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14th International Conference on Measuring Technology and Mechatronics Automation, ICMTMA 2022

作者： Hao, Junyu Li, Xiaoge School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an71012 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'an Key laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China

ISBN: (纸本)9781665499781

To deeply excavate the information contained in user data and better alleviate cold start problem, we propose KGCN++, a user information enhanced knowledge graph convolutional networks model for recommender system, which simulates nerves' influence diffusion of information of user and items in a unified framework. The model combines user information and user-item interaction information into a user knowledge graph, which advances KGCN by injecting high-order user potential information reflected in user graph and high-order influence of items reflected in item knowledge graph. In addition, we have designed the influence factors of different items on users, which are used to characterize the weight of items on the user's information dissemination, so as to distinguish users with different information. Through extensive experiments on real-world datasets, we demonstrate that KGCN++ achieves substantial gains in a variety of ecenarios, including movie and book recommendation, better performance compared to the other methods. © 2022 IEEE

关键词： Recommender systems

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A Broadband Circularly Polarized Filtering Patch Antenna with Stacked Structure

A Broadband Circularly Polarized Filtering Patch Antenna wit...

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International Conference on Computer Science and Network Technology (ICCSNT)

作者： Jinping Zhang Dmitry Kholodnyak Zhixiang Huang Yingsong Li College of Information and Communication Engineering Harbin Engineering University Harbin China Department of Microwave Electronics Saint Petersburg Electrotechnical University Russia Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China

ISBN: (数字)9798350350791

ISBN: (纸本)9798350350807

A double-layer stacked patch filtering antenna with broadband circular polarization performance is proposed in this paper. The main patch consists of a corner-truncated square patch with two shorting pins. The designed two shorting pins are located in the main patch and the upper stacked patch structure are introduced and designed, which can gain two radiation nulls for realizing the filtering effect of the proposed antenna. The circularly polarized (CP) operation is realized by corner-truncated square patch. In addition, to increase the axial ratio (AR) bandwidth, a rectangular slot is carved on the ground plane. The simulated results indicate that the researched antenna can provide the impedance bandwidth of 20.58% (2.68-3.29 GHz) and AR bandwidth of 9.5% (2.80-3.08 GHz). Moreover, The average realized gain of the simulation is 6.48 dBic over bandwidth.

关键词： Radio frequency Polarization Filtering Simulation Patch antennas Bandwidth Pins Broadband communication Broadband antennas Antennas

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Two-Hop Partial Task Offloading and Resource Allocation in Air-Ground Integrated Mobile Edge computing Network: A DRL-based Method

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IEEE Internet of Things Journal 2025年第12期12卷 21443-21456页

作者： Li, Shichao Lu, Bingji Ale, Laha Chen, Hongbin Tan, Fangqing Huang, Jingyue Guilin University of Electronic Technology Guangxi Key Laboratory of Wireless Broadband Communication and Signal Processing Guilin541004 China Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu610031 China

The integration of mobile edge computing (MEC) and air-ground integrated network is viewed as a crucial technology for Internet of remote things (IoRT) devices. It provides widespread service coverage and allows the tasks of IoRT devices to be executed by the unmanned aerial vehicles (UAVs) and the high altitude platforms (HAPs). In this paper, we investigate a joint partial task offloading, resource allocation, and UAV trajectory design problem to minimize the total task offloading delay of all IoRT devices in the air-ground integrated MEC network. Given that the problem is non-convex and hard to solve by the traditional methods, we convert it into a Markov decision process (MDP) and leverage the deep reinforcement learning method to address it. Considering the complexity of the MDP grows with the number of the IoRT devices and the UAVs increasing, the primal problem is decomposed into two subproblems: the UAV trajectory design and IoRT device power control subproblem, and the partial task offloading and resource allocation subproblem. To address these two subproblems, we apply the basic concepts of the multi-agent deep deterministic policy gradient (MADDPG) and the independent proximal policy optimization (IPPO) methods, respectively. Additionally, we introduce the enhanced prioritized experience replay and noise value to improve both the convergence performance and rate. This leads to the development of the MADDPG-improved prioritized experience replay (MADDPG-IPER) algorithm and noise value-IPPO (NV-IPPO) algorithm. Based on the solution of these two subproblems, a joint partial task offloading, resource allocation, and UAV trajectory design (JPTORAUTD) algorithm is proposed. Simulation results present that the proposed JPTORAUTD algorithm outperforms other benchmark algorithms in terms of reducing the total task offloading delay. © 2025 IEEE.

