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作者： Jiang, Zhenghong Zhou, Chunrong School of Big Data Chongqing Vocational College of Transportation Chongqing402247 China Science and Technology Department Chongqing Vocational College of Transportation Chongqing402247 China

In recent years, with the continuous improvement of the degree of industrial automation in our country, the intelligent requirements for electronic equipment are also rising. For this reason, some scholars propose to apply artificial neural network algorithm to electronic equipment to control the equipment. However, the current artificial neural network algorithm is generally implemented by FPGA. This scheme has the disadvantages of high cost and lack of applicability. Therefore, the purpose of this paper is to study the simulation of electronic equipment control method based on improved neural network algorithm. This paper uses PSpice circuit simulation software to model, simulate and optimize the design of the circuit. And the nonlinear function generator circuit, adder circuit and analog multiplier circuit are modeled, simulated and optimized by PSpice simulation software, and finally the adder circuit and the multiplier circuit are used to form a neural network overall circuit that meets the requirements to achieve Algorithmic function. After training and simulating the curve obtained by the boiling water experiment with the method in this paper, the experiment shows that the curve fitted by the polynomial is basically consistent with the curve obtained by the experiment, and the maximum error obtained by calculation is 1.35%. Through the research of this paper, it fills the blank of our country's neural algorithm in the control simulation of electronic equipment, and promotes the economic development of our country. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Timing circuits

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An Improved Bayesian Learner Based on Optimized Kernel Density Estimation and Wasserstein Soft Ensemble Strategy 12

An Improved Bayesian Learner Based on Optimized Kernel Densi...

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12th IEEE International Conference on Intelligent data Acquisition and Advanced Computing Systems: technology and Applications, IDAACS 2023

作者： Ye, Zhiwei Liu, Yuanhu Lv, Jiazhi Liu, Yingying Wu, Zhenwei Bai, Wanfang School of Computer Science Hubei University of Technology China Fiberhome Telecommunication Technologies Co Ltd. China Xining Big Data Service Administration China

ISBN: (纸本)9798350358056

Bayesian classification is a common data analysis and modeling method in data mining. In this paper, an improved ensemble method and the optimized Kernel density estimation used to Bayesian classifier. Unlike the traditional Bayesian classifier in that the conditional possibility density is calculated by an assumed statistical model, our method estimated the possibility value without model assumption, they are obtained by kernel density estimator with optimized window width and the Wasserstein distance is used to solve the problem of how to cause the classifier difference in the integration process. The optimized window parameters are solveded by minimizing the Unbias Cross-Validation (UCV) objective function using Chaos-Particle Swarm Optimization (CPSO). Wasserstein distance is innovational used to evaluate the similarity between possibility distributions and generates weights of base classifiers. The final ensemble outputs are calculated by a soft voting strategy. The experimental results illustrate the effectiveness and improvements of the proposed method in terms of density curve fitting, ensemble ability, and overall classification accuracy. © 2023 IEEE.

关键词： CPSO Ensemble Bayesian classifier Kernel density estimation Wasserstein distance

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Design of a Virtual Simulation Experimental Teaching Platform Based on the Internet of Things 13

Design of a Virtual Simulation Experimental Teaching Platfor...

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13th International Conference on Information technology in Medicine and Education, ITME 2023

作者： Xiehua, Yu Shengjie, You Rentao, Lin School of Computer and Information MinNan Science and Technology University Quanzhou China University of Fujian Province Key Laboratory of Big Data and Artificial Intelligence Quanzhou China

ISBN: (纸本)9798350319156

The virtual simulation experiment teaching system based on the Internet of Things simulates the Internet of Things sensing device in the form of software, and integrates relatively new teaching methods and theoretical knowledge, designs and completes a characteristic, can adapt to various needs of the Internet of Things experiment teaching system, and strives to provide more efficient protection and help for teachers' teaching tasks and students' learning tasks. To a large extent, it can solve the problems in the design and implementation of the Internet of Things application system, provide students with practical operation opportunities in the way of virtual simulation, combine theory and practice, and cultivate application-oriented talents urgently needed by the industry. © 2023 IEEE.

