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Supervised topic models with weighted words: multi-label document classification None

Yue-peng ZOU, Ji-hong OUYANG, Xi-ming LI

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 4,   Pages 513-523 doi: 10.1631/FITEE.1601668

Abstract: Supervised topic modeling algorithms have been successfully applied to multi-label document classificationExperimental results demonstrate that CF-weight based algorithms are competitive with the existing supervisedtopic models.

Keywords： Supervised topic model     Multi-label classification     Class frequency     Labeled latent Dirichlet allocation    

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Special Topic on environment and sustainable development

Frontiers of Chemical Science and Engineering 2017, Volume 11, Issue 3,   Pages 291-292 doi: 10.1007/s11705-017-1667-6

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Emerging topic identification from app reviews via adaptive online biterm topic modeling Research Article

Wan ZHOU, Yong WANG, Cuiyun GAO, Fei YANG,yongwang@ahpu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 5,   Pages 678-691 doi: 10.1631/FITEE.2100465

Abstract: Emerging topics in highlight the topics (e.g., software bugs) with which users are concerned during certain periods. Identifying emerging topics accurately, and in a timely manner, could help developers more effectively update apps. Methods for identifying emerging topics in based on s or clustering methods have been proposed in the literature. However, the accuracy of is reduced because reviews are short in length and offer limited information. To solve this problem, an improved (IETI) approach is proposed in this work. Specifically, we adopt techniques to reduce noisy data, and identify emerging topics in using the adaptive online biterm . Then we interpret the implicature of emerging topics through relevant phrases and sentences. We adopt the official app changelogs as ground truth, and evaluate IETI in six common apps. The experimental results indicate that IETI is more accurate than the baseline in identifying emerging topics, with improvements in the F1 score of 0.126 for phrase labels and 0.061 for sentence labels. Finally, we release the codes of IETI on Github (https://github.com/wanizhou/IETI).

Keywords： App reviews     Emerging topic identification     Topic model     Natural language processing    

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Ensemble enhanced active learning mixture discriminant analysis model and its application for semi-supervised Research Article

Weijun WANG, Yun WANG, Jun WANG, Xinyun FANG, Yuchen HE

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 12,   Pages 1814-1827 doi: 10.1631/FITEE.2200053

Abstract: As an indispensable part of process monitoring, the performance of relies heavily on the sufficiency of process knowledge. However, data labels are always difficult to acquire because of the limited sampling condition or expensive laboratory analysis, which may lead to deterioration of classification performance. To handle this dilemma, a new strategy is performed in which enhanced is employed to evaluate the value of each unlabeled sample with respect to a specific labeled dataset. Unlabeled samples with large values will serve as supplementary information for the training dataset. In addition, we introduce several reasonable indexes and criteria, and thus human labeling interference is greatly reduced. Finally, the effectiveness of the proposed method is evaluated using a numerical example and the Tennessee Eastman process.

Keywords： Semi-supervised     Active learning     Ensemble learning     Mixture discriminant analysis     Fault classification    

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Unknown fault detection for EGT multi-temperature signals based on self-supervised feature learning and

Frontiers in Energy 2023, Volume 17, Issue 4,   Pages 527-544 doi: 10.1007/s11708-023-0880-x

Abstract: Data-based methods of supervised learning have gained popularity because of available Big Data and computingHowever, the common paradigm of the loss function in supervised learning requires large amounts of labeledTherefore, a fault detection method based on self-supervised feature learning was proposed to addressThe self-supervised representation learning uses a sequence-based Triplet Loss.The model can detect progressive faults very quickly and achieve improved results for comparison without

Keywords： fault detection     unary classification     self-supervised representation learning     multivariate nonlinear    

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Topic discovery and evolution in scientific literature based on content and citations Article

Hou-kui ZHOU, Hui-min YU, Roland HU

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 10,   Pages 1511-1524 doi: 10.1631/FITEE.1601125

Abstract: document citation relations and the con-tent of the document itself via a probabilistic generative modelThe citation-content-LDA topic model exploits a two-level topic model that includes the citation informationThe model parameters are estimated by a collapsed Gibbs sampling algorithm.We also propose a topic evolution algorithm that runs in two steps: topic segmentation and topic dependencyWe have tested the proposed citation-content-LDA model and topic evolution algorithm on two online datasets

