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Improvement of Federal Average Algorithm Based on CRITIC Weighting Method 2

Improvement of Federal Average Algorithm Based on CRITIC Wei...

引用

2nd International Conference on Artificial Intelligence and intelligent Information Processing, AIIIP 2023

作者： Yu, Qiantong Wei, Nianqing Mi, Yiyang College of Software Chengdu University of Information Technology Chengdu610225 China Sichuan Province Chengdu University of Information Technology Key Laboratory of Software Automatic Generation and Intelligent Service Chengdu610225 China

ISBN: (纸本)9798350371451

Federated learning is a machine learning technology that can protect data privacy. Its federated average algorithm updates the global model through weights. In view of the influence of subjective factors in the calculation of data weights by analytic hierarchy process (AHP), a federated average algorithm based on CRITIC weighting method is proposed to process multi-source data from the perspective of data diversity and volatility. In the experiment, the CRITIC weighting method is used to calculate the importance of each attribute in the data, and it is used as the model aggregation parameter to calculate the weight of each client data. By applying the federated average algorithm in horizontal federated learning to the data model training fusion, the accuracy of data model establishment is improved. In terms of both theoretical basis and practical research, experiments and analysis on the collected data sets show that the proposed method is executable. The experimental results show that the proposed method improves the accuracy by about 4 % compared with the federal average algorithm, reduces the communication overhead of the federal learning and training process, and improves the overall efficiency. © 2023 IEEE.

关键词： Analytic hierarchy process

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Multi-station low-visibility prediction based on spatio-temporal fusion model

Multi-station low-visibility prediction based on spatio-temp...

引用

2024 International Conference on Image, Signal Processing, and Pattern Recognition, ISPP 2024

作者： Xiao, Nan Luo, Fei Cao, Liang College of Software Engineering Chengdu University of Information Technology Sichuan Chengdu China Yunnan R&D Institute of Natural Disaster Chengdu University of Information Technology Yunnan Kunming China Automatic Software Generation and Intelligent Service Key Laboratory of Sichuan Province Sichuan Chengdu China CMA - CUIT Meteorological Software Engineering Joint Research Center Sichuan Chengdu China

ISBN: (纸本)9781510680425

The accurate prediction of low-visibility has important application value to the normal operation of navigation, aviation and other transportation vehicles. However, the current low-visibility prediction task mostly uses time series model to extract temporal features, but neglects the spatial correlation between national observation stations. Additionally, those time series model require that the input data must be limited to the Euclidean space. However, due to the discrete distribution of stations, the input data required for multi-station low-visibility prediction does not have a regular spatial structure. To address those limitation, we proposed a weighted location feature convolution based on graph convolution. The construction of the weighted location feature convolution takes into account the distance and pitch angle, reduces the influence caused by the dispersion of national observation stations, and enables the convolution kernel to be shared in different local meteorological spaces. The convolution as a substitute for matrix multiplication within Sequence to Sequence framework, then cascaded the decoder to deal with the spatio-temporal dependence relationship of different levels, leading spatio-temporal fusion model CPGDC. The experimental results show that the CPGDC model achieves has higher accuracy in predicting low-visibility values across multiple national observation stations. © 2024 SPIE.

关键词： Input output programs

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Improvement of Federal Average Algorithm Based on CRITIC Weighting Method

Improvement of Federal Average Algorithm Based on CRITIC Wei...

引用

Artificial Intelligence and intelligent Information Processing (AIIIP), International Conference on

作者： Qiantong Yu Nianqing Wei Yiyang Mi College of software Chengdu University of Information Technology Chengdu China Key Laboratory of Software Automatic Generation and Intelligent Service Sichuan Province Chengdu University of Information Technology Chengdu China

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On Corpus Construction and Joint Modeling Method for Clinical Pulse Feeling and Palpation Information Extraction and Lexical Analysis of Traditional Chinese Medicine 22

On Corpus Construction and Joint Modeling Method for Clinica...

