bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Identity Authentication Method Based on Multi-modal Feature Fusion
Authors: Chen Jiabo , Cai Linxi , Tu Yuyang , Dong Rui , An Diankun , Zhang Baili 
Published: April 2021

Abstract
At present, many judicial organs have adopted the daily attendance system based on face recognition to strengthen the supervision of community correction personnels. In order to prevent a few personnels from using pre-prepared photos and videos to deceive the face recognition system, this paper proposes an identity verification scheme with liveness detection based on dynamic combination of multimodal features. The main idea is as follows. Firstly, during face verification, the user is required to read out random numbers on the screen. Secondly, generating dynamic combination of speech, voiceprint, lip-reading and other verification methods according to the user’s risk personas, so as to achieve a balance between convenience and security. In addition, in view of the low accuracy of lip-reading recognition in practice, this paper changes the traditional lip-reading recognition based on morpheme to lip-reading recognition based on classifier. By optimizing the interactive content, the distinction of pronunciation between different Chinese characters is increased, and the accuracy of lip-reading recognition is significantly improved.
Iterative parameter estimation of nonlinear systems
Authors: Yin Miao , Zhao Li 
Published: April 2021

Abstract
This paper discusses the identification methods for nonlinear systems. Using the negative gradient search, the gradient based iterative algorithm is derived to determine the parameters of the nonlinear system. In addition, in order to improve the computational efficiency, the gradient-based iterative algorithm based on the model transformation is derived. The basic principle is to transform a complex nonlinear system into a linear or simple nonlinear system. Finally, a numerical example is given to verify the effectiveness of the proposed methods.
Multi-target detection method based on YOLOv4 convolutional neural network
Authors: Liu Jinhui 
Published: April 2021

Abstract
In order to solve the problems of low detection accuracy, false detection and high miss detection rate of small targets in target detection tasks, this paper is a multi-target detection method based on YOLOv4 convolutional neural network. The proposed method is based on YOLOv4. The semantic information of high-level features is first propagated to the low-level network through FPN sampling, and then it is fused with the high-resolution information of the underlying features to improve the detection effect of small target detection objects. The information transmission path from the bottom to the top is enhanced by downsampling the feature pyramid, and finally the feature maps of different layers are fused to achieve relevant predictions. Experiments prove that the method proposed in this paper has good results.
Improved Convolutional Neural Network for Biomedical Text Classification
Authors: Ma Zhenzhen 
Published: April 2021

Abstract
In recent years, the combination of biomedical field and computer field is booming. Obtaining useful information from a large amount of biomedical text information is a research topic of great significance. Convolutional neural network has a good ability to extract useful features, so it is widely used in the field of text classification. In this paper, a novel approach for biomedical text classification based on improved convolutional neural network is proposed to solve the problem that deep convolutional neural network has a large amount of computation and can not perceive the relationship between levels well. In this paper, we use the combination of deep separable convolution and void convolution to improve the convolutional neural network. At the same time, we use the attention mechanism to classify biomedical literature. In addition, focusing loss function is used to improve the imbalance of biomedical texts. Experimental results show that the classification model in this paper is effective for biomedical texts.
Research on the Legal Protection of Personal Information in the Big Data Era
Authors: Deng Xianghua , Yan Meiyuan 
Published: April 2021

Abstract
With the scale and convenience of information storage and transmission, human society has entered the era of big data. In this era, information has become the most valuable and potentially influential resource, and personal information that is closely related to citizens’ daily lives is no exception, and is used by all stakeholders. Improper collection of personal information, fraudulent use of personal information, and malicious use of personal information have disrupted the peaceful life of citizens, thereby threatening personal and property safety issues are gradually exposed. In the era of big data, personal information protection is facing new challenges of data personality shaping and modern power control. Strengthening the protection of personal information is an urgent need in the era of big data.
Lip Reading using Local-Adjacent Feature Extractor and Multi-Level Feature Fusion
Authors: Zhang Chengwei , Zhao Haotian 
Published: April 2021

