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International IEEE/EMBS Conference on Neural Engineering, CNE

作者： Dan Nie Xiao-Wei Wang Li-Chen Shi Bao-Liang Lu MOE-Microsoft Key Laboratory of Intelligent Computing and Intelligent System Shanghai Jiaotong University Shanghai China Shanghai Jiao Tong University Shanghai CN

This study aims at finding the relationship between EEG signals and human emotions. EEG signals are used to classify two kinds of emotions, positive and negative. First, we extracted features from original EEG data and used a linear dynamic system approach to smooth these features. An average test accuracy of 87.53% was obtained by using all of the features together with a support vector machine. Next, we reduced the dimension of features through correlation coefficients. The top 100 and top 50 subject-independent features were achieved, with average test accuracies of 89.22% and 84.94%, respectively. Finally, a manifold model was applied to find the trajectory of emotion changes.

关键词： Accuracy Electroencephalography Motion pictures Emotion recognition Trajectory Humans Manifolds

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A study of an irrelevant variability normalization based discriminative training approach for LVCSR

A study of an irrelevant variability normalization based dis...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yu Zhang Jian Xu Zhi-Jie Yan Qiang Huo Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems China Microsoft Research Asia Beijing China University of Science and Technology Hefei China

This paper presents a discriminative training (DT) approach to irrelevant variability normalization (IVN) based training of feature transforms and hidden Markov models for large vocabulary continuous speech recognition. A speaker-clustering based method is used for acoustic sniffing and maximum mutual information (MMI) is used as a training criterion. Combined with unsupervised adaptation of feature transforms, the IVN-based DT approach achieves a 14.5% relative word error rate reduction over an MMI-trained baseline system on a Switchboard-1 conversational telephone speech transcription task.

关键词： Training Hidden Markov models Acoustics Transforms Speech Speech recognition Feature extraction

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An Empirical Comparative Study on Two Large-Scale Hierarchical Text Classification Approaches

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International Journal of Computer Processing of Languages 2011年第4期23卷 309-325页

作者： JIAN ZHANG HAI ZHAO LIQING ZHANG BAO-LIANG LU Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai 200240 China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiaotong University 800 Dongchuan Rd. Shanghai 200240 China Department of Computer Science Virginia Tech Blacksburg VA 24061 USA

Patent classification is a large scale hierarchical text classification (LSHTC) task. Though comprehensive comparisons, either learning algorithms or feature selection strategies, have been fully made in the text categorization field, few work was done for a LSHTC task due to high computational cost and complicated structural label characteristics. For the first time, this paper compares two popular learning frameworks, namely, hierarchical support vector machine (SVM) and k -nearest neighbor ( k -NN) that are applied to a LSHTC task. Our experimental results show that the latter outperforms the former for the LSHTC task, which is quite different from the existing results for normal text categorization tasks. In addition, this paper compares different similarity measures and ranking strategies in k -NN framework for LSHTC task. From our empirical study, conclusions can be drawn that k -NN is more appropriate for the LSHTC task than hierarchical SVM. BM25 outperforms other similarity measures and ListWeak gains a better performance than other ranking strategies. Our empirical results also indicate that using all the labels of the retrieved neighbors can remarkably improve classification performance over only using the first label of the retrieved neighbors.

关键词： Large-scale text classification hierarchical text classification text classification k -NN hierarchical SVM similarity measure ranking strategies

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A Game Theoretic Model and Tree Analysis Method for Fair Exchange Protocols

A Game Theoretic Model and Tree Analysis Method for Fair Exc...

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Theoretical Aspects of Software Engineering (TASE)

作者： Guoqiang Li Yonggen Gu Xiuting Tao Jie Tao BASICS School of Software MOE-MS Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University Shanghai China School of Information and Engineering HUzhou Teacher's College Zhejiang China College of Mathematics Physics and Information Engineering Zhejiang Normal University China

Exchange protocols are an important theoretic basis to make secure electronic commerce and electronic business transactions possible, in which the fairness is a crucial property. To ensure and verify the property, a specific model is proposed, based on the extensive game with imperfect information. Fairness is built in the protocol game and the corresponding game tree. To verify the property, a tree analysis method is offered, and a linear time algorithm is given. As a case study, some flaws of ASW protocol are found.

关键词： Protocols Games Computational modeling Game theory Educational institutions Analytical models Electronic commerce

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Estimate vigilance level in driving simulation based on sparse representation

Estimate vigilance level in driving simulation based on spar...

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International Conference on Machine Learning and Cybernetics

作者： Liu, Hong-Jun Yu, Hong-Bin Ren, Qing-Sheng Lu, Hong-Tao Department of Computer Science and Engineering MOE-Microsoft Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)9781424465262

Avoiding fatal accidents caused by low vigilance level in driving is very important in our daily lives. Electroencephalography(EEG) has been proved very effective for measuring the level of vigilance. In this paper, we distinguish vigilance level into three classes which are 'alert', 'fatigue' and 'sleeping' by using sparse representation classification(SRC). Six features from each frequency band are got from samples of EEG data. Random feature is used to reduce the dimension of features. Actually there is almost no training process before the classification. The accuracy in classification of three classes reaches about 90 % on average. © 2010 IEEE.

