bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparison of Text Mining Classification Algorithms in Interbank Money Transfer Application
Authors: Masripah Siti , Utami Lila Dini , Amalia Hilda , Nurlaela Dini , Ryansyah Muhamad , Yusuf Lestari 
Published: November 2020

Abstract
Funds transfer is a series of orders from the sender whose purpose is to move money from the sender to the recipient. The high interbank transaction fees imposed on each bank makes people use an interbank money transfer application, interbank money transfer transactions such as the Flip application are much in demand by the public because there are no administrative fees imposed on users. Opinion of the users of the application is processed using a text mining classification algorithm, namely the Naïve Bayes Algorithm and k-NN, the two algorithms are compared to produce which algorithm has high accuracy in processing the opinion of the flip money transfer application. Based on this matter, researchers conducted a sentiment analysis of the Flip Application, K-Nearest Neighbor (k-NN). After conducting research on sentiment analysis of Flip Applications, the Naïve Bayes classification algorithm has an accuracy of 91.25% and an ROC curve with an AUC value of 0.500. Whereas K-Nearest Neighbor has an accuracy of 85.25% and an ROC curve with an AUC value of 0.937. The Naïve Bayes algorithm can be said to be ”good classification” and the public can make the decision to use the Flip Application.
Optimization Of The Decision Tree Algorithm Used Particle Swarm Optimization In The Selection Of Digital Payments
Authors: Ariyati I , Rosyida S , Ramanda K , Riyanto V , Faizah S , Ridwansyah  
Published: November 2020

Abstract
One of the developments of information technology in Indonesia was the developments in Fintech (financial technology) that made it easy for people to access financial products, facilitated online transactions and also increased financial literacy. The development of fintech occurs when the use of cash was reduced to cashless when making payment transactions so that transactions would be more practical, easy, safe and comfortable. The purpose of this study to improve the quality of decision tree modeling and accuracy in the selection of digital payments. This research was focused on determining the fintech applications that were widely used by the people of Indonesia, namely Gopay or Ovo by comparing the advantages of applications and features of each fintech application. Optimization method of Decision Tree Algorithm (C4.5) and Particle Swarm Optimization by selecting several attributes including the level of ease of use, data security, application trust and convenience, maximum balance increase, discounts, cashback, ease of top-ups, range of existing merchants, return the money and customer complaint services. The results of the development of a decision tree algorithm based on particle swarm optimization provide a good classification and increase the validation value in the selection of digital payments.
Expert System For Detection Glaucoma Disease Using Certainty Factor Method
Authors: Hardi SM , Surbakti F P , Elviwani  
Published: November 2020

Abstract
Glaucoma is an eye disease that attacks nerves of human eye and it’s often not realized by sufferer until further damage to the eye nerve occurs. Glaucoma is second most common cause of permanent blindness according to Indonesian Ministry of Health. Permanent blindness caused by glaucoma can be avoided by early detection of disease and appropriate regular treatment. Early detection needed in an effort to prevent glaucoma can be done using an expert system. Expert system is an artificial intelligence program that can make reasoning and thinking like an expert in a particular field. Expert systems with certainty factor methods can describe level of certainty of expert expressions such as possible, most likely, and almost certain. Thus everyone can use an expert system to do glaucoma examination. In this research, diagnosis results will be obtained in the form of a percentage of a person’s CF value for glaucoma which is calculated using the certainty factor method based on the CF value of the symptoms entered by the user. Based on results of tests conducted, there are 8 out of 9 case data that are in accordance with the results of expert system diagnosis using certainty factor method that has been built. In other words, expert system has 90% accuracy in diagnosing glaucoma.
Support Aggregate Analytic Window Function over Large Data with Spilled State
Authors: Shi Xing , Wang Chao 
Published: November 2020

Abstract
Analytic function, also called window function, is to query the aggregation of data over a sliding window. For example, a simple query over the online stock platform is to return the average price of a stock of the last three days. These functions are commonly used features in SQL databases. They are supported in most of the commercial databases. With the increasing usage of cloud data infra and machine learning technology, the frequency of queries with analytic window functions rises. Some analytic functions only require const space in memory to store the state, such as SUM, AVG, while others require linear space, such as MIN, MAX. When the window is extremely large, the memory space to store the state may be too large. In this case, we need to spill the state to disk, which is a heavy operation. In this paper, we proposed an algorithm to manipulate the state data in the disk to reduce the disk I/O to make spill available and efficiency. We analyze the complexity of the algorithm with different data distribution.
Real-Time Scheduling Parallel Tasks on Multicore Platforms
Authors: Lei Zhenyang , Lei Xiangdong , Long Jun 
Published: November 2020

