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Phase spectrum based automatic ship detection in synthetic aperture radar images

Journal of Ocean engineering and Science 2021年第2期6卷 185-195页

作者： Miaohui Zhang Baojun Qiao Ming Xin Bo Zhang Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifengChina Institute of Data and Knowledge Engineering Henan UniversityKaifengChina School of Computer Science and Engineering Beihang UniversityBeijingChina

This paper proposes an automatic ship detection approach in Synthetic Aperture Radar(SAR)Images using phase *** proposed method mainly contains two stages:Firstly,sea-land segmentation of SAR Images is one of the key stages for SAR image application such as sea-targets detection and recognition,which are easily detected only in sea *** order to eliminate the influence of land regions in SAR images,a novel land removing method is *** removing method employs a Harris corner detector to obtain some image patches belonging to land,and the probability density function(PDF)of land area can be estimated by these ***,an appropriate land segmentation threshold is accordingly ***,an automatic ship detector based on phase spectrum is *** proposed detector is free from various idealized assumptions and can accurately detect ships in SAR *** results demonstrate the efficiency of the proposed ship detection algorithm in diversified SAR images.

关键词： Ship detection saliency detection phase spectrum sea-land segmentation

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Self-assembly of a new 3D platelike ternary-oxo-cluster:An efficient catalyst for the synthesis of pyrazoles

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Chinese Chemical Letters 2022年第1期33卷 354-357页

作者： Guoping Yang Yufeng Liu Xiaoling Lin Bangming Ming Ke Li Changwen Hu Jiangxi Province Key Laboratory of Synthetic Chemistry Jiangxi Key Laboratory for Mass Spectrometry and InstrumentationEast China University of TechnologyNanchang 330013China Jiangxi Engineering Technology Research Center of Nuclear Geoscience Data Science and System East China University of TechnologyNanchang 330013China Key Laboratory of Cluster Science of Ministry of Education School of Chemistry and Chemical EngineeringBeijing Institute of TechnologyBeijing 100081China

A new metal-oxo-clusters-based inorganic framework[NaCo_(2)Mo_(2)O_(7)(OH)_(3)]_(n)(NaCoMo),named as 3D platelike ternary-oxo-cluster,has been hydrothermally synthesized and characterized by single-crystal X-ray diffraction structure analysis,FT-IR spectroscopy,powder X-ray diffraction(PXRD),scanning electron microscope(SEM),energy-dispersive X-ray spectroscopy(EDS)analyses,X-ray photoelectron spectroscopy(XPS)and thermogravimetric analysis(TGA).Structure analysis reveals that there are no classical building units in NaCoMo,and the asymmetric units of NaCoMo are directly extended into a new platelike 3D *** functional theory calculations(DFT)indicates that the crystal formation process is exothermic and the structure is extremely *** addition,the compound presents excellent catalytic activity in the condensation and cyclization reaction of sulfonyl hydrazides and 1,3-diketones to synthesize pyrazoles,and the yield of the desired product is up to 99%.The successful synthesis of NaCoMo represents the discovery of a new kind of non-classical polyoxometalates.

关键词： Polyoxometalates Oxo clusters 3D inorganic framework Condensation reaction Pyrazoles

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CLASCN： Candidate Network Selection for Efficient Top-κ keyword Queries over databases

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Journal of Computer Science & Technology 2007年第2期22卷 197-207页

作者：张俊彭朝晖王珊聂惠静 School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing 100872 China Computer Science and Technology College Dalian Maritime University Dalian 116026 China

keyword Search Over Relational databases （KSORD） enables casual or Web users easily access databases through free-form keyword queries. Improving the performance of KSORD systems is a critical issue in this area. In this paper, a new approach CLASCN （Classification, Learning And Selection of Candidate Network） is developed to efficiently perform top-κ keyword queries in schema-graph-based online KSORD systems. In this approach, the Candidate Networks （CNs） from trained keyword queries or executed user queries are classified and stored in the databases, and top-κ results from the CNs are learned for constructing CN Language Models （CNLMs）. The CNLMs are used to compute the similarity scores between a new user query and the CNs from the query. The CNs with relatively large similarity score, which are the most promising ones to produce top-κ results, will be selected and performed. Currently, CLASCN is only applicable for past queries and New All-keyword-Used （NAU） queries which are frequently submitted queries. Extensive experiments also show the efficiency and effectiveness of our CLASCN approach.

