bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on Container Big Data Application
Authors: Xu Shibo , Wang Jing , Li Jichun , Cao Wensheng 
Published: August 2020

Abstract
The container transport is the most important equipment of global supply chain, which connects economy of most countries in the world. A huge amount of data will be generated during container transportation. In this paper, the current transportation risks and problems in container transportation are presented in detail, and the construction plan of the container big data platform is proposed, including how to obtain data, what types of data are obtained and how the big data platform optimizes the existing transportation safety and efficiency, and finally specific recommendations are put forward to better promote the construction of container big data platform.
Collaborative Filtering Recommendation Algorithm Based on User Characteristics and User Interests
Authors: Li Shangsong , Li Xuesong 
Published: August 2020

Abstract
Among many e-commerce platforms, collaborative filtering recommendation algorithm is currently the most widely used recommendation technology. In order to alleviate the deficiencies of the traditional user-based collaborative filtering algorithm in cold start and recommendation accuracy, this paper proposes a collaborative filtering recommendation algorithm based on user characteristics and user interests. The similarity of the algorithm in this paper is composed of user score similarity, user attribute feature similarity and user interest similarity, in which user registration information is used to extract attribute features to calculate user feature similarity; use the number of user evaluations of project attributes to measure users’ interest in different project attributes, use the similarity calculation formula to calculate the interest similarity value between users. The user attribute feature similarity and user interest similarity are combined with the user rating similarity to obtain the final similarity for recommendation. Finally, a simulation experiment is performed on the MovieLens movie data set. Through the experimental results, it can be seen that the improved collaborative filtering algorithm based on user characteristics and user interests not only solves the cold start problem, but also improves the recommendation accuracy.
Research on Spatial Pattern of Cultural and Creative Industries in Shanghai Based on POI Data
Authors: Zhao Qian , Hu Ziyi , Wu Tianyan 
Published: August 2020

Abstract
The combination of big data and cultural and creative industry provides reference for the smart layout of urban planning. Based on nuclear density estimation, location entropy analysis and Ripley’s K function analysis, this paper uses ArcGIS10.2 to study the spatial pattern of cultural and creative industries in Shanghai based on the point of interest (POI) data of various cultural and creative industries in Shanghai. The results show that the spatial distribution of Shanghai’s cultural and creative industries is characterized by agglomeration, presenting a “multi-core” spatial structure, and the industry space of various cultural and creative industries is distributed as “center - periphery”, showing different distribution patterns. The results also show that the multi-scale spatial clustering of all kinds of cultural and creative industries shows different manifestations. They all converge on a larger spatial scale and have a larger range of spatial choices.Then, there are obvious regional differences in the professional functions of various cultural and creative industries. The mature blocks show multi-functional elements and multi-advantageous industries are featured by agglomeration. The functions of the developing blocks are relatively single.
Personal Data Protection and Anonymization in the Process of Data Commodity Trading
Authors: Su Jing , Gai Jiale , Si Yaqing , Zheng Xinyu , Li Guangkai , Liu Zhixue 
Published: August 2020

Abstract
Big data brings tremendous commercial value, but at the same time, it also leads to personal information leakage and other problem. This paper analyses the laws and regulations of the personal information and anonymization at home and abroad, and discusses the legal identification criterion of personal information, personal data and data anonymization systematically. And combining with the practice, this paper provides general methods for anonymizing data with personal privacy when they are used or traded by companies.
Study on Taxi Driver Decision-Making of Beijing Capital International Airport Based on M / M / S model
Authors: Xu Baiyan , Liu Jiawei , Chen Long , JiabinDeng  
Published: August 2020

Abstract
Based on the ideas of queuing theory and data fitting, this paper uses Dev-cpp software to write C ++ programs and establishes a driver decision model. Firstly, the selection scheme of taxi drivers for Beijing Capital International Airport is given, and the rationality of the model and its dependence on related factors are analyzed using Dev-cpp software programming. Secondly, through the M / M / S model in the queuing theory model, it is verified that the “boarding point” arrangement scheme with the highest total boarding efficiency can be achieved under the premise of ensuring safety.
Identifying the Dimensions of Data Science as a Space
Authors: Huang Ruxin , Cai Xinlin , Liu Yuhang 
Published: August 2020

Abstract
An increasing number of data with great potential value are produced continuously, which exceeds the capacity of the existing computing system and promotes the generation of new basic theories about big data and its processing. Such problems as the relationship between big data and small data and that between big data and the existing scientific system need to be accurately solved by the academic circle through the establishment of data science. However, data science is still in its infancy and the basic dimensions of data science need to be identified. In this paper, five categories demanding for establishment of data science are summarized. A new way to understand big data that gives the most important speculation and research object of data science from a global perspective is developed.
DNS based Measurement and Analysis of IPv6 Domain Name Support
Authors: Li Feng , Cao Huaping , Ma Weijun , Hu Chao , Wang Zhanfeng , Lu Xiyu 
Published: August 2020

