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检索条件"任意字段=Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition"

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Semantic Aware Online Detection of Resource Anomalies on the Cloud 8

Semantic Aware Online Detection of Resource Anomalies on the...

引用

8th IEEE international conference on Cloud computing Technology and Science (CloudCom)

作者： Bhattacharyya, Arnamoy Jandaghi, Seyed Ali Jokar Sotiriadis, Stelios Amza, Cristiana Univ Toronto Dept Elect & Comp Engn Toronto ON Canada

ISBN: (纸本)9781509014453

As cloud based platforms become more popular, it becomes an essential task for the cloud administrator to efficiently manage the costly hardware resources in the cloud environment. Prompt action should be taken whenever hardware resources are faulty, or configured and utilized in a way that causes application performance degradation, hence poor quality of service. In this paper, we propose a semantic aware technique based on neural network learning and pattern recognition in order to provide automated, real-time support for resource anomaly detection. We incorporate application semantics to narrow down the scope of the learning and detection phase, thus enabling our machine learning technique to work at a very low overhead when executed online. As our method runs "life-long" on monitored resource usage on the cloud, in case of wrong prediction, we can leverage administrator feedback to improve prediction on future runs. this feedback directed scheme with the attached context helps us to achieve an anomaly detection accuracy of as high as 98.3% in our experimental evaluation, and can be easily used in conjunction with other anomaly detection techniques for the cloud.

关键词： pattern recognition

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Optical Music recognition as the Case of Imbalanced pattern recognition: A Study of Single Classifiers 8th

Optical Music Recognition as the Case of Imbalanced Pattern ...

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8th international conference on Knowledge, Information, and Creativity Support Systems (KICSS)

作者： Jastrzebska, Agnieszka Lesinski, Wojciech Warsaw Univ Technol Fac Math & Informat Sci Plac Politech 1 PL-00660 Warsaw Poland Univ Bialystok Fac Math & Comp Sci Ul Sosnowa 64 PL-15887 Bialystok Poland

ISBN: (纸本)9783319190907;9783319190891

the article is focused on a particular aspect of classification, namely the imbalance of recognized classes. the paper contains a comparative study of results of musical symbols classification using known algorithms: k-nearest neighbors, k-means, Mahalanobis minimal distance, and decision trees. Authors aim at addressing the problem of imbalanced pattern recognition. First, we theoretically analyze difficulties entailed in the classification of music notation symbols. Second, in the enclosed case study we investigate the fitness of named single classifiers on real data. Conducted experiments are based on own implementations of named algorithms with all necessary image processing tasks. Results are highly satisfying.

关键词： pattern recognition Classification Imbalanced data

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the Method of Probabilistic Nodes Combination in 2D Information Retrieval, pattern recognition and Biometric Modeling

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8th international conference on Image Processing and Communications (IP and C)

作者： Jakobczak, Dariusz Jacek Tech Univ Koszalin Dept Elect & Comp Sci Sniadeckich 2 PL-75453 Koszalin Poland

ISBN: (纸本)9783319238142;9783319238135

Proposed method, called Probabilistic Nodes Combination (PNC), is the method of 2D curve modeling and interpolation using the set of key points. Nodes are treated as characteristic points of unknown object for modeling and recognition. Identification of shapes or symbols need modeling and each model of the pattern is built by a choice of probability distribution function and nodes combination. PNC modeling via nodes combination and parameter. as probability distribution function enables curve parameterization and interpolation for each specific object or symbol. Two-dimensional curve is modeled and interpolated via nodes combination and different functions as continuous probability distribution functions: polynomial, sine, cosine, tangent, cotangent, logarithm, exponent, arc sin, arc cos, arc tan, arc cot or power function.

关键词： pattern recognition Shape modeling Curve interpolation PNC method Nodes combination Probabilistic modeling

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Evaluating the complexity degree of electromagnetic environment utilizing morphological pattern spectrum 8

Evaluating the complexity degree of electromagnetic environm...

