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Natural Language Processing Journal 2024年 7卷

作者： Chuanjun Zhao Rong Feng Xuzhuang Sun Lihua Shen Jing Gao Yanjie Wang School of Information Shanxi University of Finance and Economics Taiyuan 030006 Shanxi China Economic Big Data Shanxi Province Key Laboratory Shanxi University of Finance and Economics Taiyuan 030006 Shanxi China Shanxi Information Technology Application Innovation Engineering Research Center Taiyuan 030006 Shanxi China

Fine-grained sentiment analysis, commonly referred to as aspect-based sentiment analysis (ABSA), has garnered substantial attention in both academic and industrial circles. ABSA focuses on unveiling the sentiment orientation associated with specific entities or attributes within textual data, resulting in a more precise depiction of intricate emotional nuances. However, due to the extensive range of applications for ABSA, certain domains face challenges such as constrained dataset sizes and the absence of exhaustive, high-quality corpora, leading to issues like few-shot learning and resource scarcity scenarios. To address the issue of limited training dataset sizes, one viable approach involves the utilization of text-based context generation to expand the dataset. In this study, we amalgamate Bert-based text generation with text filtering algorithms to formulate our model. Our model fully leverages contextual information using the Bert model, with a particular emphasis on the interrelationships between sentences. This approach effectively integrates the relationships between sentences and labels, resulting in the creation of an initial data augmentation corpus. Subsequently, filtering algorithms have been devised to enhance the quality of the initial augmentation corpus by eliminating low-quality generated data, ultimately yielding the final text-enhanced dataset. Experimental findings on the Semeval-2014 Laptop and Restaurant datasets demonstrate that the enhanced dataset enhances text quality and markedly boosts the performance of models for aspect-level sentiment classification.

关键词： Low resources Context-based text generation Text filtering Aspect-level sentiment classification

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Roof greening in major Chinese cities possibly afford a large potential carbon sink

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Science Bulletin 2024年第20期69卷 3216-3220页

作者： Chao Yang Yinghui Zhang Min Chen Song Zhu Yuzhi Tang Zhixin Zhang Wei Ma Huizeng Liu Junyi Chen Bohui Tang Dejin Zhang Zhengdong Huang Xuqing Wang Wei Tu Cuiling Liu Tiezhu Shi Haiying Xu Aihong Cui Fanyi Meng Tianhong Zhao Kai Guo Wenhao Guo Bi Fan Quande Qin Wuyang Hong Haishan Wu Bing Wang Jinrong Zeng Ka Wai Wu Qingquan Li Key Laboratory for Geo-Environmental Monitoring of Great Bay Area Ministry of Natural Resources&Guangdong Key Laboratory of Urban InformaticsShenzhen UniversityShenzhen 518060China School of Architecture and Urban Planning Shenzhen UniversityShenzhen 518060China Key Laboratory of Virtual Geographic Environment Ministry of EducationNanjing Normal UniversityNanjing 210023China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Shenzhen Zhiyuan Space Innovation Technology Co. LtdShenzhen 518060China Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China School of Geography and Ocean Science Nanjing UniversityNanjing 210008China School of Geography Nanjing Normal UniversityNanjing 210023China School of Civil Engineering Chongqing Jiaotong UniversityChongqing 400074China Institute for Advanced Study&Shenzhen University Shenzhen 518060China Tiandu-Shenzhen University Deep Space Joint Laboratory Shenzhen UniversityShenzhen 518060China Faculty of Land Resource Engineering Kunming University of Science and TechnologyKunming 650093China Center for Hydrogeology and Environmental Geology China Geological SurveyBaoding 071051China Department of Landscape Architecture and Urban Planning Texas A&M UniversityTX 77840USA Department of Geography Hong Kong Baptist UniversityHong Kong 999077China College of Civil and Transportation Engineering Shenzhen UniversityShenzhen 518060China College of Big Data and Internet Shenzhen Technology UniversityShenzhen 518118China Faculty of Geosciences and Environmental Engineering Southwest Jiaotong UniversityChengdu 611756China College of Management Shenzhen UniversityShenzhen 518060China WeBank(Shenzhen)Co. LtdShenzhen 518060China Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong Kong 100872China Esri C

