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research on patent recommendation method based on graph neural network

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Research on patent recommendation method based on graph neur...

International Conference on Computer Supported Cooperative Work in Design

作者： Enchao Zhang Weidong Liu Shi Liu Yan Cao College of Computer Science Inner Mongolia University China Institute of Scientific and Technical Information of China China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China Inner Mongolia Key Laboratory of Social Computing and Data Processing China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

Intellectual property transactions have shown a strong growth momentum in recent years, but the patent transaction market has been plagued by the matching degree of consumers and sellers, resulting in frequent problems such as low patent transformation efficiency and poor transaction quality. This paper proposes a method of recommending patents to consumers by experts to improve the environment of patent transactions. Through the analysis of the past transaction information of the patent, the effective path information of the target is extracted. The graph neural network is used to describe the characteristics and semantics among experts, patents and consumers, and then capture the potential weight among them through the common attention mechanism, and then dynamically integrate them to predict the occurrence of recommendation behavior. The paper makes reasonable use of social information and expert information in the transaction, which significantly improves the rationality and accuracy of expert recommendation.

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LEARNING A LOW-RANK FEATURE REPRESENTATION: ACHIEVING BETTER TRADE-OFF BETWEEN STABILITY AND PLASTICITY IN CONTINUAL LEARNING

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arXiv 2023年

作者： Liu, Zhenrong Li, Yang Gong, Yi Wu, Yik-Chung Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Shenzhen Research Institute of Big Data Shenzhen China

In continual learning, networks confront a trade-off between stability and plasticity when trained on a sequence of tasks. To bolster plasticity without sacrificing stability, we propose a novel training algorithm called LRFR. This approach optimizes network parameters in the null space of the past tasks’ feature representation matrix to guarantee the stability. Concurrently, we judiciously select only a subset of neurons in each layer of the network while training individual tasks to learn the past tasks’ feature representation matrix in low-rank. This increases the null space dimension when designing network parameters for subsequent tasks, thereby enhancing the plasticity. Using CIFAR-100 and TinyImageNet as benchmark datasets for continual learning, the proposed approach consistently outperforms state-of-the-art methods. © 2023, CC BY-NC-ND.

关键词： Stability

Movable Antenna Enabled Near-Field Communications: Channel Modeling and Performance Optimization

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arXiv 2024年

作者： Zhu, Lipeng Ma, Wenyan Xiao, Zhenyu Zhang, Rui Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore School of Electronic and Information Engineering Beihang University Beijing100191 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

Movable antenna (MA) technology offers promising potential to enhance wireless communication by allowing flexible antenna movement. To maximize spatial degrees of freedom (DoFs), larger movable regions are required, which may render the conventional far-field assumption for channels between transceivers invalid. In light of it, we investigate in this paper MA-enabled near-field communications, where a base station (BS) with multiple movable subarrays serves multiple users, each equipped with a fixed-position antenna (FPA). First, we extend the field response channel model for MA systems to the near-field propagation scenario. Next, we examine MA-aided multiuser communication systems under both digital and analog beamforming architectures. For digital beamforming, spatial division multiple access (SDMA) is utilized, where an upper bound on the minimum signal-to-interference-plus-noise ratio (SINR) across users is derived in closed form. A low-complexity algorithm based on zero-forcing (ZF) is then proposed to jointly optimize the antenna position vector (APV) and digital beamforming matrix (DBFM) to approach this bound. For analog beamforming, orthogonal frequency division multiple access (OFDMA) is employed, and an upper bound on the minimum signal-to-noise ratio (SNR) among users is derived. An alternating optimization (AO) algorithm is proposed to iteratively optimize the APV, analog beamforming vector (ABFV), and power allocation until convergence. For both architectures, we further explore MA design strategies based on statistical channel state information (CSI), with the APV updated less frequently to reduce the antenna movement overhead. Simulation results demonstrate that our proposed algorithms achieve performance close to the derived bounds and also outperform the benchmark schemes using dense or sparse arrays with FPAs. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal to noise ratio

Movable-Antenna Array Enhanced Beamforming: Achieving Full Array Gain with Null Steering

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arXiv 2023年

作者： Zhu, Lipeng Ma, Wenyan Zhang, Rui Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

