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Recommendation System Based on Perceptron and Graph Convolution Network

Computers, Materials & Continua 2024年第6期79卷 3939-3954页

作者： Zuozheng Lian Yongchao Yin Haizhen Wang College of Computer and Control Engineering Qiqihar UniversityQiqihar161006China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar UniversityQiqihar161006China

The relationship between users and items,which cannot be recovered by traditional techniques,can be extracted by the recommendation algorithm based on the graph convolution *** current simple linear combination of these algorithms may not be sufficient to extract the complex structure of user interaction *** paper presents a new approach to address such issues,utilizing the graph convolution network to extract association *** proposed approach mainly includes three modules:Embedding layer,forward propagation layer,and score prediction *** embedding layer models users and items according to their interaction information and generates initial feature vectors as input for the forward propagation *** forward propagation layer designs two parallel graph convolution networks with self-connections,which extract higher-order association relevance from users and items separately by multi-layer graph ***,the forward propagation layer integrates the attention factor to assign different weights among the hop neighbors of the graph convolution network fusion,capturing more comprehensive association relevance between users and items as input for the score prediction *** score prediction layer introduces MLP(multi-layer perceptron)to conduct non-linear feature interaction between users and items,***,the prediction score of users to items is *** recall rate and normalized discounted cumulative gain were used as evaluation *** proposed approach effectively integrates higher-order information in user entries,and experimental analysis demonstrates its superiority over the existing algorithms.

关键词： Recommendation system graph convolution network attention mechanism multi-layer perceptron

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Multiparticipant Federated Feature Selection Algorithm With Particle Swarm Optimization for Imbalanced data Under Privacy Protection

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 1002-1016页

作者： Hu, Ying Zhang, Yong Gong, Dunwei Sun, Xiaoyan China University of Mining and Technology School of Information and Control Engineering Xuzhou221008 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun130012 China

In the real world, data describing the same learning task may be distributed in different institutions (called participants), and these participants cannot share their own data due to the need of privacy protection. How to select an optimal feature subset on the basis of ensuring the data privacy of each participant is an important and challenging topic. At the same time, data on part participants show imbalanced or even miss some classes, further increasing the difficulty of solving this kind of problem. In view of this, by introducing a trusted third party to process and integrate optimal information from participants, this article proposes a multiparticipant federated evolutionary feature selection algorithm for imbalanced data under privacy protection. First, a multilevel joint sample filling strategy with multiple participants (called sampling-rough selection-fine tuning strategy) is proposed to fill imbalanced or empty classes on each participant while ensuring data privacy. Then, a federated evolutionary feature selection method based on particle swarm optimization (PSO) is proposed by periodically sharing the optimal feature subsets obtained by PSOs on participants. Finally, the proposed algorithm is applied to 15 test datasets, and compared with several typical imbalanced feature selection algorithms and two kinds of ensemble feature selection algorithms. The experimental results show that the proposed algorithm can significantly promote the ability of each participant on processing imbalanced datasets, and improve the classification accuracy of obtained feature subsets while protecting the participants' data privacy. © 2020 IEEE.

关键词： Clustering algorithms

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A DDoS Identification Method for Unbalanced data CVWGG

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Computers, Materials & Continua 2024年第12期81卷 3825-3851页

作者： Haizhen Wang Na Jia Yang He Pan Tan College of Computer and Control Engineering Qiqihar UniversityQiqihar161006China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar UniversityQiqihar161006China

