bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Application of Big Data Technology in the Construction of Smart Campus in Vocational Colleges
Authors: Guofeng Xu , Mingzhu Li 
Published: March 2021

Abstract
Informational education is a new requirement for education in the information age and it is an inevitable trend in the development of education modernization. In the rapid development of the Internet, the use of cloud computing technology can play a greater role in the construction of smart campuses. It can not only improve the school’s digital service management level, but also promote the progress of education. This article briefly introduces the concept and necessity of smart campus construction, analyzes the role of big data technology in the construction of smart campus, and proposes smart campus construction measures based on big data.
Research on Near far field transform algorithm based on probe compensation
Authors: Zhang Yiyue , Jia Yunfei , Liu Xingxun , Li Jinsong 
Published: March 2021

Abstract
In order to solve the problem of sidelobe error caused by probe pattern in current antenna planar near-field and far-field transformation, a new near-field and far-field transform algorithm with probe compensation is studied by combining the advantages of traditional pattern development and edge current approximation method. By measuring the array antenna in a microwave anechoic chamber, the traditional mode expansion method is compared with the hybrid algorithm. The results show that the measurement accuracy of this method is 1.445dB higher than that of the traditional method, and the calculation efficiency is significantly improved, which proves the effectiveness and engineering practicability of this method.
Application of WebCrawler in Information Search and Data Mining
Authors: Qinghua Chen 
Published: March 2021

Abstract
With the development of network technology, the scope of information search and the ability of data mining are also increasing. On this basis, a WebCrawler technology is proposed. WebCrawler technology plays a very important role in improving the ability of data information search. This article mainly analyzes the working principle of WebCrawler technology and the principle of Web information search and data mining, and focuses on the application in information search.
Using AI to Enhance Recruitment Effect
Authors: Liu Jia , Chang Shih-Hsuan , Xu Yu-Ci , Wu Guo-An , Chang Shih-Feng 
Published: March 2021

Abstract
In today’s rapid economic development, the competitiveness of enterprises is more and more reflected in the talent competition, and the important means to obtain talents is recruitment. With the development of economy, recruitment channels are becoming more and more diversified. Enterprises will choose recruitment channels according to various reasons to recruit suitable candidates for the vacant positions of the enterprise. However, after finding the suitable people, how to keep them or hire them. After the recruitment of employees, whether the performance of employees is related to the recruitment channels has an important reference role for the selection of human resources. We use AI technology to measure the performance of employees through the three dimensions of subjective initiative, job satisfaction and job self dedication. Therefore, it has theoretical and practical value for improving the recruitment effect and contribution.
Research on Particle Swarm Optimization Algorithm
Authors: Wang Shouya , Zhu Jiabing , Shen Xiaobo , Wang Qianchun , Shu Renyi , Cai Jun 
Published: March 2021

Abstract
Aiming at the unsatisfactory performance of the particle swarm optimization algorithm in the optimization process of complex problems and being easy to fall into premature convergence, a power law function to improve the weight value of the particle swarm algorithm is proposed in the paper. It is compared with the linearly decreasing weight particle swarm algorithm, and the test is performed on four typical single-peak and multi-peak functions. The test results show that the particle swarm optimization algorithm in this paper has a better optimization effect.
Providing Domain Specific Model via Universal No Data Exchange Domain Adaptation
Authors: Wang Lixu , Liang Songtao , Gao Feng 
Published: March 2021

Abstract
High quality Data and its derivative machine learning (ML) models are gradually becoming commercial commodity and provided to work effectively in an increasing number of areas. These ML model providers possess a set of trained models along with immense amount of source data stored on their servers. In order to obtain specific models, providers will require consumers to upload their domain-specialized data and conduct domain adaptation at the server side. However, considering the protection of the private information reflected by consumers’ training data, as well as maintaining the commercial competitiveness of ML service, it is best that there is no data exchange between servers and consumers. Besides, consumers’ data is always lack of supervision, i.e., classification labels, thus we are searching for how to conduct unsupervised domain adaptation (UDA) with no data exchange among domains in this work. We are the first to propose a novel memory cache based adversarial training (AT) strategy for UDA at the target side without the source data (the existence of source data is an essential requirement for regular AT). And our method includes a multiple pseudo labelling operation which is more accurate and robust than single pseudo labelling. The AT and multiple labelling work collaboratively to extract shared features among domains and adapt the learning model more specific to target domain. We carry out extensive evaluation experiments with a number of data sets and baselines, and according to the results, our proposed method can perform very well and exceed the state-of-art performance on all tasks. In the end, we also discuss how to extend our method to partial and open-set domain adaptation.
Generating Anime Sketches With C-GAN
Authors: Wang Ziyuan 
Published: March 2021

