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14th international Symposium on Pervasive Systems, Algorithms and Networks (I-SPAN) / 11th international conference on Frontier of Computer Science and Technology (FCST) / 3rd international Symposium of Creative computing (ISCC)

作者： Wang, Jing Chen, Shiqiang Xiang, Changcheng Yu, Ya Li, Jun Hubei Univ Nationalities Sch Informat & Engn Enshi Hubei Peoples R China Hubei Univ Nationalities Sch Sci Enshi Hubei Peoples R China

ISBN: (纸本)9781538608401

To solve the problem of real-time and regional identification in an intelligent control classroom, both edge and extended moment invariants are employed in this paper. By computing and visually analysing the moment invariants from a classroom video image, a set of 10-dimensional edge invariant moments is chosen, which is stable, practical and provides adequate real-time performance. the pattern recognition method of a support vector machine is used to distinguish the humans from the objects in the classroom image. Experimental results are provided to show that this group of moments achieves excellent results, and the recognition rate can reach 99.81%.

关键词： Hu moment visual analysis support vector machine pattern recognition

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Proceedings - 10th IEEE international conference on Computer and Information Technology, CIT-2010, 7th IEEE international conference on Embedded Software and Systems, ICESS-2010, ScalCom-2010

Proceedings - 10th IEEE International Conference on Computer...

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10th IEEE international conference on Computer and Information Technology, CIT-2010, 7th IEEE international conference on Embedded Software and Systems, ICESS-2010, 10th IEEE Int. Conf. Scalable computing and Communications, ScalCom-2010

ISBN: (纸本)9780769541082

the proceedings contain 461 papers. the topics discussed include: distributed lock manager for distributed file system in shared-disk environment;an asymmetric data conversion scheme based on binary tags;scalable orchestration strategy for automatic service composition;an energy efficient clustering scheme for self-organizing distributed wireless sensor networks;conceptual multi-level hierarchy for evaluation and classification;QoS assessment over multiple attributes;feature selection of gene expression data using regression model;incremental emerging patterns mining for identifying safe and non-safe power load lines;methods of pattern extraction and interval prediction for equipment maintenance;text detection using multilayer separation in real scene images;face recognition using layered linear discriminant analysis and small subspace;and a rotate-based best neighborhood matching algorithm for high definition image error concealment.

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On Some Scientific Results of the ICPR-2020

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pattern recognition AND IMAGE ANALYSIS 2021年第3期31卷 357-363页

作者： Gurevich, I. B. Moroni, D. Pascali, M. A. Yashina, V. V. Russian Acad Sci Fed Res Ctr Comp Sci & Control Bldg 244 Vavilov Str Moscow 119333 Russia Pisa Natl Res Council Natl Res Council Italy Res Area Pisa Inst Informat Sci & Technol A Faedo I-56124 Pisa Italy

this special issue of PRIA is devoted to some scientific results and trends of the 25th international conference on pattern recognition (Virtual, Milano, Italy, January 10-15, 2021). Two important events of ICPR-2020 are represented in this special issue: (1) the paper of Professor Ching Yee Suen (Centre for pattern recognition and Machine Intelligence, Department of Computer Science and Software Engineering, Concordia University, Montreal, QC, Canada)-the recent winner of IAPR very prestigious K.S. Fu Prize for a year of 2020. the paper based on his lecture "From handwriting to human personality and facial beauty" presented at the ICPR 2020;(2) Special issue "ICPR-2020 Workshop "Image Mining. theory and Applications." the analysis of the scientific contribution of IMTA-VII-2021 allows us to draw the following conclusions: (1) the construction of a unified mathematical theory of image analysis is still far from complete. (2) there is considerable interest in the development of new mathematical methods for analyzing and evaluating information presented in the form of images. (3) there is a tendency to expand the mathematical apparatus in the development of new methods of image analysis and recognition by involving in this process areas of mathematics that were not previously used in image analysis. (4) the gap between the capabilities of new mathematical methods of image analysis and recognition and their actual use in solving applied problems remains significant. (5) there is an excessive use of neural networks in solving applied problems of image analysis and image recognition, and quite often without proper justification and interpretation of the results. the special issue includes articles based on the workshop papers selected by the IMTA-VII-2021 Program Committee for publication in PRIA. the PRIA special issue "Scientific Resume of the 25th international conference on pattern recognition" is prepared by the National Committee for pattern recognition and Image A

关键词： automation of data analysis biomedical applications biomedical images conditional generative adversarial networks data mining descriptive image analysis face beauty identity-preserved image mining intellectual data analysis mathematical theory of image analysis mathematical theory of pattern recognition

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Laya Estimation for Hindustani Classical Vocals, Devoid of Rhythmic Indicators 10

Laya Estimation for Hindustani Classical Vocals, Devoid of R...

