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Spatial-temporal changes and driving factors of eco-environmental quality in the Three-North region of China

Journal of Arid Land 2023年第3期15卷 231-252页

作者： LONG Yi JIANG Fugen DENG Muli WANG Tianhong SUN Hua Research Center of Forestry Remote Sensing&Information Engineering Central South University of Forestry and TechnologyChangsha 410004China Key Laboratory of Forestry Remote Sensing Based Big Data&Ecological Security for Hunan Province Changsha 410004China Key Laboratory of National Forestry&Grassland Administration on Forest Resources Management and Monitoring in Southern Area Changsha 410004China

Eco-environmental quality is a measure of the suitability of the ecological environment for human survival and socioeconomic *** the spatial-temporal distribution and variation trend of eco-environmental quality is essential for environmental protection and ecological *** remote sensing ecological index(RSEI)can quickly and objectively quantify eco-environmental quality and has been extensively utilized in regional ecological environment *** this paper,Moderate Resolution Imaging Spectroradiometer(MODIS)images during the growing period(July-September)from 2000 to 2020 were obtained from the Google Earth Engine(GEE)platform to calculate the RSEI in the three northern regions of China(the Three-North region).The Theil-Sen median trend method combined with the Mann-Kendall test was used to analyze the spatial-temporal variation trend of eco-environmental quality,and the Hurst exponent and the Theil-Sen median trend were superimposed to predict the future evolution trend of eco-environmental *** addition,ten variables from two categories of natural and anthropogenic factors were analyzed to determine the drivers of the spatial differentiation of eco-environmental quality by the geographical *** results showed that from 2000 to 2020,the RSEI in the Three-North region exhibited obvious regional characteristics:the RSEI values in Northwest China were generally between 0.2 and 0.4;the RSEI values in North China gradually increased from north to south,ranging from 0.2 to 0.8;and the RSEI values in Northeast China were mostly above *** average RSEI value in the Three-North region increased at an average growth rate of 0.0016/a,showing the spatial distribution characteristics of overall improvement and local degradation in eco-environmental quality,of which the areas with improved,basically stable and degraded eco-environmental quality accounted for 65.39%,26.82%and 7.79%of the total study area,*** Hurst

关键词： eco-environmental quality remote sensing ecological index Google Earth Engine Hurst exponent geographical detector Three-North region of China

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Bilateral Network with Residual U-blocks and Dual-Guided Attention for Real-time Semantic Segmentation

Bilateral Network with Residual U-blocks and Dual-Guided Att...

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2023 China Automation Congress, CAC 2023

作者： Liao, Liang Wan, Liang Liu, Mingsheng Li, Shusheng College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang China School of Computer Science and Engineering Northeastern Univiversity Shenyang China

ISBN: (纸本)9798350303759

When some application scenarios need to use semantic segmentation technology, like automatic driving, the primary concern comes to real-time performance rather than extremely high segmentation accuracy. To achieve a good trade-off between speed and accuracy, two-branch architecture has been proposed in recent years. It treats spatial information and semantics information separately which allows the module to be composed of two networks both not heavy. However, the process of fusing features with two different scales becomes a performance bottleneck for many nowaday two-branch models. In this research, we design a new fusion mechanism for two-branch architecture which is guided by attention computation. To be precise, we use the Dual-Guided Attention (DGA) module we proposed to replace some multi-scale transformations with the calculation of attention which means we only use several attention layers of near linear complexity to achieve performance comparable to frequently-used multi-layer fusion. To ensure that our module can be effective, we use Residual U-blocks (RSU) to build one of the two branches in our networks which aims to obtain better multi-scale features. Extensive experiments on Cityscapes and CamVid dataset show the effectiveness of our method. On Cityscapes, our light version network without pretrain weight can achieve 71.1% mIoU at 163 FPS on a single Nvidia RTX 3070 using full resolution images(1024×2048pix). And the large version can achieve 77.9% mIoU with a speed of 43 FPS which still reaches the real-time criterion. Our code and module has been open sourced at https://***/LikeLidoA/Mymodule. © 2023 IEEE.

