bluuebunny/crossref_metadata_2025_split huggingface dataset:
Identifying Influential Nodes in Social Networks Based on Social Strength
Authors: Lee Yanli , Fu Yan 
Published: November 2020

Abstract
Identifying influential nodes in social networks is significant in controlling the outbreak of epidemics, conducting advertisements for e-commercial products, predicting popular scientists or papers, and so on. Many methods have been proposed by paying much more attention to the role of nodes, while the social strength between nodes is rarely considered. Especially in unweighted networks, social strength is not explicit information. In this paper, we first characterize the social strength of unweighted networks, and then proposed a LOVital method based on the social strength. Numerical results on the relation network of Weibo dataset and the other datasets from various fields show that LOVital can much more accurately identify influential users.
Scheme design of Smart Platform for the Agricultural Science and Technology Park
Authors: Wang Yali , Sun Zhiguo , Wang Xiaoli , Yang Chenxue 
Published: November 2020

Abstract
With the rapid development of the global IoT and various information technologies, the construction of “smart city” has been gradually put on the agenda, and at the same time, the smart management of the National Agricultural Science and Technology Park will also become the key content of smart city construction. This paper proposes a scheme design of smart platform for the agricultural science and technology park, and lists the basic contents of the construction of the smart park. The proposed scheme design of smart platform applies 5G, AI, cloud computing, Internet of Things, mobile Internet and other new ICT technologies to solve the long-term pain points faced by traditional agricultural science and technology parks, including poor service experience, weak comprehensive security, low operational efficiency, high management costs, difficult business innovation, and so on. Because of the construction of the smart park has many contents and a long construction period, this paper discusses the specific design options of smart platform for the agricultural science and technology park. It can provide users with better service perception and help enterprises achieve innovative development through digital transformation. Based on the proposed smart platform, the business subsystem of the agricultural science and technology park is opened horizontally, through data fusion to business integration, to help customers more efficiently and agilely solve business problems that are difficult or even impossible to solve with traditional technical means. The future development and the impact on the life of people.
Development Mode Study of Jingdezhen Ceramic Vertical E-commerce Based on Cloud Computing
Authors: He Fubao , Ji Yanhua 
Published: November 2020

Abstract
As a new application service mode, cloud computing has become the focus of the integration between Internet and the traditional industries. It is infiltrating into e-commerce gradually and changing the development mode of e-commerce. This paper analyses the development status and the problems of the Jingdezhen ceramics e-commerce simply’ expounds the characteristics of cloud computing and the advantages of vertical e-commerce and the influence of cloud computing on the development mode of Jingdezhen ceramic e-commerce. On the basis, this paper tries to study the development mode of Jingdezhen ceramic vertical e-commerce based on cloud computing. And the overall architecture of Jingdezhen ceramic vertical e-commerce platform based on cloud computing is given in order to promote the further development of Jingdezhen ceramic e-commerce.
Low-Power Wide Area Network Construction Method Based on LoRa Technology for Agricultural Science and Technology Parks
Authors: Sun Zhiguo , Ji Zhiqiang , Wang Hongyan , Huang He , Wang Zheng , Ding Yongbo , Yu Tao , Yang Chenxue 
Published: November 2020

Abstract
The Internet of Things (IoT) requires different but complementary types of wireless network technologies, having their own merits from distance, power consumption, capacity and cost perspectives. This paper introduces a comparative study of LoRa wireless communication network technology for the long-distance and low-power wide-area network scenario deployment. In this paper, we propose a low-power wide-area network construction method based on LoRa technology for an agricultural science and technology park in Ningxia, Western China. Specifically, we design the optimal placement of LoRa sensors in the facility greenhouse by measuring the LoRa signal quality on the spot in the traditional brick wall sunlight greenhouse. In the traditional brick wall solar greenhouse, the success rate of data communication between LoRa terminal and LoRa gateway reaches 100%. Experiment shows that the coverage distance of the LoRa network reaches 3km in the most common conditions in agricultural science and technology park, i.e., the height of the LoRa gateway is 10 meters, and the maximum transmission power is 27dbm.
A Brief Analysis of the Construction of Private Gardens in Suzhou under the Theory of Spatial Narratology
Authors: Li Tianyingzi 
Published: November 2020

Abstract
At present, there are few researches on the exploration of Chinese classical private gardens from the perspective of spatial narration. This paper discusses the spatial narration of the classical private gardens in The Humble Administrator’s Garden of Suzhou. From the perspective of artistic conception creation, node setting, path arrangement, space combination and gardening techniques, the narrative scene in the garden is analyzed in all aspects. Thus it helps us to have a deeper understanding of Chinese classical gardens.
The Function and Enlightenment of Cloud Network Convergence in Fighting against the New Crown Epidemic
Authors: Wang Yin-chuan , Wang Lian-qing , Zhang Na 
Published: November 2020

