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Research on Semantic Communication Based on Joint Control Mechanism of Shallow and Deep Neural Network

Chinese Journal of Electronics 2025年第2期34卷 698-711页

作者： Xie Wenwu Xiong Ming Ren Ziqing Wang Ji Yang Zhihe School of Information Science and Engineering Hunan Institute of Science and Technology Yueyang 414006 China School of Computer Science Hefei Normal University Hefei 230001 China College of Physical Science and Technology Central China Normal University Wuhan 430079 China

With the rapid development of deep learning, various semantic communication models are emerging, but the current semantic communication models still have much room for improvement in the coding layer. For this reason, a joint-residual neural networks (Joint-ResNets) framework based on the joint control of shallow neural networks (SNNs) and deep neural networks (DNNs) is proposed to cope with the problems in semantic communication coding. The framework synergizes SNNs and DNNs based on their shared utility, and uses variable weight \begin{document}$\alpha$\end{document} term to control the ratio of SNNs and DNNs to fully utilize the simplicity of SNNs and the richness of DNNs. The article details the construction of the Joint-ResNets framework and its canonical use in classical semantic communication models, and illustrates the control mechanism of the variable weight \begin{document}$\alpha$\end{document} term in the Joint-ResNets framework and its importance in balancing the model complexity between SNNs and DNNs. The article takes the task-oriented communication model in the device edge collaborative reasoning system as an example for experimentation and analysis. The experimental validation shows that DNNs and SNNs can be combined in a more effective way to standardize semantic coding, which improves the overall predictive performance, interpretability, and robustness of semantic communication models, and this framework is expected to bring new breakthroughs in the field of semantic communication.

关键词： Semantic communication Residual neural networks (ResNets) Joint control mechanisms Edge reasoning

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Porous FeP/FeO Deposited on Carbon Cloth with Accelerated Kinetics for the Hydrogen Evolution Reaction

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Journal of Wuhan University of technology(Materials science) 2024年第6期39卷 1434-1439页

作者： YU Hai ZHAO Yujia LIU Su ZUO Lei LEI Qianqian HU Ruiling LU Jianguo ZHAO Min WANG Congrong WANG Cunyong ZHANG Miao YANG Lei ZHONG Jin CAO Chunbin School of Physics and Materials Engineering and Key Laboratory for Photoelectric Detection Science and Technology of Education Department of Anhui Province Hefei Normal UniversityHefei 230601China School of Physics and Material Science Anhui UniversityHefei 230039China Department of Chemistry and Materials Engineering Hefei UniversityHefei 230601China School of Computer Science and Technology Hefei Normal UniversityHefei 230601China School of Science Anhui Agricultural UniversityHefei 230036China

FeP/FeO was prepared on carbon cloth(CC)via hydrothermal method,heat treatment in air,and phosphorization in ***/FeO/CC presents a porous and loose morphology which is conducive to the exposure of active sites and the transfer of ***/FeO/CC requires the low overpotentials of 257 and 117 mV(*** hydrogen electrode(RHE))to achieve the current density of 10 mA_·cm^(-2)for oxygen evolution reaction(OER)and hydrogen evolution reaction(HER)in alkaline KOH solution,*** small Tafel slope values of 36.1 mV_·dec^(-1)(for OER)and 96.2 mV_·dec^(-1)(for HER)indicate that FeP/FeO/CC exhibits the fast electrocatalytic reactive kinetics for OER and *** particular,the reaction kinetics of FeP/FeO/CC accelerated with the progress of *** charge-transfer resistance(R_(ct))of FeP/FeO/CC is only 11Ω.Excellent bifunctional electrocatalytic performances of FeP/FeO/CC should be attributed to the porous morphology and the lower charge-transfer resistance.