Abstract
At present, the IPv4 addresses has been fully allocated, and the worldwide deployment of IPv6 addresses is accelerating due to its advantages. In order to investigate current development of IPv6, the IPv6 addresses of domain names are firstly resolved by DNS servers, and the obtained IPv6 addresses data, mainly including the geographic location statistics of IPv6, the proportion of southeast coastal cities, the comprehensive statistics of top-level domain names, etc., can be used to analyze the domain name supported by IPv6. The analysis of the measurement results has great guiding significance for further understanding the future development and application of IPv6.
Application status and optimization prospect of China’s electricity prosperity index
Authors: Wang Xiang , Guo Lijie , Zhang Lili , Wu Shanshan 
Published: August 2020

Abstract
Electric power data can more realistically and objectively reflect the development trend of the national economy. The application of the boom index based on electric power data is an effective way for macroeconomic monitoring and early warning. This paper combs the application of the power prosperity index of the power industry, especially power grid companies, and finds that There are problems such as insufficient application of power data, traditional synthesis methods, irregular data processing, unscientific selection of benchmark indicators, and incomplete evaluation of index validity. Finally, the paper proposes the future direction of the research and application of power boom index optimization.
Bank Queuing Optimization Based on Markov Process
Authors: He Yuqi , Li Haoxuan 
Published: August 2020

Abstract
Queuing optimization is a troubling problem in the field of operations research and it is difficult to solve completely due to the complexity and uncertainty of the queuing environment. Among them, bank queuing optimization is the most widespread and representative in the queuing theory. This article focuses on using customer arrival and service of cashiers to establish birth-death process and its transfer rate matrix. By introducing service intensity and customer satisfaction index, it is concluded that the optimal number of open cashiers in different time period can be calculated and predicted.
Research on Travelers’ Travel Behaviour in Urban Central Business Districts Based on Nested Logit Model
Authors: Ying Dan , Guo Zhida , Ye Tianrui 
Published: August 2020

Abstract
From the perspective of individual travel, this paper studies the behavior of travel mode selection of travelers in downtown business districts. In this regard, this paper uses survey data on Dalian residents’ travel to summarize the travel characteristics of travelers and to explore the key factors that influence travelers’ choice of travel mode. Using all samples and considering that travelers with different travel purposes may have differences in the choice of travel modes, the travel behavior of travelers in the business center is modeled. Finally, the Nested Logit(NL)model is used to predict the traffic mode split rate of travelers in the central business district. And the variation trend of traffic mode split rate is predicted when the important influencing factor (car ownership) in the model changes. The results show that the sharing rate of cars increases significantly, while that of other modes of transportation decreases, and the fastest decline is in the order of bus and walking. This indicates that car ownership has a certain influence on the sharing rate of buses and cars. Therefore, reducing car ownership can, to a certain extent, discourage the use of cars and increase the proportion of travelers who choose public transportation.
Analysis of CO Concentration Monitoring Data in Apron Environment
Authors: Xu Enhua , Xu Yan 
Published: August 2020

Abstract
The paper analysed CO concentration monitoring data in apron environment to explore the influence factors, and proposed the additive model for CO considering the influence of internal and external factors. Linear interpolation filling the missing values could effectively solve the problem of data missing and improved the effect of the addictive model of ARIMA and multivariate linear regression. The addictive calibration model by ARIMA and Multiple regressions for CO was reconstructed based on linear interpolation filling. The error analysis showed that the accuracy of CO was improved. The prediction effect was also improved by considering the interaction effect.
Deep Learning Semantic Statistical Features’ Labeling Of SAR Image Based On K-MEANS Clustering And Wavelet Transform
Authors: Peng Jinxi , Su Yuanqi , Xue Xiaorong , Song Donghong , Xue Xiaoyong , Wu Aihua 
Published: August 2020

