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18th Pacific-Asia Conference on Advances in Knowledge Discovery and data mining, PAKDD 2014

作者： Liu, Bo Jing, Liping Yu, Jian Li, Jia Beijing Key Lab. of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing 100044 China College of Information Science and Technology Agricultural University of Hebei Hebei 071000 China

ISBN: (纸本)9783319066042

The core tasks of graph based semi-supervised learning (GSSL) are constructing a proper graph and selecting suitable supervisory information. The ideal graph is able to outline the intrinsic data structure, and the ideal supervisory information could represent the whole data. In this paper, we propose a new graph learning method, called constrained least squares regression (CLSR), which integrates the supervisory information into graph learning process. To learn a more adaptive graph, regression coefficients and neighbor relations are combined in CLSR to capture the global and local data structures respectively. Moreover, as byproduct of CLSR, a new strategy is presented to select the high-quality data points as labeled samples, which is practical in real applications. Experimental results on different real world datasets demonstrate the effectiveness of CLSR and the sample selection strategy. © 2014 Springer International Publishing.

关键词： Least squares approximations

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A simple calculation method of rail head surface defect area under the high speed condition

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Journal of Computational Information Systems 2014年第24期10卷 10683-10691页

作者： Lyu, Guohao Yin, Hui Huang, Hua Huang, Yaping Jaing, Xinlan Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China

Area is the most important piece of information in rail head surface defect detection. However, under the high speed condition, e.g. over 300 km/h, it is hard to obtain the area information because of the partial loss of length and width information. In order to solve this problem, a simple and fast algorithm is proposed. This algorithm adopts the line-scan calibration, and calculates the defect areas which the line-scan camera can get and cannot get respectively. In the traditional algorithm, the area is calculated by counting the number of pixels. Comparing with the traditional method, the experiment shows that our algorithm has the same complexity, but better accuracy in area calculation.

关键词： Calibration

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A Bayesian Graph Neural Network for EEG Classification - A Win-Win on Performance and Interpretability 39

A Bayesian Graph Neural Network for EEG Classification - A W...

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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Wang, Jing Ning, Xiaojun Shi, Wangjun Lin, Youfang Beijing Jiaotong University School of Computer and Information Technology China Beijing Jiaotong University Beijing Key Lab of Traffic Data Analysis and Mining China Caac Key Laboratory of Intelligent Passenger Service of Civil Aviation Beijing China

ISBN: (纸本)9798350322279

With the deepening of neuroscience research, data mining of brain signals is becoming an emerging topic. Among various brain signals, electroencephalography (EEG) has attracted more and more attention due to its advantages of non-invasiveness, portability, and low cost. EEG modeling and analysis play a vital role in human healthcare. Although many machine learning algorithms have been successfully applied to data mining of EEG signals, few of them achieve a win-win in classification performance and interpretability. In this paper, we propose a Bayesian graph neural network named BayesEEGNet. Considering an electrical impulse between two nodes in the brain as a Poisson process, the countless electrical impulses generated by the brain in a period are represented as an infinite number of connection probability graphs. After coupling and transforming these probability graphs, we interpret the brain's electrical activity state as the brain's perceptual state. Benefiting from the joint optimization of Bayesian modules and deep neural networks, our model shows superior classification performance in sleep stage classification and emotion recognition tasks. Meanwhile, our model is able to learn interpretable functional connectivity relationships between EEG channels without any prior knowledge. © 2023 IEEE.

关键词： Electroencephalography

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Common latent space identification for heterogeneous co-transfer clustering

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Neurocomputing 2017年 269卷 29-39页

作者： Yang, Liu Jing, Liping Liu, Bo Yu, Jian Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China School of Computer Science and Technology Tianjin University Tianjin China College of Information Science and Technology Agricultural University of Hebei Baoding China

With the rapid development of collection techniques, it is easy to gather various data which come from different domains, such as images, videos, documents, and etc, how to group these heterogeneous data becomes a research issue. Traditional techniques handle these clustering tasks separately, that is one task for one domain, so that they ignore the interactions among domains. In this paper, we present a co-transfer clustering method to deal with these separate tasks together with the aid of co-occurrence data which contain some instances represented in different domains. The proposed method consists of two steps, one is to learn the subspace of different domains that uncovers the latent common topics and respects the intrinsic geometric structure, the next is to simultaneously cluster the instances in all domains via the symmetric nonnegative matrix factorization method. A series of experiments on real-world data sets have shown the performance of the proposed method is better than the state-of-the-art methods. © 2017 Elsevier B.V.