Conventional beamforming with fixed-position antenna (FPA) arrays has a fundamental trade-off between maximizing the signal power (array gain) over a desired direction and simultaneously minimizing the interference power over undesired directions. To overcome this limitation, this letter investigates the movable antenna (MA) array enhanced beamforming by exploiting the new degree of freedom (DoF) via antenna position optimization, in addition to the design of antenna weights. We show that by jointly optimizing the antenna positions vector (APV) and antenna weights vector (AWV) of a linear MA array, the full array gain can be achieved over the desired direction while null steering can be realized over all undesired directions, under certain numbers of MAs and null-steering directions. The optimal solutions for AWV and APV are derived in closed form, which reveal that the optimal AWV for MA arrays requires only the signal phase adjustment with a fixed amplitude. Numerical results validate our analytical solutions for MA array beamforming and show their superior performance to the conventional beamforming techniques with FPA arrays. Copyright © 2023, The Authors. All rights reserved.

关键词： Beamforming

research and application of artificial intelligence based webshell detection model: A literature review

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arXiv 2024年

作者： Ma, Mingrui Han, Lansheng Zhou, Chunjie Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China The Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China

Webshell, as the"culprit" behind numerous network attacks, is one of the research hotspots in the field of cybersecurity. However, the complexity, stealthiness, and confusing nature of webshells pose significant challenges to the corresponding detection schemes. With the rise of Artificial Intelligence (AI) technology, researchers have started to apply different intelligent algorithms and neural network architectures to the task of webshell detection. However, the related research still lacks a systematic and standardized methodological process, which is confusing and redundant. Therefore, following the development timeline, we carefully summarize the progress of relevant research in this field, dividing it into three stages: Start Stage, Initial Development Stage, and In-depth Development Stage. We further elaborate on the main characteristics and core algorithms of each stage. In addition, we analyze the pain points and challenges that still exist in this field and predict the future development trend of this field from our point of view. To the best of our knowledge, this is the first review that details the research related to AI-based webshell detection. It is also hoped that this paper can provide detailed technical information for more researchers interested in AI-based webshell detection tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

Toward Building Human-Like Sequential Memory Using Brain-Inspired Spiking Neural Models

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IEEE Transactions on Neural Networks and Learning Systems 2025年第6期36卷 10143-10155页

作者： Malu Zhang Xiaoling Luo Jibin Wu Ammar Belatreche Siqi Cai Yang Yang Haizhou Li School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Key Laboratory of Higher Education of Sichuan Province for Enterprise Informationalization and Internet of Things Sichuan University of Science and Engineering Yibin China Department of Data Science and Artificial Intelligence Department of Computing The Hong Kong Polytechnic University Hung Hom Hong Kong Department of Computer and Information Sciences Faculty of Engineering and Environment Northumbria University Newcastle upon Tyne U.K. Department of Electrical and Computer Engineering National University of Singapore Lower Kent Ridge Rd Singapore Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong (CUHK) Shenzhen China

The brain is able to acquire and store memories of everyday experiences in real-time. It can also selectively forget information to facilitate memory updating. However, our understanding of the underlying mechanisms and coordination of these processes within the brain remains limited. However, no existing artificial intelligence models have yet matched human-level capabilities in terms of memory storage and retrieval. This study introduces a brain-inspired spiking neural model that integrates the learning and forgetting processes of sequential memory. The proposed model closely mimics the distributed and sparse temporal coding observed in the biological neural system. It employs one-shot online learning for memory formation and uses biologically plausible mechanisms of neural oscillation and phase precession to retrieve memorized sequences reliably. In addition, an active forgetting mechanism is integrated into the spiking neural model, enabling memory removal, flexibility, and updating. The proposed memory model not only enhances our understanding of human memory processes but also provides a robust framework for addressing temporal modeling tasks.

关键词： Neurons Biological system modeling Computational modeling Brain modeling Dendrites (neurons) Oscillators Neural activity Membrane potentials Encoding Adaptation models

Gradient Alignment Improves Test-Time Adaptation for Medical Image Segmentation 39

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Gradient Alignment Improves Test-Time Adaptation for Medical...