关键词： relational database keyword search top-κ query candidate network

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An integrated classification model for massive short texts with few words 19

An integrated classification model for massive short texts w...

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2019 International Conference on Robotics Systems and Vehicle Technology, RSVT 2019

作者： Tang, Xuetao Zhu, Yi Hu, Xuegang Li, Peipei Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology China

ISBN: (纸本)9781450362429

The excellent performance of short texts classification has emerged in the past few years. However, massive short texts with few words like invoice data are different with traditional short texts like tweets in its no contextual and less semantic information, which hinders the application of conventional classification algorithms. To address these problems, we propose an integrated classification model for massive short texts with few words. More specifically, the word embedding model is introduced to train the word vectors of massive short texts with few words to form the feature space, and then the vector representation of each instance in texts is trained based on sentence embedding. With this integrated model, higher level representations are learned from massive short texts with few words. It can boost the performance of the base subsequent classifiers such as K-Nearest Neighbor. Extensive experiments conducted on dataset including 16 million real data demonstrate the superior classification performance of our proposed model compared with all competing state-of-the-art models. © 2019 Association for Computing Machinery.

关键词： Embeddings

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Analysis of bandwagon and average hybrid attack model against trust-based recommender systems

Analysis of bandwagon and average hybrid attack model agains...

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2011 International Conference on Management of e-Commerce and e-Government, ICMeCG 2011

作者： Zhang, Fuguo School of Information and Technology Jiangxi University of Finance and Economics Nanchang China Institute of Information Resource Management Jiangxi University of Finance and Economics Nanchang China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

ISBN: (纸本)9780769545448

Recommender systems have been accepted as a vital application on the web by offering product advice or information that users might be interested in. Despite its success, similarity-based collaborative filtering suffers from some significant limitations, such as scalability, sparsity and recommendation attack. Prior work has shown incorporating trust mechanism into traditional collaborative filtering recommender systems can improve these limitations. However, trust-based recommender systems are also known to be vulnerable to profile infection attacks. Malicious users can inject a large number of biased profiles into such a system in order to make recommendations that favor or disfavor given items. In this paper, we propose a bandwagon and average hybrid attack model and analysis the effectiveness of the attack model against topic-level trust-based recommender algorithm. The results of our experiments conducted on well-known dataset show that the hybrid attack model is more effective than other attack models. © 2011 IEEE.

关键词： Recommender systems

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Cutting Learned Index into Pieces: An In-depth Inquiry into Updatable Learned Indexes 39

Cutting Learned Index into Pieces: An In-depth Inquiry into ...

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39th IEEE International Conference on data engineering, ICDE 2023

作者： Ge, Jiake Shi, Boyu Chai, Yanfeng Luo, Yuanhui Guo, Yunda He, Yinxuan Chai, Yunpeng Moe Key Laboratory of Data Engineering and Knowledge Engineering China Renmin University of China School of Information China

ISBN: (纸本)9798350322279

Numerous high-performance updatable learned indexes have recently been designed to support the writing requirements in practical systems. Researchers have proposed various strategies to improve the availability of updatable learned indexes. However, it is unclear which strategy is more profitable. Therefore, we deconstruct the design of learned indexes into multiple dimensions and in-depth evaluate their impacts on the overall performance, respectively. Through the in-depth exploration of learned indexes, we reckon that the approximation algorithm is the most crucial design dimension for improving the performance of the learned indexes rather than the popular works that focus on the learned index structure. Moreover, this paper makes a comprehensive end-to-end evaluation based on a high-performance key-value store to answer people's concerns about which learned index is better and whether learned indexes can outperform traditional ones. Finally, according to end-to-end and in-depth evaluation results, we give some constructive suggestions on designing a better learned index in these dimensions, especially how to design an excellent approximate algorithm to improve the lookup and insertion performance of learned indexes. © 2023 IEEE.

关键词： Approximation algorithms

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Moment+: Mining closed frequent itemsets over data stream

Moment+: Mining closed frequent itemsets over data stream

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4th International Conference on Advanced data Mining and Applications, ADMA 2008

作者： Li, Haifeng Chen, Hong Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)3540881913

Closed frequent itemsets(CFI) mining uses less memory to store the entire information of frequent itemsets thus is much suitable for mining stream. In this paper, we discuss recent CFI mining methods over stream and presents an improved algorithm Moment+ based on the existent one Moment. Moment+ focuses on the problem of mining CFI over data stream sliding window and proposes a new structure Extended Closed Enumeration Tree(ECET) to store the CFIs and nodes' BPN which is introduced to reduce the search space, with which new mining method is designed to mine more rapidly with little memory cost sacrifice. The experimental results show that this method is effective and efficient. © 2008 Springer-Verlag Berlin Heidelberg.

