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A Heuristic Sampling Method for Maintaining the Probability Distribution

Journal of Computer Science & Technology 2021年第4期36卷 896-909页

作者： Jiao-Yun Yang Jun-Da Wang Yi-Fang Zhang Wen-Juan Cheng Lian Li Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei 230601China National Smart EldeTca/re International Science and Technology Coopemtion Base Hefei University of Technology Hefei 230601China School of Computer Science and Information Engirwering Hefei University of TechnologyHefei 230601China School of Mathematics Hefei University of TechnologyHefei 230601China

Sampling is a fundamental method for generating data *** many data analysis methods are developed based on probability distributions,maintaining distributions when sampling can help to ensure good data analysis ***,sampling a minimum subset while maintaining probability distributions is still a *** this paper,we decompose a joint probability distribution into a product of conditional probabilities based on Bayesian networks and use the chi-square test to formulate a sampling problem that requires that the sampled subset pass the distribution test to ensure the ***,a heuristic sampling algorithm is proposed to generate the required subset by designing two scoring functions:one based on the chi-square test and the other based on likelihood *** on four types of datasets with a size of 60000 show that when the significant difference level,a,is set to 0.05,the algorithm can exclude 99.9%,99.0%,93.1%and 96.7%of the samples based on their Bayesian networks-ASIA,ALARM,HEPAR2,and ANDES,*** subsets of the same size are sampled,the subset generated by our algorithm passes all the distribution tests and the average distribution difference is approximately 0.03;by contrast,the subsets generated by random sampling pass only 83.8%of the tests,and the average distribution difference is approximately 0.24.

关键词： Bayesian network chi-square test sampling probability distribution

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Efficient approximation of the maximal preference scores by lightweight cubic views

Efficient approximation of the maximal preference scores by ...

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15th International Conference on Extending database Technology, EDBT 2012

作者： Chen, Yueguo Cui, Bin Du, Xiaoyong Tung, Anthony K. H. Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE China Department of Computer Science Peking University Beijing China School of Information Renmin University of China Beijing China School of Computing National University of Singapore Singapore Singapore

ISBN: (纸本)9781450307901

Given a multi-features data set, a best preference query (BPQ) computes the maximal preference score (MPS) that the tuples in the data set can achieve with respect to a preference function. BPQs are very useful in applications where users want to efficiently check whether many individual data sets contain tuples that are of interest to them. Although a BPQ can be naïvely answered by issuing a top-1 query and computing the score from the returned tuple, doing so might require to load a larger number of tuples externally. In this paper, we address the problem of efficient processing BPQs by using lightweight cubic (3-dimensional) views. With these in-memory views, the MPSs of BPQs can be efficiently estimated with an error bound guaranteed, by paying only a small number of I/Os. Extensive experimental results over real-life data sets show that our approximate solution can achieve the efficiency of up to three orders of magnitude compared to exact solutions, with certain accuracy guaranteed. © 2012 ACM.

关键词： best preference score materialized views preference query top-k query

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Windows Attention Based Pyramid Network for Food Segmentation 7

Windows Attention Based Pyramid Network for Food Segmentatio...

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7th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2021

作者： Dong, Xiaoxiao Wang, Wei Li, Haisheng Cai, Qiang Beijing Technology and Business University School of Computer Science and Engineering Beijing10048 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing China National Engineering Laboratory for Agri-Product Quality Traceability China

ISBN: (纸本)9781665441490

Recently, food segmentation has obtained growing attention in the field of computer vision for its great potential in human health. Most of existing methods utilize deep visual features extracting from Convolutional Neural Networks (CNNs) for food segmentation. However, these works ignore characteristics of food images and are thus difficult to achieve optimal segmentation performance. Compared with general image segmentation, food images usually do not exhibit unique spatial layout and common semantic patterns. In this paper, we address the food image segmentation task by capturing richer contextual and boundary information. The previous works capture image representation by multi-scale feature fusion, we propose a Windows Attention based Pyramid Network (WAPNet) to adaptively combine local features with global dependencies. Specifically, WAPNet combines Feature Pyramid Network (FPN) with Window Attention to weight multi-scale features, and then extract richer marginal information. In addition, we utilize a multimodality pre-training approach Recipe Learning Module (ReLeM) that explicitly provides segmentation model with rich semantic food knowledge. And by introducing Locality and Windows design, calculating self-attention according to Windows, We demonstrate promising performance on a new proposed food image benchmark for semantic segmentation. © 2021 IEEE.

