknown. Furthermore, homogenization of degree allows one to compute solitary wave solutions of nonlinear PDEs that do not have solitons. Examples include the Fisher and FitzHugh-Nagumo equations, and a combined KdV-Burgers equation. When applied to a wave equation with a cubic source term, one gets a "bi-soliton" solution describing the coalescence of two wavefronts. The method is largely algorithmic and implemented in *** Codes 35C08, 37K40, 35Q53, 68W30 Copyright © 2023, The Authors. All rights reserved.

关键词： Solitons

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An enhanced Walrus Optimizer with opposition-based learning and mutation strategy for data clustering

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Array 2025年 26卷

作者： Abualigah, Laith Alomari, Saleh Ali Almomani, Mohammad H. Zitar, Raed Abu Migdady, Hazem Saleem, Kashif Smerat, Aseel Snasel, Vaclav Ezugwu, Absalom E. Computer Science Department Al al-Bayt University Mafraq25113 Jordan Faculty of Science and Information Technology Jadara University Irbid21110 Jordan Department of Mathematics Facility of Science The Hashemite University P.O box 330127 Zarqa13133 Jordan Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates CSMIS Department Oman College of Management and Technology Barka320 Oman Department of Computer Science & Engineering College of Applied Studies & Community Service King Saud University Riyadh11362 Saudi Arabia Faculty of Educational Sciences Al-Ahliyya Amman University Amman19328 Jordan Computer Technologies Engineering Mazaya University College Nasiriyah Iraq Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Poruba-Ostrava 70800 Czech Republic Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom2520 South Africa

Data clustering plays a crucial role in various domains, such as image processing, pattern recognition, and data mining. Traditional clustering techniques often suffer from limitations like sensitivity to initialization, poor convergence, and entrapment in local optima. To address these challenges, this paper proposes an Enhanced Walrus Optimizer (IWO) tailored for clustering tasks. The proposed IWO integrates two powerful strategies–Opposition-Based Learning (OBL) and Mutation Search Strategy (MSS)–to improve population diversity and prevent premature convergence, thereby enhancing both exploration and exploitation capabilities. These enhancements enable more accurate and stable identification of cluster centers. The effectiveness of IWO is validated through extensive experiments on multiple benchmark clustering datasets and compared against several state-of-the-art metaheuristic algorithms, including PSO, GWO, AOA, and others. The results demonstrate that IWO achieves better results, indicating improved compactness and separation of clusters. Statistical validation using p-values and ranking scores further confirms the superiority of the proposed method. These findings suggest that IWO offers a robust and flexible framework for solving complex clustering problems. Future work will explore hybrid deep learning-integrated models and parallel implementations to enhance scalability. © 2025

关键词： Swarm intelligence

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Dynamics of Multi-Strain Malware Epidemics over Duty-Cycled Wireless Sensor Networks 4

Dynamics of Multi-Strain Malware Epidemics over Duty-Cycled ...

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4th International Balkan Conference on Communications and Networking, BalkanCom 2021

作者： Fedorov, Dmitriy Tabarak, Yrys Dadlani, Aresh Kumar, Muthukrishnan Senthil Kizheppatt, Vipin Nazarbayev University Department of Electrical and Computer Engineering Nur-Sultan Kazakhstan PSG College of Technology Department of Applied Mathematics and Computational Sciences Coimbatore India Birla Institute of Technology and Science Pilani Department of Electrical and Electronics Engineering Goa India

ISBN: (纸本)9781665402583

Insights on the salient features of malicious software spreading over large-scale wireless sensor networks (WSNs) in low-power Internet of Things (IoT) are not only essential to project, but also mitigate the persistent rise in cyber threats. While the analytical findings on single malware spreading dynamics are well-established, the interplay among multiple malware strains with heterogeneous infection rates in power-limited WSNs yet remain unexplored. Inspired by compartmental modeling in epidemiology, we present the mean-field approximation for a novel stochastic epidemic model of two mutually exclusive malware strains spreading over WSNs with sleep/awake modes of energy consumption. Referred as the susceptible-infected by strain 1 or by strain 2-susceptible with duty cycles (SI1I2SD), we then derive the basic reproduction number to characterize the sufficient conditions for the existence and stability of the infection-free and endemic equilibrium states. Simulation results show the predictive capability of the proposed model for energy-efficient WSNs evolving as random geometric graphs against uniformly connected networks. © 2021 IEEE.

