bluuebunny/crossref_metadata_2025_split huggingface dataset:
Application of explainable machine learning based on Catboost in credit scoring
Authors: Qi Ji , Yang Ruicheng , Wang Pucong 
Published: June 2021

Abstract
Credit scoring is the core part of an institution’s lending. As artificial intelligence is used in various fields, credit rating is also under the same topic of accepting technological changes. Combining credit evaluation and machine learning can incorporate relatively comprehensive features into the credit evaluation process. Through the excellent performance of Catboost, while ensuring accuracy, it demonstrates the explainability of the model as much as possible, avoiding the traditional trust problem of the black-box model. Explainability is proposed to the machine learning model, which reduces the difficulty of processing large amounts of data and the threshold for non-professionals to understand the model. In this article, the dataset is the personal loan data of LendingClub obtained through python. By analyzing the data through Catboost, we can derive excellent results in applying the explainability of machine learning in personal credit evaluation.
A Multi-dimensional Scientometrics Analysis on Alfalfa Research
Authors: Zhu Heying , Zong Yanxiang , Yang Yanping , Li Huoyou , Dong Yu , Wu Qingqiang 
Published: June 2021

Abstract
Alfalfa is known as “the king of forage”. It not only has high yield, but also has good quality. Studying the development process of alfalfa will help scholars in this field grasp the future research direction. In this paper, the data of alfalfa field from 2009 to August 14, 2020 retrieved on Web of Science is taken as the research object. Firstly, this paper analyses the current research status by using basic statistical methods. The selected analysis dimensions include time, country, funding source etc. Then, we conduct topic clustering research based on Latent Dirichlet Allocation (LDA) model and the scientometrics method to compare and analyze its interests from both the overall and time perspectives. Finally, the experimental results are presented in diagrams. The results reveal that the development of alfalfa field tends to be stable. China and the United States have made remarkable achievements in the field of alfalfa. The topics integration and decomposition trend is weak, and the research content has strong coherence.
Improving the Transferability of Adversarial Examples with Image Affine Transformation
Authors: Yin Heng , Zhang Hengwei , Li Zheming , Liu Zhilin 
Published: June 2021

Abstract
Deep learning is widely regarded as a black-box technology. We all know its performance is very good, but we have limited understanding of why it is so good. At present, there are many researches on the interpretability of deep neural network. By studying adversarial example, we can understand the internal semantics of neural network and find the decision boundary with problems, which in turn helps to improve the robustness and performance of neural network and its interpretability. With the development of adversarial example research, more and more adversarial example generation methods are proposed. Although the attack from adversarial example poses a great security threat to the deep learning system, it can also be used as an effective tool to measure the robustness and reliability of the model, and the attack and defense are two mutually promoting processes. Therefore, how to generate adversarial example with stronger attack ability is worth further study. And this study proposes a method named Affine-Invariant Method, aimed to improve the transferability of adversarial examples in black-box environment.
The application of big data in the analysis of the impact of urban floods: A case study of Qianshan River Basin
Authors: Shao Weiwei , Su Xin , Lu Jie , Liu Jiahong , Yang Zhiyong , Cao Yongqiang , Yang Zhaohui , Wang Kaibo 
Published: June 2021

Abstract
In recent years, with the acceleration of climate change and urban modernization, the flood risk of economic and social development has increased. The problem of urban flood disasters needs to be solved urgently. It is particularly important to conduct flood disaster loss assessment research for further flood control and disaster reduction, and emerging big data have opened a new direction for solving the problem of urban flooding. In this study, the Qianshan River Basin in Guangdong Province was used as an example. The TELEMAC model was used to simulate the inundation process under the design rainstorms of 50a and 100a in the Qianshan River Basin. Based on the heat map service, route planning service, and search service functions of Baidu Maps and Amap open platforms, this study used crawler technology to integrate multi-source data, including traffic survey data, geospatial data, population heat map data at different times (day/night) on weekdays and non-weekdays, and point of interest (POI) data from various industries. A dynamic assessment model of flood disaster loss for traffic, population, and POIs was constructed. The research results could greatly improve the timeliness and accuracy of urban flood disaster analysis, early warning, monitoring, and disaster risk assessment.
Smart Campus Microservice Recommendation Algorithm Based on Neighborhood Clustering and Time Collaboration
Authors: Lian Jin , Gao Ming 
Published: June 2021