Keywords： Topic extraction     Topic evolution     Evaluation method    

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NLWSNet: a weakly supervised network for visual sentiment analysis in mislabeled web images

Luo-yang Xue, Qi-rong Mao, Xiao-hua Huang, Jie Chen,mao_qr@ujs.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 9,   Pages 1267-1412 doi: 10.1631/FITEE.1900618

Abstract: Large-scale datasets are driving the rapid developments of deep convolutional neural networks for . However, the annotation of large-scale datasets is expensive and time consuming. Instead, it is easy to obtain weakly labeled web images from the Internet. However, noisy labels still lead to seriously degraded performance when we use images directly from the web for training networks. To address this drawback, we propose an end-to-end network, which is robust to mislabeled web images. Specifically, the proposed attention module automatically eliminates the distraction of those samples with incorrect labels by reducing their attention scores in the training process. On the other hand, the special-class activation map module is designed to stimulate the network by focusing on the significant regions from the samples with correct labels in a approach. Besides the process of feature learning, applying regularization to the classifier is considered to minimize the distance of those samples within the same class and maximize the distance between different class centroids. Quantitative and qualitative evaluations on well- and mislabeled web image datasets demonstrate that the proposed algorithm outperforms the related methods.
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NGAT: attention in breadth and depth exploration for semi-supervised graph representation learning Research Articles

Jianke HU, Yin ZHANG,yinzh@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 3,   Pages 409-421 doi: 10.1631/FITEE.2000657

Abstract: To alleviate oversmoothing, we propose a nested graph network (NGAT), which can work in a semi-supervised

Keywords： Graph learning     Semi-supervised learning     Node classification     Attention    

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Personalized topic modeling for recommending user-generated content Article

Wei ZHANG, Jia-yu ZHUANG, Xi YONG, Jian-kou LI, Wei CHEN, Zhe-min LI

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 5,   Pages 708-718 doi: 10.1631/FITEE.1500402

Abstract: A generative model that combines hierarchical topic modeling and matrix factorization is proposed.Empirical results show that our model outperforms other state-of-the-art models, and can provide interpretabletopic structures for users and items.

Keywords： User-generated content (UGC)     Collaborative filtering (CF)     Matrix factorization (MF)     Hierarchical topic    

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Multi-domain Knowledge Convergence Trajectory Analysis of Strategic Emerging Industries Based on Citation Network and Text Information

Liu Yufei, Miao Zhongzhen, Li Lingfeng, Kong Dejing

Strategic Study of CAE 2020, Volume 22, Issue 2,   Pages 120-129 doi: 10.15302/J-SSCAE-2020.02.016

Abstract: It utilizes a graph neural network model and encodes the citation network, title, and abstract of the

Keywords： emerging industries     knowledge convergence     graph neural networks     citation network     topic model    

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Green Heating System——Topic of Critical Importance

Song Zhi-ping

Strategic Study of CAE 2001, Volume 3, Issue 6,   Pages 9-14

Abstract:

Nowadays a topic of most concern is how to protect the global climate change and meet the increasing

Keywords： sustainable development     water resource     combined heat and power     heat pump     poly generation    

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Topicmodeling for large-scale text data

Xi-ming LI,Ji-hong OUYANG

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 6,   Pages 457-465 doi: 10.1631/FITEE.1400352

Abstract: This paper develops a novel online algorithm, namely moving average stochastic variational inference (MASVI), which applies the results obtained by previous iterations to smooth out noisy natural gradients. We analyze the convergence property of the proposed algorithm and conduct a set of experiments on two large-scale collections that contain millions of documents. Experimental results indicate that in contrast to algorithms named ‘stochastic variational inference’ and ‘SGRLD’, our algorithm achieves a faster convergence rate and better performance.

Keywords： Latent Dirichlet allocation (LDA)     Topic modeling     Online learning     Moving average    

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Seismic input of NPP & topic of seismic-isolated research for AP1000 nuclear island

Xia Zufeng

Strategic Study of CAE 2013, Volume 15, Issue 4,   Pages 52-56

Abstract:

The article introduces seismic input of nuclear power plant in the world briefly, and mentions some exploratory work for seismic-isolated foundation of nuclear island in France, Japan and China. The article mainly focuses on a general concept design of nuclear island seismic-isolated foundation for AP1000 units by our institute. There are more useful information for seismic input of nuclear power plant & seismic-isolated foundation of nuclear island as a reference.

