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Sentence segmentation for classical Chinese based on LSTM with radical embedding

The Journal of China Universities of Posts and Telecommunications 2019年第2期26卷 1-8页

作者： Han Xu Wang Hongsu Zhang Sanqian Fu Qunchao Liu Jun School of software Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT)Ministry of EducationBeijing 100876China The Key Laboratory of Rich-Media Knowledge Organization and Service of Digital Publishing Content Institute of Scientific and Technical Information of ChinaBeijing 100038China Insitute of Quantitative Social Science Harvard UniversityCambridgeMAUSA Department of statistics Harvard UniversityCambridgeMAUSA

A low-than character feature embedding called radical embedding is proposed,and applied on a long-short term memory(LSTM) model for sentence segmentation of pre-modern Chinese *** dataset includes over 150 classical Chinese books from 3 different dynasties and contains different literary ***-conditional random fields(LSTM-CRF) model is a state-of-the-art method for the sequence labeling *** model adds a component of radical embedding,which leads to improved *** results based on the aforementioned Chinese books demonstrate better accuracy than earlier methods on sentence segmentation,especial in Tang’s epitaph texts(achieving an F1-score of 81.34%).

关键词： LSTM radical embedding sentence segmentation

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Pyramid Pooling Dense Convolutional Neural Network for Multi-focus Image Fusion

Pyramid Pooling Dense Convolutional Neural Network for Multi...

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IEEE International Conference on Cloud computing and Intelligence Systems (CCIS)

作者： Yi Li Xuanjing Shen Haipeng Chen College of Software Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

ISBN: (数字)9781728138633

ISBN: (纸本)9781728138640

In recent years, image fusion becomes one of the important technique in information fusion fields. In this paper, we proposed a Deep Learning-based pyramid pooling dense convolutional neural network for multi-focus image fusion problem. The PDenseCNN we proposed can accurately detect the clear regions and generate a fused decision feature map, afterward, morphological operation and guided filter are used to optimize the initial map. Finally, it can obtain a clear fusion image based on weighted-sum strategy. The experimental results proved that the proposed method can obtain accurate decision maps and state-of-art fusion results on both visual effect and objective evaluation assessment.

关键词： Economic indicators

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Parking Space Prediction Algorithm Based on Recurrent Neural Network and Ensemble Learning Algorithm

Parking Space Prediction Algorithm Based on Recurrent Neural...

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IEEE International Conference on Cloud computing and Intelligence Systems (CCIS)

作者： Kedi Lv Haipeng Chen Yingda Lv Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China Center for Computer Fundamental Education Jilin University Changchun China

ISBN: (数字)9781728138633

ISBN: (纸本)9781728138640

The essence of parking space prediction is a practical application of time series prediction problem. It provides effective auxiliary information for traffic management and personal travel. As a new demand, they face challenges in data acquisition, accuracy and robustness. The parking space prediction algorithm based on recurrent neural network (RNN) and ensemble learning algorithm is proposed in this paper. However, because the neural network needs large data training, the lack of parking space data in specific areas is a common challenge for researchers. In this paper, a new network model structure based on ensemble learning algorithm is proposed, which is called E-RNN. The ensemble learning algorithm model is used as the primary learner and the neural network as the secondary learner for the ensemble learning. By using the adaptability of the algorithm in ensemble learning to the collective data, the neural network can get better prediction effect under the training of small data sets. Finally, the parameters of the new model are optimized by using particle swarm optimization (PSO) algorithm, and a better model is obtained. The experimental results show that the algorithm has high accuracy and robustness for parking space prediction with a small data sets.

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A pseudo label based dataless naive bayes algorithm for text classification with seed words 27

A pseudo label based dataless naive bayes algorithm for text...