Abstract
Firstly, this paper describes the challenge of the new coronary pneumonia epidemic to the ability of information service support. Secondly, it analyzes the main problems and related concepts of cloud network convergence, and points out the new problems faced by cloud network converge. Thirdly, this paper summarizes the current status and significant characteristics of the construction and development of cloud network convergence, and points out the important role and performance of cloud network convergence in fighting the new crown epidemic through specific examples; Finally, it analyzes the risks of cloud network convergence and the countermeasures was proposed.
Application of machine learning in optimizing b-value acquisition strategy of diffusion Magnetic Resonance Imaging
Authors: Ye Chen , Xu Daoyun , Wang Lihui , Wang Rongpin , Zhu Yuemin 
Published: November 2020

Abstract
The b-value acquisition strategy of diffusion Magnetic Resonance Imaging (dMRI) is very important for medical clinical application, especially the low b-value strategy. However, the choice of b-values is affected by several factors: for example, different tissue, different regions of tissue, the dependence of dMRI signals on b-values are different. Specifically, dMRI signals in areas with faster blood circulation may be more sensitive to low b-values (b<50 s/mm2); in addition, to obtain the diffusion or perfusion information from the diffusion-weighted (DW) signal, fitting methods are required, which also affected by low b-values. In this paper, Convolutional Neural Network (CNN), a machine learning based method is first used for learning the different characteristics of the DW signals in different regions of tissue and generated by different b-value acquisition strategy, and then analyse the dependence of DW signals on low b-values in different regions of the tissue. Finally, to study the dependence of the fitting methods on low b-values, which to determine the b-value acquisition strategy. The results show that the b-value acquisition strategy are different in different perfusion regions and using different fitting methods.
LiDAR target fusion and algorithm detection based on improved YOLO
Authors: Yu Zhuoquan , Liu Jingyao , Xu Wenyang , Liu Yiding , Lu Chenji 
Published: November 2020

Abstract
Abstract. In order to achieve safe driving behavior, the most important point of automatic driving is to detect the target. At present, the judgment of obstacles is based on single sensor, so it is difficult to detect some complex road environment, and it is easy to be missed. Therefore, this paper proposes another system device combined with color camera technology in lidar. This is another detection method proposed on the basis of Yolo, which improves the detection ability of small targets such as non motor vehicles and people. This is based on the Yolo algorithm, using images and other samples to obtain relevant useful data, and finally build the detection system model. Finally, the sensor is introduced to combine the color image and the deep image in order to improve the detection accuracy. Finally, the fusion of decision-making level is verified by test samples. The results show that the improved YOLO algorithm and decision-level fusion algorithm have higher target detection accuracy, can meet the real-time requirements, and can reduce the miss detection rate of small and weak targets such as non-motorized vehicles and pedestrians. Therefore, the method proposed in this paper has good performance and broad application prospects, while taking into account both accuracy and real-time.
Improved application of transfer learning in network traffic classification
Authors: Shang Fengjun , Li Saisai , He Jinlong 
Published: November 2020

Abstract
When using machine learning for traffic classification, there is such an assumption: the training data and the test data are independently and identically distributed. However, in reality, the assumption that the flow characteristics obey the same distribution may no longer hold because of conceptual drift or regional changes. Existing models will not be able to effectively classify new traffic. The transfer learning method TrAdaBoost has achieved great success in traffic classification and other aspects, but there are some problems, such as too much attention to the difficult-to-classify instances in the target domain, and failure to consider the wrong-classified instances in the source domain. In this study, the method of introducing weight correction factors in TrAdaBoost is used to make the iteration of weights more reasonable, and the effectiveness of this method is proved through theoretical analysis and experiments.
Modified particle swarm optimization algorithm and its application in neural network
Authors: Xu Qiulin 
Published: November 2020

Abstract
Intelligent optimization algorithm solves the optimization problem by simulating some natural phenomena and provides a new method for optimization theory. As a swarm based stochastic optimization algorithm, particle swarm optimization (PSO) is widely used in various optimization problems due to its simple model and fast convergence speed. However, particle swarm optimization algorithm still has some problems to be solved, such as premature phenomenon and balancing global exploration and local exploitation capabilities. In order to solve these problems, a nonlinear inertial weights and adaptive position updating strategy based on chaos is proposed. The algorithm can effectively avoid premature convergence of particle swarm, and has better convergence and faster convergence rate. In this paper, the modified particle swarm optimization algorithm(MPSO) is used as a learning algorithm for neural network optimization, and the MPSO is used to optimize the connection weights and thresholds of the neural network. In this paper, three sets of standard classification data sets are used for testing. Experimental results and statistical analysis verify the good performance of the MSPO.
Research and Implementation of Machine Programming Based on Agile Thinking
Authors: Zhou Jinlian , Chang Huili , Xie Tao 
Published: November 2020