关键词： Semantics

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Automatic extraction of foreground objects from Mars images

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Geo-Spatial Information Science 2012年第1期15卷 17-25页

作者： WANG Shuliang LIU Chang WU Shangru NIE Qianqian WANG Yongtao ZENG Shi ZHU Haifeng International School of Software Wuhan University129 Luoyu RoadWuhan 430079China Key Laboratory of Data Mining and Knowledge Engineering Inner Mongolia Autonomous RegionInner Mongolia UniversityHohhot 010021China Department of Geography East Michigan UniversityYpsilantiMI 48197USA

A novel method is proposed to automatically extract foreground objects from Martian surface *** characteristics of Mars images are distinct,*** illumination,low contrast between foreground and background,much noise in the background,and foreground objects with irregular *** the context of these characteristics,an image is divided into foreground objects and background *** filtering is first applied to rectify ***,wavelet transformation enhances contrast and denoises the ***,edge detection and active contour are combined to extract contours regardless of the shape of the *** results show that the method can extract foreground objects from Mars images automatically and accurately,and has many potential applications.

关键词： automatic object extraction Mars images homomorphic filtering wavelet transformation active contour edge detection

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Generalized Unique Game Problem

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Journal of Computational Information Systems 2012年第16期8卷 6551-6557页

作者： Cui, Peng Key Laboratory of Data Engineering and Knowledge Engineering School of Information Resource Management Renmin University of China Beijing 100872 China

In this paper, the author defines Generalized Unique Game Problem (GUGP), where weights of the edges are allowed to be negative. Two special types of GUGP are illuminated, GUGP-NWA, where the weights of all edges are negative, and GUGP-PWT (ρ), where the total weight of all edges are positive and the negative-positive ratio is at most ρ. The author investigates the counterpart of the Unique Game Conjecture on GUGP-PWT (ρ). The author shows that Unique Game Conjecture on GUGP-PWT (1) holds true, and Unique Game Conjecture on GUGP-PWT (1=2) holds true, if the 2-to-1 Conjecture holds true. The author poses an open problem whether Unique Game Conjecture holds true on GUGP-PWT (ρ) with 0 < ρ < 1. © 2012 Binary Information Press.

关键词： Approximation algorithms

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High-Quality Noise Detection for knowledge Graph Embedding with Rule-Based Triple Confidence 18th

High-Quality Noise Detection for Knowledge Graph Embedding w...

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18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Hong, Yan Bu, Chenyang Wu, Xindong Ministry of Education Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Mininglamp Academy of Sciences Mininglamp Technology Beijing China

ISBN: (纸本)9783030891879

knowledge representation learning is usually used in knowledge reasoning and other related fields. Its goal is to use low-dimensional vectors to represent the entities and relations in a knowledge graph. In the process of automatic knowledge graph construction, the complexity of unstructured text and the incorrect text may make automatic construction tools unable to accurately obtain the semantic information in the text. This leads to high-quality noise with matched entity types but semantic errors. Currently knowledge representation learning methods assume that the knowledge in knowledge graphs is completely correct, and ignore the noise data generated in the process of automatic construction of knowledge graphs, resulting in errors in the vector representation of entities and relations. In order to reduce the negative impact of noise data on the construction of a representation learning model, in this study, a high-quality noise detection method with rule information is proposed. Based on the semantic association between triples in the same rule, we propose the concept of rule-based triple confidence. The calculation strategy of triple confidence is designed inspired by probabilistic soft logic (PSL). The influence of high-quality noise data in the training process of the model can be weakened by this confidence. Experiments show the effectiveness of the proposed method in dealing with high-quality noise. © 2021, Springer Nature Switzerland AG.

关键词： knowledge graph

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Fast & scalable distributed set similarity joins for big data analytics 33

Fast & scalable distributed set similarity joins for big dat...