As the popularity and dependence on the Internet increase,DDoS(distributed denial of service)attacks seriously threaten network *** accurately distinguishing between different types of DDoS attacks,targeted defense strategies can be formulated,significantly improving network protection *** attacks usually manifest as an abnormal increase in network traffic,and their diverse types of attacks,along with a severe data imbalance,make it difficult for traditional classification methods to effectively identify a small number of attack *** solve this problem,this paper proposes a DDoS recognition method CVWGG(Conditional Variational Autoencoder-Wasserstein Generative Adversarial Network-gradient penalty-Gated Recurrent Unit)for unbalanced data,which generates less noisy data and high data quality compared with existing *** mainly includes unbalanced data processing for CVWG,feature extraction,and *** uses the CVAE(Conditional Variational Autoencoder)to improve the WGAN(Wasserstein Generative Adversarial Network)and introduces a GP(gradient penalty)term to design the loss function to generate balanced data,which enhances the learning ability and stability of the ***,the GRU(Gated Recurrent Units)are used to capture the temporal features and patterns of the ***,the logsoftmax function is used to differentiate DDoS attack *** PyCharm and Python 3.10 for programming and evaluating performance with metrics such as accuracy and precision,the results show that the method achieved accuracy rates of 96.0%and 97.3%on two datasets,***,comparison and ablation experiment results demonstrate that CVWGG effectively mitigates the imbalance between DDoS attack categories,significantly improves the classification accuracy of different types of attacks and provides a valuable reference for network security defense.

关键词： Conditional variational autoencoder generating adversarial networks DDoS attack

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A survey for light field super-resolution

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High-Confidence Computing 2024年第1期4卷 118-129页

作者： Mingyuan Zhao Hao Sheng Da Yang Sizhe Wang Ruixuan Cong Zhenglong Cui Rongshan Chen Tun Wang Shuai Wang Yang Huang Jiahao Shen State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Key Laboratory of Data Science and Intelligent Computing International Innovation InstituteBeihang UniversityHangzhou 311115China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Compared to 2D imaging data,the 4D light field(LF)data retains richer scene’s structure information,which can significantly improve the computer’s perception capability,including depth estimation,semantic segmentation,and LF ***,there is a contradiction between spatial and angular resolution during the LF image acquisition *** overcome the above problem,researchers have gradually focused on the light field super-resolution(LFSR).In the traditional solutions,researchers achieved the LFSR based on various optimization frameworks,such as Bayesian and Gaussian *** learning-based methods are more popular than conventional methods because they have better performance and more robust generalization *** this paper,the present approach can mainly divided into conventional methods and deep learning-based *** discuss these two branches in light field spatial super-resolution(LFSSR),light field angular super-resolution(LFASR),and light field spatial and angular super-resolution(LFSASR),***,this paper also introduces the primary public datasets and analyzes the performance of the prevalent approaches on these ***,we discuss the potential innovations of the LFSR to propose the progress of our research field.

关键词： Light field super-resolution Convolutional neural network Transformer Sub-aperture image Epipolar-plane image

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Few-Shot Partial Multi-View Learning

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2023年第10期45卷 11824-11841页

作者： Zhou, Yuan Guo, Yanrong Hao, Shijie Hong, Richang Luo, Jiebo Ministry of Education Hefei University of Technology The Key Laboratory of Knowledge Engineering with Big Data Hefei230009 China Hefei University of Technology The School of Computer Science and Information Engineering Hefei230009 China University of Rochester The Department of Computer Science RochesterNY14627 United States

It is often the case that data are with multiple views in real-world applications. Fully exploring the information of each view is significant for making data more representative. However, due to various limitations and failures in data collection and pre-processing, it is inevitable for real data to suffer from view missing and data scarcity. The coexistence of these two issues makes it more challenging to achieve the pattern classification task. Currently, to our best knowledge, few appropriate methods can well-handle these two issues simultaneously. Aiming to draw more attention from the community to this challenge, we propose a new task in this paper, called few-shot partial multi-view learning, which focuses on overcoming the negative impact of the view-missing issue in the low-data regime. The challenges of this task are twofold: (i) it is difficult to overcome the impact of data scarcity under the interference of missing views;(ii) the limited number of data exacerbates information scarcity, thus making it harder to address the view-missing issue in turn. To address these challenges, we propose a new unified Gaussian dense-anchoring method. The unified dense anchors are learned for the limited partial multi-view data, thereby anchoring them into a unified dense representation space where the influence of data scarcity and view missing can be alleviated. We conduct extensive experiments to evaluate our method. The results on Cub-googlenet-doc2vec, Handwritten, Caltech102, Scene15, Animal, ORL, tieredImagenet, and Birds-200-2011 datasets validate its effectiveness. The codes will be released at https://***/zhouyuan888888/UGDA. © 1979-2012 IEEE.