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27th International Conference on Computational Linguistics, COLING 2018

作者： Li, Ximing Yang, Bo College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China

ISBN: (纸本)9781948087506

Traditional supervised text classifiers require a large number of manually labeled documents, which are often expensive to obtain. Recently, dataless text classification has attracted more attention, since it only requires very few seed words of categories that are much cheaper. In this paper, we develop a pseudo-label based dataless Naive Bayes (PL-DNB) classifier with seed words. We initialize pseudo-labels for each document using seed word occurrences, and employ the expectation maximization algorithm to train PL-DNB in a semi-supervised manner. The pseudo-labels are iteratively updated using a mixture of seed word occurrences and estimations of label posteriors. To avoid noisy pseudo-labels, we also consider the information of nearest neighboring documents in the pseudo-label update step, i.e., preserving local neighborhood structure of documents. We empirically show that PL-DNB outperforms traditional dataless text classification algorithms with seed words. Especially, PL-DNB performs well on the imbalanced dataset. © 2018 COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings. All rights reserved.

关键词： Iterative methods

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An Unmanned Aerial Vehicle Pose Estimation System Guided by Desired Shot

An Unmanned Aerial Vehicle Pose Estimation System Guided by ...

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IEEE International Conference on Automation, Electronics and Electrical engineering (AUTEEE)

作者： Xinglei Dou Qijian Ding Qiao Du Min Yang Yuzhou Huo Xin Wang College of Software Engineering Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineer of Ministry of Education Jilin University Changchun China

ISBN: (数字)9781728150307

ISBN: (纸本)9781728150314

Unmanned aerial vehicles (UAVs) are widely used in aerial photography nowadays for their strong maneuverability, good image quality and high cost performance, while they have limited battery capacity and difficulty in operation. It is difficult to reach the ideal pose and get the desired shot by manual manipulation in a short time. To solve these problems, an UAV pose estimation system guided by desired shot is constructed. Either a sequence of images or a video is taken as input. The user interacts with the system to express the desired shot. The 3D-reconstruction is done by Structure from Motion (SfM). Then the Perspective-n-Point (PnP) problem is solved to estimate the ideal pose of the UAV. The experimental results proves that the system is effective and valid.

关键词： Three-dimensional displays Feature extraction Unmanned aerial vehicles Cameras Pose estimation Software engineering Data mining

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CircRNAFisher: a systematic computational approach for de novo circular RNA identification

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中国药理学报（英文版） 2019年第1期40卷 55-63页

作者： Guo-yi Jia Duo-lin Wang Meng-zhu Xue Yu-wei Liu Yu-chen Pei Ying-qun Yang Jing-mei Xu Yan-chun Liang Peng Wang School of Life Sciences Shanghai University Shanghai 200444 China Laboratory of Systems Biology Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai 201210 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun 130012 China Laboratory of Systems Biology Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai 201210 China Laboratory of Systems Biology Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai 201210 China School of Life Science and Technology Shanghai Tech University Shanghai 201210 China

Circular RNAs (circRNAs) are emerging species of mRNA splicing products with largely unknown *** several computational pipelines for circRNA identification have been developed,these methods strictly rely on uniquely mapped reads overlapping back-splice junctions (BSJs) and lack approaches to model the statistical significance of the identified ***,we reported a systematic computational approach to identify circRNAs by simultaneously utilizing BSJ overlapping reads and discordant BSJ spanning reads to identify ***,we developed a novel procedure to estimate the P-values of the identified circRNAs.A computational cross-validation and experimental validations demonstrated that our method performed favorably compared to existing circRNA detection *** created a standalone tool,CircRNAFisher,to implement the method,which might be valuable to computational and experimental scientists studying circRNAs.

关键词： circRNA RNA-Seq alternative splicing pipeline

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MDPL-net: Multi-layer Dictionary Learning Network with Added Skip Dense Connections

MDPL-net: Multi-layer Dictionary Learning Network with Added...

