检索结果-内蒙古大学图书馆

Controllable chaos in the coupled waveguide-optomechanical system with phase shifter

Science China(Physics,Mechanics & Astronomy) 2025年第4期68卷 26-34页

作者： Deng-Wei Zhang Pei-Qi Qin Li-Li Zheng Zhi-Ming Zhan Xin-You Lü Department of Mathematics and Physics Luoyang Institute of Science and TechnologyLuoyang 471023China School of Computer and Information Engineering School of SoftwareLuoyang Institute of Science and TechnologyLuoyang 471023China School of Artificial Intelligence Jianghan UniversityWuhan 430056China School of Physics Huazhong University of Science and TechnologyWuhan 430074China

We theoretically investigate chaotic dynamics in an optomechanical system composed of a whispering-gallery-mode(WGM)microresonator and a *** find that tuning the optical phase using a phase shifter and modifying the coupling strength via a unidirectional waveguide(IWG)can induce chaotic *** underlying reason for this phenomenon is that adjusting the phase and coupling strength via the phase shifter and IWG bring the system close to an exceptional point(EP),where field localization dynamically enhances the optomechanical nonlinearity,leading to the generation of chaotic *** addition,due to the sensitivity of chaos to phase in the vicinity of the EP,we propose a theoretical scheme to measure the optical phase perturbations using *** work may offer an alternative approach to chaos generation with current experimental technology and provide theoretical guidance for optical signal processing and chaotic secure communication.

关键词： cavity optomechanics chaos exceptional point

来源：评论

学校读者我要写书评

暂无评论

Clustering and Artificial Intelligence-based Prediction of Ecologically Sustainable Species Introductions

IAENG International Journal of Computer Science

引用

IAENG International Journal of computer Science 2025年第4期52卷 1159-1168页

作者： Liu, Shuqiao Zhang, Zhao Zhou, Hongyan Chen, Xue-Bo School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

There is a growing interest in sustainable ecosystem development, which includes methods such as scientific modeling, environmental assessment, and development forecasting and planning. However, due to insufficient survey data in many current development areas, development progress is delayed and stagnant. To address this situation, this paper proposes a SWOT-TOPSIS-K-Means (STK) data analysis and evaluation model to analyze ecological factors, which can realize a comprehensive and complete data analysis with fewer samples. Decision tree (DT), random forest (RF), and multilayer perceptron (MLP) neural network models were constructed from the results of this analysis, and statistical tests such as r-squared, mean absolute error, and cross-validation are used to further confirm the performance efficiency of the computational prediction models to provide real-time prediction research solutions. For this purpose, data from research scholars on species introduction in ecosystem development were selected for testing. The results show that the proposed assessment model and modeling results satisfy all accuracy-related acceptance requirements. Among them, MLP is better than DT and RF. In summary, the STK assessment model and the MLP prediction model can provide a basis for the selection and development of ecological factors. © (2025), (International Association of Engineers). All rights reserved.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Minimization of the Training Makespan in Hybrid Federated Split Learning

引用

IEEE Transactions on Mobile Computing 2025年第6期24卷 5400-5417页

作者： Tirana, Joana Tsigkari, Dimitra Iosifidis, George Chatzopoulos, Dimitris University College Dublin School of Computer Science Ireland Telefónica Research Spain Delft University of Technology Department of Software Technology Delft2628 Netherlands

Parallel Split Learning (SL) allows resource-constrained devices that cannot participate in Federated Learning (FL) to train deep neural networks (NNs) by splitting the NN model into parts. In particular, such devices (clients) may offload the processing task of the largest model part to a computationally powerful helper, and multiple helpers may be employed and work in parallel. In hybrid federated and split learning (HFSL), on the other hand, devices can participate in the training process through any of the two protocols (SL and FL), depending on the system's characteristics. This could considerably reduce the maximum training time over all clients (makespan), especially in highly heterogeneous scenarios. In this paper, we study the joint problem of the training protocol selection, client-helper assignments, and scheduling decisions, to minimize the training makespan. We prove this problem is NP-hard and propose two solution methods: one based on the decomposition of the problem by leveraging its inherent symmetry, and a second fully scalable one. Through numerical evaluations using our testbed's measurements, we build a solution strategy comprising these methods. Moreover, this strategy finds a near-optimal solution and achieves a shorter makespan than the baseline schemes by up to 71%. © 2025 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

FedMPS: A Robust Differential Privacy Federated Learning based on Local Model Partition and Sparsification for Heterogeneous IIoT Data