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10th IEEE international conference on Electronics, computing and Communication Technologies (IEEE CONECCT)

作者： Bhatta, Srinivasa Puranika Bharadwaj, Suhas Nagaraj Shadakshari, Sumukh Bhat, Ashwini PES Univ Dept Elect & Commun Bengaluru India

ISBN: (纸本)9798350385939;9798350385922

Laya, also known as Tempo, is an integral part of music. the human body intuitively sways or taps to the tempo while listening to any rhythmic music. Laya depends on two parameters. the first parameter is the number of beats and its structure. the second parameter is the speed. Tempo estimation has previously been achieved for music containing percussive elements. these elements can either be from rhythmic instruments or they can be repetitive melodic sounds. the challenge in Hindustani music, comprising of only vocals, is the lack of any such easily distinguishable rhythmic elements. Hence, existing methods do not deliver satisfactory results. the method proposed in this paper, to extract the tempo has been designed to work exclusively with Hindustani vocal music. this work achieves a minimum accuracy of 95%. the method has been tested for different Taalas, Ragas, and a wide range of tempi.

关键词： pattern Matching MFCCs Tempo Estimation PLP Hindustani Music

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Variable masking pattern-based QR codes for high security 10

Variable masking pattern-based QR codes for high security

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10th IEEE international conference on Electronics, computing and Communication Technologies (IEEE CONECCT)

作者： Pasala, Anjaneyulu Mukherjee, Subhro CMR Inst Technol AI&DS Bangalore Karnataka India Upgrad Bangalore Karnataka India

ISBN: (纸本)9798350385939;9798350385922

QR codes are the de facto standard for the digital transfer of data. At the same time, QR code manipulation has become increasingly prevalent, causing serious fraud. To secure QR codes further, we propose scrambling QR codes using a novel method named Random Masking. Proposed Scrambled QR codes can be detected and read only by secure QR scanners. the Scrambled QR codes cannot be manipulated without removing the random mask using a secret key. If scrambled QR codes are replaced by counterfeit QR codes, the secure scanners fail to scan such QR codes. We observed no performance bottleneck or loss in error correction of QR codes due to random masking. We also made the Scrambled QR codes compatible with the existing hardware used for the generation and scanning of QR codes with a simple software upgrade. Our focus is on securing QR patterns without concerning QR payload.

关键词： Security QR Codes Data Security Encryption

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CircleSense: A Pervasive computing System for Recognizing Social Activities

CircleSense: A Pervasive Computing System for Recognizing So...

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11th Annual IEEE international conference on Pervasive computing and Communications (PerCom)

作者： Liang, Guanqing Cao, Jiannong Zhu, Weiping Hong Kong Polytech Univ Dept Comp Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781467345750;9781467345736

Social activities have great impact for human beings in psychological health and social relationship. the recognition of social activities can unobtrusively recognize and record users' daily social activities, enabling users to better manage their life. Existing work of social activity recognition focus on recognizing a limited set of social activities and are mainly based on the patterns of individual user such as location pattern, vocal pattern, or others. However, social activities inherently exhibit the patterns with respect to multiple users rather individual user. In this paper, we introduce the concept of social circle, to extract social patterns associated with multiple users in a generic set of social activities. A social circle refers to a set of users frequently gathering to conduct certain social activities. Based on social circle, we design a system called CircleSense that supports accurate recognition of a generic set of social activities. We validate the effectiveness of CircleSense through the real trace collected by 10 volunteers. the result shows that CircleSense outperforms existing methods in terms of accuracy of social activity recognition.

关键词： Pervasive computing Social Activity recognition Social Circle

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Adaptive and Natural computing Algorithms - 10th international conference, ICANNGA 2011, Proceedings

Adaptive and Natural Computing Algorithms - 10th Internation...

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10th international conference on Adaptive and Natural computing Algorithms, ICANNGA 2011

ISBN: (纸本)9783642202810

the proceedings contain 84 papers. the topics discussed include: new measure of boolean factor analysis quality;mechanisms of adaptive spatial integration in a neural model of cortical motion processing;self-organized short-term memory mechanism in spiking neural network;approximation of functions by multivariable hermite basis: a hybrid method;using pattern recognition to predict driver intent;neural networks committee for improvement of metal's mechanical properties estimates;logarithmic multiplier in hardware implementation of neural networks;a robust learning model for dealing with missing values in many-core architectures;a model of saliency-based selective attention for machine vision inspection application;grapheme-phoneme translator for Brazilian Portuguese;and improvement of inventory control under parametric uncertainty and constraints.