Abstract
Lip reading is a widely used technology, which aims to infer text content from visual information. To represent lip information more efficiently and reduce the network parameters, most networks will first extract features from lip images and then classify the features. In recent studies, most researchers adopt convolutional networks to extract information from pixels which contain a lot of useless information, limiting the improvement of model accuracy. In this paper, we designed a graph structures and a lip segmentation network to effectively represent changes in the shape of the lips in adjacent frames and the ROI in local frame and propose two feature extractors, named U-net-based local feature extractor and graph-based adjacent feature extractor. We proposed a very challenging dataset to simulate extreme environments, including highly variable face properties, light intensity and so on. Finally, we designed several different levels of feature fusion methods. The experimental results on the proposed challenging dataset show that the model can effectively extract the useful information from content irrelevant information very well. The accuracy of our proposed model is 9.1% higher than that of baseline. This result shows that our proposed model can better adapt to the application of the wild environment.
Design and Implementation of Distributed Call-Center Based on Soft-Switch
Authors: Deng Yanling , Deng Yin 
Published: April 2021

Abstract
Traditional PSTN-based Call-Center fail to meet agent distribution deployments and lower operating costs. With the development of NGN technology, Soft-Switch-based Call-Center has solved the pain points of enterprises. This paper analyzes the special needs of distributed Call-Center system, and on the basis of analyzing and studying Soft-Switch technology, the platform architecture of distributed Call-Center system based on VoIP is given. This paper focuses on the support process of SIP protocol for call control function of Call-Center system, as well as the key equipment in Soft-Switch system. Finally, a practical Soft-Switch system implementation scheme is introduced in conjunction with the project.
Research on Image Feature Extraction of Pavement Crack Disease
Authors: Qiao Heng , Zhang Zhihua , Zhang Xinxiu , Yan Jingping 
Published: April 2021

Abstract
The traditional highway disease detection technology and method are inefficient and can not meet the needs of modern detection. In this paper, the identification and extraction method of pavement crack disease characteristics is proposed. In the crack disease image, the grey image is transformed to highlight the crack information and background colour. Then, the effects of various image enhancement are compared, and a better algorithm template is selected to remove the black and white noise on the grey image, and the image with high accuracy is obtained. Finally, the image is segmented by stretching threshold, and the crack features are extracted by morphological closed operation. Based on ENVI+IDL development platform, an experimental system for pavement crack disease detection is developed. The experimental results show that the experimental system can effectively detect and extract pavement crack diseases and realize batch processing of multiple disease images.
Research on Network Threat and Situation Assessment Method of Electric Power Information System
Authors: Liang Yunhui , Zhang Linghao , Wang Sheng , Zhang Jie , Zhang Juling , Wu Ming 
Published: April 2021

Abstract
With the rapid development of information and communication technology, the national electric power information system has been extensively developed, and the electric power information system is becoming more and more intelligent, complicated and networked. At the same time, the complexity of the power information system has also brought about the problem of network security, and various network threats have put forward higher requirements for the network security of the power information system. How to evaluate the cyber threat of power information system has become an important work of power information system network security. This article uses the network threat attribute indicators that the power information system faces, and combines the situation assessment method to conduct a situation assessment on the network security of the power information system.
Regional Development of E-Commerce Based on Big Data Evaluation Model
Authors: Li Lin , Qin Fang , Wang Can , Sun Jianyan , Zeng WeiJia , Yu LinLin 
Published: April 2021

Abstract
In order to improve the authenticity and reliability of the evaluation data of regional e-commerce for reflecting the regional credit level in the business platform, this paper proposes a regional credibility evaluation model based on big data, which integrates the behavior characteristics of regional economic development and the judgment of the credibility of comments. Based on the analysis of regional economic behavior, the features include regional activity, regional reliability and regional contribution, while the credibility of comments is calculated based on the quality of regional text. The experimental results show that this method can quickly screen out the high reliability region, and can effectively alleviate the cold start problem. In the regional credibility evaluation model, two groups of features are proposed: regional behavior feature and regional text quality feature. The credibility of regional behavior, the credibility of regional social relations and the quality of regional comments are comprehensively evaluated. These two sets of features solve the problems of incomplete features and data cold start in the evaluation model of regional credibility.
Industrial Value Chain Model and Big Data Application for developing green agriculture in China
Authors: Hu Die , Chen Yuxue , Hu Menghan 
Published: April 2021

