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IEEE Conference on Wireless Communications and networking

作者： Zhuo Li Guangyue Lu Yuxin Li Hao Zhou Huan Li Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications Xi'an China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Automatic Modulation Recognition (AMR) is becoming increasingly crucial in the industrial internet, and it ensures more reliable communication for devices within this vast and intricate network. Although recent application of neural networks, notably Graph Convolutional network (GCN) in the AMR domain shows promising results, it fails to account for temporal features within modulation signals, resulting in a loss of recognition precision. Given that modulation signals possess both time and space features, this paper proposes a hybrid deep learning model named LAGNet, which combines Long Short-Term Memory (LSTM) and GCN. The model uses an attention mechanism to map the LSTM outputs into a graph and then employs the GCN to extract the spatial features of the signal. Subsequently, the temporal features and the spatial features are combined together for modulation signal classification. Experimental results show that LAGNet not only outperforms several advanced models in classification accuracy across all signal-to-noise ratios, but also requires fewer learnable parameters.

关键词： Deep learning Accuracy Neural networks Modulation Pattern classification Feature extraction Reliability

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Scratch Each Other’s Back: Incomplete Multi-modal Brain Tumor Segmentation Via Category Aware Group Self-Support Learning

Scratch Each Other’s Back: Incomplete Multi-modal Brain Tum...

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International Conference on computer Vision (ICCV)

作者： Yansheng Qiu Delin Chen Hongdou Yao Yongchao Xu Zheng Wang National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering

Although Magnetic Resonance Imaging (MRI) is very helpful for brain tumor segmentation and discovery, it often lacks some modalities in clinical practice. As a result, degradation of prediction performance is inevitable. According to current implementations, different modalities are considered to be independent and non-interfering with each other during the training process of modal feature extraction, however they are complementary. In this paper, considering the sensitivity of different modalities to diverse tumor regions, we propose a Category Aware Group Self-Support Learning framework, called GSS, to make up for the information deficit among the modalities in the individual modal feature extraction phase. Precisely, within each prediction category, predictions of all modalities form a group, where the prediction with the most extraordinary sensitivity is selected as the group leader. Collaborative efforts between group leaders and members identify the communal learning target with high consistency and certainty. As our minor contribution, we introduce a random mask to reduce the possible biases. GSS adopts the standard training strategy without specific architectural choices and thus can be easily plugged into existing incomplete multi-modal brain tumor segmentation. Remarkably, extensive experiments on BraTS2020, BraTS2018, and BraTS2015 datasets demonstrate that GSS can improve the performance of existing SOTA algorithms by 1.27-3.20% in Dice on average. The code is released at https://***/qysgithubopen/GSS.

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The Entrapment Problem in Random Walk Decentralized Learning

The Entrapment Problem in Random Walk Decentralized Learning

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IEEE International Symposium on Information Theory

作者： Zonghong Liu Salim El Rouayheb Matthew Dwyer Department of Electrical and Computer Engineering Rutgers University New Brunswick NJ USA Network Cyber and Computational Sciences Division DEVCOM Army Research Laboratory Adelphi MD USA

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

This paper explores decentralized learning in a graph-based setting, where data is distributed across nodes. We investigate a decentralized SGD algorithm that utilizes a random walk to update a global model based on local data. Our focus is on designing the transition probability matrix to speed up convergence. While importance sampling can enhance centralized learning, its decentralized counterpart, using the Metropolis-Hastings (MH) algorithm, can lead to the entrapment problem, where the random walk becomes stuck at certain nodes, slowing convergence. To address this, we propose the Metropolis-Hastings with Levy Jumps (MHLJ) algorithm, which incorporates random perturbations (jumps) to overcome entrapment. We theoretically establish the convergence rate and error gap of MHLJ and validate our findings through numerical experiments.

关键词： Monte Carlo methods Perturbation methods Distributed databases Data models Convergence Information theory

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Heavy Rainfall Prediction Model Using Sample Entropy Derived from GNSS-PWV and PSO-SVM 14th

Heavy Rainfall Prediction Model Using Sample Entropy Derived...

