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检索条件"机构=Key Lab. Machine Intelligence and Advanced Computing"

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A dynamic competitive swarm optimizer based-on entropy for large scale optimization

A dynamic competitive swarm optimizer based-on entropy for l...

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International Workshop on advanced Computational intelligence (IWACI)

作者： Wen-Xiao Zhang Wei-Neng Chen Jun Zhang School of Data and Computer Science Sun Yat-sen University Guangzhou China Key Lab. of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

In this paper, a dynamic competitive swarm optimizer (DCSO) based on population entropy is proposed. The new algorithm is derived from the competitive swarm optimizer (CSO). The new algorithm uses population entropy to make a quantitative description about the diversity of population, and to divide the population into two sub-groups dynamically. During the early stage of the execution process, to speed up convergence of the algorithm, the sub-group with better fitness will have a small size, and worse large sub-group will learn from small one. During the late stage of the execution process, to keep the diversity of the population, the sub-group with better fitness will have a large size, and small worse sub-group will learn from large group. The proposed DCSO is evaluated on CEC'08 benchmark functions on large scale global optimization. The simulation results of the example indicate that the new algorithm has better and faster convergence speed than CSO.

关键词： Sociology Statistics Entropy Optimization Convergence Heuristic algorithms Benchmark testing

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Interest Tree Based Information Dissemination via Vehicular Named Data Networking

Interest Tree Based Information Dissemination via Vehicular ...

引用

International Conference on Computer Communications and Networks (ICCCN)

作者： Xiaokun Li Siyang Wang Weigang Wu Xu Chen Bin Xiao Ministry of Education Sun Yet-Sen University Guangdong Province Key Lab. of Big Data Analysis and Processing Key Lab. of Machine Intelligence and Advanced Computing Guangzhou Guangdong China Department of Computing Hong Kong Polytechnic University Hong Kong China

Named Data Networking (NDN) is a promising technology for content centric networks, and it is suitable for vehicular networks since no IP architecture is required. Quite a number of solutions have been proposed for vehicular NDN (V- NDN), but high communication cost due to frequent topology changes caused by high mobility of vehicles is still a challenge to be addressed. In this paper, we study how to disseminate traffic information to vehicles via V-NDN. Different from existing works, we consider navigation route based data interests, i.e., a vehicle is concerned about the traffic information along road segments planned to take. According to such a data interest scenario, we propose a tree based data interest structure and associated maintenance operations to merge identical data interests due to overlapping navigation routes among different vehicles. With the tree based data interest management, the number of interest packets can be significantly reduced. Then, we propose trigger based mechanisms for data interest packet re-sending and forwarding, which can avoid unnecessary interest packets re-sending. With our design, traffic information can be disseminated to interested nodes with high success ratio and low communication cost simultaneously. Simulations via SUMO and ndnSIM confirm such advantages of our work.

关键词： Roads Navigation Vehicular ad hoc networks Network topology Data communication Prototypes Wireless communication

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Personalizing EEG-based affective models with transfer learning 25

Personalizing EEG-based affective models with transfer learn...

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25th International Joint Conference on Artificial intelligence, IJCAI 2016

作者： Zheng, Wei-Long Lu, Bao-Liang Center for Brain-like Computing and Machine Intelligence Department of Computer Science and Engineering China Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering China Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China

Individual differences across subjects and nonstationary characteristic of electroencephalography (EEG) limit the generalization of affective braincomputer interfaces in real-world applications. On the other hand, it is very time consuming and expensive to acquire a large number of subjectspecific lab.led data for learning subject-specific models. In this paper, we propose to build personalized EEG-based affective models without lab.led target data using transfer learning techniques. We mainly explore two types of subject-to-subject transfer approaches. One is to exploit shared structure underlying source domain (source subject) and target domain (target subject). The other is to train multiple individual classifiers on source subjects and transfer knowledge about classifier parameters to target subjects, and its aim is to learn a regression function that maps the relationship between feature distribution and classifier parameters. We compare the performance of five different approaches on an EEG dataset for constructing an affective model with three affective states: positive, neutral, and negative. The experimental results demonstrate that our proposed subject transfer framework achieves the mean accuracy of 76.31% in comparison with a conventional generic classifier with 56.73% in average.

关键词： Electroencephalography

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Deep knn for medical image classification 23rd

Deep knn for medical image classification

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23rd International Conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2020

作者： Zhuang, Jiaxin Cai, Jiabin Wang, Ruixuan Zhang, Jianguo Zheng, Wei-Shi School of Data and Computer Science Sun Yat-sen University Guangzhou China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou China Pazhou Lab Guangzhou China

ISBN: (纸本)9783030597092

Human-level diagnostic performance from intelligent systems often depends on large set of training data. However, the amount of availab.e data for model training may be limited for part of diseases, which would cause the widely adopted deep learning models not generalizing well. One alternative simple approach to small class prediction is the traditional k-nearest neighbor (kNN). However, due to the non-parametric characteristics of kNN, it is difficult to combine the kNN classification into the learning of feature extractor. This paper proposes an end-to-end learning strategy to unify the kNN classification and the feature extraction procedure. The basic idea is to enforce that each training sample and its K nearest neighbors belong to the same class during learning the feature extractor. Experiments on multiple small-class and class-imbalanced medical image datasets showed that the proposed deep kNN outperforms both kNN and other strong classifiers. © Springer Nature Switzerland AG 2020.

关键词： Nearest neighbor search

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Particle swarm optimization with Monte-Carlo simulation and hypothesis testing for network reliability problem

Particle swarm optimization with Monte-Carlo simulation and ...

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International Workshop on advanced Computational intelligence (IWACI)

作者： Lu-Yao Wu Wei-Neng Chen Hao-Hui Deng Jun Zhang Yun Li Advanced Computing Ministry of Education Guangzhou China Sun Yat-sen University Guangzhou China Key Lab. of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China Sun Yat-sen University Guangzhou China

The performance of Monte-Carlo Simulation(MCS) is highly related to the number of simulation. This paper introduces a hypothesis testing technique and incorporated into a Particle Swarm Optimization(PSO) based Monte-Carlo Simulation(MCS) algorithm to solve the complex network reliability problem. The function of hypothesis testing technique is to reduce the dispensable simulation in network system reliability estimation. The proposed technique contains three components: hypothesis testing, network reliability calculation and PSO algorithm for finding solutions. The function of hypothesis testing is to abandon unpromising solutions; we use Monte-Carlo simulation to obtain network reliability; since the network reliability problem is NP-hard, PSO algorithm is applied. Since the execution time can be better decreased with the decrease of Confidence level of hypothesis testing in a range, but the solution becomes worse when the confidence level exceed a critical value, the experiment are carried out on different confidence levels for finding the critical value. The experimental results show that the proposed method can reduce the computational cost without any loss of its performance under a certain confidence level.

关键词： Reliability Testing Monte Carlo methods Optimization Particle swarm optimization Complex networks Probability

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English to Chinese translation: How Chinese character matters? 29

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

引用

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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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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High-order graph-based neural dependency parsing 29

High-order graph-based neural dependency parsing

引用

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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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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