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2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings

作者： Wan, Wei Yong, Xue Jie, Guang Linyan, Bai Ying, Wang Ai, Jianwen Li, Yinjie State Key Laboratory of Remote Sensing Science Beijing Normal University Chinese Academy of Sciences P.O. Box 9718 Beijing 100101 China Department of Computing London Metropolitan University 166-220 Holloway Road London N7 8DB United Kingdom China Center for Resource Satellite Data and Application Beijing 100830 China Graduate School of the Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781424428083

In this paper, a method using multi-resource remotely sensed data and ground base data for quantitative determination of aerosol optical properties was demonstrated. The model exploits the synergy of TERRA and AQUA MODerate resolution Imaging Spectrometer (MODIS) data, to simultaneously retrieve both Aerosol Optical Thickness (AOT) and surface reflectance. The ground station data routinely coming from AERosol Robotic NETwork (AERONET) of ground-based sun- and sky-scanning radiometers were assimilated as variables describing model initial states. To meet the increased computational needs caused by the complex and computational intensity, the algorithm was migrated to run as parallel processing on a Grid platform. Experimental results were presented with a realistic pplication, using data collected by MODIS over China mainland. The results show that Grid-enabled model allowed on-demand large volume of ground-based data assimilation with parameters, and achieved improvement both in retrieval accuracy and computing performance. © 2008 IEEE.

关键词： Optical properties

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A short-term output power prediction model of wind power based on deep learning of grouped time series

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European Journal of Electrical Engineering 2020年第1期22卷 29-38页

作者： Wang, Yongsheng Gao, Jing Xu, Zhiwei Li, Leixiao College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot010018 China Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application for Agriculture and Animal Husbandry Hohhot010018 China College of Data Science and Application Inner Mongolia University of Technology Hohhot010080 China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service Hohhot010080 China Institute of Computing Technology Chinese Academy of Sciences Beijing100080 China

The output power prediction of wind power plants is an important guarantee to improve the utilization rate of wind energy and reduce wind curtailment. However, due to the strong randomness of wind energy, the ultra-short-term prediction accuracy of wind power output is poor. In view of the problem above, a prediction model based on deep learning of grouped time series (LW-CLSTM) was proposed in this paper. Based on this model, the authors attempted to explore a prediction method of wind power output. For this, first the multivariate data of wind power was fused, cleaned, dimension-reduced, and standardized, and the time period characteristics of the output power itself were extracted. Afterwards, it proposes a time sliding window (TSW) algorithm, and constructs a neural network input data set. Then a deep neural network prediction model combining the Convolutional Neutral Network (CNN) and Long-Short Term Memory (LSTM) was established, and the regression evaluation criteria for output power forecast accuracy in wind power production were designed, to compare the proposed model with four other prediction models. Finally, the experiments on the TensorFlow platform using real data show that this model has better prediction accuracy than the other four models, reaching a prediction accuracy rate of 92.5%, which verified the effectiveness of this prediction method. © 2020 International Information and Engineering Technology Association. All rights reserved.

关键词： Wind power

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Taxi travel time based Geographically Weighted Regression Model (GWR) for modeling public housing prices in Singapore 29

Taxi travel time based Geographically Weighted Regression Mo...

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29th International Conference on Geoinformatics, Geoinformatics 2022

作者： Wang, Yi'an Cai, Fangyi Cheng, Shih-Fen Wu, Bo Cao, Kai School of Geographic Sciences East China Normal University Shanghai China East China Normal University Key Laboratory of Geographic Information Science Ministry of Education Shanghai China East China Normal University Key Lab. of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities Ministry of Natural Resources Shanghai China National University of Singapore Department of Geography Singapore School of Computing and Information Systems Singapore Management University Singapore School of Geography and Environment Jiangxi Normal University Jiangxi Nanchang China