关键词： Internet of things

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Predicting Employee Attrition Using Machine Learning Approaches 9

Predicting Employee Attrition Using Machine Learning Approac...

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9th International Symposium on Computer and Information Processing technology, ISCIPT 2024

作者： Jin, Aitong Yang, Zicong Liu, Xiaoshan Lou, Shang Zhuang, Zhipeng Zhang, Shixuan Zhang, Chunfu Zhuhai College of Science and Technology Department of Big Data Zhuhai China Zhuhai College of Science and Technology Department of Computer Zhuhai China The Chinese University of Hong Kong Hkstr Department of Mechanical and Automation Engineering China Zhuhai College of Science and Technology Department of Life Sciences Zhuhai China

ISBN: (纸本)9798350388404

Employee attrition has become a focal point of interest for researchers and human resource managers alike. Analyzing factors leading to employee attrition plays a vital role in organizational stability, cost management, and reputation. This study aims to analyze such factors and predict employee attrition using machine learning and ensemble learning methods. A comparison of four machine learning methods and three ensemble learning methods was conducted, utilizing grid search for hyperparameter tuning to identify the optimal model. Additionally, exploratory data analysis of employee data was employed to identify factors contributing to employee attrition. The results indicate that Random Forest exhibits the highest accuracy, approximately 95%. Overtime, stock options level, and job satisfaction were identified as key factors leading to employee attrition. These findings can assist organizations in reducing employee attrition by addressing key factors contributing to turnover, thereby enhancing job satisfaction and improving organizational stability and reputation. © 2024 IEEE.

关键词： Job satisfaction

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Ontology-Based Decision-Making Support in Real-Time Strategy Game

Ontology-Based Decision-Making Support in Real-Time Strategy...

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International Conference on

作者： Yaochu Wang Jian Yang Guoli Yang School of Automation Science and Engineering South China University of Technology Guangzhou China Department of Big Data Intelligence Advanced Institute of Big Data Beijing China

Real-Time Strategy (RTS) games have attracted millions of players due to their characteristics of diverse scenes and flexible decision-making mechanisms. However, the mechanisms, contents, and operations of RTS games could be quite complex for beginners. The frustration and poor beginner experience caused by the high level of difficulty in getting started may lead novice players to give up on a specific RTS game due to a lack of sense of achievement. To address this issue and enhance the gaming experiences of beginners, this article proposes to assist beginners in familiarizing themselves with the mechanisms of the games and evaluating the opponents' abilities based on ontology. Firstly, an ontology will be built according to the properties of game units, which can provide beginners with a clear and comprehensive conceptual framework to understand game mechanisms and operations. Secondly, a knowledge-based reasoning method will be proposed to help beginners evaluate their opponents' abilities. Using a specific RTS game as a demonstration, the ontology is built and a reasoning example is proposed. By analyzing and reasoning about the abilities of their opponents' game units, this reasoning method can help beginners develop more effective strategies and tactics to enhance their experience in the game.

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SparseCoder: Identifier-Aware Sparse Transformer for File-Level Code Summarization 31

SparseCoder: Identifier-Aware Sparse Transformer for File-Le...

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31st IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2024

作者： Wang, Yanlin Huang, Yanxian Guo, Daya Zhang, Hongyu Zheng, Zibin School of Software Engineering Sun Yat-sen University China School of Computer Science and Technology Sun Yat-sen University China School of Big Data and Software Engineering Chongqing University China

