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Image Segmentation Using Fuzzy C-means Optimized by Ant Lion Optimization 10

Image Segmentation Using Fuzzy C-means Optimized by Ant Lion...

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10th IEEE international Conference on Intelligent data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Jin, Can Ye, Zhiwei Yan, Lingyu Cao, Ye Zhang, Aixin Ma, Lie Hu, Xiang Hu, Jiwei Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China Wuhan Jingzhi Technol Co Ltd Wuhan Hubei Peoples R China Wuhan FiberHome Tech Serv Co Ltd Wuhan Peoples R China Fujian Prov Univ Fujian Prov Key Lab Data Intens Comp Fuzhou Peoples R China Fujian Prov Univ Key Lab Intelligent Comp & Informat Proc Fuzhou Peoples R China

ISBN: (纸本)9781728140698

Image segmentation is the indispensable part in the field of computer vision. there are tremendous methods for handling this task such as Otsu-thresholding and Fuzzy C-means (FCM). However, the segmentation results of the images are occasionally unsatisfactory due to the presence of noise. In this paper, two kinds of spatial information consisting of the relative position information and the intensity information of the neighborhood pixels in an image are taken into consideration in constructing the objective function in FCM. Moreover, Ant Lion Optimization, one of the recently proposed optimization algorithms is utilized to optimize the relevant index. Bio-inspired ALO has the robust ability to find optimal parameters in search spaces. So the proposed approach to image segmentation based on Fuzzy C-Means (FCM) and Ant Lion Optimization (ALO) may alleviate this problem to a certain degree. A series of experimental validation has been implemented for demonstrating the performance of the proposed approach in the end of the paper.

关键词： Image segmentation Fuzzy c-means spatial information Ant lion optimization

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Index structures for information filtering under the vector space model

Index structures for information filtering under the vector ...

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Proceedings of the 10th international Conference on data Engineering

作者： Yan, Tak W. Garcia-Molina, Hector Stanford Univ Stanford United States

ISBN: (纸本)0818654007

With the ever increasing volumes of electronic information generation, users of information systems are facing an information overload. It is desirable to support information filtering as a complement to traditional retrieval mechanism. the number of users, and thus profiles (representing users' long-term interests), handled by an information filtering system is potentially huge, and the system has to process a constant stream of incoming information in a timely fashion. the efficiency of the filtering process is thus an important issue. In this paper, we study what data structures and algorithms can be used to efficiently perform large-scale information filtering under the vector space model, a retrieval model established as being effective. We apply the idea of the standard inverted index to index user profiles. We devise an alternative to the standard inverted index, in which we, instead of indexing every term in a profile, select only the significant ones to index. We evaluate their performance and show that the indexing methods require orders of magnitude fewer I/Os to process a document than when no index is used. We also show that the proposed alternative performs better in terms of I/O and CPU processing time in many cases.

关键词： Signal filtering and prediction

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Artificial neural networks for non destructive testing of concrete structures

Artificial neural networks for non destructive testing of co...

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10th international Conference on Engineering Applications of Neural Networks, EANN 2007

作者： Cannas, Barbara Carcangiu, Sara Cau, Francesca Fanni, Alessandra Montisci, Augusto Testoni, Pietro Department of Electrical and Electronic Engineering University of Cagliari Piazza d'Armi 09123 Cagliari Italy

In this paper, the determination of defects in concrete structures using an ultrasound technique is discussed. A diagnostic model for concrete pillars by means of Multi Layer Perceptron neural networks is developed to locate and classify the defects. Finite Elements numerical techniques have been used to model a concrete pillar of specified size (i.e., rectangular cross section and 2 meters in span) affected by defects of different position and size. the numerical analyses enable to obtain several received signals containing the fault information. these signals have been processed by a feature extractor system, whose purpose is to reduce the data dimensionality and to compute suitable features. Results showed good accuracy in the identification of the position and entity of the faults.

关键词： Concrete construction

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10th international workshop on Artificial Intelligence and Security (AISec 2017) 17

10th International Workshop on Artificial Intelligence and S...