关键词： FeP/FeO/CC OER HER water splitting

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Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

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Chinese Journal of Electronics 2024年第1期33卷 231-244页

作者： Ming CHEN Yajian JIANG Xiujuan LEI Yi PAN Chunyan JI Wei JIANG College of Information Science and Engineering Hunan Normal University School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Computer Science Department BNU-HKBU United International College

Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

关键词： Drug-target interactions Signed heterogeneous network Link sign prediction Graph neural networks

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A Contrastive Learning Framework for Keyphrase Extraction

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Data Intelligence 2024年第4期6卷 1032-1056页

作者： Jing Song Xian Zu Fei Xie Department of Electronic Information and Electrical Engineering Hefei Normal UniversityLianhua RoadHefei 230601China Department of Computer and Artificial Intelligence Hefei Normal UniversityLianhua RoadHefei 230601China Department of Safety Science and Engineering University of Science and Technology of ChinaHefei 230026China

Keyphrase extraction aims to extract important phrases that reflect the main topics of a document. Recently, deep learning methods are used to model semantic information and rank candidates based on the similarities between the n-grams and the document. However, existing keyphrase extraction methods mainly caused the keyphrase extraction task to be independent of the embedding. Based on the fact that phrases that are semantically closer to the document are more likely to become keyphrases, we propose a novel contrastive learning strategy for supervised keyphrase extraction by integrating local and global Information of the document. A pre-trained RoBERTa model is used to model contextual information of sub-words in the document. Then, the embedding vectors of n-grams and the document are calculated by the convolution neural layers. Finally, we propose a novel loss function for efficiently ranking candidate phrases by combining n-gram features and document embeddings during the training of the model.

关键词： Keyphrase extraction Contrastive learning Supervised n-gram features Document embedding

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A Novel CAPTCHA Recognition System Based on Refined Visual Attention

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computers, Materials & Continua 2025年第4期83卷 115-136页

作者： Zaid Derea Beiji Zou Xiaoyan Kui Monir Abdullah Alaa Thobhani Amr Abdussalam School of Computer Science and Engineering Central South UniversityChangsha410083China College of Computer Science and Information Technology Wasit UniversityWasit52001Iraq Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha67714Saudi Arabia Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China

Improving website security to prevent malicious online activities is crucial,and CAPTCHA(Completely Automated Public Turing test to tell computers and Humans Apart)has emerged as a key strategy for distinguishing human users from automated ***-based CAPTCHAs,designed to be easily decipherable by humans yet challenging for machines,are a common form of this ***,advancements in deep learning have facilitated the creation of models adept at recognizing these text-based CAPTCHAs with surprising *** our comprehensive investigation into CAPTCHA recognition,we have tailored the renowned UpDown image captioning model specifically for this *** approach innovatively combines an encoder to extract both global and local features,significantly boosting the model’s capability to identify complex details within CAPTCHA *** the decoding phase,we have adopted a refined attention mechanism,integrating enhanced visual attention with dual layers of Long Short-Term Memory(LSTM)networks to elevate CAPTCHA recognition *** rigorous testing across four varied datasets,including those from Weibo,BoC,Gregwar,and Captcha 0.3,demonstrates the versatility and effectiveness of our *** results not only highlight the efficiency of our approach but also offer profound insights into its applicability across different CAPTCHA types,contributing to a deeper understanding of CAPTCHA recognition technology.

关键词： Text-based CAPTCHA recognition refined visual attention web security computer vision

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A Dual-Layer Attention Based CAPTCHA Recognition Approach with Guided Visual Attention

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computer Modeling in Engineering & sciences 2025年第3期142卷 2841-2867页

作者： Zaid Derea Beiji Zou Xiaoyan Kui Alaa Thobhani Amr Abdussalam School of Computer Science and Engineering Central South UniversityChangsha410083China College of Computer Science and Information Technology Wasit UniversityWasit52001Iraq Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China

