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New lower bounds for two multicolor vertex Folkman numbers

New lower bounds for two multicolor vertex Folkman numbers

引用

international conference on Computer and Management

作者： Shao, Zehui Liang, Meilian He, Jiandong Xu, Xiaodong School of Information Science and Technology Chengdu University Chengdu 610106 China Key Laboratory of Pattern Recognition and Intelligent Information Processing Sichuan Province China School of Mathematics and Information Science Guangxi University Nanning 530004 China Guangxi Academy of Sciences Nanning 530007 China

For a graph G, G → (a1, a2, · · ·, ar)v means that in every r-coloring of the vertices in G, there exists a monochromatic ai-clique of color i for some i∈{1, 2, · · ·, r}. The vertex Fo... 详细信息

ISBN: (纸本)9781424492831

For a graph G, G → (a1, a2, · · ·, ar)v means that in every r-coloring of the vertices in G, there exists a monochromatic ai-clique of color i for some i∈{1, 2, · · ·, r}. The vertex Folkman number is defined as Fv(a1, a2, ···, ar;k) = min{|V(G)|: G → (a1, a2, ···, ar)v and Kk G}. In general, computing lower and upper bounds for vertex Folkman numbers is difficult. In this note, based on theoretical analysis and computation, we show that Fv(2, 3, 3;4) ≥ 19 and Fv(3, 3, 3;4) ≥ 24, and suggest a cyclic graph of order 91 which may give an upper bound for Fv(3, 3, 3;4). ©2011 IEEE.

关键词： Graph theory

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Feature Selection for Mobility pattern recognition in Mobile Ad-Hoc Networks

Feature Selection for Mobility Pattern Recognition in Mobile...

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international conference on Wireless Communications, Networking and Mobile computing (WiCom)

作者： S. M. Mousavi Faculty of Computer Engineering University of Qom Qom Iran

Mobility is one of the most challenging issues in mobile Ad-Hoc networks which has significant impact on performance of variety of network protocols. To deal with this matter the protocol designers should be able to analyze movement behavior of mobile nodes in a particular wireless network. In our previous works we have proposed a simple mobility pattern recognition method which can classify mobility traces into different mobility model classes. The main issue here is finding appropriate features which can classify different mobility traces into mobility classes accurately. In this paper we try to introduce suitable and minimal features to make our mobility pattern recognition method able to distinguish and classify different mobility models more accurate. Simulation results show significant efficiency of our proposed mobility pattern recognition method using appropriate feature sets introduces in this paper.

关键词： Mobile communication Measurement Ad hoc networks Mobile computing Markov processes Feature extraction pattern recognition

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SIGAda 2011 - Proceedings of the 2011 ACM international conference on Ada and Related Technologies

SIGAda 2011 - Proceedings of the 2011 ACM International Conf...

引用

2011 Annual international conference of the Association for computing Machinery's Special Interest Group on the Ada Programming Language, SIGAda 2011

ISBN: (纸本)9781450310284

The proceedings contain 10 papers. The topics discussed include: how to measure and optimize reliable embedded software;service-oriented architecture (SOA) concepts and implementations;improving quality of Ada software with range analysis;SF1: introduction to Ada;designing and checking coding standards for Ada;building embedded real-time applications;a parallel programming model for Ada;stack safe parallel recursion with paraffin;how to make Ada go viral;software vulnerabilities precluded by SPARK;enhancing SPARK's contract checking facilities using symbolic execution;an Ada design pattern recognition tool for AADL performance analysis;improving quality of Ada software with range analysis;making the non-executable ACATS tests executable;and DO-178C: the next avionics safety standard.

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Advancements in Arabic-to-English hierarchical machine translation

Advancements in Arabic-to-English hierarchical machine trans...

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15th international conference of the European Association for Machine Translation, EAMT 2011

作者： Huck, Matthias Vilar, David Stein, Daniel Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany DFKI GmbH Berlin Germany

ISBN: (纸本)9789081486118

In this paper we study several advanced techniques and models for Arabic-to-English statistical machine translation. We examine how the challenges imposed by this particular language pair and translation direction can be successfully tackled within the framework of hierarchical phrase-based translation. We extend the state-of-the-art with a novel cross-system and cross-paradigm lightlysupervised training approach. In addition, for following recently developed techniques we provide a concise review, an empirical evaluation, and an in-depth analysis: soft syntactic labels, a discriminative word lexicon model, additional reorderings, and shallow rules. We thus bring together complementary methods that previously have only been investigated in isolation and mostly on different language pairs. Combinations of the methods yield significant improvements over a baseline using a usual set of models. The resulting hierarchical systems perform competitive on the large-scale NIST Arabic-to-English translation task. © 2011 European Association for Machine Translation.

