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Extracting Named Entity Using Entity Labeling in Geological Text Using Deep Learning Approach

Journal of Earth Science 2023年第5期34卷 1406-1417页

作者： Qinjun Qiu Miao Tian Zhong Xie Yongjian Tan Kai Ma Qingfang Wang Shengyong Pan Liufeng Tao State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR Chinese Academy of Surveying and MappingBeijing 100036China Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities MNRShanghai 20063China 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 Chengdu Geological Environment Monitoring Station Chengdu 610036China Wuhan Zondy Cyber Science&Technology Co. Ltd.Wuhan 430074China

Artificial intelligence(AI) is the key to mining and enhancing the value of big data, and knowledge graph is one of the important cornerstones of artificial intelligence, which is the core foundation for the integration of statistical and physical representations. Named entity recognition is a fundamental research task for building knowledge graphs, which needs to be supported by a high-quality corpus, and currently there is a lack of high-quality named entity recognition corpus in the field of geology, especially in Chinese. In this paper, based on the conceptual structure of geological ontology and the analysis of the characteristics of geological texts, a classification system of geological named entity types is designed with the guidance and participation of geological experts, a corresponding annotation specification is formulated, an annotation tool is developed, and the first named entity recognition corpus for the geological domain is annotated based on real geological reports. The total number of words annotated was 698 512 and the number of entities was 23 345. The paper also explores the feasibility of a model pre-annotation strategy and presents a statistical analysis of the distribution of technical and term categories across genres and the consistency of corpus annotation. Based on this corpus, a Lite Bidirectional Encoder Representations from Transformers(ALBERT)-Bi-directional Long Short-Term Memory(BiLSTM)-Conditional Random Fields(CRF) and ALBERT-BiLSTM models are selected for experiments, and the results show that the F1-scores of the recognition performance of the two models reach 0.75 and 0.65 respectively, providing a corpus basis and technical support for information extraction in the field of geology.

关键词： ontology geological reports named entity recognition geological corpus construction semi-automated annotation platforms deep learning

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Toward Intelligent Financial Advisors for Identifying Potential Clients:A Multitask Perspective

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Big data Mining and Analytics 2022年第1期5卷 64-78页

作者： Qixiang Shao Runlong Yu Hongke Zhao Chunli Liu Mengyi Zhang Hongmei Song Qi Liu Department of Anhui Province Key Laboratory of Big Data Analysis and Application(BDAA) School of Computer Science and TechnologyUniversity of Science and Technology of China Department of College of Management and Economics Tianjin University School of Management Hefei University of Technology Department of Information Technology China Merchants Bank

Intelligent Financial Advisors(IFAs)in online financial applications(apps)have brought new life to personal investment by providing appropriate and high-quality portfolios for *** real-world scenarios,identifying potential clients is a crucial issue for IFAs,i.e.,identifying users who are willing to purchase the ***,extracting useful information from various characteristics of users and further predicting their purchase inclination are ***,two critical problems encountered in real practice make this prediction task challenging,i.e.,sample selection bias and data *** this study,we formalize a potential conversion relationship,i.e.,user→activated user→client and decompose this relationship into three related ***,we propose a Multitask Feature Extraction Model(MFEM),which can leverage useful information contained in these related tasks and learn them jointly,thereby solving the two problems *** addition,we design a two-stage feature selection algorithm to select highly relevant user features efficiently and accurately from an incredibly huge number of user feature ***,we conduct extensive experiments on a real-world dataset provided by a famous fintech *** results clearly demonstrate the effectiveness of MFEM.

关键词： Intelligent Financial Advisor(IFA) potential client identification MultiTask Learning(MTL) feature selection

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Individual tree detection and counting based on high-resolution imagery and the canopy height model data

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地球空间信息科学学报（英文版） 2024年第6期27卷中插23,2162-2178页

作者： Ye Zhang Moyang Wang Joseph Mango Liang Xin Chen Meng Xiang Li Key Laboratory of Geographic Information Science(Ministry of Education)and School of Geographic Sciences East China Normal UniversityShanghaiChina Department of Transportation and Geotechnical Engineering University of Dar es SalaamDar es salaamTanzania College of Surveying and Geographic Informatics Tongji UniversityShanghaiChina Cadre School of Shanghai Municipal Bureau of Planning and Natural Resources ShanghaiChina School of Ecology and Environmental Sciences East China Normal UniversityShanghaiChina Key Laboratory of Geographic Information Science(Ministry of Education)and School of Geographic Sciences East China Normal UniversityShanghaiChina Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration East China Normal UniversityShanghaiChina Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities(Ministry of Natural Resources) East China Normal UniversityShanghaiChina

