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International Symposium on Micro-NanoMechatronics and Human Science (MHS)

作者： Adi, Wibowo Sekiyama, Kosuke Nagoya Univ Dept Micronano Syst Engn Nagoya Aichi 4648603 Japan Diponegoro Univ Dept Informat Semarang Central Java Indonesia

ISBN: (纸本)9781479966783

In this paper, we investigate an alternative double-stranded probe (dsProbe) system as detection platform that employs a similarity-based classifiers in DNA strand displacement reactions. We formulate label propagation based on DNA toehold domain as classifier, where a node propagates to neighboring nodes according to their proximity. We observe mismatch attributes of DNA strand sequences to recognize the effect of this model. The attributes are position, length and location number of mismatches. We carry out simulation using Nupack software and also experimental investigation.

关键词： DNA biochemistry biology computing molecular biophysics molecular configurations pattern classification DNA strand displacement reactions DNA strand sequences DNA toehold domain Nupack software double-stranded DNA probes dsProbe system multitargeting strand similarity-based classifiers Classification algorithms Educational institutions Fluorescence Probes Sensors Software biology computing DNA molecular biology sorting algorithm Probes Educational institutions Molecular Conformation Pattern recognition Classifiers computer software biochemistry Sensors Fluorescence DNA strand

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Geometric PDEs on weighted graphs for semi-supervised classification 13

Geometric PDEs on weighted graphs for semi-supervised classi...

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13th International Conference on Machine Learning and Applications (ICMLA)

作者： Toutain, Matthieu Elmoataz, Abderrahim Lezoray, Olivier Normandie Univ UNICAEN ENSICAEN GREYC UMR CNRS 6072 Caen France

ISBN: (纸本)9781479974153

In this paper, we consider the adaptation of two Partial Differential Equations (PDEs) on weighted graphs, p-Laplacian and eikonal equations, for semi-supervised classification tasks. These equations are a discrete analogue of well known geometric PDEs, which are widely used in image processing. While the p-Laplacian on graphs was intensively used in data classification, few works relate to the eikonal equation for data classification. The methods are illustrated through semi-supervised classification tasks on databases, where we compare the two algorithms. The results show that these methods perform well regarding the state-of-the-art and are applicable to the task of semi-supervised classification.

关键词： database management systems graph theory partial differential equations pattern classification data classification databases eikonal equations geometric PDE image processing p-Laplacian equations semisupervised classification tasks weighted graphs Classification algorithms Databases Equations Image processing Labeling Standards Vectors data classification Image processing Database eikonal equation database management systems sorting algorithm Partial differential equations Pattern recognition Graph theory Differential equations

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High-Accuracy Recognition of Muscle Activation Patterns Using a Hierarchical Classifier 5

High-Accuracy Recognition of Muscle Activation Patterns Usin...

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5th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Rivera, Luis A. Smith, Nicholas R. DeSouza, Guilherme N. Univ Missouri Dept Elect & Comp Engn Vis Guided & Intelligent Robot Lab Columbia MO 65211 USA

ISBN: (纸本)9781479931262

Systems based on Surface Electromyography (sEMG) signals require some form of machine learning algorithm for recognition and classification of specific patterns of muscle activity. These algorithms vary in terms of the number of signals, feature selection, and the classification algorithm used. In our previous work, a technique for recognizing muscle patterns using a single sEMG signal, called Guided Under-determined Source Signal Separation (GUSSS), was introduced. This technique relied on a very small number of features to achieve good classification accuracies for a small number of gestures. In this paper, an enhanced version called Hierarchical GUSSS (HiGUSSS) was developed to allow for the classification of a large number of hand gestures while preserving a high classification accuracy.

关键词： Accuracy Feature extraction Muscles Sensors Support vector machines Training Vectors muscles Feature extraction Support Vector Network Sensors Functional training Gestures sorting algorithm Machine learning algorithms SEMA5B gene Recognition (Psychology) signals Training Cloning Vectors Hierarchical

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A Robust Online Signature Based Cryptosystem 14

A Robust Online Signature Based Cryptosystem

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14th International Conference on Frontiers in Handwriting Recognition, ICFHR 2014

作者： Bhateja, Ashok K. Chaudhury, Santanu Saxena, P.K. Scientific Analysis Group Defence R and D Organization Delhi India Department of Electrical Engineering Indian Institute of Technology Delhi India