China is experiencing accelerated urbanisation,with a large number of people moving from rural to urban areas[1].It has resulted in large losses in the net primary production(NPP),biodiversity and carbon stocks and an increase in environmental pollution and CO_(2)emissions[2–4].In 2015,196 countries signed the Paris Agreement and committed to setting long-term goals to jointly manage climate change and reduce their individual emissions,aiming to control the increase in global average temperature from the pre-industrial level to below 2℃and to curtail the temperature rise within 1.5℃till the end of the 21st century[5].China is bolstering its efforts to achieve the climate change mitigation goals and has announced a plan for achieving carbon neutrality by 2060[6].The carbon neutrality goal poses a challenge to the current policies promoting rapid urbanisation across China.

关键词： jointly carbon goals

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Neural mechanisms of top-down divided and selective spatial attention in visual and auditory perception

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Brain Science Advances 2023年第2期9卷 95-113页

作者： Zhongtian Guan Meng Lin Qiong Wu Jinglong Wu Kewei Chen Hongbin Han Dehua Chui Xu Zhang Chunlin Li School of Biomedical Engineering Capital Medical UniversityBeijing 100069China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Capital Medical UniversityBeijing 100069China Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application Capital Medical UniversityBeijing 100069China Peking University First Hospital Beijing 100034China Biomedical Engineering Laboratory Graduate School of Natural Science and TechnologyOkayama University3-1-1 Tsushima-nakaOkayamaJapan Key Laboratory of Biomimetic Robots and Systems Ministry of EducationBeijing Institute of TechnologyBeijing 100081China Computational Image Analysis Banner Alzheimer’s Institute and Banner Good Samaritan Medical CentrePET CentrePhoenixAZ 85006USA Radiology Peking University Third HospitalBeijing 100191China Neuroscience Research Institute/Peking University Third Hospital Beijing 100191China

Top-down attention mechanisms require the selection of specificobjects or locations;however,the brain mechanism involved when attention is allocated across different modalities is not well *** aim of this study was to use functional magnetic resonance imaging to define the neural mechanisms underlyingdivided and selective spatial attention.A concurrent audiovisual stimulus was used,and subjects were prompted to focus on a visual,auditory and audiovisual stimulus in a Posner *** results confirmed the better performance of selectiveattention compared to devided *** found differences in the activation level of the frontoparietal network,visual/auditorycortex,the putamen and the salience network under differentattention *** further used Granger causality(GC)toexplore effective connectivity differences between *** in GC connectivity between visual and auditory selective tasksreflected the visual dominance effect under spatial *** addition,our results supported the role of the putamen inredistributing attention and the functional separation of the *** summary,we explored the audiovisual top-down allocation of attention and observed the differences in neuralmechanisms under endogenous attention modes,which revealedthe differences in cross-modal expression in visual and auditory attention under attentional modulation.

关键词： top-down mechanism divided spatial attention selective spatial attention fMRI Granger causality

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Development of a nondestructive prediction model for nutrient content of orchid leaves using different hyperspectral sensors

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Computers and Electronics in Agriculture 2025年 237卷

作者： Deng, Shuwen Chen, Lu Li, Qingnv Feng, Yao Li, Chengfeng Li, Shuang Wu, Shasha Zhai, Junwen College of Landscape Architecture and Art Fujian Agriculture and Forestry University Fujian Fuzhou350002 China The Innovation and Application Engineering Technology Research Center of Ornamental Plant Germplasm Resources in Fujian Province Fujian Fuzhou350002 China National Long Term Scientific Research Base for Fujian Orchid Conservation Fujian Fuzhou350002 China Ningde Hywison Big Data Technology Co. Ltd. Fujian Ningde352100 China

