bluuebunny/crossref_metadata_2025_split huggingface dataset:
Towards Discovery of Differences of Source Code Through Semantic Difference Checking
Authors: Han Qingdi , Gao Wu 
Published: April 2021

Abstract
Source code difference analysis plays an important role in software development, maintenance and regression testing. Many existing research methods are text-based, however, they are subject to code movement and alignment limitations, resulting in imprecise difference analysis. In addition, some methods are lexical and syntax-based, which ignore the key semantic information of the code and make the analysis results imprecise. In this paper, we propose a source code difference analysis technique based on semantic difference checking to improve the problem of inaccurate difference analysis. We implement the prototype, and exploit static analysis and textual difference detection tools to pro-process the programs, then perform semantic difference checking on the code with textual differences, and define the semantic differences. We conduct experiments on two C programs with differences and the results show that our approach can effectively and accurately detect code differences.
Unified SQL Query Middleware for Heterogeneous Databases
Authors: Zhang Huiran , Zhang Cheng , Hu Rui , Liu Xi , Dai Dongbo 
Published: April 2021

Abstract
With the development of modern computer technology, the data which needs to be stored and processed by modern application systems becomes more and more varied. While traditional relational databases still dominate, NoSQL databases are developing rapidly and occupy a place in the entire industry. Therefore, more and more applications need to connect to multiple database systems at the same time. However, the different characteristics supported by different database systems and the differences in query syntax rules make multi-database application development very complicated. This paper talks about a unified query middleware system that supports federated analytical queries with standard SQL syntax for various data sources and can perform joins, aggregates and filter on data from different data sources in a single query.
Research on Intelligent Control Method of Fire Emergency Lighting Distribution in High Rise Buildings
Authors: Zhong Yi 
Published: April 2021

Abstract
Aiming at the problem of high energy consumption in the traditional intelligent control method of fire emergency lighting distribution in high-rise buildings, the intelligent control method of fire emergency lighting distribution in high-rise buildings is proposed. By using the data processing model of fire emergency lighting light environment of high-rise buildings, the data of fire emergency lighting light environment of high-rise buildings are normalized. By calculating the distance between lights and using the optimal mathematical model, the intelligent control algorithm of fire emergency lighting power distribution of high-rise buildings is designed. Finally, through the development of high-rise building fire emergency lighting power distribution intelligent control scheme, to achieve high-rise building fire emergency lighting power distribution intelligent control. Dialux is used to simulate the lighting system with dimming control. The simulation results show that the minimum illumination, total illumination uniformity and longitudinal illumination uniformity of fire emergency passage of high-rise buildings meet the lighting requirements under different walking speed and traffic flow conditions.
Time series forecast of sales volume based on XGBoost
Authors: Zhang Lingyu , Bian Wenjie , Qu Wenyi , Tuo Liheng , Wang Yunhai 
Published: April 2021

Abstract
Some problems such as the decline of new labor force, the increase of retired labor force emerge because of the complex and changeable market environment, consequently exacerbating the staffing problem in the retail industry. Also, the unreasonable distribution of personnel, there are few people in busy hours and too many people in idle hours, which causes waste of labor. To address this issue, we analyzed the time series of sales volume in the retail industry in detail, and processed the data with feature engineering for predicting the in-store sales volume in the future. At the same time, other features such as weather and temperature are added to improve the accuracy of the model. Considering the characteristics of the data, we choose XGBoost as the prediction model. The experiments on real-world datasets verified better performance of proposed model compared with other state-of-art models.
Lane Detection of Smart Car based on Deep Learning
Authors: Yang Dongdong , Bao Wenzhi , Zheng Kai 
Published: April 2021

Abstract
Deep learning brings higher accuracy to lane detection, and the speed of the model is becoming faster and faster. The lane detection is regarded as the classification task after image gridding, and a simplified lane structure loss function is proposed, which is more suitable for the car track. The lightweight SqueezeNet is used as the backbone network, and the deep learning model is applied to the intelligent car of raspberry pie. Through the experiment, the real-time detection speed reaches 23FPS, which can complete the task of lane tracking. Compared with traditional methods, deep learning model has better robustness.
Overview of Generative Adversarial Networks
Authors: Songyuan Li , Fan Mengxuan , Chen Renqiu 
Published: April 2021

Abstract
The study of generative adversarial networks (GAN) provides a new approach and framework for computer vision and makes an outstanding choice for cross-domain image translation problems. In this work, the basic framework structure of GAN as well as the evolution of GAN are summarized. It is known that the unsupervised domain adaptation algorithms attempt to map representations between the two domains, or learn to extract features that are domain–invariant. An approach that learns in an unsupervised manner can be a transformation in the pixel space from one domain to the other. Finally, some existing problems of GAN and its potential future are also summarized and discussed.
Application of simulated annealing particle swarm optimization in complex three-dimensional path planning
Authors: Wangsheng Fang , Chong Wang , Ruhua Zhao 
Published: April 2021

