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Stochastic SEIR epidemic models with virus mutation and logistic growth of susceptible populations

Mathematics and Computers in Simulation 2023年第1期212卷 289-309页

作者： Wang, Qi Xiang, Kainan Zhu, Chunhui Zou, Lang Hunan Key Laboratory for Computation and Simulation in Science and Engineering & Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education & School of Mathematics and Computational Science Xiangtan University Hunan Province Xiangtan City411105 China National Center for Applied Mathematics in Hunan China

This article introduces a kind of stochastic SEIR models containing virus mutation and logistic growth of susceptible populations, whose deterministic versions have a positively invariant set and globally asymptotically stable equilibrium points. For these stochastic SEIR epidemic models, we prove they have a unique global positive solution, and also obtain sufficient conditions respectively for survival and extinction of the infectious disease. Eventually, we validate our theoretical findings using numerical simulations. © 2023 International Association for Mathematics and Computers in Simulation (IMACS)

关键词： Viruses

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Research on patent classification based on hierarchical label semantics 3

Research on patent classification based on hierarchical labe...

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3rd International Conference on education, Knowledge and Information Management, ICEKIM 2022

作者： Chen, Jie Zhao, Shu Lu, Fan Liu, Feng Zhang, Yangping Ministry of Education Anhui University Key Laboratory of Intelligent Computing and Signal Processing Hefei China Anhui University School of Computer Science and Technology Hefei China

ISBN: (纸本)9781665416665

Patent classification is an essential task in patent information management and knowledge mining. Most existing studies are based on the textual content of individual patent texts (e.g., titles and abstracts) for classification, but the patent also has label information with hierarchical structure and semantic description. However, the semantics correlation of patent texts and their labels have been largely ignored. In this paper, we propose a novel framework called HLSPC for patent classification by leveraging the hierarchical semantics correlation of patent texts and their labels. Specifically, we first apply a patent representation learning module for capturing the semantics representation of patent texts and hierarchical labels. Then, we design a label attention learning module to build the semantics correlation between patent texts and hierarchical labels, which enhances patent representation. Finally, we deploy a multi-level fusion module to get the refined category prediction for each patent which can preserve both local and global hierarchical prediction information. Extensive experimental results on two English patent datasets demonstrate the effective power of the HLSPC model. © 2022 IEEE.

关键词： Semantics

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Deep Reinforcement Learning based intelligent Routing for Multi-service Traffic SDN 5

Deep Reinforcement Learning based Intelligent Routing for Mu...

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5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023

作者： Wang, Lingjiao Li, Jiachen Xiang Tan University Key Laboratory of Intelligent Computing and Information Processing Ministry of Education China Xiang Tan University School of Automation and Electronic Information China

ISBN: (纸本)9798350357738

The development of network has imposed higher requirements on QoS (Quality of Service) for routing, and the emergence of traffic classification techniques, software defined networking (SDN), and programmable network devices has made it possible to quickly identify user needs and route for service traffic requirements. Deep reinforcement learning routing algorithms for existing QoS compliant routing algorithms perform poorly in terms of generalizability. A deep graph reinforcement learning intelligent routing algorithm DDGL applied to SDN scenarios is proposed, which uses GNN to extract the link feature information of SDN data plane for deep reinforcement learning routing decision making, and comprehensively takes into account the performance metrics such as latency, packet loss, throughput, and bandwidth, to improve the algorithm generalization ability and enhance the SDN routing performance. © 2023 IEEE.

关键词： Reinforcement learning

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A Fractional-Order Logarithmic Adaptive Filtering Algorithm Under α-Stable Noise 6

A Fractional-Order Logarithmic Adaptive Filtering Algorithm ...

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6th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2023

作者： Sun, Dazhi Yu, Canping Li, Li Li, Yingsong Xiao, Wudang Chen, Yong Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China Aircraft Airworthiness Institute China Academy of Civil Aviation Science and Technology Center of Aviation Safety Technology Cast Beijing101399 China

ISBN: (纸本)9798350399059

In practical applications, noise is more appropriately modelled as an a-stable random process than as a Gaussian process. To cope with a-stable noise, a fractional-order stochastic gradient descent (SGD) method is invented to adaptive filtering (AF) algorithms. When both input signal and noise follow α-stable random processes, the AF algorithms based on mean square error (MSE) criteria can be affected. Nevertheless, the fractional-order AF (FoAF) algorithm can effectively address this issue. For the purpose of better stability performance of the FoAF algorithm, different cost functions (CFs) are introduced into fractional-order SGD method. Based on the fractional-order derivative of a logarithmic CF (LCF), a fractional-order logarithmic adaptive filtering (FoLAF) algorithm is formed in this condition. Trials show that the FoLAF algorithm achieves faster convergence, excellent steady-state performance and superior tracking performance. © 2023 IEEE.

