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检索条件"任意字段=7th International Workshop on Graph-Based Representations in Pattern Recognition"

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7th international workshop on Artificial Intelligence and pattern recognition, IWAIPR 2021

7th International Workshop on Artificial Intelligence and Pa...

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7th international workshop on Artificial Intelligence and pattern recognition, IWAIPR 2021

ISBN: (纸本)9783030896904

the proceedings contain 42 papers. the special focus in this conference is on Artificial Intelligence and pattern recognition. the topics include: Convolutional Neural Networks as Support Tools for Spinocerebellar Ataxia Detection from Magnetic Resonances;a Hybrid Brain-Computer Interface using Extreme Learning Machines for Motor Intention Detection;peripheral Nerve Segmentation in Ultrasound Images Using Conditioned U-Net;Assessing the Relationship Between Binarization and OCR in the Context of Deep Learning-based ID Document Analysis;pattern recognition Strategies to Classify Traced Neurons;evaluation of Hepatic Fibrosis Stages Using the Logical Combinatorial Approach;a Proposal of Condition Monitoring with Missing Data and Small-Magnitude Faults in Industrial Plants;a Proposal of Metric for Improving Remaining Useful Life Prediction in Industrial Systems;a Fuzzy Logic Proposal for Diagnosis Multiple Incipient Faults in a Power Transformer;A Machine Learning based Approach for Estimation of the Lung Affectation Degree in CXR Images of COVID-19 Patients;a Robust Fault Diagnosis Strategy in Mechanical Systems Using Pythagorean Fuzzy Sets;prediction of Diabetes Mortality in Mexico City Applying Data Science;balancing of Samples in Class Hierarchy;feature Selection with Class Hierarchy for Imbalance Problems;SVM based EVM for Open Space Problems;data Drive Fuzzy Cognitive Map for Classification Problems;extracting Composite Summaries from Qualitative Data;A Novel Initial Population Construction Heuristic for the DINOS Subgroup Discovery Algorithm;a Survey on the Methods to Determine the Sensitivity of Textual Documents: Solutions and Problems to Solve;automatic Classification of Diabetic Foot Ulcers Using Computer Vision Techniques;deep Neural Network to Detect Gender Violence on Mexican Tweets.

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pattern recognition Strategies to Classify Traced Neurons 7th

Pattern Recognition Strategies to Classify Traced Neurons

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7th international workshop on Artificial Intelligence and pattern recognition (IWAIPR)

作者： Lopez-Cabrera, Jose D. Hernandez-Perez, Leonardo A. Lorenzo-Ginori, Juan, V Univ Cent Marta Abreu Las Villas Informat Res Ctr Santa Clara 54830 Cuba Empresa Telecomunicac Cuba SA Santa Clara Cuba

ISBN: (纸本)9783030896911;9783030896904

this paper addresses two strategies for pattern recognition in high-dimension data sets, obtained from databases of digitally traced neurons. the first strategy has as distinctive characteristic that the features are obtained both from the whole neuron and from the axon and the dendrites as well. On the other hand, the second strategy is based in feature extraction from data sequences obtained from the decomposition of the traced neuron. Both strategies generate a wide variety of datawhich result in highly dimensional data sets. Two supervised pattern recognition alternatives were implemented for feature selection. When using the first strategy, the percentage of correctly classified cases raised up to 94.55% once the fusion of data extraction alternatives is performed and the feature selection methods are applied. these results are maintained for three different sets of traced neurons pertaining different regions of the cerebral cortex. By means of the second strategy, a group or recursive feature elimination alternatives was evaluated. Using this strategy, the mean percentage of correctly classified cases achieved was 78.5% and 76.79% which are comparable to those obtained using multiple iterations and the computational time was reduced three to four times. the results obtained encourage the use of the proposed strategies to reduce the dimensionality of the data sets obtained from the traced neurons.

关键词： Feature selection pattern recognition Neuron classification Traced neurons

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the use of graph theory in the implementation of psychodiagnostic projects on the example of researching the leadership qualities of participants in software development teams 7

The use of graph theory in the implementation of psychodiagn...