关键词： Hierarchical systems

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Dynamic Successive Feed-Forward Neural Network for Learning Fuzzy Decision Tree

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13th international conference on Rough Sets, Fuzzy Sets and Granular computing (RSFDGrC)

作者： Singh, Manu Pratap Dr BR Ambedkar Univ Dept Comp Sci ICIS Agra Uttar Pradesh India

ISBN: (纸本)9783642218804;9783642218811

Fuzzy decision trees have been substantiated to be a valuable tool and more efficient than neural networks for pattern recognition task due to some facts like computation in making decisions are simpler and important features can be selected automatically during the design process. Here we present a feed forward neural network which learns fuzzy decision trees during the descent along the branches for its classification. Every decision instances of decision tree are represented by a node in neural network. The neural network provides the degree of membership of each possible move to the fuzzy set << good move >> corresponding to each decision instance. These fuzzy values constitute the core of the probability of selecting the move out of the set of the children of the current node. This results in a natural way for driving the sharp discrete-state process running along the decision tree by means of incremental methods on the continuous-valued parameters of the neural network. A simulation program in C has been deliberated and developed for analyzing the consequences. The effectiveness of the learning process is tested through experiments with three real-world classification problems.

关键词： Decision tree pattern classification fuzzy system artificial neural networks fuzzy logic

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11th international Work-conference on Artificial Neural Networks, IWANN 2011

11th International Work-Conference on Artificial Neural Netw...

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11th international Work-conference on Artificial Neural Networks, IWANN 2011

ISBN: (纸本)9783642215001

The proceedings contain 154 papers. The special focus in this conference is on Visual computing. The topics include: A Geometrical Method of Diffuse and Specular Image Components Separation;Optical Flow Reliability Model Approximated with RBF;video and Image Processing with Self-Organizing Neural Networks;parallelism in Binary Hopfield Networks;Multi-parametric Gaussian Kernel Function Optimization for Ε-SVMr Using a Genetic Algorithm;face recognition System in a Dynamical Environment;memetic Pareto Differential Evolutionary Neural Network for Donor-Recipient Matching in Liver Transplantation;Studying the Hybridization of Artificial Neural Networks in HECIC;processing Acyclic Data Structures Using Modified Self-Organizing Maps;visual Features Extraction Based Egomotion Calculation from a Infrared Time-of-Flight Camera;On the Performance of the μ-GA Extreme Learning Machines in Regression Problems;a Hybrid Evolutionary Approach to Obtain Better Quality Classifiers;neural Network Ensembles with Missing Data Processing and Data Fusion Capacities: Applications in Medicine and in the Environment;hybrid Artificial Neural Networks: Models, Algorithms and Data;automatic recognition of Daily Living Activities Based on a Hierarchical Classifier;prediction of Functional Associations between Proteins by Means of a Cost-Sensitive Artificial Neural Network;Hybrid (Generalization-Correlation) Method for Feature Selection in High Dimensional DNA Microarray Prediction Problems;Model Selection with PLANN-CR-ARD;Gender recognition Using PCA and DCT of Face Images;efficient Face recognition Fusing Dynamic Morphological Quotient Image with Local Binary pattern;feature Weighting in Competitive Learning for Multiple Object Tracking in Video Sequences;a Growing Neural Gas Algorithm with Applications in Hand Modelling and Tracking;object Representation with Self-Organising Networks;visual Mining of Epidemic Networks.

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13th international conference on Rough Sets, Fuzzy Sets, Data Mining and Granular computing, RSFDGrC 2011

13th International Conference on Rough Sets, Fuzzy Sets, Dat...