Abstract
Particle Swarm Optimization (PSO) has achieved good results in UAV path planning, but there is still the phenomenon of abandoning the global optimal path and choosing the local optimal one. In order to improve the ability of particle swarm in path planning, a simulated annealing particle swarm algorithm is proposed. First, tent reverse learning is used to initialize the population so that the algorithm is evenly distributed in space. Then annealing operation is performed after iteration once, which has better local path judgment ability and avoids the phenomenon of local optimum to some extent, so as to find a more satisfactory path. Simulated annealing particle swarms can find a clear and satisfactory path with high stability through the complex three-dimensional path planning simulation of UAV.
Multi level Recommendation System of College Online Learning Resources Based on Multi Intelligence Algorithm
Authors: Zheng HU 
Published: April 2021

Abstract
In order to accurately grasp the actual search habits of online learning objects and establish a more reasonable multi-level recommendation scheme, a multi-level recommendation system of online learning resources in Colleges and Universities Based on multi intelligent algorithm is designed. With the help of the given topology, the multi-level recommendation behavior in the system is analyzed. Then combined with various types of Resource Recommendation application units, the hardware execution environment of the system is built. On this basis, a multiple intelligent neural network model is established. Combined with multi intelligent recommendation technology, the actual Resource Recommendation behavior related to learning objects is determined, and the software execution environment of the system is built. Combined with the structure of relevant hardware equipment, the design of multi-level recommendation system for online learning resources in colleges and universities based on multiple intelligent algorithms is completed. The experimental results show that the system designed in this paper records a larger number of learning objects, but the recommended waiting time is relatively shorter. The system can accurately grasp the actual search habits of online learning objects and establish a more reasonable multi-level recommendation implementation scheme.
Automotive Parking Assistant Testing Scene analysis and evaluation research
Authors: Xue Liang , Leng Yue , Lian Xin 
Published: April 2021

Abstract
Since the existing automatic parking testing procedure scene is way too simple and idealized, it’s necessary to explore a set of effective verification procedure for the complex scene of Automatic Parking Assistant, which can evaluate the overall performance of the automatic parking function of different vehicles comprehensively, objectively and accurately. In the actual vehicle parking process, there are a lot of harsh and complex scenes which cannot be covered by the current verification system. This paper aims to build a set of effective verification system for the complex scene of automatic parking based on Artificial Intelligence technology. It can comprehensively, objectively and accurately evaluate the comprehensive performance of the automatic parking function of different vehicles.
A Toxic Comment Classification Model Based on Ensemble
Authors: Xu Jian , Zhai Yuqing 
Published: April 2021

Abstract
Accurate classification of toxic comments in different languages is an important task in today’s international social networking platforms. In order to improve the classification of toxic comments in different languages, this paper combines the advantages of high accuracy of monolingual model and strong generalization ability of multi-language model, and adopts the ensemble of multilingual model and monolingual model to classify toxic comments. For the monolingual model, the monolingual pre-training model is fine-tuned with labeled task data; for the multilingual model, before fine-tuning the model with labeled task data, a further pre-training is applied to train model on unlabeled data, which aims to make full use of the large amount of unlabeled data and reduce the dependency of amount of labeled comment data while improving the classification effect. Comparative experiments on Conversasion AI’s multilingual toxic comment dataset show that the model in this paper has improved results on different evaluation metrics compared to the XLM-RoBERTa multilingual fine-tuning model, illustrating the validity of the model.
Research on students’ classroom behavior based on big data analysis and hidden Markov model
Authors: Tang Mingwei , Chen Xiaoliang , Mao Hongyun , Yang Tian , Xie Jianhua , Zhu Lin , Zhong Shifen 
Published: April 2021

Abstract
Under the traditional mode of classroom teaching, there is a problem that students’ classroom behaviours are inconsistent with their classroom results. In view of the above problems, this paper analyses students’ classroom behaviours through big data, and gives a hidden Markov model. The model can establish the organic relationship between classroom behaviours and students’ results, and adjust the contents that affect classroom behaviours in real time in the process of teaching. The results show that the model is effective, and big data analysis plays an important role in the analysis of curriculum behaviours.
Research on the application of animation design based on digital media technology
Authors: Liu Ying , Zhang Xinyue 
Published: April 2021