引用

IEEE Internet of Things Journal 2025年第10期12卷 13757-13768页

作者： Wang, Danxin Gao, Yuyang Pang, Shanchen Zhang, Chen Zhang, Xiaoman Li, Ming School of Qingdao Institute of Software College of Computer Science and Technology Shandong Key Laboratory of Intelligent Oil and Gas Industrial Software Qingdao China Shandong Inspur Database Technology Co. Ltd Jinan China

In the emerging Industrial Internet of Things (IIoT) applications, Federated Learning (FL) enables model training without the need to transmit raw data directly. Nevertheless, transmitting model parameters could still reveal private information. To further protect local model parameters, Differential Privacy combined with FL (DPFL) has been introduced. Nonetheless, adding noise in DPFL can severely impact model performance, especially in non-independent and identically distributed (non-iid) data scenarios typical of IIoT environments. It is necessary to carefully balance privacy preservation and utility. In this paper, we propose a robust differential privacy Federated learning scheme leveraging local Model Partition and Sparsification (namely, FedMPS) for heterogeneous IIoT scenarios. The local model is divided into a shared part, which is sparsified before adding noise to mitigate its impact, and a private part that remains on the client. We provide a theoretical analysis of the privacy guarantees. Extensive experiments on common datasets, including Fashion-MNIST, CIFAR-10 and CIFAR-100, demonstrate that the proposed approach achieves a better privacy-utility trade-off, with a 10 compared to baseline methods, and performs well especially in non-iid scenarios. © 2025 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Multimodal Neural Machine Translation Based on Knowledge Distillation and Anti-Noise Interaction

引用

computers, Materials & Continua 2025年第5期83卷 2305-2322页

作者： Erlin Tian Zengchao Zhu Fangmei Liu Zuhe Li School of Software Zhengzhou University of Light IndustryZhengzhou450001China School of Computer Science and Technology Zhengzhou University of Light IndustryZhengzhou450001China

Within the realm of multimodal neural machine translation(MNMT),addressing the challenge of seamlessly integrating textual data with corresponding image data to enhance translation accuracy has become a pressing *** saw that discrepancies between textual content and associated images can lead to visual noise,potentially diverting the model’s focus away from the textual data and so affecting the translation’s comprehensive *** solve this visual noise problem,we propose an innovative KDNR-MNMT *** combines the knowledge distillation technique with an anti-noise interaction mechanism,which makes full use of the synthesized graphic knowledge and local image interaction masks,aiming to extract more effective visual ***,the KDNR-MNMT model adopts a multimodal adaptive gating fusion strategy to enhance the constructive interaction of different modal *** integrating a perceptual attention mechanism,which uses cross-modal interaction cues within the Transformer framework,our approach notably enhances the quality of machine translation *** confirmthemodel’s performance,we carried out extensive testing and assessment on the extensively utilized Multi30K *** outcomes of our experiments prove substantial enhancements in our model’s BLEU and METEOR scores,with respective increases of 0.78 and 0.99 points over prevailing *** accomplishment affirms the potency of our strategy for mitigating visual interference and heralds groundbreaking advancements within themultimodal NMT domain,further propelling the evolution of this scholarly pursuit.

关键词： Knowledge distillation anti-noise interaction mask occlusion door control fusion

来源：评论

学校读者我要写书评

暂无评论

Service Function Chain Deployment with Intrinsic Dynamic Defense Capability

引用

IEEE Transactions on Mobile Computing 2025年第6期24卷 5418-5432页

作者： Wang, Ran Cai, Lundan Wu, Qiang Niyato, Dusit Nanjing University of Aeronautics and Astronautics Collaborative Innovation Center of Novel Software Technology and Industrialization College of Computer Science and Technology Nanjing China Nanyang Technological University School of Computer Science and Engineering Singapore

The Service Function Chain (SFC) leverages Network Function Virtualization (NFV) and software-Defined Networking (SDN) for flexible deployment, creating customized service chains tailored to specific applications. As NFV and SDN technologies play crucial roles in the SFC implementation, any security risk that arises in an NFV/SDN network can potentially pose a threat to SFC. Thus, SFC becomes vulnerable to network security attacks. To address this, intrinsic security technologies, including moving target defense and mimic defense, offer proactive protection against both known and unknown threats. It is expected to break through traditional security protection mechanisms such as "enhanced", "plug-in"and "passive"defense. This paper proposes an intrinsic dynamic defense architecture to equip SFC with active defense capabilities, shifting from passive reactive mechanism based on prior knowledge to an active defense against various attacks. The architecture comprises two models and five modules, including a sub-pool partitioning algorithm that enhances heterogeneity across sub-pools by splitting the heterogeneous replica pool into several sub-pools among replica VNFs. To meet Quality of Service (QoS) requirements like latency, cost, and security, we formulate a multi-objective optimization problem with three objectives: latency, cost, and defense success rate. Following that, we propose a dynamic Deep Reinforcement Learning (DRL)-based deployment algorithm. This algorithm selects appropriate VNFs based on heterogeneity and historical information, improving SFC and VNF security against external attacks. Extensive experiments validate that our architecture significantly enhances network security, provided that this improvement comes at the expense of limited cost and latency. © 2002-2012 IEEE.