Abstract
The basic algorithm research of smart campus construction is the basic path for education to benefit from the development of science and technology. Based on the application environment of higher education teaching and management, according to the teaching periodicity and the closed-loop characteristics of the business system in universities, a neighborhood-based user attribute clustering algorithm is used in this paper to form user clusters, which are coupled micro-application clustering with periodic time. Finally, through normalized conversion and Pearson correlation coefficient analysis, the Top-N recommendation list is obtained, and a recommendation algorithm suitable for smart campus microservice system is constructed. In this paper, matrix sparsity and cold start are discussed, and data compensation is carried out by analyzing the general behavior patterns of teachers and students, which effectively solves the problem of insufficient recommendation list caused by matrix sparsity and cold start. Upon data analysis before and after the algorithm deployment, the algorithm effectively reduces the number of times that users use the search function.
Edge detection algorithm of medical image based on Canny operator
Authors: Xu Ziqi , Ji Xiaoqiang , Wang Meijiao , Sun Xiaobing 
Published: June 2021

Abstract
Edge detection is an important part of image segmentation, in this paper, the edge detection algorithm based on traditional Canny operator for medical images is studied. The Canny operator is improved using Otsu algorithm and double-gate limit detection method, and the ability of Canny operator edge detection is strengthened. The simulation of the algorithm is realized on the computer platform by MATLAB, and the experimental results are analyzed from two image objective evaluation indexes of information entropy and mean square error. The experimental results show that compared with the traditional Canny algorithm, the improved adaptive double threshold Canny algorithm has better edge detection effect, richer image details, better noise suppression and less false edges.
Credit decision system based on combination weight and eXtreme Gradient Boosting algorithm
Authors: Youlve Chen , Kaiyun Bi , Jiangtian Chen 
Published: June 2021

Abstract
According to the demand of bank credit business, a credit decision system is established. The system mainly uses principal component analysis and eXtreme Gradient Boosting algorithm to establish a credit risk assessment model. When calculating the index weight, it proposes a more accurate method: combination weight method, which combines the advantages of entropy weight method and expert scoring method to calculate the relatively accurate weight. Then, the credit risk of each enterprise is analyzed quantitatively. Through the results, we give the corresponding credit strategies of banks in different situations.
Image Segmentation of Pitaya Disease Based on Genetic Algorithm and Otsu Algorithm
Authors: Yan Haokun , Ma Jiachen , Feng Zejing 
Published: June 2021

Abstract
Aiming at the problem of using traditional Otsu algorithm to segment pitaya disease images, this paper proposes the Otsu algorithm combined with genetic algorithm. The maximum variance between classes obtained by Otsu is calculated as the fitness value of the genetic algorithm to improve accuracy and improve efficiency of optimization. The experimental results show that the segmentation accuracy and efficiency of the algorithm in this paper are higher, and it has certain practical value.
Research on the impact of Credit, Brand and Service Recovery on Online Sales Based on Web Crawler Technology and Regression Analysis Methods
Authors: Luo Mi , Lin Jianzong 
Published: June 2021

