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2023 International Conference on Communication Network and Machine Learning, CNML 2023

作者： Li, Beiming Xu, Jiahao Key Laboratory of Intelligent Information Processing Technology Ministry of Industry and Information Technology Harbin Engineering University Harbin150001 China

ISBN: (纸本)9798400716683

With the development of communication countermeasure technology, the covertness, anti-interception and anti-jamming of information transmission are more and more important. In this paper, on the basis of researching and analyzing the existing covert anti-jamming communication technology, we propose a new type of covert anti-jamming communication system, a kind of transform-domain covert communication based on combining basis functions. The communication system maps part of the information into a combination of frequency points, and the same information maps different combinations of frequency points, the combination of frequency points as the amplitude spectrum of the basis function, and then another part of the information through the transform domain modulation technology modulated into both strong concealment and strong anti-jamming ability, but also has a higher information transmission efficiency of the time domain transmission waveform, to provide a new idea for China's satellite covert communication. The simulation results verify the effectiveness of the method. © 2023 ACM.

关键词： Efficiency

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IGED:Towards intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN

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Computers, Materials & Continua 2024年第8期80卷 1851-1866页

作者： Yanhua Liu Yuting Han HuiChen Baokang Zhao XiaofengWang Ximeng Liu College of Computer and Data Science Fuzhou UniversityFuzhou350108China Engineering Research Center of Big Data Intelligence Ministry of EducationFuzhou350108China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350108China College of Computer National University of Defense TechnologyChangsha410073China

As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly *** propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data *** the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization *** results show that the proposed method can efficiently distinguish normal traffic from DDoS *** with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.

关键词： DDoS real-time improved generalized entropy DNN

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Joint utility optimization algorithm for secure hybrid spectrum sharing

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High Technology Letters 2022年第4期28卷 373-383页

作者： WANG Yi HUANG Rong XU Yaohua JIANG Fang BAI Na TANG Tao HU Yanjun Key Laboratory of Intelligent Computing&Signal Processing Ministry of EducationAnhui UniversityHefei 230601P.R.China

As the rapid development of wireless communication networks has resulted in better user experiences,the spectrum resources occupied and energy consumption have increased considerably and resulted in great *** address the energy consumption and cost problems of spectrum sharing in cognitive radio networks,a hybrid spectrum sharing model combining the free spectrum of authorized users and the leased spectrum of mobile network operators is *** on the hybrid model,a function of throughput and costs,including energy consumption and transaction costs,is constructed,and a joint utility optimization problem is *** transactions between secondary users and primary users are performed on the consortium blockchain on which users can directly trade spectrum and the transaction information is *** order to improve the joint utility,the Lagrange multiplier method is used to achieve the optimal solution for the sensing time,the number of secondary users involved in sensing,and the transmission *** simulation results show that the joint utility optimization algorithm proposed in this paper can achieve higher joint utility under the constraints of the minimum throughput requirement and maximum transmission power.

关键词： spectrum sharing power control joint utility blockchain

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A Microwave Sensor for Detection of Water Adulteration and Spoilage in Milk Based on Split-Ring Resonator 7

A Microwave Sensor for Detection of Water Adulteration and S...

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7th IEEE International Conference on Electronic information and Communication Technology, ICEICT 2024

作者： Ding, Shuying Li, Yingsong Huang, Zhixiang Sinkevich, Eugene Mordachev, Vladimir Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China Belarusian State University of Informatics and Radioelectronics EMC R and D Laboratory Minsk Belarus

ISBN: (纸本)9798350384437

The paper introduces a microwave sensor based on split-ring resonators (SRR) for detecting the moisture content and spoilage of milk. This sensor is characterized by its simple structure and high sensitivity, utilizing SRR to enhance the local electric field intensity of the electromagnetic field. By measuring the transmission characteristics of microwave signals, it can accurately detect the moisture content and spoilage of milk samples. The sensor operates at a frequency of 6 GHz and has dimensions of 30×25×1.6 mm. The study establishes the relationship between resonance frequency and water content in milk through simulated data, which provides an effective method for monitoring milk quality and holds potential applications in the food industry, contributing significantly to ensuring food safety. © 2024 IEEE.