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7th international Conference "Information Technology and Interactions", IT and I 2020

作者： Lukianov, Dmytro Radyuk, Oleg Charniauskaya, Hanna Basinskaya, Irina Interdisciplinary Institute for Advanced Studies and Retraining Belarusian National Technical University 77 Partyzansky ave. Minsk220107 Belarus Individual Entrepreneur Post office box 67 Minsk-134220134 Belarus HR in IT Rogachyovskaya str. 7-36 Minsk220056 Belarus Belarusian State University 4 Nezavisimosti Avenue Minsk220030 Belarus

the article proposes consider the application of visual analysis of psychological testing results through the use of directed graph representations. It is proposed to consider the method of visual analysis by means of various representations based on the analysis of the number of connections between the elements of the system, considered as the basis for the formation of a directed graph. In the example considered in the article, it is proposed to consider the scales of the integrated test technique used as the vertices of the graph. As the main views, the application of calculating centrality properties based on the number of links, the topological structure of links, and the Page Rank parameter is demonstrated. It is proposed to use the method of visual analysis of the graph as an additional to the classical method of correlation analysis used in processing the results of the application of psychodiagnostic methods. the possibility of using visual analysis to form hypotheses about the possible optimization of the structure of the scales used in the design of psychodiagnostic techniques (test batteries) is shown. In parallel, the results of the analysis of the leadership qualities of such a category of specialists as software developers (members of development teams) are presented. © 2020 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Directed graphs

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DRSGN: Dual Revised Semantic graph Structured Network for Image-Text Matching 7

DRSGN: Dual Revised Semantic Graph Structured Network for Im...

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

作者： Yang, Xiao Wu, Xiaojun Xu, Tianyang Jiangnan University School of Artificial Intelligence and Computer Science Wuxi214122 China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

ISBN: (纸本)9781665441490

Image-Text matching has made significant contributions to bridge the gap in multi-modal retrieval for advanced pattern recognition systems. the key point of this task is mapping images and text semantics into a common space, where the correlations of similar pairs and dissimilar ones are distinguishable. To concentrate on obtaining semantics, most of existing methods utilize an external object detector to extract compatible region-level visual representations to match the word-level textual clues in captions. In addition to perform the local region matching, several recent studies design a classification task using global information in the final matching layer, but such global supervision signal is difficult to transmitted to the shallow feature descriptor learning layer. To address this issue, in this paper, we propose a novel Dual Revised Semantic graph Structured Network (DRSGN) to supplement global supervision to the regional semantics adaptively in the shallow layers. In principle, DRSGN integrates regional and global descriptors to formulate an attention supervising mechanism, aiming to simultaneously highlight the regional instances and global scene to obtain complementary visual clues. A dual semantic supervising module is then established to interact between two modalities to extract the genuine matching pairs. Finally, a semantic graph consisting of the obtained multi-modal clues is designed to perform similarity reasoning between the positional relation embedded textual nodes and semantic related visual nodes. the dedicated global signals provide complementary supervision against local regions to support improved matching capacity. the experimental results on Flikr30K and MSCOCO demonstrate the effectiveness of the proposed DRSGN, with improved matching performance against the local region-based approaches. © 2021 IEEE.

关键词： Semantics

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Dataset and Methods for Recognizing Care Activities 22

Dataset and Methods for Recognizing Care Activities

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Proceedings of the 7th international workshop on Sensor-based Activity recognition and Artificial Intelligence

作者： Sylvia Kaczmarek Martin Fiedler Andreas Bongers Sebastian Wibbeling Rene Grzeszick Fraunhofer IML Germany MotionMiners GmbH Germany

ISBN: (纸本)9781450396240

A major challenge in stationary care in hospitals is the limited amount of time for each patient due to a large overhead being created by manual documentation efforts. Studies show that it is common for caregivers to spend more than one hour per day for documentation *** this paper a novel concept for reducing the manual documentation effort by leveraging methods of human activity recognition is introduced and a corresponding dataset is published. the dataset captures different care activities like repositioning, sitting up, transfer and patient mobilization using body worn sensors in a realistic setting with multiple patients and *** evaluation of the data, two experimental setups are presented: an unsegmented case, where the duration of the care activity is unknown and a segmented case, where the beginning and the end of the activity is known beforehand. First experiments show the feasibility of recognizing care activities using different types of Neural Networks.

关键词： Signal processing Human Activity recognition Applications pattern recognition

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42nd German Conference on pattern recognition, DAGM GCPR 2020 held in parallel with 25th international Symposium on Vision, Modeling, and Visualization, VMV 2020 and 10th Eurographics workshop on Visual Computing for Biology and Medicine, VCBM 2020

42nd German Conference on Pattern Recognition, DAGM GCPR 202...