引用

22nd Chinese National Conference on Computational Linguistics, CCL 2023

作者： Wang, Yaqiang Jiang, Wen Jiang, Yongguang Shu, Hongping College of Software Engineering Chengdu University of Information Technology China Institute for Data Science and Engineering Chengdu University of Information Technology China Sichuan Key Laboratory of Software Automatic Generation and Intelligent Service China Department of Preclinical Medicine Chengdu University of Traditional Chinese Medicine China

ISBN: (纸本)9781713876229

Pulse feeling and palpation (PFP) are the most distinctive and representative clinical diagnostic methods of traditional Chinese medicine (TCM). They provide important evidence for TCM clinical practitioners to differentiate syndromes. Extracting PFP information and analyzing its lexical features have important clinical value. In this paper, we carried out research on corpus construction and joint modeling method for PFP information extraction (IE) and lexical analysis (LA) of TCM for the first time. Based on more than ten thousand TCM clinical records, we proposed a corpus construction framework, built annotation guidelines and constructed a labeled PFP IE and LA corpus to support multi-task learning. Labeling consistency evaluated by inter-annotator agreement value for the corpora achieve more than 0.94. Moreover, we attempted to jointly model PFP IE and LA tasks of TCM with a same-level-shared multi-task model, and experimental results verified effectiveness of the joint model. © 2023 China National Conference on Computational Linguistics.

关键词： Information retrieval

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An Unstructured Data Representation Enhanced Model for Postoperative Risk Prediction 21

An Unstructured Data Representation Enhanced Model for Posto...

引用

21st Chinese National Conference on Computational Linguistic, CCL 2022

作者： Wang, Yaqiang Yang, Xiao Hao, Xuechao Shu, Hongping Chen, Guo Zhu, Tao College of Software Engineering Chengdu University of Information Technology China Institute for Data Science and Engineering Chengdu University of Information Technology China Sichuan Key Laboratory of Software Automatic Generation and Intelligent Service China Department of Anesthesiology Sichuan University China

Postoperative risk prediction has a positive effect on clinical resource planning, emergency plan preparation and reducing postoperative risk and mortality. Postoperative risk prediction is mainly based on patient's basic information, laboratory tests, vital signs and other structured data, while the value of unstructured preoperative diagnosis with rich semantic information remains to be verified. Aiming at attempting this problem, an unstructured data representation enhanced postoperative risk prediction model is proposed in this paper. The model utilizes self-attention to fuse structured data with preoperative diagnosis. Through comparing with the commonly used statistical machine learning models and the state-of-the-art deep neural networks, the proposed model has not only better prediction performance, but also better interpretability. © 2022 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License.

关键词： Deep neural networks

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Four Diagnostic Description Extraction in Clinical Records of Traditional Chinese Medicine with Batch Data Oversampling 21

Four Diagnostic Description Extraction in Clinical Records o...

引用

21st Chinese National Conference on Computational Linguistic, CCL 2022

作者： Wang, Yaqiang Li, Kailun Jiang, Yongguang Shu, Hongping College of Software Engineering Chengdu University of Information Technology China Institute for Data Science and Engineering Chengdu University of Information Technology China Sichuan Key Laboratory of Software Automatic Generation and Intelligent Service China Department of Preclinical Medicine Chengdu University of Traditional Chinese Medicine China

Four diagnostic description extraction in clinical records of traditional Chinese medicine (TCM) has important application value in improving the quality and efficiency of TCM clinical syndrome differentiation and treatment. However, the extraction task is yet to be explored, and imbalanced class distribution is one of the key challenges of this task. As a first exploration of this task, we firstly constructed a TCM clinical four diagnostic description extraction corpus and then solved the domain adaptation by fine-tuning the general domain pre-trained language model based on unlabeled TCM clinical records. At last, we trained our proposed four diagnostic description extraction model by utilizing a small labeled dataset through a well-designed batch data oversampling algorithm. The experimental results show that the performance of the proposed method in this paper is better than that of the compared methods, and the proposed method improves the extraction performance F1 score of the rare class by 2.13% on average. © 2022 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License.

关键词： Extraction

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Label Embedding Enhanced Multi-label Sequence generation Model 9th

Label Embedding Enhanced Multi-label Sequence Generation Mod...

