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Social Robot Detection Method with Improved Graph Neural Networks

computers, Materials & Continua 2024年第2期78卷 1773-1795页

作者： Zhenhua Yu Liangxue Bai Ou Ye Xuya Cong institute of systems Security and Control College of Computer Science and TechnologyXi’an University of Science and TechnologyXi’an710054China

Social robot accounts controlled by artificial intelligence or humans are active in social networks,bringing negative impacts to network security and social *** social robot detection methods based on graph neural networks suffer from the problem of many social network nodes and complex relationships,which makes it difficult to accurately describe the difference between the topological relations of nodes,resulting in low detection accuracy of social *** paper proposes a social robot detection method with the use of an improved neural ***,social relationship subgraphs are constructed by leveraging the user’s social network to disentangle intricate social relationships ***,a linear modulated graph attention residual network model is devised to extract the node and network topology features of the social relation subgraph,thereby generating comprehensive social relation subgraph features,and the feature-wise linear modulation module of the model can better learn the differences between the ***,user text content and behavioral gene sequences are extracted to construct social behavioral features combined with the social relationship subgraph ***,social robots can be more accurately identified by combining user behavioral and relationship *** carrying out experimental studies based on the publicly available datasets TwiBot-20 and Cresci-15,the suggested method’s detection accuracies can achieve 86.73%and 97.86%,*** with the existing mainstream approaches,the accuracy of the proposed method is 2.2%and 1.35%higher on the two *** results show that the method proposed in this paper can effectively detect social robots and maintain a healthy ecological environment of social networks.

关键词： Social robot detection social relationship subgraph graph attention network feature linear modulation behavioral gene sequences

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Coordinated responses of leaf and nodule traits contribute to the accumulation of N in relay intercropped soybean

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Journal of Integrative Agriculture 2024年第6期23卷 1910-1928页

作者： Ping Chen Qing Du Benchuan Zheng Huan Yang Zhidan Fu Kai Luo Ping Lin Yilin Li Tian Pu Taiwen Yong Wenyu Yang College of Agronomy Sichuan Agricultural University/Sichuan Engineering Research Center for Crop Strip Intercropping System/Key Laboratory of Crop Ecophysiology and Farming System in SouthwestMinistry of Agriculture and Rural AffairsChengdu 611130China Crop Research institute Sichuan Academy of Agricultural SciencesChengdu 610066China

Maize(Zea mays L.)-soybean(Glycine max ***.)relay intercropping provides a way to enhance land ***,the late-planted soybean suffers from shading by the *** maize harvest,how the recovery growth influences the leaf and nodule traits remains unclear.A three-year field experiment was conducted to evaluate the effects of genotypes,i.e.,supernodulating(nts1007),Nandou 12(ND12),and Guixia 3(GX3),and crop configurations,i.e.,the interspecific row spacing of 45(I45),60(I60),75 cm(I75),and sole soybean(SS),on soybean recovery growth and N *** results showed that intercropping reduced the soybean total leaf area(LA)by reducing both the leaf number(LN)and unit leaflet area(LUA),and it reduced the nodule dry weight(NW)by reducing both the nodule number(NN)and nodule diameter(ND)compared with the *** correlation and principal component analysis(PCA)indicated a co-variability of the leaf and nodule traits in response to the genotype and crop configuration *** the recovery growth stages,the compensatory growth promoted soybean growth to reduce the gaps of leaf and nodule traits between intercropping and *** relative growth rates of ureide(RGR_U)and nitrogen(RGR_N)accumulation were higher in intercropping than in *** achieved more significant sucrose and starch contents compared with ***12 and GX3 showed more robust compensatory growth than nts1007 in *** the recovery growth of relay intercropping soybean improved biomass and nitrogen accumulation,ND12 gained a more significant partial land equivalent ratio(pLER)than *** I60 treatment achieved more robust compensation effects on biomass and N accumulation than the other ***,I60 showed a higher nodule sucrose content and greater shoot ureide and N accumulation than ***,intercropping ND12 with maize using an interspecific row spacing of 60 cm was optimal for both yield advantage and N accumulation.