Enhancing website security is crucial to combat malicious activities,and CAPTCHA(Completely Automated Public Turing tests to tell computers and Humans Apart)has become a key method to distinguish humans from *** text-based CAPTCHAs are designed to challenge machines while remaining human-readable,recent advances in deep learning have enabled models to recognize them with remarkable *** this regard,we propose a novel two-layer visual attention framework for CAPTCHA recognition that builds on traditional attention mechanisms by incorporating Guided Visual Attention(GVA),which sharpens focus on relevant visual *** have specifically adapted the well-established image captioning task to address this *** approach utilizes the first-level attention module as guidance to the second-level attention component,incorporating two LSTM(Long Short-Term Memory)layers to enhance CAPTCHA *** extensive evaluation across four diverse datasets—Weibo,BoC(Bank of China),Gregwar,and Captcha 0.3—shows the adaptability and efficacy of our *** approach demonstrated impressive performance,achieving an accuracy of 96.70%for BoC and 95.92%for *** results underscore the effectiveness of our method in accurately recognizing and processing CAPTCHA datasets,showcasing its robustness,reliability,and ability to handle varied challenges in CAPTCHA recognition.

关键词： Text-based CAPTCHA image recognition guided visual attention web security computer vision

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Wi-SFDAGR: WiFi-Based Cross-Domain Gesture Recognition via Source-Free Domain Adaptation

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IEEE Internet of Things Journal 2025年第13期12卷 24159-24173页

作者： Yan, Huan Zhang, Xiang Huang, Jinyang Feng, Yuanhao Li, Meng Wang, Anzhi Ou, Weihua Wang, Hongbing Liu, Zhi Guizhou Normal University School of Big Data and Computer Science Guiyang550025 China University of Science and Technology of China CAS Key Laboratory of Electromagnetic Space Information Hefei230026 China Hefei University of Technology School of Computer and Information Hefei230601 China The University of Electro-Communications Department of Computer and Network Engineering Tokyo1828585 Japan

WiFi Channel State Information (CSI)-based gesture recognition offers unique advantages, including cost-effectiveness and enhanced privacy protection, and has garnered significant attention in recent years. However, existing WiFi-based gesture recognition solutions exhibit poor generalization ability when deployed in new environment, orientation, or location. Although some methods combine labeled source domain and unlabeled target domain to learn domain-independent features, factors such as data privacy protection hinder access to source data during practical environment adaptation. Consequently, we consider realistic scenario where source data is unavailable during adaptation of unlabeled test data, and instead, a trained source domain model is used. In this paper, we propose Wi-SFDAGR, a WiFi-based Source-free Domain Adaptation Gesture Recognition framework. Specifically, we treat cross-domain as an unsupervised clustering problem, aiming to ensure that features within local neighborhoods exhibit similar prediction results while those farther apart display different prediction outcomes in the feature space. We theoretically analyze the effect of enhanced prediction consistency between neighbor points extracted from gestures on generalization error. Furthermore, we employ an attraction-dispersion network to strengthen prediction consistency among closely located features in the feature space while reducing it for distantly located features. To mitigate noise introduced during nearest neighbor sample selection in the feature space (where predictions may not align with the input sample’s prediction), we progressively improve nearby sample feature aggregation by estimating uncertainty to reweight local neighborhood predictions. Finally, extensive experiments are conducted on the Widar 3.0 and XRF55 datasets and the results show our proposed framework outperforms most cross-domain methods. © 2014 IEEE.

关键词： Differential privacy

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Optimal Production Capacity Matching for Blockchain-Enabled Manufacturing Collaboration With the Iterative Double Auction Method

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 550-562页

作者： Ying Chen Feilong Lin Zhongyu Chen Changbing Tang Cailian Chen the Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University the School of Computer Science and Technology Zhejiang Normal University IEEE the College of Physics and Electronic Information Engineering Zhejiang Normal University the Department of Automation Shanghai Jiao Tong University

The increased demand for personalized customization calls for new production modes to enhance collaborations among a wide range of manufacturing practitioners who unnecessarily trust each other. In this article, a blockchain-enabled manufacturing collaboration framework is proposed, with a focus on the production capacity matching problem for blockchainbased peer-to-peer(P2P) collaboration. First, a digital model of production capacity description is built for trustworthy and transparent sharing over the blockchain. Second, an optimization problem is formulated for P2P production capacity matching with objectives to maximize both social welfare and individual benefits of all participants. Third, a feasible solution based on an iterative double auction mechanism is designed to determine the optimal price and quantity for production capacity matching with a lack of personal information. It facilitates automation of the matching process while protecting users' privacy via blockchainbased smart contracts. Finally, simulation results from the Hyperledger Fabric-based prototype show that the proposed approach increases social welfare by 1.4% compared to the Bayesian game-based approach, makes all participants profitable,and achieves 90% fairness of enterprises.