Abstract
With the rapid development of information, communication, network and computer, the field, depth and breadth of computer applications are also rapidly expanding. The traditional production methods, development techniques and development tools of software development are no longer suitable for the system software development with larger and larger scale and more complex structure. Facing the fast changing demand, how to develop software quickly and quickly has become an urgent problem. This paper takes the agile development methodology as the support point and the core idea, puts forward to adopt the agile development thought as the core, take IDE as the tool, take machine programming as the core technology, carries on the research to the agile thinking machine programming technology, carries on the development in Visual Studio, finally, realizes carries on the programming with the machine.
An Improved Grey-Wolf Optimization Algorithm Based on Circle Map
Authors: Wang Yongliang , Wang Ting , Dong Shuxian , Yao Chen 
Published: November 2020

Abstract
To overcome the shortcomings of grey wolf optimization algorithm (GWO) such as being easy to fall into the local optimum, and the slow convergence rate in the later stage, an adaptive weighted grey-wolf optimization algorithm based on the Circle map is proposed. Firstly, in this algorithm, Circle chaotic map, which enhances the diversity of the initialization population, is introduced into the initialization of population, therefore, the search space can be searched more thoroughly; Secondly, trigonometric function and the beta distribution are introduced in the convergence factor ‘a’ and the population position update formula, which improve the convergence speed in the later period of the algorithm; Finally, the simulation experiments on the four common test functions on CEC2017 show that under the same experimental conditions, the improved grey wolf optimization algorithm improves the solution accuracy and convergence speed significantly, and its performance is obviously better than other smart optimization algorithms and other improved GWO algorithms.
Evaluation of the screening and diagnosis model for the lung adenocarcinoma protein function module based on WGCNA and machine learning
Authors: Liang Jiankun , Zhai Fei , Min Junting , Xiang Rongwu , Xiao Shen , Liang Luhua 
Published: November 2020

Abstract
This paper aims to identify lung adenocarcinoma biomarkers through WGCNA and machine learning and construct a clinical diagnosis model for lung adenocarcinoma. We used the lung cancer protein expression data from the CPTAC database to conduct differentiation analysis, built a WGCNA network of lung adenocarcinoma samples and a WGCNA network of lung tumor samples and normal samples, and assessed the overlapped module of these two networks using machine learning. GO and KEGG abundance analysis was conducted to find proteins related to lung tumor, a correlation network for proteins in the overlapped module was created to mine the target protein biomarkers, and machine learning was used to create and analyze a screening model. Therewere2317 differentially expressed proteins obtained from 213 lung tumor samples from the CPTAC database; the lung tumor network and the lung tumor para-carcinoma tissue joint network had two overlapping modules; through PPI network optimization, we mined 11 protein biomarkers related to lung adenocarcinoma. Through verification based on data outside TCGA, we found that the lung adenocarcinoma diagnosis model constructed based on the 11 protein biomarkers had high accuracy and stability, showing clinical and biological significance.
Transfer learning for the design of a digital twins-based automatic relay production line
Authors: Yang Rui , Mo Qinglong , Huang Zijuan , Zhang Yu 
Published: November 2020

Abstract
At present, the world is experiencing a new round of scientific and technological revolution and industrial transformation. It has entered a historic intersection with the transformation and upgrading of the manufacturing industry, bringing new opportunities to the industry. Digital twins are considered as an effective way to realize the interaction and integration of the manufacturing physical world and the information world, attracting great attention from the relevant academic and business sectors at home and abroad. Specifically, they use virtual models and simulation technology to design industrial production lines and predict the future operation of equipment, which contributes to the efficient operation of intelligent production processes. Nonetheless, the key learning problem of accurate modeling should be solved, so as to adapt to the random and dynamic changes in industrial equipment design. Transfer learning, an innovative learning paradigm in machine learning, is developed to solve challenging learning problems with only a few or no labelled samples in the target field.
The hybridized Harris hawk optimization and slime mould algorithm
Authors: Zhao Juan , Gao Zheng-Ming 
Published: November 2020

Abstract
Both the Harris hawk optimization (HHO) algorithm proposed in 2016 and the slime moud algorithm proposed recently had complicated disciplines for individuals to update their positions. And both of them were proved to be capable of finding the best solutions for either benchmark functions or real engineering problems. In this paper, we further hybridized the SM and HHO algorithms and allowed the individuals in swarms to take more ways to update their positions. Simulation experiments were carried out and the better performance in either accuracy or convergence rate verified the capability of the hybridization.
Nondestructive detection of yellow peach quality parameters based on 3D-CNN and hyperspectral images
Authors: Xu Shipu , Liu Yong , Hu Wenwen , Wu Yingjing , Liu Sijia , Wang Yunsheng , Liu Chang 
Published: November 2020

