bluuebunny/crossref_metadata_2025_split huggingface dataset:
A multi-strategy improved sparrow search algorithm
Authors: Ouyang Chengtian , Qiu Yaxian , Zhu Donglin 
Published: April 2021

Abstract
As a novel algorithm, the sparrow search algorithm has better optimization performance than other intelligent optimization algorithms. However, in complex problems, there is still the possibility of falling into a local optimum and relying on the initial population stage. In response to these shortcomings, a multi-strategy improved sparrow search algorithm (KLSSA) is proposed. First, in the initial population stage, K-means clustering method is used to cluster and differentiate the individual positions of sparrows, which speeds up the work efficiency of the population and gets rid of the influence of randomness. Then, the levy flight mechanism and adaptive local search strategy are respectively introduced in the calculation of the location update of the discoverer and the follower, so that the discoverer can conduct a wide range of searches more flexibly, and the follower has a more detailed search method. Through the 10 standard test functions, it can be seen that the multi-strategy improved sparrow search algorithm has stronger optimization ability and better optimization speed.
Prospect of Voiceprint Recognition Based on Deep Learning
Authors: Shan Shuaijie , Liu Jianbao , Dun Yaowu 
Published: April 2021

Abstract
As a biometric technology, voiceprint recognition is as unique as human fingerprint and pupil, so voiceprint recognition has great potential in practical application. Aiming at the feature extraction method and voiceprint recognition method, this paper first introduces the principle of voiceprint recognition, traditional MFCC, LPDA and other feature parameters and their performance; secondly, the traditional voiceprint recognition methods such as GMM and GMM-SVM are introduced, as well as their shortcomings and improvement schemes. Aiming at the shortcomings of voiceprint recognition system based on traditional algorithms in accuracy, robustness and real-time, this paper introduces the role of deep learning neural network in different stages of voiceprint recognition, and introduces the characteristics and network structure of some typical algorithms based on deep learning. Finally, according to the advantages and disadvantages of deep learning in voiceprint recognition, the development prospect and challenges of voiceprint recognition technology are analysed.
Improved Fault Location Algorithm for Radial Distribution Network Based on Power Failure Information
Authors: Han Yi , Qian Zhongmin , Shao Danlu , Lin Qiujia , Tang Zhexuan , Chen Minyu , Ai Qian 
Published: April 2021

Abstract
As the scale of the distribution network continues to increase, the importance of distribution network is getting higher and higher. Therefore, it is necessary to achieve fault location and restore it as soon as possible after the fault occurs. This paper proposes a new fault location algorithm for the radial distribution network, which is improved on the basis of traditional matrix algorithm. First, the power failure incidence matrix (PFIM) and power interruption information matrix (PIIM) is constructed based on the topological connection and fault information at each node. The fault location matrix (FLM) is then obtained through PFLM and PIIM to realize the fault location. Second, the accuracy of the proposed algorithm is verified by mathematical derivation. Finally, an 11-node radial distribution network is illustrated to testify the proposed algorithm. Results show that the improved fault location matrix algorithm proposed in this paper can effectively achieve fault location in radial distribution network.
Using the Markov chain to predict the outcome of an election in a given situation
Authors: Wan Yajie 
Published: April 2021

Abstract
This paper used Markov Chain, a mathematical model that is often utilized in predicting, to forecast the result of the election process of a person in a specific circumstance. In the way of modeling, starting from the tree diagram to the model of Markov Chain and related transition matrix, this paper aimed at the election result, which relates the object of study, a specific candidate. The research data are collected from the prompt of the research question and the calculation of coding based on the transition matrix. The result is, when it is the 64th year of that specific candidate running for the reelection campaign, he will be likely to retire from politics.
Structured information extraction for bone scan image report texts
Authors: Huang Mengge , Zhang Jingyi , Deng Tao , Man Zhengxing , Lin Qiang 
Published: April 2021

Abstract
Extracting structured information from the bone scan image report text plays a crucial role in supporting clinical analysis and research. This study summarized the structure and characteristics of 3608 bone scan image report text using dictionary-based information extraction method, including data cleaning, entity recognition, building dictionary and extraction rules. This method was used to obtain the structured data of bone scan image report text required for clinical research, and the effect evaluation was carried out on 1000 randomly selected report texts, with the precision rate and recall rate higher than 90%. The method proposed in this study is practical and could have good effect on structured results for bone scan imaging report text.
An Automatic Abstractive Text Summarization Model based on Hybrid Attention Mechanism
Authors: Wang Zhe 
Published: April 2021