关键词： Electroencephalography

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A unified character-based tagging framework for chinese word segmentation

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ACM Transactions on Asian Language Information Processing 2010年第2期9卷 1–32页

作者： Zhao, Hai Huang, Chang-Ning Li, Mu. Lu, Bao-Liang Department of Computer Science and Engineering MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University 800 Dongchuan Road Minhang District Shanghai 200240 China Language Computing Group Microsoft Research Asia 49 Zhichun Road Haidian District Beijing 100080 China

Chinese word segmentation is an active area in Chinese language processing though it is suffering from the argument about what precisely is a word in Chinese. Based on corpus-based segmentation standard, we launched this study. In detail, we regard Chinese word segmentation as a character-based tagging problem. We show that there has been a potent trend of using a character-based tagging approach in this field. In particular, learning from segmented corpus with or without additional linguistic resources is treated in a unified way in which the only difference depends on how the feature template set is selected. It differs from existing work in that both feature template selection and tag set selection are considered in our approach, instead of the previous feature template focus only technique. We show that there is a significant performance difference as different tag sets are selected. This is especially applied to a six-tag set, which is good enough for most current segmented corpora. The linguistic meaning of a tag set is also discussed. Our results show that a simple learning system with six n-gram feature templates and a six-tag set can obtain competitive performance in the cases of learning only from a training corpus. In cases when additional linguistic resources are available, an ensemble learning technique, assistant segmenter, is proposed and its effectiveness is verified. Assistant segmenter is also proven to be an effective method as segmentation standard adaptation that outperforms existing ones. Based on the proposed approach, our system provides state-of-the-art performance in all 12 corpora of three international Chinese word segmentation bakeoffs. © 2010 ACM 1530-0226/2010/06-ART5 $10.00.

关键词： Learning systems

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Flatten hierarchies for large-scale hierarchical text categorization

Flatten hierarchies for large-scale hierarchical text catego...

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International Conference on Digital Information Management

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

ISBN: (纸本)9781424475728

Hierarchies are very popular in organizing documents and web pages, hence automated hierarchical classification techniques are desired. However, the current dominant hierarchical approach of top-down method suffers accuracy decrease compared with flat classification approaches, because of error propagation and bottom nodes' data sparsity. In this paper we flatten hierarchies to relieve such accuracy decrease in top-down method, which aims to make hierarchies both effective enough to make large-scale classification tasks feasible, and simple enough to ensure high classification accuracy. We propose two flattening strategies based on these two causes of the accuracy decrease, and experimental results show that the flattening strategy designed for error propagation is more effective, which suggests that hierarchies with lots of branches at top layers can provide high classification accuracy. Besides, we analyze the computational complexity before and after flattening, which approximately agree with the experimental results. ©2010 IEEE.

关键词： Websites

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Comparative analysis of motor imagery on different scales based on brain computer interface

Comparative analysis of motor imagery on different scales ba...

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作者： Li, Junhua Zhang, Liqing Department of Computer Science and Engineering MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University Shanghai 200240 China

The event-related brain oscillatory responses have a substantial role in motor imagery experiments. There are two patterns, i.e. event-related desynchronization (ERD) and eventrelated synchronization (ERS). In clinical applications and scientific experiments, an obvious event-related potential (ERP) is pivotal to achieve required tasks. Therefore, we explore how subjects generate the obvious ERP more easily. Eight healthy subjects participated in the experiments and completed motor imagery on two different scales (index finger versus arm). The results indicate that the motor imagery of arm reaches the better performance compared with the motor imagery of index finger. Furthermore, we make a comparative analysis in averaged power spectrum in order to reveal inherent phenomenon. The analysis of power spectrum further confirms that arm motor imagery has an advantage over index finger motor imagery.

关键词： Brain computer interface

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A study on recommendation features for an RSS reader

A study on recommendation features for an RSS reader

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International Conference on Cyber-Enabled Distributed computing and Knowledge Discovery

作者： Ji, Cansheng Zhou, Jingyu School of Software MOE-MS Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)9780769542355

In the era of web 2.0, everyone can create and update content, and everyone can host a personal web site with little effort, making it hard to gather valuable information from different web sites. With RSS, people can read information from different resources in a uniform way, and in a single tool, such as an RSS reader. However, most of the RSS readers only display items in chronological order, which doesn't work well when users are inundated with too many items in the feeds. We propose using recommendation to help people find items in an RSS reader. Specifically, we consider profiled based features (i.e., text similarity and favorite fraction), update frequency, as well as PostRank values for RSS recommendation. Experimental results indicate that favorite fraction and update frequency perform better than text similarity. Additionally, we also study the effect of feature combination and find that the combination of similarity and favorite fraction performs the best. © 2010 IEEE.

关键词： Vector spaces

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Vigilance analysis based on continuous wavelet transform of EEG signals

Vigilance analysis based on continuous wavelet transform of ...

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2010 International Conference on Biomedical Engineering and Computer Science, ICBECS 2010

作者： Ouyang, Tian Lu, Hong-Tao MOE-MS Key Laboratory for Intelligent Computing and Intelligent Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781424453153

Electroencephalography (EEG) is considered a reliable indicator of a person's vigilance level. In this paper, we use EEG recordings to discriminate three vigilance states of a person, namely alert, drowsy, and sleep, while driving a car in a simulation environment. The proposed framework explores the use of continuous wavelet transform in EEG signal processing. A large set of features is extracted from the wavelet coefficients, which are computed from EEG signals with multiple wavelet functions. We use random forest to rank the plenty of features and select the most important ones for later classification. Samples of EEG data are then trained and classified by SVM (Support Vector Machine). On datasets acquired from 5 subjects, our method reveals high classification accuracy (over 96%). ©2010 IEEE.

关键词： Electroencephalography

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