Cymbidium hybrids has the advantages of strong growth, large number of flowers, and high market share. However, most orchid cultivation has problems such as over-reliance on artificial experience cultivation, lack of scientific basis for water and fertilizer application, and low degree of precision and intelligent cultivation. Monitoring the levels of N, P, and K in plant leaves facilitates real-time assessment of nutritional status while guiding optimization fertilization, thereby enhancing fertilizer efficiency and plant growth quality. Nevertheless, conventional nutrient detection methods are invasive, time-consuming, and expertise-dependent, necessitating advanced non-destructive alternatives. This study explores hyperspectral sensing technology for non-destructive monitoring of N, P, and K in hybrid orchid under greenhouse conditions. Three water-fertilization regimes were applied at strategic growth stages of hybrid orchids, with spectral data collected across four seasons using a spectrometer and a hyperspectral imaging device. Through the analysis of 720 combinations of ten spectral preprocessing methods and four modeling approaches, an optimal algorithm combination was identified. This optimal combination was integrated with the Stable Competitive Adaptive Reweighted Sampling algorithm to select spectral feature bands. Predictive models were then constructed to estimate the contents of N, P, and K in the orchid leaves. The results showed the utilization of Stable Competitive Adaptive Reweighted Sampling method, between 14.2% and 25.0% of the feature wavelengths significantly reduced the root mean square error of the models. By incorporating these methodologies, the predictive model for leaf N, P, and K contents utilizing the hyperspectral imaging device achieved determination coefficients (R2) of 0.7395, 0.7213, and 0.7055, respectively, on the test sets. For the spectrometer-based models predicting leaf N, P, and K contents, the corresponding R2 values for

关键词： Damage detection

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Machinery Fault Diagnosis Based on Weighted 2D Fault Feature Extraction and Multi-level Information Fusion

Machinery Fault Diagnosis Based on Weighted 2D Fault Feature...

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International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)

作者： Ziao Luo Zuqiang Su Feng Tan Hu Ruixing Xiaolin Hu Bin Xing School of Advanced Manufacturing Engineering Chongqing University of Posts and Telecomunications Chongqing China The 44th Research Institute of CETC Chongqing China Chongqing Innovation Center of Industrial Big-Data Co. Ltd National Engineering Laboratory for Industrial Big-data Application Technology Chongqing China

ISBN: (数字)9781728170503

ISBN: (纸本)9781728170510

Machinery fault diagnosis is critical to the reliability and safety of modern industrial systems. In order to better diagnose the faults of rotating machinery, this paper presents a fault diagnosis model based on a weighted twodimensional (2D) time-frequency spectrum and multi-level information fusion. Firstly, multiple time-frequency analysis methods are performed on the collected vibration signals to get 2D time-frequency spectrum diagram. Secondly, a feature weighting strategy is proposed to weight the 2D time-frequency features to further enhance the fault information and reduce the disturbing of noise. Then, a 2D dimensionality reduction method is applied to reduce the dimensionality of the weighted 2D timefrequency features and extract the primary fault information contained in the 2D fault features. Finally, the extracted fault features are inputted to a support vector machine (SVM) for fault diagnosis model training. The proposed multi-level information fusion strategy based on Dempster-Shafer (D-S) evidence theory is also applied to resolve the contradiction of fault diagnosis results between different features and different sensors.

关键词： Fault diagnosis Support vector machines Time-frequency analysis Evidence theory Two dimensional displays Feature extraction Machinery

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$\mathsf{NCF}$NCF: A Neural Context Fusion Approach to Raw Mobility Annotation

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IEEE Transactions on Mobile Computing 2020年第1期21卷 226-238页

作者： Renjun Hu Jingbo Zhou Xinjiang Lu Hengshu Zhu Shuai Ma Hui Xiong SKLSDE Lab Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Business Intelligence Lab Baidu Research National Engineering Laboratory of Deep Learning Technology and Application Beijing China Talent Intelligence Center Baidu Inc. Beijing China Management Science and Information Systems Department Rutgers Business School Rutgers University Newark NJ USA

Understanding human mobility patterns at the point-of-interest (POI) scale plays an important role in enhancing business intelligence in mobile environments. While large efforts have been made in this direction, most studies simply utilize POI check-ins to mine the concerned mobility patterns, the effectiveness of which is usually hindered due to data sparsity. To obtain better POI-based human mobility for mining, in this paper, we strive to directly annotate the POIs associated with raw user-generated mobility records. We propose a neural context fusion approach which integrates various context factors in people's POI-visiting behaviors. Our approach evaluates the preference and transition factors via representation learning. Notably, we incorporate an attention mechanism to deal with the randomized transitions in raw mobility. The domain knowledge factors, i.e., distance, time and popularity, remain effective and our approach further includes them from a data-driven perspective. Factors are automatically fused with a feed-forward neural network. Furthermore, we exploit a multi-head architecture to enhance the model expressiveness. Using two real-life data sets, we conduct our experimental study and find that our approach consistently outperforms the state-of-the-art baselines by at least 32 percent in accuracy. Besides, we demonstrate the utility of the obtained POI-based human mobility with a POI recommendation example.

