Electronic Research Archive最新文献_第2页

Sentence opinion mining model for fusing target entities in official government documents 面向政府公文目标实体融合的句子意见挖掘模型

IF 0.8 4区数学

Electronic Research Archive Pub Date : 2023-01-01 DOI: 10.3934/era.2023177

Xiao Ma, Teng Yang, Feng Bai, Yunmei Shi

{"title":"Sentence opinion mining model for fusing target entities in official government documents","authors":"Xiao Ma, Teng Yang, Feng Bai, Yunmei Shi","doi":"10.3934/era.2023177","DOIUrl":"https://doi.org/10.3934/era.2023177","url":null,"abstract":"When drafting official government documents, it is necessary to firmly grasp the main idea and ensure that any positions stated within the text are consistent with those in previous documents. In combination with the field's demands, By taking advantage of suitable text-mining techniques to harvest opinions from sentences in official government documents, the efficiency of official government document writers can be significantly increased. Most existing opinion mining approaches employ text classification methods to directly mine the sentential text of official government documents while disregarding the influence of the objects described within the documents (i.e., the target entities) on the sentence opinion categories. To address these issues, this study proposes a sentence opinion mining model that fuses the target entities within documents. Based on the Bi-directional long short-term (BiLSTM) and attention mechanisms, the model fully considers the attention given by a official government document's target entity to different words within the corresponding sentence text, as well as the dependency between words of the sentence. The model subsequently fuses two by using feature vector fusion to obtain the final semantic representation of the text, which is then classified using a fully connected network and softmax function. Experimental results based on a dataset of official government documents show that the model significantly outperforms baseline models such as Text-convolutional neural network (TextCNN), recurrent neural network (RNN), and BiLSTM.","PeriodicalId":48554,"journal":{"name":"Electronic Research Archive","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70245873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Barycentric rational interpolation method for solving time-dependent fractional convection-diffusion equation 求解时变分数阶对流扩散方程的质心有理插值方法

IF 0.8 4区数学

Electronic Research Archive Pub Date : 2023-01-01 DOI: 10.3934/era.2023205

Jin Li, Yongling Cheng

引用次数: 1

Uniqueness criteria for initial value problem of conformable fractional differential equation 符合分数阶微分方程初值问题的唯一性准则

IF 0.8 4区数学

Electronic Research Archive Pub Date : 2023-01-01 DOI: 10.3934/era.2023207

Y. Zou, Yujun Cui

引用次数: 0

Color image steganalysis based on quaternion discrete cosine transform 基于四元数离散余弦变换的彩色图像隐写分析

IF 0.8 4区数学

Electronic Research Archive Pub Date : 2023-01-01 DOI: 10.3934/era.2023209

Meng Xu, X. Luo, Jinwei Wang, Hao Wang

{"title":"Color image steganalysis based on quaternion discrete cosine transform","authors":"Meng Xu, X. Luo, Jinwei Wang, Hao Wang","doi":"10.3934/era.2023209","DOIUrl":"https://doi.org/10.3934/era.2023209","url":null,"abstract":"With the rapid development and application of Internet technology in recent years, the issue of information security has received more and more attention. Digital steganography is used as a means of secure communication to hide information by modifying the carrier. However, steganography can also be used for illegal acts, so it is of great significance to study steganalysis techniques. The steganalysis technology can be used to solve the illegal steganography problem of computer vision and engineering applications technology. Most of the images in the Internet are color images, and steganalysis for color images is a very critical problem in the field of steganalysis at this stage. Currently proposed algorithms for steganalysis of color images mainly rely on the manual design of steganographic features, and the steganographic features do not fully consider the internal connection between the three channels of color images. In recent years, advanced steganography techniques for color images have been proposed, which brings more serious challenges to color image steganalysis. Quaternions are a good tool to represent color images, and the transformation of quaternions can fully exploit the correlation among color image channels. In this paper, we propose a color image steganalysis algorithm based on quaternion discrete cosine transform, firstly, the image is represented by quaternion, then the quaternion discrete cosine transform is applied to it, and the coefficients obtained from the transformation are extracted to design features of the coeval matrix. The experimental results show that the proposed algorithm works better than the typical color image steganalysis algorithm.","PeriodicalId":48554,"journal":{"name":"Electronic Research Archive","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70246562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Satellite road extraction method based on RFDNet neural network 基于RFDNet神经网络的卫星道路提取方法

