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8th International Congress on Edge Computing, EDGE 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Tian, Yun Wu, Bin Shi, Jiaoli Zhang, Caicai Xu, Du School of Computer and Big Data Science Jiujiang University Jiujiang332005 China Jiujiang Key Laboratory of Network and Information Security Jiujiang332005 China School of Modern Information Technology Zhejiang Polytechnic University of Mechanical and Electrical Engineering Hangzhou310053 China

ISBN: (纸本)9783031770685

The development of cloud computing and the widespread application of cloud services have made outsourcing services more convenient. The need for individuals and businesses to store and manipulate the graph data they generate is growing rapidly. The unreliability and insecurity of cloud servers make outsourcing graph data a great risk of information leakage. To effectively protect data security, encrypting outsourced data is a useful method. The adjacent vertex query is a very commonly used and fundamental operation, and similarity search is a widely used and powerful tool to improve the scope and functionality of queries. After outsourcing encrypted sparse graph data to cloud servers, it becomes very inconvenient to use and manipulate the data. In this work, we present a scheme to realize the adjacent vertex query supporting similarity search on sparse graph data in cloud environment (SSAQ), which also protects the security of the information. This work uses edit distance and the searchable encryption principle to construct query index, and next implement the similar adjacent vertex query on cloud server. This work provides a formal security analysis, and also gives the experimental comparison and analysis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Information leakage

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Longtime behaviors of an epidemic model with nonlocal diffusions and a free boundary: rate of accelerated spreading

arXiv

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arXiv 2024年

作者： Li, Lei Wang, Mingxin School of Mathematics and Statistics Henan University of Technology Zhengzhou450001 China School of Mathematics and Information Science Henan Polytechnic University Jiaozuo454003 China

This is the third part of our series of work devoted to the dynamics of an epidemic model with nonlocal diffusions and free boundary. This part is concerned with the rate of accelerated spreading for three types of kernel functions when spreading happens. By constructing the suitable upper and lower solutions, we get the rate of the accelerated spreading of free boundary, which is closely related to the behavior of kernel functions near infinity. Our results indicate that the heavier the tail of the kernel functions are, the faster the rate of accelerated spreading is. Moreover, more accurate spreading profiles for solution component (u, v) are also obtained. Copyright © 2024, The Authors. All rights reserved.

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Research on Rule Engine Optimization Algorithm in Internet of Things Teaching Platform

Research on Rule Engine Optimization Algorithm in Internet o...

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2023 International Conference on Algorithm, Imaging Processing, and Machine Vision, AIPMV 2023

作者： Li, JianZhong Wan, Qiang Zhang, ZhiQiang School of Entrepreneurship Guangdong Innovative Technical College Guangdong DongGuan523000 China School of Computer Science and Engineering South China University of Technology Guangdong GuangZhou510000 China School of Intelligent Manufacture Shunde Polytechnic FoShan528300 China

ISBN: (纸本)9781510672444

The rule engine is an important part of the industry-education integrated Internet of Things teaching platform, and it is the basis for realizing the dynamic configuration of business rules in the practical teaching function. Combined with the data characteristics of the Internet of Things application scenario, this paper proposes a rule engine optimization algorithm based on Rete, and designs a pre-sorting algorithm based on rule frequency, which pre-sorts the order of nodes according to the frequency of use of rule patterns, with priority Match frequently used patterns, increases the sharing rate of nodes, and reduce the memory usage of the inference network. Through experimental simulation, the improved algorithm is verified, and the experimental results prove the effectiveness of the algorithm. © 2024 SPIE.

关键词： Internet of things

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Recursive Filtering for Nonlinear Systems With Self-Interferences Over Full-Duplex Relay Networks

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 2037-2040页

作者： Hailong Tan Bo Shen Qi Li Wei Qian the School of Mathematics-Physics and Finance Anhui Polytechnic UniversityWuhu 241000 the Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment Ministry of EducationAnhui Polytechnic UniversityWuhu 241000China IEEE the College of Information Science and Technology Donghua UniversityShanghai 201620 the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of EducationShanghai 201620China the School of Information Science and Engineering Hangzhou Normal UniversityHangzhou 311121China the School of Electrical Engineering and Automation Henan Polytechnic UniversityJiaozuo 454000China

Dear Editor,In this paper,a recursive filtering problem(RRP)is addressed for nonlinear systems over full-duplex relay(FDR)networks.A FDR is adopted to forward measurements of the sensor to the *** of concurrently tran... 详细信息

关键词： thereby Nonlinear filtering

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Remote Sensing Image Scene Classification Method Based on Semantic and Spatial Interactive Information 7

Remote Sensing Image Scene Classification Method Based on Se...

