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Patent Recommendation Methods Based on Transformer Encoders and Regularization Strategies

Patent Recommendation Methods Based on Transformer Encoders ...

2024 lEEE International Conference on Advanced Information, Mechanical Engineering, Robotics and Automation, AIMERA 2024

作者： Zheng, Wan Ding, Qingyan Li, Na Pan, Yu Dong, Xuecheng Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350343335

Patent recommendation service has become a major demand of all walks of life in the society. Patent data collection and analysis can not only help enterprises grasp the development status of the industry, but also provide decision support for them in the research and development process of science and technology services. In view of the problem of inaccurate patent recommendation caused by the inconsistency between the search text and the patent text length and the low accuracy of traditional patent recommendation, a word and sentence dual processing model based on Transformer encoder and regularization strategy is proposed to complete the patent recommendation task from the level of sentence granularity and word granularity. The sentence granularity feature extraction module uses the transformer encoder superimposed multi-head attention method to obtain the feature information between the sentence vector groups, so that the sentence vector fuses the feature information of each sentence, and solves the problem of low recommendation accuracy caused by the inconsistent length of the patent text and the search text;The word granularity feature extraction module uses the twin network model to extract the feature information of the sentence granularity vector using the Transformer encoder using the double splicing operation, improve the model performance and model accuracy;Introduce a strategy based on Dropout and symmetric JS divergence regularization, improve the model generalization ability and accuracy. Experiments have proved that it outperforms SBERT, BERT, MGMSN and SVM models in terms of performance. © 2024 IEEE.

关键词： Patents and inventions

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Learning Label Shift Correction for Test-Agnostic Long-Tailed Recognition 41

Learning Label Shift Correction for Test-Agnostic Long-Taile...

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41st International Conference on Machine Learning, ICML 2024

作者： Wei, Tong Mao, Zhen Zhou, Zi-Hao Wan, Yuanyu Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China School of Software Technology Zhejiang University Ningbo China

Long-tail learning primarily focuses on mitigating the label distribution shift between long-tailed training data and uniformly distributed test data. However, in real-world applications, we often encounter a more intricate challenge where the test label distribution is agnostic. To address this problem, we first theoretically establish the substantial potential for reducing the generalization error if we can precisely estimate the test label distribution. Motivated by the theoretical insight, we introduce a simple yet effective solution called label shift correction (LSC). LSC estimates the test label distribution within the proposed framework of generalized black box shift estimation, and adjusts the predictions from a pre-trained model to align with the test distribution. Theoretical analyses confirm that accurate estimation of test label distribution can effectively reduce the generalization error. Extensive experimental results demonstrate that our method significantly outperforms previous state-of-the-art approaches, especially when confronted with non-uniform test label distribution. Notably, the proposed method is general and complements existing long-tail learning approaches, consistently improving their performance. The source code is available at https://***/Stomach-ache/label-shift-correction. Copyright 2024 by the author(s)

关键词： Black-box testing

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PNRITR:Research on Positive and Negative Entity Region Perception Methods for Image-Text Retrieval 36

PNRITR:Research on Positive and Negative Entity Region Perce...

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36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Zhang, Zhiping Sun, Tao Zheng, Hongyan Liu, Hao Liu, Gengchen Yang, Zhi Wang, Xiaoyu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)1891706594

The aim of cross-modal image-text retrieval is to heighten comprehension and to create robust associations between visual and textual content. This process entails a mutual querying and synchronization across various data formats. While conventional techniques have seen enhancements via attention-based approaches, they frequently miss the dynamics between corresponding and non-corresponding entity areas within image-text pairs. These techniques, often fixated on aligning image entities with textual terms, can overlook the congruence of entity characteristics, leading to less accurate localization. This research introduces PNRITR, an innovative strategy for cross-modal retrieval that leverages label learning to discern attributes absent from images, broadening the scope beyond just positive entity areas. PNRITR has delivered notable advancements on the Flickr30K and MS-COCO datasets. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Knowledge based systems

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MTRNet: An Efficient Hybrid network Model for Surface Defect Detection of Sheet Metal 36

MTRNet: An Efficient Hybrid Network Model for Surface Defect...