关键词： relay intercropping genotype crop configuration symbiotic nitrogen fixation soybean nodule

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SNR and RSSI Based an Optimized Machine Learning Based Indoor Localization Approach:Multistory Round Building Scenario over LoRa Network

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computers, Materials & Continua 2024年第8期80卷 1927-1945页

作者： Muhammad Ayoub Kamal Muhammad Mansoor Alam Aznida Abu Bakar Sajak Mazliham Mohd Su’ud Malaysian institute of Information Technology(MIIT) Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Computing and Informatics Multimedia UniversityCyberjaya63100Malaysia Depertment of computer Science DHA Suffa UniversityKarachiSindh75500Pakistan Riphah institute of system Engineering(RISE) Faculty of ComputingRiphah International UniversityIslamabad46000Pakistan Malaysian France institute(MFI) Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneyUltimoNSW 2007Australia

In situations when the precise position of a machine is unknown,localization becomes *** research focuses on improving the position prediction accuracy over long-range(LoRa)network using an optimized machine learning-based *** order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology,this study proposed an optimized machine learning(ML)based *** signal strength indicator(RSSI)data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory round layout *** noise factor is also taken into account,and the signal-to-noise ratio(SNR)value is recorded for every RSSI *** study concludes the examination of reference point accuracy with the modified KNN method(MKNN).MKNN was created to more precisely anticipate the position of the reference *** findings showed that MKNN outperformed other algorithms in terms of accuracy and complexity.

关键词： Indoor localization MKNN LoRa machine learning classification RSSI SNR localization

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An Electrical Impedance Imaging system Towards Edge Intelligence for Non-Destructive Testing of Concrete

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Tsinghua Science and Technology 2024年第3期29卷 883-896页

作者： Abolfazl Roshanpanah Mahdi Abbasi Hani Attar Ayman Amer Mohammad R.Khosravi Ahmed A.Solyman Department of Electrical Engineering Islamic Azad UniversityScience and Research BranceTehran ***Iran Department of computer Engineering Bu-Ali Sina UniversityHamedan ***Iran Department of Energy Engineering Zarqa UniversityZarqa 13110Jordan Department of computer Engineering Persian Gulf UniversityBushehr ***Iran Department of Electrical and Electronics Engineering Faculty of Engineering and ArchitectureNişantaşıUniversityIstanbul 34398Turkey

In the construction industry,to prevent accidents,non-destructive tests are necessary and *** impedance tomography is a new technology in non-invasive imaging in which the image of the inner part of conductive bodies is reconstructed by the arrays of external electrodes that are connected on the periphery of the *** equipment is cheap,fast,and edge *** this imaging method,the image of electrical conductivity distribution(or its opposite;electrical impedance)of the internal parts of the target object is *** image reconstruction process is performed by injecting a precise electric current to the peripheral boundaries of the object,measuring the peripheral voltages induced from it and processing the collected *** an electrical impedance tomography system,the voltages measured in the peripheral boundaries have a non-linear equation with the electrical conductivity *** paper presents a cheap Electrical Impedance Tomography(EIT)instrument for detecting impurities in the concrete.A voltage-controlled current source,a micro-controller,a set of multiplexers,a set of electrodes,and a personal computer constitute the structure of the *** conducted tests on concrete with impurities show that the designed EIT system can reveal impurities with a good accuracy in a reasonable time.

关键词： non-destructive testing system concrete electrical impedance tomography

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Model of fractured medium and nondestructive control of composite materials

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Chinese Journal of Aeronautics 2024年第2期37卷 93-99页

作者： Vasily GOLUBEV Ilia NIKITIN Katerina BEKLEMYSHEVA Department of computers Science and Numerical Mathematics Moscow Institute of Physics and TechnologyDolgoprudny 141700Russia Mathematical Modeling and Management Department of InformatizationInstitute of Computer Aided Design RASMoscow 123056Russia

Non-destructive testing of composites is an important issue in the modern aircraft *** are susceptible to the barely visible impact damage which can affect the residual strength of the material and occurs both during production and *** continuum model for describing the damaged zone is *** slip theory relations used for a continuous distribution of slip planes are *** the initial stage,the isotropic background model is *** model allows the material slippage along the fractures based on the Coulomb friction law with the small viscous *** this regime,the govern system of equations becomes *** overcome this difficulty,the explicit-implicit grid-characteristic scheme is *** standard ultrasound diagnostic procedure of damaged composite materials is successfully *** with the trivial free-surface fracture model,different reactions on the compression and stretch waves are *** approach provided an effective way for the simulation of complex dynamic behavior of damage zones.