Abstract
Attentional sequence-to-sequence models based on RNN have achieved promising performances in the automatic abstractive summarization technology. However, there are still some shortcomings, including the inaccuracies and lack of key information. In this paper, an abstractive text summarization model based on the hybrid attention mechanism is presented. The model aims at adopting sentence-level attention mechanism to guide the word-level attention distribution. Moreover, this model modulates the weight of sentence-level attention value to alleviate the adverse effect of high variance on word-level attention distribution for short documents. The experimental results on the LCSTS dataset show that the presented model can effectively improve the ROUGE scores and can better summarize the source document while retaining the important information. An example in this paper shows that the model can generate summaries that are similar to human-written ones.
Research on Entity Recognition in Aerospace Engine Fields Based on Conditional Random Fields
Authors: Gao Wenchao , Zhao Shanshan , Zhu Shiying , Ren Shengbo 
Published: April 2021

Abstract
With Entity recognition is an important basic tool for many natural language processing tasks such as information extraction, question answering systems, and syntactic analysis. Entity recognition is divided into general field and specific field. There were different recognition methods for different fields. At present, one of the more common entity recognition technologies is a rule-based method, and the other is a statistical method. Due to data sets scarcity in the aerospace engine field, there is a lack of corresponding entity recognition research in the aerospace engine field. This paper proposes a statistical-based entity recognition method to identify entities in a specific field based on conditional random fields. This method constructs a training set in the aerospace engine field through manual annotation, extracts entities in the aerospace engine field, uses words, word frequency, and part-of-speech features respectively, and conducts experiments. This method can more accurately identify entities in the aerospace engine field, and finally make the accuracy rate, Recall rate and F value reach 92.7%, 87.1%, 89.8%.
Application of computed tomography angiography in thoracic aortic aneurysm
Authors: Yuan Biying , Liu Jianhua , Tian Jiale , Xu Nannan 
Published: April 2021

Abstract
Thoracic aortic aneurysm (TAA) is the most common thoracic aortic dilatation. Computed tomography angiography can effectively assess the shape, location and degree of dilatation of TAA. It can also show the blood vessels involved in TAA, including left subclavian artery, double renal artery and abdominal aorta. At present, post-processing techniques of computed tomography angiography include MPR, CPR, VR. These methods are commonly used to evaluate TAA.
Model predictive control of microbial fuel cell based on Kalman state estimation
Authors: Wang Mimmin , An Aimin , Zhao Yingying 
Published: April 2021

Abstract
Aiming at the constraints and undetectable interference in the microbial fuel cell system, a microbial fuel cell model predictive control method based on state estimation is proposed. According to the principles and actual requirements of the microbial fuel cell system, a state space model with input constraints is established. By introducing the model predictive controller, the performance of constrained optimization control is improved. Combined with the Kalman filter estimator, the impact of unmeasured interference on the predictive controller is compensated, and the control accuracy and robustness of the system are improved. The simulation experiment finally indicate that model predictive control based on kalman state estimation makes the output voltage of system reach the desired value, the input flow meets the actual demand and the cost is optimal. In addition, it has a good ability to deal with interference.
Aspect Based Sentiment Analysis of catering field reviews via RoBERTa-AOA model
Authors: Li Bo , Pan Fang , Shou Zhaoyu , Zhang Huibing 
Published: April 2021

Abstract
The emotional information included in catering field reviews has a great influence on the ordering behaviour of users. The traditional Aspect Sentiment Analysis Model based on Neural Network often has the problem of neglecting Aspect Words. This paper proposes an aspect sentiment analysis model that combines RoBERTa and Attention-over-Attention (AOA). Firstly, the model uses RoBERTa to model sentences and aspects respectively. Then, the deep semantic features of sentence and aspect were extracted by Bi-LSTM. Finally, AOA is adopted to learn the weight of aspect to sentence and sentence to aspect respectively, and the results are input into SoftMax layer for classified output. Experimental results on real Meituan catering data sets show that the accuracy of RoBERTa-AOA is about 1.7%-3.7% higher than that of other models.
Inner synchronization of Boolean networks with time delays
Authors: Liang Yi , Hu Haihui , Alimjan Gulnaz 
Published: April 2021

Abstract
The model of Boolean networks with time delays is proposed, whose delays are multiple state delays for each node. Inner synchronization problem of the Boolean networks is investigated, and necessary and sufficient condition of synchronization is established based on the semi-tensor product of matrices. Moreover, illustrative examples show the efficiency of the proposed results.
A Loan risk assessment model with consumption features for online finance
Authors: Li Li , Huang Ding 
Published: April 2021

