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AVEC 2011-the First international Audio/Visual Emotion Challenge

AVEC 2011-The First International Audio/Visual Emotion Chall...

引用

4th Bi-Annual international conference of the Humaine Association on Affective Computing and Intelligent Interaction (ACII 2011)

作者： Schuller, Bjoern Valstar, Michel Eyben, Florian McKeown, Gary Cowie, Roddy Pantic, Maja Tech Univ Munich Inst Human Machine Commun Munich Germany Imperial Coll London Intelligent Behav Understand Group London England Queens Univ Belfast Sch Psychol Belfast BT7 1NN Antrim North Ireland Univ Twente EEMCS POB 217 NL-7500 AE Twente Netherlands

ISBN: (纸本)9783642245701

the Audio/Visual Emotion Challenge and Workshop (AVEC 2011) is the first competition event aimed at comparison of multimedia processing and machine learning methods for automatic audio, visual and audiovisual emotion analysis, with all participants competing under strictly the same conditions. this paper first describes the challenge participation conditions. Next follows the data used - the SEMAINE corpus - and its partitioning into train, development, and test partitions for the challenge with labelling in four dimensions, namely activity, expectation, power, and valence. Further, audio and video baseline features are introduced as well as baseline results that use these features for the three sub-challenges of audio, video, and audiovisual emotion recognition.

关键词： Audiovisual Emotion recognition Speech Emotion recognition Facial Expression Analysis Challenge

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Human-machine learning for Intelligent Aircraft Systems

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8th international conference on Image Analysis and recognition (ICIAR) / 2nd international conference on Autonomous and Intelligent Systems (AIS)

作者： Rubin, Stuart H. Lee, Gordon SPAWAR SSC Pacific 53560 Hull St San Diego CA 92152 USA San Diego State Univ Dept Elect & Comp Engn San Diego CA 92182 USA

ISBN: (纸本)9783642215377;9783642215384

the solution for insuring the safety of tele-operated or fully unmanned autonomous systems (UASs) in the air space requires a) that the human remain in and on the loop to the maximal extent practical and b) that the UASs, which share the air space, have an intelligent backend for the processing of their sensory data. Moreover, it is necessary that this sensory processor be capable of generalizing and learning more than it was told in order that it properly handle situations not explicitly programmed for. Given the advent of advances in nanotechnology and microsystems, several research teams continue to investigate the integration of such technologies for single UASs and small swarms of UASs for military, commercial, and civilian applications. Our proposed technology can be readily adapted for transparent learning to serve as an assistant for human piloting as well as an emergency intelligent autopilot for all manner of piloted vehicles.

关键词： CBR Context-Free Grammar Heuristics KASER machine learning

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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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Discovering Correspondences between Fingerprints Based on the Temporal Dynamics of Eye Movements from Experts

Discovering Correspondences between Fingerprints Based on th...

引用

4th international Workshop on Computational Forensics

作者： Yu, Chen Busey, thomas Vanderkolk, John Indiana Univ Bloomington IN 47405 USA

ISBN: (纸本)9783642193750

Latent print examinations involve a process by which a latent print, often recovered from a crime scene, is compared against a known standard or sets of standard prints. Despite advances in automatic fingerprint recognition, latent prints are still examined by human expert primarily due to the poor image quality of latent prints. the aim of the present study is to better understand the perceptual and cognitive processes of fingerprint practices as implicit expertise. Our approach is to collect fine-grained gaze data from fingerprint experts when they conduct a matching task between two prints. We then rely on machine learning techniques to discover meaningful patterns from their eye movement data. As the first steps in this project, we compare gaze patterns from experts with those obtained from novices. Our results show that experts and novices generate similar overall gaze patterns. However, a deeper data analysis using machine translation reveals that experts are able to identify more corresponding areas between two prints within a short period of time.

关键词： fingerprint Cognitive and Behavioral Studies Computational data analysis data mining

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Research on off-line handwritten Chinese characters recognition based on biomimetic pattern

Research on off-line handwritten Chinese characters recognit...

引用

international conference on Intelligent Computation Technology and Automation

作者： Wang, Jianping Wu, Changbin Zhu, Chenghui Xu, Xiaobing Wang, Jinling School of Electrical Engineering and Automation Hefei University of Technology Hefei China School of Computer and Information Hefei University of Technology Hefei China

ISBN: (纸本)9780769543536

In this paper, the off-line Chinese character image is transformed into ellipse shape of basic Chinese characters strokes in different position. the stroke "turning" and joint or crossover of strokes is combined by basic strokes. the neural network for extracting Chinese character has been build up. the research on off-line Chinese character image is transformed into the research on double weights elliptical neural network based on biomimetic pattern. By extracting fault-tolerant features of the 4 kinds of basic stroke, 13 kinds of "turning" stroke and 7 kinds of consistency and intersection of strokes, the data-knowledge table of features is constructed. the method of bionic recognition gives the machine the ability of cognizing features of stroke type and number, features of stroke location and features of topology structural. the simple and complex handwritten Chinese characters are used to carry out simulation experiment in SCUT-IRAC-HCCLIB. the experiment results show that the algorithm exhibits a strong ability of cognizing handwritten Chinese characters. © 2011 IEEE.