Abstract
In recent years, efficient and accurate detection of fruit quality has become an important means of non-destructive testing of fruit quality. In order to solve the problems of cost, efficiency and accuracy in the non-destructive testing of yellow peach quality. This paper proposes a new method for simultaneous detection of yellow peach hyperspectral multiple quality parameters. This method uses the method of extracting and recombining wavelengths at equal intervals in the full-band spectrum instead of the characteristic wavelength selection method. It can make the hyperspectral image contain all-band spectral information. This method uses 3D-CNN to replace the original regression modelling method, which improves the accuracy of model prediction. This method uses the shared network convolutional layer method to perform multi-task learning on the sugar content and hardness of yellow peaches, and finally realizes the simultaneous detection of multiple quality parameters of yellow peaches.
Research on the Vocabulary Relevancy Algorithm of the Improved Search Engine
Authors: Wang Taoyu , Shi Xianjun , Chen Qijie 
Published: November 2020

Abstract
Aiming at the limitation of the existing search engine search algorithm and the low reliability of the derived vocabulary relevancy algorithm, a vocabulary relevancy algorithm based on search engine and vocabulary relevancy algorithm is proposed under the background of making full use of the knowledge base of Hownet. Firstly, the main defects of search engine algorithm are considered in two aspects, meaningless text and redundancy. Then, the two problems are solved by using “Hownet” and DSC algorithm. Finally, the search engine-based vocabulary relevancy algorithm is improved by integrating multiple factors such as weight reduction and noise reduction. Experimental results show that compared with the pure improvement method based on the search engine, the method of spearman coefficient and Pearson coefficient were improved, as well as reduce meaningless language segment on the calculation of correlation, verified the “Hownet”, and DSC algorithm into the search engine algorithm can effectively improve the computing performance of relevant vocabulary.
Research on Vehicle Detection Technology of Blind Spots at Night Based on CAdaBoost Algorithm
Authors: Cao Yong , Wang Botao 
Published: November 2020

Abstract
Aiming at the problem of vehicle video detection in blind areas at night, a method of vehicle detection in blind areas at night based on CAdaBoost algorithm is proposed. Since night vehicles and daytime vehicles have different illumination, visibility, vehicle edge characteristics, vehicle contours, etc., the test image is first grayed out and ROI is determined. Then, because the features of the car head and wheels at night are more obvious than other features, a parallel method is used to train multiple weak classifiers offline at the same time, mainly to train the Haar-like features of the car head and wheels of the test image. In the meantime, in order to improve the accuracy of detection, the weighted parameter is introduced to determine the role of the weak classifier in the final strong classifier. Then, the offline trained model is used to match the Haar-like features of the test vehicle’s car head and wheels respectively, and finally realize the real-time detection of vehicles in the blind area. The results show that: for vehicle detection in blind spots at night, the algorithm has a high detection accuracy and low detection time.
A Novel Data Traceability Model Based on Blockchain and Digital Watermarking in Edge Computing
Authors: Liu Caiyun , Chen Xuehong , Li Jun , Yang Shuaifeng , Sun Yan 
Published: November 2020

Abstract
Most of the current data traceability models are centralized models, which cannot meet the traceability requirements of distributed edge nodes. Blockchain is a decentralized trust management mechanism based on the Internet. Its features that are difficult to tamper with and traceable provide a new solution to secure data traceability. However, many edge nodes have limited computing and storage resources, and cannot store large amounts of blockchain information. In order to solve the above problems, we propose a novel distributed traceability model based on digital watermarking and blockchain in edge computing. The model improves the security of the traditional traceability model by dividing the internal and external areas and electing the master nodes, reduces the storage capacity of resource-constrained nodes and solves the problem of poor scalability of the blockchain. The model can be applied to widely distributed edge node traceability scenarios, and can also trace the data in the case of watermark failure.
Intelligent recommendation of related items based on naive bayes and collaborative filtering combination model
Authors: Wei Wei , Wang Zhuo , Fu Changlong , Damaševičius Robertas , Scherer Rafal , Wožniak Marcin 
Published: November 2020

AbstractNowadays, data plays a unique role in various fields. Based on the background of the era of big data, this paper collects user evaluations of certain commodities and labels the evaluations into positive emotions and negative emotions. We propose an intelligent recommendation algorithm based on Naive Bayes algorithm, which determines the user’s preference for a product according to the user’s opinion on a product, then uses the collaborative filtering algorithm to recommend other similar products according to the similarity between the product and other products, so as to realize the recommendation of other similar products. This can not only help users choose more goods in the environment, but also maximize the value of goods. In this paper, bayesian classifier was used for prediction, and the accuracy reached about 97%.