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7th International Conference on Image, Vision and Computing, ICIVC 2022

作者： Chen, Ya Zheng, Liang Zheng, Jin Cheng, Junjie Henan Kaifeng College of Science Technology and Communication Kaifeng475001 China Henan University School of Computer and Information Engineering Kaifeng475001 China

ISBN: (数字)9781665467346

ISBN: (纸本)9781665467346

Since the receptive field of convolutional neural network is limited, it is difficult to obtain the interaction relationship of global information in the image. To solve this problem, combining visual transformer and convolutional neural network, a scene classification network based on visual transformer and dense neural network is designed to obtain more discriminative features. On the one hand, visual transformer is used to establish the global information interaction mechanism, and the convolution neural network is combined to construct a double branch feature extraction network. The complementarity of image abstract semantic features and long-distance dependencies within the image is used to improve the expression ability of classification features. On the other hand, by connecting the encoders in the visual transformer with skip connection, the feature reuse is enhanced and the problem of gradient disappearance is avoided. Experiments are conducted on the public remote sensing datasets AID and NWPU-RESISC45. The experimental results show that the proposed method is able to improve the classification accuracy of remote sensing image scenes. © 2022 IEEE.

关键词： Remote sensing

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DDoS detection based on graph structure features and non-negative matrix factorization

DDoS detection based on graph structure features and non-neg...

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作者： Jing, Hengchang Wang, Jian College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China School of Management Henan University of Science and Technology Luoyang471000 China

Distributed denial of service (DDoS) attacks are the most common and harmful attack in the field of network security, the purpose of this paper is to predict the occurrence of DDoS attacks quickly, accurately and effectively. In this paper, the characteristics of DDoS attacks are analyzed in detail, graph model is used to express the structural characteristics of traffic data between the victims and bots, at the same time, considering data characteristics of traffic itself, 11 characteristics are selected at last. The method of nonnegative matrix factorization (NMF) is adopted to mine the behavior pattern of DDoS attacks, and then the DDoS bots is detected by clustering according to the projection value of nodes on the behavior pattern. By extracting the attack behavior pattern, the attack behavior can be detected. Experimental results of real dataset verify the effectiveness and validity of the method. © 2020 John Wiley & Sons, Ltd.

关键词： Non-negative matrix factorization

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A Self-Attention Based Joint Sequence Labeling Model

A Self-Attention Based Joint Sequence Labeling Model

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2022 IEEE International Conference on Artificial Intelligence and computer Applications, ICAICA 2022

作者： Wu, Eryong Liu, Xiaoming Chen, Yun Zhang, Tao Zhongyuan University of Technology School of Computer Science Henan Zhengzhou China Zhongyuan University of Technology Henan Key Laboratory on Public Opinion Intelligent Analysis Zhengzhou450007 China

ISBN: (数字)9781665499910

ISBN: (纸本)9781665499910

Many sequence tasks can be effectively treated as Sequence labeling (SL) problems in Natural Language Processing. A lot of the existing studies solve these tasks as independent sequence labeling problems, or use multiple auxiliary tasks to increase the performance of specific target tasks. These studies ignore the potential relationship and influence between multi-tasks in natural language processing. We propose an innovative and well-designed self-attention based joint sequence model (SA-JSL) in a neural network frame, which fully utilize the possible interactions and influences between many sequence labeling tasks, so as to promote and improve the performance of each sequence labeling task simultaneously. Specifically, by fusing the self-attention and joint tag mechanism, this framework model converts multiple sequence tasks into a unified sequence labeling task for processing, and effectively utilizes the potential relationship between these tasks, so as to realize the mutual promotion and improvement of multi-tasks. The joint model combines with the self-attention mechanism to obtain richer context information and improve the performance of the model. Experiments were carried out on seven popular benchmark datasets to verify the superiority of the model. © 2022 IEEE.

关键词： Natural language processing systems

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DBMAT: Research on Chinese Named Entity Recognition Using the Dilated Bidirectional Multi-layer Attentive Transformer Fusion Model 4

DBMAT: Research on Chinese Named Entity Recognition Using th...

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4th International Conference on Artificial Intelligence and computer Engineering, ICAICE 2023

作者： Yu, Tingjie Liao, Liefa Yang, Yiguo Xia, Weihuan Zou, Zhenyuan School of Information Engineering Jiangxi University of Science and Technology Jiangxi Ganzhou341000 China Jiangxi Modern Polytechnic College Jiangxi Nanchang330095 China School of Computer Engineering and Science Shanghai University Shanghai200444 China

ISBN: (纸本)9798400708831

In the realm of Chinese Named Entity Recognition tasks, conventional models have frequently fallen short in adequately addressing linguistic features and recognizing the essential role of context. To address this challenge, our research presents a unique Chinese Named Entity Recognition model, referred to as the LERT-DBMAT-CRF model. Initially, the LERT pre-trained language model incorporates language-informed pretraining strategies to enrich the semantic attributes in textual data. Subsequently, we apply the DBMAT module, unifying bidirectional Long Short-Term Memory networks with residual dilated convolutional networks, coordinated through a multi-head additive attention mechanism. This approach enhances feature extraction by employing the Exponential Linear Unit function, thus enhancing the model's capacity to capture temporal and spatial information relevant to semantic features. Lastly, a Conditional Random Field layer is introduced to exploit contextual information for label prediction. Experimental results demonstrate the exceptional model performance, achieving impressive F1 scores of 97.38% and 96.55% on the Resume dataset and the MSRA dataset, respectively, surpassing the performance of current mainstream models. © 2023 ACM.

