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International Conference on Computer Supported Cooperative Work in Design

作者： Yuejia Wu Jian-Tao Zhou College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

Knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. Knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models.

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Translational analysis of data science and causal learning in real-world clinical evaluation of traditional Chinese medicine

Science of Traditional Chinese Medicine

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Science of Traditional Chinese Medicine 2024年第1期2卷 57-65页

作者： Wei Yang Danhui Yi XiaoHua Zhou Yuanming Leng Institute of Basic Research in Clinical Medicine China Academy of Chinese Medical SciencesBeijingChina School of Statistics Renmin University of ChinaBeijingChina Beijing International Center for Mathematical Research Peking UniversityBeijingChina Department of Biostatistics School of Public HealthPeking UniversityBeijingChina National Engineering Laboratory of Big Data Analysis and Applied Technology Peking UniversityBeijingChina Department of Biostatistics School of Public HealthBoston UniversityBostonMAUSA

Real-world clinical evaluation of traditional Chinese medicine(RWCE-TCM)is a method for comprehensively evaluating the clinical effects of TCM,with the aim of delving into the causality between TCM intervention and clinical *** study explored data science and causal learning methods to transform RWD into reliable real-world evidence,aiming to provide an innovative approach for *** study proposes a 10-step data science methodology to address the challenges posed by diverse and complex data in *** methodology involves several key steps,including data integration and warehouse building,high-dimensional feature selection,the use of interpretable statistical machine learning algorithms,complex networks,and graph network analysis,knowledge mining techniques such as natural language processing and machine learning,observational study design,and the application of artificial intelligence tools to build an intelligent engine for translational *** goal is to establish a method for clinical positioning,applicable population screening,and mining the structural association of TCM characteristic *** addition,the study adopts the principle of real-world research and a causal learning method for TCM clinical *** constructed a multidimensional clinical knowledge map of“disease-syndrome-symptom-prescription-medicine”to enhance our understanding of the diagnosis and treatment laws of TCM,clarify the unique therapies,and explore information conducive to individualized *** causal inference process of observational data can address confounding bias and reduce individual heterogeneity,promoting the transformation of TCM RWD into reliable clinical *** data science improves efficiency and accuracy for implementing *** proposed data science methodology for TCM can handle complex data,ensure high-quality RWD acquisition and analysis,and provide in-depth insights into clinical benefits of *** method supp

关键词： Traditional Chinese medicine Real-world study data science Causal learning Health information system Machine learning Artificial intelligence Treatment effects

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Fast extraction of winter wheat planting area in Huang-Huai-Hai Plain using high-resolution satellite imagery on a cloud computing platform

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International Journal of Agricultural and Biological engineering 2022年第1期15卷 241-250页

作者： Dongyan Zhang Mengru Zhang Fenfang Lin Zhenggao Pan Fei Jiang Liang He Hang Yang Ning Jin National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230601China Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions(Henan University) Ministry of EducationKaifeng 475004HenanChina School of Informatics and Engineering Suzhou UniversitySuzhou 234000AnhuiChina National Meteorological Center Beijing 100081China Aerospace Information Research Institute Chinese Academy of ScienceBeijing 100101China

To extract regional winter wheat planting area using higher-resolution satellite imagery still faces many challenges due to large data size and long processing time in traditional remote sensing *** Earth Engine(GEE),a cloud computing analysis platform based on global geospatial analysis,provides a new opportunity for rapid analysis of remote sensing *** this study,high-quality Landsat-8 imagery was used to extract the winter wheat planting area from the Huang-Huai-Hai Plain in *** random forest algorithm was used to identify and map the winter wheat sown in 2019 and harvested in 2020,and Sentinel-2 imagery was used to verify the *** spectral indices,texture,and terrain features of the image were derived,and their contribution to the classification accuracy of winter wheat was evaluated by *** the top nine features were selected to form an optimal feature *** the set of thirty-four features and the optimized feature subset as the input variables of the random forest classifier,the results show that the accuracy difference between the two feature classification schemes is small,but the classification effect of all feature sets is slightly better than the optimal feature *** overall classification accuracy of sample plots verification was 86%-95%,the Kappa coefficient was between 0.7 and 0.85,and the percentage error of the total area was 5.42%.The research demonstrates a reliable method for mapping a wide range of winter wheat planting area,and provides a good prospect for exploring the precise mapping of other crops,which is of great significance to crop monitoring and agricultural development.