关键词： Wireless sensor networks

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SEMI-IMPLICIT HYBRID FINITE VOLUME/FINITE ELEMENT METHOD FOR THE GPR MODEL OF CONTINUUM MECHANICS

arXiv

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arXiv 2024年

作者： Busto, Saray Río-Martín, Laura Department of Applied Mathematics Universidade de Santiago de Compostela Santiago de Compostela15782 Spain Galician Center for Mathematical Research and Technology CITMAga Santiago de Compostela15782 Spain Department of Information Engineering and Computer Science University of Trento via Sommarive 9 Povo Trento38123 Italy Laboratory of Applied Mathematics DICAM University of Trento Via Mesiano 77 Trento38123 Italy

We present a new hybrid semi-implicit finite volume / finite element numerical scheme for the solution of incompressible and weakly compressible media. From the continuum mechanics model proposed by Godunov, Peshkov and Romenski (GPR), we derive the incompressible GPR formulation as well as a weakly compressible GPR system. As for the original GPR model, the new formulations are able to describe different media, from elastoplastic solids to viscous fluids, depending on the values set for the model’s relaxation parameters. Then, we propose a new numerical method for the solution of both models based on the splitting of the original systems into three subsystems: one containing the convective part and non-conservative products, a second subsystem for the source terms of the distortion tensor and heat flux equations and, finally, a pressure subsystem. In the first stage of the algorithm, the transport subsystem is solved by employing an explicit finite volume method, while the source terms are solved implicitly. Next, the pressure subsystem is implicitly discretised using finite elements. Within this methodology, unstructured grids are employed, with the pressure defined in the primal grid and the rest of the variables computed in the dual grid. To evaluate the performance of the proposed scheme, a numerical convergence analysis is carried out, which confirms the second order of accuracy in space. A wide range of benchmarks is reproduced for the incompressible and weakly compressible cases, considering both solid and fluid media. These results demonstrate the good behaviour and robustness of the proposed scheme in a variety of scenarios and conditions. © 2024, CC BY-NC-ND.

关键词： Finite element method

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Promoting Tailored Hotel Recommendations Based on Traveller Preferences:A Circular Intuitionistic Fuzzy Decision Support Model

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computer Modeling in engineering & sciences 2025年第5期143卷 2155-2183页

作者： Sana Shahab Ibtehal Alazman Ashit Kumar Dutta Mohd Anjum Vladimir Simic Zeljko Stevic Nouf Abdulrahman Alqahtani Department of Business Administration College of Business AdministrationPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Mathematics and Statistics College of ScienceImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11623Saudi Arabia Department of Computer Science and Information Systems College of Applied SciencesAI Maarefa UniversityAd DiriyahRiyadh13713Saudi Arabia Department of Computer Engineering Aligarh Muslim UniversityAligarh202002India Faculty of Transport and Trafc Engineering Universityf BelgradeVojode Stepe 305Belgrade110Serbia Department of Industrial Engineering and Management College of EngineeringYuan Ze University Taoyuan City320315Taiwan Deparment of Computer Science and Engineering Colege of InformaticsKorea UniversitySeongbuk-GuSeoul284Republi of Korea Department of Railway Transport and Mobile Machinery Faculty of Transport EngineeringVilnius Gediminas Technical UniversityVilnius10105Lithuania