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Chen, Ziyang Ye, Yiwen Pan, Yongsheng Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China Research & Development Institute Northwestern Polytechnical University in Shenzhen Shenzhen China Ningbo Institute Northwestern Polytechnical University Ningbo China

ISBN: (纸本)157735897X

Although recent years have witnessed significant advancements in medical image segmentation, the pervasive issue of domain shift among medical images from diverse centres hinders the effective deployment of pre-trained models. Many Test-time Adaptation (TTA) methods have been proposed to address this issue by fine-tuning pre-trained models with test data during inference. These methods, however, often suffer from less-satisfactory optimization due to suboptimal optimization direction (dictated by the gradient) and fixed step-size (predicated on the learning rate). In this paper, we propose the Gradient alignment-based Test-time adaptation (GraTa) method to improve both the gradient direction and learning rate in the optimization procedure. Unlike conventional TTA methods, which primarily optimize the pseudo gradient derived from a self-supervised objective, our method incorporates an auxiliary gradient with the pseudo one to facilitate gradient alignment. Such gradient alignment enables the model to excavate the similarities between different gradients and correct the gradient direction to approximate the empirical gradient related to the current segmentation task. Additionally, we design a dynamic learning rate based on the cosine similarity between the pseudo and auxiliary gradients, thereby empowering the adaptive fine-tuning of pre-trained models on diverse test data. Extensive experiments establish the effectiveness of the proposed gradient alignment and dynamic learning rate and substantiate the superiority of our GraTa method over other state-of-the-art TTA methods on a benchmark medical image segmentation task. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Image segmentation

A Penalty-Dual Learning Framework for Multi-User QoS-Constrained Beamforming

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A Penalty-Dual Learning Framework for Multi-User QoS-Constra...

IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Yang Li Ya-Feng Liu Fan Xu Qingjiang Shi Tsung-Hui Chang Shenzhen Research Institute of Big Data China LSEC ICMSEC AMSS Chinese Academy of Sciences China Peng Cheng Laboratory China School of Software Engineering Tongji University China School of Science and Engineering The Chinese University of Hong Kong (Shenzhen) China

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

This paper investigates a novel deep learning framework for general nonconvex quality-of-service (QoS)-constrained beamforming design problems in multi-user systems. While existing deep learning-based methods have shown great success for beamforming design with simple constraints, it is still challenging to tackle more complicated QoS constraints, where the beam-formers and the wireless channels are commonly coupled. To fill this gap, this paper proposes an augmented Lagrangian based penalty-dual training algorithm, which alternatingly trains two individual neural networks to infer the beamformers and the corresponding Lagrange multipliers, respectively. This framework is then applied for learning the energy-efficient beamformers, with judiciously designed neural network architectures based on the solution structures of the problem. Simulations demonstrate the superiority of the proposed approach over both state-of-the-art deep learning and optimization-based approaches.

关键词： Wireless communication Deep learning Training Learning systems Array signal processing Conferences Neural networks Signal processing algorithms Quality of service Energy efficiency

Spatiotemporal variation of cyanobacterial harmful algal blooms in China based on literature and media information

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International Journal of Digital Earth 2023年第1期16卷 3905-3922页

作者： Yichen Du Chen Wang Mengqiu Wang Huan Zhao Kai Yan Yunchang Mu Wenzhi Zhang Fangfang Zhang Shenglei Wanga Junsheng Li Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijingPeople’s Republic of China College of Resources and Environment University of Chinese Academy of SciencesBeijingPeople’s Republic of China Research Center of Big Data for Sustainable Development Goals BeijingPeople’s Republic of China Satellite Application Center for Ecology and Environment Ministry of Ecology and Environment of the People’s Republic of ChinaBeijingPeople’s Republic of China School of Remote Sensing and Information Engineering Wuhan UniversityWuhanPeople’s Republic of China School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijingPeople’s Republic of China

Cyanobacterial harmful algal blooms(CyanoHABs)in inland waters are now among the most pressing environmental issues worldwide,especially in *** remote sensing has limitations in monitoring CyanoHABs in small water bodies due to spatial and temporal resolution *** literature and news media have the potential to supplement satellite remote sensing in monitoring CyanoHABs,they have currently not received sufficient *** this study,we combined information on the distributions of CyanoHABs from literature and media for the first time to comprehensively assess the spatiotemporal variation in CyanoHABs in *** collected,cleaned,validated,and organized data from literature and media on CyanoHABs in China,resulting in the establishment of a comprehensive database on CyanoHABs in China's inland waters(ChinaCyanoDB)covering 198 water bodies,525 records for *** majority of water bodies with CyanoHABs(CyanoWaters)are located in the eastern China,mainly concentrated in the middle and lower Yangtze region,with a clear upward trend in their number over the last four *** ChinaCyanoDB and analytical results can provide valuable data support for monitoring and managing CyanoHABs in China while the database construction method may also be applied to other countries and regions.

关键词： CyanoHABs China literature media information spatiotemporal variation