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12th International Conference on Computing and Pattern Recognition, ICCPR 2023

作者： Qiang, Baohua Tang, Feng Zhou, Xiangyu Wang, Yuanchun Yang, Xianyi Li, Kai Guilin University of Electronic Technology Guilin China The 54th Research Institute of Cetc Shijiazhuang China Hebei Key Laboratory of Intelligent Information Perception and Processing Shijiazhuang China

ISBN: (纸本)9798400707988

To address the problems of inaccurate event element extraction, inability to directly handle long texts and errors in the fine-tuning stage of the task due to insufficient semantic information and incomplete semantic feature samples in the current Chinese domain event extraction model, this paper proposes the Chinese Event Extraction Algorithm of Multi-information Semantic Enhancements (MiSE). In the embedding stage, the algorithm is designed a coding structure that fusing multiple information: Using the Roformer model based on rotational encoding for word embedding and text embedding, and augment the Roformer word embedding matrix by mapping the word embedding matrixs of event trigger, argument information and their location information to it , forming an vector matrix that incorporates multiple information;the matrix is then placed into the BiLSTM for forward and backward chain calculation to enhance the model's ability to extract semantic information from Chinese text. In the decoding stage, using the decoding structure to obtain the set of tags with the highest probability. Comparing this algorithm with several Chinese event extraction algorithms on the Baidu DuEE dataset and its extraction results outperformed other event extraction algorithms in all evaluation metrics. © 2023 ACM.

关键词： Embeddings

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Semantic Matching Algorithm for Legal Issues Based on BERT and Graph Convolution with Multi-granularity Features 12

Semantic Matching Algorithm for Legal Issues Based on BERT a...

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12th International Conference on Computing and Pattern Recognition, ICCPR 2023

作者： Qiang, Baohua Pang, Shijie Teng, Keliang Liu, Zhiguang Liu, Weigang Guilin University of Electronic Technology Guilin China The 54th Research Institute of Cetc Shijiazhuang China Hebei Key Laboratory of Intelligent Information Perception and Processing Shijiazhuang China

ISBN: (纸本)9798400707988

In recent years, pre-training models represented by BERT have shown amazing text semantic representation capabilities supported by large-scale corpus, and now have become one of the mainstream solutions for semantic matching tasks in legal Q&A systems. However, pre-training models generally do not set specific pre-training tasks in terms of syntactic information, but implicitly and incidentally learn in large-scale unsupervised, which does not make good use of syntactic information as an important factor for representing semantics. To address this problem, this paper proposes a multi-granularity feature semantic matching model based on BERT and graph convolution. The model first obtains syntactic features of sentences in the form of dependent syntactic trees using existing tools, and then fuses syntactic features with word features and sentence features of BERT using an attention mechanism and graph convolutional neural network to enhance the characterization of text semantics and make the model better for semantic matching tasks. Experimental results show that the algorithm achieves significant improvements in the semantic matching task and outperforms the five comparison methods in the DIAC2019 dataset. Compared to the baseline model Sentenct-BERT, the accuracy, precision, recall and F1 values are improved by 0.33%, 1.18%, 1.03% and 1.05% improvement. © 2023 ACM.

关键词： Convolution

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Image enhancement based on intrascale dependencies of the second generation curvelet transform

Image enhancement based on intrascale dependencies of the se...

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MIPPR 2009 - Remote Sensing and GIS Data processing and Other Applications: 6th International Symposium on Multispectral Image processing and Pattern Recognition

作者： Hao, Hongxia Liu, Fang Jiao, Licheng School of Computer Science and Technology Xidian University Xi'an 710071 China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Xi'an 710071 China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9780819478092

This paper presents a strong noise image enhancement method based on intrascale dependencies of the second generation curvelet transform. Observing that the immediate four neighbor coefficients bear the most important dependencies, we use spatial clustering property of the intrascale neighbor coefficients to separate noise and signal of interest, and to deal with them differently, i.e. to suppress noise and strengthen edges. Comparing our approach with Starck's enhancement model (Starck et al., 2003), we experimentally find that for high noise level images, our method outperforms the starck's system in noise suppression and signal strengthening and produces better enhancement results. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Image enhancement

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Adaptive Synchronization between Two Different Complex Networks with Time-Varying Delay Coupling

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Chinese Physics Letters 2009年第6期26卷 61-64页

作者：陈建芮焦李成吴建设王晓华 Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligence Information Processing Xidian University Xi'an 710071 College of Science Inner Mongolia University of Technology Inner Mongolia 010051

A new general network model for two complex networks with time-varying delay coupling is presented. Then we investigate its synchronization phenomena. The two complex networks of the model differ in dynamic nodes, the number of nodes and the coupling connections. By using adaptive controllers, a synchronization criterion is derived. Numerical examples are given to demonstrate the effectiveness of the obtained synchronization criterion. This study may widen the application range of synchronization, such as in chaotic secure communication.

