bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on Multiple Abnormal Behaviour Detection Methods for Surveillance Video
Authors: Tan Quange , Zhang Yali 
Published: October 2020

Abstract
This paper uses the centroid acceleration of the moving target and the aspect ratio operator of the outer frame of the moving target to identify the human moving target. It uses inter-frame difference method to detect stagnation, uses 3D convolutional neural networks to detect fall, and analyzes the experimental results. Abnormal behaviour detection can assist in case investigation, large-scale activities, and security in key areas, etc., and help ensure social harmony and stability.
Applying Anchored Instruction in Crowdsourcing Platforms to Promote Career Competence of Digital Media Students
Authors: Liu Ming , Xie Tao , Ding Jinxiao 
Published: October 2020

Abstract
The gap between the career competence and real needs of operating post significantly affects the development of enterprises. This paper combines the anchored instructional strategies and crowdsourcing to innovate the practice teaching mode of digital media specialty in vocational colleges. The goal is to explore the possibility of improving the career competence of students. A preliminary evaluation is conducted based on a 4-month longitudinal study. Thirty-nine junior students were tracked and 32 of them responded. The instruments included two popular crowdsourcing platforms in China and a testing scale of career competencies. The data was collected via Internet survey first followed by a telephone interview if the students did not respond within a fixed time. The results show that the crowdsourcing platform is an appropriate training environment where students can understand more market needs through the platform and students’ experience has been greatly improved through training and cooperation. In conclusion, the approach proposed in this study is useful in promoting career competence of digital media students in vocational colleges.
Application of Data Mining in the Coal Enterprises in ERP
Authors: Zhong Ruowu , Wang Huiping 
Published: October 2020

Abstract
With the development of science and technology, and to meet the need of the coal enterprise development, the ERP system has been more and more used in coal enterprises day-to-day management, and has played the increasing important role. With the wide application of ERP system, how to deal with a lot of data and information for reasonable application becomes the focus of attention. Data mining is an effective analysis and processing of various data technology, and is being applied in the coal enterprise ERP system. Firstly, the technology of data mining is introduced. Secondly the concrete application of data mining technology in coal enterprises ERP system is introduced and analysed.
Ontology Alignment Optimization Method Based on NSGA-II
Authors: Feng Shaorong 
Published: October 2020

Abstract
In order to solve the problem of optimizing the mapping result integration of three distinct similarity measurement technologies (grammar based similarity measurement, linguistic based similarity measurement and taxonomy based similarity measurement), a method based on NSGA-II is proposed. Compared with the traditional genetic algorithm, this method can achieve the maximum mapping recall ratio, precision ratio and f-measure value at the same time. The ontology mapping results obtained by this method can solve the problem of preference for recall or precision. The experimental results indicate that the put forward method is feasible.
Predicting Individual’s Posting Behaviors in Social Network with Markov Chain
Authors: Qiao Fengcai , He Su , Deng Jinsheng 
Published: October 2020

Abstract
Online social networks like twitter are self-organized systems with emergent behaviors from the individual interactions. Predicting the users’ behaviors like posting, replying or retweeting is one of the most important issue. In the paper, we present a method to model the twitter users’ posting behavior with the other users’ response. We also contrast the simulation precision with the model that does not involve the other users’ response. We make use of a stochastic model with Markov chain to predict a specific user’s next tweeting behavior. From the result by contrasting the simulation data and the real data, we demonstrate that the engineering method is able to predict individual posting behaviors based on time windows. In addition, the proposed model involving other users’ response is better than that only considering posting time sequences.
Fisheye Rectified Image Super-resolution Using Deep Symmetrical Neural Network
Authors: Zhang Yuanfa , Li Weili , Yin Xiaoqing , Deng Jinsheng 
Published: October 2020

Abstract
Influenced by the fast developments of imaging technologies, fisheye cameras have been widely applied. But the resolution of images captured by fisheye cameras is limited due to lens distortions. In our work, we use a deep symmetrical network model to deal with single fisheye rectified image super-resolution task. It is shown by the experiments that the proposed method can achieve satisfactory results for fisheye rectified image super-resolution.
Using Open-Source Learning Platform(Moodle) in University Teachers’ Professional Development
Authors: Xiao Qin 
Published: October 2020