关键词： Resource allocation

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Blockchain-Based Multi-Cloud Data Storage System Disaster Recovery

Blockchain-Based Multi-Cloud Data Storage System Disaster Re...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wang, Feiyu Zhou, Jian-Tao College of Computer Science Inner Mongolia University Inner Mongolia Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

ISBN: (纸本)9798350337020

Cloud storage services have been used by most businesses and individual users. However, data loss, service interruptions and cyber attacks often lead to cloud storage services not being provided properly, and these incidents have caused financial losses to users. Second, traditional and single-cloud model disaster recovery services are no longer suitable for the current complex cloud storage systems. Therefore, a scheme to provide disaster recovery for cloud storage services in a multi-cloud storage environment is needed in real production. In this paper, we propose a disaster recovery scheme based on blockchain technology. The proposed scheme outlined in this study aims to address the issue of data availability within the cloud storage landscape. The proposed scheme achieves this goal by dividing data into hot and cold categories, verifying the integrity of copy data via blockchain technology, and utilizing blockchain networks to manage multi-cloud storage systems. Experimental findings demonstrate that the proposed scheme yields superior results in terms of computation and time overheads. © 2023 IEEE.

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Multi-Layer Visual Perception for No-Reference Image Quality Assessment 6

Multi-Layer Visual Perception for No-Reference Image Quality...

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6th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2023

作者： Qi, Junwei Wang, Yingzhen Wang, Qingchun Li, Yingsong College of Computer Science and Technology Harbin Engineering University Harbin150001 China College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Shanghai Yanding Information Technology Co. Ltd. Shanghai201210 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui Hefei230601 China

ISBN: (纸本)9798350399059

In this work, we propose a no-reference image quality evaluation approach, aiming to solve the problem that the traditional convolutional neural network is insufficient to express the global information of the image. Therefore, in order to make the captured image features have the coherence of global context information, we combine a Transformer structure widely used in NLP (natural language processing) with the traditional CNN to realize the attention mechanism in the human visual perception characteristics. Firstly, the self-attention mechanism of Transformer is used to learn the global representation of the image from the multi-layer features extracted from different levels of CNN. Additionally, to forecast the ultimate image quality score, the global and local attributes are combined. Finally, the proposed algorithm is evaluated on three public datasets commonly used in CSIQ [1], TID2013 [2] and LIVE [3]. Compared with eight traditional no-reference image quality evaluation algorithms and five methods based on deep learning, the proposed algorithm achieves competitive results. © 2023 IEEE.

关键词： Convolutional neural networks

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pp2-linear codes: rank and kernel

arXiv

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arXiv 2022年

作者： Shi, Minjia Wang, Shukai Li, Xiaoxiao Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematics Sciences Anhui University Anhui Hefei China

A code C is called pp2-linear if it is the Gray image of a pp2-additive code, where p > 2 is prime. In this paper, the rank and the dimension of the kernel of pp2-linear codes are studied. Two bounds of the rank of a Z3Z9-linear code and the dimension of the kernel of a pp2-linear code are given, respectively. For each value of these bounds, we give detailed construction of the corresponding code. Finally, pairs of rank and the dimension of the kernel of 39-linear codes are also *** Codes 94B05 © 2022, CC0.

关键词： Additives

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AUCSeg: AUC-oriented pixel-level long-tail semantic segmentation 24

AUCSeg: AUC-oriented pixel-level long-tail semantic segmenta...

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Proceedings of the 38th International Conference on Neural Information processing Systems

作者： Boyu Han Qianqian Xu Zhiyong Yang Shilong Bao Peisong Wen Yangbangyan Jiang Qingming Huang Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and School of Computer Science and Tech. University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Peng Cheng Laboratory School of Computer Science and Tech. University of Chinese Academy of Sciences School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Key Laboratory of Big Data Mining and Knowledge Management CAS