Individual Tree Detection-and-Counting(ITDC)is among the important tasks in town areas,and numerous methods are proposed in this *** their many advantages,still,the proposed methods are inadequate to provide robust results because they mostly rely on the direct field *** paper presents a novel approach involving high-resolution imagery and the Canopy-Height-Model(CHM)data to solve the ITDC *** new approach is studied in six urban scenes:farmland,woodland,park,industrial land,road and residential ***,it identifies tree canopy regions using a deep learning network from high-resolution *** then deploys the CHM-data to detect treetops of the canopy regions using a local maximum algorithm and individual tree canopies using the region ***,it calculates and describes the number of individual trees and tree *** proposed approach is experimented with the data from Shanghai,*** results show that the individual tree detection method had an average overall accuracy of 0.953,with a precision of 0.987 for woodland ***,the R2 value for canopy segmentation in different urban scenes is greater than 0.780 and 0.779 for canopy area and diameter size,*** results confirm that the proposed method is robust enough for urban tree planning and management.

关键词： Individual tree detection-and-counting(ITDC) deep learning high-resolution imagery Canopy Height Model data(CHM)

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Traffic optimization method at five-way intersection based on green building materials and signal processing

Traffic optimization method at five-way intersection based o...

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2022 International Conference on Signal Processing and Communication Security, ICSPCS 2022

作者： Liang, Zijun Zhan, Xuejuan Kong, Wei Zhao, Yuanyuan Niu, Yiran Peng, Min School of Urban Construction and Transportation Hefei University China Anhui Province Transportation Big Data Analysis and Application Engineering Laboratory China Anhui Province Ordinary University Key Laboratory of Traffic Information and Security China

ISBN: (纸本)9781510660151

With the rapid development of China's economy, the development speed and scale of transportation engineering have also expanded rapidly. But the problem of environmental pollution problems have also gradually emerged, and the use of environmental protection materials has become an inevitable trend of green transportation development. This paper puts forward the traffic optimization method at five-way intersection based on green building materials and signal processing. Firstly, the green and environment protection hot-melt reflective marking paint and color asphalt concrete pavement materials are used to construct the travel space of motor vehicles, non-motor vehicles and pedestrians at five-way intersection. Then, on this basis, the phase design method based on clipping method and the corresponding signal timing method are proposed to optimize the traffic flow travel time at five-way intersection. Finally, the evaluation and analysis is done by the VISSIM traffic simulation software, the results show that this method based on green building materials effectively defines the travel space of five-way intersection. On this premise, the traffic efficiency is further improved through signal processing optimization technology, which effectively reduces the number of conflict point at the five-way intersection and provides a basis for the research on the optimal control of deformed intersections based on green building materials and signal processing. © 2022 SPIE.

关键词： Computer software

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A method for correcting characteristic X-ray net peak count from drifted shadow peak

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Nuclear Science and Techniques 2023年第11期34卷 155-167页

作者： Lin Tang Xing‑Ke Ma Kai‑Bo Shi Yeng‑Chai Soh Hong‑Tao Shen College of Electronic Information and Electrical Engineering Chengdu UniversityChengdu 610106China Guangxi Key Laboratory of Nuclear Physics and Nuclear Technology Guangxi Normal UniversityGuilin 541004China National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Anhui UniversityHefei 230039China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore College of Nuclear Technology and Automation Engineering Chengdu University of TechnologyChengdu 610059China

To correct spectral peak drift and obtain more reliable net counts,this study proposes a long short-term memory(LSTM)model fused with a convolutional neural network(CNN)to accurately estimate the relevant parameters of a nuclear pulse signal by learning of samples.A predefined mathematical model was used to train the CNN-LSTM model and generate a dataset composed of distorted pulse *** trained model was validated using simulated *** relative errors in the amplitude estimation of pulse sequences with different degrees of distortion were obtained using triangular shaping,CNN-LSTM,and LSTM *** a result,for severely distorted pulses,the relative error of the CNN-LSTM model in estimating the pulse parameters was reduced by 14.35%compared with that of the triangular shaping *** slightly distorted pulses,the relative error of the CNN-LSTM model was reduced by 0.33%compared with that of the triangular shaping *** model was then evaluated considering two performance indicators,the correction ratio and the efficiency ratio,which represent the proportion of the increase in peak area of the two characteristic peak regions of interest(ROIs)to the peak area of the corrected characteristic peak ROI and the proportion of the increase in peak area of the two characteristic peak ROIs to the peak areas of the two shadow peak ROI,*** measurement results of the iron ore samples indicate that approximately 86.27%of the decreased peak area of the shadow peak ROI was corrected to the characteristic peak ROI,and the proportion of the corrected peak area to the peak area of the characteristic peak ROI was approximately 1.72%.The proposed CNN-LSTM model can be applied to X-ray energy spectrum correction,which is of great significance for X-ray spectroscopy and elemental content analyses.