Abstract
Generating a decent colored anime drawing from black-and-white sketches is not a simple task in the anime industry. For this problem, a novel deep learning model is introduced based on Conditional Generative Adversarial Networks(C-GAN), which can automatically colorize the anime sketches and output satisfactory pictures. By testing the structure of this model, a typical structure is found to have the best performance.
A Scanner Jitter Test Method of CR System Based on Improved Hough Transform Algorithm
Authors: Wenming Guo , Huilin Zhao 
Published: March 2021

Abstract
Computed radiology system (CR) test technology has been applied in the testing of industrial welding defect after the promulgation and implementation of digital radiographic imaging test standards. The performance of CR system directly affects the testing rate of welding defects. Therefore, testing and evaluating the performance of CR system becomes the premise and guarantee for the application of CR system. In this paper, based on the testing image of CR phantom and the simulation of scanner dither results, an algorithm combining fault-tolerant voting and improved point-based hoof transform is proposed to test the performance index of scanner dither in CR system. Experimental results show that the proposed algorithm can test not only the row and column dithering, but also the subtle arc dithering. Compared with standard Hough transform algorithm, this algorithm has higher accuracy and can avoid the influence of other image indicators around the T-shaped image quality indicator used in CR phantom.
Research on the Application of Mind Map in Multimedia Network Teaching Environment
Authors: Guo Qinrong 
Published: March 2021

Abstract
With the rapid development of Internet technology, multimedia network teaching has become a new teaching mode in the teaching work of major universities. In the context of multimedia technology, the use of mind maps to carry out teaching activities in various disciplines can give full play to the advantages of both, which can use mind maps to present the knowledge learned in an intuitive and vivid way. This not only helps students sort out their knowledge, but also enables students to master important and difficult knowledge faster and more efficiently, and enhance their memory and thinking skills. This is also conducive to the cultivation of students’ divergent systems, systematic thinking and creative logical thinking. Based on this, this article will discuss and study the application of mind mapping in college multimedia network teaching environment. The author hopes that the combination of multimedia technology and mind mapping can maximize the effectiveness of teaching.
Medical Record Storage Model Based on Credibility Assessment H-Algorand Consensus Mechanism
Authors: Sun Jiatai , Zhao Zengnan , Zhang Minghui , Wang Ning , Wang Lihua 
Published: March 2021

Abstract
In order to solve the problems of poor security and low throughput of the current electronic medical records, this paper proposes an electronic medical record storage model based on H-Algorand consensus mechanism of credibility assessment combined with blockchain technology. To ensure the normal operation of blockchain system effectively, the model used a credibility assessment algorithm to obtain the trust value and replace the account balance in the H-Algorand consensus mechanism with the trust value as the node weight. The results show that the storage model adopted here has higher security and throughput efficiency.
Improved Object Detection using Data Enhancement method based on Generative Adversarial Nets
Authors: Ruiqiang Zhang , Jiajia Liu , Yu Zeng , Kejia Pan , Lin Huang 
Published: March 2021

Abstract
In deep learning-based object detection, especially in face detection, small target and small face has always been a practical and common difficult problem due to its low resolution, blurred image, less information and more noise. In some applications, sensing image data is hard to collect, leading to limited object detection performance. In this paper, we investigate using a generative adversarial network model to augment data for object detection in images. We use generative adversarial network to generate the diverse objects based on the current image data. An improved generative adversarial network is added in the network and a new loss funtion is applied during the trianing process to generate diverse and high-quality traing images. Experiments show that images generated by generative adversarial network have higher quality than counterparts.
On the In-application Value of Emotional Intelligence Interaction Based on Interactive Art Installation
Authors: Zhou Xinyu 
Published: March 2021

Abstract
The design of interactive art installation, which integrates interactivity, artistry and education, leads people to think their relationship with nature and society by human-installation interactive movement. Due to the repaid development of science and technology, the simple interaction with settled program cannot satisfy people’s demands. Instead, they want to step into the world of installation through personalized emotional catharsis. The current mode of communication will be changed by the in-application program within the interactive art installation. The single installation totally produced by artists’ thought will be replaced by the finished installation product with the participation of the audience. In this way, participants will immerse in the story plot and feel various emotional changes psychologically by sound, image and touch, which relies on the interactive art installation. The Listen is taken as the example to spread the idea of sharing the ecological nature of human beings and the world. Arduino, an open source platform, will be studied to show the change of modern interactive communication mode, exploring the impact of programming tool on the development of future interactive installation and providing some new ideas for the integration of artistic creation and science and technology in the future.
Resource Allocation for High-Speed Train Communication Based on Deep Reinforcement Learning
Authors: Zhou Lanlan , Yu Jianguo 
Published: March 2021