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42nd German Conference on pattern recognition, DAGM GCPR 2020 held in parallel with 25th international Symposium on Vision, Modeling, and Visualization, VMV 2020 and 10th Eurographics workshop on Visual Computing for Biology and Medicine, VCBM 2020

ISBN: (纸本)9783030712778

the proceedings contain 34 papers. the special focus in this conference is on pattern recognition. the topics include: Adversarial Synthesis of Human Pose from Text;long-Term Anticipation of Activities with Cycle Consistency;multi-stage Fusion for One-Click Segmentation;neural Architecture Performance Prediction Using graph Neural Networks;discovering Latent Classes for Semi-supervised Semantic Segmentation;Riemannian SOS-Polynomial Normalizing Flows;automated Water Segmentation and River Level Detection on Camera Images Using Transfer Learning;Does SGD Implicitly Optimize for Smoothness?;Looking Outside the Box: the Role of Context in Random Forest based Semantic Segmentation of PolSAR Images;semantic Bottlenecks: Quantifying and Improving Inspectability of Deep representations;haar Wavelet based Block Autoregressive Flows for Trajectories;center3D: Center-based Monocular 3D Object Detection with Joint Depth Understanding;constellation Codebooks for Reliable Vehicle Localization;towards Bounding-Box Free Panoptic Segmentation;proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks;unsupervised Part Discovery by Unsupervised Disentanglement;on the Lifted Multicut Polytope for Trees;conditional Invertible Neural Networks for Diverse Image-to-Image Translation;image Inpainting with Learnable Feature Imputation;4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving;bias Detection and Prediction of Mapping Errors in Camera Calibration;inline Double Layer Depth Estimation with Transparent Materials;a Differentiable Convolutional Distance Transform Layer for Improved Image Segmentation;PET-Guided Attention Network for Segmentation of Lung Tumors from PET/CT Images;self-supervised Disentanglement of Modality-Specific and Shared Factors Improves Multimodal Generative Models;multimodal Semantic Forecasting based on Conditional Generation of Future Features;learning to Identify Physical Parameters from Video Using Differentiable

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pattern recognition on aerospace images using deep neural networks 7

Pattern recognition on aerospace images using deep neural ne...

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7th IEEE international workshop on Metrology for AeroSpace (MetroAeroSpace)

作者： Saetchnikov, Ivan Skakun, Victor Tcherniavskaia, Elina Belarusian State Univ Radio Phys Dept Minsk BELARUS Belarusian State Univ Phys Dept Minsk BELARUS

ISBN: (纸本)9781728166360

pattern recognition is one of the most important tasks in aerospace image processing. Various methods based on convolutional neural networks attain state-of-the-art accuracy;however, their effectiveness on exact images is influenced by the chosen architecture and its training parameters. this work present methods based on convolutional neural networks for pattern recognition on the aerospace images. A possibility for objects segmentation into ten classes is demonstrated on example of the multispectral images from the World View 3 satellite. Four networks with different architectures were built, trained and optimized parametrically based on the auto-encoder neural networks. Segmentation results has been analyzed by means of three parameters: training Jacard Index, testing Jacard Index and weight numbers. the positive impact of the properly selected shearing augmentation on extension of a small marked dataset is discussed. the influence of the nonequilibrium classes on the segmentation accuracy and how to account this feature during training of deep neural networks is pointing out.

关键词： convolutional neural network deep learning unmanned aerial vehicle pattern recognition segmentation autoencoder aerospace image

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A Survey on the Methods to Determine the Sensitivity of Textual Documents: Solutions and Problems to Solve 7th

A Survey on the Methods to Determine the Sensitivity of Text...

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7th international workshop on Artificial Intelligence and pattern recognition (IWAIPR)

作者： Morales Escobar, Saturnino Job Ruiz Shulcloper, Jose Juarez Landin, Cristina Perez Garcia, Osvaldo Andres Ruiz Castilla, Jose Sergio Univ Autonoma Estado Mexico UAEM Ctr Univ UAEM Valle Mexico Atizapan De Zaragoza Estado De Mexic Mexico Univ Informat Sci Res Grp Log Combinatorial Pattern Recognit Havana Cuba Univ Informat Sci Invest Havana Cuba Univ Autonoma Estado Mexico UAEM Ctr Univ UAEM Valle Chalco Valle Chalco Solidaridad Estado De Mexic Mexico CENATAV DATYS Equipo Invest Min Datos Havana Cuba Univ Autonoma Estado Mexico UAEM Ctr Univ UAEM Texcoco Texcoco Estado De Mexic Mexico