Abstract
[Purpose] Because Synthetic Aperture Radar (SAR) image contains much speckle, at present, it’s hard to obtain good results for SAR image with the traditional image classification methods. However, SAR image has rich texture information which facilitates SAR image segmentation. There is a Deep Learning method of K-means data clustering that is used to calculate the optimal k value to avoid more-segmentation or less-segmentation, the advantages of fuzzy theory and neural network are used to improve the classification accuracy of image processing.[Methods] According to the statistical characteristics of SAR images and the semantics of fuzzy neural networks analysis, an efficient image segmentation method based on Deep Learning Semantic analysis and wavelet transform is proposed to achieve precision of classification; Firstly, the texture features of SAR image are extracted by Deep Learning Semantic clustering; Secondly, the SAR image are characterized according to SAR semantics. Based on one of the Deep Learning Semantic k-means algorithms, the best k-value iterations of SSE(Sum of the Squared Errors)and SC(Silhouette Coefficient) are performed, and the suitable value k is selected; Finally, by Deep Learning segmentation based on the texture feature of the SAR image and the filtered gray component vector, and the SAR image is classified to facilitate the change detection of images.[Results] The experimental results show that a good k value detection is performed on the CPU/GPU platform. The SAR images is compared with two different results between the wavelet filtering and Deep Learning semantic method images which are single or multiple standard classified through detection-change SAR is better than the Empirical Approach, then the labels are classified for the accuracy and computational efficiency that are calculated. The classification results were accurately ameliorated. [Conclusions] The similarity calculation of k-value cluster is an important precondition, so it’s necessary to select the optimal k-value suitable for the segmentation condition. The method achieves are taken an adequate SAR image analysis of effect and improvement for performance by semantic classification of k-means clustering.
A novel method for global minimization combined filled function method and dimensionality reduction technique
Authors: Wang Weixiang , Shang Youlin , Sun Guanglei 
Published: August 2020

Abstract
This paper introduces a new filled function method based on the dimensionality reduction technique for the global minimization problem. By utilizing the so called α - dense curves, we first transform the original function of n-variables into a single variable function, and then minimize the transformed function with the filled function method. The constructed filled function contains just one parameter which can be adjusted readily during the iterative process. The theoretical properties of the filled function are discussed, and the filled function algorithm is given. At last, a few numerical experiments are included.
Research on Sales Forecast Based on Prophet-SARIMA Combination Model
Authors: Zhao Jiantao , Zhang Chunwei 
Published: August 2020

Abstract
The stable development of e-commerce enterprises cannot be separated from scientific replenishment planning, and forecasting the sales volume of products helps e-commerce to make a reasonable replenishment plan, so as to speed up cash flow and reduce inventory pressure. Based on the in-depth study of Prophet model and seasonal differential autoregressive moving average model, as well as the analysis of daily sales volume of an e-commerce product from January 2015 to March 2018, a Prophet-SARIMA combination model for sales volume prediction is proposed. The principle of the combination model is to use prophet and SARIMA model to forecast the sales data respectively, and then weighted combination to get the final forecast results. Analyze and compare the prediction effect of single model and combination model. The first mock exam shows that the Prophet-SARIMA combination model has higher accuracy and stability in sales volume forecasting than single model. The sales volume forecast results of Prophet SARIMA combination model can provide an important basis for the e-commerce to supplement orders scientifically.
Survey and Statistical Analysis of Hangzhou Citizens’ Awareness of Harmful Blue Light and Consciousness of Protection Against Blue Light
Authors: Shen Qiuxin , Xu Liting , Jiang Yiyan , Zheng Ran , Zhang Yiping 
Published: August 2020

Abstract
The blue light hazards of electronics display screen have been concerned by people, and the corresponding protection of the blue light hazards has become the need of consumers. We took the citizens of Hangzhou as the sample survey object and we got the cognition degree of the citizens of Hangzhou to the harmful blue light and blue light-proof mobile phone membrane with the methods of the questionnaire survey and SPSS data analysis. The results showed that most people were not aware of the dangers of blue light, and many people had more or less certain eye problems. At the same time, the use of blue light-proof mobile phone membrane was not popular. The public still needs to increase their awareness of the dangers of blue light as well as the awareness of blue light prevention.
Research on Comprehensive Development of Shandong Province Based on Bayesian Quantile Regression
Authors: Guo Mengdi , Zhao Xiaoshan , Ge Yang 
Published: August 2020

Abstract
Based on the theme of coordinated development in Shandong Province, the article proposes coordinated indicators based on the three aspects of economy, society and ecology, and uses the principal component analysis method and entropy method to integrate the three aspects of economy, society and ecology in Shandong The indicators were selected. Then the Bayesian quantile model was used to construct an evaluation model and the coordinated development of Shandong Province was evaluated. It shows that Shandong Province needs to vigorously develop ecology and vigorously develop indicators that affect Shandong Province’s great changes if it wants to rapidly increase its comprehensive strength.
The Construction of College Students’ Habit Guidance System based on Action Control Theory
Authors: Li Guangyu 
Published: August 2020