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36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Hao, Huijuan Zhu, Sijian Chen, Yu Yi, Changle Zhao, Hongge Feng, Yue Yang, Rong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)1891706594

Addressing the challenges posed by the insufficient computational power of low-spec devices to achieve the inference efficiency of prevailing deep learning models, alongside the variability in type and shape within industrial metal sheet datasets, while also catering to the demand for high-precision detection, this paper proposes a novel single-stage detection architecture termed MTRNet. In this paper, we introduce three key enhancements: Firstly, to reduce the number of parameters, we propose a novel Partial Depth Convolution (PDC) structure. By minimizing redundant computations, we aim to enhance the efficiency of spatial feature extraction. Secondly, we propose a Convolution Transformer (CTR) structure that replaces the self-attention module with a convolutional module. This modification addresses the computational inefficiency inherent in the self-attention mechanism of existing Visual Transformers (ViT). Finally, we introduce a novel Attention Convolution Transformer (ACTR) structure to enhance the extraction of global and local feature information. This architecture seamlessly integrates the strengths of both attention and convolution mechanisms, working synergistically to improve performance. Our proposed MTRNet network demonstrates superior detection performance compared to existing industrial standards, achieving a detection accuracy of 81.5% on the NEU-DET dataset. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Deep learning

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An adaptive time-convolutional network online prediction method for ocean observation data 36

An adaptive time-convolutional network online prediction met...

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36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Li, Enjing Li, Xiang Zhao, Zhigang Wu, Lu Wang, Yinglong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)1891706594

Deep learning is particularly important in the field of time series data analysis, and has been applied to tasks such as marine data prediction. However, there is a’concept drift’ problem in marine observation data, which leads to performance degradation and catastrophic forgetting of traditional deep learning models used in online scenarios. For this reason, this paper proposes OL-TCN, an adaptive temporal convolutional neural network online prediction deep learning model, which is more suitable for marine online learning and inference scenarios. We make the following innovations: Inside the model, model performance is enhanced by incorporating a multi-head attention mechanism and introducing an automatic machine learning enhancement in the residual cell. Outside the model, a model repository approach is used to effectively cope with the complexity and evolutionary challenges of data streams. The experimental results verify that the OL-TCN model is effective and feasible in marine time series processing. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Spatio-temporal data

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JNMR: Joint Non-Linear Motion Regression for Video Frame Interpolation

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IEEE Transactions on Image Processing 2023年 32卷 5283-5295页

作者： Liu, Meiqin Xu, Chenming Yao, Chao Lin, Chunyu Zhao, Yao Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

Video frame interpolation (VFI) aims to generate predictive frames by motion-warping from bidirectional references. Most examples of VFI utilize spatiotemporal semantic information to realize motion estimation and interpolation. However, due to variable acceleration, irregular movement trajectories, and camera movement in real-world cases, they can not be sufficient to deal with non-linear middle frame estimation. In this paper, we present a reformulation of the VFI as a joint non-linear motion regression (JNMR) strategy to model the complicated inter-frame motions. Specifically, the motion trajectory between the target frame and multiple reference frames is regressed by a temporal concatenation of multi-stage quadratic models. Then, a comprehensive joint distribution is constructed to connect all temporal motions. Moreover, to reserve more contextual details for joint regression, the feature learning network is devised to explore clarified feature expressions with dense skip-connection. Later, a coarse-to-fine synthesis enhancement module is utilized to learn visual dynamics at different resolutions with multi-scale textures. The experimental VFI results show the effectiveness and significant improvement of joint motion regression over the state-of-the-art methods. The code is available at https://***/ruhig6/JNMR. © 1992-2012 IEEE.

关键词： Interpolation

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Glare-SNet: Unsupervised Glare Suppression Balance network 27th

Glare-SNet: Unsupervised Glare Suppression Balance Network

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Li, Pei Zuo, Chengyu Wei, Wangjuan Pan, Xiaoying Wang, Zhanhao School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an China Shaanxi Key Laboratory of Intelligent Media Computing and interaction Xi’an China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an China

ISBN: (纸本)9783031781681

In light of the problems associated with glare and halo effects in low-light images, as well as the inadequacy of existing processing algorithms in handling details, a glare suppression balance network based on unsupervised learning has been designed and implemented in this study. A network that perceives brightness through light decomposition has been proposed, and an illumination learning network has been designed. The idea of integration has been employed to form an unsupervised glare suppression balance network, aiming to tackle the issues of glare and the accompanying halo effects. Through objective and subjective evaluations of real images, it has been proven that the method introduced in this paper surpasses other methods in suppressing glare and enhancing the overall quality of low-light images. Furthermore, it has been shown to exhibit good performance and effectiveness in target detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image enhancement

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Research on Satellite network Traffic Prediction Algorithm Based on Gray Wolf Algorithm Optimizing GRU and Spatiotemporal Analysis 15

Research on Satellite Network Traffic Prediction Algorithm B...