Abstract
While online finance is rapidly growing, it is a big challenge to evaluate the loan risk of users based on the data on the Internet. In this study, we used basic user information and added the user’s consumption features to construct a loan risk assessment model that integrates features. We extract consumption features from two aspects: firstly, construct consumption portrait features through statistical analysis and clustering; secondly, combine convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) networks to extract sequence features in consumption, and add attention mechanisms to improve the evaluation effect. Finally, the features are combined and fed into the fully connected layer, and the probability of default loan is calculated through the activation function. Our data set comes from an online finance company. The experimental results show that the loan assessment model combined with consumption features does improve the accuracy of default loans under the AUC and KS indicators.
Attention Relational Graph Convolution Networks for Relation Prediction in Knowledge Graphs
Authors: Wang Shuo , Zhong Yi , Wang Chengpeng 
Published: April 2021

AbstractRecently, the tasks based on knowledge graph (KG), such as question answering, information retrieval, are more and more widely used. However, due to the incompleteness of the relations (links) between entities, it is very important to study the relations prediction based on KG to complete the missing relations between entities. In recent years, graph convolution networks (GCNs) have been a new method to solve the reasoning of knowledge graph. However, the existing knowledge graph relation prediction model based on GCNs fails to consider the importance between nodes. In order to obtain more abundant relations information between entities (nodes), inspired by the graph attention network (GAT), we propose an attention weighted relational graph convolutional network (denoted as AWR-GCN), which is used as an encoder of the encoder-decoder model for relationship prediction, and the decoder is a linear model. Compared with the advanced methods in the commonly used relational prediction data sets, our model has a certain performance improvement and reached advanced level.
Face Manipulation with Generative Adversarial Network
Authors: Xu Longpeng 
Published: April 2021

Abstract
Generative adversarial network has appeared as an effective image manipulation tool in recent years and has been widely used. The GAN-based manipulation of face images is also possible and tools including DeepFake are already misused. In this paper, we discuss the pros and cons of face manipulation with generative adversarial network. We find that this technique can be very useful for recovering masked face and further improving face recognition accuracy.
Named entity recognition method of Chinese EMR based on BERT-BiLSTM-CRF
Authors: Gao Wenchao , Zheng Xiaohui , Zhao Shanshan 
Published: April 2021

Abstract
With the widespread use of Chinese electronic medical records, the extraction of medical named entities and the resolution of the polysemous term of entities are of great significance to the analysis and processing of patient information and disease diagnosis. This paper uses a BERT Chinese pre-training vector that does not rely on manual feature selection, combines BiLSTM and CRF Chinese named entity recognition algorithm model, and applies it to the processing of the CCKS2020 electronic medical record data set. This paper conducts experimental tests and comparisons between the BERT-BiLSTM-CRF model and other models. The results show that the results of this model are better than other models, and it can accurately identify multiple entity categories and has a good application prospect.
Research on Interior Scene Design Based on Cinema4d Technology
Authors: Meng FeiYan , Zhang Ziyan 
Published: April 2021

Abstract
This paper completes the 3D virtual interior scene modeling and design based on Cinema4d software technology. Through a full range of simulation analysis, to select environmentally friendly materials, match reasonable space structures, make full use of the space, and improve the utilization of indoor space. By making models of various parts of the interior design and building walls and windows, the entire interior space is designed. Finally, the interior design was rendered through lighting to ensure the expected design effect. Experiments show that the realism of 3D indoor scene design is higher than other similar methods, and it is suitable for 3D virtual scene design. It is hoped that the research in this article can provide some reference on feasibility for interior scene design.
Research on Facial Expression Recognition Method Based on Bilinear Convolutional Neural Network
Authors: Zhang Yong , Yi Weikang 
Published: April 2021

Abstract
Facial expressions express different emotions through different degrees of distortion of facial muscles, and the expression images spontaneously generated by people in real scenes are susceptible to interference from different illumination angles and postures. An extraction based on bilinear convolutional neural network is proposed. This is an end-to-end method for emotion classification based on second-order features of expression images. This method optimizes the network structure by adding a batch normalization layer, increasing the maximum pooling core, and replacing the fully connected layer with a global average pooling layer. Finally, the bilinear normalization method is used to convert one-dimensional features into two the dimensional features complete the final classification. Through testing on the FER2013 dataset, this method obtains an accuracy of 73.2%, which is 0.5% higher than the current state-of-the-art method, and proves that extracting image second-order features is more conducive to expression classification than first-order features. In addition, compared with other mainstream methods, the proposed improved model has higher recognition accuracy.
Traffic sign detection based on Haar and adaBoost classifier
Authors: Wang Wenhao , Gao Hui , Chen Xiaobing , Yu Zhenyang , Jiang Mingxing 
Published: April 2021

Abstract
Intelligent transportation system is a hot issue in the field of computer vision. Traffic sign detection is an important part of intelligent transportation system. A traffic sign detection algorithm based on Haar feature and AdaBoost algorithm is proposed in this paper. Firstly, Haar features are extracted from the positive and negative samples of the training set, and then these features are used to train the AdaBoost cascade classifier. Finally, the Haar features are extracted from the test image, and the possible traffic signs in the image are detected, the experimental results show that the method can achieve good detection effect, and provide a new idea for the detection of traffic signs.
Research on The Numerical Accuracy of Network Public Opinion Propagation Model Based on Runge-Kutta Method
Authors: Fan Lidan , Zhao Sifan , Han Fengqing 
Published: April 2021