ISBN: (纸本)9783030896911;9783030896904

the identification of sensitive information, whether personal or institutional, is a fundamental step when dealing with the problem of information leakage. this problem is one of the most pressing to which companies and research centers dedicate a considerable amount of material and intellectual resources, as a particular case, to the development of methods or the application of some already known ones to the identification of sensitive information. this increased the proposals with promising results, but without yet offering a totally satisfactory solution to the problem. Under these conditions, it is considered necessary to make a critical analysis of the existing methods and techniques and their future projections. In this paper, a review of the proposals for the determination of sensitivity in textual documents is presented and a taxonomy is introduced to better understand the approaches with which this problem has been approached in the context of information leakage. Starting from the critical analysis and the practical needs raised by experts in the areas of possible application, lines of research on this subject are outlined that include the development of methods for the automation of the classification of sensitive textual documents. Possible extensions that these studies may have in similar application areas are proposed based on other information carriers, such as the cases of images, recordings and other forms of information object, each of which entails levels of complexity that merit studies analogous to the one carried out in this work.

关键词： Information leakage Document sensitivity Information protection systems Information objects Supervised classification

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graph Convolutional Regression of Cardiac Depolarization from Sparse Endocardial Maps 11th

Graph Convolutional Regression of Cardiac Depolarization fr...

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11th international workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2020 held in Conjunction with MICCAI 2020

作者： Meister, Felix Passerini, Tiziano Audigier, Chloé Lluch, Èric Mihalef, Viorel Ashikaga, Hiroshi Maier, Andreas Halperin, Henry Mansi, Tommaso Friedrich-Alexander University Pattern Recognition Lab Erlangen Germany Siemens Healthineers Digital Technology and Innovation Princeton United States Siemens Healthineers Digital Technology and Innovation Erlangen Germany Johns Hopkins University School of Medicine Cardiac Arrhythmia Service Baltimore United States

ISBN: (纸本)9783030681067

Electroanatomic mapping as routinely acquired in ablation therapy of ventricular tachycardia is the gold standard method to identify the arrhythmogenic substrate. To reduce the acquisition time and still provide maps with high spatial resolution, we propose a novel deep learning method based on graph convolutional neural networks to estimate the depolarization time in the myocardium, given sparse catheter data on the left ventricular endocardium, ECG, and magnetic resonance images. the training set consists of data produced by a computational model of cardiac electrophysiology on a large cohort of synthetically generated geometries of ischemic hearts. the predicted depolarization pattern has good agreement with activation times computed by the cardiac electrophysiology model in a validation set of five swine heart geometries with complex scar and border zone morphologies. the mean absolute error hereby measures 8 ms on the entire myocardium when providing 50% of the endocardial ground truth in over 500 computed depolarization patterns. Furthermore, when considering a complete animal data set with high density electroanatomic mapping data as reference, the neural network can accurately reproduce the endocardial depolarization pattern, even when a small percentage of measurements are provided as input features (mean absolute error of 7 ms with 50% of input samples). the results show that the proposed method, trained on synthetically generated data, may generalize to real data. © 2021, Springer Nature Switzerland AG.

关键词： Mapping

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Local Binary pattern based graph Construction for Hyperspectral Image Segmentation 1

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12th IAPR-TC15 workshop on graph-based representations in pattern recognition (GbRPR)

作者： Tabia, Kaouther Desquesnes, Xavier Lucas, Yves Treuillet, Sylvie Univ Orleans Lab PRISME 12 Rue Blois 6744 F-45067 Orleans 2 France

ISBN: (数字)9783030200817

ISBN: (纸本)9783030200817;9783030200800

Building highly discriminative graph has an important impact on the quality of graph-based hyperspectral image segmentation. For this purpose, we propose to weight graph edges using Local Binary pattern (LBP) descriptor that takes into account the texture information of the hyperspectral images. Nodes in the graph embed spectral LBP features computed from the different hyperspectral bands, while edges encode the spatial relationship between these features. the multiphase level set method is then applied on the constructed graph to segment the image. We validate the proposed method, using Overlapping Score evaluation metric, on several popular hyperspectral images. the results show that our method is very efficient compared to other state-of-the-art one.

关键词： Hyperspectral image graph LBP Segmentation Multiphase level set

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