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ACM SIGKDD Workshop on Mining Data Semantics 2012, MDS'12 - SIGKDD 2012

作者： Zhao, Shu Xu, Xiansheng Hua, Bo Zhang, Yanping School of Computer Science and Technology Anhui University Hefei Anhui Province 230039 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei Anhui Province 230039 China

ISBN: (纸本)9781450315463

In this paper, we present a novel idea to solve maximum flow problem in the directed networks. Given a directed flow network which we call original network here, we propose a method of Contracting Neighbor-node-set Approach (CNA) based on Quotient Space Theory. It selects some nodes from fine-grained original network as equivalence class to form a new node in the new network. The coarse-grained new network is called quotient network. Then we apply classic algorithms on quotient network to solve the maximum flow of original network. The performance in practice of our algorithm is better than that of classic algorithms and the method of simplifying network. Experimental results show that CNA can approximate accurately solve the maximum flow of original network . The correctness of maximum flow is over 95%. At the same time, the node scale reduces to 55.45% of node scale of original network and edge scale reduces to 32.10% of edge scale of original network, averagely. Copyright © 2012 ACM.

关键词： Equivalence classes

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Secure two-party computation of the quadratic function's extreme minimal value

Secure two-party computation of the quadratic function's ext...

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2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2012

作者： Fen, Wu Hong, Zhong Runhua, Shi Hongsheng, Huang School of Computer Science and Technology Anhui University Anhui China Key Laboratory of Intelligent Computing and Signal Processing Anhui University Ministry of Education Anhui China Department of Operation and Maintence in China Mobile Xuancheng China

ISBN: (纸本)9781467300223

Privacy-Preserving Computational Geometry (PPCG) is a special Secure Multi-party Computation, which is a hot research in information security. This paper presented a special PPCG problem of secure two-party computing the quadratic function's extreme minimal value on a secret interval. Based on Paillier's additive homomorphic and secure comparison protocol, a novel protocol for this problem is proposed. Furthermore, its correctness, security and efficiency are analyzed. The analysis results show that the proposed protocol is much more efficient than current protocol, which can be applied in some military and commercial fields. © 2012 IEEE.

关键词： Computational geometry

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A new warped discrete fourier transform algorithm and its application to whisper enhancement

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ICIC Express Letters, Part B: Applications 2012年第1期3卷 21-28页

作者： Zhou, Jian Liang, Ruiyu Tao, Liang Zhao, Li Zou, Cairong Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education Southeast University No. 2 Sipailou Nanjing 210096 China School of Computer Science and Technology Anhui University No. 3 Feixi Road Hefei 230601 China College of Computer and Information Hohai University No. 1 Xikang Road Nanjing 210098 China

Warped Discrete Fourier Transform (WDFT) is able to increase frequency resolution at any selected parts of spectral axis without extending the number of sampling points. However, in ordinary WDFT synthesis block, inverse WDFT is imperfectly approximated since the WDFT related matrices are ill-conditioned. In this paper, we propose an innovative WDFT algorithm based on matching pursuit where an overcomplete set consisting of a series of complex exponent basis signals is constructed in order to minimize reconstruction error. The process is performed solely in the warped spectrum domain. The proposed algorithm is evaluated with speech signal reconstruction and used in whispered speech enhancement system. The experimental results show that signal can be perfectly synthesized without matrix inversion and the warped spectrum extracted using the proposed WDFT algorithm outperforms DFT and conventional WDFT based spectrum in whispered speech intelligibility enhancement. © 2012 ISSN 2185-2766.

关键词： signal reconstruction

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Tensor classification for P300-based brain computer interface

Tensor classification for P300-based brain computer interfac...