关键词： Neural networks

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Discovering Reliable Information Extraction Patterns with Pre-Trained Model for Text with Writing Style

Discovering Reliable Information Extraction Patterns with Pr...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bu, Chenyang Liu, Jiacheng Liu, Jiaxuan Ji, Shengwei Yang, Hongbin School of Computer Science and Information Engineering Ministry of Education Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Anhui Hefei230027 China School of Artificial Intelligence and Big Data Hefei University China

ISBN: (纸本)9798350337020

Large-scale pre-trained models such as GPT and BERT have demonstrated remarkable performance in information extraction tasks. However, their black-box nature poses challenges for reliability and interpretability. In contrast, rule- based extraction methods have better interpretability, but typically require domain experts to manually establish rules, limiting their generalization ability. In industry, there is often a demand for reliable knowledge extraction to reduce the time spent on manual verification of each piece of knowledge. In this paper, we explore the idea of combining GPT and symbolic-based methods to automatically discover reliable extraction patterns in text with a particular writing style. This method leverages the characteristics of high information density and similar writing patterns in text with a specific writing style to generate verifiable and reliable patterns. We conduct experiments on two datasets with a specific writing style to demonstrate its effectiveness, validating the idea of combining large models for reliable information extraction pattern discovery in the tested datasets. © 2023 IEEE.

关键词： Information extraction Reliable pattern Writing style data

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Learning-based Bundling Strategy for Two Products under Uncertain Consumer's Valuations

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IEEE Transactions on engineering Management 2025年 72卷 1970-1982页

作者： Fang, Lang Tang, Jiafu Pan, Zhendong Dongbei University of Finance and Economics School of Management Science & Engineering Liaoning Key Laboratory of Big Data Management and Optimization Decision Dalian116025 China

Should a firm engage in bundling to boost revenue when consumer's valuations of products are heterogeneous and uncertain? In recent years, technological advances have made it possible for firms to use large amounts of available data to make decisions under demand-side information uncertainty. However, it remains unclear exactly how they can dynamically optimize bundling and pricing decisions by learning from uncertain consumer's valuations. To answer this question, we study the bundling and pricing decisions of a monopoly firm offering a basic product and a premium product over a finite T periods. We first analyze the equilibrium outcomes of pure component strategies (PCS) and pure bundling strategies (PBS) under deterministic consumer's valuations. We then introduce a learning-based bundling strategy (LBBS) framework to make decisions dynamically over time. It employs Thompson Sampling to estimate the fractions of low-valuation consumers (LVC) of two products, allowing the firm to adjust its decisions based on updated beliefs about consumer's valuation distributions. We demonstrate the robust performance of the LBBS and show the interesting findings. That is, the fraction of LVC of premium product, the ratio between high- and low-valuation (RHL) of basic product, the ratio of low-valuation (RLV) and the ratio of high-valuation (RHV) of two products wield significant influence on the adoption of bundling strategy and its possibility. These findings offer practical guidance to firm's practitioners regarding when and which PBS to adopt and how to improve PBS decisions according to the fractions of LVC of products. We also extend the model to uniform distribution of consumer's valuations and correlated consumer's valuations to test our results. © 1988-2012 IEEE.