Abstract
In modern society with rich information, the behavior of college students born after the year 2000 shows new characteristics. In the face of temptations, as well as the significant differences in learning methods between universities and high schools, freshmen are prone to be confused and shaken in their self-control, which brings pressure of academic difficulties. In order to help freshmen quickly adapt to the university learning methods and develop good learning habits, based on the theory of behavior control, this paper constructs the habit guidance system of freshmen from three aspects: Students’ network behavior, work and rest habits, and reading interest. We achieved satisfactory results through two years’ practice in Beihang University.
Improving Abstractive Summarization via Dilated Convolution
Authors: Jin Dawei , Kang Ruizhi , Zhang Hongjun , Hao Wening , Chen Gang 
Published: August 2020

AbstractIn this paper, a sequence-to-sequence based hybrid neural network model is proposed for abstractive summarization. Our method utilizes Bi-directional Long Short-Term Memory (Bi-LSTM) and multi-level dilated convolutions (MDC) to capture the global semantic information and semantic-unit level information, respectively. In decoding phrase, our model generates words according to summary relevant information captured by attention mechanism. Experiment shows that this proposed model outperforms several strong baselines on both of Gigawords corpus and DUC-2004 task.
Health Prediction of Airborne System Based on Grey Prediction
Authors: Wang Jiakun , Zhang Tianwei , Luo Caikun 
Published: August 2020

Abstract
With the development of civil aircraft technology, the scale of civil aviation industry in China is expanding, and the service capacity is gradually improving. Civil aviation industry has become an important industry in China’s economic development. With the increasing complexity, integration and intelligence of aircraft, it is more and more difficult to diagnose and maintain the fault of airborne system. The traditional fault diagnosis and maintenance methods cannot meet the requirements of modern aircraft development. Especially in the field of aircraft maintenance, the thought of aircraft maintenance has gone through the transformation process from “preventive maintenance” to “reliability maintenance”, and the maintenance method of aircraft has gradually changed from the previous scheduled maintenance to the situation-based maintenance.
Research on the Protection of Tort in the Process of Service Data Circulation
Authors: Su Jing , Sun Wenfeng , Si Yaqing , Wang Qinghong , Zheng Jin 
Published: August 2020

Abstract
With the advent of the era of “big data”, service data has released increasingly higher commercial value. Incidents such as personal information leakage and data product infringement that frequently occur in the process of data circulation seriously infringe the rights of data subjects and data controllers. This article takes service data as an example, combines the existing laws and regulations at home and abroad, analyzes the legal attributes of service data products and the definition of the rights of related subjects, and proposes corresponding tort protection suggestions from the two perspectives of personal information protection and data product protection.
A Statistical Analysis of Smartphone Use and Public Eye Health Based on the Survey in Hangzhou, Zhejiang Province
Authors: Shen Qiuxin , Xu Liting , Jiang Yiyan , Zheng Ran , Zhang Yiping 
Published: August 2020

Abstract
In terms of the way of information dissemination, mobile phone has become an essential tool of communication in people’s daily life. In this paper, 382 valid questionnaires were collected and SPSS data analysis was conducted to investigate the situation of smartphone use and eye health of Hangzhou citizens. The results of the survey and analysis showed that most people had very bad habits of using mobile phones, which were harmful to their eyes. Many people more or less had certain eye problems, and there was a long way to go to protect their eyes.
Upper Arm Action Recognition for Self Training with a Smartphone
Authors: Wu Jialing , Song Weixin , Lai Xiaoying , Li Xiao 
Published: August 2020

Abstract
The action recognition for upper arm training, in low-cost and effective way, has great application in both sport training and rehabilitation training. However,they almost require extra and expensive equipments. This paper proposes an approach for real-time recognition of upper arm actions based on Hidden Markov Model (HMM) only one sensor and one smartphoneare needed. Data collected by a sensor and Action Pattern Sets are established with HMM training. Empirical Results with the smartphone, one for self-coaching for badminton technique improvement and another for arm injuries rehabilitation, validating the effectiveness and usability. Some problems found on site show that the next step is to further optimize the Action Pattern Sets to improve the overall accuracy and users’subjective satisfaction.
Mechanical Fault Diagnosis of Circuit Breakers Based on XGBoost and Time-domain Features
Authors: Qi Jiajin , He Qingkui , Jiang Yijun , Xu Yinfei 
Published: August 2020

Abstract
In order to improve the efficiency of feature extraction of mechanical vibration signal of circuit breaker and the reliability of state recognition of circuit breakers, a mechanical fault diagnosis method of high voltage circuit breaker based on XGBoost is adopted. Firstly, 17 time-domain features are extracted from the measured vibration signals of circuit breakers, constructing feature vector and the separability of eigenvectors is analyzed. Then the feature vector is input into XGBoost, the depth and size of the tree are optimized to realize the high reliability discriminant analysis of the mechanical state of circuit breaker. Experiments on vibration data of circuit breakers prove that, this method has high efficiency in feature extraction and the overall recognition accuracy is high.