关键词： Non-destructive testing Composite materials Numerical modeling Continuum model Grid-characteristic method

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Design of a Lightweight Compressed Video Stream-Based Patient Activity Monitoring system

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computers, Materials & Continua 2024年第1期78卷 1253-1274页

作者： Sangeeta Yadav Preeti Gulia Nasib Singh Gill Piyush Kumar Shukla Arfat Ahmad Khan Sultan Alharby Ahmed Alhussen Mohd Anul Haq Department of computer Science&Applications Maharshi Dayanand UniversityRohtak124001India University institute of Technology Rajiv Gandhi Proudyogiki VishwavidyalayaBhopal462033India Department of computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand Department of computer Science College of Computer and Information SciencesMajmaah UniversityAl Majmaah11952Kingdom of Saudi Arabia Department of computer Engineering College of Computer and Information SciencesMajmaah UniversityAl Majmaah11952Kingdom of Saudi Arabia

Inpatient falls from beds in hospitals are a common *** falls may result in severe *** problem can be addressed by continuous monitoring of patients using *** advancements in deep learning-based video analytics have made this task of fall detection more effective and *** with fall detection,monitoring of different activities of the patients is also of significant concern to assess the improvement in their *** computation-intensive models are required to monitor every action of the patient *** requirement limits the applicability of such ***,to keep the model lightweight,the already designed fall detection networks can be extended to monitor the general activities of the patients along with the fall *** by the same notion,we propose a novel,lightweight,and efficient patient activity monitoring system that broadly classifies the patients’activities into fall,activity,and rest classes based on their *** whole network comprises three sub-networks,namely a Convolutional Neural Networks(CNN)based video compression network,a Lightweight Pose Network(LPN)and a Residual Network(ResNet)Mixer block-based activity recognition *** compression network compresses the video streams using deep learning networks for efficient storage and retrieval;after that,LPN estimates human ***,the activity recognition network classifies the patients’activities based on their *** proposed system shows an overall accuracy of approx.99.7% over a standard dataset with 99.63% fall detection accuracy and efficiently monitors different events,which may help monitor the falls and improve the inpatients’health.

关键词： Fall detection activity recognition human pose estimation accuracy

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QARF: A Novel Malicious Traffic Detection Approach via Online Active Learning for Evolving Traffic Streams

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Chinese Journal of Electronics 2024年第3期33卷 645-656页

作者： Zequn NIU Jingfeng XUE Yong WANG Tianwei LEI Weijie HAN Xianwei GAO School of computer Science and Technology Beijing Institute of Technology School of Space Information Space Engineering University

In practical abnormal traffic detection scenarios,traffic often appears as drift,imbalanced and rare labeled streams,and how to effectively identify malicious traffic in such complex situations has become a challenge for malicious traffic *** have extensive studies on malicious traffic detection with single challenge,but the detection of complex traffic has not been widely *** adaptive random forests(QARF) is proposed to detect traffic streams with concept drift,imbalance and lack of labeled *** is an online active learning based approach which combines adaptive random forests method and adaptive margin sampling *** achieves querying a small number of instances from unlabeled traffic streams to obtain effective *** conduct experiments using the NSL-KDD dataset to evaluate the performance of *** is compared with other state-of-the-art *** experimental results show that QARF obtains 98.20% accuracy on the NSL-KDD *** performs better than other state-of-the-art methods in comparisons.