ISBN: (纸本)9781479943340

Cryptography is the backbone for the security systems. The main challenge in use of the Cryptosystems is maintaining the confidentiality of the cryptographic key. A Cryptosystem which encrypts the data using biometric features improves the security of the data and overcomes the problems of key management and key confidentiality. Fuzzy Vault Scheme proposed by Juels and Sudan [1] binds the secret key and the biometric template, so that extraction of the secret without the biometric data is infeasible. Physical signature is a biometric that is widely accepted and is used for proving the authenticity of a person in legal documents, bank transactions etc. Electronic devices such as digital tablets capture azimuth, altitude and pressure along with x any y coordinates at fixed time interval. This paper describes a robust online signature based cryptosystem to hide the secret by binding it with invariant online signature templates. The invariant templates of the signature are derived from artificial neural network based classifier. The entire signature is divided into fixed number of time slices. Important features are extracted based on the consistency of the feature in the slices of the genuine signature. Binary back propagation based neural network for each feature, each subset of slices for a user is trained by a weighted back propagation algorithm. The decisions of these networks are combined using AdaBoost algorithm. The proposed scheme is highly robust as it works well for all kinds of signatures and is independent of the number of zero crossing and high curvature points in the signature trajectory. © 2014 IEEE.

关键词： Classification algorithms Cryptography Decoding Feature extraction Handwriting recognition Polynomials Training AdaBoost algorithm back propagation cryptosystem fuzzy vault online signature variance cryptography back propagation handwriting recognition sorting algorithm signatures Feature extraction DECODING Polynomial Functional training secret key Variance

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SVM is not always confident: Telling whether the output from multiclass SVM is true or false by analysing its confidence values 16

SVM is not always confident: Telling whether the output from...

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2014 16th IEEE International Workshop on Multimedia Signal Processing, MMSP 2014

作者： Yamasaki, Toshihiko Maeda, Takaki Aizawa, Kiyoharu Department of Information and Communication Engineering University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo Japan

ISBN: (纸本)9781479958962

This paper presents an algorithm to distinguish whether the output label that is yielded from multiclass support vector machine (SVM) is true or false without knowing the answer. Such judgment is done only by the confidence analysis based on the pre-training/testing using the training data. Such true/false judgment is useful for refining the output labels. We experimentally demonstrate that the decision value difference between the top candidate and the second candidate is a good measure. In addition, a proper threshold can be determined by the pre-training/testing using only the training data. Experimental results using three standard image datasets demonstrate that our proposed algorithm can improve Matthews correlation coefficient (MCC) much better than simply thresholding the decision value for the top candidate.

关键词： data analysis support vector machines MCC Matthews correlation coefficient confidence analysis confidence values decision value difference decision value thresholding false judgment image datasets multiclass SVM multiclass support vector machine output label true judgment Accuracy Classification algorithms Machine learning algorithms Support vector machines Testing Training Training data Support Vector Network Training data Machine learning algorithms sorting algorithm MCC Genes False multi-class Functional training

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CATE: AN OPEN AND HIGHLY CONFIGURABLE FRAMEWORK FOR PERFORMANCE EVALUATION OF PACKET CLASSIFICATION algorithmS 15

CATE: AN OPEN AND HIGHLY CONFIGURABLE FRAMEWORK FOR PERFORMA...

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Simulation Conference

作者： Wladislaw Gusew Sven Hager Bjorn Scheuermann Humboldt University of Berlin Berlin Germany

ISBN: (纸本)9781467397414

Network packet classification is the central building block for important services such as QoS routing and firewalling. Accordingly, a wide range of classification schemes has been proposed, each with its own specific set of characteristics. But while novel algorithms keep being developed at a high pace, there barely exists tool support for proper benchmarking, which makes it hard for researchers and engineers to evaluate and compare those algorithms in changing scenarios. In this paper, we present the Classification algorithm Testing Environment (CATE). CATE consistently and reproducibly extracts the key performance characteristics, such as memory footprint and matching speed, for a predefined set of classification algorithms from a highly customizable set of benchmarks. In addition, we demonstrate that CATE can be used to gain new insights on both the input parameter sensitivity and the scalability of even well-studied algorithms.

关键词： Performance Evaluation performance characteristics BENCHMARKS Packet algorithms sorting algorithm Scalability Building blocks TEST ENVIRONMENT

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A Novel pattern recognition model for real-time voice data input 5

A Novel pattern recognition model for real-time voice data i...