IF 0.8 4区数学

Electronic Research Archive Pub Date : 2023-01-01 DOI: 10.3934/era.2023223

Weichi Liu, Gaifang Dong, Mingxin Zou

{"title":"Satellite road extraction method based on RFDNet neural network","authors":"Weichi Liu, Gaifang Dong, Mingxin Zou","doi":"10.3934/era.2023223","DOIUrl":"https://doi.org/10.3934/era.2023223","url":null,"abstract":"The road network system is the core foundation of a city. Extracting road information from remote sensing images has become an important research direction in the current traffic information industry. The efficient residual factorized convolutional neural network (ERFNet) is a residual convolutional neural network with good application value in the field of biological information, but it has a weak effect on urban road network extraction. To solve this problem, we developed a road network extraction method for remote sensing images by using an improved ERFNet network. First, the design of the network structure is based on an ERFNet; we added the DoubleConv module and increased the number of dilated convolution operations to build the road network extraction model. Second, in the training process, the strategy of dynamically setting the learning rate is adopted and combined with batch normalization and dropout methods to avoid overfitting and enhance the generalization ability of the model. Finally, the morphological filtering method is used to eliminate the image noise, and the ultimate extraction result of the road network is obtained. The experimental results show that the method proposed in this paper has an average F1 score of 93.37% for five test images, which is superior to the ERFNet (91.31%) and U-net (87.34%). The average value of IoU is 77.35%, which is also better than ERFNet (71.08%) and U-net (65.64%).","PeriodicalId":48554,"journal":{"name":"Electronic Research Archive","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70246690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Derivations of finite-dimensional modular Lie superalgebras $ overline{K}(n, m) $ 有限维模李超代数的导数$ overline{K}(n, m) $

IF 0.8 4区数学

Electronic Research Archive Pub Date : 2023-01-01 DOI: 10.3934/era.2023217

Dan Mao, Keli Zheng

引用次数: 0

Deciphering and identifying pan-cancer RAS pathway activation based on graph autoencoder and ClassifierChain 基于图自编码器和分类链的泛癌RAS通路激活解码与识别

IF 0.8 4区数学

Electronic Research Archive Pub Date : 2023-01-01 DOI: 10.3934/era.2023253

Jianting Gong, Yingwei Zhao, Xiantao Heng, Yongbing Chen, Pingping Sun, Fei He, Zhiqiang Ma, Zilin Ren

{"title":"Deciphering and identifying pan-cancer RAS pathway activation based on graph autoencoder and ClassifierChain","authors":"Jianting Gong, Yingwei Zhao, Xiantao Heng, Yongbing Chen, Pingping Sun, Fei He, Zhiqiang Ma, Zilin Ren","doi":"10.3934/era.2023253","DOIUrl":"https://doi.org/10.3934/era.2023253","url":null,"abstract":"<abstract> <p>The goal of precision oncology is to select more effective treatments or beneficial drugs for patients. The transcription of ‘‘hidden responders’’ which precision oncology often fails to identify for patients is important for revealing responsive molecular states. Recently, a RAS pathway activation detection method based on machine learning and a nature-inspired deep RAS activation pan-cancer has been proposed. However, we note that the activating gene variations found in KRAS, HRAS and NRAS vary substantially across cancers. Besides, the ability of a machine learning classifier to detect which KRAS, HRAS and NRAS gain of function mutations or copy number alterations causes the RAS pathway activation is not clear. Here, we proposed a deep neural network framework for deciphering and identifying pan-cancer RAS pathway activation (DIPRAS). DIPRAS brings a new insight into deciphering and identifying the pan-cancer RAS pathway activation from a deeper perspective. In addition, we further revealed the identification and characterization of RAS aberrant pathway activity through gene ontological enrichment and pathological analysis. The source code is available by the URL <ext-link ext-link-type=\"uri\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:href=\"https://github.com/zhaoyw456/DIPRAS\">https://github.com/zhaoyw456/DIPRAS</ext-link>.</p> </abstract>","PeriodicalId":48554,"journal":{"name":"Electronic Research Archive","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70247114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Numerical simulation analysis for the effect of water content on the intelligent compaction quality of roadbed 含水量对路基智能压实质量影响的数值模拟分析