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Rainfall Prediction Using Data Visualisation Techniques 10

Rainfall Prediction Using Data Visualisation Techniques

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10th international conference on Cloud computing, Data Science and Engineering (Confluence)

作者： Joshi, Yogesh Kumar Chawla, Udit Shukla, Shipra Amity Univ Uttar Pradesh Dept CSE Noida UP India

ISBN: (纸本)9781728127910

the volume of big data has opened up great opportunities for prediction and analysis of different aspects of weather. Data Visualisation is common in day to day life. Various charts and graphs are used to illustrate the practical approach towards the classification of rainfall with the help of data visualisation methods. Since it was impossible to analyze the large datasets earlier, the data visualisation techniques has made easier to plot the graphs for the better understanding of the weather. With the help of data visualisation patterns such as the highest, lowest and average rainfall in the States/Union Territories the weather of India has been visualised. In this paper, the rainfall pattern in the States/Union Territories of India was successfully visualised. the pattern identifies drought prone region in India, decrease in the annual rainfall over the century and heavy rainfall in the coastal regions of India.

关键词： Data Visualisation Weather Analysis Big Data Data Analytics Rainfall pattern

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Impact of Feature Selection and Engineering in the Classification of Handwritten Text 3

Impact of Feature Selection and Engineering in the Classific...

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3rd international conference on computing for Sustainable Global Development (INDIACom)

作者： Kaushik, Anupama Gupta, Himanshu Latwal, Digvijay Singh Maharaja Surajmal Inst Technol Dept IT Delhi India

ISBN: (纸本)9789380544199

Feature selection forms an important aspect of machine learning and character recognition. It is a process of selecting the most important features (attributes) from the dataset. Accurate feature selection results in significant reduction in the number of irrelevant (constant, redundant) attributes thereby, reducing the processing time and increasing the accuracy of the model without any loss of information. this paper focuses on the impact of feature selection and engineering in the classification of handwritten text by identifying and extracting those attributes of the training dataset that will contribute most towards the classification task using classifiers like J48, NaiveBayes and Sequential Minimal Optimization (SMO). this results in improved accuracy of the classifiers as compared to the work reported earlier. Further, a comparative performance evaluation of the classifiers used for OCR and pattern recognition is done. Initial classification performance of all the classifiers listed above was recorded on the raw dataset. Finally, the dataset was transformed after performing relevant feature selection and engineering on its attributes. the same classifiers were again trained on the transformed dataset and their accuracy was recorded. this paper uses the widely used MNIST dataset of handwritten digits for training the classifiers.

关键词： Character recognition J-48 MNIST Dataset NaiveBayes Optical Character recognition (OCR) Principal Components Analysis (PCA) Sequential Minimal Optimization (SMO) WEKA pattern recognition Machine Learning

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VOWEL: A Local Online Learning Rule for Recurrent Networks of Probabilistic Spiking Winner-Take-All Circuits 25

VOWEL: A Local Online Learning Rule for Recurrent Networks o...

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25th international conference on pattern recognition (ICPR)

作者： Jang, Hyeryung Skatchkovsky, Nicolas Simeone, Osvaldo Kings Coll London KCLIP Lab Dept Engn London England

ISBN: (纸本)9781728188089

Networks of spiking neurons and Winner-Take-All spiking circuits ( WTA-SNNs) can detect information encoded in spatio-temporal multi-valued events. these are described by the timing of events of interest, e.g., clicks, as well as by categorical numerical values assigned to each event, e.g., like or dislike. Other use cases include object recognition from data collected by neuromorphic cameras, which produce, for each pixel, signed bits at the times of sufficiently large brightness variations. Existing schemes for training WTA-SNNs are limited to rate-encoding solutions, and are hence able to detect only spatial patterns. Developing more general training algorithms for arbitrary WTA-SNNs inherits the challenges of training (binary) Spiking Neural Networks (SNNs). these amount, most notably, to the non-differentiability of threshold functions, to the recurrent behavior of spiking neural models, and to the difficulty of implementing backpropagation in neuromorphic hardware. In this paper, we develop a variational online local training rule for WTA-SNNs, referred to as VOWEL, that leverages only local pre- and post-synaptic information for visible circuits, and an additional common reward signal for hidden circuits. the method is based on probabilistic generalized linear neural models, control variates, and variational regularization. Experimental results on real-world neuromorphic datasets with multi-valued events demonstrate the advantages of WTA-SNNs over conventional binary SNNs trained with state-of-the-art methods, especially in the presence of limited computing resources.

关键词： Probabilistic Spiking Neural Networks Neuromorphic computing

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