Abstract
Shared resources on the multicore chip, such as main memory, are increasingly becoming a point of contention. Traditional real-time task scheduling strategies focus on solely on the CPU, and do not take in account memory access and cache effects. In this paper, we propose real-time parallel scheduling (PRTTS) strategy on multicore platforms. Each task is represented as a directed acyclic graph (DAG). Tasks priorities are assigned according to task periods. In PRTTS scheduling strategy priorities of tasks which access memory are promoted over priorities of tasks not accessing memory. Tasks which read/write data in cache dynamically have their priority increased above all tasks. The results of simulation experiment show that proposed new scheduling strategy offers better performance in terms of core utilization and schedulability rate of tasks.
Research on Enterprise Service Governance Based on Service Mesh
Authors: Tian Yuchuan , Hao Haitian , Xu Xiaojian , Liang Bing 
Published: November 2020

Abstract
With the technology development, enterprise applications are rapidly changing from the traditional centralized application architecture to the distributed, microservices architecture (MSA). However, with the further development of MSA, enterprises are facing new challenges while enjoying the convenience brought by microservices, e.g. how to manage a large number of services, how to quickly locate and troubleshoot these services, how to avoid the performance issues, etc. As the next generation of MSA, service mesh is attracting more and more attentions and can be used to solve above problems. On top of service mesh, this paper proposes one mechanism that largely customizes service mesh implementation technologies. Furthermore, we implement this mechanism in a real project of one local large financial company. The final result illustrates that the mechanism is not only feasible but also effective to solve aforementioned service governance problems.
Community Management System Based on Embedded WEB Server Data Transmission Method
Authors: Yang Faquan , Su Huana , Huang Mei , Cai Zihong , Lan Di 
Published: November 2020

Abstract
in this paper, we design a data transmission method based on embedded WEB server of the community management system, the system through the embedded WEB server hang community intelligent systems, intelligent community system within the family in all the various functions of subsystems such as uploaded information fusion and data processing, implementation of the intelligent management of community operating.
The Method to Add Sky Background in 3D Virtual Engine
Authors: Chu Weijun , Liu Qing 
Published: November 2020

Abstract
In GTENG engine, scene manager is used to establish the corresponding mesh nodes of skybox or sky hemisphere, and sky texture files are set up in the order of the built-in texture name. These sky texture files are placed in the directory that can be searched by state manager, and they can be called when using to generate sky background. This paper gives the general process of creating skybox and sky hemisphere. By classifying and defining the outer, inner and cloud layer of skybox, the composition of their texture files is explained. Further, the general procedure of making skybox texture by using Terragen software is discussed. The methods of making outer and inner sky texture are illustrated, including setting and adjusting atmosphere, light, water, camera, coordinate, rendering and texture block. The application shows that it is a very effective and connvenient method to add sky background in GTENG engine cooperating with Terragen software to create skybox texture, and the effect of sky background in the application of 3D visual simulation is realistic.
Filling Algorithm for the Missing Value of Network Stability Monitoring Results Based on Big Data
Authors: Lyu Pinpin 
Published: November 2020

Abstract
In the research field of network stability monitoring based on big data, there are some disadvantages. For example, the processing method of the missing value of results influences execution effect, interfere the final filling effect and can only be applied in small data set. Therefore, a missing value filling algorithm for network stability monitoring results based on big data is proposed. The evaluation index of network stability monitoring based on big data is selected, and data missing mode and missing value filling mechanism are analyzed. The filling algorithm for the missing value of results based on big data is designed, the missing value filling parameter framework is constructed, the missing value filling parameters are processed, and the filling of the missing value of network stability detection results is completed. Compared with the traditional algorithm, the experimental results show that the efficiency of the designed missing value filling algorithm is 70.3% higher than that of the traditional filling algorithm, and the accuracy rate is far higher than the traditional method.
Driving Assistance System Based on Deep Learning and Traditional Vision
Authors: Bian Zhenwei , Yu Tao , Zhang Xin , Gong-Ye Xiaoyan 
Published: November 2020

Abstract
Relevant technologies such as computer vision and artificial intelligence are cheaper and easier to implement than detection technologies implemented by hardware such as lidar and radar. Cars are equipped with advanced intelligent driving assistance systems to prevent or reduce traffic accidents. In this context, this paper will identify and analyze the most important traffic lights, vehicles, and lane lines in traffic. Based on ImageNet pre-training, SqueezeNet builds fine-tuned network recognition traffic lights. Aims to achieve an assisted driving system that integrates deep learning and traditional vision. The final model size is only 7.84MB, the recognition accuracy is as high as 94.95%, and the processing speed is 12.4ms / frame. The single-frame processing speed of recognizer of YOLO v3 trained vehicle and classifier of B-CNN trained vehicle is up to 24.47ms. Using computer vision and mathematical operations, image perspective transformation, and polynomial fitting to analyze lane lines has the advantage of reducing cost.
Intelligent Analysis of Big Data for the Preventing of External Force Destruction on High-voltage Transmission Lines
Authors: Wu Peng 
Published: November 2020