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14th China Satellite Navigation Conference, CSNC 2024

作者： Wu, Fanming Li, Dengao Zhao, Jinhua Feng, Ran Shi, Danyang Zhang, Xinfang Zhao, Jumin College of Data Science Taiyuan University of Technology Taiyuan030024 China College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan030024 China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan030024 China Shanxi Province Engineering Technology Research Center of Spatial Information Network Taiyuan030024 China

ISBN: (纸本)9789819969272

There is a growing interest to use Global Navigation Satellite System (GNSS) inversed PWV for heavy rainfall prediction. When heavy rainfall occurs, it requires the atmosphere to contain sufficient water vapour and undergo strong upward motion. However, existing models using GNSS-PWV for heavy rainfall prediction have not accounted for the effect of the complex motion of water vapour. In this paper, an hourly heavy rainfall prediction model using sample entropy derived from GNSS-PWV and PSO-SVM is proposed. The sample entropy of GNSS-PWV is used to measure the complexity of the water vapour movement process before heavy rainfall occurs. Meanwhile, combining GNSS-PWV time domain features and co-located meteorological data, the occurrence of hourly heavy rainfall events is predicted by support vector machine optimized with particle swarm. To verify the validity and feasibility of the proposed algorithm, Hong Kong's HKSC station in Sham Shui Po is used for the model to train and test. The result shows that probability of detection, false alarm rate and critical success index of the proposed algorithm in this paper have significantly improved over other heavy rainfall prediction models. © 2024, Aerospace Information Research Institute.

关键词： Support vector machines

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A 3D Memristive Cubic Map with Dual Discrete Memristors: Design, Implementation, and Application in Image Encryption

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Gao, Suo Iu, Herbert Ho-Ching Erkan, Ugur Simsek, Cemaleddin Toktas, Abdurrahim Cao, Yinghong Wu, Rui Mou, Jun Li, Qi Wang, Chunpeng Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering Perth6009 Australia Faculty of Engineering Electrical and Electronics Engineering Respectively Karamanoglu Mehmetbey University Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Discrete chaotic systems based on memristors exhibit excellent dynamical properties and are more straightforward to implement in hardware, making them highly suitable for generating cryptographic keystreams. However, most existing memristor-based chaotic systems rely on a single memristor. This paper introduces a novel discrete chaotic system employing dual memristors, named the 3D memristive cubic map with dual discrete memristors (3D-MCM). The 3D-MCM system demonstrates richer and more intricate dynamical behaviors compared to its single-memristor counterparts, as verified through bifurcation diagrams, Lyapunov exponent spectra, and complexity analyses. Notably, the system exhibits coexisting attractors, substantially enhancing its dynamical complexity. Hardware implementation of the 3D-MCM attractors confirms its feasibility for industrial applications. To illustrate the system's potential in encryption tasks, this study integrates the quaternary-based permutation and dynamic emanating diffusion (QPDED-IE) scheme for image encryption with the *** results demonstrate that the QPDED-IE scheme based on the 3D-MCM exhibits strong diffusion and confusion properties, effectively resisting cryptanalytic attacks. © 1991-2012 IEEE.

关键词： Bifurcation (mathematics)

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Design and Implementation of Links Generation for Inter Domain Routing System 12

Design and Implementation of Links Generation for Inter Doma...

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12th International Conference on Information Science and Technology, ICIST 2022

作者： Wang, Yu Qiao, Zhi Yin, Junru Zhang, Mingliang College of Software Henan University of Engineering Zhengzhou China College of Information and Electrical Engineering China Agriculture University Beijing China College of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Network Research Department Zhengzhou China

ISBN: (数字)9781665485821

ISBN: (纸本)9781665485821

Due to the inherent flaws of BGP protocol, the security of inter domain routing system has been regularly threatened. Since it is difficult to achieve collaborative defense against existing resources and strategies of multiple autonomous systems, this paper designs and organizes them into autonomous system community. Within the community, a precomputing multi-link generation mechanism is proposed based on genetic algorithm. Before the inter domain routing system is attacked, the link set can be generated as complete as possible in advance. Once an attack occurs, the community will quickly select proper precomputing links to replace the failed ones, in order to ensure the stability of inter domain communication. The effectiveness of the mechanism has been proved in function and performance through simulation experiments. © 2022 IEEE.

关键词： Genetic algorithms

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Evolutionary Multitasking Based on Team Learning Strategy 6

Evolutionary Multitasking Based on Team Learning Strategy

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6th Asian Conference on Artificial Intelligence Technology, ACAIT 2022

作者： Li, Wei Xu, Qingzheng Gao, Xinyu Wang, Lei Xi'an University of Technology School of Computer Science and Engineering Xi'an China National University of Defense Technology College of Information and Communication Wuhan China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

ISBN: (纸本)9781665453110

Multi-factorial optimization (MFO) is a popular optimization mechanism recently, which aims to optimize multiple tasks in a single run. Generally, the multi-factorial optimization algorithm uses the random learning strategy to generate offspring, which leads to an inefficient exploration ability of the algorithm. To address this problem, this paper proposed a simple and effective strategy called team learning strategy (TLS). The proposed learning strategy divided the population into excellent team and ordinary team. Different teams employ different learning strategies. Moreover, nine MFO problems were used to assess the effectiveness of the proposed strategy. The experimental results show that the team learning strategy can improve the performance of the algorithm. © 2022 IEEE.