Abstract
In order to explore the relationship between credit, brand, service recovery and the merchandise sales in online transactions, based on web crawler technology and regression analysis methods, the paper empirically analyzes the co-influence of credit, brand and service recovery on online merchandise sales volume by using the data of Baijiu in Tmall platform. The research results show that seller credit score has a significant positive effect on online merchandise sales volume; Merchandise brand has no significant impact on online merchandise sales volume, but brand as a moderator has a significant positive effect on the relationship between seller credit score and merchandise sales volume; The seller’s service recovery has no significant influence on the sales volume of online merchandises.
Optimal Network Defense Decision-making Method Based on Delay-Time Differential Game
Authors: Mi Yan , Zhang Hengwei , Sun Pengyu , Wang Jindong 
Published: June 2021

Abstract
In the process of attack and defense, there will be a period of delay in the implementation of the decision-making and strategy, therefore, the paper is based on the theory of the dynamic and time-delay differential game theory, and puts forward the network security status evolution model, and puts forward the network attack defense delay differential game model, and the design of the method of the optimal defense strategy of the most optimal defense strategy is designed, and the optimal decision is achieved. Finally, the validity of the model and the algorithm is verified by experiment.
An Improved Just-in-time-learning Based Soft Sensor Method Using Dimensionality Reduction Analysis
Authors: Chen Yu , Ye Tong 
Published: June 2021

Abstract
In recent years, the data-driven soft sensor has achieved unprecedented development, which can update the model in real-time and save many costs. Most traditional methods are only suitable for single-mode processes, but the existing working conditions are generally multi-modal. In this paper, we propose an improved JITL method to modeling the complex industrial process. First, we apply AE to reduce the data dimensionality, exploring the intrinsic structure of data. Then a JITL strategy is adopted to select similar samples on the low-dimensional data and use them for the local model. Finally, PLS is developed for each model and predicts the variables. A three-phase flow case verifies the effectiveness of the proposed method. We find that compared with other methods, the proposed method has the highest accuracy.
Remote sensing inversion modeling of chlorophyll-a concentration in Wuliangsuhai Lake based on BP neural network
Authors: Fu Xueliang , Liu Wenyao , Hu Hua 
Published: June 2021

Abstract
Monitoring chlorophyll-a concentration in Wuliangsuhai Lake can effectively grasp the primary production of Wuliangsuhai Lake. The traditional manual sampling monitoring method is time-consuming and laborious, compared with the monitoring method, the monitoring range of chlorophyll-a in lake water by remote sensing is not only wide, long time sequence, but also lower than that of the original. Based on the Landsat 8 remote sensing image data of Wuliangsuhai Lake and the measured data of chlorophyll-a concentration sampling points in Wuliangsuhai Lake, the spectral reflectance of each sampling point was extracted through the longitude and latitude coordinates of each sampling point, the first to fifth band spectral reflectance combination of Landsat 8 remote sensing image data is used as input, and the measured chlorophyll-a concentration is used as output, 26 BP neural network models were constructed to Retrieve Chlorophyll-a Concentration in Wuliangsuhai Lake. Through the verification and analysis of all the models, it is found that the inversion model with spectral reflectance of band 1, 2, 3, 4 and 5 as input has the best performance.
WGCNA revisited: Module indetification
Authors: Yan Shaomin , Wu Guang 
Published: June 2021

Abstract
WGCNA is a very popular R language software package used in biomedical field. It is mainly designed to deal with gene microarray samples to find clusters (modules), which include highly correlated genes, in biomedical studies. Theoretically, it results in the hierarchical modules, which are different from the results based on the edge density inside module and outside modules. In this study, we address this difference and indicate that caution should be exercised when using these two methods to interpret the implication of studies.
Research on urban density model and resilient development model under the guidance of carbon emission
Authors: Shen Shifang , Yu Yiting 
Published: June 2021

Abstract
In this study, we used time series data from 2001 to 2018 to quantify the relationship between urban density and carbon emissions in Shanghai. The regression equation is used to analyze the relationship between urban carbon emissions and urban density in the past ten years. According to national policies and the calculation of peak carbon emissions, the urban density under the peak carbon emissions is derived as the appropriate urban density. This paper puts forward the concept of urban density index, calculates the density index of Shanghai and analyzes it.
Research on the Influence of Students Gender on Students Examination Scores——Based on Logistic regression model
Authors: Hu Sen , Hu Yue 
Published: June 2021