Abstract
By constructing an intelligent analysis network, we can analyze autonomously and at the same time interact with the data. It makes the analysis more accurate, and it can construct a systematic model of the results. It can share to each smart terminal through the collation and analysis of the cloud computing library. The process will be transmitted to the background. Through the integration of big data the background collates a model of analysis and share to all the terminals. This will upgrade the ability of thinking on the terminals.
Detecting the Overlapping Community by Using Extract Method
Authors: Ma Yuejiao , Liu Yongjun , Tao Ji 
Published: November 2020

Abstract
The power overlapping communities are getting more and more attention in the complex network. In many networks, communities are not disjoint. It is very difficulty to get complete information of the network, such as the WWW, the local community detection has been studied by researcher. In this paper,we applied the local community detection to detecting the overlapping communities. Based on the algorithm in [11]. In this paper, we make an improvement to let the source node selected in each time is fixed, and then the algorithm has a certain stability.Even though the experimental results have no great improvement, but our improvement makes the algorithm has a certain stability.
Higher Education Online Courses Personalized Recommendation Algorithm Based on Score and Attributes
Authors: Wu Wenbin , Wang Bixi , Zheng Wenfeng , Liu Yan , Yin Lirong 
Published: November 2020

Abstract
Education through the online platform has gained popularity. While the online course keeps increasing, its benefit does not increase in a proportional way. Personalized recommendation systems can well solve this problem by mining users’ interests and preferences. Aiming at the cold start problem, this paper improves the collaborative filtering algorithm by combining the user score and project attribute characteristics of the online course platform. The network communication technology is used to obtain the user ratings and project attribute data to verify the feasibility of the recommendation algorithm, adjust the parameters in the model, and compare the accuracy of the algorithm. The designed algorithm can provide accurate and rapid personalized recommendation services, which is convenient for users and conducive to the development of the platform. The improved algorithm solves the cold start problem compared with the traditional algorithm with a significantly improved prediction accuracy. The scheme can also be modified according to the changes in user preferences and can achieve good real-time recommendation effect.
Bootstrap Estimating the Long Memory Parameter of Long Memory Time Series
Authors: Xing Yuhong 
Published: November 2020

Abstract
This paper evaluates the finite sample performances of three bootstrap methods, sieve AR bootstrap (SARB), fractional differencing sieve bootstrap (FDSB) and fractional differencing block bootstrap (FDBB), inestimating the long memory parameter in long memory time series. Extensive simulations show that the FDSB method outperforms others when estimating long memory parameter, and has stable estimated results in most cases.
Mode Control System of Intelligent Lighting Scene
Authors: Yang Faquan , Liang Porong , Cai Zihong , Lan Di 
Published: November 2020

Abstract
In this paper, through the wireless data communication technology to digital control of lighting, reach the purpose of reference light intelligent control, through the single chip microcomputer technology for home lighting more in terms of intensity, color, steering gear (each shift 6 or above) setting, a variety of tie-in, in order to meet the people under the condition of different patterns of lighting requirements of light.
Object Detection in Remote Sensing Images with Mask R-CNN
Authors: Gan Yuhang , You Shucheng , Luo Zhengyu , Liu Ke , Zhang Tao , Du Lei 
Published: November 2020

Abstract
With the wide applications of remote sensing technology in engineering, the demands of efficient object detection algorithms for remote sensing images have also been significantly increased in recent years. Traditional detection methods have the shortcomings of low accuracy and poor robustness, which is difficult to be applied to remote sensing images with complex background and varieties of objects. Recently, the deep convolutional neural networks have already shown great advances in object detection and outperformed many traditional methods. In this work, we study the performance of a region proposal-based method, Mask R-CNN, for detecting airplane and ship in remote sensing images. Specifically, we add an FPN module to improve the accuracy of small objects detection, and a mask branch is used to describe the shape of objects. In addition, we used a series of data augmentation strategies during training for meeting the CNN’s requirement of training samples diversity. The experimental results show that our model has superior performance in object detection of remote sensing images.
Analysis of Aflatoxin B1 in Peanut Oil by near Infrared Spectroscopy
Authors: Hu Zhui , Cai Zihao , Yang Xingxing , Xu Qiang , Zheng Xiao , Ye Jianping , Yang Junsheng 
Published: November 2020