关键词： Evolutionary algorithms

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Measuring Encrypted DNS Service with TLS1.3 Support over IPv6

Measuring Encrypted DNS Service with TLS1.3 Support over IPv...

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IEEE Symposium on computers and Communications (ISCC)

作者： Liang Jiao Jianqiang Li Wenxiu Zhang Tianyu Cui Yi Zhou Yujia Zhu Qingyun Liu Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences National Computer Network Emergency Response Technical Team/Coordination Center of China Shandong Branch National Computer Network Emergency Response Technical Team/Coordination Center School of Information Science and Engineering University of Jinan Zhongguancun Laboratory

ISBN: (数字)9798350354232

ISBN: (纸本)9798350354249

The Encrypted Domain Name System (DNS) and Encrypted Server Name Indication (ESNI) are recently proposed to enhance network security and privacy protection; we refer to these schemes collectively as domain name encryption technologies. Previous research has shown that the destination IP address accessed by the user cannot be associated with common web services such as websites because a large number of websites are hosted through cloud or CDN over IPv6. However, encrypted DNS, as an internet infrastructure service, is typically deployed independently by the service provider rather than hosted through cloud or CDN. In this paper, we propose a method to discover the unique service provider of encrypted DNS resolvers on a large-scale encrypted traffic with TLS1.3 support over IPv6. The model utilizes a Siamese network to determine whether two IPv6 resolver addresses belong to the same service provider of encrypted DNS, even if the DNS query is protected by ESNI. Through a comprehensive analysis of two real-world datasets, which include encrypted DNS data and common web data, we find that the implementation of TLS1.3, especially ESNI, does not impact the association of encrypted DNS server addresses. Our model achieves an accuracy rate of 95.29%.

关键词： Measurement Cloud computing Privacy Correlation Web services network security Domain Name System Cryptography Servers Protection

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Msqat: A Multi-Dimension Non-Intrusive Speech Quality Assessment Transformer Utilizing Self-Supervised Representations

SSRN

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SSRN 2023年

作者： Shen, Kailai Yan, Diqun Dong, Li Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China

Convolutional neural networks (CNNs) have been widely utilized as the main building block for many \textit{non-intrusive} speech quality assessment (NISQA) methods. A new trend is to add a self-attention mechanism based on CNN to better capture long-term global content. However, it is not clear whether the pure attention-based network is sufficient to obtain good performance in NISQA. To this end, a framework named \textbf{M}ulti-dimension \textit{non-intrusive} \textbf{S}peech \textbf{Q}uality \textbf{A}ssessment \textbf{T}ransformer (MSQAT) is proposed. To strengthen the interactions of various speech regions between local and global, three multi-dimensional modules are devised. Additionally, speech quality varies not only in different frames, but also in different frequencies. Thus, a two-branch structure is designed to better evaluate the quality of speech. Experimental results demonstrate that the proposed MSQAT has state-of-the-art performance on three standard datasets (NISQA Corpus, Tencent Corpus, and PSTN Corpus). © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Fast Crop Pest Detection Using Lightweight Feature Extraction and Knowledge Distillation

Fast Crop Pest Detection Using Lightweight Feature Extractio...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Ze Yang Xianliang Jiang Guang Jin Junkai Huang Jie Bai Dingxin Yu Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China

Pest detection is critical for achieving effective pest control. However, the current deep learning-based pest detection algorithm is unsuitable for deployment on resource-limited edge devices due to its extensive computation and long inference time. Although lightweight models have been widely used for practical detection, their insufficient feature extraction capability leads to a decline in detection accuracy. This paper proposes a fast algorithm for crop pest detection based on lightweight feature extraction and knowledge distillation. Firstly, we introduce partial convolution and propose a lightweight feature extraction module, C3Faster, which reduces the model's computation and speeds up model inference while ensuring effective feature extraction. Secondly, we use knowledge distillation to improve the model's detection accuracy by using teacher networks to assist in training. Finally, we created a dataset, CropPest6, consisting of six crop pest categories and conducted experiments. The experimental results demonstrate that our method reduces the detection time, number of parameters, and computation by 17%, 38%, and 44%, respectively, compared to the baseline model. Furthermore, our method achieves 93.9% Precision, 93.6% Recall, and 97.5% mean Average Precision (mAP), demonstrating its practical suitability for fast crop pest detection.

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