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IEEE International Conference on Data Mining (ICDM)

作者： Zhao Zhang Yulin Sun Zheng Zhang Yang Wang Lin Wu Meng Wang School of Computer Science and Technology Soochow University Suzhou China Ministry of Education Key Laboratory of Knowledge Engineering with Big Data School of Computer Science and Information Engineering Hefei University of Technology Hefei China Bio-Computing Research Center Harbin Institute of Technology (Shenzhen) Shenzhen China

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

Dictionary learning (DL) is powerful for representation learning, while it fails to capture the deep hierarchical information hidden in data. In this paper, we propose a new generalized end-to-end mulita-layer representation learning architecture referred to as Multi-layer Dictionary Pair Learning Network (MDPL-net) for the deep sparse and hierarchical representation of images. To enable MDPL-net to conduct accurate classification, MDPL-net clearly integrates the skip connection end-to-end network and multi-layer deep sparse dictionary learning into a unified architecture. The representation learning module has several hidden DL blocks, where each hidden DL block has a dictionary pair learning (DPL) layer, a batch-norm layer and an activation function layer, and the DL blocks are connected in a feed-forward manner. To further improve the information flow and maintain the privileged features between different DL blocks, a novel skip dense connectivity pattern is deployed between hidden DL blocks, which can obtain more stable and discriminative features. The DPL layer jointly formulates the discriminative synthesis dictionary and analysis dictionary by minimizing reconstruction error within each batch over the feature maps from front layers. Extensive results on benchmark databases demonstrate the effectiveness of MDPL-net for discriminative representation and robust image classification.

关键词： Dictionaries Databases Conferences Machine learning Data mining Image reconstruction Image classification

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A Fuzzy Clustering Based Collaborative Filtering Algorithm for Time-aware POI Recommendation

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Journal of Physics: Conference Series 2021年第1期1746卷

作者： Minghao Yin Yanheng Liu Xu Zhou Geng Sun College of Computer Science and Technology Jilin University Changchun Jilin China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin China Center for Computer Fundamental Education Jilin University Changchun Jilin China

Location based social network develops and gets widely concern along with the population and widespread use of mobile. Point of interest(POI) recommendation become one of the most widely application among location-based service. To get better POI recommendation performance, a fuzzy clustering based collaborative filtering algorithm (FCCF) for time-aware POI recommendation is proposed in this paper. It first constructs the user feature vector from users' check-in behaviours. Individual's check-in behaviour can be under the influence of location region and time slots, so user's feature consists of two parts. One is the vising frequency of each user in different location regions, and the other is the vising frequency of each user in different time slots. Next fuzzy c-means is adopted due to its simplicity to group users according to user feature vector. Then the user similarity computation can be limited in the similar small user groups. In the end, a collaborative filtering algorithm is applied to recommend a number of top-N POIs at a given time for the target user. Some experiments are conducted and the comparative results on Foursquare and Gowalla show that FCCF has higher precision and recall value than the comparative algorithms.

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Correction to: MRI enhancement based on visual-attention by adaptive contrast adjustment and image fusion

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Multimedia Tools and Applications 2021年第9期80卷 13019-13019页

作者： Rui Zhu Xiongfei Li Xiaoli Zhang Xiaowei Xu Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China Department of Information Sciences University of Arkansas at Little Rock Little Rock USA

A Correction to this paper has been published: https://***/10.1007/s11042-021-10782-7

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Learning optimal data augmentation policies via bayesian optimization for image classification tasks

arXiv

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arXiv 2019年

作者： Zhang, Chunxu Cui, Jiaxu Yang, Bo College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

In recent years, deep learning has achieved remarkable achievements in many fields, including computer vision, natural language processing, speech recognition and others. Adequate training data is the key to ensure the effectiveness of the deep models. However, obtaining valid data requires a lot of time and labor resources. Data augmentation (DA) is an effective alternative, which can generate new labeled data based on existing data using label-preserving transformations. Although we can benefit a lot from DA, designing appropriate DA policies requires a lot of expert experience and time consumption, and the evaluation of searching the optimal policies is costly. So we raise a new question in this paper: how to achieve automated data augmentation at as low cost as possible? We propose a method named BO-Aug for automating the process by finding the optimal DA policies using the Bayesian optimization approach. We validate the BO-Aug on three widely used image classification datasets, including CIFAR-10, CIFAR- 100 and SVHN. Experimental results show that the proposed method can achieve state-of-the-art or near advanced classification accuracy at a relatively low policies search cost, and the searched policies based on a specific dataset are transferable across different neural network architectures or even different datasets. Code to reproduce our experiments is available at https://***/zhangxiaozao/BOAug. Copyright © 2019, The Authors. All rights reserved.

关键词： Image classification

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