关键词： Cost accounting Pricing Training data mining Artificial intelligence Uncertainty Profitability Supply chain management Sustainable development Surveys

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A Frequency and Polarization Reconfigurable Transparent Water Antenna

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Progress in Electromagnetics Research C 2025年 152卷 177-186页

作者： Li, Lei Gao, Jing Nan, Jingchang School of Electronics and Information Engineering Liaoning Technical University Huludao125105 China Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Huludao125105 China

A novel frequency and polarization reconfigurable water patch antenna is proposed for radio communication in the UHF band. Based on theoretical analysis and simulation results, water is an ideal material for designing transparent liquid ground. Water enhances outstanding transparency, excellent aesthetics, and high optical stealth performance for a wider range of application scenarios. The entire structure is made with polyvinyl chloride material and distilled water, except for the feed structure. By filling different cavities with liquid water, five different operating states are obtained in 1.924–2.5 GHz (26.1%), 1.67–2.33 GHz (33%), 0.644–2.288 GHz (112.1%), 1.975–2.54 GHz (25%), and 1.748–2.108 GHz (18.7%), achieving frequency reconfigurability. The antenna can be flexibly switched between linear polarization (LP) and two right-handed circular polarization (RHCP) states. The results show that the 3 dB axial ratio (AR) bandwidth covers 1.93–2.08 GHz (7.5%) and 2.06–2.132 GHz (3.5%). The antenna achieves high optical transparency of 100% and a peak gain of 7.97 dBi. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Circular polarization

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Digital twin-enabled adaptive scheduling strategy based on deep reinforcement learning

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Science China(Technological Sciences) 2023年第7期66卷 1937-1951页

作者： GAN XueMei ZUO Ying ZHANG AnSi LI ShaoBo TAO Fei School of Mechanical Engineering Guizhou UniversityGuiyang 550025China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang 550025China

The modern complicated manufacturing industry and smart manufacturing tendency have imposed new requirements on the scheduling method,such as self-regulation and self-learning *** traditional scheduling methods cannot meet these needs due to their ***-learning is an inherent ability of reinforcement learning(RL) algorithm inhered from its continuous learning and trial-and-error ***-regulation of scheduling could be enabled by the emerging digital twin(DT) technology because of its virtual-real mapping and mutual control *** paper proposed a DT-enabled adaptive scheduling based on the improved proximal policy optimization RL algorithm,which was called explicit exploration and asynchronous update proximal policy optimization algorithm(E2APPO).Firstly,the DT-enabled scheduling system framework was designed to enhance the interaction between the virtual and the physical job shops,strengthening the self-regulation of the scheduling ***,an innovative action selection strategy and an asynchronous update mechanism were proposed to improve the optimization algorithm to strengthen the self-learning ability of the scheduling ***,the proposed scheduling model was extensively tested in comparison with heuristic and meta-heuristic algorithms,such as wellknown scheduling rules and genetic algorithms,as well as other existing scheduling methods based on reinforcement *** comparisons have proved both the effectiveness and advancement of the proposed DT-enabled adaptive scheduling strategy.

关键词： agent reinforcement learning digital twin adaptive scheduling generalization

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Epilepsy dynamics of an astrocyte-neuron model with ammonia intoxication

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Chinese Physics B 2023年第2期32卷 128-135页

作者：袁治轩独盟盟于羊羊吴莹 State Key Laboratory for Strength and Vibration of Mechanical Structures Shaanxi Engineering Laboratory for Vibration Control of Aerospace StructuresSchool of Aerospace EngineeringXi'an Jiaotong UniversityXi'an 710049China School of Mathematics and Data Science Shaanxi University of Science and TechnologyXi'an 710000China

Experimental records in the cerebral cortex indicate that ammonia injection can lead to seizures. Considering that astrocytes play a vital role in mediating the uptake and absorption of ammonium ions in the extracellular space of the cortical circuit, we constructed a new astrocyte neuron coupling model, which is composed of a neuron and its astrocytes connected through the extracellular space, taking into account of the influence of extracellular ammonium ions. The numerical results verified the previous experimental observation that obtained epileptic firing modes of neurons and postsynaptic GABA reversal potential depolarization triggered by ammonia injection. In addition, we also determined the concentration–response relationship between the ammonium ion concentration and the time of entering epilepsy and predicted the threshold of the ammonium ion concentration for the onset of epilepsy.

关键词： ammonia neuron astrocyte epilepsy

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