关键词： Adaptive filtering

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A Finite Element Variational Multiscale Method for Stationary Incompressible Magnetohydrodynamics Equations

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Advances in Applied Mathematics and Mechanics 2023年第5期15卷 1142-1165页

作者： Huayi Huang Yunqing Huang Qili Tang Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of IntelligentComputing&Information Processing of Ministry of EducationSchool of Mathematics and Computational ScienceXiangtan UniversityXiangtanHunan 411105China

In this paper,we propose a variational multiscale method(VMM)for the stationary incompressible magnetohydrodynamics *** method is defined by large-scale spaces for the velocity field and the magnetic field,which aims to solve flows at high Reynolds *** provide a new VMM formulation and prove its stability and ***,some numerical experiments are presented to indicate the optimal convergence of our method.

关键词： Variational multiscale method stationary incompressible magnetohydrodynamics large-scale spaces stability and convergence high Reynolds numbers

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2023 7th International Conference on Communication and Information Systems An Improved IOTA Protocol Based on Optimal Midway Walk 7

2023 7th International Conference on Communication and Infor...

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7th International Conference on Communication and Information Systems, ICCIS 2023

作者： Wu, Hongcheng Zhi, Hui Liu, Yangyang Anhui University School of Electronic and Information Engineering Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Hefei230601 China Hefei Zhilian Technology Industry Investment Co. Ltd Hefei230601 China

ISBN: (纸本)9798350330540

IOTA (Internet of Things Applications) protocol aims to provide a secure and trustworthy protocol for realizing data-value interactions between IoT devices, and it has become the next possible direction for the development of Blockchain applications. IOTA protocol has the problem that the time-consuming cumulative weight update grows too fast with the increase of the number of transactions, this paper proposed an improved IOTA protocol based on optimal midway walk, the improved protocol uses cutting-edge transaction selection algorithm of weighted Monte Carlo Markov random walk to reduce cumulative weight update delay. Firstly, this improved IOTA protocol mainly ensures the efficiency of transaction confirmation without losing the security of consensus by introducing the optimal midway walk method, i.e., the starting point of each walk is chosen to depart at an optimal location. Secondly, this paper designs the indirect verification identity record algorithm and the layer degree of confirmation principle are designed to realize the improved IOTA protocol's resilience and resistance to Parasite chain attack. The experimental results show that the improved IOTA using random function to select the midway walk starting point from i-50λ, to i-75λsubstantially solves the system computational burden problem and greatly reduces the cumulative weight update delay. © 2023 IEEE.

关键词： cumulative weight IOTA midway walk Parasite chain attack

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Few-Shot Object Detection in SAR images via Gaussian Flow Class-Balanced Differential Aggregation

Few-Shot Object Detection in SAR images via Gaussian Flow Cl...

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signal, Information and Data processing (ICSIDP), IEEE International Conference on

作者： Po Zhao Jie Chen Chaochen Zhang Ke Ma Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electronics and Information Engineering Anhui University Hefei China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Currently, few-shot object detection (FSOD) methods for synthetic aperture radar (SAR) images are severely understudied. The high confidentiality and scarcity of SAR data, coupled with the susceptibility of targets in SAR images to significant changes in orientation, make existing models sensitive to variations in novel class examples. This sensitivity leads to challenges in extracting class prototypes and accurately estimating class centers for novel classes. To solve the above issues, we propose a few-shot object detection algorithm for SAR images via Gaussian Flow Class-Balanced Differential Aggregation. Specifically, we first design a class-balanced difference feature aggregation module to solve the problems of difficult class distribution estimation and inaccurate class center estimation of novel classes in SAR images. Second, the contextual information perception module is designed to extract the most representative support features into fine-grained prototypes and aggregate them with the query features using the matching paradigm to solve the imbalance between foreground targets and background in SAR images. Finally, experimental results on MSAR-AIR dataset indicate that our model realizes the best detection properties on SAR images compared to existing models.