关键词： Computer vision

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Few-Shot Node Classification on Attributed Networks Based on Prototypical Network 19

Few-Shot Node Classification on Attributed Networks Based on...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Zhao, Yaliang Zhang, Guangming Wang, Jinke Henan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng China

ISBN: (纸本)9798350346558

In today's society, the interactions between various entities form complicated attributed networks. The node classification task as the core task of analyzing attribute networks has gained a considerable amount of attention from researchers. The existing graph neural network-based node classification approaches all demand lots of labeled data, but a high percentage of node classes in practical scenarios contain only restricted labeled data. So their classification effect is not good. In response to this problem, we put forward an improved graph prototypical network (IGPN) to enhance the few-shot node classification's accuracy. In particular, we start by obtaining the embedding representations of nodes on the attributed network through a graph attention network. Then combined with the node's degree information and neighbor information, the importance coefficient of all nodes in the support set is learned. After that, we can obtain the prototype representation of every class. Finally, the node classification can be completed by the distance of the query nodes' representation from all class prototypes. Our method is preferable to the baseline method on three datasets according to a large number of experiments. © 2022 IEEE.

关键词： Large dataset

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A CEEMDAN-QPSO-BiLSTM Based Hybrid Model for Water Quality Parameter Forecasting 3

A CEEMDAN-QPSO-BiLSTM Based Hybrid Model for Water Quality P...

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3rd International Conference on Electronic Information engineering and Computer, EIECT 2023

作者： Guo, Yongwen Chen, Hongming Huang, Juntao Lin, Yilei Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan China Shenzhen University College of Physics and Optoelectronic Engineering Shenzhen China

ISBN: (纸本)9798350357707

Water contamination is a major concern worldwide due to its impact on the global ecology. Accurate forecasting of water quality parameters is crucial for effective water contamination control. However, existing models face challenges in accurately predicting water quality series due to their nonlinearity and non-stationarity. Additionally, further investigation is needed to find out the performance of the Bidirectional Long Short-Term Memory network (BiLSTM) when combined with optimization of signal processing and adjustment of hyperparameters. In this article, we introduce a novel hybrid approach named CEEMDAN-QPSO-BiLSTM, which combines three techniques-Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Quantum behaved Particle Swarm optimization (QPSO), and BiLSTM-for predicting water quality parameters. To evaluate the effectiveness of this hybrid model, we developed four benchmark models, namely LSTM, BiLSTM, PSO-BiLSTM, and QPSO-BiLSTM, and compared their predictive accuracy with that of our hybrid approach. The training and testing of these models utilized actual water quality data from the Chinese National Marine Environmental Monitoring Center's official system, specifically focusing on Dissolved Oxygen (DO), pH value, and Chemical Oxygen Demand (COD) parameters. The experimental results show that the CEEMDAN-QPSO-BiLSTM surpasses the baseline models, providing better error performance and prediction accuracy. © 2023 IEEE.

关键词： Empirical mode decomposition

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Differentially Private Discrete Multi-Attributed Network Releasing

Differentially Private Discrete Multi-Attributed Network Rel...

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2022 International Conference on Signal Processing, Computer Networks, and Communications, SPCNC 2022

作者： Lin, Yinghao Wang, Yuye Zhao, Shihao Qiao, Baojun Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Kaifeng China Shenzhen Research Institute of Henan University ShenZhen China

ISBN: (纸本)9781510664616

In the area of social network, different attributes have different effects on the structure of network. Most of the existing privacy protection methods for attributed networks ignore the situation which different attributes have different effects on the network structure. They protect the privacy of the attributes indiscriminately. In respect of the issues above, a differentially private discrete multi-attributed network releasing method is proposed. Firstly, a probability model of discrete multi-attributed network is structured and the correlation parameter between multiple attributes and network structure is defined. The factor with different effects of different attributes on network structure is added into the model. Then, the algorithm uses the correlation parameter to establish the partition model of metadata and divides the metadata into different groups. As the group has different network model and attribute between each other, the groups are independence. The differential privacy of discrete multi-attributed network is realized through sanitizing parameters of the model and allocating metadata using exponential mechanism. Finally, experiment on real datasets verifies that the algorithm can satisfy the characteristics of the discrete multi-attributed network. It can also improve the efficiency and data availability. © 2023 SPIE.