关键词： Additives

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Deep Learning Based Take-Over Performance Prediction and Its Application on Intelligent Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Liu, Weimin Li, Qingkun Wang, Wenjun Wang, Zhenyuan Zeng, Chao Cheng, Bo State Key Laboratory of Automotive Safety and Energy Center for Intelligent Connected Vehicles and Transportation School of Vehicle and Mobility Tsinghua University Beijing China Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing China College of Information Science and Engineering Henan University of Technology Zhengzhou China

Take-over performance plays a significant role in evaluating drivers' state, and serves as a crucial reference for enhancing control transitions in the context of conditionally automated driving. In this study, we aim to predict minimum anticipated collision time (min ACT), an indicator of drivers' take-over performance, in expectation of promoting safer take-overs via deep learning, so that drivers' state detriment of take-over safety could be adjusted accordingly with intelligent human-machine interaction algorithms predictably. By incorporating multi-source information including drivers' state, drivers' demographics, surrounding traffic features as well as driver-vehicle interaction characteristics, network model “ACTNet” was proposed to facilitate continuous estimation. Depthwise separable convolution and non-local self-attention were utilized to prevent overfitting and establish spatial dependency over fixation heatmap, respectively. To overcome data distribution imbalance, class balanced loss was used in conjunction with regression loss to realize more accurate predictions. Driving simulator experiment was conducted with dataset collected for the subsequent verification of the proposed algorithm. Potentialities of deep learning methods were highlighted for take-over studies, contributing to the design of intelligent human-machine interaction systems in conditional automation. Our findings present a valid method of deep learning in predicting drivers' take-over performance and meanwhile have implications for the development of intelligent adaptive take-over time budget regulation and dynamic drivers' state adjustment algorithms. IEEE

关键词： Automation

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Rational construction of enzyme-like stereoselective magnetic chiral nanozymes

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Nano Research 2025年第4期18卷 316-324页

作者： Xuan Chen Shenli Wang Xiaoxiao Zhang Stefanos Mourdikoudis Chengxiao Song Leung Siu Lun Juan Xie Wing-leung Wong Jorge Perez-Juste Isabel Pastoriza-Santos Kwok-Yin Wong Guangchao Zheng Colloidal Physics Group Key Laboratory of Materials PhysicsMinistry of EducationSchool of Physics and Laboratory of Zhongyuan LightZhengzhou UniversityZhengzhou 450001China Institute of Quantum Materials and Physics Henan Academy of SciencesZhengzhou 450046China College of Food Science and Technology Henan University of TechnologyZhengzhou 450001China State Key Laboratory of Chemical Biology and Drug Discovery Department of Applied Biology and Chemical TechnologyThe Hong Kong Polytechnic UniversityHong Kong 999077China CINBIO Universidade de VigoDepartment of Physical ChemistryCampus Universitario Lagoas Marcosende36310 VigoSpain

Recent studies support that magnetic chiral nanozymes,integrating the features of chirality,magnetism,and enzyme-like catalysis,provide new insights into the synthetic methodologies and applications of chiral *** this study,we present the design of novel magnetic chiral cobalt superstructures(CoSSs)synthesized by the regulation of complex formation kinetics of Co3+with chiral ligands(L-or D-tartaric acid)under varying metal-to-ligand molar ratios and solvent *** approach yielded a series of CoSSs with varying symmetry from high to *** chiral CoSSs exhibited chirality-dependent peroxidase(POD)-like activity,demonstrating a high affinity of L-CoSSs towards substrates,with a chiral selective factor of approximately *** addition,the magneto-optical effects of the chiral CoSSs significantly enhanced their chiroptical performance from ultraviolet-visible(UV-vis)to near-infrared *** a magnetic field,the affinity of chiral CoSSs for substrates increases,while the chiral selective factor was modified to *** research on magnetic chiral CoSSs nanozymes opens promising new avenues for the application of artificial enzymes in fields,such as antibacterial technology,drug delivery,and biocatalysis.

关键词： chiral cobalt superstructures chirality origin solvent effect magnetic circular dichroism enzyme-like stereoselectivity

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