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2012 IEEE International Conference on Acoustics, Speech, and signal processing, ICASSP 2012

作者： Onishi, Akinari Phan, Anh Huy Matsuoka, Kiyotoshi Cichocki, Andrzej Kyushu Institute of Technology Graduate School of Life Science and System Engineering Department of Brain Science and Engineering 2-4 Hibikino Wakamatsu Kitakyushu Fukuoka Japan RIKEN Brain Science Institute Laboratory for Advanced Brain Signal Processing 2-1 Hirosawa Wako Saitama Japan

ISBN: (纸本)9781467300469

Classification methods have been widely applied in most brain computer interfaces (BCIs) that control devices for better quality of life. Most existing classification methods for P300-based BCIs extract features based on temporal structure related to P300 components of event-related potentials (ERPs). Some others exploit the spatial distribution of ERPs optimally selected by recursive channel elimination. However, none of them employed multilinear structures which exploit hidden features in P300-based BCI data. In this paper, we propose a new feature extraction method based on tensor decomposition for ERP-based BCIs. The method seeks an optimal feature subspace simultaneously spanned by temporal and spatial bases, and additional bases which indicate a variant of ERPs obtained by different degrees of polynomial fittings. The proposed method has been evaluated by both the BCI competition III data set II and the affective face driven paradigm data set, and achieved 92% and 95% classification accuracies respectively, which were better than those of most existing P300-based BCI algorithms. © 2012 IEEE.

关键词： Discriminant analysis

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Off-Line Handwritten Signature Verification Using Contourlet Transform and Co-occurrence Matrix

Off-Line Handwritten Signature Verification Using Contourlet...

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International Workshop on Frontiers in Handwriting Recognition

作者： Assia Hamadène Youcef Chibani Hassiba Nemmour Speech Communication and Signal Processing Laboratory Faculty of Electronics and Computer Science University of Science and Technology Houari Boumediene Algiers Algeria

We address, in this work, a new feature generation method for two different approaches of off-line handwritten signature verification (HSV), writer-dependent and writer-independent HSV. The proposed method uses conjointly the contourlet transform and the co-occurence matrix. The contourlet transform allows capturing contour segment directions of the handwritten signature, while the co-occurrence matrix allows describing the number of directions. Experiments are conducted on the well known CEDAR dataset and the classification through the support vector machines (SVM). The obtained results show the effective use of the Contourlet transform for handwritten signature verification comparatively to the state of the art.

关键词： Handwriting recognition Transforms Computed tomography Vectors Support vector machines Laplace equations Filter banks

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SVM-DSmT combination for off-line signature verification

SVM-DSmT combination for off-line signature verification

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International Conference on Computer, Information and Telecommunication Systems (CITS)

作者： Nassim Abbas Youcef Chibani Speech Communication and Signal Processing Laboratory Faculty of Electronics and Computer Science University of Science and Technology of Houari Boumedienne Algiers Algeria

We propose in this work a signature verification system based on decision combination of off-line signatures for managing conflict provided by the SVM classifiers. The system is basically divided into three modules: i) Radon Transform-SVM, ii) Ridgelet Transform-SVM and iii) PCR5 combination rule based on the generalized belief functions of Dezert-Smarandache theory. The proposed framework allows combining the normalized SVM outputs and uses an estimation technique based on the dissonant model of Appriou to compute the belief assignments. Decision making is performed through likelihood ratio. Experiments are conducted on the well known CEDAR database using false rejection and false acceptance criteria. The obtained results show that the proposed combination framework improves the verification accuracy compared to individual SVM classifiers.

关键词： Support vector machines Transforms Biometrics Forgery Training Estimation

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Handwritten Digit Recognition Based on a DSmT-SVM Parallel Combination

Handwritten Digit Recognition Based on a DSmT-SVM Parallel C...