Abstract
With the development of information technology, teachers’ professional development based on network has attracted more and more attention due to its ease of use, openness, sharing and knowledge accumulation. This paper studies the importance of teachers’ subjective consciousness and self-development needs in the information technology environment in the university teachers’ professional development, discusses how to update teachers’ professional development attitude and adapt to the new role of teachers in the information technology environment; Moodle(or Magic lamp in China), namely: modular object-oriented dynamic learning environment, is a course management system (CMS) designed and developed by Australian teacher Martin Dougiamas based on the theory of social constructivism. The paper analyzes the internal relationship between Moodle related functional modules and teachers’ professional development, and the possibility of realization. After in-depth analysis of characteristics and advantages of Moodle as a technology platform and the support and application value of Moodle platform for university teachers’ professional development, this paper constructs a new mode of teachers’ professional development with Moodle as the interactive platform, which is “one platform, two footholds, three ways and multi-body participation”.
The Study on Basic Fatigue Properties of the New Axles Steel and Axle Fatigue Assessment
Authors: Liu Qingfeng , Lyu Yawen , Yan Liang , Zou Hua 
Published: October 2020

Abstract
With the development of the high speed railway construction, we poise a higher demand for the performance of EMU. This subject is designed to get the information about the new axle materials’ S-N curve by testing the fatigue property of axle materials. With the help of finite element simulation, finite element model of wheel pair could be built. Through the method of numerical simulation, we could know the stress distributing when wheel pair are stressed, and evaluate the intensity of axles.
Foot Pronation Detection Based on Plantar Pressure Measurement
Authors: Yu Junchao , Gao Weidong , Jiang Weining 
Published: October 2020

Abstract
In the paper, we design a foot pronation detection system based on plantar pressure sensors. The system includes: collecting the pressure sensor signals by reasonably arranging the pressure sensors; collecting the plantar pressure signal value of the test object which has been walking for a period of time; taking the proportional calculation, determining the fuzzy sets, designing the membership function, the fuzzy processing was carried out and the fuzzy rules was established by the IF-THEN rules. Finally the gait phase was deduced according to the fuzzy output signal, and the system function was realized by pronation analysis algorithm. The experimental results show that the system has an important value for sports risk prediction.
Surface Water Quality Classification Based on MobileNetV2
Authors: Hao Jingbo , Tao Yang 
Published: October 2020

Abstract
Water quality detection is important for water resources protection. There is an urgent need for convenient and precise water quality detection methods. In this paper a deep learning based detection method is proposed. A MobileNetV2 classifier is constructed and tested aiming at surface water quality classification. The experimental results show that the classifier has excellent performance and can also be deployed on edge devices conveniently.
A Safety Evaluation Method of IMA Dynamic Reconfiguration Process Based on CPN
Authors: Liu Lei , Zhao Wei , Jiang Zeyong , Wang Shihai 
Published: October 2020

Abstract
With the rapid development of integrated modular avionics (IMA), the dynamic reconfiguration of IMA provides great advantages in resource utilization and aircraft configuration and acts as a valid means for resource failure management. Due to the complexity of dynamic reconfiguration process, it is difficult to analyze and evaluate the safety of dynamic reconfiguration process. The Architecture Analysis and Design Language (AADL) is effective in modeling such process of the embedded real-time system. Colored Petri Net (CPN) has advantages of simulation and evaluation. Therefore, this paper model the IMA dynamic reconfiguration process based on AADL, and transfer it to CPN model to analyze and evaluate the safety of the process of dynamic reconfiguration. Finally, a case study is provided to indicate the effectiveness of the method.
Data Analysis for Field Orienteering Heat Training
Authors: Yang Hui , Sun Yanfei , Xue Xiangfeng , Hou Zhibin , Yang Shuojun 
Published: October 2020

Abstract
Orienteering is a kind of intelligent sport that the athletes are selected on the base of integrative evaluation of the physical, skill and psychological qualities. In this paper, a method is supposed to evaluate and select the seeded player with data such as total time consumption, split time, positioning efficiency and operation efficiency. It can be concluded with data series collected from two distinctive heat training courses that, 1) the operation efficiency and it’s standard deviation of 5th player achieved the optimization, and 2) the solution in this paper can be popularized for selective heat training in common primary school or college with limited training equipment.
Comparison of Chinese Character Correct and Error Classifier for Overseas Students Based on Handwriting Motion Characteristics
Authors: Zhang Jun , Zhang Xiwen 
Published: October 2020

Abstract
The quality of Chinese characters written by foreign students is reflected not only in the result of writing, but also in the writing movement. 25 characteristics of handwriting movement were selected from 39 parameters, such as time, space, movement and pressure. Based on the feature of handwriting motion, 6 classifiers, such as Decision Tree and Artificial Neural Network, are selected to compare the performance of them. Experiments show that the accuracy of DT algorithm is the best regardless of whether the target characters are known or not. Handwriting movement features are effective representations of the quality of Chinese characters written by foreign students.
U-shaped Feature Extractor Used on Mask R-CNN for Cell Nuclei Image Segmentation
Authors: Zhu Qing , Huang Xiaoyang , Wang Liansheng , Li Jonathan 
Published: October 2020