关键词： Annotations Trajectory Mobile computing Computer architecture Business intelligence Neural networks Semantics

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Spatial optimization of rice systems with ratoon rice increases production and reduces methane emissions

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European Journal of Agronomy 2025年 170卷

作者： Ding, Qian Li, Yuechen Cao, Ruyin Chen, Jin Yao, Xiong Zhang, Wujun Chongqing Engineering Research Center for Remote Sensing Big Data Application Chongqing Jinfo Mountain National Field Scientific Observation and Research Station for Karst Ecosystem School of Geographical Sciences Southwest University Chongqing 400715 China Key Laboratory of Remote Sensing Application and Innovation Chongqing 401147 China Key Laboratory of Monitoring Evaluation and Early Warning of Territorial Spatial Planning Implementation Ministry of Natural Resources Chongqing 401147 China School of Resources and Environment University of Electronic Science and Technology of China Sichuan Chengdu 611731 China State Key Laboratory of Remote Sensing Science Faculty of Geographical Science Beijing Normal University Beijing 100875 China Chongqing Rice Ratooning Research Center Chongqing Academy of Agricultural Sciences Chongqing 401329 China

The ratoon rice system (RR) has potential for increasing rice production and reducing environmental impacts. However, research on the spatial optimization of rice systems with RR to balance rice production and environmental protection is limited. We explored the trade-offs in production and methane emissions between RR, the single-season rice system (SR), and the double-season rice system (DR) by employing spatial optimization as a research paradigm. On the basis of 70 field sites, we analyzed the yield and methane emission differences among RR, SR and DR. A random forest model was employed to assess the suitability of RR in China's rice fields. The potential of RR to increase production and reduce methane emissions in medium- and high-suitability areas under different spatial cultivation strategies was quantified through scenario simulations. We found that the annual yield of RR (12.40 ± 1.98 t ha⁻¹) was significantly higher than that of SR (73.53 %) but less than that of DR (1.35 %). However, its methane emissions (0.42 ± 0.19 t ha⁻¹) were lower than those of DR (38.25 %) but higher than those of SR (27.43 %). Suitability analysis revealed that 73.82 % of rice fields nationwide are suitable for RR, with 99.21 % suitability in DR areas. Scenario simulations indicated that, with the optimal spatial layout of rice systems, RR could increase rice production by 14.55 % without increasing methane emissions or reduce methane emissions by 5.31 % while maintaining rice production. This study demonstrated the significant potential of RR to increase rice production while reducing environmental impacts through spatial optimization and offered an innovative approach for sustainable rice production planning in China. © 2025 Elsevier B.V.

关键词： China Methane emission Production Ratoon rice Suitability

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Improving aerosol retrieval from FY-3D/MERSI by parameterizing a binary quadratic spectral surface reflectance model with urban percentage and vegetation index

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Atmospheric research 2025年 326卷

作者： Weiqian Ji Leiku Yang Xin Pei Huan Liu Yidan Si Kaimin Sun Yizhe Fan Ping Zhang Xiaoqian Cheng Xiaofeng Lu School of Surveying and Land Information Engineering Henan Polytechnic University Jiaozuo 454003 China Henan Province Spatial Big Data Acquisition Equipment Development and Application Engineering Technology Research Center Jiaozuo 454003 China National Satellite Meteorological Center (National Centre for Space Weather) China Meteorological Administration Beijing 10081 China Innovation Center for FengYun Meteorological Satellite (FYSIC) China Meteorological Administration Beijing 10081 China Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites China Meteorological Administration Beijing 10081 China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan 430072 China