关键词： gamma-ray bursts gamma-rays relativity

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A Multimodal Semantic Model of Packaging Sorting Field Based on Deep Learning

A Multimodal Semantic Model of Packaging Sorting Field Based...

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International Conference on Cyberspace Data and Intelligence, CyberDI 2020 and the International Conference on Cyber-Living, Cyber-Syndrome and Cyber-Health, CyberLife 2020

作者： Shen, Xuehao Xiao, Falong Wu, Yuezhong Li, Changyun Dai, Honggang School of Traffic Engineering Hunan University of Technology Zhuzhou China Intelligent Information Perception and Processing Technology Hunan Province Key Laboratory Zhuzhou China China Quality Mark Certification Group Shandong CO. LTD. Jinan China

ISBN: (纸本)9789813343351

In order to solve the practical problem of modal gap between different packaging and sorting data in multimodal environment, and not applying traditional machine learning methods directly, by introducing a deep learning feature extraction method, a semantic model of feature representation of multimodal data in packaging and sorting field is proposed based on deep learning. This method proposes a multimodal neural network. For each modal, there is an independent multi-layer sub-neural network corresponding to it. First, converting features under different modalities into features of the same modal, and passing a network layer shared by all modes above these sub-neural networks to establish connections between different modes;then adopting a structured sparse feature selection method to obtain the importance weights of different modalities, and selecting the most important features for the current learning task, so as to eliminate redundant information and noise;finally constructing a multimodal joint shared feature semantic model and applying in the field of packaging sorting. Experimental results show that the proposed model has significant effects and is significantly better than other similar algorithms. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Extraction

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Three-dimensional source reconstruction for bioluminescence tomography using the LSQR method with preconditioning

Three-dimensional source reconstruction for bioluminescence ...

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MIPPR 2009 - Medical Imaging, Parallel processing of Images, and Optimization Techniques: 6th International Symposium on Multispectral Image processing and Pattern Recognition

作者： Yu, Jingjing Liu, Fang Jiao, Licheng He, Xiaowei School of Computer Science and Technology Xidian University Xi'an 710071 China Scholl of Physics and Information Technology Shaanxi Normal University Xi'an 710062 China Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education of China Xi'an 710071 China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China School of Information Science and Technology Northwest University Xi'an 710069 China

ISBN: (纸本)9780819478085

As a novel optical molecular imaging modality, Bioluminescence Tomography (BLT) aims at quantitative reconstruction of the bioluminescent source distribution inside the biological tissue from the optical signals measured on the living animal surface, which is a highly ill-posed inverse problem. In this paper, with the finite element method solving the diffusion equation, an iterative regularization algorithm, referred to as least square QR-factorization (LSQR), is applied to the inverse problem in BLT. The affect of the preconditioning strategy on the LSQR method (PLSQR) for BLT is also investigated. The Simulations with a heterogeneous mouse chest phantom demonstrate that by incorporating a priori knowledge of the permissible source region, the LSQR method can reconstruct the source accurately and stably. Moreover, by employing preconditioning strategy, PLSQR outperforms LSQR in terms of source power density and convergence speed. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Inverse problems

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Texture image segmentation using Brushlet-domain hidden Markov models

Texture image segmentation using Brushlet-domain hidden Mark...

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MIPPR 2009 - Remote Sensing and GIS Data processing and Other Applications: 6th International Symposium on Multispectral Image processing and Pattern Recognition

作者： Liu, Fang Yang, Kai Hao, Hongxia Hou, Biao Zhong, Hua School of Computer Science and Technology Xidian University Xi'an Shanxi 710071 China Intelligent Perception and Image Understanding Key Lab. of Ministry of Education of China Xi'an Shanxi 710071 China Intelligent Information Processing Laboratory Xidian University Xi'an Shanxi 710071 China

ISBN: (纸本)9780819478092

By researching the Brushlet domain coefficients of texture images, we found that the distribution of the magnitudes of Brushlet domain coefficients roughly meet rayleigh distribution. And there are correlations between Brushlet coefficients in adjacent scales. Therefore, Rayleigh Mixture Model (RMM) is used to characterize the statistics of the magnitudes of Brushlet coefficients. To capture the inter-scale persistence of Brushlet coefficients, a "four to four" models with markov property is adopted in this paper. On the basis, by combining with the multi-scale Bayesian segmentation method, we propose a multiscale Bayesian texture segmentation algorithm that is based on a Brushlet domain hidden Markov tree (BruHMT) model. The experiment results indicate that our method is feasible and effective. Especially for coarse texture, our method is superior than texture segmentation method using Wavelet domain hidden Markov tree (WHMT) model. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Hidden Markov models

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A Practical Approach to Constructing a Geological Knowledge Graph:A Case Study of Mineral Exploration Data

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Journal of Earth Science 2023年第5期34卷 1374-1389页