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5th International Conference on Confluence 2014 - The Next Generation Information Technology Summit

作者： Sen, Yogesh Kumar Chaurasiya, R.K. Verma, Shrish Dept. of Electronics and Telecommunication National Institute of Technology Raipur India

ISBN: (纸本)9781479942367

The classical front end analysis in speech recognition is a spectral analysis which parameterizes the speech signal into feature vectors. This paper proposes a voice recognition model that is able to automatically classify and recognize a voice signal with background noise. The model uses the concept of spectrogram, pitch period, short time energy, zero crossing rate, mel frequency scale and cepestral coefficient in order to calculate feature vectors. The k-Nearest Neighbor (k-NN) classification is used for classification and recognition of real-time input signal. Analytical hierarchical process is used for deciding the weightage of different features.

关键词： Accuracy Classification algorithms Real-time systems Speech Speech recognition Training AHP k-nearest neighbor pattern classification pitch-period Speech recognition Pattern recognition Voice Signal sorting algorithm real-time systems Analytic hierarchy process Speech Functional training k nearest neighbor audio data Training

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Feature and Weight Selection Using Tabu Search for Improving the Recognition Rate of Duct Anomaly

Feature and Weight Selection Using Tabu Search for Improving...

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2014 IEEE International Conference on Robotics and Biomimetics (ROBIO)

作者： Wang Yongxiong Kai Li Univ Shanghai Sci & Technol Dept Automat Shanghai 200093 Peoples R China

ISBN: (纸本)9781479973972

In real-time anomaly detection problems, reducing the dimensionality and improving recognition rate are two most crucial problems. The unbalanced data distribution is one of main reasons of leading to low recognition rate. In this paper, a hybrid approach using Tabu search (TS) and ensemble classification algorithm is proposed. Tabu search is simultaneously applied to select features and weights of ensemble classification. To relieve unbalanced data problems, three policies are used: taking advantages of the cost function of TS to attach more importance to high recognition rate of minority class in the process of feature selection, constructing new sample sets by using oversampling and undersampling, and using ensemble classification method to improve the detection accuracy at low false positive rates. Experimental results show that the approach is effective to improve the classification accuracy of unbalanced class.

关键词： tabu search Feature extraction Recognition (Psychology) Cost functions Undersampling sorting algorithm data distribution Oversampling Classification Methods duct TBXAS1 gene CACNA1C gene

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Suffix Array Construction in External Memory Using D-Critical Substrings

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ACM TRANSACTIONS ON INFORMATION SYSTEMS 2014年第1期32卷 1-1页

作者： Nong, Ge Chan, Wai Hong Zhang, Sen Guan, Xiao Feng Sun Yat Sen Univ Dept Comp Sci Guangzhou 510275 Guangdong Peoples R China Hong Kong Inst Educ Dept Math & Informat Technol Kowloon Hong Kong Peoples R China SUNY Coll Oneonta Dept Math Comp Sci & Stat Oneonta NY 13820 USA

We present a new suffix array construction algorithm that aims to build, in external memory, the suffix array for an input string of length n measured in the magnitude of tens of Giga characters over a constant or integer alphabet. The core of this algorithm is adapted from the framework of the original internal memory SA-DS algorithm that samples fixed-size d-critical substrings. This new external-memory algorithm, called EM-SA-DS, uses novel cache data structures to construct a suffix array in a sequential scanning manner with good data spatial locality: data is read from or written to disk sequentially. On the assumed external-memory model with RAM capacity Omega ((nB)(0.5)), disk capacity O(n), and size of each I/O block B, all measured in log n-bit words, the I/O complexity of EM-SA-DS is O(n/B). This work provides a general cache-based solution that could be further exploited to develop external-memory solutions for other suffix-array-related problems, for example, computing the longest-common-prefix array, using a modern personal computer with a typical memory configuration of 4GB RAM and a single disk.

关键词： Suffix array sorting algorithm external memory algorithms Performance

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Granular Computing Classification algorithms Based on Distance Measures between Granules from the View of Set

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2014年第unknown期2014卷 656790-656798页

作者： Liu, Hongbing Liu, Chunhua Wu, Chang-an Xinyang Normal Univ Sch Comp & Informat Technol Xinyang 464000 Peoples R China

Granular computing classification algorithms are proposed based on distance measures between two granules from the view of set. Firstly, granules are represented as the forms of hyperdiamond, hypersphere, hypercube, and hyperbox. Secondly, the distance measure between two granules is defined from the view of set, and the union operator between two granules is formed to obtain the granule set including the granules with different granularity. Thirdly the threshold of granularity determines the union between two granules and is used to form the granular computing classification algorithms based on distance measures (DGrC). The benchmark datasets in UCI Machine Learning Repository are used to verify the performance of DGrC, and experimental results show that DGrC improved the testing accuracies.

关键词： sorting algorithm View distance measurement

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