Keywords： nuclear power plant     seismic design     AP1000     seismic-isolated foundation    

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Self-supervised graph learning with target-adaptive masking for session-based recommendation Research Article

Yitong WANG, Fei CAI, Zhiqiang PAN, Chengyu SONG,wangyitong20@nudt.edu.cn,caifei08@nudt.edu.cn,panzhiqiang@nudt.edu.cn,songchengyu@nudt.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 1,   Pages 73-87 doi: 10.1631/FITEE.2200137

Abstract: Existing approaches use mainly recurrent neural networks (RNNs) or (GNNs) to model the sequential patternsignore the over-smoothing issue of GNNs, or directly use cross-entropy loss with a softmax layer for modelTo tackle the above issues, we propose a self-supervised graph learning with (SGL-TM) method.Finally, we combine the main supervised component with the auxiliary self-supervision module to obtainthe final loss for optimizing the model parameters.

Keywords： Session-based recommendation     Self-supervised learning     Graph neural networks     Target-adaptive masking    

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Quality-related locally weighted soft sensing for non-stationary processes by a supervised Bayesian network Research Articles

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 9,   Pages 1234-1246 doi: 10.1631/FITEE.2000426

Abstract:

It is necessary to construct an adaptive model to cope with process non-stationaries.

Keywords： 软测量；有监督贝叶斯网络；隐变量；局部加权建模；质量预测    

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Title Author Date Type Operation

Supervised topic models with weighted words: multi-label document classification

Yue-peng ZOU, Ji-hong OUYANG, Xi-ming LI

Journal Article

Special Topic on environment and sustainable development

Journal Article

Emerging topic identification from app reviews via adaptive online biterm topic modeling

Wan ZHOU, Yong WANG, Cuiyun GAO, Fei YANG,yongwang@ahpu.edu.cn

Journal Article

Ensemble enhanced active learning mixture discriminant analysis model and its application for semi-supervised

Weijun WANG, Yun WANG, Jun WANG, Xinyun FANG, Yuchen HE

Journal Article

Unknown fault detection for EGT multi-temperature signals based on self-supervised feature learning and

Journal Article

Topic discovery and evolution in scientific literature based on content and citations

Hou-kui ZHOU, Hui-min YU, Roland HU

Journal Article

NLWSNet: a weakly supervised network for visual sentiment analysis in mislabeled web images

Luo-yang Xue, Qi-rong Mao, Xiao-hua Huang, Jie Chen,mao_qr@ujs.edu.cn

Journal Article

NGAT: attention in breadth and depth exploration for semi-supervised graph representation learning

Jianke HU, Yin ZHANG,yinzh@zju.edu.cn

Journal Article

Personalized topic modeling for recommending user-generated content

Wei ZHANG, Jia-yu ZHUANG, Xi YONG, Jian-kou LI, Wei CHEN, Zhe-min LI

Journal Article

Multi-domain Knowledge Convergence Trajectory Analysis of Strategic Emerging Industries Based on Citation Network and Text Information

Liu Yufei, Miao Zhongzhen, Li Lingfeng, Kong Dejing

Journal Article

Green Heating System——Topic of Critical Importance

Song Zhi-ping

Journal Article

Topicmodeling for large-scale text data

Xi-ming LI,Ji-hong OUYANG

Journal Article

Seismic input of NPP & topic of seismic-isolated research for AP1000 nuclear island

Xia Zufeng

Journal Article

Self-supervised graph learning with target-adaptive masking for session-based recommendation

Yitong WANG, Fei CAI, Zhiqiang PAN, Chengyu SONG,wangyitong20@nudt.edu.cn,caifei08@nudt.edu.cn,panzhiqiang@nudt.edu.cn,songchengyu@nudt.edu.cn

Journal Article

Quality-related locally weighted soft sensing for non-stationary processes by a supervised Bayesian network

Journal Article