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Joint extraction method for entity relations from mineral resources reports integrating dependency parsing and improved graph convolutional networks

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Ore Geology Reviews 2025年 182卷

作者： Qiu, Qinjun Jin, Xiangguo Tian, Miao Wu, Qirui Tao, Liufeng Chen, Jianguo Xie, Zhong Technology Innovation Center for Geological Data Intelligent Application Department of Natural Resources of Jiangsu Province 210012 China School of Computer and Science China University of Geosciences Hubei Wuhan430074 China National Engineering Research Center for Geographic Information System Hubei Wuhan430074 China Key Laboratory of Geological Survery and Evaluation of Ministry of Education Hubei Wuhan430074 China School of Future Technology China University of Geosciences Hubei Wuhan430074 China Wuhan University Hubei Wuhan430074 China

Geological reports, as crucial technical documents reflecting the outcomes of geological survey work, encapsulate extensive expert and domain knowledge. Geological knowledge graphs integrate vast amounts of data, facilitating efficient and rapid extraction of knowledge embedded within geoscientific data. The extraction of geological entity relations is a key method in creating these knowledge graphs. Existing techniques for extracting geological entities and their relations encounter difficulties such entity overlap, relation overlap, and the challenge of obtaining deep semantic information because of the vastness and complexity of geological data. Our study suggests a collaborative extraction model for entity relations that integrates dependency syntactic relations with a graph convolutional network (GCN) in order to address these problems. This model learns dependency syntactic structures and deep semantic information by building a GCN that includes dependency syntactic relations. A pointer network decoder is then added to increase entity relation extraction efficiency. Dependencies between words in a phrase, such as subject-verb and verb-object relations, are revealed via dependency syntactic analysis. By structuring these dependencies into a graph, the model captures syntactic structural information. Through operations involving adjacency matrices and feature matrices, the model effectively propagates and aggregates node information, thereby capturing the global dependency syntactic structure and deep semantic information of sentences. The integration of dependency syntactic relations with GCN processing enables the model to more accurately comprehend entity relations within sentences. Results from experiments show that this model successfully tackles problems like overlapping entity relations and the challenge of gleaning deep semantic information from geological texts. It achieves a 79.73% accuracy rate and a 77.98% F1 score on geological text datasets. © 2025

关键词： Geochemical surveys

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Asymptotic solution of a wide moving jam to a class of higher-order viscous traffic flow models

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Applied Mathematics and Mechanics(English Edition) 2018年第5期39卷 609-622页

作者： Chunxiu WU College of Mathematics and Computer Science Quanzhou Normal UniversityQuanzhou 362000Fujian ProvinceChina Fujian Provincial Key Laboratory of Data Intensive Computing Quanzhou 362000Fujian ProvinceChina Key Laboratory of Intelligent Computing and Information Processing Quanzhou 362000Fujian ProvinceChina

The boundary-layer method is used to study a wide moving jam to a class of higher-order viscous models. The equations for characteristic parameters are derived to determine the asymptotic solution. The sufficient and essential conditions for the wide moving jam formation are discussed in detail, respectively, and then used to prove or disprove the existence of the wide moving jam solutions to many well-known higher-order models. It is shown that the numerical results agree with the analytical results.

关键词： higher-order traffic flow model wide moving jam boundary-layer method,weighted essentially nonoscillatory (WENO) scheme

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A Cascaded Co-evolutionary Model for Attribute Reduction and Classification Based on Coordinating Architecture with Bidirectional Elitist Optimization

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Chinese Journal of Electronics 2017年第1期26卷 13-21页

作者： DING Weiping WANG Jiandong LI Yuehua CHENG Xueyun School of Computer Science and Technology Nantong University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Provincial Key Laboratory for Computer Information Processing Technology Soochow University College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics

A cascaded co-evolutionary model for Attribute reduction and classification based on Coordinating architecture with bidirectional elitist optimization(ARC-CABEO) is proposed for the more practical applications. The regrouping and merging coordinating strategy of ordinary-elitist-role-based population is introduced to represent a more holistic cooperative co-evolutionary framework of different populations for attribute reduction. The master-slave-elitist-based subpopulations are constructed to coordinate the behaviors of different elitists, and meanwhile the elitist optimization vector with the strongest balancing between exploration and exploitation is selected out to expedite the bidirectional attribute co-evolutionary reduction process. In addition, two coupled coordinating architectures and the elitist optimization vector are tightly cascaded to perform the co-evolutionary classification of reduction subsets. Hence the preferring classification optimization goal can be achieved better. Some experimental results verify that the proposed ARC-CABEO model has the better feasibility and more superior classification accuracy on different UCI datasets, compared with representative algorithms.