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13th international conference on Rough Sets, Fuzzy Sets, Data Mining and Granular computing, RSFDGrC 2011

ISBN: (纸本)9783642218804

The proceedings contain 56 papers. The special focus in this conference is on Rough Sets, Fuzzy Sets, Data Mining and Granular computing. The topics include: An Improved Variable Precision Model of Dominance-Based Rough Set Approach;rough Numbers and Rough Regression;covering Numbers in Covering-Based Rough Sets;on Coverings of Rough Transformation Semigroups;covering Rough Set Model Based on Multi-granulations;a Descriptive Language Based on Granular computing – Granular Logic;optimization and Adaptation of Dynamic Models of Fuzzy Relational Cognitive Maps;sensitivity Analysis for Fuzzy Linear Programming Problems;estimation of Parameters of the Empirically Reconstructed Fuzzy Model of Measurements;Towards Faster Estimation of Statistics and ODEs Under Interval, P-Box, and Fuzzy Uncertainty: From Interval Computations to Rough Set-Related Computations;dominance-Based Rough Set Approach for Possibilistic Information Systems;creating Fuzzy Concepts: the One-Sided Threshold, Fuzzy Closure and Factor Analysis Methods;position Paper: Pragmatics in Fuzzy Theory;regularization of Fuzzy Cognitive Maps for Hybrid Decision Support System;on Designing of Flexible Neuro-Fuzzy Systems for Nonlinear Modelling;time Series Processing and Forecasting Using soft computing Tools;fuzzy Linear Programming – Foreign Exchange Market;fuzzy Optimal Solution of Fuzzy Transportation Problems with Transshipments;Fuzzy Optimal Solution of Fully Fuzzy Project Crashing Problems with New Representation of LR Flat Fuzzy Numbers;a Prototype System for Rule Generation in Lipski’s Incomplete Information Databases;rough Set Based Uncertain Knowledge Expressing and Processing;how to Reconstruct the System’s Dynamics by Differentiating Interval-Valued and Set-Valued Functions;symbolic Galois Lattices with pattern Structures;accumulated Cost Based Test-Cost-Sensitive Attribute Reduction.

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11th international Work-conference on Artificial Neural Networks, IWANN 2011

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11th international Work-conference on Artificial Neural Networks, IWANN 2011

ISBN: (纸本)9783642214974

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Learning Readers' News Preferences with Support Vector Machines

Learning Readers' News Preferences with Support Vector Machi...

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10th international conference on Artificial Neural Networks and Genetic Algorithms

作者： Hensinger, Elena Flaounas, Ilias Cristianini, Nello Univ Bristol Intelligent Syst Lab Bristol BS8 1TH Avon England

ISBN: (纸本)9783642202667

We explore the problem of learning and predicting popularity of articles from online news media. The only available information we exploit is the textual content of the articles and the information whether they became popular by users clicking on them or not. First we show that this problem cannot be solved satisfactorily in a naive way by modelling it as a binary classification problem. Next, we cast this problem as a ranking task of pairs of popular and non-popular articles and show that this approach can reach accuracy of up to 76%. Finally we show that prediction performance can improve if more content-based features are used. For all experiments, Support Vector Machines approaches are used.

关键词： pattern recognition Data mining Applications Machine learning

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Robust Eye Movement recognition Using EOG Signal for Human-Computer Interface

Robust Eye Movement Recognition Using EOG Signal for Human-C...

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2nd international conference on software Engineering and computing Systems (ICSECS 2011)

作者： Aungsakun, Siriwadee Phinyomark, Angkoon Phukpattaranont, Pornchai Limsakul, Chusak 15 Kanjanavanich Rd Hat Yai 90112 Songkhla Thailand

ISBN: (纸本)9783642221903

Electrooculography (EOG) signal is one of the useful biomedical signals. Development of EOG signal as a control signal has been paid more increasing interest in the last decade. In this study, we are proposing a robust classification algorithm of eight useful directional movements that it can avoid effect of noises, particularly eye-blink artifact. Threshold analysis is used to detect onset of the eye movements. Afterward, four beneficial time features are proposed that are peak and valley amplitude positions, and upper and lower lengths of two EOG channels. Suitable threshold conditions were defined and evaluated. From experimental results, optimal threshold values were selected for each parameters and classification accuracies approach to 100% for three subjects testing. To avoid the eye-blink artifact, the first derivative was additionally implemented.

关键词： Electrooculography (EOG) signal eye movement eye blink artifact robust pattern recognition human-machine interface (HMI)

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