关键词： Peak correction Triangular shaping Deep learning Long short-term memory Convolutional neural network X-ray fluorescence spectroscopy Silicon drift detector

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FairLISA: fair user modeling with limited sensitive attributes information 23

FairLISA: fair user modeling with limited sensitive attribut...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Zheng Zhang Qi Liu Hao Jiang Fei Wang Yan Zhuang Le Wu Weibo Gao Enhong Chen Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence Hefei University of Technology

User modeling techniques profile users' latent characteristics (e.g., preference) from their observed behaviors, and play a crucial role in decision-making. Unfortunately, traditional user models may unconsciously capture biases related to sensitive attributes (e.g., gender) from behavior data, even when this sensitive information is not explicitly provided. This can lead to unfair issues and discrimination against certain groups based on these sensitive attributes. Recent studies have been proposed to improve fairness by explicitly decorrelating user modeling results and sensitive attributes. However, most existing approaches assume that fully sensitive attribute labels are available in the training set, which is unrealistic due to collection limitations like privacy concerns, and hence bear the limitation of performance. In this paper, we focus on a practical situation with limited sensitive data and propose a novel FairLISA framework, which can efficiently utilize data with known and unknown sensitive attributes to facilitate fair model training. We first propose a novel theoretical perspective to build the relationship between data with both known and unknown sensitive attributes with the fairness objective. Then, based on this, we provide a general adversarial framework to effectively leverage the whole user data for fair user modeling. We conduct experiments on representative user modeling tasks including recommender system and cognitive diagnosis. The results demonstrate that our FairLISA can effectively improve fairness while retaining high accuracy in scenarios with different ratios of missing sensitive attributes.

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TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders

arXiv

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arXiv 2023年

作者： Cheng, Mingyue Liu, Qi Liu, Zhiding Zhang, Hao Zhang, Rujiao Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application State Key Laboratory of Cognitive Intelligence School of Data Science University of Science and Technology of China Anhui Hefei230027 China

Enhancing the expressive capacity of deep learning-based time series models with self-supervised pre-training has become ever-increasingly prevalent in time series classification. Even though numerous efforts have been devoted to developing self-supervised models for time series data, we argue that the current methods are not sufficient to learn optimal time series representations due to solely unidirectional encoding over sparse point-wise input units. In this work, we propose TimeMAE, a novel self-supervised paradigm for learning transferrable time series representations based on transformer networks. The distinct characteristics of the TimeMAE lie in processing each time series into a sequence of non-overlapping sub-series via window-slicing partitioning, followed by random masking strategies over the semantic units of localized sub-series. Such a simple yet effective setting can help us achieve the goal of killing three birds with one stone, i.e., (1) learning enriched contextual representations of time series with a bidirectional encoding scheme;(2) increasing the information density of basic semantic units;(3) efficiently encoding representations of time series using transformer networks. Nevertheless, it is a non-trivial to perform reconstructing task over such a novel formulated modeling paradigm. To solve the discrepancy issue incurred by newly injected masked embeddings, we design a decoupled autoencoder architecture, which learns the representations of visible (unmasked) positions and masked ones with two different encoder modules, respectively. Furthermore, we construct two types of informative targets to accomplish the corresponding pretext tasks. One is to create a tokenizer module that assigns a codeword to each masked region, allowing the masked codeword classification (MCC) task to be completed effectively. Another one is to adopt a siamese network structure to generate target representations for each masked input unit, aiming at performing the mask

关键词： Time series

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Hawk‐eye‐inspired perception algorithm of stereo vision for obtaining orchard 3D point cloud navigation map

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 987-1001页

作者： Zichao Zhang Jian Chen Xinyu Xu Cunjia Liu Yu Han College of Engineering China Agricultural UniversityBeijingChina Jiangsu Province and Education Ministry Co‐sponsored Synergistic Innovation Center of Modern Agricultural Equipment Jiangsu UniversityZhenjiangChina Key Laboratory of Spatial‐temporal Big Data Analysis and Application of Natural Resources in Megacities MNRShanghaiChina Department of Aeronautical and Automotive Engineering Loughborough UniversityLoughboroughLeicestershireUK College of Water Resources and Civil Engineering China Agricultural UniversityBeijingChina State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijingChina Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural ResourcesShenzhenChina

The binocular stereo vision is the lowest cost sensor for obtaining 3D *** the weakness of long‐distance measurement and stability,the improvement of accuracy and stability of stereo vision is urgently required for application of precision *** address the challenges of stereo vision long‐distance measurement and stable perception without hardware upgrade,inspired by hawk eyes,higher resolution perception and the adaptive HDR(High Dynamic Range)were introduced in this *** the function from physiological structure of‘deep fovea’and‘shallow fovea’of hawk eye,the higher resolution reconstruction method in this paper was aimed at ac-curacy *** by adjustment of pupils,the adaptive HDR method was proposed for high dynamic range optimisation and stable *** various light conditions,compared with default stereo vision,the accuracy of proposed algorithm was improved by 28.0%evaluated by error ratio,and the stability was improved by 26.56%by disparity *** fixed distance measurement,the maximum improvement was 78.6%by standard *** on the hawk‐eye‐inspired perception algorithm,the point cloud of orchard was improved both in quality and *** hawk‐eye‐inspired perception algorithm contributed great advance in binocular 3D point cloud recon-struction in orchard navigation map.