关键词： Co-transfer clustering Collective matrix factorization Heterogeneous feature spaces Symmetric nonnegative matrix factorization

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Fuzzy cognitive maps learning algorithm based on modified covariance matrix adaptation evolution strategy

Fuzzy cognitive maps learning algorithm based on modified co...

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2023 International Conference on Cloud Computing, Performance Computing, and Deep Learning, CCPCDL 2023

作者： Li, Zhuofan Wang, Jingping Zhang, Yingjun Huang, Hua Yin, Hui Xu, Hongli Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China Information Technology Center Beijing Jiaotong University Beijing100044 China Key Laboratory of Beijing for Railway Engineering Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9781510666474

Fuzzy cognitive maps (FCMs) are an efficient soft computing tool commonly used for modeling and analyzing complex systems, and have significant advantages in knowledge representation and fast reasoning. At present, many researchers have devoted themselves to solving the learning problem of FCMs. However, most of the existing learning algorithms are prone to local convergence and are not suitable for large-scale FCMs learning problems. Moreover, the learning problem of FCMs is often a complex non-convex optimization problem, which is easy to cause the final learning result of the algorithm to be only a local optimal solution. To address these limitations, this paper proposes a novel algorithm for learning FCMs by combining multimodal optimization and modified covariance matrix adaptation evolution strategy (CMA-ES), termed as MCMA-ES. First, the learning problem of FCMs is modeled as a multimodal optimization problem (MMOP), and then a multimodal optimization algorithm is proposed by combining the detect multimodal clustering (DMC) and CMA-ES, and the cubic chaos factor is combined to enhance the capability of local search. Finally the proposed algorithm is combined with the decomposition strategy to learn FCMs. Experiments are carried out on 5 benchmark datasets, and MCMA-ES is also applied to 15 groups of large-scale gene regulatory system reconstruction problems. The experimental results show that MCMA-ES has high learning generalization and accuracy. © 2023 SPIE.

关键词： Fuzzy Cognitive Maps

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Improved physical layer for energy-efficient M2M communications over cellular networks

Improved physical layer for energy-efficient M2M communicati...

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International Conference on Software, Telecommunications and Computer Networks (SoftCOM)

作者： Ming Liu Matthieu Crussière Jean-François Hélard Beijing Key Lab of Transportation Data Analysis and Mining Beijing Jiaotong University Beijing China IETR INSA Rennes France

The existing GSM networks are promising candidates to enable the machine-to-machine (M2M) communications due to their global deployment. However, these networks are initially designed for mobile telephony, and need to be renovated to adapt to the new M2M applications. In this paper, we address the perspective of upgrading the channel coding scheme of the GSM networks for the M2M communications. Concretely, we investigate two possible solutions, namely concatenated codes (convolutional and Reed-Solomon) and Turbo codes. It is shown that the Turbo coding scheme can simultaneously improve both the service coverage and the energy efficiency performance, and is therefore suitable for the future M2M applications. Finally, it is demonstrated that the enhanced channel coding scheme leads to the reduced energy consumption, or equivalently a longer battery life, in the typical M2M scenarios.

关键词： Turbo codes Energy consumption GSM Convolutional codes Gain

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Towards efficient design of fixed-point iterative receiver for coded MIMO-OFDM systems

Towards efficient design of fixed-point iterative receiver f...

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International Conference on Telecommunications (ICT)

作者： Rida El Chall Fabienne Nouvel Maryline Hélard Yvan Kokar Ming Liu INSA IETR Rennes France Beijing Key Lab of Transportation Data Analysis and Mining Beijing Jiaotong University Beijing China

In recent years, iterative receiver has been widely considered since it is able to achieve high data rates and reliable communications for coded MIMO-OFDM systems. However, such a receiver is extremely challenging in practical implementation. In this paper, we present an efficient fixed-point arithmetic of iterative receiver based on low-complexity K-Best decoder in order to reduce the hardware costs in terms of area and execution time. The performance of fixed-point receiver is then evaluated with different modulation orders and channel models. Moreover, a testbed for MIMO-OFDM system using wireless open access research platform (WARP) is introduced in order to evaluate the performance of the receiver under real system conditions.

关键词： Receivers Decoding Iterative decoding MIMO Detectors Bit error rate Measurement

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An approach for Chinese-Japanese named entity equivalents extraction using inductive learning and Hanzi-Kanji mapping table

An approach for Chinese-Japanese named entity equivalents ex...