关键词： Google Earth Engine regional scale winter wheat rapid mapping

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Wind-Bell Index: Towards Ultra-Fast Edge Query for Graph databases

Wind-Bell Index: Towards Ultra-Fast Edge Query for Graph Dat...

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International Conference on data engineering

作者： Rui Qiu Yi Ming Yisen Hong Haoyu Li Tong Yang School of Computer Science and National Engineering Laboratory for Big Data Analysis Technology and Application Peking University Beijing China Academy for Advanced Interdisciplinary Studies Peking University Peng Cheng Laboratory Shenzhen China

Graphs are good at presenting relational and structural information, making it powerful in the representation of various data. For the efficient storage and processing of graph-like data, graph databases have been rapidly developed and extensively studied. However, graph databases mostly use adjacency lists as their basic data structure (e.g., Neo4j), which could result in poor performance of edge due to the skewed degree distribution of *** design the Wind-Bell Index to address this problem. Wind-Bell Index is a memory-efficient index data structure, which can be attached to existing graph databases to speed up the edge. We have fully implemented our data structure in Neo4j, the most popular graph database today, and conduct theoretical and experimental analysis to evaluate the performance. Theoretical results prove the high query efficiency of our algorithm. And experimental results show that the average edge query speed is increased by hundreds of times compared with the original query interface of Neo4j. We believe that the excellent performance and scalability of Wind-Bell Index make it suitable for the application in a variety of graph databases.

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An Optimized Variational Bayesian Point Set Matching Approach using Coordinate Ascent and Simulated Annealing

An Optimized Variational Bayesian Point Set Matching Approac...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Fangjun Luan Shitong Liu Shuai Yuan Shenyang Branch of National Special Computer Engineering Technology Research Center Shenyang China School of Computer Science and Engineering Shenyang Jianzhu University Shenyang China Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang China

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

Aiming at the interference problem of outliers in point set matching, we propose an optimized variational Bayesian point set matching algorithm using coordinate ascent and simulated annealing. For the complex terms in the objective function, we introduce the coordinate ascending strategy and use the Monte Carlo integral stochastic approximation and control variable method to directly optimize the parameters. To enhance the generalization ability of the algorithm and avoid falling into the local optimal solution, we use the simulated annealing scheme to optimize the spatial mapping of the point set. Finally, the affine transform point set matching experiment and the non-rigid point set matching experiment represent our algorithm have a good matching effect and high accuracy.

关键词： Monte Carlo methods Annealing Integral equations Simulated annealing Transforms Interference Approximation algorithms

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PISketch: Finding Persistent and Infrequent Flows 3

PISketch: Finding Persistent and Infrequent Flows

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3rd ACM SIGCOMM 2022 Workshop on Formal Foundations and Security of Programmable Network Infrastructures, FFSPIN 2022

作者： Fan, Zhuochen Hu, Zhoujing Wu, Yuhan Guo, Jiarui Liu, Wenrui Yang, Tong Wang, Hengrui Xu, Yifei Uhlig, Steve Tu, Yaofeng School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Peking University China Computer Science Department University of California Los Angeles United States School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom ZTE Corporation China

ISBN: (纸本)9781450393294

Finding persistent and inactive activity periods is very helpful in practice, for example to detect intrusion activities. Most of the literature focuses on finding persistent flows or frequent flows. No previous work is able to find persistent and infrequent flows. In this paper, we propose a novel sketch data structure, PISketch, to find persistent and infrequent flows in real time. The key idea of PISketch is to define a weight and its Reward and Penalty System for each flow to combine and balance the information of both persistency and infrequency, and to keep high-weighted flows in a limited space through a strategy. We implement PISketch on P4 and CPU platforms, and compare the performance of PISketch with two strawman solutions (On-Off + CM sketch, and PIE + CM sketch), in terms of finding persistent and infrequent flows. Our experimental results demonstrate the advantage of PISketch, by comparing it to two strawman solutions: 1) The F1 Score of PISketch is around 22.1% and 57.6% higher than two strawman solutions, respectively;2) The Average Relative Error (ARE) of PISketch is around 820.9 (up to 1188.8) and 126.2 (up to 265.6) times lower than two strawman solutions, respectively;3) The insertion throughput of PISketch is around 1.23 and 16.5 times higher than two strawman solutions, respectively. Moreover, we implement two concrete cases of PISketch through end-to-end experiments. All of our codes are available at GitHub. © 2022 ACM.

