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Group Activity Recognition with Group Interaction Zone

Group Activity Recognition with Group Interaction Zone

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International Conference on Pattern Recognition

作者： Young-Ji Kim Nam-Gyu Cho Seong-Whan Lee Department of Computer Science and Engineering Korea University Seoul Korea Department of Brain and Cognitive Engineering Korea University Seoul Korea

ISBN: (纸本)9781479952106

In this paper, we address the problem of recognizing group activities that include interactions between human objects based on their motion trajectory analysis. In order to resolve the complexity and ambiguity problems caused by a large number of human objects, we propose a Group Interaction Zone (GIZ) to detect meaningful groups in a scene so as to be robust against noisy information. Two novel features, Group Interaction Energy feature and Attraction and Repulsion Features, are proposed to better describe group activities within a GIZ. We demonstrate the effectiveness of our method with other methods on the public BEHAVE dataset.

关键词： Feature extraction Trajectory Conferences Pattern recognition computer vision Clustering algorithms Computational modeling

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An Improved Graph Model for Chinese Spell Checking 3

An Improved Graph Model for Chinese Spell Checking

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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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Learning hierarchical translation spans

Learning hierarchical translation spans

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2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014

作者： Zhang, Jingyi Utiyama, Masao Sumita, Eiichro Zhao, Hai Department of Computer Science and Engineering Center for Brain-Like Computing and Machine Intelligence Shanghai Jiao Tong Unviersity Shanghai200240 China Key Laboratory Shanghai Education Commission Intelligent Interaction Cognitive Engineering Shanghai Jiao Tong Unviersity Shanghai200240 China National Institute of Information and Communications Technology 3-5Hikaridai Keihanna Science City Kyoto619-0289 Japan

ISBN: (纸本)9781937284961

We propose a simple and effective approach to learn translation spans for the hierarchical phrase-based translation model. Our model evaluates if a source span should be covered by translation rules during decoding, which is integrated into the translation system as soft constraints. Compared to syntactic constraints, our model is directly acquired from an aligned parallel corpus and does not require parsers. Rich source side contextual features and advanced machine learning methods were utilized for this learning task. The proposed approach was evaluated on NTCIR-9 Chinese-English and Japanese-English translation tasks and showed significant improvement over the baseline system. © 2014 Association for Computational Linguistics.

关键词： Syntactics

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NEURAL NETWORKS FOR SUPERVISED PITCH TRACKING IN NOISE

NEURAL NETWORKS FOR SUPERVISED PITCH TRACKING IN NOISE

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Kun Han DeLiang Wang Department of Computer Science and Engineering & Center for Cognitive and Brain Sciences The Ohio State University Columbus OH 43210-1277 USA

ISBN: (纸本)9781479928941

Determination of pitch in noise is challenging because of corrupted harmonic structure. In this paper, we extract pitch using supervised learning, where probabilistic pitch states are directly learned from noisy speech. We investigate two alternative neural networks modeling the pitch states given observations. The first one is the feedforward deep neural network (DNN), which is trained on static frame-level features. The second one is the recurrent deep neural network (RNN) capable of learning the temporal dynamics trained on sequential frame-level features. Both DNNs and RNNs produce accurate probabilistic outputs of pitch states, which are then connected into pitch contours by Viterbi decoding. Our systematic evaluation shows that the proposed pitch tracking approaches are robust to different noise conditions and significantly outperform current state-of-the-art pitch tracking techniques.

关键词： pitch PITCHES Noise Neural network Viterbi decoding recurrent neural nets Ship motion Semi-supervised Learning tracking

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LEARNING SPECTRAL MAPPING FOR SPEECH DEREVERBERATION

LEARNING SPECTRAL MAPPING FOR SPEECH DEREVERBERATION

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Kun Han Yuxuan Wang DeLiang Wang Department of Computer Science and Engineering & Center for Cognitive and Brain Sciences The Ohio State University Columbus OH 43210-1277 USA

ISBN: (纸本)9781479928941

Reverberation distorts human speech and usually has negative effects on speech intelligibility, especially for hearing-impaired listeners. It also causes performance degradation in automatic speech recognition and speaker identification systems. Therefore, the dereverberation problem must be dealt with in daily listening environments. We propose to use deep neural networks (DNNs) to learn a spectral mapping from the reverberant speech to the anechoic speech. The trained DNN produces the estimated spectral representation of the corresponding anechoic speech. We demonstrate that distortion caused by reverberation is substantially attenuated by the DNN whose outputs can be resynthesized to the dereverebrated speech signal. The proposed approach is simple, and our systematic evaluation shows promising dereverberation results, which are significantly better than those of related systems.

关键词： Speech Speech recognition Reverberation Speech Intelligibility degradation of performance associated system distortion system evaluation negative effects Hearing Disorders

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Hybrid brain-computer interface based on EEG and NIRS modalities

Hybrid brain-computer interface based on EEG and NIRS modali...

