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<title cf:type="text"><![CDATA[Editorial department of the Journal of National University of Defense Technology -->专栏：机器学习]]></title>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Multi-channel graph attention network with disentangling capability for social recommendation]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202203001]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[A Multi-channel graph attention network social recommendation model with disentangling capability was proposed. This model mainly included three modules:the deep clustering module, the aggregation module based on multi-channel graph attention network, and the rating prediction module. Among them, the deep clustering module was used to group users and items. The clustering results can be used to split user-user social graph and user-item interaction graph into multiple subgraph to learn user interest groups and users′ interests in different types of items. The aggregation module learns the attention of different sub-graphs to the prediction results. The rating prediction module input the learned user representation vector and item representation vector into the multilayer perceptron for rating prediction. Extensive experiments on multiple real-world datasets demonstrate that the proposed method is better than other social recommendation algorithms. Specifically, compared with the latest graph neural networks method for social recommendation, the root mean square error is respectively reduced by 2.26% and 2.07% on the Ciao and Epinions datasets, and the mean absolute error is respectively reduced by 2.58% and 3.06%.]]></description>
<pubDate>2022/6/2 10:28:10</pubDate>
<category><![CDATA[专栏：机器学习]]></category>
<author><![CDATA[HONG Mingli, WANG Jing, JIA Caiyan]]></author>
<atom:author xmlns:atom="http://www.w3.org/2005/Atom">
<atom:name>HONG Mingli, WANG Jing, JIA Caiyan</atom:name>
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<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202203001]]></guid><cfi:id>11</cfi:id><cfi:read>true</cfi:read></item>
<item>
<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Asymptotic cluster analysis of single-leader Cucker-Smale model with local interchange function and free will]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202203002]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[In order to study the relationship between cluster formation and radius, the Cucker-Smale model with a hierarchical structure of a single leader and the leader speed invariable was considered. The influence of free will on cluster was discussed. The sufficient conditions that the radius has a lower bound (the lower bound was related to the velocity difference, particle number, communication intensity, etc.) were obtained by proving. When the radius was greater than the lower bound, the cluster would be generated. The related conclusion was verified by MATLAB numerical simulation.]]></description>
<pubDate>2022/6/2 10:28:10</pubDate>
<category><![CDATA[专栏：机器学习]]></category>
<author><![CDATA[ZHAO Ziyu, LIU Yicheng]]></author>
<atom:author xmlns:atom="http://www.w3.org/2005/Atom">
<atom:name>ZHAO Ziyu, LIU Yicheng</atom:name>
</atom:author>
<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202203002]]></guid><cfi:id>10</cfi:id><cfi:read>true</cfi:read></item>
<item>
<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Smooth principal component analysis network image recognition algorithm with fusion graph embedding]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202203003]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[PCANet (principal component analysis network) is a simple deep learning algorithm with excellent performance in the field of image recognition. Integrating the idea of graph embedding into PCANet, a new image recognition algorithm Smooth-PCANet was proposed. In order to verify the effectiveness of the Smooth-PCANet algorithm, adequate experiments were performed on different data sets such as face, handwritten characters, and images. Compared with several image recognition algorithms based on deep learning, the experiments demonstrated that the Smooth-PCANet achieves higher recognition performance than the PCANet and avoids overfitting more effectively, with a significant advantage in small samples training.]]></description>
<pubDate>2022/6/2 10:28:10</pubDate>
<category><![CDATA[专栏：机器学习]]></category>
<author><![CDATA[CHEN Feiyue, ZHU Yulian, TIAN Jialue, JIANG Ke]]></author>
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<atom:name>CHEN Feiyue, ZHU Yulian, TIAN Jialue, JIANG Ke</atom:name>
</atom:author>
<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202203003]]></guid><cfi:id>9</cfi:id><cfi:read>true</cfi:read></item>
<item>
<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Multi-instance multi-label learning for labels with directed acyclic graph structures in protein function prediction]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202203004]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[In MIML (multi-instance multi-label learning) tasks, labels are often correlated with each other, and DAG (directed acyclic graph) is a common hierarchically structure which often occurs in the prediction of gene ontology biological functions of proteins. Considering the labels with directed acyclic graph structures in MIML, a novel algorithm named MIMLDAG (multi-instance multi-label directed acyclic graph) was proposed. MIMLDAG trained a low-dimensional subspace of shared labels from the feature space of original datasets, minimized the rank loss by a stochastic gradient descent method, and then incorporated the inner DAG hierarchical structure of labels for optimizing the output labels. MIMLDAG was applied to predict the protein functions in multiple datasets, and the results show that MIMLDAG possesses higher efficiency and predictive performance.]]></description>