ISBN: (纸本)9798331314385

The Area Under the ROC Curve (AUC) is a well-known metric for evaluating instance-level long-tail learning problems. In the past two decades, many AUC optimization methods have been proposed to improve model performance under long-tail distributions. In this paper, we explore AUC optimization methods in the context of pixel-level long-tail semantic segmentation, a much more complicated scenario. This task introduces two major challenges for AUC optimization techniques. On one hand, AUC optimization in a pixel-level task involves complex coupling across loss terms, with structured inner-image and pairwise inter-image dependencies, complicating theoretical analysis. On the other hand, we find that mini-batch estimation of AUC loss in this case requires a larger batch size, resulting in an unaffordable space complexity. To address these issues, we develop a pixel-level AUC loss function and conduct a dependency-graph-based theoretical analysis of the algorithm's generalization ability. Additionally, we design a Tail-Classes Memory Bank (T-Memory Bank) to manage the significant memory demand. Finally, comprehensive experiments across various benchmarks confirm the effectiveness of our proposed AUCSeg method. The code is available at https://***/boyuh/AUCSeg.

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Low Coupling Sparse Array Based on Uniform Linear Array Fitting Method

Low Coupling Sparse Array Based on Uniform Linear Array Fitt...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Yichang Yao Yingsong Li Liping Li Nikola Zlatanov College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Faculty of Computer and Engineering Sciences Innopolis University Innopolis Russia

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

In order to solve the problem that traditional sparse arrays only emphasize high degrees of freedom and ignore low coupling, uniform array fitting method can design arrays with low coupling. However, the existing array coupling based on uniform array fitting method still has room for decline, and the value of weight function is not the minimum, so the performance of array DOA estimation needs to be further optimized. Therefore, based on the uniform matrix fitting method, a uniform matrix fitting 6-base-layer array is designed. By extending the spacing of each subarray of the uniform subarray in the base layer, the number of small array element spacing pairs in the differential cooperative array is reduced, and the coupling between the elements of the whole sparse array is reduced as a whole. The performance of DOA estimation of the designed array is simulated and analyzed by using spatial smoothing multiple signal classification algorithm.

关键词： Couplings Mutual coupling Direction-of-arrival estimation Smoothing methods Simulation Fitting Estimation

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Drug-Protein Interaction Prediction by Fusion of Attention and Graph Neural Network

Drug-Protein Interaction Prediction by Fusion of Attention a...

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第42届中国控制会议

作者： Tianrui Chen Shouheng Tuo Zengyu Feng School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an Key Laboratory of Big Data and Intelligent Computing

The identification of drug-protein interactions(DTIs) is a critical step in drug development and ***,detecting these interactions using scientific methods presents a formidable *** models often struggle to extract relevant information from drug-protein sequences,and they fail to consider pre-existing interactions between ***,we introduce a deep learning model,DPAG,based on attention mechanisms and graph neural networks,which overcomes these *** leverages information from both the drug and protein sequences,as well as the association between drug-protein pairs,to predict *** conducted multiple experiments to evaluate the performance of the model in benchmark datasets,and the results showed that DPAG achieved excellent performance in terms of AUC,AUPR,precision,and *** findings suggest that DPAG holds significant potential as a promising tool for predicting DTIs in drug development and *** is available at https://***/veres se/DPAG.

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A Novel Underdetermined Blind Source Separation Algorithm of Frequency-hopping signals via Time-frequency Analysis

TechRxiv

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TechRxiv 2023年

作者： Li, Yingsong Wang, Yifan Li, Yibing Sun, Qian The College of Information and Communication Engineering Harbin Engineering University Harbin150001 China The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230601 China

To address the significant performance degradation of conventional underdetermined blind source separation algorithms for frequency-hopping (FH) signals under time-frequency (TF) overlapping conditions, this paper presents a novel three-stage scheme based on the TF distribution of FH signals. In the first stage, key parameters of the FH signal are estimated using a TF binary graph. In the second step, the initial mixing matrix is estimated for non-overlapping and overlapping carrier frequencies employing density peaks clustering and tensor decomposition methods, respectively. In the third step, the final mixing matrix, directions of arrival (DOA), and source signals are estimated using the expectation-maximization algorithm within the nonnegative matrix factorization model. Finally, different segments of the FH signals are spliced together based on the DOAs of different source signals. Comprehensive experimental results demonstrate the superior performance of the proposed algorithm compared to state-of-the-art algorithms. Even at a signal-to-noise ratio of 5 dB, the correlation coefficient of the estimated source signals can still reach 0.91. © 2023, CC BY.

关键词： Blind source separation

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