关键词： adaptive high dynamic range binocular stereo vision hawk‐eye‐inspired perception point cloud of orchard super‐resolution generative adversarial network

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TGNN: A GNN-Based Method with Multi-entity Node for Personal Banking Time Prediction 5th

TGNN: A GNN-Based Method with Multi-entity Node for Person...

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5th China Conference on Intelligent Networked Things, CINT 2022

作者： Mo, Zhikang Shao, Qixiang Wu, Likang Yu, Runlong Liu, Qi Xu, Jiexin Song, Hongmei Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application State Key Laboratory of Cognitive Intelligence School of Computer Science and Techonology University of Science and Technology of China Hefei China Department of Information Technology China Merchant Bank Shenzhen China

ISBN: (纸本)9789811989148

The offline business of banks is deeply involved in bank clients’ financial lives and directly affects the satisfaction of clients. For offline banking scenarios, accurate prediction of clients’ banking time is essential for the service system of the banks, which can be used for multiple downstream tasks, such as queuing time prediction, real-time queuing planning, etc. However, the problem of banking time prediction remains open due to many technical and domain challenges, such as Anonymous Client Problem (ACP) and Sparse Interaction Problem (SIP). The existing works mainly utilize the traditional machine learning method and are relatively plain in modeling this practical scenario thus leading to an unpromising performance. Different from existing works, we predict the personal banking time by modeling the clients and banking services in the banking process through mining a large number of banking records. In this paper, we propose a Tuple-node-based Graph Neural Network (TGNN) framework to learn representations of clients and services of banks and estimate the personal banking time based on the representations. Empirical studies on two real-world datasets collected from a fintech bank demonstrate the effectiveness of TGNN. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data mining

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Mathematical model for predicting fungal growth and decomposition rates based on improved Logistic equations

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International Journal of Agricultural and Biological Engineering 2023年第1期16卷 60-65页

作者： Jiayong Pan Wenxin Le Zi’ao Wang Jian Chen College of Engineering China Agricultural UniversityBeijing 100083China Shenzhen Key Laboratory of Intelligent Microsatellite Constellation Shenzhen 518107GuangdongChina Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities MNRShanghai 200063China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural ResourcesShenzhen 518000GuangdongChina Jiangsu Province and Education Ministry Co-sponsored Synergistic Innovation Center of Modern Agricultural Equipment Jiangsu UniversityZhenjiang 212013China Key Laboratory of Smart Agricultural Technology in Tropical South China Ministry of Agriculture and Rural AffairsGuangzhou 510642China State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China State Key Laboratory of Clean Energy Utilization Zhejiang UniversityHangzhou 310013China

As the function of the decomposition of fungi has been clearly researched in the global carbon cycle,it is obviously of value to explore the decomposition rate of fungal *** study analyzed the relationship between environmental factors and biodiversity step by *** order to explore the interaction between the fungi and the relationship between the decomposition rate of fungi with time,the model based on the Logistic model was built and the Lotka-Volterra model was employed in the condition of two kinds of fungi existing in an environment with limited *** changing trend of population number and decomposition rate of several fungi under different environmental conditions can be predicted through the *** illustrate the applicability of the model,Laetiporus conifericola and Hyphoderma setigerum were applied as *** results showed that the higher the degree of population diversity,the greater the decomposition rate,and the higher the decomposition efficiency of the *** rich species diversity is conducive to accelerating the decomposition of litter,lignocellulose,and the circulation of the entire *** on the above model and using the data from measuring the mycelial elongation rate of each isolate at 10℃,16℃,and 22℃ under standardized laboratory conditions,the growth patterns of the five fungi combinations were *** results revealed a general increase in growth rate with increasing temperature,which verifies the accuracy of the ***,it also revealed that the total decomposition rate after fungal incorporation was negatively correlated with the decomposition rate of a fungal single *** on the above model,predictions can be made for fungal growth in different environments,and suitable environments for fungal growth can be *** the future,the model can be further optimized,and lignin and cellulose decomposition factors can be added to fit the decomposition of *** application

关键词： Logistic equations fungal decomposition mathematical model

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