Abstract
The cell nuclei segmentation of is a challenging task in microscopy image analysis. The problems of noise, small cell nuclei, and few training data samples in the data set will all affect the effectiveness of the model to varying degrees. This paper presents a new approach to nuclear image segmentation based on convolutional neural networks. Our approach is based on Mask R-CNN with some modification, which combines low-level semantic features for model training. In order to make the model specific to each low-level feature map, the attention mechanism was used to assign weights to each low-level feature map, making the model learning more purposeful. Our method achieves an average precision value of 62.8%, which is 2.7% higher than that of the Mask R-CNN (ResNet50) basic model and 6.3% of the Mask R-CNN (ResNet101) basic model.
An Improved Random Forest Algorithm
Authors: Yu Ying , Wang Lingyun , Huang Hao , Yang Wenwen 
Published: October 2020

Abstract
A random forest algorithm with an improved decision tree and an optimal tree integration is proposed. First, an improved ID3 decision tree is proposed. Next, when the trees are integrated, each decision tree is assigned a voting right consistent with the out-of-bag accuracy of the decision tree. Finally, the experimental results show that the improved random forest algorithm is efficient and the classification performance is improved compared with the traditional random forest algorithm.
Research on Attack Detection Method of Microgrid Central Controller Based on Convolutional Neural Network
Authors: Xi Wei , He Shengguo , Chen Rui , Xu Yongjun , Li Weifan , Zhou Guoqi , Yu Weiguo , He Hongyan , Huang Zhiwei , Yu Yang , Yao Hao , Liu Hui , Xiao Shengyuan , Yue Zhihui , Liu Wei 
Published: October 2020

Abstract
The microgrid central controller (MGCC) integrates the functions of control, monitoring, and communication in microgrid system, and has powerful capabilities of information collection and data processing. However, with the development of microgrid system worldwide, the information security management capabilities of the MGCC are poor, If information / network attacks cannot be actively detected and identified, it will easily reduce the reliability of the microgird system operation. Attackers can use abnormal information or use the MGCC as a springboard to further attack the upper-layer system. Aiming at the above problems, this paper presents an attack detection method based on convolutional neural network, and a detailed design process of attack detection model of the MGCC is proposed. In the attack detection method, the important data streams in the MGCC are used as the input of the convolutional neural network model, then the convolutional neural network model detects or classifies these data streams, finally, intercept the data flow with attack behavior and give a warning prompt, and forward data without attack behaviors normally.
RETRACTED: Data Analysis and Application Research in Sports Field under Big Data Environment
Authors: Ding Peng 
Published: October 2020

Abstract
This paper expounds data mining theory and sports data analysis theory, such as data mining concept, mining method and mining flow. Taking the data analysis in basketball and track and field as an example, this paper discusses the application of data mining in the field of sports industry, mass physique monitoring and physical education data analysis at home and abroad, and analyzes the influence of big data on sports, mainly changing the methods of competitive sports training, promoting the innovation of sports research methods and the transformation of sports communication methods, and promoting the development of the world sports industry.
Architecture Design and Key Technologies Study of Omnichannel Business Platform for Electric Power Marketing
Authors: Lin Hong , Ouyang Hong , Fang Xuemin , Wang Jinzhi , Yuan Bao , Yang Wei 
Published: October 2020

Abstract
Building the omnichannel business platform for the electric power marketing and integrating all online service resources, with the features of customer aggregation, business integration and data sharing, are an inevitable pathway for the power grid enterprise to establish the customer-centric modern service system and realize its digital transformation strategy. The overall architecture of the omnichannel business platform is proposed and the detailed design for the eight shared service centres are given respectively for user centre, ticket centre, electronic bill centre, payment centre, order centre, points centre, online customer service centre, and messaging centre. Some typical application scenarios are described to illustrate the internal working mechanism of the shared service centres and the cooperation mechanism among them, such as management of comprehensive energy business, online interaction service for the electric power customer, and instant messaging delivery. The key technology applications and practices are discussed for the implementation of the business platform, such as improving data processing capabilities, building integrated services with fast response, and developing digital operational capabilities. The development prospects of the business platform are concluded finally, whose service centres would be gradually be evolved into an important component of the strategic business platform for the power grid enterprise by continuously precipitating the business capabilities and establishing the competent service centres.
Analysis of Key Problems in the Optimal Design of Satellite-based Precise Point Positioning Messages
Authors: Liu Cheng , Zhang Weixing , Liu Xiaolei , Gao Weiguang , Lu Jun 
Published: October 2020

