ISSN 1000-1239 CN 11-1777/TP

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    Journal of Computer Research and Development    2020, 57 (12): 2479-2480.   DOI: 10.7544/issn1000-1239.2020.qy1201
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    Motor Imagery Classification Based on Multiscale Feature Extraction and Squeeze-Excitation Model
    Jia Ziyu, Lin Youfang, Liu Tianhang, Yang Kaixin, Zhang Xinwang, Wang Jing
    Journal of Computer Research and Development    2020, 57 (12): 2481-2489.   DOI: 10.7544/issn1000-1239.2020.20200723
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    Brain-computer Interface (BCI) technology based on motor imagery (MI) can establish communication between the human brain and outside world. It has been widely used in medical rehabilitation and other fields. Owing to the characteristics of the motor imagery EEG signals,such as non-linear, non-stationary, and low signal-noise ratio, it is a huge challenge to classify motor imagery EEG signals accurately. Hence, we propose a novel multiscale feature extraction and squeeze-excitation model which is applied for the classification of motor imagery EEG signals. Firstly, the proposed deep learning module, which is based on multiscale structure, automatically extracts time domain features, frequency domain features and time-frequency domain features. Then, the residual module and squeeze-excitation module are applied for feature fusion and selection, respectively. Finally, fully connected network layers are used to classify motor imagery EEG signals. The proposed model is evaluated on two public BCI competition datasets. The results show that the proposed model can effectively improve the recognition performance of motor imagery EEG signals compared with the existing several state-of-the-art models. The average accuracy on the two datasets is 78.0% and 82.5%, respectively. Moreover, the proposed model has higher application value because it classifies motor imagery EEG signals efficiently without manual feature extraction when spatial information is insufficient.
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    Learning to Rank for Evolution Trend Evaluation of Online Public Opinion Events
    Qin Tao, Shen Zhuang, Liu Huan, Chen Zhouguo
    Journal of Computer Research and Development    2020, 57 (12): 2490-2500.   DOI: 10.7544/issn1000-1239.2020.20200725
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    Public opinion events in social networks have a bearing on social harmony and stability. Analyzing the evolution trend of events so as to manage and control them is able to reduce the impact of malignant online public opinion. However, the lack of labelled data and the limited relevant resources makes the effective management of online public opinion challenging and complicated. To solve those difficulties, we propose a learning-to-rank algorithm for the events evolution trend evaluation, which makes full use of the expert knowledge in the labelled data and the correlation between labelled and unlabelled data to select important public opinion for management, in turn, improves the management efficiency. Firstly, based on the experiences and demands of public opinion management, we design a measurable, accessible and meaningful hierarchical index system, which is derived from the three most important factors of events, for evolution trend evaluation. Secondly, we build an evaluation model for evolution trend evaluation based on the graph convolutional network. Specifically, our model uses the local sensitive Hash algorithm to mine the structural information from the data node’s neighborhood and generates the mixed feature of the data node and its neighbor. Finally, we design different loss functions for the labelled and unlabelled data respectively, in order to realize the comprehensive utilization of the expert knowledge in the labelled data and the spatial structure information in the unlabelled data. We verify the efficiency of the proposed model on public datasets MQ 2007-semi and MQ 2008-semi. We also build a real-world public opinion event dataset to verify the practicability and generalization of the proposed algorithm. The experimental results show that the proposed model can evaluate the public opinion event evolution trend with limited expert knowledge, and provide decision support for public opinion event management with limited resources.
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    Hybrid Human-Machine Active Search over Knowledge Graph
    Wang Meng, Wang Jingting, Jiang Yinlin, Qi Guilin
    Journal of Computer Research and Development    2020, 57 (12): 2501-2513.   DOI: 10.7544/issn1000-1239.2020.20200750
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    Effective search over knowledge graphs can provide support for applications such as question answering and semantic search. However, when the user cannot give a clear query, accurately capturing the user’s interest and finding the answer are difficult for machines. Hybrid human-machine active search provides a pathway to bridge the gap between users and machines. Hybrid human-machine active search is a kind of interactive search, and it is originated from the thought of active learning in machine learning field. The core idea is to let the machine issue questions to the user, to obtain information from the user feedback, and then based on this information to eventually capture user intent and return answers. In this paper, we stand on recent advances in knowledge graph representation learning techniques and propose a hybrid human-machine active search in the vector space of a knowledge graph. Specifically, the knowledge graph is first embedded into the low-dimensional vector space, which quantizes the characteristics of entities and relationships, and at the same time, the user’s interests and preferences are embedded into the same space. Then, the machine actively proposes questions to the user, and gets the feedback information by asking the user to rate the specific entity, thus updating the user preference positioning in the vector space. We design an evaluation method to measure the user’s interest in a specific entity based on the Euclidean distance between the preference point and other entities, and finally find the final target entity to return to the user after multiple turns of human-machine interaction. In the experiment part, we conduct experiments on the knowledge graph embedding and the active search respectively, and the experimental results show that the proposed method is effective.
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    Rule-Guided Joint Embedding Learning of Knowledge Graphs
    Yao Siyu, Zhao Tianzhe, Wang Ruijie, Liu Jun
    Journal of Computer Research and Development    2020, 57 (12): 2514-2522.   DOI: 10.7544/issn1000-1239.2020.20200741
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    In recent years, numerous research works have been devoted to knowledge graph embedding learning which aims to encode entities and relations of the knowledge graph in continuous low-dimensional vector spaces. And the learned embedding representations have been successfully utilized to alleviate the computational inefficiency problem of large-scale knowledge graphs. However, most existing embedding models only consider the structural information of the knowledge graph. The contextual information and literal information are also abundantly contained in knowledge graphs and could be exploited to learn better embedding representations. In this paper, we focus on this problem and propose a rule-guided joint embedding learning model which integrates the contextual information and literal information into the embedding representations of entities and relations based on graph convolutional networks. Especially for the convolutional encoding of the contextual information, we measure the importance of a piece of contextual information by computing its confidence and relatedness metrics. For the confidence metric, we define a simple and effective rule and propose a rule-guided computing method. For the relatedness metric, we propose a computing method based on the representations of the literal information. We conduct extensive experiments on two benchmark datasets, and the experimental results demonstrate the effectiveness of the proposed model.
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