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作者： Xu, Jinan Chen, Yufeng Ru, Kuang Zhang, Yujie Araki, Kenji Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Haidian District Beijing100044 China Hokkaido University Sapporo-shi060-0814 Japan

Named Entity Translation Equivalents extraction plays a critical role in machine translation (MT) and cross language information retrieval (CLIR). Traditional methods are often based on large-scale parallel or comparable corpora. However, the applicability of these studies is constrained, mainly because of the scarcity of parallel corpora of the required scale, especially for language pairs of Chinese and Japanese. In this paper, we propose a method considering the characteristics of Chinese and Japanese to automatically extract the Chinese-Japanese Named Entity (NE) translation equivalents based on inductive learning (IL) from monolingual corpora. The method adopts the Chinese Hanzi and Japanese Kanji Mapping Table (HKMT) to calculate the similarity of the NE instances between Japanese and Chinese. Then, we use IL to obtain partial translation rules for NEs by extracting the different parts from high similarity NE instances in Chinese and Japanese. In the end, the feedback processing updates the Chinese and Japanese NE entity similarity and rule sets. Experimental results show that our simple, efficient method, which overcomes the insufficiency of the traditional methods, which are severely dependent on bilingual resource. Compared with other methods, our method combines the language features of Chinese and Japanese with IL for automatically extracting NE pairs. Our use of a weak correlation bilingual text sets and minimal additional knowledge to extract NE pairs effectively reduces the cost of building the corpus and the need for additional knowledge. Our method may help to build a large-scale Chinese-Japanese NE translation dictionary usingmonolingual corpora. Copyright © 2017 The Institute of Electronics, Information and Communication Engineers.

关键词： Mapping

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Exploring bilingual parallel corpora for syntactically controllable paraphrase generation 29

Exploring bilingual parallel corpora for syntactically contr...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Liu, Mingtong Yang, Erguang Xiong, Deyi Zhang, Yujie Sheng, Chen Hu, Changjian Xu, Jinan Chen, Yufeng School of Computer Science and Information Technology & Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China College of Intelligence and Computing Tianjin University Tianjin China Lenovo Research AI Lab Beijing China

ISBN: (纸本)9780999241165

Paraphrase generation is of great importance to many downstream tasks in natural language processing. Recent efforts have focused on generating paraphrases in specific syntactic forms, which, generally, heavily relies on manually annotated paraphrase data that is not easily available for many languages and domains. In this paper, we propose a novel end-to-end framework to leverage existing large-scale bilingual parallel corpora to generate paraphrases under the control of syntactic exemplars. In order to train one model over the two languages of parallel corpora, we embed sentences of them into the same content and style spaces with shared content and style encoders using cross-lingual word embeddings. We propose an adversarial discriminator to disentangle the content and style space, and employ a latent variable to model the syntactic style of a given exemplar in order to guide the two decoders for generation. Additionally, we introduce cycle and masking learning schemes to efficiently train the model. Experiments and analyses demonstrate that the proposed model trained only on bilingual parallel data is capable of generating diverse paraphrases with desirable syntactic styles. Fine-tuning the trained model on a small paraphrase corpus makes it substantially outperform state-of-the-art paraphrase generation models trained on a larger paraphrase dataset. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Syntactics

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ReS2TIM: Reconstruct Syntactic Structures from Table Images

ReS2TIM: Reconstruct Syntactic Structures from Table Images

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International Conference on Document analysis and Recognition

作者： Wenyuan Xue Qingyong Li Dacheng Tao Beijing Key Lab of Transportation Data Analysis and Mining Beijing Jiaotong University China Faculty of Engineering The University of Sydney Australia

Tables often represent densely packed but structured data. Understanding table semantics is vital for effective information retrieval and data mining. Unlike web tables, whose semantics are readable directly from markup language and contents, the full analysis of tables published as images requires the conversion of discrete data into structured information. This paper presents a novel framework to convert a table image into its syntactic representation through the relationships between its cells. In order to reconstruct the syntactic structures of a table, we build a cell relationship network to predict the neighbors of each cell in four directions. During the training stage, a distance-based sample weight is proposed to handle the class imbalance problem. According to the detected relationships, the table is represented by a weighted graph that is then employed to infer the basic syntactic table structure. Experimental evaluation of the proposed framework using two datasets demonstrates the effectiveness of our model for cell relationship detection and table structure inference.

关键词： Syntactics Visualization Semantics Feature extraction data mining Training Magnetic resonance imaging

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