ISBN: (数字)9798350309881

ISBN: (纸本)9798350309881

In this research, a taxi travel time based Geographically Weighted Regression model (GWR) is proposed and utilized to model the public housing price in the case study of Singapore. In addition, a comparison between the proposed taxi data driven GWR and other models, such as ordinary least squares model (OLS), GWR model based on Euclidean distance and GWR model based on public transport travel time, have also been carried out. Results indicates that taxi travel time based GWR model has better fitting performance than the OLS model, and slightly better than the Euclidean distance-based GWR model, however, it is not as good as the GWR model based on public transport travel time according to the metric of Adjusted R2. These experiments indicate that the public transport travel time may has a major part to play in modeling the public housing resale prices compared to taxi travel time or driving time, and both the taxi travel time and public transport travel time can better explain the public housing resale prices in Singapore compared to Euclidean distance in the GWR modeling. © 2022 IEEE.

关键词： Travel time

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Deep Learning Based Search and Recommendation of Literature in Wanfang Knowledge Base

Deep Learning Based Search and Recommendation of Literature ...

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6th International Conference on Intelligent computing and Signal Processing (ICSP)

作者： Tongtong Li Qiang Lin Xiaoyan Chen Yanru Guo Shaofang Zhao School of Mathematics and Computer Science Northwest Minzu University Lanzhou Gansu China Dynamic Flow Data Computing and Application Laboratory Northwest Minzu University Lanzhou Gansu China Key Laboratory of Chinese National Language and Information Technology Ministry of Education Northwest Minzu University Lanzhou Gansu China

Classifying Chinese text is a hot but difficult topic in natural language processing field. In this work, we aim to quickly recommend the required documents for users via searching keywords in a massive literature database. We use "Wanfang data Knowledge Service Platform Journal Document User Behavior Log data" as the object to research and build a text classifier based on deep learning techniques. It is proposed to perform data cleaning and data extraction for user browsing behavior logs and user download behavior logs. Firstly, Chinese word segmentation and keyword extraction are carried out on the document title information, and the keyword data set and document keyword data set based on the document name are constructed. Finally, using the traditional deep learning model and convolutional neural network model to build a text classifier for training and classification, as a model for users to search for keywords recommended for literature. Experimental results show that the constructed model is able to effectively classify and recommend documents with the users' search keywords, and extract keywords from document names in the construction model. The proposed model TEXTCNN V2 obtains a value for accuracy, precision and F-1 score is 0.9132, 0.9220 and 0.9154, respectively.

关键词： Deep learning Training Computational modeling Text categorization Search problems data models Convolutional neural networks

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Multispectral Pan-sharpening via Dual-Channel Convolutional Network with Convolutional LSTM Based Hierarchical Spatial-Spectral Feature Fusion

arXiv

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arXiv 2020年

作者： Wang, Dong Bai, Yunpeng Li, Ying School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing Northwestern Polytechnical University Xian China School of Computing and Information Systems University of Melbourne VIC3010 Australia

Multispectral pan-sharpening aims at producing a high resolution (HR) multispectral (MS) image in both spatial and spectral domains by fusing a panchromatic (PAN) image and a corresponding MS image. In this paper, we propose a novel dual-channel network (DCNet) framework for MS pan-sharpening. In our DCNet, the dual-channel backbone involves a spatial channel to capture spatial information with a 2D CNN, and a spectral channel to extract spectral information with a 3D CNN. This heterogeneous 2D/3D CNN architecture can minimize causing spectral information distortion, which typically happens in conventional 2D CNN models. In order to fully integrate the spatial and spectral features captured from different levels, we introduce a multi-level fusion strategy. Specifically, a spatial-spectral CLSTM (S2-CLSTM) module is proposed for fusing the hierarchical spatial and spectral features, which can effectively capture correlations among multi-level features. The S2-CLSTM module attaches two fusion ways: the intra-level fusion via bi-directional lateral connections and inter-level fusion via the cell state in the S2-CLSTM. Finally, the ideal HR-MS image is recovered by a reconstruction module. Extensive experiments have been conducted at both simulated lower scale and the original scale of real-world datasets. Compared with the state-of-the-art methods, the proposed DCNet achieves superior or competitive performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

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Improving Security data Access Control for Multi-Authority Cloud Storage

Improving Security Data Access Control for Multi-Authority C...