关键词： Fault tolerance

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Stepwise feature selection using Multiple Kernel learning

Stepwise feature selection using Multiple Kernel Learning

引用

international Workshop on Soft Computing Applications and Knowledge Discovery 2011, SCAKD 2011 - In Conjunction with the 4th international conference on pattern recognition and machine Intelligence, PReMI 2011

作者： Jumutc, Vilen Riga Technical University Meza 1/4 LV-1658 Riga Latvia

In this paper we propose a novel more flexible approach for the simultaneous feature selection and classification using Support Vector machine and recent major advances of it, namely Multiple Kernel learning. Using a quite simple kernel assembly scheme in the following paper we will indicate that feature selection and classification could be done in one step without applying computationally intensive and maybe inadequate filtering or wrapper approach. Later imply that to achieve dimensionality reduction, tractable and more compact as well as comprehensively accurate model it is necessary to accomplish all of above goals by "training" SVM only once. Actually we apply some additional prerequirement that resulted in a ranking criteria that could be provided by any domain expert or created by our algorithm using Linear SVM by itself. Provided experimental results verify that our approach is comparable or even more accurate and robust than other feature extraction/ selection schemes tested on public UCI datasets.

关键词： Support vector machines

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Audio-Based Emotion recognition from Natural Conversations Based on Co-Occurrence Matrix and Frequency Domain Energy Distribution Features

Audio-Based Emotion Recognition from Natural Conversations B...

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4th Bi-Annual international conference of the Humaine Association on Affective Computing and Intelligent Interaction (ACII 2011)

作者： Sayedelahl, Aya Fewzee, Pouria Kamel, Mohamed S. Karray, Fakhri Univ Waterloo Pattern Anal & Machine Intelligence Lab Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9783642245701

Emotion recognition from natural speech is a very challenging problem. the audio sub-challenge represents an initial step towards building an efficient audio-visual based emotion recognition system that can detect emotions for real life applications (i.e. human-machine interaction and/or communication). the SEMAINE database, which consists of emotionally colored conversations, is used as the benchmark database. this paper presents our emotion recognition system from speech information in terms of positive/negative valence, and high and low arousal, expectancy and power. We introduce a new set of features including Co-Occurrence matrix based features as well as frequency domain energy distribution based features. Comparisons between well-known prosodic and spectral features and the new features are presented. Classification using the proposed features has shown promising results compared to the classical features on both the development and test data sets.

关键词： Speech Emotion recognition Co-Occurrence Matrix Frequency Domain Energy Distribution

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Forecasting the U.S. stock market via Levenberg-Marquardt and Haken artificial neural networks using ICA&PCA pre-processing techniques

Forecasting the U.S. stock market via Levenberg-Marquardt an...

引用

作者： Sergey, Golovachev National Research University Higher School of Economics Moscow Department of World Economics and International Affairs Russia

Artificial neural networks (ANN) is an approach to solving different tasks. In this paper we forecast U.S. stock market movements using two types of artificial neural networks: a network based on the Levenberg-Marquardt learning mechanism and a synergetic network which was described by German scientist Herman Haken. the Levenberg-Marquardt ANN is widely used for forecasting financial markets, while the Haken ANN is mostly known for the tasks of image recognition. In this paper we apply the Haken ANN for the prediction of the stock market movements. Furthermore, we introduce a novation concerning preprocessing of the input data in order to enhance the predicting power of the abovementioned networks. For this purpose we use Independent Component Analysis (ICA) and Principal Component Analysis (PCA). We also suggest using ANNs to reveal the "mean reversion" phenomenon in the stock returns. the results of the forecasting are compared with the forecasts of the simple auto-regression model and market index dynamics.

关键词： Principal component analysis

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Evaluation of a Resource Allocating Network with Long Term Memory Using GPU

Evaluation of a Resource Allocating Network with Long Term M...

引用

10th international conference on Artificial Neural Networks and Genetic Algorithms

作者： Ribeiro, Bernardete Quintas, Ricardo Lopes, Noel Univ Coimbra Dept Informat Engn P-3000 Coimbra Portugal CISUC P-3000 Coimbra Portugal

ISBN: (纸本)9783642202667

Incremental learning has recently received broad attention in many applications of pattern recognition and data mining. With many typical incremental learning situations in the real world where a fast response to changing data is necessary, developing a parallel implementation (in fast processing units) will give great impact to many applications. Current research on incremental learning methods employs a modified version of a resource allocating network (RAN) which is one variation of a radial basis function network (RBFN). this paper evaluates the impact of a Graphics Processing Units (GPU) based implementation of a RAN network incorporating Long Term Memory (LTM) [4]. the incremental learning algorithm is compared with the batch RBF approach in terms of accuracy and computational cost, both in sequential and GPU implementations. the UCI machine learning benchmark datasets and a real world problem of multimedia forgery detection were considered in the experiments. the preliminary evaluation shows that although the creation of the model is faster with the RBF algorithm, the RAN-LTM can be useful in environments with the need of fast changing models and high-dimensional data.

关键词： Incremental learning CPU Computing

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Beyond analytical modeling, gathering data to predict real agents' strategic interaction

Beyond analytical modeling, gathering data to predict real a...

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作者： Tagiew, Rustam Institute for Computer Science TU Bergakademie Freiberg Germany

this paper presents research proposals on the interdisciplinary research infrastructure for understanding human reasoning in game-theoretic terms. Strategic reasoning impacts human decision making in social, economical and competitive interactions. the provided introduction summarizes concepts from AI, game theory and psychology. First result is a concept of interdisciplinary game description language as a part of the focused interdisciplinary research infrastructure. the need of this domain-specific language is motivated and is aimed to accelerate the current developments. As second result, the paper provides a summary of ongoing research and its significance.

关键词： Game theory

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