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8th IEEE international conference on Communication Software and Networks, ICCSN 2016

作者： Jun, Dong Bing, Li Shuangshuang, Chen Hui, Han State Key Laboratory of Complex Electromagnetic Environmental Effects on Electronics and Information System Postbox 085 Luoyang471003 China Forth Department Mechanical Engineering College He Ping Xi Lu 97# Shijiazhuang050003 China

ISBN: (纸本)9781509017805

In this work, a novel feature extraction technique for evaluating the complexity degree of Electromagnetic Environment (EME) is proposed by utilizing the morphological pattern spectrum (MPS) based on the mathematical morphology theory. Four types of morphological operators, mean the morphological dilate, morphological erosion, morphological open and morphological close, are utilized for computing the MPS. EME signals with four complexity degree are simulated to evaluate the effectiveness of the presented method. Experimental results have demonstrated the MPS calculated by morphological erosion operator to be very effective for discriminating the complexity degree of EME. © 2016 IEEE.

关键词： Mathematical morphology

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Multi-Feature Selection of Handwriting for Gender Identification Using Mutual Information 15

Multi-Feature Selection of Handwriting for Gender Identifica...

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15th international conference on Frontiers in Handwriting recognition (ICFHR)

作者： Tan, Jun Suen, Ching Y. Bi, Ning Nobile, Nicola Sun Yat Sen Univ Sch Math & Computat Sci Guangzhou 510275 Guangdong Peoples R China Concordia Univ Ctr Pattern Recognit & Machine Intelligence Montreal PQ H3G 1M8 Canada

ISBN: (纸本)9781509009817

this paper presents a new flexible approach to predict the gender of the writers from their handwriting samples. Handwriting features can be extracted from different methods. therefore, the multi-feature sets are irrelevant and redundant. the conflict of the features exists in the sets, which affects the accuracy of classification and the computing cost. this paper proposes a Mutual Information (MI) approach, that focuses on feature selection. the approach can decrease redundancies and conflicts. In addition, it extracts an optimal subset of features from the writing samples produced by male and female writers. the classification is carried out using a Support Vector Machine (SVM) on two databases. the first database comes from the ICDAR 2013 competition on gender prediction, the other database contains the Registration Document-Form (RDF) database in Chinese. the proposed and compared methods were evaluated on both databases. Results from the methods highlight the importance of feature selection for gender prediction from handwriting.

关键词： Gender prediction feature selection kernel method mutual information handwriting machine learning

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A Windowing Approach for Activity recognition in Sensor Data Streams 8

A Windowing Approach for Activity Recognition in Sensor Data...

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8th international conference on Ubiquitous and Future Networks (ICUFN)

作者： Al Machot, Fadi Mayr, Heinrich C. Ranasinghe, Suneth Alpen Adria Univ Klagenfurt Inst Appl Informat Klagenfurt Austria

ISBN: (纸本)9781467399913

Determining the appropriate data window size for online sensor data streams to recognize a specific activity is still a challenging task. In particular, when new sensor events are recorded. this paper proposes a windowing algorithm which presents promising results to recognize complex activities, e.g., in a smart home environment. the underlying basic idea is to analyze the sensor data in order to identify the set of "best fitting sensors": it contains those sensors that most contribute to the recognition task, and therefore should be considered in a window. To validate our approach, we applied it on the CASAS data set which is an international data set for activity recognition. Based on the promising results, we believe that this algorithm can assist to detect human activities. thus, our approach might be used in Active and Assisted Living Environments (AAL), where activity recognition is required to distinguish the types of help, a person needs to master his/her daily life activities.

关键词： Activity recognition Smart homes pattern recognition Active and Assisted Living (AAL) Pervasive computing

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Stroke Tissue pattern recognition Based on CT Texture Analysis 9th

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9th international conference on Computer recognition Systems (CORES)

作者： Ostrek, Grzegorz Nowakowski, Artur Jasionowska, Magdalena Przelaskowski, Artur Szopinski, Kazimierz Warsaw Univ Technol Fac Math & Informat Sci Ul Koszykowa 75 PL-00662 Warsaw Poland Warsaw Univ Technol Inst Radioelect Ul Nowowiejska 15-19 PL-00665 Warsaw Poland Med Univ Warsaw Dept Dent & Maxillofacial Radiol Fac Med & Dent Ul Nowogrodzka 59 PL-02006 Warsaw Poland