关键词： Blockchain iterative double auction manufacturing collaboration production capacity matching

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An Analysis of Worm Dynamics with Honeypot Feedback in Scale-Free Networks

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第1期52卷 90-102页

作者： Xue, Jiali Ren, Jianguo Feng, Liping Wang, Lei College of Computer Science and Technology at Jiangsu Normal University Xuzhou221116 China College of Computer Science and Technology at Jiangsu Normal University Xuzhou221116 China Department of Computer Science at Xinzhou Teacher University Xinzhou034000 China Hydrology Bureau of Yishu-Si Water Resources Management Bureau Xuzhou221116 China

Scale-free networks’ complex topology makes them an ideal target for worm attacks. Research has demonstrated that honeypot technology holds enormous promise for resolving worm-related problems. In this paper, we propose a novel propagation model based on honeypot feedback, namely H-SAIR. After capturing the virus successfully, the honeypot collects assault behaviors, analyzes attack paths, generates immunity information, and provides immune feedback to adjacent nodes to promote active protection. First, we introduce honeypot node H and affected node A based on the SIR model. Then, we use dynamics analysis to compute the model's propagation threshold R0, proving the global stability of the disease-free equilibrium P0 and the existence of the endemic equilibrium P*. Finally, the experimental results indicate that when R0 is less than 1, the H-SAIR model effectively constrains the spread of worms. With the increase in feedback rate, the containment effect will become stronger. Meanwhile, we can effectively control the spread of worms by strategically placing enough honeypots at hub sites, thereby maintaining network security. © (2025), (International Association of Engineers). All rights reserved.

关键词： feedback mechanisms honeypots scale-free networks stability analysis worms

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Learning Discriminatory Information for Object Detection on Urine Sediment Image

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computer Modeling in Engineering & sciences 2024年第1期138卷 411-428页

作者： Sixian Chan Binghui Wu Guodao Zhang Yuan Yao Hongqiang Wang College of Computer Science and Technology Zhejiang University of TechnologyHangzhou310023China The Hefei Institutes of Physical Science Chinese Academy of SciencesHefei230071China Department of Digital Media Technology Hangzhou Dianzi UniversityHangzhou310018China School of Computer Science University of Nottingham Ningbo ChinaNingbo315100China

In clinical practice,the microscopic examination of urine sediment is considered an important in vitro examination with many broad *** the amount of each type of urine sediment allows for screening,diagnosis and evaluation of kidney and urinary tract disease,providing insight into the specific type and ***,manual urine sediment examination is labor-intensive,time-consuming,and *** machine learning based object detection methods require hand-crafted features for localization and classification,which have poor generalization capabilities and are difficult to quickly and accurately detect the number of urine *** learning based object detection methods have the potential to address the challenges mentioned above,but these methods require access to large urine sediment image ***,only a limited number of publicly available urine sediment datasets are currently *** alleviate the lack of urine sediment datasets in medical image analysis,we propose a new dataset named UriSed2K,which contains 2465 high-quality images annotated with expert *** main challenges are associated with our dataset:a large number of small objects and the occlusion between these small *** manuscript focuses on applying deep learning object detection methods to the urine sediment dataset and addressing the challenges presented by this ***,our goal is to improve the accuracy and efficiency of the detection algorithm and,in doing so,provide medical professionals with an automatic detector that saves time and *** propose an improved lightweight one-stage object detection algorithm called *** proposed algorithm comprises a local context attention module and a global background suppression module,which aid the detector in distinguishing urine sediment features in the *** local context attention module captures context information beyond the object region,

关键词： Object detection attention mechanism medical image urine sediment

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