Abstract
The continuous development of science and technology is making digital media technology widely used. In the field of animation design, the application of digital media technology shows great advantages; it not only improves the efficiency of animation design, but also promotes the healthy development of China’s animation industry. The article firstly draws attention to this industry by the sharp increase of digital media practitioners, analyses the combination of digital media technology into animation design and the advantages of the combination development, and puts forward the view that digital media technology is conducive to improving the efficiency of animation design and the more skilled digital media technology is, the higher the efficiency of animation design is, and puts forward strategies to optimize the application of digital technology in animation design in the case practice. It also proposes strategies to optimise the application of digital technology in animation design in the case practice, hoping to provide reference for improving the technology of animation design.
Design of Plant Breeding System Based on one M2M Standard
Authors: Liu Zeyu , Bu Fanru , Yin Xinyu , Wang Xiuxiu , Zhang Zhongjie 
Published: April 2021

Abstract
With the continuous upgrading of the Internet of Things, cloud platform and intelligent control technology, the development of plant cultivation has become intelligent. Aiming at the problems of low intelligent degree of environmental monitoring, in this paper, combined with agricultural Internet of Things, a plant cultivation system based on one M2M platform was designed and developed. In addition, this paper takes succulent seedlings as the experimental object and designs a succulent seedling cultivation system to achieve the control expectation of this plant cultivation system.
NAUI : Neural Attentive User Interest Model for Cross-Domain CTR Prediction
Authors: Wang Ren , Xie Zhihong , Qi Gaofeng , Li Ping 
Published: April 2021

Abstract
In the existing works for recommender systems, the design of Cross-Domain CTR prediction is less mentioned. In order to solve the data sparsity problem in CTR prediction, Cross-Domain Recommendation (CDR) leverages a wealth of information from a source domain to improve the CTR prediction performance on a target domain with sparse information, which is better than Single-Domain CTR prediction. Ads are usually displayed with natural content, which gives Cross-Domain CTR prediction the opportunity for problemsolving. In this paper, we propose a CDR novel named NAUI which can leverage auxiliary data information to improve the CTR prediction performance. NAUI utilizes an extracting user interest method which reduces excessive punishment for active users, and jointly training Cross Net and MLP components together on CDR. NAUI can capture more information about nonlinear features and combined features efficiently capture implicit and explicit high-order features interactions, and greatly improve the expression ability of the model on Cross-Domain CTR recommendation. Furthermore, we add an auxiliary classifier to the deep neural network to improve recommendation performance. Our experiment results has demonstrated that NAUI outperforms several frequently state-of-the-art methods of CTR prediction.
Carbamoylated chitosan hydrogels with improved viscoelastic properties and stability for potential 3D cell culture applications
Authors: Marapureddy Sai Geetha , Hivare Pravin , Kumar Siddhant , Gupta Sharad , Thareja Prachi 
Published: April 2021

Abstract
We demonstrate a benign and straightforward method to modify the chitosan (CH) by carbamoylation. The free amines on CH are converted into carbamyl functionalities by reacting with potassium cyanate (KCNO). One wt% CH solution, when reacted with KCNO ⩾ 0.1 M, leads to the sol–gel transition of CH through the hydrogen bonding to form carbamoylated chitosan (CCH) hydrogel. Gelation time of CCH decreases with an increase in the KCNO concentration and an interconnected porous network is formed as observed under SEM. Rheological studies show that while one wt% CH solution is a viscous liquid, the CCH hydrogel with 0.5 M KCNO has a storage modulus (G') of 104 Pa. The CCH hydrogel is proved to be non-cytotoxic and promotes the attachment and growth of the small lung cancer model A549, and the neuroblastoma SH-SY5Y cell lines. CCH hydrogel also promotes the differentiation of SH-SY5Y cells into neuronal cells, as supported by immunostaining and thus demonstrating its utility as a versatile scaffold for three-dimensional cell-culture systems.
Crop-specific exposure to extreme temperature and moisture for the globe for the last half century
Authors: Jackson Nicole D , Konar Megan , Debaere Peter , Sheffield Justin 
Published: April 2021

Abstract
Global assessments of climate extremes typically do not account for the unique characteristics of individual crops. A consistent definition of the exposure of specific crops to extreme weather would enable agriculturally-relevant hazard quantification. To this end, we develop a database of both the temperature and moisture extremes facing individual crops by explicitly accounting for crop characteristics. To do this, we collate crop-specific temperature and moisture parameters from the agronomy literature, which are then combined with time-varying crop locations and high-resolution climate information to quantify crop-specific exposure to extreme weather. Specifically, we estimate crop-specific temperature and moisture shocks during the growing season for a 0.25∘ spatial grid and daily time scale from 1961 to 2014 globally. We call this the Agriculturally-Relevant Exposure to Shocks (ARES) model and make all ARES output available with this paper. Our crop-specific approach leads to a smaller average value of the exposure rate and spatial extent than does a crop-agnostic approach. Of the 17 crops included in this study, 13 had an increase in exposure to extreme heat, while 9 were more exposed to extreme cold over the past half century. All crops in this study show a statistically significant increase in exposure to both extreme wetness and dryness. Cassava, sunflowers, soybeans, and oats had the greatest increase in hot, cold, dry, and wet exposure, respectively. We compare ARES model results with the EM-DAT disaster database. Our results highlight the importance of crop-specific characteristics in defining weather shocks in agriculture.
Global perspective on CO2 emissions of electric vehicles
Authors: Märtz Alexandra , Plötz Patrick , Jochem Patrick 
Published: April 2021