关键词： Representation learning Sensitivity Prototypes Estimation Object detection Interference Feature extraction Radar polarimetry Data models Synthetic aperture radar

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Representation Purification for End-to-End Speech Translation 31

Representation Purification for End-to-End Speech Translatio...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zhang, Chengwei Zhou, Yue Zhao, Rui Chen, Yidong Shi, Xiaodong School of Informatics Xiamen University China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University Xiamen China

ISBN: (纸本)9798891761964

Speech-to-text translation (ST) is a cross-modal task that involves converting spoken language into text in a different language. Previous research primarily focused on enhancing speech translation by facilitating knowledge transfer from machine translation, exploring various methods to bridge the gap between speech and text modalities. Despite substantial progress made, factors in speech that are not relevant to translation content, such as timbre and rhythm, often limit the efficiency of knowledge transfer. In this paper, we conceptualize speech representation as a combination of content-agnostic and content-relevant factors. We examine the impact of content-agnostic factors on translation performance through preliminary experiments and observe a significant performance deterioration when content-agnostic perturbations are introduced to speech signals. To address this issue, we propose a Speech Representation Purification with Supervision Enhancement (SRPSE) framework, which excludes the content-agnostic components within speech representations to mitigate their negative impact on ST. Experiments on MuST-C and CoVoST-2 datasets demonstrate that SRPSE significantly improves translation performance across all translation directions in three settings and achieves preeminent performance under a transcript-free setting. © 2025 Association for Computational Linguistics.

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A Blockchain-Based Framework for Secure Storage and Sharing of Resumes

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Computers, Materials & Continua 2022年第9期72卷 5395-5413页

作者： Huanrong Tang Changlin Hu Tianming Liu Jianquan Ouyang Key Laboratory of Intelligent Computing and Information Processing Ministry of EducationComputer Science College of Xiangtan UniversityXiangtanHunan ProvinceChina Department of Computer Science The University of GeorgiaAthensGeorgiaUSA

In response to problems in the centralized storage of personal resumes on third-party recruitment platforms,such as inadequate privacy protection,inability of individuals to accurately authorize downloads,and inability to determine who downloaded the resume and when,this study proposes a blockchain-based framework for secure storage and sharing of *** can employ an authorized access mechanism to protect their privacy *** proposed framework uses smart contracts,interplanetary file system,symmetric encryption,and digital signatures to protect,verify,and share *** keys are split and stored in multiple depositories through secret-sharing technology to improve the security of these *** key escrow incentives are implemented using smart contracts to automatically verify the correctness of keys and encourage the active participation of honest key escrow *** framework combines blockchain and searchable symmetric encryption technology to realize multikeyword search using inverted indexing and Bloom filters and verify search results on the *** search service fees are charged through *** after the search results are verified can the search service provider obtain the search fee,thus ensuring fair and efficient search for encrypted *** framework is decentralized,secure,and tamper-evident,and achieves controlled sharing while protecting personal privacy and information security.

关键词： Resume blockchain interplanetary file system secret sharing searchable encryption

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Machine Learning Based FDTD Method for 3D Electromagnetic Simulation

Machine Learning Based FDTD Method for 3D Electromagnetic Si...

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2024 IEEE International Conference on Computational Electromagnetics, ICCEM 2024

作者： Dai, Jinpeng Xie, Guoda Hou, Guilin Zhang, Liyuan Huang, Zhixiang Anhui University The Key Laboratory of Intelligent Computing and Signal Processing Hefei China Education of Anhui Province Anhui Province Key Laboratory of Target Recognition and Feature Extraction Anhui Luan237000 China Ministry of Education The Key Laboratory of Intelligent Computing and Signal Processing Hefei China Institute of Energy Hefei Comprehensive National Science Center Hefei230031 China Institute of Plasma Physics Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China Anhui Province Anhui University Key Laboratory of Electromagnetic Environmental Sensing Hefei230601 China Anhui University Hefei China

ISBN: (纸本)9798350383317

This article introduces an innovative and efficient deep learning-assisted Finite-Difference Time-Domain (DL-FDTD) method in the field of computational electromagnetics. This method ingeniously integrates the Gated Recurrent Unit (GRU) network and Particle Swarm Optimization (PSO) algorithm into the traditional FDTD framework, termed as the PSO-GRU-FDTD model. A key innovation of this model is the application of the particle swarm algorithm, which significantly simplifies the parameter tuning process, thereby accelerating model development. This advancement represents a notable breakthrough from the complex parameter adjustment process typical in traditional neural network models. Moreover, compared to the traditional Long Short-Term Memory (LSTM) networks, the GRU network excels in simplicity, convenience, and efficiency, while also ensuring accuracy. Ultimately, this method is applied to three-dimensional electromagnetic simulation and emulation. Numerical results demonstrate that this approach exhibits outstanding performance in both simulation accuracy and efficiency. © 2024 IEEE.

关键词： Electromagnetic simulation

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