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IEEE International Conference on Big data and Cloud computing (BdCloud)

作者： Jian Wang Kehua Wu Chunxiao Ye Xiaofeng Xia Fei Ouyang Colleage of Computer Science Chongqing University Chongqing China Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing University Chongqing China Ecological Environment Big Data Application Center Chongqing Ecological Environment Bureau Chongqing China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Cloud computing have brought a lot of advantages, such as reducing the hardware cost and providing a convenient cloud storage service. More and more people choose to put their private data in the cloud. To improve data security and privacy, Wu et al. proposed a new extensive data access control scheme for multi-authority cloud storage systems (NEDAC-MACS) on the basis of the scheme of Yang et al. In this paper, we propose a new scheme that can resist the collusion of the cloud server and user to improve the security of NEDAC-MACS. The cryptographic analysis show that our scheme is still secure in the random oracle.

关键词： Cloud computing Public key Resists Encryption Access control

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Ppt4j: Patch Presence Test for Java Binaries

Ppt4j: Patch Presence Test for Java Binaries

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International Conference on Software Engineering (ICSE)

作者： Zhiyuan Pan Xing Hu Xin Xia Xian Zhan David Lo Xiaohu Yang The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Software Engineering Application Technology Lab Huawei Hangzhou China The Hong Kong Polytechnic University Hong Kong China School of Computing and Information Systems Singapore Management University Singapore

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

The number of vulnerabilities reported in open source software has increased substantially in recent years. Security patches provide the necessary measures to protect software from attacks and vulnerabilities. In practice, it is difficult to identify whether patches have been integrated into software, especially if we only have binary files. Therefore, the ability to test whether a patch is applied to the target binary, a.k.a. patch presence test, is crucial for practitioners. However, it is challenging to obtain accurate semantic information from patches, which could lead to incorrect results. In this paper, we propose a new patch presence test framework named Ppt4j (Patch Presence Test forJava Binaries). Ppt4j is designed for open-source Java libraries. It takes Java binaries (i.e. bytecode files) as input, extracts semantic information from patches, and uses feature-based techniques to identify patch lines in the binaries. To evaluate the effectiveness of our proposed approach Ppt4j, we construct a dataset with binaries that include 110 vulnerabilities. The results show that Ppt4j achieves an F1 score of 98.5% with reasonable efficiency, improving the baseline by 14.2%. Furthermore, we conduct an in-the-wild evaluation of Ppt4j on JetBrains IntelliJ IDEA. The results suggest that a third-party library included in the software is not patched for two CVEs, and we have reported this potential security problem to the vendor.

关键词： Java Semantics Feature extraction Libraries Security Software measurement data mining

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Granular Ball Concept-Cognitive Learning for Fuzzy Contexts: Self-Adaptive Concept Splitting

SSRN

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SSRN 2024年

作者： Wang, Qijun Lin, Yidong Li, Jinjin Lin, Menglei Liang, Taoju School of Mathematics and Statistics Minnan Normal University Zhangzhou363000 China Fujian Key Laboratory of Granular Computing and Application Minnan Normal University Zhangzhou363000 China Institute of Meteorological Big Data-Digital Fujian Minnan Normal University Zhangzhou363000 China

Concept-cognitive learning (CCL) has significantly advanced cognitive science by simulating human brain processes, particularly in dealing with fuzzy data. Despite recent achievements, challenges persist in achieving complete and accurate cognition in fuzzy contexts, reflected in 1) the lack of facticity in pseudo-concept spaces;2) the limited scalability of generalized concepts;3) the subjectivity introduced by numerous parameters. To overcome these challenges, we develop two innovative approaches by integrating granular ball computing theory into granular ball concept-cognitive learning (GBCCL) and its simplified variant (P-GBCCL), designed to enhance the performance of CCL. In this paper, we present a refined concept-splitting method that captures nuanced characteristics, thereby improving the scalability of concepts. Additionally, combining with Gaussian kernel, a novel similarity degree is defined to recognize new clues and design concept cognitive mechanism based on granular ball theory subsequently. To optimize memory usage in GBCCL, we introduce the concept of `pseudo-granular ball' and present a streamlined model, P-GBCCL. Finally, experimental results demonstrate the effectiveness, facticity and objectivity of our proposed methods in achieving cognitive learning in the fuzzy context. © 2024, The Authors. All rights reserved.