关键词： Metadata

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Multi-scale fusion clustering algorithm for urban travel features 3

Multi-scale fusion clustering algorithm for urban travel fea...

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2023 3rd International Conference on Intelligent Traffic Systems and Smart City, ITSSC 2023

作者： Zhang, Zixiang Zhao, Zhilong Deng, Jiaming Zhao, Yingying Xu, Tao Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Key Laboratory of Geographical Information Science Ministry of Education East China Normal University Shanghai200241 China School of Computer and Information Engineering Henan University Kaifeng475004 China International Education College Henan University Zhengzhou450046 China Henan Technology Innovation Center of Spatio-Temporal Big Data Henan University Zhengzhou450046 China Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan University Zhengzhou450046 China

ISBN: (纸本)9781510672963

Urban travel data typically encompass various temporal and spatial scales and features. Nonetheless, existing traffic travel feature analysis often focuses on a single spatial scale, which may overlook crucial information and hinder features at different scales. Consequently, this could reduce the accuracy of travel pattern recognition. To solve this problem, we introduce a multi-scale fusion transformation algorithm. In our approach, we first establish a multi-level taxi travel data grid system. Then, we apply the I-Clique clustering algorithm to perform grid clustering at each level of the system to identify taxi travel feature regions of different scales. Subsequently, a multi-scale fusion pyramid model is constructed utilizing the acquired travel feature regions at various scales. Finally, we determine the transformation relationships between scales in the pyramid model, enabling the identification of travel feature areas across both known and unknown scales. This approach facilitates the recognition of travel features within cities at any scale. Experimental results demonstrate that the multi-scale fusion transformation algorithm enhances our understanding of spatial aggregation patterns and the hierarchical structure of geographical entities, offering a novel approach for multi-scale urban traffic analysis. © 2024 SPIE. All rights reserved.

关键词： Clustering algorithms

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Hierarchical Scalable data Lake Design for Game Engine 8

Hierarchical Scalable Data Lake Design for Game Engine

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8th International Conference on big data and Information Analytics, bigDIA 2022

作者： Yang, Mingbao Zhou, Peng Li, Shaobo Pu, Ruiqiang College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang China School of Mechanical Engineering Guizhou University Guiyang China Guizhou University State Key Laboratory of Public Big Data Guiyang China

ISBN: (数字)9781665487962

ISBN: (纸本)9781665487962

With the rapid development of information technology, people are more and more inseparable from smart devices, and people and devices produce a large amount of data all the time. How to store and analyze massive data has become a research hotspot of the times. However, researchers mainly study the high reliability, high availability, high fault tolerance, high throughput, and high concurrent processing of big data systems, but there are few studies on designing hierarchical and scalable data lakes. In this study, a layered and extensible design of the data lake is carried out, which reduces the complexity of implementing business indicators during data analysis, and at the same time improves code reuse, scalability, development efficiency and code security. In addition, in order to avoid the problem of data accumulation or even job execution failure due to the skew of ip dimension table data, this paper designs the ip dimension table rowkey. This paper designs a set of real-time and offline big data analysis data lake architecture based on game engine and applies it to the actual production environment. © 2022 IEEE.

关键词： big data

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Fair Personalized Learner Modeling Without Sensitive Attributes 25

Fair Personalized Learner Modeling Without Sensitive Attribu...