ISBN: (纸本)9798350330663

Code summarization aims to generate natural language descriptions of source code, facilitating programmers to understand and maintain it rapidly. While previous code summarization efforts have predominantly focused on method-level, this paper studies file-level code summarization, which can assist programmers in understanding and maintaining large source code projects. Unlike method-level code summarization, file-level code summarization typically involves long source code within a single file, which makes it challenging for Transformer-based models to understand the code semantics for the maximum input length of these models is difficult to set to a large number that can handle long code input well, due to the quadratic scaling of computational complexity with the input sequence length. To address this challenge, we propose SparseCoder, an identifier-aware sparse transformer for effectively handling long code sequences. Specifically, the SparseCoder employs a sliding window mechanism for self-attention to model short-term dependencies and leverages the structure message of code to capture long-term dependencies among source code identifiers by introducing two types of sparse attention patterns named global and identifier attention. To evaluate the performance of SparseCoder, we construct a new dataset FILE-CS for file-level code summarization in Python. Experimental results show that our SparseCoder model achieves state-of-the-art performance compared with other pre-trained models, including full self-attention and sparse models. Additionally, our model has low memory overhead and achieves comparable performance with models using full self-attention mechanism. Furthermore, we verify the generality of SparseCoder on other code understanding tasks, i.e., code clone detection and code search, and results show that our model outperforms baseline models in both tasks, demonstrating that our model can generate better code representations for various downstream tasks. Our

关键词： Semantics

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data Masking for Chinese Electronic Medical Records with Named Entity Recognition

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Intelligent Automation & Soft Computing 2023年第6期36卷 3657-3673页

作者： Tianyu He Xiaolong Xu Zhichen Hu Qingzhan Zhao Jianguo Dai Fei Dai School of Computer Science Nanjing University of Information Science and TechnologyNanjing21000China Geospatial Information Engineering Research Center Xinjiang Production and Construction CorpsShihezi832003China College of Big Data and Intelligent Engineering Southwest Forestry UniversityKunming650224China

With the rapid development of information technology,the electronifi-cation of medical records has gradually become a *** China,the population base is huge and the supporting medical institutions are numerous,so this reality drives the conversion of paper medical records to electronic medical *** medical records are the basis for establishing a smart hospital and an important guarantee for achieving medical intelligence,and the massive amount of electronic medical record data is also an important data set for conducting research in the medical ***,electronic medical records contain a large amount of private patient information,which must be desensitized before they are used as open ***,to solve the above problems,data masking for Chinese electronic medical records with named entity recognition is proposed in this ***,the text is vectorized to satisfy the required format of the model ***,since the input sentences may have a long or short length and the relationship between sentences in context is not *** this end,a neural network model for named entity recognition based on bidirectional long short-term memory(BiLSTM)with conditional random fields(CRF)is ***,the data masking operation is performed based on the named entity recog-nition results,mainly using regular expression filtering encryption and principal component analysis(PCA)word vector compression and *** addi-tion,comparison experiments with the hidden markov model(HMM)model,LSTM-CRF model,and BiLSTM model are conducted in this *** experi-mental results show that the method used in this paper achieves 92.72%Accuracy,92.30%Recall,and 92.51%F1_score,which has higher accuracy compared with other models.

关键词： Named entity recognition Chinese electronic medical records data masking principal component analysis regular expression

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Design and development of immersive 3D virtual simulation experiment teaching platform for internet of things

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Multimedia Tools and Applications 2024年 1-15页

作者： Li, Yanan Li, Congcong Wang, Yijin Teng, Guifa College of Information Science & amp Technology Hebei Agricultural University No.289 Lingyusi Street Hebei Province Baoding071001 China Hebei Key Laboratory of Agricultural Big Data No.289 Lingyusi Street Hebei Province Baoding071001 China

The learning content of the Internet of Things (IoT) professional courses is closely related to reality, and has the characteristics of comprehensiveness, intersection and application. In order to solve the limitations of traditional practice teaching methods in time and space, and enhance the interaction of practice teaching, this project took the Internet of Things technology and application in virtual smart city as the research object, combined with advanced information technology, based on Bloom’s teaching objectives, a 3D virtual simulation experiment teaching platform composed of virtual equipment, and integrated data management and analysis services was established. The platform constructs a number of IoT 3D virtual simulation scenarios that emphasize professional technology and reflect the cutting-edge technology to create an immersive teaching environment. In addition, the platform contains multiple comprehensive experimental projects for exploratory research-based professional courses. Through the innovative experimental mode of integrating games, competitions, breakthroughs, explorations and flipped experiments, students’ interest in learning can be enhanced, and the overall quality of students’ practical ability, innovative spirit and learning ability can be improved. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Virtual environments