关键词： infrequent items persistent items sketch weight fusion

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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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IEEE International Conference on Systems, Man and Cybernetics

作者： Feiyu Wang Jian-tao Zhou College of Computer Science Inner Mongolia University Hohhot Inner Mongolia China 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 Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China

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.

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BMDP:Blockchain-Based Multi-Cloud Storage data Provenance

BMDP:Blockchain-Based Multi-Cloud Storage Data Provenance

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International Conference on Computer Supported Cooperative Work in Design

作者： Feiyu Wang Jian-Tao Zhou Xu Guo College of Computer Science Inner Mongolia University Hohhot Inner Mongolia China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

In a multi-cloud storage system, provenance data records all operations and ownership during its lifecycle, which is critical for data security and audibility. However, recording provenance data also poses some challenging security and storage issues. In this paper, we present a secure and efficient multi-cloud storage data source scheme, BMDP. We use blockchain technology to ensure the secure storage of provenance data and design a smart contract to utilize the provenance data to ensure the proper operation of the multi-cloud storage system. Finally, we analyze the scheme’s safety and do simulation experiments to show that the scheme has practicality.

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KVSAgg: Secure Aggregation of Distributed Key-Value Sets

KVSAgg: Secure Aggregation of Distributed Key-Value Sets

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International Conference on data engineering

作者： Yuhan Wu Siyuan Dong Yi Zhou Yikai Zhao Fangcheng Fu Tong Yang Chaoyue Niu Fan Wu Bin Cui School of Computer Science and National Engineering Laboratory for Big Data Analysis Technology and Application Peking University Beijing China Peng Cheng Laboratory Shenzhen China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

In global data analysis, the central server needs the global statistic of the user data stored in local clients. In such cases, an Honest-but-Curious central server might put user privacy at risk in trying to collect individual statistics of each user. In response, the secure aggregation provides a solution for calculating global statistics without revealing users’ privacy data. However, existing secure aggregation protocols only focus on the data in the form of vectors or common sets, which limits their application scope. We formalize a general problem—key-value set secure aggregation—that not only includes secure vector aggregation and private set union but also supports more applications. To address the proposed problem, we devise our solution (called the KVSAgg framework) that promises satisfactory performance in security, efficiency, and accuracy. Our key technique is a homomorphic transform algorithm (called HyperIBLT) that is not only capable of bidirectionally transforming data between key-value sets and vectors, but also able to transform sum operation of sets to addition of vectors. We implement KVSAgg on both CPU and GPU platforms and perform the evaluation on three use cases including federated learning, distributed data counting, and finding global hot items. Compared with our baselines, KVSAgg simultaneously achieves the best security, efficiency higher by orders of magnitude, and zero-error in nearly all cases. All codes are open-source anonymously.

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REncoder: A Space-Time Efficient Range Filter with Local Encoder

REncoder: A Space-Time Efficient Range Filter with Local Enc...

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International Conference on data engineering

作者： Ziwei Wang Zheng Zhong Jiarui Guo Yuhan Wu Haoyu Li Tong Yang Yaofeng Tu Huanchen Zhang Bin Cui School of Computer Science and National Engineering Laboratory for Big Data Analysis Technology and Application Peking University Beijing China Peng Cheng Laboratory Shenzhen China ZTE Corporation Beijing China Tsinghua University Beijing China

A range filter is a data structure to answer range membership queries. Range queries are common in modern applications, and range filters have gained rising attention for improving the performance of range queries by ruling out empty range queries. However, state-of-the-art range filters, such as SuRF and Rosetta, suffer either high false positive rate or low throughput. In this paper, we propose a novel range filter, called REncoder. It organizes all prefixes of keys into a segment tree, and locally encodes the segment tree into a Bloom filter to accelerate queries. REncoder supports diverse workloads by adaptively choosing how many levels of the segment tree to store. We theoretically prove that the error of REncoder is bounded and derive the asymptotic space complexity under the bounded error. We conduct extensive experiments on both synthetic datasets and real datasets. The experimental results show that REncoder outperforms all state-of-the-art range filters.

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