Abstract
When students enter the middle school stage, students of different genders will show different advantages in different subjects. In order to explore the influence of students’ gender characteristics on students’ specialties, this paper uses logit regression to conduct an empirical analysis of students’ performance. This article uses student achievement data, which uses different subject scores as explanatory variables and student gender as the explanatory variable. Finally, it is found that student gender factors will have a significant impact on student performance in different subjects. Based on the analysis of student performance data, this article believes that schools should formulate different education programs according to the gender factors of students, so as to teach students in accordance with their aptitude.
Research on Input-output Efficiency of Junior High School-A Case Study of DEA-OLS Model
Authors: Gao Jiacheng , Liu Yue 
Published: June 2021

Abstract
Junior high school education has a strong influence on students’ first major decision in life, which leads to a large proportion of their investment in educational resources, so it is necessary to evaluate their comprehensive efficiency. Based on the questionnaire data of principals from 2014 school year to 2015 school year of China Education Tracking Survey, this paper uses DEA-OLS model to calculate and analyze the input-output efficiency of junior high schools, and finds that the input-output efficiency of junior high schools is low on average. Compared with computer students, the efficiency of teacher-student ratio has a significant negative impact. According to this, it is proposed that junior high schools should make their own adjustment plans in combination with teachers’ teaching quality and the amount of teachers’ resources, as well as the curriculum arrangement of students’ computer skills training and the amount of computer resources.
Underestimated marine stratocumulus cloud feedback associated with overly active deep convection in models
Authors: Hirota N , Ogura T , Shiogama H , Caldwell P , Watanabe M , Kamae Y , Suzuki K 
Published: June 2021

Abstract
Cloud feedback remains the largest source of uncertainty in equilibrium climate sensitivity (ECS). Many studies have attempted to narrow uncertainties in cloud feedback and ECS by proposing observable metrics with high skill at predicting future climate, referred to as emergent constraints. These constraints are often associated with clouds, convection, and circulation, and are interrelated. However, physical explanations for these connections remain unclear. Here, we propose a new mechanism relating convection and clouds across multiple climate models. Some models show overly active deep convection on daily timescales in the subtropical low cloud regions, which contributes to weaker subsidence inversion and smaller amounts of low-level clouds. Such models predict smaller shortwave (SW) cloud feedback. Using precipitation frequency in these regions as an emergent constraint, encapsulating this mechanism, models with lower SW cloud feedback (<0.50 W m−2 °C−1) are found to exhibit erroneously frequent convection. Our results suggest that further improvements in understanding and better modeling of cloud and convective systems are necessary for accurate climate predictions.
Zooming in on Arctic urban nature: green and blue space in Nadym, Siberia
Authors: Fedorov R , Kuklina V , Sizov O , Soromotin A , Prihodko N , Pechkin A , Krasnenko A , Lobanov A , Esau I 
Published: June 2021

Abstract
Urban landscape combines built-up areas with strongly altered natural (green and blue) and other open spaces. Voluminous literature examines urban socio-environmental interactions in tropical and temperate cities, whereas high-latitude cities are rarely considered. Here, we create a historical perspective on urban green (vegetation) and blue (water) spaces in a sub-Arctic city of Nadym in Russia. Our study explores a novel way to combine quantitative information from satellite imagery and biometric studies with qualitative information from interviews with stakeholders and residents. Such a joint analysis helps to understand dynamics of the urban green and blue space as well as its value for society. Furthermore, we propose objective indicators reflecting societal values of spaces in connection with recreational and ecological services. By contrast to temperate city studies, we found that green space is less used in summer, but still highly valued, deep lakes are used and valued more than warmer shallow lakes, and winter white space do not shrink but enhance the urban public space. Satellite images reveal inevitable loss of green space to urban construction and its remediation by artificial plantings (almost by 30% at present), whereas less valued blue space decreased almost three-fold. Interviews reveal that shallow lakes have reduced recreational values due to ice bottom and algae bloom. High values are attributed to deep artificial lakes, which are more than ten times deeper than natural lakes and do not freeze throughout in winter. Our biometric studies show that trees in urban environment are significantly taller than in the corresponding undisturbed areas. Since majority of the Arctic cities are built using very similar planning ideas and technologies, our findings shall help objective appreciation of green and blue spaces in other settlements.
Cleaner air has contributed one-fifth of US maize and soybean yield gains since 1999
Authors: Lobell David B , Burney Jennifer A 
Published: June 2021