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24th ACM-SIGSAC Conference on Computer and Communications Security (ACM CCS)

作者： Biggio, Battista Freeman, David Miller, Brad Sinha, Arunesh Univ Cagliari Cagliari Italy Facebook Inc Menlo Pk CA USA Google Inc Mountain View CA USA Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781450349468

Artificial Intelligence (AI) and Machine Learning (ML) provide a set of useful analytic and decision-making techniques that are being leveraged by an ever-growing community of practitioners, including many whose applications have security-sensitive elements. However, while security researchers often utilize such techniques to address problems and AI/ML researchers develop techniques for Big data analytics applications, neither community devotes much attention to the other. Within security research, AI/ML components are usually regarded as black-box solvers. Conversely, the learning community seldom considers the security/privacy implications entailed in the application of their algorithms when they are designing them. While these two communities generally focus on different directions, where these two fields do meet, interesting problems appear. Researchers working in this intersection have raised many novel questions for both communities and created a new branch of research known as secure learning. the AISec workshop has become the primary venue for this unique fusion of research.

关键词： Adversarial Learning Secure Learning Malware Detection

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SE-UXNet: A medical imaging segmentation network based on attention mechanism 10

SE-UXNet: A medical imaging segmentation network based on at...

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2020 10th international workshop on Computer Science and Engineering, WCSE 2020

作者： Huang, Zhongyu Hu, Wenxin Zheng, Wei School of Data Science and Engineering East China Normal University China Information Technology services East China Normal University China

ISBN: (纸本)9789811447877

Automatic medical image segmentation is an important part of medical image analysis, and plays an indispensable role in computer-aided diagnosis. Recently, FCN (Fully Convolutional Network) and U-Net have become the main frameworks for image segmentation task. Based on U-Net, the U-Net++ is proposed to predict robust segmentation maps. However, it is difficult for both U-Net and U-Net++ to obtain better results as the model depth grows. In this paper we propose a new medical image segmentation network SE-UXNet using attention mechanism. this model bridges the encoder-decoder models of two different structures to increase model capacity, and recalibrates the weight of the features in the bridge process by SE blocks. Using the Dice coefficient and mIoU as evaluation criteria, we evaluate our model by performing nuclei, liver, prostate, polyps and pulmonary nodule segmentation tasks. the results show that, the Dice coefficients of the above segmentation tasks using SE-UXNet were 92.75%, 90.49%, 90.34%, 95.33% and 73.44%, respectively, all higher compared to results of the U-Net and U-Net++. the experimental results also show that the method we proposed improves the accuracy of medical image segmentation. © WCSE 2020.

关键词： Image segmentation

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Intellectual analysis and basic modeling of complex threats 10

Intellectual analysis and basic modeling of complex threats

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10th Anniversary international Scientific and Technical Conference on Secure Information Technologies, BIT 2019

作者： Korneev, Nikolai Merkulov, Vyacheslav Faculty of Integrated Security of Fuel and Energy Complex Gubkin Russian State University of Oil and Gas National Research University Moscow Russia Department of Data Analysis Decision-Making and Financial Technology Financial University under the Government of the Russian Federation Moscow Russia

the paper describes the basic principles of complex threats modeling, and the task of complex threats detection is formalized. the proposed modeling principles are based on the idea of identifying the links between elementary threats as part of a complex one. As an example, the process of constructing a complex threat model based on the proposed modeling rules is given. Based on the examples presented in the work, the paper includes the description of tasks while working with complex threats: the tasks of complex threats detection, the identification of their inner structure and purposes of the implementation. Based on the formulated principles of basic modeling, the paper also gives a formal statement of complex threats detection problem, which explains the possibility for applying data mining algorithms and big data processing technologies in the construction of protection systems against complex threats and developing the neurographic theory of complex security. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0)

关键词： Big data

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10th international Conference on Similarity Search and Applications, SISAP 2017

10th International Conference on Similarity Search and Appli...