作者： Qinjun Qiu Bin Wang Kai Ma Hairong Lü Liufeng Tao Zhong Xie Collaborative Innovation Center of Geo-Information Technology for Smart Central Plains Zhengzhou 450046China Key Laboratory of Spatiotemporal Perception and Intelligent Processing Ministry of Natural ResourcesZhengzhou 450046China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang 443002China College of Computer and Information Technology China Three Gorges UniversityYichang 443002China Department of Automation Tsinghua UniversityBeijing 100084China

Open data initiatives have promoted governmental agencies and scientific organizations to publish data online for *** of geoscience focuses on processing georeferenced quantitative data(e.g.,rock parameters,geochemical tests,geophysical surveys and satellite imagery)for discovering new *** knowledge is the cognitive result of human knowledge of the spatial distribution,evolution and interaction patterns of geological objects or *** graphs(KGs)can formalize unstructured knowledge into structured form and have been used in supporting decision-making *** this paper,we propose a novel framework that can extract the geological knowledge graph(GKG)from public reports relating to a modelling *** on the analysis of basic questions answered by geology,we summarize and abstract geological knowledge elements and then explore a geological knowledge representation model with three levels of“geological conceptsgeological entities-geological relations”to describe semantic units of geological knowledge and their logic ***,based on the characteristics of mineral resource reports,the geological knowledge representation model oriented to“object relationships”and the hierarchical geological knowledge representation model oriented to“process relationships”are proposed with reference to the commonly used geological knowledge graph *** research in this paper can provide some implications for the formalization and structured representation of geological knowledge graphs.

关键词： mineral resource report geological knowledge knowledge graph ontology hierarchical knowledge representation model

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Federated Multitask Learning for HyperFace

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第5期3卷 788-797页

作者： Zhang, Chen Bai, Hang Zhang, Yonggang Niu, Xinyi Yu, Bin Gao, Yuan Xie, Yu Xidian University School of Computer Science and Technology Xi'an710071 China Shaanxi Province Science and Technology Resources Coordination Center Xi'an710071 China Xidian University Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education Xi'an710071 China Shanxi University Key Laboratory of Computational Intelligence and Chinese Information Processing Ministry of Education Taiyuan030006 China

Multitask learning (MTL) is a promising field in machine learning owing to its capability to improve the generalization performance of all tasks by sharing knowledge among the related tasks. MTL has attracted a large amount of attention in the multitask-related community. In recent years, with the rapid development of distributed machine learning, MTL in a distributed environment becomes a hot research topic. Although MTL can reap great fruit in a distributed environment, the sensitive information, such as photos and voice recordings of the owner, in distributed data may be leaked during training. Consequently, in the distributed settings, one prominent challenge of MTL is to prevent the disclosure of sensitive private data. In this article, we propose a novel approach, federated multitask learning (FMTL), which applies federated learning to MTL in a distributed environment in order to protect the MTL model from being leaked and to better optimize the utility of the MTL model. In FMTL, all the participants independently train their local models on their own dataset in parallel and only transmit their model updates to the central server for aggregation at every epoch. As a result, their learning accuracy is enhanced beyond what is achieved only on their own training data. In this manner, it can achieve the best tradeoff between the utility and privacy: participants not only preserve the privacy of their own data, but also benefit from the models of other participants. The experimental results on annotated facial landmarks in the wild (AFLW) and annotated faces in the wild (AFW) benchmark datasets verify the effectiveness of our framework. © 2020 IEEE.

关键词： Linearization

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Visualization and visual analysis of cyberspace metaphors based on continuous cartogram 2

Visualization and visual analysis of cyberspace metaphors ba...

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2023 2nd International Conference on Environmental Remote Sensing and Geographic information technology, ERSGIT 2023

作者： Liu, Longhui Zhou, Yang Shi, Qunshan Hu, Xiaofei Shi, Shihao PLA Strategic Support Force Information Engineering University Institute of Geospatial Information Henan Zhengzhou450001 China Collaborative Innovation Center of Geo-Information Technology for Smart Central Plains Zhengzhou450001 China Key Laboratory of Spatiotemporal Perception and Intelligent Processing Ministry of Natural Resources Zhengzhou450001 China

ISBN: (纸本)9781510672949

Cyberspace mapping has emerged as a new and compelling area of research within the field of cartography. This paper addresses the challenge of overlapping points in cyberspace visualization, which poses difficulties for visual analysis. To tackle this issue, we propose a cyberspace metaphorical cartogram map that combines metaphorical mapping and continuous cartogram technology. Additionally, based on our method, we use our method to construct a cyberspace map using global IPv6 data from the Asia-Pacific Network information Center (APNIC) to analyze the current development pattern of the global IPv6 network. Experimental results demonstrate the effectiveness of our method in avoiding node duplication, aligning with the reading logic of geospatial maps, and providing an efficient visualization approach for data comprehension. © 2024 SPIE.

关键词： Visualization

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