Abstract
Precise point positioning (PPP) technology for satellite navigation has received considerable attention because of its wide area coverage, uniform accuracy, and small number of ground reference sites. The use of satellite-based broadcasting technique enables the realization of PPP at the globe, even for users on the oceans. However, due to limited and valuable satellite downlink resources, the optimal design of satellite-based broadcasting message is of great significance. In this study, the influences of some key factors on the accuracy loss of products are analyzed, including the PPP message broadcasting interval, the message data bit width, and the scale factor. On this basis, the effect on the final positioning accuracy of the user is evaluated. Broadcasting design ideas and suggestions for major augmentation products, including Global Navigation Satellite System satellite orbits and clock corrections, through a simulation calculation based on a precise ephemeris are given, thereby providing references for the optimal design of satellite-based augmentation PPP service messages.
The Design of the Control System of Fire Control and Smoke Extraction Robot Based on PLC
Authors: Jiao Hongwei , Ma Zhenming , Hua Wanren 
Published: October 2020

Abstract
The purpose of this paper is to design a plc-based control system of fire control and smoke extraction robot, which can realize the purpose of water cannon, vehicle body movement and non-line-of-sight operation. This control system is mainly composed of PLC control system, video system, remote control system, motor drive system. PLC Through CANopen message transmission protocol, the function of distributed control system is realized. The main task is to receive the CAN command of remote control, so as to realize the command control of vehicle body action (multi-way valve), yuntai action and water cannon action. The design meets the design requirements, has good operability and reliability, in the industry has a certain promotion significance.
Improved Fuzzy Hierarchical Clustering Algorithm and its Application in Software Re-architecting
Authors: Gao Sheng , Lu Chenghua , Miao Ning 
Published: October 2020

AbstractComputer technology changes with each passing day. As a member of software system, the architecture of legacy system becomes complex and difficult to adapt to complex software functional requirements in the process of evolution. It has not only affected the late legacy system maintenance work, but also to the enterprise has brought serious losses. In order to solve the above problems, this paper presents a Fuzzy Hierarchical Clustering algorithm Based on Information Loss, FHCBIL. There are two improvements in FHCBIL algorithm: On the one hand, this algorithm extends the weights allocation of entity features, which solves the problem that the two element relation can not reflect the influence degree of entity feature on entity. On the other hand, FHCBIL algorithm uses the information loss as a similarity calculation method, avoiding the problem of sensitive data set to the irregular data set in the clustering process. In this paper, we further apply FHCBIL algorithm in software re-architecting, give the model of software re-architecting, and implement new nested software architecture. In order to evaluate the quality of the new architecture, we evaluate the new architecture from two aspects: the rationality of the new architecture hierarchy and the accuracy of entity division. The experimental results show that the new architecture based on FHCBIL algorithm has high cohesion and low coupling. And the accuracy of the new architecture is high.
Language Governance of International Cities in Artificial Intelligent Technology Age
Authors: Zhang Jing 
Published: October 2020

Abstract
Technological advances, especially the development of mobile Internet and artificial intelligence are playing a more important role on urban language life. Thus, exploring the new phenomena and new challenges that language life in international cities may face, especially the role that intelligent technology can play in multilingual communication has important theoretical and practical significance for the governance of urban language life. This study takes the international metropolitan cities as the research scope in order to analyze the language governance in the international metropolitan cities based on AI technologies. It hopes that the results of this study could provide some implications and insights to enhance people’s ability of using AI technologies and mobile apps to solve communication problems.
Monitoring Method of Internet of Things for Classified Recovery of Medical Waste
Authors: Wang Chao , Ma Yanxia , Meng Fanwen 
Published: October 2020

Abstract
In order to overcome the shortcomings of the existing technology and hit the medical waste disposal of black industry chain, this paper provides an IoT monitoring method for medical waste treatment, which can strictly monitor medical waste from the source, processing by digital, back in any link problems, the use of big data to find abnormal problems, guide the development of medical enterprise, to ensure the safety of people’s life.
Botnets Attack Detection Using Machine Learning Approach for IoT Environment
Authors: Htwe Chaw Su , Thant Yee Mon , Su Thwin Mie Mie 
Published: October 2020

Abstract
The era of the Internet of Things (IoT) is very rapidly developing with millions of devices that are useful in the smart home, smart city, and many other smart systems for education, organization and so on. On the other side, attackers are mostly targeting these devices. After infecting the malware attacks on these devices, they become bots that are controlled by attackers, and these will be targeted to the organizations not only for stealing important information but also for breaking down the network. Although some security mechanisms have developed to protect against cyber-attacks, most such systems are rule-based systems, like public IDS systems. And also, the formal rule-based detection could be circumvented by the malware attackers’ knowledge. Therefore, the machine learning-based detection scheme is the replacement for the lack of previous detection techniques. The proposed detection architecture is based on machine learning methodology, like the CART algorithm and public IDS dataset, named N-BaIoT. The experimental results indicate that the detection accuracy of the selected classifier, CART is significantly better than that of the Naïve Bayes classifier, and the overall detection rate using CART is reached up to 99%.