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9th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2020

作者： Wang, Yaqiang Yan, Feifei Wang, Xiaofeng Tang, Wang Shu, Hongping College of Software Engineering Chengdu University of Information Technology ChengduSichuan610225 China Sichuan Key Laboratory of Software Automatic Generation and Intelligent Service ChengduSichuan610225 China School of Electronic Engineering Chengdu University of Information Technology ChengduSichuan610225 China Sunsheen Inc. ChengduSichuan610225 China

ISBN: (纸本)9783030604561

Existing sequence generation models ignore the exposure bias problem when they apply to the multi-label classification task. To solve this issue, in this paper, we proposed a novel model, which disguises the label prediction probability distribution as label embedding and incorporate each label embedding from previous step into the current steps LSTM decoding process. It allows the current step can make a better prediction based on the overall output of the previous prediction, rather than simply based on a local optimum output. In addition, we proposed a scheduled sampling-based learning algorithm for this model. The learning algorithm effectively and appropriately incorporates the label embedding into the process of label generation procedure. Through comparing with three classical methods and four SOTA methods for the multi-label classification task, the results demonstrated that our proposed method obtained the highest F1-Score (reaching 0.794 on a chemical exposure assessment task and reaching 0.615 on a clinical syndrome differentiation task of traditional Chinese medicine). © 2020, Springer Nature Switzerland AG.

关键词： Embeddings

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A Context Enhanced Attention Network for Aspect-Based Sentiment Classification

A Context Enhanced Attention Network for Aspect-Based Sentim...

引用

Big Data & Artificial Intelligence & software Engineering (ICBASE), International Conference on

作者： Yinglin Zhu Wenbin Zheng Jiaoling Zheng College of Software Engineering Chengdu University of Information Technology Chengdu China Software Automatic Generation and Intelligent Service Key Laboratory of SiChuan Province Chengdu China

Aspect-based sentiment classification is a fine-grained task in sentiment analysis, it aims to detect the sentiment polarity of an aspect in a given context. Existing approaches mostly focused on the processing of aspect words, which often fail to adequately model the context via aspect terms. However, the surrounding words in context usually have an important impact on the sentiment polarity of aspect words. This paper proposes a context enhanced attention network (CE-ATT) to enhance context modeling for aspect-based sentiment classification. Firstly, the text sequence was divided into left and right contexts by a given aspect. Then, the Bidirectional Long Short Term Memory (BiLSTM) was used to model the left context and aspect, the right context and aspect, and both sides context and aspect, respectively. After summing these contextual representations, an attention mechanism was used to concentrate on the parts that have an important influence on the aspect word. The experimental results on three benchmark datasets demonstrate the effectiveness of the CE-ATT method.

关键词： Sentiment analysis Benchmark testing Task analysis Long short term memory Context modeling software engineering

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Enhanced sparse based discriminative topic representation for text categorization 6

Enhanced sparse based discriminative topic representation fo...

引用

6th International Conference on Systems and Informatics, ICSAI 2019

作者： Zheng, Wenbin Zhang, Wenxu Yue, Xi Gao, Yan Tang, Hong College of Software Engineering Chengdu University of Information Technology Chengdu610225 China Software Automatic Generation and Intelligent Service Key Laboratory of SiChuan Province Chengdu610225 China College of Engineering Sichuan Normal University Chengdu610068 China

ISBN: (纸本)9781728152561

This paper presents an enhanced sparse based discriminative topic representation method for text categorization. By constructing category center vectors and combining with the latent Dirichlet allocation model, a more discriminative dictionary is obtained, which can describe the relationship between topic and word well. Furthermore, an enhanced sparse representation of documents can be generated with a L1/2 regularization in order to achive a good relationship between document and topic. The experimental results show that our proposed approach achieves more stable classification performance and obtains more high sparse degree. © 2019 IEEE.

关键词： Statistics

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Enhanced Sparse based Discriminative Topic Representation for Text Categorization

Enhanced Sparse based Discriminative Topic Representation fo...

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International Conference on Systems and Informatics (ICSAI)

作者： Wenbin Zheng Wenxu Zhang Xi Yue Yan Gao Hong Tang Software Automatic Generation and Intelligent Service Key Laboratory of SiChuan Province Chengdu China College of Software Engineering Chengdu University of Information Technology Chengdu China College of Engineering Sichuan Normal University Chengdu China

ISBN: (数字)9781728152561

ISBN: (纸本)9781728152578

关键词： Semantics Dictionaries Open systems Text categorization Training Sparse matrices Large scale integration

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