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NI-GDBA: Non-Intrusive Distributed Backdoor Attack Based on Adaptive Perturbation on Federated Graph Learning 25

NI-GDBA: Non-Intrusive Distributed Backdoor Attack Based on ...

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34th ACM Web Conference, WWW 2025

作者： Li, Ken Shi, Bin Wei, Jiazhe Dong, Bo School of Computer Science and Technology Xi’an Jiaotong University Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an China School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an China School of Distance Education Xi’an Jiaotong University Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an China

ISBN: (纸本)9798400712746

Federated Graph Learning (FedGL) is an emerging Federated Learning (FL) framework that learns the graph data from various clients to train better Graph Neural Networks(GNNs) model. Owing to concerns regarding the security of such framework, numerous studies have attempted to execute backdoor attacks on FedGL, with a particular focus on distributed backdoor attacks. However, all existing methods posting distributed backdoor attack on FedGL only focus on injecting distributed backdoor triggers into the training data of each malicious client, which will cause model performance degradation on original task and is not always effective when confronted with robust federated learning defense algorithms, leading to low success rate of attack. What’s more, the backdoor signals introduced by the malicious clients may be smoothed out by other clean signals from the honest clients, which potentially undermining the performance of the attack. To address the above significant shortcomings, we propose a non-intrusive graph distributed backdoor attack(NI-GDBA) that does not require backdoor triggers to be injected in the training data. Our attack trains an adaptive perturbation trigger generator model for each malicious client to learn the natural backdoor from the GNN model downloading from the server with the malicious client’s local data. In contrast to traditional distributed backdoor attacks on FedGL via trigger injection in training data, our attack on different datasets such as Molecules and Bioinformatics have higher attack success rate, stronger persistence and stealth, and has no negative impact on the performance of the global GNN model. We also explore the robustness of NI-GDBA under different defense strategies, and based on our extensive experimental studies, we show that our attack method is robust to current federated learning defense methods, thus it is necessary to consider non-intrusive distributed backdoor attacks on FedGL as a novel threat that requires custom d

关键词： Federated learning

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Anomaly Detection in Multi-Level Model Space

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IEEE Transactions on big data 2025年

作者： Chen, Ao Zhou, Xiren Fan, Yizhan Chen, Huanhuan University of Science and Technology of China School of Computer Science and Technology Hefei230027 China University of Science and Technology of China School of Big Data Hefei230027 China

Anomaly detection (AD) is gaining prominence, especially in situations with limited labeled data or unknown anomalies, demanding an efficient approach with minimal reliance on labeled data or prior knowledge. Building upon the framework of Learning in the Model Space (LMS), this paper proposes conducting AD through Learning in the Multi-Level Model Spaces (MLMS). LMS transforms the data from the data space to the model space by representing each data instance with a fitted model. In MLMS, to fully capture the dynamic characteristics within the data, multi-level details of the original data instance are decomposed. These details are individually fitted, resulting in a set of fitted models that capture the multi-level dynamic characteristics of the original instance. Representing each data instance with a set of fitted models, rather than a single one, transforms it from the data space into the multi-level model spaces. The pairwise difference measurement between model sets is introduced, fully considering the distance between fitted models and the intra-class aggregation of similar models at each level of detail. Subsequently, effective AD can be implemented in the multi-level model spaces, with or without sufficient multi-class labeled data. Experiments on multiple AD datasets demonstrate the effectiveness of the proposed method. © 2015 IEEE.

关键词： Adversarial machine learning

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