Abstract
With the advent of high-speed railway era, users’ demands for high link capacity and high reliability are increasing. In this paper, a resource allocation algorithm based on deep reinforcement learning (DRL) is proposed in the high-speed railway (HSR) scenario. The communication link between high-speed train and high-speed train (T2T) is taken as an agent, and the link capacity and packet transmission success probability (PTSP) are taken as optimization objectives, which are mapped into the reward function. The agents gain experience by constantly interacting with the HSR communication environment to update the neural networks in DRL and eventually learned the optimization policy. The simulation shows that when the trains’ speed is 350km/h and the transmission packet size is 2385Bits, the high reliability of T2T link can still be maintained, and the capacity of high-speed train to Infrastructure (T2I) link is improved by 1Mbps compared with the baseline, which proves that the proposed algorithm can mitigate the interference introduced by spectrum reuse and doppler shift.
Research on Personnel File Management Based on Computer Information Technology
Authors: Juan Du 
Published: March 2021

Abstract
Informatization has become an inevitable trend in office, and it is also the main way for modern business society to improve work efficiency. From the observation of the reform of file management in practice, its main direction is based on computer technology. This research selects three aspects based on computer technology, including information resource management, technology management and service management. It mainly discusses the integration and utilization of information resources, technology management promotion measures and innovative service management system paths.
Decision making method of human resource demand selection in cloud manufacturing environment
Authors: Ye Baozhong , Chen Jian , Zhao Jing 
Published: March 2021

Abstract
In order to solve the problem of human resource selection of demander for provider in cloud manufacturing environment, this paper proposes a selection decision method from the perspective of provider. Firstly, the evaluation index system of demander is established, and the objective weight of each index is obtained by entropy weight method according to the demander’s demand information. Secondly, the subjective weight of each index is determined by improved AHP method and the comprehensive weight of each index is obtained by combining the subjective and objective weight. Then, the traditional TOPSIS method is improved to evaluate the demander and provide reference for the selection of supplier. Finally, an example is given to verify the effectiveness and practicability of the method.
Research on Safety Performance Appraisal Strength Based on Motivational Mechanisms
Authors: Guo Chenggong , Chen Jun , Jiang Yaxuan , Chang Jiaxi 
Published: March 2021

Abstract
As a quantitative tool and implementation means, performance appraisal and motivational mechanisms play an important role in the enterprise safety management system. Taking the negative motivation strength as the research object, this paper established an evaluation method and calculation model of enterprise safety performance motivation. A comparative analysis of the safety performance motivation of four electric power enterprises and three industries was conducted to examine the evaluation method and calculation model. The results illustrated the characteristics of safety performance appraisal strength of those enterprises and industries. This study can further enhance the awareness of safety responsibility of all employees in enterprises, provide a reference for other industries to implement safety performance reward and punishment mechanism and a useful reference for enterprises to improve safety management.
Design method of measurement errors of MEMS-IMU in attitude capture clothing
Authors: Yang Zhi , Zhang Zhonghui , Wang Dezhi 
Published: March 2021

Abstract
Attitude capture clothing is widely used in virtual anchor, virtual fitting and other fields, and MEMS-IMU is the key component to realize high cost-performance attitude capture clothing. However, a precision turntable is needed in order to estimate gyroscope’s static biasing, and measurement errors of MEMS-IMU are difficult to be obtained. Based on the problem, both attitude and gyroscope’s static biasing are estimated in field through a Kalman filter, a design method of measurement errors is proposed for improving attitude’s accuracy. In the proposed method, a mathematical model of measurement errors is given, and the optimal values of measurement errors are obtained by analysing the relationship between attitude’s accuracy and measurement errors. A MEMS-IMU with name of MPU9250 and size of 3 mm*3 mm* 1 mm is used for verifying the feasibility of the proposed method. Experimental results show pitch, roll and yaw deviate from the reference values by 0.008°, 0.006° and 0.6° respectively when the MEMS-IMU keeps movement at a constant speed in arbitrary trajectory, which is significant for promoting the application of MEMS-IMU in attitude capture clothing.
Verification of Air Data Computer Software using Formal Methods
Authors: Xu Jian , Zhang Xinai , Zhao Yi , Xu Bing 
Published: March 2021