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34th ACM Web Conference, WWW 2025

作者： Xu, Hefei Hou, Min Wu, Le Liu, Fei Yang, Yonghui Bai, Haoyue Hong, Richang Wang, Meng Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Anhui Hefei China Institute of Dataspace Hefei Comprehensive National Science Center Anhui Hefei China

ISBN: (纸本)9798400712746

Personalized learner modeling uses learners’ historical behavior data to diagnose their cognitive abilities, a process known as Cognitive Diagnosis (CD). This is essential for web-based learning services such as learning resource recommendation and adaptive testing. However, prior studies have shown that CD models may unfairly correlate learners’ abilities with sensitive attributes (e.g., gender, region), leading to biased outcomes. While existing approaches mitigate this issue by decorrelating sensitive attributes from the modeling process, privacy concerns make collecting such attributes impractical. Furthermore, the presence of multiple sensitive attributes complicates fairness improvements. In this paper, we explore how to achieve fair personalized learner modeling without relying on any sensitive attribute input. We introduce a novel fairness objective tailored for personalized learner modeling and design a max-min strategy to facilitate both sensitive information inference and fair CD modeling. In the max step, we infer pseudo-labels by maximizing the fairness objective, while in the min step, we retrain the CD model by minimizing it. Additionally, we provide a theoretical guarantee that our framework reduces the upper bound of fairness generalization error. Extensive experiments demonstrate that the proposed framework significantly outperforms existing methods. © 2025 Copyright held by the owner/author(s).

关键词： Supervised learning

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Blockchain-Based Multi-Cloud data Storage System Disaster Recovery

Blockchain-Based Multi-Cloud Data Storage System Disaster Re...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wang, Feiyu Zhou, Jian-Tao College of Computer Science Inner Mongolia University Inner Mongolia Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

ISBN: (纸本)9798350337020

Cloud storage services have been used by most businesses and individual users. However, data loss, service interruptions and cyber attacks often lead to cloud storage services not being provided properly, and these incidents have caused financial losses to users. Second, traditional and single-cloud model disaster recovery services are no longer suitable for the current complex cloud storage systems. Therefore, a scheme to provide disaster recovery for cloud storage services in a multi-cloud storage environment is needed in real production. In this paper, we propose a disaster recovery scheme based on blockchain technology. The proposed scheme outlined in this study aims to address the issue of data availability within the cloud storage landscape. The proposed scheme achieves this goal by dividing data into hot and cold categories, verifying the integrity of copy data via blockchain technology, and utilizing blockchain networks to manage multi-cloud storage systems. Experimental findings demonstrate that the proposed scheme yields superior results in terms of computation and time overheads. © 2023 IEEE.

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A Fine-Grained Anomaly Detection Method Fusing Isolation Forest and knowledge Graph Reasoning 19th

A Fine-Grained Anomaly Detection Method Fusing Isolation For...

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19th International Conference on Web Information Systems and Applications, WISA 2022

作者： Xu, Jie Zhou, Jiantao College of Computer Science Engineering Research Center of Ecological Big Data Ministry of Education National and Local Joint Engineering Research Center of Mongolian Intelligent Information Processing Technology Inner Mongolia Cloud Computing and Service Software Engineering Laboratory Inner Mongolia Social Computing and Data Processing Key Laboratory Inner Mongolia Discipline Inspection and Supervision Big Data Key Laboratory Inner Mongolia Big Data Analysis Technology Engineering Laboratory Inner Mongolia University Hohhot China

ISBN: (纸本)9783031203084

Anomaly detection aims to find outliers data that do not conform to expected behaviors in a specific scenario, which is indispensable and critical in current safety environments related studies. However, when performing outlier detection on a large-scale multidimensional dataset, most of the traditional methods make no distinctions between classes of outliers and lack the reasoning ability and the explainability of the analysis results, which leads to the low accuracy of global outlier detection and the inability to effectively identify local outliers. In this paper, we propose a fine-grained anomaly detection method which well utilizes isolation forest and knowledge graph reasoning tactics. First, a better extracting data feature method and a more reasonable weighted strategy are used to find global outliers based on the traditional isolation forest algorithm, and then we construct a custom rule base according to the in-depth research and analysis of global abnormal data, and at last the ontology knowledge is reasoned based on such rule base to achieve the detection of local outliers. Through analysis of extensive experiment results, our method could effectively discover more abnormal data without big loss of time costs, has strong generalization, and correspondingly improves the performance for abnormal detection. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anomaly detection

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