关键词： Training Streams Random forests

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Accurate and efficient floor localization with scalable spiking graph neural networks

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Satellite Navigation 2024年第1期5卷 191-206页

作者： Fuqiang Gu Fangming Guo Fangwen Yu Xianlei Long Chao Chen Kai Liu Xuke Hu Jianga Shang Songtao Guo College of computer Science Chongqing UniversityChongqingChina Department of Precision Instrument Tsinghua UniversityBeijingChina institute of Data Science German Aerospace Center(DLR)JenaGermany School of computer Science China University of GeosciencesWuhanChina

Floor localization is crucial for various applications such as emergency response and rescue,indoor positioning,and recommender *** existing floor localization systems have many drawbacks,like low accuracy,poor scalability,and high computational *** this paper,we first frame the problem of floor localization as one of learning node embeddings to predict the floor label of a ***,we introduce FloorLocator,a deep learning-based method for floor localization that integrates efficient spiking neural networks with powerful graph neural *** approach offers high accuracy,easy scalability to new buildings,and computational *** results on using several public datasets demonstrate that FloorLocator outperforms state-of-the-art ***,in building B0,FloorLocator achieved recognition accuracy of 95.9%,exceeding state-of-the-art methods by at least 10%.In building B1,it reached an accuracy of 82.1%,surpassing the latest methods by at least 4%.These results indicate FloorLocator’s superiority in multi-floor building environment localization.

关键词： Indoor positioning Deep learning Floor localization Spiking neural networks Graph neural networks

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A Privacy Preservation Method for Attributed Social Network Based on Negative Representation of Information

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computer Modeling in Engineering & Sciences 2024年第7期140卷 1045-1075页

作者： Hao Jiang Yuerong Liao Dongdong Zhao Wenjian Luo Xingyi Zhang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei230601China Chongqing Research institute School of Computer Science and Artificial IntelligenceWuhan University of TechnologyWuhan430070China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and TechnologyHarbin Institute of TechnologyShenzhen518055China

Due to the presence of a large amount of personal sensitive information in social networks,privacy preservation issues in social networks have attracted the attention of many *** by the self-nonself discrimination paradigmin the biological immune system,the negative representation of information indicates features such as simplicity and efficiency,which is very suitable for preserving social network ***,we suggest a method to preserve the topology privacy and node attribute privacy of attribute social networks,called ***,a negative survey-based method is developed to disturb the relationship between nodes in the social network so that the topology structure can be kept ***,a negative database-based method is proposed to hide node attributes,so that the privacy of node attributes can be preserved while supporting the similarity estimation between different node attributes,which is crucial to the analysis of social *** evaluate the performance of the AttNetNRI,empirical studies have been conducted on various attribute social networks and compared with several state-of-the-art methods tailored to preserve the privacy of social *** experimental results show the superiority of the developed method in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topology disturbing and attribute hiding *** experimental results show the superiority of the developed methods in preserving the privacy of attribute social networks and demonstrate the effectiveness of the topological interference and attribute-hiding components.

关键词： Attributed social network topology privacy node attribute privacy negative representation of information negative survey negative database

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Unveiling of Terahertz Emission from Ultrafast Demagnetization and the Anomalous Hall Effect in a Single Ferromagnetic Film

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Chinese Physics Letters 2024年第4期41卷 45-50页

作者：蓝志强李章顺徐浩然刘凡金钻明彭滟朱亦鸣 Terahertz Technology Innovation Research institute Terahertz Spectrum and Imaging Technology Cooperative Innovation CenterShanghai Key Lab of Modern Optical SystemUniversity of Shanghai for Science and TechnologyShanghai 200093China

Using THz emission spectroscopy,we investigate the elementary spin dynamics in ferromagnetic single-layer Fe on a sub-picosecond *** demonstrate that THz radiation changes its polarity with reversal of the magnetization applied by the external magnetic *** addition,it is found that the sign of THz polarity excited from different sides is defined by the thickness of the Fe layer and Fe/dielectric *** on the thickness and symmetry dependences of THz emission,we experimentally distinguish between the two major contributions:ultrafast demagnetization and the anomalous Hall *** experimental results not only enrich understanding of THz electromagnetic generation induced by femtosecond laser pulses but also provide a practical way to access laser-induced ultrafast spin dynamics in magnetic structures.

关键词： magnetic magnetization polarity

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