Abstract
The rise of big technology gives a good way for transformation and upgrading of traditional agriculture to modern agriculture. It has great significance to enhance the competitiveness of the agricultural industrial chain, increase farmers’ income and improve agricultural efficiency by applying it into management of agriculture industry chain. However, the utilization of big data in agriculture is not well, facing all kinds of negative conditions and challenges. To overcome this situation, this paper tries to combine the big data technology and value chain model to construct an efficient industrial chain for green agriculture with the case of Hubei Province in China.
Intelligent planning method for large-scale low-orbit constellation supplementary network
Authors: Cui Wenhui , Zhang Zhibin , Cao Xiaolong , Zhao Xiangwen , Sun Jingfeng 
Published: April 2021

Abstract
Large LEO constellations have become increasingly prominent in the aerospace field. When some satellites in the constellation fail, the traditional SOC (Streets-of-Coverage) constellation coverage analysis method can no longer evaluate the coverage of the constellation accurately. A method for calculating the space coverage performance of the large constellation with an incomplete configuration using a single-satellite space coverage stacking method is deduced. The constellation TT&C (Telemetry Tracking and Command) planning method based on spatial coverage assessment is proposed. The genetic algorithm is taken to illustrate the general steps of the intelligent planning of constellation complementary network, and the intelligent planning model design for multiple constellation TT&C restriction and covering the optimal target is realized.
Application of Variational Mode Decomposition and Deep Learning in Short-Term Power Load Forecasting
Authors: Yu Ping , Fang Jie , Xu Yubao , Shi Qiang 
Published: April 2021

Abstract
Accurate load forecasting of power system operation and development is of great significance. Because of the power load time series has strong nonlinear, the traditional forecasting model does not apply. Therefore, a short-term load forecasting model based on variational model (VMD) and length (LSTM) is proposed. Firstly, the VMD decomposes the original load sequence to get the modal of different size frequency component. Then, phase space reconstruction (PSR) organizes the modal components into deep learning inputs. Then, the LSTM network is employed to predict each group of modal components. Finally, all the modal component predictive value addition to the power load to predict the future. The experimental results show that compared with the BP, LSTM and EEMD - LSTM model, the model completely weakens the non-stationary load sequence, minimize the prediction error, reached the highest prediction accuracy.
The Influence of Artificial Intelligence on French Interpretation Teaching and Its Development Prospect
Authors: Yi Guo 
Published: April 2021

Abstract
As interpretation courses are generally offered in colleges and universities, French interpretation courses not only reflect the similarities between French interpretation courses and English interpretation courses, but also have many special features. With the development of science and technology, artificial intelligence translation tools are also the product of social development, which has a vital influence on both interpretation work and interpretation teaching. This paper not only introduces the situation of artificial intelligence translation products, but also introduces its influence on French interpretation teaching, which is the representative of minority language, especially it plays an important role. This paper also introduces methods on how to cultivate talents who are suitable for the current situation of social development, These talents should not only with high-level interpretation skills, but also with diversified and compound skills.
Thoughts on the development of artificial intelligence combined with RPA
Authors: Zhang Xuan , Wen Zhanpeng 
Published: April 2021

Abstract
As companies focus on digital innovation, robotic process automation (RPA), has attracted widespread attention in many different industries. The automation of repetitive tasks can increase efficiency while reducing human error. Robots will not be tired, will not be bored, and will perform tasks accurately to help their human counterparts improve and reduce their free energy to focus on higher-level tasks. In addition to simple RPA, intelligent automation can also be achieved by integrating machine learning and artificial intelligence with robotic process automation to automate repetitive tasks and increase human-like perception and prediction layers.
Research on Online Shopping Packaging Recycling Strategy under Big Data Environment
Authors: Kong Fanrong , Lu Hongxia 
Published: April 2021

Abstract
Online shopping has greatly facilitated people’s lives, but not properly recycled express packaging, not only cause waste of resources, but also destroy the environment, and “reduce, reuse, resource” as the principle of circular economy. Based on the data of 309 questionnaires, this paper analyzes the present situation of online shopping packaging recovery in China. It is expected to play a certain role in realizing the efficient and recycling utilization of resources, protecting the environment and promoting the green and sustainable development of the express industry.
Research on Innovation and Entrepreneurship of Higher Vocational Education Based on CiteSpace
Authors: Li Yanjun , Wei Haoyu , Wei Shengyu , Zhang Li 
Published: April 2021