With the increasing complexity of hotel selection,traditional decision-making models often struggle to account for uncertainty and interrelated ***-criteria decision-making(MCDM)techniques,particularly those based on fuzzy logic,provide a robust framework for handling such *** paper presents a novel approach to MCDM within the framework of Circular Intuitionistic Fuzzy Sets(C-IFS)by combining three distinct methodologies:Weighted Aggregated Sum Product Assessment(WASPAS),an Alternative Ranking Order Method Accounting for Two-Step Normalization(AROMAN),and the CRITIC method(Criteria Importance Through Inter-criteria Correlation).To address the dynamic nature of traveler preferences in hotel selection,the study employs a comprehensive set of criteria encompassing aspects such as location proximity,amenities,pricing,customer reviews,environmental impact,safety,booking flexibility,and cultural *** CRITIC method is used to determine the importance of each criterion by assessing intercriteria *** is employed for the systematic evaluation of alternatives,considering their additive relationships and providing a weighted *** further analyzes the results obtained from AROMAN,incorporating both positive and negative aspects for a comprehensive *** integration of C-IFS enhances the model’s ability to manage uncertainty and imprecision in the decision-making *** a case study,we demonstrate the effectiveness of this integrated approach,offering decision-makers valuable insights for selecting the most suitable hotel option in alignment with the diverse preferences of contemporary *** research contributes to the evolving field of decision science by showcasing the practical applicability of these methodologies within a C-IFS framework for complex decision scenarios.

关键词： Multi-criteria decision-making circular intuitionistic fuzzy sets hotel recommendations

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Applying data mining technique to predict trends in air pollution in Mumbai 1

Applying data mining technique to predict trends in air poll...

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1st International Conference on Advances in Computational science and engineering, ICACSE 2020

作者： Sadhasivam, Jayakumar Muthukumaran, V. Thimmia Raja, J. Vinothkumar, V. Deepa, R. Nivedita, V. Vellore Institute of Technology Vellore India Department of Mathematics School of Applied Science REVA University Bengaluru64 India School of Computing Kalasalingam Academy of Research and Education Tamilnadu 626128 India Department of Computer Science and Engineering MVJ College of Engineering Bangalore India Telangana Hyderabad India Department of Computer Science and Engineering Dr. MGR Educational and Research Institute Chennai India

Prediction of air quality is a topic of great interest in air quality research due to direct association with health effect. The prediction provides pre-information to the overall population of the area about the status of pollution on which they can take precautionary measures and can protect their health. The problem arises when the level of SO2, NO2 and residual suspended particulate matters in the air increases than that of theirs restricted level. In this paper, the Prophet Algorithm, open source software, is applied to predict the trend of air pollution in the city of Mumbai, Maharashtra. The Prophet is machine learning algorithm to forecast and also to predict time series data. It is based on additive model where non-linear trends are fit with yearly and weekly seasonality. The graphical results are generated after using this algorithm which shows the trending pattern of the pollutants in the air of Mumbai. © Published under licence by IOP Publishing Ltd.

关键词： Air quality

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Unsupervised Learning for Detection of Radio Frequency Interference Signals

Unsupervised Learning for Detection of Radio Frequency Inter...

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Brahampal Singh Vijai Pal Singh Anukool Bajpai Chetan Bulla Swapnil Parikh Ramya Maranan Department of CS & IT Trinity Institute of Innovations in Professional Studies Delhi India Department of Applied Mathematics Trinity institute of innovations in professional studies Uttar Pradesh India Department of Computer Science GGSIP University New Delhi India Department of Business Intelligence Synechron Bangalore Karnataka India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Gujarat India Department of Research and Innovation Saveetha School of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

In order to ensure that wireless communication systems operate at their best, it's crucial to develop reliable methods for detecting RF interference. This study compares two unsupervised anomaly detection algorithms, Support Vector Data Description (SVDD) and One-Class Support Vector Machine (SVM), using a small training database. We evaluated these algorithms by detecting various types of RF interference signals, including Continuous Interference (CI), Continuous Wave Interference (CWI), and Modulated Continuous Wave Interference (MCWI). While previous works have made strides in RF interference detection, they have some limitations. For example, some methods may struggle with accurately detecting RF interference in dynamic environments or when faced with varying interference patterns. Additionally, certain algorithms may exhibit reduced performance when dealing with low signal-to-noise ratios or in the presence of complex interference sources. To address these challenges, both SVDD and One-Class SVM (OCS) algorithms are used and described the methodology for evaluating their performance. Throughout the experiments, how well SVDD and OCS could identify abnormal interference signals are measured using the precision metric. The findings revealed that employing normalization techniques like feature scaling over a range or robust normalization significantly improved the accuracy of RF interference detection. Importantly, SVDD consistently outperformed OCS in this regard.

关键词： Support vector machines Wireless communication Training Measurement Accuracy Databases Heuristic algorithms

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