bluuebunny/crossref_metadata_2025_split huggingface dataset:
A q-rung orthopair fuzzy multiple attribute group decision making method based on generalized Maclaurin symmetry mean and Dombi t-norm and t-conorm
Authors: Xu Hui , Zhang Runtong 
Published: April 2021

Abstract
Generalized Maclaurin symmetry mean (GMSM) operator is an effective tool in the process of multi-attribute group decision making (MAGDM) with the characteristic of capturing the interrelationships between multiple arguments. at the same time, q-rung orthopair fuzzy set (q-ROFS) is a good tool to describe uncertainty and fuzziness. To effectively aggregate q-rung orthopair fuzzy information based on the extension of Dombi operations, some novel operators and ideal properties of the proposed operators are put forward in this study. Further, this research present a novel method to MAGDM based on the proposed operators. Finally, the applicability of new method can be proved by a numerical experiment. A detailed parametric analysis and a comparative analysis are also discussed to highlight the flexibility and superiority of the method proposed in this paper.
Research on Network Intrusion Detection Method Based on Machine Learning
Authors: Xu Guorong 
Published: April 2021

Abstract
After years of development, the Internet has penetrated into all areas of society, and various network intrusions occur almost every day. In order to enhance the security of the network, intrusion detection technology has received more and more attention. Network intrusion detection is a branch of computer security. Its goal is to automatically and effectively detect intrusion traffic in the network and provide timely warnings. Network intrusion detection technology has become an important means to resist network intrusion in recent years, and network intrusion detection systems of various scales have been applied in the network. Due to the diversity of intrusion methods, traditional digital authentication and firewalls and other network security measures are difficult to meet the needs of network intrusion detection. The machine learning boom has gradually emerged, and machine learning methods have begun to be applied to the field of intrusion detection, which has become a research hotspot in this field. This article discusses machine learning methods, discusses adding machine learning algorithms to the network intrusion detection link to achieve effective detection of network intrusion traffic, so as to obtain a good network intrusion detection effect.
GAU-Net: U-Net Based on Global Attention Mechanism for brain tumor segmentation
Authors: Gan Xiuling , Wang Lidan , Chen Qi , Ge Yongjie , Duan Shukai 
Published: April 2021

Abstract
Deep learning has shown great advantages in biomedical image segmentation. The classic model U-Net uses a stacked encoding-decoding structure of convolution operations for feature extraction and pixel-level classification. The stacking of convolutional layers can expand the receptive field, but it is still a local operation and cannot capture long-distance dependence. Therefore, in this work, we propose a Global Attention Mechanism that combines channel attention module and spatial attention module and integrates different convolutions in it. Besides, we design a residual module for the traditional up and down sampling blocks. And finally, we combine them with U-Net to propose a new global attention network GAU-Net. We perform experiments on the dataset BraTS2018. Our model has increased the mIoU from 0.65 to 0.75 with only 5.4% of U-Net parameters. At the same time, the inference time is also significantly shortened with relatively good performance.
GAU-Nets: Graph Attention U-Nets for Image Classification
Authors: Zhao Haoran , Zhang Chengwei 
Published: April 2021

Abstract
Graph neural network is a research hotspot in the field of deep learning recently, and its application has become more and more extensive. A new graph neural network model is proposed, Graph Attention U-Nets(GAU-Nets for short), which has strong processing capabilities for graph structures. In GAU-Nets, the graph data structure is preliminarily processed through GCN, and then extracted the features by the graph pooling and Unpooling blocks. We innovatively added the attention mechanism to GAU-Nets to avoid forgetting the important information. We have done a lot of image classification experiments on MS-COCO and other datasets. The experimental results prove that GAU-Nets performs better than other traditional graph neural network models. Without bells and whistles, our GAU-Nets method has an accuracy of 69.1% on the MS-COCO data set and 82.1% on the VOC 2007 data set, which has surpassed all benchmark methods.
Research on Anti-noise of Image Compression Based on Variational Autoencoder
Authors: Gengzeng Li 
Published: April 2021