关键词： Microwave sensors

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HRPose: Real-Time High-Resolution 6D Pose Estimation Network Using Knowledge Distillation

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Chinese Journal of Electronics 2023年第1期32卷 189-198页

作者： GUAN Qi SHENG Zihao XUE Shibei Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management

Real-time six degrees-of-freedom(6D)object pose estimation is essential for many real-world applications, such as robotic grasping and augmented reality. To achieve an accurate object pose estimation from RGB images in real-time, we propose an effective and lightweight model, namely high-resolution 6D pose estimation network(HRPose). We adopt the efficient and small HRNetV2-W18 as a feature extractor to reduce computational burdens while generating accurate 6D poses. With only 33% of the model size and lower computational costs, our HRPose achieves comparable performance compared with state-of-the-art models. Moreover, by transferring knowledge from a large model to our proposed HRPose through output and feature-similarity distillations, the performance of our HRPose is improved in effectiveness and efficiency. Numerical experiments on the widely-used benchmark LINEMOD demonstrate the superiority of our proposed HRPose against state-of-the-art methods.

关键词： Knowledge engineering Computational modeling Pose estimation Grasping Benchmark testing Feature extraction Real-time systems

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Improving BERT Fine-Tuning via Self-Ensemble and Self-Distillation

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Journal of Computer Science & Technology 2023年第4期38卷 853-866页

作者：许一格邱锡鹏周浬皋黄萱菁 School of Computer Science Fudan UniversityShanghai 200433China Shanghai Key Laboratory of Intelligent Information Processing Fudan UniversityShanghai 200433China Huawei Technologies Co. Ltd.Hangzhou 310052China

Fine-tuning pre-trained language models like BERT have become an effective way in natural language processing(NLP)and yield state-of-the-art results on many downstream *** studies on adapting BERT to new tasks mainly focus on modifying the model structure,re-designing the pre-training tasks,and leveraging external data and *** fine-tuning strategy itself has yet to be fully *** this paper,we improve the fine-tuning of BERT with two effective mechanisms:self-ensemble and *** self-ensemble mechanism utilizes the checkpoints from an experience pool to integrate the teacher *** order to transfer knowledge from the teacher model to the student model efficiently,we further use knowledge distillation,which is called self-distillation because the distillation comes from the model itself through the time *** on the GLUE benchmark and the Text Classification benchmark show that our proposed approach can significantly improve the adaption of BERT without any external data or *** conduct exhaustive experiments to investigate the efficiency of the self-ensemble and self-distillation mechanisms,and our proposed approach achieves a new state-of-the-art result on the SNLI dataset.

关键词： BERT deep learning fine-tuning natural language processing(NLP) pre-training model

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Asymptotically Optimal Sequence Sets With Low/Zero Ambiguity Zone Properties

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IEEE Transactions on information Theory 2025年第6期71卷 4785-4796页

作者： Tian, Liying Song, Xiaoshi Liu, Zilong Li, Yubo Northeastern University School of Computer Science and Engineering Shenyang110819 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110189 China University of Essex School of Computer Science and Electronics Engineering ColchesterCO4 3SQ United Kingdom Yanshan University School of Information Science and Engineering Qinhuangdao066004 China Hebei Key Laboratory of Information Transmission and Signal Processing Qinhuangdao066004 China

Sequences with low/zero ambiguity zone (LAZ/ZAZ) properties are useful in modern communication and radar systems operating over mobile environments. This paper first presents a new family of ZAZ sequence sets motivated by the "modulating" zero correlation zone (ZCZ) sequences which were first proposed by Popovic and Mauritz. We then introduce a second family of ZAZ sequence sets with comb-like spectrum, whereby the local Doppler resilience is guaranteed by their inherent spectral nulls in the frequency domain. Finally, LAZ sequence sets are obtained by exploiting their connection with a novel class of mapping functions. These proposed unimodular ZAZ and LAZ sequence sets are cyclically distinct and asymptotically optimal with respect to the existing theoretical bounds on ambiguity functions. © 1963-2012 IEEE.