Abstract
Aflatoxin is natural highly toxic substances. Aflatoxin B1 is the most common one, and is usually detected in moldy peanut, corn and other foods. In this paper, for the detection of aflatoxin, near-infrared spectroscopy technology was applied, and three preprocessing methods, two characteristic wavelength extraction algorithms and three parameter optimization methods were adopted to establish a variety of models for the quantitative prediction of aflatoxin B1 content in peanut oil. The influences of different spectral preprocessing methods, characteristic wavelength extraction methods, and parameter optimization algorithms on the prediction performances of these models were studied. Additionally, the ways to find an optimal quantitative prediction model and qualitative identification model were explored.
Research on Artificial Intelligence Ethics in the Field of Art Design
Authors: Li Yueen , Gu Jin , Wang Liyang 
Published: November 2020

Abstract
Through the analysis of the ethical dilemmas encountered by artificial intelligence in the field of art design, the method of systematic analysis is used to provide the methods and models of art ethics research under the condition of artificial intelligence, and the implementation of artificial intelligence ethical standards is adjusted to study new ethical principles., And strive to maximize the practical effectiveness of artificial intelligence ethics; establish a reliable art creation norm under artificial intelligence conditions. On the basis of emphasizing technical research, the largest possible research on humanities investigation. And from the research and top-level design of artificial intelligence ethics, promote the improvement of people’s livelihood and welfare, promote the healthy development of the industry, and grasp the initiative of a new round of technological revolution. Seize the period of strategic development opportunities for artificial intelligence, accelerate the pace of ethical research and innovation, build a competitive advantage in the development of artificial intelligence, build an intelligent society as soon as possible, and use technology to benefit the people.
Analysis on Reasonable Operation Mode of Newly Built Sediment Laden Reservoir
Authors: Liu Yanhong , Meng Jinjie , Wang Wenting , Jin Zhiwei 
Published: November 2020

Abstract
The operation mode of the reservoir should be selected according to the task, Reservoir type, Reservoir capacity, reservoir runoff and sediment of the reservoir. For the river with much sediment, the different Operation mode of reservoir will affect the Sediment deposition pattern and sedimentation rate of the reservoir, and have a great impact on the difference of the reservoir. The selection of operation mode of newly-built reservoir with sediment is directly related to the operation period and operation benefit of the reservoir. How to select the operation mode of the reservoir to maximize the benefit of the reservoir has been discussed and studied by water conservancy workers in recent years. Taking Gahaishan reservoir in Kulun banner of Inner Mongolia as an example, the paper uses one-dimensional reservoir sediment mathematical model, combined with suspended load unbalanced sediment transport equation, flow sediment carrying capacity formula and resistance formula suitable for sediment laden rivers, and uses existing sedimentation data of reservoirs in similar basins for verification. This paper briefly describes how to calculate the sediment deposition for the newly-built sediment laden reservoir and how to reasonably select the appropriate operation mode.
Research on the Construction of Teaching Quality Evaluation System
Authors: Liu Hongcheng , Zhu Yang 
Published: November 2020

Abstract
With the increasing application of information management methods to teaching management activities in Colleges and universities, it is imperative to use information system to realize the intelligent, automatic and accurate evaluation of teaching quality. This paper takes the evaluation system of teaching quality as the research object, focusing on the design and implementation of the evaluation system of teaching quality, so that we can evaluate the teaching performance of teachers fairly, reasonably and objectively from different perspectives such as students, leaders, supervision experts and colleagues, and fully mobilize the enthusiasm of teachers to complete the teaching work through the application of the system.
Research on Cloud Service Reliability Evaluation Model from the Perspective of Equal Protection 2.0
Authors: Li Zhijie , Suo Hui 
Published: November 2020

Abstract
In the era of equal assurance 2.0, network evaluation has brought great challenges to enterprises, institutions and evaluation institutions. The evaluation framework of cloud service reliability is proposed, which is divided into four characteristics: accessibility, continuity, recoverability and data backup, and further divided into sub characteristics of mutual correlation and combination of subjective and objective. It provides a measurement method for each sub feature The evaluation results are obtained by processing and calculating.
Neural Network Analysis Model of Air Environmental Factors for Urticaria Skin Disease Susceptibility
Authors: Wang Weihua 
Published: November 2020

Abstract
There is a certain correlation between the occurrence of skin diseases and external inducing factors, among which air quality is one of the important factors. Based on the analysis of outpatient medical records in a certain period of time, taking urticaria as a representative, the degree of skin disease susceptibility in outpatient medical records was divided. Combined with the changes of air quality indicators in the same period, the prediction model for analyzing the correlation between urticaria skin diseases and air quality indicators was established by using artificial neural network method, and a new way of skin disease risk prediction was discussed.