Abstract
With the rapid development of airborne system and software, the requirement of software’s robustness, adaptability and reliability improves continually. Failure of software function may cause severe property loss and personnel casualties. Formal methods are mathematically based techniques for the specification, development, and verification of software aspects of digital systems that have obvious advantages in improving safety and reliability of airborne software. This paper takes Air Data Computer software as an example for formal verification in the whole software development process including requirements process, design process, coding process and integration process to demonstrate a set of formal methods application in the airborne software verification process.
Research on Ground Profiling Control of Combine Harvester Based on the Symmetric Algorithm
Authors: Wang Guoqiang , Su Rui 
Published: March 2021

Abstract
Combine harvester is an important part of agricultural modernization, and the function of the header is more important. The header is mainly composed of a transmission device, cutter, and reel. In the process of practical application, the harvest efficiency of different types of the combined cutting machine is much higher than that of manual harvesting. In this paper, a ground profiling sensing mechanism is designed by building a dynamic model. The results show that: the ground profiling sensor mechanism of combine header based on the symmetric algorithm can truly reflect the agricultural terrain data, and provide data support for the action of digging shovel and improve the profiling accuracy of the combine.
Highly sensitive T
1–T
2 dual-mode MRI probe based on ultra-small gadolinium oxide-decorated iron oxide nanocrystals
Authors: Miao Yashuang , Chen Peirong , Yan Miao , Xiao Jianmin , Hong Biao , Zhou Ke , Zhang Guilong , Qian Junchao , Wu Zhengyan 
Published: March 2021

Abstract
Single-mode magnetic resonance imaging (MRI) contrast agents (CAs) in clinical settings are easily disturbed by calcification, bleeding, and adipose signals, which result in inaccurate diagnoses. In this study, we developed a highly efficient T
1–T
2 dual-mode MRI CA using an ultra-small gadolinium oxide-decorated magnetic iron oxide nanocrystal (GMIO). The gadolinium element could effectively alter the magnetic properties of the GMIO from soft-ferromagnetism to superparamagnetism. In addition, when the Gd/Fe ratio was 15% (designated as GMIO-2), the GMIO-2 possessed the best superparamagnetism and highest magnetism. Subsequently, T
1 and T
2 values of GMIO-2 were measured through a series of turbo spin-echo images and then multi-spin echo sequence, respectively. Based on this, T
1 and T
2 relaxivities of GMIO-2 were calculated and were the highest (r
1: 1.306 m M−1 s−1 and r
2: 234.5 m M−1 s−1) when compared to other groups. The cytotoxicity of GMIO-2 was negligible under a wide range of dosages, thus exhibiting excellent cell biocompatibility. Moreover, GMIO-2 could quickly diffuse into cells, leading to its effective accumulation. The systemic delivery of GMIO-2 resulted in an excellent T
1–T
2 dual-mode MRI contrast effect in kidneys, which is expected to improve the diagnosis of kidney lesions. Therefore, this work provides a promising candidate for the development of a T
1–T
2 dual-mode MRI CA.
Inorganic chemoreactive nanosonosensitzers with unique physiochemical properties and structural features for versatile sonodynamic nanotherapies
Authors: Dong Caihong , Hu Hui , Sun Liping , Chen Yu 
Published: March 2021

Abstract
The fast development of nanomedicine and nanobiotechnology has enabled the emerging of versatile therapeutic modalities with high therapeutic efficiency and biosafety, among which nanosonosensitizer-involved sonodynamic therapy (SDT) employs ultrasound (US) as the exogenous activation source for inducing the production of reactive oxygen species (ROS) and disease therapy. The chemoreactive nanosonosensitizers are the critical components participating in the SDT process, which generally determine the SDT efficiency and therapeutic outcome. Compared to the traditional and mostly explored organic sonosensitizers, the recently developed inorganic chemoreactive nanosonosensitizers feature the distinct high stability, multifunctionality and significantly different SDT mechanism. This review dominantly discusses and highlights two types of inorganic nanosensitizers in sonodynamic treatments of various diseases and their underlying therapeutic mechanism, including US-activated generation of electrons (e−) and holes (h+) for facilitating the following ROS production and delivery of organic molecular sonosensitizers. Especially, this review proposes four strategies aiming for augmenting the SDT efficiency on antitumor and antibacterial applications based on inorganic sonosensitizers, including defect engineering, novel metal coupling, increasing electric conductivity and alleviating tumor hypoxia. The encountered challenges and critical issues facing these inorganic nanosonosensitzers are also highlighted and discussed for advancing their clinical translations.