Abstract
Plug-in electric vehicles (PEVs) are a promising option for greenhouse gas (GHG) mitigation in the transport sector - especially when the fast decrease in carbon emissions from electricity provision is considered. The rapid uptake of renewable electricity generation worldwide implies an unprecedented change that affects the carbon content of electricity for battery production as well as charging and thus the GHG mitigation potential of PEV. However, most studies assume fixed carbon content of the electricity in the environmental assessment of PEV and the fast change of the generation mix has not been studied on a global scale yet. Furthermore, the inclusion of up-stream emissions remains an open policy problem. Here, we apply a reduced life cycle assessment approach including the well-to-wheel emissions of PEV and taking into account future changes in the electricity mix. We compare future global energy scenarios and combine them with PEV diffusion scenarios. Our results show that the remaining carbon budget is best used with a very early PEV market diffusion; waiting for cleaner PEV battery production cannot compensate for the lost carbon budget in combustion vehicle usage.
Development Status and Application of Under Pressure Operation Equipment Technology
Authors: Tang Shiguo , Qin Fang , Dong Liangliang , Sun Hanwen , Zheng Li , Luo Hong , Wu Ke , Luo Wei 
Published: April 2021

Abstract
With the development and development of unconventional oil and gas reservoirs, the problem of difficult completion and workover has gradually become apparent, and pressured operations have gradually been valued by major oilfields and enterprises. In order to promote the development of pressure-related workover related equipment and technology in our country, and overcome the difficulties of late start and little experience, this article focuses on the development status of pressured operation. The main contents include: a brief introduction to the dangers of annulus pressure and the distribution statistics of annulus pressure wells at home and abroad; introduction to the development process of annulus operations; and introduction of a series of pressure equipment products that have matured or formed an annulus at home and abroad, and The research status of the key components of the pressure-proof operating equipment, such as the blowout preventer and the floating slip, is summarized. Finally, China’s domestically developed independent research and development of pressure-operating equipment is analyzed and considered. Based on the operating concept, enhance the trust and confidence of enterprises and oilfields in pressured operations; technical development should be oriented towards integration, characterization, and digitization, and systemized and serialized pressured operating equipment and process technology. The combination of pressure operation technology and equipment with repair and drill forms a drill-finish-repair integrated pressure operation system.
Numerical Analysis of Casing Damage in Heavy Oil Thermal Recovery Wells
Authors: Geng Lijun , Liu De , Han Liang , Feng Shuo 
Published: April 2021

Abstract
With the decrease in the world’s thin oil resources, heavy oil resources gradually become the main resources in the current and coming period. However, the casing damage of thermal recovery wells seriously restricts the development and exploitation of heavy oil. By investigating geological conditions, production status, and casing damage from in Liaohe Oilfield, the mechanism of casing damage in heavy oil thermal recovery wells was analyzed. Through establishing a casing-cement sheath-formation model and using a thermosetting coupling model of transient heat transfer, the temperature field and stress field of casing in the perforation section were simulated and studied, and the influence law of thermal stress generated by high temperature on the casing was analyzed. The damage of perforated casing is mainly caused by thermal stress, making the casing bear the alternating load of high temperature and high pressure, research shows. After multiple rounds of steam huff and puff, the residual stress of casing increases with the period, resulting in casing fatigue failure. Meanwhile, sand production seriously abrades casing, causing stress concentration of casing and then accelerating casing damage. By studying the casing optimization design, the occurrence of casing damage in heavy oil thermal recovery wells can be reduced and even prevented.
A Landslide Displacement Calculation Method Based on Correlation Analysis of Point Cloud Intensity Images
Authors: Xie Mowen , Liu Weinan , Wang Hongfei , Li Qingbo 
Published: April 2021

Abstract
This article proposes a new point cloud data analysis method to expand the applicability of 3D laser scanning technology for landslide deformation monitoring. First, the laser intensity value of the point cloud needs to be corrected. The corrected, discrete point cloud data is converted into a two-dimensional false color image based on its laser intensity. Then, a digital image correlation algorithm is introduced to analyse the point cloud intensity raster images. The displacement field of the landslide on a specific projection plane is calculated. Finally, the calculated landslide displacement field is matched with the digital elevation model of the landslide. The spatial displacement field of the landslide surface was calculated by comparing the elevations before and after the deformation. Laboratory experiments show that this method can monitor the deformation and failure of the landslide model as well as calculate the spatial displacement of the landslide surface.