关键词： Attribute reduction for classification Bidirectional elitist optimization Coordinating architecture Cascaded co-evolutionary model

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A Fast Calculation of Metric Scores for Learning Bayesian Network

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International Journal of Automation and computing 2012年第1期9卷 37-44页

作者： Qiang Lv Xiao-Yan Xia Pei-De Qian School of Computer Science and Technology Soochow University Suzhou 215006 PRC Jiangsu Provincial Key Lab for Computer Information Processing Technology Suzhou 215006 PRC

Frequent counting is a very so often required operation in machine learning algorithms. A typical machine learning task, learning the structure of Bayesian network （BN） based on metric scoring, is introduced as an example that heavily relies on frequent counting. A fast calculation method for frequent counting enhanced with two cache layers is then presented for learning BN. The main contribution of our approach is to eliminate comparison operations for frequent counting by introducing a multi-radix number system calculation. Both mathematical analysis and empirical comparison between our method and state-of-the-art solution are conducted. The results show that our method is dominantly superior to state-of-the-art solution in solving the problem of learning BN.

关键词： Frequent counting radix-based calculation ADtree learning Bayesian network metric score

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Prof. Rudolf Emil Kalman OBITUARY

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IEEE CONTROL SYSTEMS MAGAZINE 2017年第1期37卷 151-152页

作者： Antoulas, Athanasios Georgiou, Tryphon T. Khargonekar, Pramod P. Ozguler, A. Bulent Sontag, Eduardo D. Yamamoto, Yutaka JiangSu Provincial Key Lab for Computer Information Processing Technology School of Computer Science and Technology Soochow University Suzhou China

Recounts the career and contributions of Professor Rudolf Emil Kalman.

关键词： Obituaries Kalman, Rudolf Emil

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Wireless Service Prediction Algorithm in Mobile Social Environment 2

Wireless Service Prediction Algorithm in Mobile Social Envir...

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2017 IEEE 2nd International Conference on Big Data Analysis（ICBDA 2017）

作者： Hui Zhang Min Wang Jiangsu Key Lab of Wireless Communications Nanjing University of Posts and Telecommunications Provincial Key Laboratory for Computer Information Processing Technology Soochow University

ISBN: (纸本)9781509036202

First,according to characteristics of mobile social environment,by using optimization models based on similarity degree and interaction degree respectively,the optimal correlated users can be selected for analyzing two main factors of a target user's behaviors(***-term habits and shortterm influences);furthermore,an adaptive update strategy based on fuzzy theory is proposed to describe the importance of two factors in real time and quantitative ***,an improved Apriori theory is introduced to predict user service behaviors accurately;particularly,a new update mechanism for Apriori sample database is built to effectively integrate the samples of optimal correlated ***,simulation results verify the effectiveness of proposed algorithm.

关键词： mobile social environment user behavior prediction fuzzy theory adaptive update strategy improved Apriori theory

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IDSGA: A high-performance search model for differential-neural cryptanalysis

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Digital Signal processing: A Review Journal 2025年 164卷

作者： Hu, Zhiwen Li, Lang Zhu, Siqi Hu, Yemao College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal University Hengyang421002 China

Differential-neural cryptanalysis poses critical security threats to internet of things-embedded lightweight block ciphers, outperforming traditional methods but requiring efficient input difference identification. Current solutions are constrained by the opacity of neural networks and the prohibitive computational demands during search processes. Therefore, an input difference search model IDSGA is proposed in this paper. IDSGA model innovatively combines cryptographic theory with deep learning through two key processes. First, feature purification enhances differential characteristics by eliminating redundant patterns while preserving attack-relevant statistical properties through traditional differential probability integration. Second, multi-dimensional distribution mapping enables quantitative dataset evaluation by transforming purified data into interpretable statistical metrics. It enables our model to break through the reliance on neural network evaluation. Moreover, IDSGA model constructs a more optimal search path by integrating a genetic algorithm to deal with the problem of differential search of full inputs with high computational complexity. A quantum variant theoretically extends these capabilities. The experimental results show that the execution time of the IDSGA model is 93% less than that of Gohr. For the CARX cipher, the input differences found by IDSGA are more optimal. This work establishes a new paradigm for differential-neural cryptanalysis by decoupling dataset evaluation from neural network training dependencies. © 2025 Elsevier Inc.