关键词： Mining

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A sampling-based algorithm for approximating maximum average value region in wireless sensor network

A sampling-based algorithm for approximating maximum average...

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International Workshop on Scheduling and Resource Management for Parallel and Distributed Systems

作者： Zhang, Hao Wu, Zhongbo Li, Deying Chen, Hong School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China

ISBN: (纸本)9780769541570

in wireless sensor network, sensory readings are often noisy due to the imprecision of measuring hardware and the disturbance of deployment environment, so it is often inaccurate if we use individual sensor readings to answer queries. In this paper, we consider a useful application of sensor network: maximum average value region query. This query returns the region with the maximum average value among all possible regions in the network, where the region is a fix-sized circle pre-defined by users. Using the average value of a region to answer the query, noises between sensors will be neutralized with each other, which will make the results more reliable. However, because of the huge amount of possible regions in the network, it is costly to process the query exactly. Therefore, w e propose a sampling-based algorithm AMAVR to deal with the problem approximately. AMAVR uses a background value to prun e the useless regions which cannot be the result. A further optimization strategy is also given to handle the situation that, background value based filter does not work when some individual sensor nodes have higher values than their neighbors. By using both of the two techniques, the scale of the sampling population can be effectively reduced, that is, we cost less energy to get a satisfying result. At last, the conducted simulations demonstrate the energy efficiency of the proposed methods in our paper. © 2010 IEEE.

关键词： Sensor nodes

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A framework for personalized management of domain ontologies

A framework for personalized management of domain ontologies

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2012 IEEE/ACIS 11th International Conference on Computer and Information Science, ICIS 2012

作者： Liu, Runnan Wang, Puwei Zheng, Shixian Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Ministry of Education Beijing 100872 China

ISBN: (纸本)9780769546940

As the sharable and reusable domain knowledge, domain ontology increasingly serves as a foundation for semantic Web. Personalized management of domain ontologies is to provide personalized views of domain ontologies to users during run time according to user preferences. It helps that users can focus on their interested parts, instead of the whole. It increases the efficiency of ontology-based application. This paper proposes a framework for personalized management of domain ontologies. In this framework, a user model firstly is introduced to describe user preferences. Secondly, domain ontologies are decomposed into moderate-scale modules with high cohesion and low coupling. During run time, modules that a user is interested in are selected out based on the user's preferences. Finally, the selected modules are combined to construct personalized views of domain ontologies for the user. © 2012 IEEE.

关键词： Ontology

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Self-Adaptive Imbalanced Domain Adaptation With Deep Sparse Autoencoder

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 1293-1304页

作者： Zhu, Yi Wu, Xindong Li, Yun Qiang, Jipeng Yuan, Yunhao Yangzhou University School of Information Engineering Yangzhou225012 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education of China Hefei230002 China

Domain adaptation aims to transfer knowledge between different domains to develop an effective hypothesis in the target domain with scarce labeled data, which is an effective method for remedying the problem of labeled data requirement in deep learning. In reality, it is unavoidable that the dataset has a large gap in the number of positive and negative instances across different categories in source and target domains, which is the imbalanced domain adaptation problem. However, since the imbalanced degree always varies greatly in different source- and target-domain datasets, most of the existing imbalanced domain adaptation models fix the imbalanced parameters, which cannot adapt to the change of the proportion between positive and negative instances in different domains. To address this problem, in this article, we propose a self-adaptive imbalanced domain adaptation method via a deep sparse autoencoder, which can adjust the model automatically according to the imbalanced extent for bridging the chasm of domains. More specifically, the self-adaptive imbalanced cross-entropy loss is designed for emphasizing more on minority categories and compensating the bias of training loss automatically. In addition, to alleviate the deficient problem of labeled data, we further propose the unlabeled information incorporating method by minimizing the distribution discrepancy of high-level representation space between the source and target domains. Experiments on several real-world datasets demonstrate the effectiveness of our method compared to other state-of-the-art methods. © 2020 IEEE.

关键词： Noise abatement

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