Abstract
Crop productivity is potentially affected by several air pollutants, although these are usually studied in isolation. A significant challenge to understanding the effects of multiple pollutants in many regions is the dearth of air quality data near agricultural fields. Here we empirically estimate the effect of four key pollutants (ozone (O3), particulate matter (PM), sulfur dioxide (SO2), and nitrogen dioxide (NO2)) on maize and soybean yields in the United States using a combination of administrative data and satellite-derived yield estimates. We identify clear negative effects of exposure to O3, PM, and SO2 in both crops, using yields measured in the vicinity of monitoring stations. We also show that while stations measuring NO2 are too sparse to reliably estimate a yield effect, the strong gradient of NO2 concentrations near power plants allows us to more precisely estimate NO2 effects using satellite measured yield gradients. The presence of some powerplants that turn on and others that shut down during the study period are particularly useful for attributing yield gradients to pollution. We estimate that total yield losses from these pollutants averaged roughly 5% for both maize and soybean over the past two decades. While all four pollutants have statistically significant effects, PM and NO2 appear more damaging to crops at current levels than O3 and SO2. Finally, we find that the significant improvement in air quality since 1999 has halved the impact of poor air quality on major crops and contributed to yield increases that represent roughly 20% of overall yield gains over that period.
Tropical rainfall monitoring with commercial microwave links in Sri Lanka
Authors: Overeem Aart , Leijnse Hidde , van Leth Thomas C , Bogerd Linda , Priebe Jan , Tricarico Daniele , Droste Arjan , Uijlenhoet Remko 
Published: June 2021

Abstract
Commercial microwave links (CMLs) from cellular telecommunication networks can provide a valuable ‘opportunistic’ source of high-resolution space-time rainfall information, complementing traditional in-situ measurement devices (rain gauges, disdrometers) and remote sensors (weather radars, satellites). Their greatest potential lies in areas with low gauge densities and lack of weather radars, often in developing countries with a subtropical or tropical climate and generally large spatial rainfall variability. Here, the open-source R package RAINLINK is employed to retrieve CML rainfall maps covering the majority of Sri Lanka for a 3.5 month period based on CML data from on average 1140 link paths. These are compared locally to hourly and daily rain gauge data, as well as to rainfall maps from the dual-frequency precipitation radar on board the global precipitation measurement core observatory satellite. The potential of CMLs for real-time tropical rainfall monitoring is demonstrated.
Construction and experimental study of Langmuir model for multi-molecular layer adsorption of porous materials
Authors: Jucheng Gao , Zhaohe Zhang , Chunhui Liu , Weiwei Zhang , Yingchun Wang 
Published: June 2021

Abstract
In this paper, a Langmuir isothermal adsorption model was developed based on the assumption of multi-molecular layer adsorption, and then adsorption experiments were carried out using coconut shell activated carbon samples as adsorbent. The results showed that multi-molecular layer Langmuir isothermal adsorption model is more widely used than the traditional single-molecular layer Langmuir adsorption model, and it is also more simple, more intuitive and more standardized than the BET adsorption equation based on the multi-molecular layer adsorption assumption, and its application is more convenient.