2021 IEEE International Conference on Progress in Informatics and Computing (PIC)最新文献

Exploring Multi-Layer Convolutional Neural Networks for Railway Safety Text Classification 多层卷积神经网络在铁路安全文本分类中的应用

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687014

Taocun Yang, Xin Liu, Guohua Li, Ming-rui Dai, Lei Tian, Yan Xie

{"title":"Exploring Multi-Layer Convolutional Neural Networks for Railway Safety Text Classification","authors":"Taocun Yang, Xin Liu, Guohua Li, Ming-rui Dai, Lei Tian, Yan Xie","doi":"10.1109/PIC53636.2021.9687014","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687014","url":null,"abstract":"With the rapid development of China High-Speed Rail, massive text data related to railway safety has been accumulated. When analyzing and understanding this data, classifying railway accident report text is essential and tedious work. Usually, such classification tasks are manually done by experts and workers in the railway safety department. Traditional data mining algorithms have been applied in these tasks to classify the text automatically. However, due to the complexity of the text data, classification algorithms sometimes fail and have insufficient learning ability. Meanwhile, the rise of machine learning enables us to deal with these complex problems effectively. In this paper, we propose an end-to-end multi-layer convolutional neural networks model to classify the railway safety-related text. We update the CNN part of the traditional model by increasing layers and adding a multi-height convolutional kernel. Additionally, we develop a data-preprocessing strategy to obtain the neat input data and reduce the complexity of the task. Experiments show that our proposed method achieves competitive performance and is suitable for railway safety-related text classification problems.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124976624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on an Two-Channel ACNN-LSTM Model for Financial Text Sentiment Analysis 金融文本情感分析的双通道ACNN-LSTM模型研究

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687020

Hanxiao Shi, Liqiang You, Mimi Ren, Xiaojun Li

引用次数: 0

Research of Imbalanced Classification Based on Cascade Forest 基于级联林的不平衡分类研究

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687091

M. Shi, Fangxin Lin, Ying Qian, Liang Dou

{"title":"Research of Imbalanced Classification Based on Cascade Forest","authors":"M. Shi, Fangxin Lin, Ying Qian, Liang Dou","doi":"10.1109/PIC53636.2021.9687091","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687091","url":null,"abstract":"With the rapid development of science, the quantity of data is increasing exponentially. And unprecedented opportunities are provided by machine learning and data mining. While data classification is commonly used as a primary data processing method, the diversity of data is also a great challenge. Among those, problems caused by class imbalance are attracting more attention, and there are also a number of strategies and improvement of original algorithms are proposed. Gcforest is a new integrated learning algorithm proposed by Professor Zhou Zhihua in 2017. It has the advantages of few super parameters, suitable for small-scale data sets and strong model expression ability. However, the algorithm does not optimize the unbalanced data classification. Inspired by the improvement of other ensemble learning algorithms for unbalanced data classification, this paper applies a variety of under sampling strategies to the cascaded forest of gcforest. Through experimental comparison, it has achieved better or similar performance than the current advanced learning algorithms for unbalanced data sets on a variety of typical unbalanced data sets.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114906714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Survey on the Identification of Causal Relation in Texts 语篇因果关系识别研究综述

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687029

Mingyue Han, Yinglin Wang

引用次数: 1

Data-Driven Product Design and Axiomatic Design 数据驱动的产品设计和公理设计

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687021

Bin Yang, R. Xiao

引用次数: 2

Research on Knowledge Distillation Algorithm of Object Detection 目标检测中的知识蒸馏算法研究

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687066

Xue-fang Wang, Wenbin Zhang, Yuchun Chu, Peishun Liu, Qilin Yin, Qi Li

{"title":"Research on Knowledge Distillation Algorithm of Object Detection","authors":"Xue-fang Wang, Wenbin Zhang, Yuchun Chu, Peishun Liu, Qilin Yin, Qi Li","doi":"10.1109/PIC53636.2021.9687066","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687066","url":null,"abstract":"The Algorithms of object detection are usually difficult to deploy on low-end devices due to the large amount of computation, but knowledge distillation can solve this problem by training small models to learn the already trained complex network models, realizing model compression, and effectively reducing the amount of computation. How to transfer rich knowledge from teachers to students is a key step in the knowledge distillation. To solve this problem, this paper uses the knowledge of the teacher to guide the student network training in feature extraction, target classification and frame prediction, and proposes a distillation algorithm based on multi-scale attention mechanism, which uses attention mechanism to integrate different scale features. The correlation of features between different channels is learned by assigning weights to the features of each channel. The distillation algorithm proposed in this paper is based on YOLOv4, so it can strengthen the student network to learn the key knowledge of the teacher network, and make the knowledge of the teacher network How to the student network better. Experimental analysis shows that it can effectively improve the detection accuracy of the student network. The size of the model is only 6.4% of the teacher network, but the speed is increased by 3 times, and mAP is 5.7% higher than the original student network and 2.1% lower than the teacher network.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130404409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deeply Feature Fused Video Super-resolution Network 深度融合视频超分辨率网络

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687037

Jingmin Yang, Zhensen Chen, Li Xu

引用次数: 0

A Differentially Private Data Transmission Scheme for Advanced Metering Infrastructures 先进计量基础设施的差分私有数据传输方案

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687013

Li Yan, Chao Ma, Cong Wang, Ziqing Zhu, Gaozhou Wang, Huijian Wang

引用次数: 0

Optimal Scale Combinations Selection for Incomplete Generalized Multi-scale Decision Systems 不完全广义多尺度决策系统的最优尺度组合选择

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687000

Qiong Mou, Yunlong Cheng

引用次数: 0

Reliability Modeling and Analysis of Hospital Information System Based on Microservices 基于微服务的医院信息系统可靠性建模与分析

2021 IEEE International Conference on Progress in Informatics and Computing (PIC) Pub Date : 2021-12-17 DOI: 10.1109/PIC53636.2021.9687027

Zheng Liu, Huiqun Yu, Guisheng Fan, Liqiong Chen

{"title":"Reliability Modeling and Analysis of Hospital Information System Based on Microservices","authors":"Zheng Liu, Huiqun Yu, Guisheng Fan, Liqiong Chen","doi":"10.1109/PIC53636.2021.9687027","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687027","url":null,"abstract":"In recent years, modern hospital has a large scale, complex relationship, and hospital information system (HIS) due to rapid development of computer networks. But there is still a big gap for reliable use of clinical information and management system, especially in terms of fault prevention. The microservice architecture has great advantages for development and delivery of complex system. This paper proposes a novel microservice reliability model (MSRM) for HIS based on the formalism of Predicate Petri net (PrT net). First, microservice reliability requirement design is given and PrT net is used to model the reliability of microservice, and the corresponding syntax and semantics are also presented. Then the redundancy and circuit breaker is designed by using PrT net, a composition strategy is proposed and the reliability of microservices is analyzed qualitatively and quantitatively. Based on the constructed MSRM, the correctness of PrT net modeling and effectiveness of the strategies have been proven theoretically. Finally, a public healthcare case is used to explain modeling process, and verify the effectiveness of proposed method. Experimental results show that the strategy for HIS microservice reliability is effective.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116013270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1