The Medium Resolution Spectral Imager (MERSI) Dark Target (DT) algorithm, adapted from the Moderate Resolution Imaging Spectroradiometer (MODIS) DT algorithm, demonstrated aerosol retrieval performance similar to that of MODIS Collection 6.1 (C6.1) DT products. However, discrepancies arose in urban areas, where MERSI retrievals tended to exhibit noticeable positive biases, primarily due to the inadequate representation of urban surface reflectance. To address this issue, this study developed an enhanced algorithm by parameterizing a binary quadratic spectral surface reflectance model using urban percentage (UP) and vegetation index, named the MERSI DT_UP algorithm. Global validation against Aerosol Robotic Network (AERONET) measurements showed that MERSI DT_UP retrievals in 2019 exhibited greater accuracy than MERSI DT retrievals. The correlation coefficient between satellite retrievals and AERONET for MERSI DT_UP ( R = 0.882) was higher than that for MERSI DT ( R = 0.875), and the mean bias was reduced to −0.001 from 0.018. Additionally, the percentage of matchups falling within the expected error envelope (within EE%) of ± (0.05 + 0.2τ) increased by 1.5 %. Across different urbanization levels, MERSI DT_UP retrievals showed significant reductions in mean biases (ranging from 0.003 to 0.217) and notable increases in within EE% (ranging from 2.2 % to 49.3 %) compared to original results. Moreover, improvements were also observed on moderately vegetated and moderately bright surfaces, as demonstrated by the error dependence analysis. The proposed surface reflectance model supports the algorithm development for MERSI and holds potential for application to other sensors.

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Author Correction: Mapping annual 10-m soybean cropland with spatiotemporal sample migration

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Scientific data 2024年第1期11卷 565页

作者： Hongchi Zhang Zihang Lou Dailiang Peng Bing Zhang Wang Luo Jianxi Huang Xiaoyang Zhang Le Yu Fumin Wang Linsheng Huang Guohua Liu Shuang Gao Jinkang Hu Songlin Yang Enhui Cheng Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing 100094 China. International Research Center of Big Data for Sustainable Development Goals Beijing 100094 China. University of Chinese Academy of Sciences Beijing 100094 China. Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing 100094 China. pengdl@***. International Research Center of Big Data for Sustainable Development Goals Beijing 100094 China. pengdl@***. Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing 100094 China. zhangbing@***. University of Chinese Academy of Sciences Beijing 100094 China. zhangbing@***. Jiangxi Nuclearindustry Surveying and Mapping Institute Group Co. Ltd Nanchang 330038 China. College of Land Science and Technology China Agricultural University Beijing 100083 China. Geospatial Sciences Center of Excellence Department of Geography Geospatial Sciences South Dakota State University Brookings SD 57007 USA. Department of Earth System Science Tsinghua University Beijing 100084 China. Institute of Applied Remote Sensing & Information Technology Zhejiang University Hangzhou 310058 China. National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui University Hefei 230601 China. Innovation Academy for Microsatellites Chinese Academy of Sciences Shanghai 200120 China.

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application of Text Mining Techniques in railway Safety Supervision System

Application of Text Mining Techniques in Railway Safety Supe...

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2018 International Conference on Civil and Hydraulic Engineering, IConCHE 2018

作者： Li, X.Q. Shi, T.Y. Li, P. Gao, F. Xiang, W.G. Postgraduate Department China Academy of Railway Sciences Beijing100081 China Institute of Computing Technologies China Academy of Railway Sciences Beijing100081 China Research and Application Innovation Centre for Big Data Technology in Railway China Academy of Railway Sciences Beijing100081 China

railway safety is the most important in railway transportation. It is the cornerstone of all railway work and involves a wide range of departments. In the age of unstructured big data, it is an urgent problem to solve unstructured data, which contains a lot of information value by using advanced technology. This paper introduces the application of text analysis in railway safety supervision system based on massive data of accident and fault analysis report, analysis the importance of text mining techniques for railway safety system, the key technology of text analysis. Finally, based on the railway Security big data application Platform, we mainly introduce two classic functions of full-text retrieval and text classification based on text analysis technology on this platform. The application of the platform proves that text analysis plays a key role in railway safety supervision system. © Published under licence by IOP Publishing Ltd.

关键词： Text processing

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