关键词： Quality of service

来源：评论

学校读者我要写书评

暂无评论

Road Defect Detection Model Based on YOLOv8

IAENG International Journal of Computer Science

引用

IAENG International Journal of computer Science 2025年第4期52卷 986-994页

作者： Cong, Shengqiang Zhang, Chunna Yu, Yang Yue, Xiaoping Zhao, Jinchi Shen, Yuming School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning Anshan114051 China University of Science and Technology Liaoning Liaoning Anshan114051 China

The detection of road defects is crucial for ensuring vehicular safety and facilitating the prompt repair of roadway imperfections. Existing YOLOv8-based models face the following issues: extraction capabilities and insufficient feature representation in complex scenarios, slow bounding box regression speed. To address these challenges, we propose the YOLOv8-DSW model. Firstly, we incorporate the Dilation-wise Residual (DWR) module into the C2f module in the neck, improving the detection performance of multi-scale feature maps. Secondly, We incorporate the SENetv2 attention mechanism into the neck of the YOLOv8 model to augment feature expression capabilities. Finally, we introduce Wise-IoU (WIoU) to enhance the speed of bounding box regression. Experimental results indicate that YOLOv8-DSW enhances the mean Average Precision at 50% (mAP50) by 2.5% in comparison to the YOLOv8n, significantly improving detection accuracy and provide valuable method for the development of road defect detection. © (2025.0), (International Association of Engineers). All Rights Reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Classifying extended,localized and critical states in quasiperiodic lattices via unsupervised learning

引用

Chinese Physics B 2025年第1期34卷 422-427页

作者： Bohan Zheng Siyu Zhu Xingping Zhou Tong Liu School of Computer Science and Technology School of SoftwareNanjing University of Posts and TelecommunicationsNanjing 210023China Institute of Quantum Information and Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China Department of Applied Physics School of ScienceNanjing University of Posts and TelecommunicationsNanjing 210003China

Classification of quantum phases is one of the most important areas of research in condensed matter *** this work,we obtain the phase diagram of one-dimensional quasiperiodic models via unsupervised ***,we choose two advanced unsupervised learning algorithms,namely,density-based spatial clustering of applications with noise(DBSCAN)and ordering points to identify the clustering structure(OPTICS),to explore the distinct phases of the Aubry–André–Harper model and the quasiperiodic p-wave *** unsupervised learning results match well with those obtained through traditional numerical ***,we assess similarity across different algorithms and find that the highest degree of similarity between the results of unsupervised learning algorithms and those of traditional algorithms exceeds 98%.Our work sheds light on applications of unsupervised learning for phase classification.

关键词： quantum phase quasiperiodic machine learning

来源：评论

学校读者我要写书评

暂无评论

Weighted Attribute Based Conditional Proxy Re-Encryption in the Cloud

引用

computers, Materials & Continua 2025年第4期83卷 1399-1414页

作者： Xixi Yan Jing Zhang Pengyu Cheng Henan Key Laboratory of Network Cryptography Technology Network Information Security and Cryptography LaboratorySchool of SoftwareHenan Polytechnic UniversityJiaozuo454003China School of Computer Science and Technology Henan Polytechnic UniversityJiaozuo454000China

Conditional proxy re-encryption(CPRE)is an effective cryptographic primitive language that enhances the access control mechanism and makes the delegation of decryption permissions more granular,but most of the attribute-based conditional proxy re-encryption(AB-CPRE)schemes proposed so far do not take into account the importance of user attributes.A weighted attribute-based conditional proxy re-encryption(WAB-CPRE)scheme is thus designed to provide more precise decryption rights *** introducing the concept of weight attributes,the quantity of system attributes managed by the server is reduced *** the same time,a weighted tree structure is constructed to simplify the expression of access structure *** conditional proxy re-encryption,large amounts of data and complex computations are outsourced to cloud servers,so the data owner(DO)can revoke the user’s decryption rights directly with minimal *** scheme proposed achieves security against chosen plaintext attacks(CPA).Experimental simulation results demonstrated that the decryption time is within 6–9 ms,and it has a significant reduction in communication and computation cost on the user side with better functionality compared to other related schemes,which enables users to access cloud data on devices with limited resources.

关键词： Cloud service conditional proxy re-encryption user revocation weighted attribute

来源：评论

学校读者我要写书评

暂无评论

A survey on cross-user federated recommendation

引用

Science China(Information Sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：