<pubDate>2022/6/2 10:28:10</pubDate>
<category><![CDATA[专栏：机器学习]]></category>
<author><![CDATA[WU Jiansheng, TANG Shidi, MEI Dejin, ZHU Yanxiang, DIAO Yemin]]></author>
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<atom:name>WU Jiansheng, TANG Shidi, MEI Dejin, ZHU Yanxiang, DIAO Yemin</atom:name>
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<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202203004]]></guid><cfi:id>8</cfi:id><cfi:read>true</cfi:read></item>
<item>
<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Decision solving algorithm for multiple optimal solution combinatorial optimization problem]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202203005]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Oriented to the combinatorial optimization problem with fixed sum of goods and multiple optimal solutions, the problem formulation was given by two examples:the fixed sum real number subset problem and buying wings problem. A integer state and a real number state multi-optimal solution dynamic programming algorithm based on 0-1 decision recursive search was put forward on the foundation of analysis of some classical methods like enumeration. In order to cope with the problem of time complexity tending to the extreme O(m<sup>n</sup>) when the number of optimal solutions is large for the proposed algorithms, two improved algorithms, the same decision path fusion algorithm and the 0-x decision based algorithm were proposed. The computation time of the improved algorithms is consistent with the proportional relation with O(nb+nm) on the whole in experiments, which indicates that these algorithms have good performance for this type of problem.]]></description>
<pubDate>2022/6/2 10:28:10</pubDate>
<category><![CDATA[专栏：机器学习]]></category>
<author><![CDATA[HU Zhenzhen, YUAN Weilin, LUO Junren, ZOU Mingwo, CHEN Jing]]></author>
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<atom:name>HU Zhenzhen, YUAN Weilin, LUO Junren, ZOU Mingwo, CHEN Jing</atom:name>
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<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202203005]]></guid><cfi:id>7</cfi:id><cfi:read>true</cfi:read></item>
<item>
<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[3D channel-wise attention network for spatio-temporal traffic raster flow prediction]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202203006]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Urban traffic flow forecasting is of great significance for traffic management and public safety. However, the correlations of traffic raster flow change with time. There are global spatio-temporal correlations in the city, and the contributions of channel-wise features vary on each city region. To tackle these challenges and make more accurate prediction, a novel spatio-temporal neural network model, named 3D-CANet (three-dimensional channel-wise attention network), was designed. A 3D-InnerCA (three-dimensional inner-channel attention) unit was proposed to dynamically capture the global spatio-temporal correlations for different channel-wise features. Meanwhile, an InterCA (inter-channel attention) unit was designed to adaptively recalibrate the contributions of different channel-wise features on each region. The experimental results on three real-world traffic raster flow datasets demonstrate that the predictive performance of the 3D-CANet model was better than the others,which proved the validity of the model proposed.]]></description>
<pubDate>2022/6/2 10:28:10</pubDate>
<category><![CDATA[专栏：机器学习]]></category>
<author><![CDATA[TONG Kainan, LIN Youfang, LIU Jun, GUO Shengnan, WAN Huaiyu]]></author>
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<atom:name>TONG Kainan, LIN Youfang, LIU Jun, GUO Shengnan, WAN Huaiyu</atom:name>
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<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202203006]]></guid><cfi:id>6</cfi:id><cfi:read>true</cfi:read></item>
<item>
<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Active learning method based on instability sampling]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202203007]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Traditional active learning methods select examples by only considering the predictions of the current model. However, these methods neglect the information of the previous trained models, which reflect the stability of the prediction sequence for each unlabeled example during the active learning stage. Thus, a novel active learning method with instability sampling was proposed, which attempted to estimate the potential utility of each unlabeled examples for improving the model performance based on the difference among predictions of the previous models. The proposed method measured the instability of unlabeled example based on the difference between the posterior probabilities predicted by the previous models, and the example with the largest instability was selected to be queried. Extensive experiments were conducted on multiple datasets with diverse classification models. The experimental results validate the effectiveness of the proposed method.]]></description>
<pubDate>2022/6/2 10:28:10</pubDate>
<category><![CDATA[专栏：机器学习]]></category>
<author><![CDATA[HE Hua, XIE Mingkun, HUANG Shengjun]]></author>
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<atom:name>HE Hua, XIE Mingkun, HUANG Shengjun</atom:name>
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<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202203007]]></guid><cfi:id>5</cfi:id><cfi:read>true</cfi:read></item>
<item>