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International Winter Workshop on brain-computer Interface (BCI)

作者： Min-Ho Lee Siamac Fazli Jan Mehnert Seong-Whan Lee Department of Brain and Cognitive Engineering Korea University Seoul Korea Department of Computer Science Berlin Institute of Technology Berlin Germany

Non-invasive brain-computer interfaces (BCIs) allow users to control external devices by their intentions. Currently, most BCI systems are synchronous, which means, they rely on cues or tasks to which a subject has to react. It would be more useful for users if they could control a device at their own will (i.e., asynchronous BCIs). However, previous asynchronous BCI systems that rely on non-invasive electroencephalogram (EEG) measurements, are not accurate and stable enough for real world applications. Previously, hybrid BCI systems, relying on simultaneous EEG and near-infrared spectroscopy (NIRS) measurements, have been shown to increase the classification performance of synchronous motor imagery (MI) tasks. In this study, we present a first report on an asynchronous multi-modal hybrid BCI, based on simultaneous EEG and near-infrared spectroscopy (NIRS) measurements and propose novel subject-dependent classification strategies for combining both measurements.

关键词： Electroencephalography brain-computer interfaces Accuracy Band-pass filters Digital filters Performance evaluation Visualization

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Neural network based bilingual language model growing for statistical machine translation

Neural network based bilingual language model growing for st...

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2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014

作者： Wang, Rui Zhao, Hai Lu, Bao-Liang Utiyama, Masao Sumita, Eiichro Department of Computer Science and Engineering Center for Brain-Like Computing and Machine Intelligence Shanghai Jiao Tong University Shanghai200240 China Multilingual Translation Laboratory MASTAR Project National Institute of Information and Communications Technology 3-5 Hikaridai Keihanna Science City Kyoto619-0289 Japan Key Laboratory Shanghai Education Commission Intelligent Interaction Cognitive Engineering Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9781937284961

Since larger n-gram Language Model (LM) usually performs better in Statistical Machine Translation (SMT), how to construct efficient large LM is an important topic in SMT. However, most of the existing LM growing methods need an extra monolingual corpus, where additional LM adaption technology is necessary. In this paper, we propose a novel neural network based bilingual LM growing method, only using the bilingual parallel corpus in SMT. The results show that our method can improve both the perplexity score for LM evaluation and BLEU score for SMT, and significantly outperforms the existing LM growing methods without extra corpus. © 2014 Association for Computational Linguistics.

关键词： Neural networks

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Information geometry meets BCI spatial filtering using divergences

Information geometry meets BCI spatial filtering using diver...

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International Winter Workshop on brain-computer Interface (BCI)

作者： Wojciech Samek Klaus-Robert Müller Department of Computer Science Berlin Institute of Technology (TU Berlin) Berlin Germany Department of Brain and Cognitive Engineering Korea University Seoul Republic of Korea

Algorithms using concepts from information geometry have recently become very popular in machine learning and signal processing. These methods not only have a solid mathematical foundation but they also allow to interpret the optimization process and the solution from a geometric perspective. In this paper we apply information geometry to brain-computer Interfacing (BCI). More precisely, we show that the spatial filter computation in BCI can be cast into an information geometric framework based on divergence maximization. This formulation not only allows to integrate many of the recently proposed CSP algorithms in a principled manner, but also enables us to easily develop novel CSP variants with different properties. We evaluate the potentials of our information geometric framework on a data set containing recordings from 80 subjects.

关键词： Covariance matrices Spatial filters Robustness Information geometry Linear programming

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brain-computer interface for smart vehicle: Detection of braking intention during simulated driving

Brain-computer interface for smart vehicle: Detection of bra...

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International Winter Workshop on brain-computer Interface (BCI)

作者： Jeong-Woo Kim Il-Hwa Kim Stefan Haufe Seong-Whan Lee Dept. of Brain and Cognitive Engineering Korea Univ. Seoul Korea Dept. of Computer Science Berlin Institute of Technology Berlin Germany

Neuro-driving simulation framework was proposed in this article for studying neural correlates of braking intention in diversified driving situations. In addition, the possibility was investigated that these neural correlates can be used to detect a participant's braking intention prior to the behavioral response. Electroencephalographic (EEG) and electromyographic (EMG) signals were measured from fifteen participants during they were exposed to several kinds of traffic situations in a neuro-driving simulation framework. After that, the novel characteristic feature was extracted from the measured signals to categorize according to whether the driver intended to brake or not. This proposed novel feature consists of readiness potential (RP), event-related desynchronization (ERD) and event-related potential (ERP) as used in a previous study. The prediction performance of braking intention based on the proposed feature combination exhibited superior prediction performance than simple ERP feature used in a previous study.

关键词： Vehicles Feature extraction Electroencephalography Accidents brain modeling Electromyography computers

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Information-theoretic objective functions for lifelong learning

Information-theoretic objective functions for lifelong learn...

引用

2013 AAAI Spring Symposium

作者： Zhang, Byoung-Tak School of Computer Science and Engineering Graduate Programs in Cognitive Science and Brain Science Seoul National University Seoul 151-742 Korea Republic of

ISBN: (纸本)9781577356028

Conventional paradigms of machine learning assume all the training data are available when learning starts. However, in lifelong learning, the examples are observed sequentially as learning unfolds, and the learner should continually explore the world and reorganize and refine the internal model or knowledge of the world. This leads to a fundamental challenge: How to balance long-term and short-term goals and how to trade-off between information gain and model complexity? These questions boil down to "what objective functions can best guide a lifelong learning agent?" Here we develop a sequential Bayesian framework for lifelong learning, build a taxonomy of lifelong-learning paradigms, and examine information-theoretic objective functions for each paradigm, with an emphasis on active learning. The objective functions can provide theoretical criteria for designing algorithms and determining effective strategies for selective sampling, representation discovery, knowledge transfer, and continual update over a lifetime of experience. © 2013, Association for the Advancement of artificial intelligence.

关键词： Learning systems

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