Abstract
Based on the CiteSpace, the core Chinese journals in CNKI general library from 2005 to September 2020 were searched, 2855 papers on innovation and entrepreneurship were obtained and analyzed. The development of innovation and entrepreneurship in Higher Vocational Education in China was objectively analyzed by drawing the knowledge map of the research authors, research institutions and research topics in this field. The results show that the research authors and research institutions form less cooperation, in the research institutions, with the increasing number of coastal economic development zones, vocational education innovation and entrepreneurship research main force to the relatively developed areas of the transfer. Research is carried out on hot issues such as political education, modern apprenticeship system and the integration of dictatorship. It is suggested that we should continue to strengthen the research on the integration of “proprietary” and pay attention to the improvement of the curriculum system and teaching methods according to local conditions. The specific content of innovation and entrepreneurship education should be improved to diversify innovation and entrepreneurship.
Research on Judicial Big Data Text Mining and Sentencing Prediction Model
Authors: Xu Juan 
Published: April 2021

Abstract
In the era of big data, computer data has grown tremendously, especially in unstructured text data, and its growth rate is amazing. For this reason, in the application of judicial big data, people need to apply automated text mining technology to process unstructured data and predict the specific value of information. This article summarizes the research design and technical background content based on previous work experience. The author discusses the analysis of specific research results from three aspects: pre-processing of research data, establishment of sentencing prediction models, model testing and evaluation.
MLDRL: A multi-layer distributed reinforcement learning framework with multiple trainers
Authors: Zhang Jiarui , Li Yuan , Wang Xuewen 
Published: April 2021

Abstract
Synchronous distributed sampling is widely used in various distributed reinforcement learning algorithms. However, it is difficult to scale to a large case due to significant synchronization and communication costs. In this paper, we propose a scalable layered distributed reinforcement learning framework by introducing multiple trainers. We use a parameter processor to coordinate parameters obtained from multiple trainers and introduce worker nodes to reduce the communication cost among the parameter processor and the trainers. We do experiments in a multi-agent environment, and the results show that the proposed framework significantly accelerates the training efficiency of the reinforcement learning algorithm.
A Hybrid Deep Learning Framework for Estimation of Elbow Flexion Force via Electromyography
Authors: Lu Wei , Gao Lifu , Zhang Qianqian , Li Zebin 
Published: April 2021

Abstract
Real-time and accurate estimation is beneficial for intention recognition, muscle rehabilitation evaluation and artificial limb control. However, it is difficult to estimate the elbow flexion force accurately. The aim of our model is to estimate elbow flexion muscle force, which can be used for elbow joint health assessment and prosthetic control studies. This paper proposed an end-to-end deep learning framework by fusing Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) neural network with attention mechanism, which is more suitable for time series EMG signal to improve the feature extraction ability and achieve a high flexion force estimation accuracy. Experimental results indicated that the proposed method can automatically extract the proper features of elbow motion behaviors without professional knowledge in feature extraction model. Moreover, the experimental result shows that the proposed framework performs well in the accuracy and generalization ability, outperforming the state-of-the-art methods.
Analysis of International Situation on Rateless Codes Based on Big Data
Authors: Zhang Xian , Chen Dunkui , Peng Lingling , Chen Yingchun , Zhang Chi 
Published: April 2021

Abstract
As one of the key technologies of the new generation of high mobile wireless communication, rateless codes have drawn attention of countries all over the world. From the patents, we investigated the development trend, the patent source and distribution, main organizations, research hotspots and core patents by using analysis tools including the PatSnap database, Innography and the Insights. The results show that the quantities of patent applications has generally increased since 2006. China, American and Korea ranked the top 3 of the number of patents. Top three organizations for patents applications were Qualcomm Incorporated, Thomson Licensing and Huawei Technologies Co., Ltd. The research hotspots mainly focus on arrangements for detecting or preventing errors in the information received(H04L1) and coding, decoding or code conversion, for error detection or error correction et al.(H03M13).
On Application of Artificial Intelligence in Electric Power Service Field
Authors: Huang TongNan 
Published: April 2021

Abstract
with the development of science and technology, artificial intelligence has gradually come into people’s vision, which has brought great convenience to people’s work and life. Especially in the field of electric power service, the application of artificial intelligence technology has greatly promoted the development of the industry. In order to better integrate this technology into the power service industry, this paper analyzes the specific application of artificial intelligence technology in the field of power service. And based on the current situation and application scenarios of artificial intelligence development, and this paper also puts forward constructive suggestions on how to develop artificial intelligence technology in the industry.