Abstract
In a SIsIoR public opinion transmission model with discussion mechanism and emotional tendency of public opinion, government intervention is added to list the corresponding dynamic differential equation. It is found that there is no exact solution to the equation. Theoretically, the equation satisfies Routh-Hurwitz stability and Lipschitz continuity, and finally runs to the local equilibrium point. Explicit Euler method and 4th order Runge-Kutta method are used for numerical simulation, and the results are real and effective. By comparing the two methods, it is found that the numerical results of the fourth-order Runge-Kutta method are more accurate, and the iteration step size is shorter, which is more consistent with the theoretical expectation. This method is more suitable for the simulation of network public opinion dissemination, and has the advantages of stronger verifiability and higher numerical accuracy of the model. The simulation of network public opinion transmission based on Runge-Kutta method can provide reliable numerical basis for the guidance of public opinion under the environment of netizens’ emotional tendency, and provide scientific decision-making basis for the government to intervene in network public opinion transmission.
Prediction model of physical activity level and diabetes mellitus based on artificial neural network
Authors: yu Wang , xiaofei Liu , pei Zhang , jinying Li , yiting Wang 
Published: April 2021

Abstract
Objective: clinical practice shows that the control and treatment of chronic diseases, in addition to drugs, exercise health management is more significant, but also the cheapest, no side effects, thorough intervention. It has been proved many times that exercise can control and prevent chronic diseases. Through the related research of sports intervention, because the researcher selects the experimental object and the movement type difference, thus obtains the different research conclusion. Different types of people use different types of exercise, to achieve different exercise results, especially in the group of chronic patients. Therefore, to discuss the establishment of evaluation and selection system of exercise mode in chronic disease exercise management is the time requirement of health promotion for the aged patients. Methods 1242 patients with different degrees of diabetes in Henan Province were selected and their informed consent was obtained. Using the physical activity scale (IPAQ) and the long form questionnaire, the questionnaire was issued and collected from July 1,2020 to December 31,2020 by the uniformly trained physical examination physician, the data were analyzed by artificial neural network. Results: this study has found a certain correlation between diabetes and physical activity level whose data is incomplete, but has showed an overall positive correlation.
Traffic sign detection and recognition based on CNN-ELM
Authors: Li Wenju , Na Xinyuan , Su Pan , Zhang Qing 
Published: April 2021

Abstract
Aiming at the problem of low traffic sign recognition rate and slow speed, a traffic sign recognition algorithm combining CNN and Extreme Learning Machine is proposed. First, the ResNet50 network is used to extract image features, and then the Region Proposal Network (RPN) is used to generate proposals from the extracted image feature maps. Finally, the extreme learning machine is used to classify the generated proposals, and the fully connected layer is used for regression prediction. The experiment shows that compared with the Faster R-CNN model, the CNN+ELM improves the recognition accuracy on the TT-100K dataset 7.7% and reduces the training time per epoch by 32 seconds.
Study on reflection separation based on orthogonal polarization images
Authors: Zhang Jinghua , Zhang Yan , Shi Zhiguang , Li Biao , Zhang Yu , Ling Feng , Suo Yuchang , Liu Di 
Published: April 2021

Abstract
In this paper, a deep learning method based on polarization image is proposed. Firstly, the polarization characteristics of reflected light and transmitted light are analyzed by using the Fresnel’s law. Then, based on the polarization characteristics that the reflected light is polarized in the perpendicular direction and the transmitted light is polarized in the parallel direction, the polarization images obtained from the parallel direction and the perpendicular direction are input into the Encoder-Decoder net at the same time, and the net is trained by L1 loss. The experimental results show that by combining the polarization information and deep learning method, the reflected component and transmitted component can be effectively separated.
Screw hole location for laptop based on improved RCF
Authors: Huang Yafei , Hu Jiwei , Lou Ping 
Published: April 2021

Abstract
The recent application of computer vision to disassembly and remanufacturing has pushed forward this field significantly. In disassembling the laptop, we find that the existing positioning screw methods such as hough transform and template matching were not suitable for black laptops, so we propose a new method to solve this problem. We use RCF network to locate the screw hole contour, and make two improvements according to the characteristics of small screw hole size: 1. A feature integration module is added to RCF network to increase multi-scale features.2. A modulating factor is added to the loss function to enhance the attention to the indistinguishable pixels. Our approach has shown good results on black laptops and has higher ODS and OIS than the original RCF network and the existing method.