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15th International Conference on Communication Software and networks, ICCSN 2023

作者： Cong, Ligang Shi, Baoyu Di, Xiaoqiang Ding, Huiying Chen, Chuanhui School of Computer Science and Technology Changchun University of Science and Technology Changchun China Changchun University of Science and Technology Jilin Key Laboratory of Network and Information Security Changchun China

ISBN: (纸本)9798350329360

Accurate traffic prediction is crucial for the performance improvement of satellite networks, which can help balance network load, optimize routing, and effectively improve network performance. To better plan traffic while capturing the spatial and temporal dependencies of traffic, we propose a new neural network-based traffic prediction method for satellite networks, namely the temporal graph convolutional attention (ACW-GRU) model, which is combined with the Graph Convolutional Neural network (GCN), the Gated Recurrent Unit (GRU), and the Soft Attention mechanism. The GCN is used to analyze the complex topology to capture spatial correlation, and the GRU is used to analyze the dynamic changes of traffic data to capture temporal correlation, while the Grey Wolf Optimization (GWO) is used to optimize the hyperparameters of the GRU model, and the soft attention mechanism is used to integrate the global temporal information and adjust the importance at different time points. The importance of the traffic data at distant time points is also not ignored, thus improving the accuracy of the prediction. The simulation results show that the ACW-GRU model is superior in fitting the flow data compared with the traditional GRU, LSTM, and FARIMA models, and its root mean square error (RMSE) is reduced by 24.7%, 40.4%, and 51.2%, respectively. Therefore, the ACW-GRU model can predict the traffic data of the satellite network more accurately, thus providing clearer guidance for defining the demand of applications and the allocation of network resources. © 2023 IEEE.

关键词： Topology

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Design and implementation of cooperative multi-agent based on MAPPO 5

Design and implementation of cooperative multi-agent based o...

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5th International Conference on Telecommunications, Optics, and computer Science, TOCS 2024

作者： Wang, Haolong Fei, Rong Chen, Ruizhu School of Computer Science and Engineering Xi'an University of Technology Xi'an China Shannxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China

ISBN: (纸本)9781510691667

In the past few years, with the continuous development of deep reinforcement learning, the application of deep reinforcement has been set off within the realms of self-driving vehicle technology, game games, and natural language processing, and the proximal policy optimization algorithm has emerged as a focal point in the sphere of deep reinforcement learning research. In this paper, the Proximal Policy Optimization (PPO) algorithm and the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm are studied, and a cooperative multi-agent system is designed and implemented based on the MAPPO algorithm in the field of StarCraft game, which is compared with the popular Q-Value Mixing (QMIX) reinforcement learning algorithm in recent years, and proposes a direction for subsequent system improvement. In the proximal policy optimization algorithm, the traditional policy gradient algorithm, natural policy gradient algorithm, trust domain policy optimization algorithm, proximal policy optimization algorithm, and independent proximal policy optimization algorithm are studied. In the multi-agent proximal policy optimization algorithm, the centralized training decentralized execution architecture, athlete critic method, value standardization, experience playback, and GAE (GAE) adopted by the MAPPO algorithm are studied. Designing and implementing a cooperative multi-agent system, introducing the StarCraft environmental campaign. Regarding the specifics of the training process, the policy gradient algorithm is used to update the policy network, the time series difference algorithm is improved, and the generalized advantage estimation is used to update the value network. Compared with the QMIX learning algorithm, the model trained by the MAPPO algorithm has the advantages of faster convergence and better stability. © 2025 SPIE.

关键词： Deep reinforcement learning

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Large-scale Satellite Optical network Simulation Architecture and Performance Evaluation

Large-scale Satellite Optical Network Simulation Architectur...

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2024 Asia Communications and Photonics Conference, ACP 2024 and International Conference on Information Photonics and Optical Communications, IPOC 2024

作者： Wang, Xinjun Zhang, Zhenrong Zhu, Kangqi Hua, Nan School of Computer Electronic and Information Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning China Department of Electronic Engineering Tsinghua University Beijing China

We evaluate the accuracy and performance of different simulation modes in MegaStarSim, analyze end-to-end routing strategies in large-scale satellite optical networks, and assess link quality across various inter-sate... 详细信息

ISBN: (纸本)9798350379266

关键词： Satellite links

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