关键词： Frequency domain analysis

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SDGNN: Symmetry-Preserving Dual-Stream Graph Neural Networks

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1717-1719页

作者： Jiufang Chen Ye Yuan Xin Luo the College of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065 the Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China the College of Computer and Information Science Southwest UniversityChongqing 400715China IEEE

Dear Editor,This letter proposes a symmetry-preserving dual-stream graph neural network(SDGNN) for precise representation learning to an undirected weighted graph(UWG). Although existing graph neural networks(GNNs) are influential instruments for representation learning to a UWG, they invariably adopt a unique node feature matrix for illustrating the sole node set of a UWG.

关键词： representation preserving undirected

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A Novel Grades Prediction Method for Undergraduate Students by Learning Explicit Conditional Distribution

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4837-4848页

作者： Zhang, Na Liu, Ming Wang, Lin Liu, Shuangrong Sun, Runyuan Yang, Bo Zhu, Shenghui Li, Chengdong Yang, Cheng Cheng, Yuhu China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Shandong Key Laboratory of Intelligent Buildings Technology Jinan250101 China University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan250022 China Quan Cheng Laboratory Jinan250100 China Shandong Jianzhu University Shandong Key Laboratory of Intelligent Buildings Technology Jinan250101 China Shandong Jianzhu University School of Information and Electrical Engineering Jinan250101 China Ministry of Education Engineering Research Center of Intelligent Control for Underground Space Xuzhou221116 China

Educational data mining (EDM) offers an effective solution to predict students' course grades in the next term. Conventional grade prediction methods can be viewed as regressing an expectation of the probability distribution of the student's grade, typically called single-value grade prediction. The reliable prediction outcomes of these methods depend on the complete input information related to students. However, next-term grade prediction often encounters the challenge of incomplete input information due to the inaccessibility of future data and the privacy of data. In this scenario, single-value grade prediction struggles to assess students' academic status, as it may not be represented and assessed by relying on a singular expectation value. This limitation increases the risk of misjudgment, and may lead to errors in educational decision-making. Considering the challenge of collecting complete input information, we shift from traditional single-value predictions to forecasting the explicit probability distribution of the course grade. The probability distribution of the grade can assess the students' academic status by providing probabilities corresponding to all possible grade values rather than relying solely on an expectation value, which offers the foundation to support the educators' decision-making. In this article, the course grade distribution prediction (CGDP) model is proposed, aiming to estimate an explicit conditional probability distribution of course grades in the next term. This model can identify at-risk students, offering comprehensive decision-making information for educators and students. To ensure precise distribution predictions, a calibration method is also employed to improve the alignment between predicted and actual probabilities. Experimental results verify the effectiveness of the proposed model in early grade warning for undergraduates, based on real university data. © 2020 IEEE.

关键词： Probability distributions

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Identifying important nodes of hypergraph:An improved PageRank algorithm

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Chinese Physics B 2025年第4期34卷 162-171页

作者： Yu-Hao Piao Jun-Yi Wang Ke-Zan Li School of Mathematics and Computing Science Guilin University of Electronic TechnologyGuilin 541004China School of Information and Communication Guilin University of Electronic TechnologyGuilin 541004China Guangxi Wireless Broadband Communication and Signal Processing Key Laboratory Guilin University of Electronic TechnologyGuilin 541004China

Hypergraphs can accurately capture complex higher-order relationships,but it is challenging to identify their important *** this paper,an improved PageRank(ImPageRank)algorithm is designed to identify important nodes in a directed *** algorithm introduces the Jaccard similarity of directed *** comparing the numbers of common neighbors between nodes with the total number of their neighbors,the Jaccard similarity measure takes into account the similarity between nodes that are not directly connected,and can reflect the potential correlation between *** improved susceptible–infected(SI)model in directed hypergraph is proposed,which considers nonlinear propagation mode and more realistic propagation *** addition,some important node evaluation methods are transferred from undirected hypergraphs and applied to directed ***,the ImPageRank algorithm is used to evaluate the performance of the SI model,network robustness and *** of real networks demonstrate the excellent performance of the proposed algorithm and provide a powerful framework for identifying important nodes in directed hypergraphs.

关键词： hypergraph important node PageRank susceptible-infected(SI)model centrality index

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