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33rd IEEE International Conference on data engineering, ICDE 2017

作者： Rong, Chuitian Lin, Chunbin Silva, Yasin N. Wang, Jianguo Lu, Wei Du, Xiaoyong Tianjin Polytechnic University China Arizona State University United States University of California San Diego United States Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China China

ISBN: (纸本)9781509065431

Set similarity join is an essential operation in big data analytics, e.g., data integration and data cleaning, that finds similar pairs from two collections of sets. To cope with the increasing scale of the data, distributed algorithms are called for to support large-scale set similarity joins. Multiple techniques have been proposed to perform similarity joins using MapReduce in recent years. These techniques, however, usually produce huge amounts of duplicates in order to perform parallel processing successfully as MapReduce is a shared-nothing framework. The large number of duplicates incurs on both large shuffle cost and unnecessary computation cost, which significantly decrease the performance. Moreover, these approaches do not provide a load balancing guarantee, which results in a skewness problem and negatively affects the scalability properties of these techniques. To address these problems, in this paper, we propose a duplicate free framework, called FS-Join, to perform set similarity joins efficiently by utilizing an innovative vertical partitioning technique. FS-Join employs three powerful filtering methods to prune dissimilar string pairs without computing their similarity scores. To further improve the performance and scalability, FS-Join integrates horizontal partitioning. Experimental results on three real datasets show that FS-Join outperforms the state-of-Theart methods by one order of magnitude on average, which demonstrates the good scalability and performance qualities of the proposed technique. © 2017 IEEE.

关键词： MapReduce

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Vulnerabilities static detection for Web applications with false positive suppression

Vulnerabilities static detection for Web applications with f...

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2010 IEEE International Conference on Information Theory and Information Security, ICITIS 2010

作者： Huang, Jianjun Liang, Bin Zhong, Jiagui Wang, Qianqian Cai, Jingjing Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE Beijing 100872 China School of Information Renmin University of China Beijing 100872 China Beijing Venustech Cybervision Co. Ltd Beijing 100094 China

ISBN: (纸本)9781424469406

Web applications become more and more important, and the corresponding security problems have been concerned about. This paper presents TASA, an ASP static analyzer, which employs a path-sensitive, inter-procedural and context-sensitive data flow analysis, mainly concerning the taint propagation and sanitization. This paper also discusses some techniques used in TASA, such as sanitization routines modeling, ASP specific features, alias analysis and path-related routines modeling, to prune false positives. Experiments on four open source applications show that TASA has a rate of false positive of 4.98% and it can avoid certain false warnings owing to the proposed approaches. © 2010 IEEE.

关键词： data flow analysis

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3D Super-Resolution Reconstruction Based on Multi-view Representation

3D Super-Resolution Reconstruction Based on Multi-view Repre...

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Chinese Intelligent Systems Conference, CISC 2019

作者： Du, Yujia Zheng, Yanping Li, Haisheng Tan, Li School of Computer and Information Engineering Beijing Technology and Business University Beijing100048 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing China National Engineering Laboratory for Agri-Product Quality Traceability Beijing China

ISBN: (纸本)9789813296978

Despite the progressed achievement on three-dimensional generation, the results of the research on high-resolution models are not satisfactory. In order to generate 3D objects in high resolution, we implement a 3D model super-resolution reconstruction system. First, a set of orthogonal depth maps are acquired based on each input 3D model, which has a low resolution. Then we upscale these images by training a super-resolution network. The high-resolution orthogonal depth maps we obtained include rich information and are used to carve models in high resolution. Absolutely, our system is a reconstruction process from images to high quality 3D objects in voxel space. Considering the effect of the resolution of the image on the reconstruction results, we design a suitable super-resolution network to obtain a high-resolution depth map. We have conducted experiment on a subset of ShapeNet dataset and the experimental results show the effectiveness of our method. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Three dimensional computer graphics

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3D Shape Classification Based on Point Convolutional Neural Network Combining Multi-geometric Features

3D Shape Classification Based on Point Convolutional Neural ...

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Chinese Intelligent Systems Conference, CISC 2019

作者： Zeng, Guang Wu, Yujuan Li, Haisheng Tan, Li School of Computer and Information Engineering Beijing Technology and Business University Beijing100048 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing China National Engineering Laboratory for Agri-Product Quality Traceability Beijing China

ISBN: (纸本)9789813296978

Point cloud has become increasingly prevalent in 3D computer vision tasks owing to its simplicity, flexibility and powerful representation capability. However, for 3D shapes classification, how to obtain discriminative and effective feature descriptors from point clouds is still a challenging task. In this paper, we implement a point convolutional neural network combining multi-geometric feature to classify 3D shapes. We firstly obtain hidden geometric information from raw input point. And then a set of point information that consists of three-dimensional coordinates, normal vectors and curvatures is input to network. The extracted feature combining local geometric information can represent 3D shapes more effectively. Experiments on the Modelnet40 dataset show the effectiveness of the proposed method. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Convolution

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