<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Method for individual activities recognition incorporating spatial preference and semantics]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202203008]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[The recognition of individual activities helps in the realisation of functions such as user profiling, personalized recommendations, abnormal behaviour detection, city-wide group behaviour analysis and resource allocation optimisation. A recognition method for the semantics of individual activities based on sparse social media check-in data was proposed. The temporal periodicity and tendency features of activity behaviors were extracted from the check-in data, and a spatial preference quantification algorithm was utilized to extract the preferences of groups and individuals from the spatial relevance between individual and group activities. The natural language embedding model BERT was used to extract the semantics of POIs (point of interest). The temporal features, spatial preference features and text features of POI′s names constituted the joint spatio-temporal features characterizing group and individual preferences, and the joint features were classified by the extreme gradient boosting classifier to obtain the activity semantic recognition results. With the results of comparison experiments and ablation experiments on the Foursquare dataset, it was validated that the model proposed can effectively improve the accuracy of activity semantics recognition.]]></description>
<pubDate>2022/6/2 10:28:10</pubDate>
<category><![CDATA[专栏：机器学习]]></category>
<author><![CDATA[GUO Maozu, CHEN Jiadong, ZHANG Bin, ZHAO Lingling, LI Yang]]></author>
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<atom:name>GUO Maozu, CHEN Jiadong, ZHANG Bin, ZHAO Lingling, LI Yang</atom:name>
</atom:author>
<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202203008]]></guid><cfi:id>4</cfi:id><cfi:read>true</cfi:read></item>
<item>
<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Algorithm for model decision tree with multi-kernel Bayesian optimization]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202203009]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[In the construction of the model decision tree, there are many parameters and the parameter combination is complex. The use of grid search and other parameter tuning methods will consume a lot of time, which will affect the improvement of the model performance. A model decision tree with multi-kernel bayesian optimization was proposed. In order to deal with the characteristics of different classified data, three Gaussian processes were used for modeling optimization. The Bayesian optimization technique was used to select the best parameter combination. The experimental results show that the proposed algorithm is better than the traditional model decision tree method in parameter optimization, and can find the global optimal parameter value in the case of a few iterations. To a certain extent, it improves the classification performance of the algorithm and saves a lot of parameter adjustment time.]]></description>
<pubDate>2022/6/2 10:28:10</pubDate>
<category><![CDATA[专栏：机器学习]]></category>
<author><![CDATA[GAO Honglei, MEN Changqian, WANG Wenjian]]></author>
<atom:author xmlns:atom="http://www.w3.org/2005/Atom">
<atom:name>GAO Honglei, MEN Changqian, WANG Wenjian</atom:name>
</atom:author>
<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202203009]]></guid><cfi:id>3</cfi:id><cfi:read>true</cfi:read></item>
<item>
<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Crowdsourced label inference algorithm using double-confidence]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202203010]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Since the workers have significant differences in the knowledge level and evaluation criteria, the quality of the collected labels varies a lot. It′s of key importance to improve the quality of labels and learning models in crowdsourced label learning. A novel double-confidence inference algorithm was proposed to solve the problem of crowdsourced label inference. The workers′ confidence was obtained via the data distribution characteristics and label information, and then the label was inferred by this confidence so as to improve the quality of the integrated label. The experimental results show that the proposed algorithm outperforms other ground truth inference algorithms only based on label information.]]></description>
<pubDate>2022/6/2 10:28:10</pubDate>
<category><![CDATA[专栏：机器学习]]></category>
<author><![CDATA[ZHANG Lin, JIANG Gaoxia, WANG Wenjian]]></author>
<atom:author xmlns:atom="http://www.w3.org/2005/Atom">
<atom:name>ZHANG Lin, JIANG Gaoxia, WANG Wenjian</atom:name>
</atom:author>
<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202203010]]></guid><cfi:id>2</cfi:id><cfi:read>true</cfi:read></item>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Context-aware deep weakly supervised image hashing learning method]]></title>
<link><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202203011]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[Existing deep supervised image hashing approaches rely on substantial labeled image data, which is very difficult to be widely applied in reality. By utilizing tags associated with images as the supervision information, a context-aware deep weakly supervised image hashing method was proposed. The method enhanced the image region representations by adaptively capturing the relevant context information of image region features, and raised the discrimination of the learnt hash codes by introducing a discrimination loss. Extensive experiments on two public datasets show the effectiveness of the method.]]></description>
<pubDate>2022/6/2 10:28:10</pubDate>
<category><![CDATA[专栏：机器学习]]></category>
<author><![CDATA[LIU Meng, ZHOU Di, TIAN Chuanfa, QI Mengjin, NIE Xiushan]]></author>
<atom:author xmlns:atom="http://www.w3.org/2005/Atom">
<atom:name>LIU Meng, ZHOU Di, TIAN Chuanfa, QI Mengjin, NIE Xiushan</atom:name>
</atom:author>
<guid><![CDATA[http://journal.nudt.edu.cn/gfkjdxxben/article/abstract/202203011]]></guid><cfi:id>1</cfi:id><cfi:read>true</cfi:read></item>
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