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检索条件"机构=Center for Brain-Like Computing and Machine Intelligence"

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EEG-Based Emotion Recognition with Manifold Regularized Extreme Learning machine

EEG-Based Emotion Recognition with Manifold Regularized Extr...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Yong Peng Jia-Yi Zhu Wei-Long Zheng Bao-Liang Lu Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University

ISBN: (纸本)9781424479276

EEG signals, which can record the electrical activity along the scalp, provide researchers a reliable channel for investigating human emotional states. In this paper, a new algorithm, manifold regularized extreme learning machine (MRELM), is proposed for recognizing human emotional states (positive, neutral and negative) from EEG data, which were previously evoked by watching different types of movie clips. The MRELM can simultaneously consider the geometrical structure and discriminative information in EEG data. Using differential entropy features across whole five frequency bands, the average accuracy of MRELM is 81.01%, which is better than those obtained by GELM (80.25%) and SVM (76.62%). The accuracies obtained from high frequency band features (β, γ) are obviously superior to those of low frequency band features, which shows β and γ bands are more relevant to emotional states transition. Moreover, experiments are conducted to further evaluate the efficacy of MRELM, where the training and test sets are from different sessions. The results demonstrate that the proposed MRELM is a competitive model for EEG-based emotion recognition.

关键词： Electroencephalography Emotion recognition Support vector machines Training Accuracy Motion pictures brain modeling

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Hedge Detection and Scope Finding by Sequence Labeling with Normalized Feature Selection 14

Hedge Detection and Scope Finding by Sequence Labeling with ...

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14th Conference on Computational Natural Language Learning, CoNLL 2010

作者： Zhang, Shaodian Zhao, Hai Zhou, Guodong Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Dept of Computer Science and Engineering Shanghai Jiao Tong University China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University China School of Computer Science and Technology Soochow University China

ISBN: (纸本)1932432841

This paper presents a system which adopts a standard sequence labeling technique for hedge detection and scope finding. For the first task, hedge detection, we formulate it as a hedge labeling problem, while for the second task, we use a two-step labeling strategy, one for hedge cue labeling and the other for scope finding. In particular, various kinds of syntactic features are systemically exploited and effectively integrated using a large-scale normalized feature selection method. Evaluation on the CoNLL-2010 shared task shows that our system achieves stable and competitive results for all the closed tasks. Furthermore, post-deadline experiments show that the performance can be much further improved using a sufficient feature selection. © 2010 Association for Computational Linguistics

关键词： Wooden fences

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Multimodal Emotion Recognition using EEG and Eye Tracking Data

Multimodal Emotion Recognition using EEG and Eye Tracking Da...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Wei-Long Zheng Bo-Nan Dong Bao-Liang Lu Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University

ISBN: (纸本)9781424479276

This paper presents a new emotion recognition method which combines electroencephalograph (EEG) signals and pupillary response collected from eye tracker. We select 15 emotional film clips of 3 categories (positive, neutral and negative). The EEG signals and eye tracking data of five participants are recorded, simultaneously, while watching these videos. We extract emotion-relevant features from EEG signals and eye tracing data of 12 experiments and build a fusion model to improve the performance of emotion recognition. The best average accuracies based on EEG signals and eye tracking data are 71.77% and 58.90%, respectively. We also achieve average accuracies of 73.59% and 72.98% for feature level fusion strategy and decision level fusion strategy, respectively. These results show that both feature level fusion and decision level fusion combining EEG signals and eye tracking data can improve the performance of emotion recognition model.

关键词： Electroencephalography Feature extraction Emotion recognition Accuracy brain modeling Videos Entropy

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Real-time head detection with kinect for driving fatigue detection

Real-time head detection with kinect for driving fatigue det...

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20th International Conference on Neural Information Processing, ICONIP 2013

作者： Cao, Yang Lu, Bao-Liang Center for Brain-like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China MOE-Microsoft Key Lab. for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China

ISBN: (纸本)9783642420504

Nowadays, depth cameras such Microsoft Kinect make it easier and cheaper for us to capture depth images. It becomes practical to use depth images for detection in consumer-grade products. In this paper, we propose a novel and simple real-time method to detect human head in depth image for our driving fatigue detection system, based on the elliptical shape of human head. Experiments show that our method can successfully detect human head in different light conditions and across different head poses. We integrate this detection algorithm into our driving fatigue detection system, and see remarkable improvements both in detection rate and detection speed. © Springer-Verlag 2013.

关键词： Computers

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Automated quality assessment of web pages from textual content

Automated quality assessment of web pages from textual conte...

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2012 International Conference on machine Learning and Cybernetics, ICMLC 2012

作者： Wang, Xiao-Lin Zhao, Hai Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University 800 Dong Chuan Rd. Shanghai 200240 China

ISBN: (纸本)9781467314855

Given the vastness of Internet, search engines have to find not only relevant but also high-quality web pages to satisfy users' information need. At present, most quality assessing methods for web pages are based on link analysis and user feedbacks. Considering that users acquire information from web pages mainly through reading their text, this paper addresses automated quality assessment of web pages from textual content. This paper surveys related works on assessing text's quality, summarizes quality-related features, and examines them with a real-word data set. Experimental results show that features based on the length of text are the most effective, while combining length features with other features such as part-of-speech tags and readability can further improve the accuracy. © 2012 IEEE.