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10th international Conference on Similarity Search and Applications, SISAP 2017

ISBN: (纸本)9783319684734

the proceedings contain 23 papers. the special focus in this conference is on Similarity Search and Applications. the topics include: Cost models and scheduling policies for quality-controlled similarity queries;cache and priority queue based approximation technique for a stream of similarity search queries;a benchmarking tool for approximate nearest neighbor algorithms;sketches with unbalanced bits for similarity search;an extreme-value-theoretic foundation for similarity applications;multivariate analysis and distributional support;high-dimensional simplexes for supermetric search;improving k-NN graph accuracy using local intrinsic dimensionality;dynamic time warping and the (windowed) dog-keeper distance;fast similarity search with the earth mover's distance via feasible initialization and pruning;a new perspective on the tree edit distance;good and bad neighborhood approximations for outlier detection ensembles;scalable similarity search for molecular descriptors;self-indexed motion planning;practical space-efficient data structures for high-dimensional orthogonal range searching;semantic similarity group by operators for metric data;succinct quadtrees for road data;on competitiveness of nearest-neighbor-based music classification;dataset proximity mining for governing the data lake;similarity-based browsing through large lists (extended abstract).;malware discovery using behaviour-based exploration of network traffic and concepts and challenges for effective retrieval considering users, tasks, and data.

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structures of covariance matrix in handwritten character recognition

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Joint IAPR 10th international workshop on Structural and Syntactic Pattern Recognition, SSPR 2004 and 5th international workshop on Statistical Techniques in Pattern Recognition, SPR 2004

作者： Raudys, Šarūnas Iwamura, Masakazu Vilnius Gediminas Technical University Saul tekio 11 Vilnius Lithuania Tohoku University Aoba 05 Aramaki Aoba-ku Sendai980-8579 Japan

ISBN: (纸本)9783540225706

the integrated approach is a classifier established on statistical estimator and artificial neural network. this consists of preliminary data whitening transformation which provides good starting weight vector, and fast training of single layer perceptron (SLP). If sample size is extremely small in comparison with dimensionality, this approach could be ineffective. In the present paper, we consider joint utilization of structures and conventional regularization techniques of sample covariance matrices in order to improve recognition performance in very difficult case where dimensionality and sample size do not differ essentially. the techniques considered reduce a number of parameters estimated from training set. We applied our methodology to handwritten Japanese character recognition and found that combination of the integrated approach, conventional regularization and various structurization methods of covariance matrix outperform other methods including optimized Regularized Discriminant Analysis (RDA). © Springer-Verlag Berlin Heidelberg 2004.

关键词： Covariance matrix

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Optimization of Digital Twins in the workshop 10th

Optimization of Digital Twins in the Workshop

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10th international workshop of Advanced Manufacturing and Automation, IWAMA 2020

作者： Xu, Tao Liu, Lilan Chen, Chen Guo, Kai Yuan, Shibo Miao, Qiang Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai China Shanghai Bluebirdme Co. Ltd. Shanghai China

ISBN: (纸本)9789813363175

For digital twin in current time, some applications have gradually matured. But there are still some unsatisfactory, such as t oversized production line models, the hardware requirements have also increased, so this article introduces the triangle folding algorithm to optimize the model, light-weight modelling of the digital twin model of the production line, complete the establishment of the digital model of the production line, and solves the problem that the entire virtual and real synchronous digital twin 3D model is too huge. there is also the problem of data collection and filtering in the production line. A large number of complex data makes the digital twin have requirements for the capacity and efficiency of the hardware. In order to reduce the load of data, Use multi-dimensional Bloom Filter algorithm technology to query the collected data set to reduce redundancy, complete the collection and filtering process, improve the accuracy of the data. through the application of these two algorithms in practice, it shows the optimization of the digital twins in the workshop. © 2021, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： 3D modeling

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Exploiting schemas in data synchronization

Exploiting schemas in data synchronization

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10th international workshop on database Programming Languages, DBPL 2005

作者： Foster, J. Nathan Greenwald, Michael B. Kirkegaard, Christian Pierce, Benjamin C. Schmitt, Alan University of Pennsylvania United States Bell Labs Lucent Technologies BRICS University of Aarhus Denmark INRIA Rhône-Alpes France

ISBN: (纸本)3540309519

Increased reliance on optimistic data replication has led to burgeoning interest in tools and frameworks for synchronizing disconnected updates to replicated data. We have implemented a generic synchronization framework, called Harmony, that can be used to build state-based synchronizers for a wide variety of tree-structured data formats. A novel feature of this framework is that the synchronization process - in particular, the recognition of conflicts - is driven by the schema of the structures being synchronized. We formalize Harmony's synchronization algorithm, state a simple and intuitive specification, and illustrate how it can be used to synchronize trees representing a variety of specific forms of application data, including sets, records, and tuples. © Springer-Verlag Berlin Heidelberg 2005.

关键词： data processing

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