IF 0.8 4区数学

Electronic Research Archive Pub Date : 2023-01-01 DOI: 10.3934/era.2023254

Yuan Ma, Y. Luan, Shuangquan Jiang, Jianming Zhang, Chuan Wang

{"title":"Numerical simulation analysis for the effect of water content on the intelligent compaction quality of roadbed","authors":"Yuan Ma, Y. Luan, Shuangquan Jiang, Jianming Zhang, Chuan Wang","doi":"10.3934/era.2023254","DOIUrl":"https://doi.org/10.3934/era.2023254","url":null,"abstract":"In the process of intelligent compaction of roadbeds, the water content of the roadbed is one of the important influencing factors of compaction quality. In order to analyze the effect of water content on the compaction quality of roadbeds, this paper is developed by secondary development of Abaqus finite element numerical simulation software. At the same time, the artificial viscous boundary was set to eliminate the influence of boundary conditions on the results in the finite element modeling process, so that the numerical simulation can be refined to model. On this basis, the dynamic response analysis of the roadbed compaction process is performed on the finite element numerical simulation results. This paper established the correlation between compaction degree and intelligent compaction index CMV (Compaction Meter Value) and then analyzed the effect of water content on the compaction quality for the roadbed. The results of this paper show that the amplitude of the vertical acceleration is almost independent of the moisture content, and the vertical displacement mainly occurs in the static compaction stage. The vertical displacement changes sharply in the first 0.5 s when the vibrating wheel is in contact with the roadbed. The main stage of roadbed compaction quality increase is before the end of the first compaction. At the end of the first compaction, the roadbed compaction degree increased rapidly from 80% to 91.68%, 95.34% and 97.41%, respectively. With the increase in water content, the CMV gradually increased. At the end of the second compaction, CMV increased slightly compared with that at the end of the first compaction and stabilized at the end of the second compaction. The water content of the roadbed should be considered to be set slightly higher than the optimal water content of the roadbed by about 1% during the construction of the roadbed within the assumptions of this paper.","PeriodicalId":48554,"journal":{"name":"Electronic Research Archive","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70247121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Local porosity of the free boundary in a minimum problem 最小问题中自由边界的局部孔隙率

IF 0.8 4区数学

Electronic Research Archive Pub Date : 2023-01-01 DOI: 10.3934/era.2023277

Yuwei Hu, Jun Zheng

引用次数: 0

Word-level dual channel with multi-head semantic attention interaction for community question answering 基于词级双通道、多头语义注意交互的社区问答

IF 0.8 4区数学

Electronic Research Archive Pub Date : 2023-01-01 DOI: 10.3934/era.2023306

Jinmeng Wu, Hanyu Hong, Yaozong Zhang, Y. Hao, Lei Ma, Lei Wang

{"title":"Word-level dual channel with multi-head semantic attention interaction for community question answering","authors":"Jinmeng Wu, Hanyu Hong, Yaozong Zhang, Y. Hao, Lei Ma, Lei Wang","doi":"10.3934/era.2023306","DOIUrl":"https://doi.org/10.3934/era.2023306","url":null,"abstract":"The semantic matching problem detects whether the candidate text is related to a specific input text. Basic text matching adopts the method of statistical vocabulary information without considering semantic relevance. Methods based on Convolutional neural networks (CNN) and Recurrent networks (RNN) provide a more optimized structure that can merge the information in the entire sentence into a single sentence-level representation. However, these representations are often not suitable for sentence interactive learning. We design a multi-dimensional semantic interactive learning model based on the mechanism of multiple written heads in the transformer architecture, which not only considers the correlation and position information between different word levels but also further maps the representation of the sentence to the interactive three-dimensional space, so as to solve the problem and the answer can select the best word-level matching pair, respectively. Experimentally, the algorithm in this paper was tested on Yahoo! and StackEx open-domain datasets. The results show that the performance of the method proposed in this paper is superior to the previous CNN/RNN and BERT-based methods.","PeriodicalId":48554,"journal":{"name":"Electronic Research Archive","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70248101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0