关键词： Genetic algorithms

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Contourlet HMT model with directional feature

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Science China(information Sciences) 2012年第7期55卷 1563-1578页

作者： WANG XiangHai 1,2,3 ,CHEN MingYing 1 ,SONG ChuanMing 1,2 ,XU MengChun 1 & FANG LingLing 3 1 College of computer and information technology,Liaoning Normal University,Dalian 116029,China 2 State key laboratory for Novel Software technology,Nanjing University,Nanjing 210093,China 3 provincial key laboratory for computer information processing technology,Soochow University,Suzhou 215006,China College of Computer and Information Technology Liaoning Normal University Dalian China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China

According to Shanno's information theory,the directional feature of texture is defined as the value of directional variable when an image signal attains a singularity of random *** terms of this definition,we calculate the texture's directional features using Tamura's method and study the directional probability distribution of Contourlet *** we find that the directional features tend to be conveyed across parent and child *** on this conclusion,we establish a novel probability distribution model of hidden direction variables under the condition of hidden state variable's distribution,named Contourlet HMT model with directional *** structure and training method of the model are presented as ***,an unsupervised context-based image segmentation algorithm is proposed on the basis of the proposed *** effectiveness is verified via extensive experiments carried out on several synthesized images and remote sensing images.

关键词： directional feature Contourlet HMT Contourlet HMT with directional feature unsupervised texture segmentation

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Toward the Completely Independent Spanning Trees Problem on BCube 9

Toward the Completely Independent Spanning Trees Problem on ...

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2017 9th IEEE International Conference on Communication Software and Networks (ICCSN 2017)

作者： Ting Pan Baolei Cheng Jianxi Fan Cheng-Kuan Lin Dongfang Zhou School of Computer Science and Technology Soochow University Provincial Key Laboratory for Computer Information Processing Technology Soochow University

ISBN: (纸本)9781509038237;9781509038220

Completely independent spanning trees（CISTs） are important construct which can be used in data center networks for multi-node broadcasting, one-to-all broadcasting, reliable broadcasting, and secure message distribution, etc. As a recently proposed server-centric data center network, BCube has many good properties. By focusing on the connections between servers, we define the logic graph of BCube as LBCube. We study the construction of CISTs with small diameter on BCubek and show that there are n/2 [（n-1）/2 if n is odd] CISTs with the diameter 2 k + 3 on L-BCubek by presenting a recursive algorithm.

关键词： BCube completely independent spanning trees diameter data center network vertex-disjoint

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Micro-Expression Recognition Based on Spatio-Temporal Feature Extraction of key Regions

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computers, Materials & Continua 2023年第10期77卷 1373-1392页

作者： Wenqiu Zhu Yongsheng Li Qiang Liu Zhigao Zeng College of Computer Science Hunan University of TechnologyZhuzhou412007China Intelligent Information Perception and Processing Technology Hunan Province Key LaboratoryZhuzhou412007China

Aiming at the problems of short duration,low intensity,and difficult detection of micro-expressions(MEs),the global and local features of ME video frames are extracted by combining spatial feature extraction and temporal feature *** on traditional convolution neural network(CNN)and long short-term memory(LSTM),a recognition method combining global identification attention network(GIA),block identification attention network(BIA)and bi-directional long short-term memory(Bi-LSTM)is *** the BIA,the ME video frame will be cropped,and the training will be carried out by cropping into 24 identification blocks(IBs),10 IBs and uncropped *** alleviate the overfitting problem in training,we first extract the basic features of the preprocessed sequence through the transfer learning layer,and then extract the global and local spatial features of the output data through the GIA layer and the BIA layer,*** the BIA layer,the input data will be cropped into local feature vectors with attention weights to extract the local features of the ME frames;in the GIA layer,the global features of the ME frames will be ***,after fusing the global and local feature vectors,the ME time-series information is extracted by *** experimental results show that using IBs can significantly improve the model’s ability to extract subtle facial features,and the model works best when 10 IBs are used.

关键词： Micro-expression recognition attention mechanism long and short-term memory network transfer learning identification block

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