关键词： Scalability

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Graph domain adaptation: A generative view

arXiv

引用

arXiv 2021年

作者： Cai, Ruichu Wu, Fengzhu Li, Zijian Wei, Pengfei Yi, Lingling Zhang, Kun School of Computing Guangdong University of Technology Guangdong Provincial Key Laboratory of Public Finance and Taxation with Big Data Application Guangzhou510006 China School of Computing Guangdong University of Technology Guangzhou510006 China School of Computing Guangdong University of Technology Guangzhou510006 China Co. Ltd Department of Philosophy Carnegie Mellon University PittsburghPA15213 United States

—Recent years have witnessed tremendous interest in deep learning on graph-structured data. Due to the high cost of collecting labeled graph-structured data, domain adaptation is important to supervised graph learning tasks with limited samples. However, current graph domain adaptation methods are generally adopted from traditional domain adaptation tasks, and the properties of graph-structured data are not well utilized. For example, the observed social networks on different platforms are controlled not only by the different crowd or communities but also by the domain-specific policies and the background noise. Based on these properties in graph-structured data, we first assume that the graph-structured data generation process is controlled by three independent types of latent variables, i.e., the semantic latent variables, the domain latent variables, and the random latent variables. Based on this assumption, we propose a disentanglement-based unsupervised domain adaptation method for the graph-structured data, which applies variational graph auto-encoders to recover these latent variables and disentangles them via three supervised learning modules. Extensive experimental results on two real-world datasets in the graph classification task reveal that our method not only significantly outperforms the traditional domain adaptation methods and the disentangled-based domain adaptation methods but also outperforms the state-of-the-art graph domain adaptation algorithms. © 2021, CC BY.

关键词： Graphic methods

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Ppt4J: Patch Presence Test for Java Binaries

arXiv

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arXiv 2023年

作者： Pan, Zhiyuan Hu, Xing Xia, Xin Zhan, Xian Lo, David Yang, Xiaohu The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Software Engineering Application Technology Lab Huawei Hangzhou China The Hong Kong Polytechnic University Hong Kong Hong Kong School of Computing and Information Systems Singapore Management University Singapore

The number of vulnerabilities reported in open source software has increased substantially in recent years. Security patches provide the necessary measures to protect software from attacks and vulnerabilities. In practice, it is difficult to identify whether patches have been integrated into software, especially if we only have binary files. Therefore, the ability to test whether a patch is applied to the target binary, a.k.a. patch presence test, is crucial for practitioners. However, it is challenging to obtain accurate semantic information from patches, which could lead to incorrect results. In this paper, we propose a new patch presence test framework named Ppt4J (Patch Presence Test for Java Binaries). Ppt4J is designed for open-source Java libraries. It takes Java binaries (i.e. bytecode files) as input, extracts semantic information from patches, and uses feature-based techniques to identify patch lines in the binaries. To evaluate the effectiveness of our proposed approach Ppt4J, we construct a dataset with binaries that include 110 vulnerabilities. The results show that Ppt4J achieves an F1 score of 98.5% with reasonable efficiency, improving the baseline by 14.2%. Furthermore, we conduct an in-the-wild evaluation of Ppt4J on JetBrains IntelliJ IDEA. The results suggest that a third-party library included in the software is not patched for two CVEs, and we have reported this potential security problem to the vendor. © 2023, CC BY-NC-ND.

关键词： Open source software

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