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International Workshop on Frontiers in Handwriting Recognition

作者： Nassim Abbas Youcef Chibani Hassiba Nemmour Speech Communication and Signal Processing Laboratory Faculty of Electronics and Computer Science University of Science and Technology Houari Boumediene Algiers Algeria

We propose in this work a new handwritten digit recognition system based on parallel combination of SVM classifiers for managing conflict provided between their outputs. Firstly, we evaluate different methods of generating features to train the SVM classifiers that operate independently of each other. To improve the performance of the system, the outputs of SVM classifiers are combined through the Dezert-Smarandache theory. The proposed framework allows combining the calibrated SVM outputs issued from a sigmoid transformation and uses an estimation technique based on a supervised model to compute the belief assignments. Decision making is performed by maximizing the new Dezert-Smarandache probability. The performance evaluation of the proposed system is conducted on the well known US Postal Service database. Experimental results show that the proposed combination framework improves the recognition rate even when individual SVM classifiers provide conflicting outputs.

关键词： Support vector machines Handwriting recognition Error analysis Databases Accuracy Estimation Character recognition

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One-class versus bi-class SVM classifier for off-line signature verification

One-class versus bi-class SVM classifier for off-line signat...

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IEEE International Conference on Multimedia Computing and Systems (ICMCS)

作者： Yasmine Guerbai Youcef Chibani Nassim Abbas Speech Communication and Signal Processing Laboratory Faculty of Electronics and Computer Science University of Science and Technology of Houari Boumedienne Algiers Algeria

Support vector machines (SVMs) have become an alternative tool for pattern recognitions, and more specifically for Handwritten Signature Verification Systems (HSVS). Usually, the bi-class SVMs (B-SVM) are used for separating between genuine and forged signatures. However, in practice, only genuine signatures are available. In this paper, we investigate the use of one-class SVM (OC-SVM) for handwritten signature verifications. Experimental results conducted on the standard CEDAR database show the effective use of the one-class SVM compared to the bi-class SVM.

关键词： Support vector machines Databases Biological system modeling Physiology Classification algorithms Equations Pattern recognition

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Localized detection of speech recognition errors

Localized detection of speech recognition errors

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IEEE Spoken Language technology Workshop

作者： Svetlana Stoyanchev Philipp Salletmayr Jingbo Yang Julia Hirschberg Department of Computer Science Columbia University USA Signal Processing and Speech Communication Laboratory Graz University of Technology Austria

We address the problem of localized error detection in Automatic Speech Recognition (ASR) output. Localized error detection seeks to identify which particular words in a user's utterance have been misrecognized. Identifying misrecognized words permits one to create targeted clarification strategies for spoken dialogue systems, allowing the system to ask clarification questions targeting the particular type of misrecognition, in contrast to the “please repeat/rephrase” strategies used in most current dialogue systems. We present results of machine learning experiments using ASR confidence scores together with prosodic and syntactic features to predict whether 1) an utterance contains an error, and 2) whether a word in a misrecognized utterance is misrecognized. We show that by adding syntactic features to the ASR features when predicting misrecognized utterances the F-measure improves by 13.3% compared to using ASR features alone. By adding syntactic and prosodic features when predicting misrecognized words F-measure improves by 40%.

关键词： Accuracy Speech recognition Syntactics Humans Speech Training Feature extraction

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MFCC and vector quantization for Arabic fricatives speech/speaker recognition

MFCC and vector quantization for Arabic fricatives speech/sp...

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IEEE International Conference on Multimedia Computing and Systems (ICMCS)

作者： Fatma Zohra. Chelali Amar. Djeradi Speech communication and signal processing laboratory Faculty of Electronics engineering and computer science University of Science and Technology of Houari Boumedienne Algiers Algeria

This article develops a speaker-dependent Arabic phonemes recognition system using MFCC analysis and the VQ-LBG algorithm. The system is examined with and without vector quantization in order to analyze the effect of compression in an acoustic parameterization phase. Our experimental results show that vector quantization using a codebook of size 16 achieves good results compared to the system without quantization for a majority of the phonemes studied.

关键词： Mel frequency cepstral coefficient Speech Feature extraction Vectors Speaker recognition Speech recognition

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