关键词： Websites

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A machine learning method to distinguish machine translation from human translation 29

A machine learning method to distinguish machine translation...

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29th Pacific Asia Conference on Language, Information and Computation, PACLIC 2015

作者： Li, Yitong Wang, Rui Zhai, Hai Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai200240 China

This paper introduces a machine learning ap-proach to distinguish machine translation texts from human texts in the sentence level au-Tomatically. In stead of traditional methods, we extract some linguistic features only from the target language side to train the predic-Tion model and these features are independent of the source language. Our prediction mod-el presents an indicator to measure how much a sentence generated by a machine translation system looks like a real human translation. Furthermore, the indicator can directly and ef-fectively enhance statistical machine transla-Tion systems, which can be proved as BLEU score improvements.

关键词： machine translation

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Neural network language model for Chinese pinyin input method engine 29

Neural network language model for Chinese pinyin input metho...

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29th Pacific Asia Conference on Language, Information and Computation, PACLIC 2015

作者： Chen, Shen-Yuan Wang, Rui Zhao, Hai Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai200240 China

Neural network language models (NNLMs) have been shown to outperform traditional n-gram language model. However, too high computational cost of NNLMs becomes the main obstacle of directly integrating it into pinyin IME that normally requires a real-Time response. In this paper, an efficient solution is proposed by converting NNLMs into back-off n-gram language models, and we integrate the converted NNLM into pinyin IME. Our exper-imental results show that the proposed method gives better decoding predictive performance for pinyin IME with satisfied efficiency.

关键词： Computational linguistics

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High-order graph-based neural dependency parsing 29

High-order graph-based neural dependency parsing

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29th Pacific Asia Conference on Language, Information and Computation, PACLIC 2015

作者： Zhang, Zhisong Zhao, Hai Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai200240 China

In this work, we present a novel way of using neural network for graph-based dependency parsing, which fits the neural network into a simple probabilistic model and can be furthermore generalized to high-order parsing. Instead of the sparse features used in traditional methods, we utilize distributed dense feature representations for neural network, which give better feature representations. The proposed parsers are evaluated on English and Chinese Penn Treebanks. Compared to existing work, our parsers give competitive performance with much more efficient inference.

关键词： Graphic methods

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An Improved Graph Model for Chinese Spell Checking 3

An Improved Graph Model for Chinese Spell Checking

引用

3rd CIPS-SIGHAN Joint Conference on Chinese Language Processing, CLP 2014

作者： Xin, Yang Zhao, Hai Wang, Yuzhu Jia, Zhongye Department of Computer Science and Engineering Center for Brain-Like Computing and Machine Intelligence Shanghai Jiao Tong University Shanghai200240 China Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai200240 China

In this paper, we propose an improved graph model for Chinese spell checking. The model is based on a graph model for generic errors and two independentlytrained models for specific errors. First, a graph model represents a Chinese sentence and a modified single source shortest path algorithm is performed on the graph to detect and correct generic spelling errors. Then, we utilize conditional random fields to solve two specific kinds of common errors: the confusion of "?" (at) (pinyin is 'zai' in Chinese), "?" (again, more, then) (pinyin: zai) and "?" (of) (pinyin: de), "?" (-ly, adverb-forming particle) (pinyin: de), "?" (so that, have to) (pinyin: de). Finally, a rule based system is exploited to solve the pronoun usage confusions: "?" (she) (pinyin: ta), "?" (he) (pinyin: ta) and some others fixed collocation errors. The proposed model is evaluated on the standard data set released by the SIGHAN Bake-off 2014 shared task, and gives competitive result. © 2014 CLP 2014 - 3rd CIPS-SIGHAN Joint Conference on Chinese Language Processing. All rights reserved.

关键词： Graph theory

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English to Chinese translation: How Chinese character matters? 29

English to Chinese translation: How Chinese character matter...

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29th Pacific Asia Conference on Language, Information and Computation, PACLIC 2015

作者： Wang, Rui Zhao, Hai Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai200240 China

Word segmentation is helpful in Chinese natural language processing in many aspects. However it is showed that different word segmentation strategies do not affect the performance of Statistical machine Translation (SMT) from English to Chinese significantly. In addition, it will cause some confusions in the evaluation of English to Chinese SMT. So we make an empirical attempt to translation English to Chinese in the character level, in both the alignment model and language model. A series of empirical comparison experiments have been conducted to show how different factors affect the performance of character-level English to Chinese SMT. We also apply the recent popular continuous s- pace language model into English to Chinese SMT. The best performance is obtained with the BLEU score 41.56, which improve base- line system (40.31) by around 1.2 BLEU s- core.

关键词： Computer aided language translation

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