ISBN: (纸本)9783319262277;9783319262253

the main objective of this paper is a texture-based solution to the problem of acute stroke tissue recognition on computed tomography images. Our proposed method of early stroke indication was based on two fundamental steps: (i) segmentation of potential areas with distorted brain tissue (selection of regions of interest), and (ii) acute stroke tissue recognition by extracting and then classifying a set of well-differentiating features. the proposed solution used various numerical image descriptors determined in several image transformation domains: 2D Fourier domain, polar 2D Fourier domain, and multiscale domains (i. e., wavelet, complex wavelet, and contourlet domain). the obtained results indicate the possibility of relatively effective detection of early stroke symptoms in CT images. Selected normal or pathological blocks were classified by LogitBoost with the accuracy close to 75% with the use of our adjusted cross-validation procedure.

关键词： Hypodensity recognition Stroke recognition Multiscale domain

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Automatic features reduction procedures in palm vein recognition 8

Automatic features reduction procedures in palm vein recogni...

引用

8th international conference on Advanced Computer Science and Information Systems, ICACSIS 2016

作者： Yunanto, Prasti Eko Nugroho, Hertog Budi, W. Tjokorda Agung School of Computing Telkom University Bandung Indonesia Electric Engineering Department Bandung State of Polytechnic Bandung Indonesia

ISBN: (纸本)9781509046294

Feature or dimensionality reduction has become one of fundamental problem in the field of pattern recognition such as biometrics. Selecting the number of feature or dimension has become one challenge. Instead selecting number of feature manually, this work proposed a procedure for feature reduction by finding the correlation between recognition rates and number of features. the procedure started with collecting recognition rates from available classes against a number of features and then calculated some variables from the distribution to be used as anchors for estimating number of features in case there are new classes to be added. this study was applied on a palm vein biometrics system which used DCT and k-PCA as features extraction method. the results of the experiment showed that the procedure was able to achieve a number of features that have an average offset of less than 6 from those obtained from direct observation and an average error of 1.1% from the real recognition rates. © 2016 IEEE.

关键词： Biometrics

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An Improved Classification Algorithm Based on Exposure pattern 8

An Improved Classification Algorithm Based on Exposure Patte...

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8th international conference on Cyber-Enabled Distributed computing and Knowledge Discovery (CyberC)

作者： Shao, Kaixia Zhou, Xiaofeng Hohai Univ Inst Comp & Informat Nanjing Jiangsu Peoples R China Dept Hohai Univ Inst Comp & Informat Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781509051540

In this paper, an improved classification method NCEEP based on EP is proposed, and this algorithm is improved on the basis of the original CEEP classification algorithm. In this paper also cited a optimal discretization method to discretize pre process data set, and select the most effective minimum support and minimum growth rate threshold. Experiments on the data set in the UCI machine learning database show that the efficiency of the improved classification algorithm proposed in this paper is obviously improved.

关键词： essential Emerging patterns NCEEP classification algorithm total score calculation

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proceedings of the international conference on Cloud computing Technology and Science, CloudCom

Proceedings of the International Conference on Cloud Computi...

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8th IEEE international conference on Cloud computing Technology and Science, CloudCom 2016

ISBN: (纸本)9781509014453

the proceedings contain 96 papers. the topics discussed include: service performance pattern analysis and prediction of commercially available cloud providers;CloudTax: a CloudSim-extension for simulating tax systems on cloud markets;nested buddy system: a new block address allocation scheme for ISPs and IaaS providers;towards green transportation: fast vehicle velocity optimization for fuel efficiency;deadline-aware energy management in data centers;instance type selection in proactive horizontal auto-scaling;device-level IoT with virtual I/O device interconnection;variability management in IaaS;deadline-aware energy management in data centers;using virtual desktop infrastructure to improve power efficiency in grinfy system;instance type selection in proactive horizontal auto-scaling;and dependency-aware and resource-efficient scheduling for heterogeneous jobs in clouds.

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