Abstract
We introduced the Variational Compression Autoencoder (VCAE) that introduces noise training, and uses KL divergence to balance compression and anti-noise. Variational autoencoders improve the anti-noise performance of the compression framework through noise training and learning decoupled latent representations. Experiments on the Kodak datasets show that our framework is superior to other deep learning compression frameworks such as compressed autoencoders in the MS-SSIM indicator under the same bit error rate.
Research on Gesture Recognition Method Based on Deep Learning
Authors: Li Yizhi 
Published: April 2021

Abstract
Gestures are necessary and indispensable in people’s ordinary life communication. And gestures have become an important part of human-computer interaction. With the gradual maturity of human-computer interaction systems, the role of gestures in human-computer interaction is becoming more and more important. Due to the variety of gestures in practical use, coupled with factors such as environment and light changes, it makes it very difficult for computers to recognize the correct gestures from image information. Deep learning has achieved certain research results through several years of development, which provides new research ideas for gesture recognition. This paper firstly introduces the common methods and processes of gesture recognition, and then proposes a new convolutional neural network structure by studying convolutional neural networks and adding Inception structure to the network to improve the performance of the network. Finally, experimental validation is conducted, and the experimental results show that the new convolutional neural network achieves an average recognition rate of 97.8% on the test set, which is a good result.
Recognition of navel orange image with complex background based on residual network
Authors: Li Xiaoxin , Tan Yumei , Lu Xinxin , Zhang Bo 
Published: April 2021

Abstract
In order to solve the problem that the detection effect of navel orange recognition in complex background is poor, we propose a navel orange recognition method based on residual network. In this study, the navel orange classification dataset was constructed and labeled, and the performance of five classic convolutional neural network models on this dataset was evaluated, including AlexNet, Improved LeNet, SqueezeNet, ResNet-18, GoogLeNet. The results present significant accuracy obtained by the ResNet-18 model, with accuracy of 98.27%, which is more suitable for navel orange image recognition in complex background.
Design of online course knowledge recommendation system based on improved learning diagnosis model
Authors: Gan Baiqiang , Zhang Chi , Dong Qiuping , Sun Wei 
Published: April 2021

Abstract
In order to better manage curriculum resources, give full play to the practical value and efficiency of university curriculum, provide an efficient and convenient interface for the majority of students and curriculum resources, and meet the different needs of different students in learning, this paper aims at the current content-based recommendation algorithm cannot solve the problem of user interest drift, online curriculum resource sharing platform cannot directly communicate with students this paper proposes a learning diagnosis method which combines the similarity of courses and user interest model to calculate the similarity. According to the actual characteristics and attributes of online course resources, and using the improved algorithm, this paper designs and implements a set of online course knowledge recommendation system based on the improved learning diagnosis model, to effectively alleviate the problems of low user interest and direct communication between online and offline classroom users in the current online course learning process.
Multi Physical Field Simulation of Irreversible Electroporation
Authors: Xu Chao , Zhang Yi 
Published: April 2021

Abstract
Irreversible electroporation (IRE) uses non thermoelectric pulse to ablate tumours, which is an effective cancer treatment method. The pulse is transmitted through a minimally invasive needle electrode inserted into the target tissue and causes cell death by creating nanoscale membrane defects. Irreversible electroporation has been shown to be safe and effective in the treatment of tumours of the brain, liver, kidney, pancreas and prostate located near key blood vessels and nerves. Determining the parameters accurately of the applied pulsed electric field to kill all tumour cells and minimize damage to healthy tissue is the key to the success of IRE for the treatment of malignant tumours. In this paper, according to the researches of irreversible electroporation in the treatment of tumour in recent years, the distribution of electric field and temperature in tissue during IRE is calculated by numerical method, which provides a methodological basis for the treatment of tumour ablation with IRE.
Application of Transition probability based on random graph and Markov chain for the Monitoring of Complex Network
Authors: Mao Hanqi , Liu Sihua 
Published: April 2021

Abstract
In recent years, the method of graph theory has been used in the process of solving many practical problems, especially the transition probability of random graphs. Because it can be used to solve the problem of numerical variables with random phenomena, it is also practical. In the middle, the Markov chain provides a transformation and functional relationship from a set of discrete vectors to the state space, and is often used to describe the transition from one initial state to another end state in various state spaces. The process and the transition process are random. The research in this paper is based on the transition probability of the random graph and the Markov chain to establish a mathematical model, so that complex networks can be monitored, including the ability to predict the long-term steady state of complex networks. The required transition time, the quantitative relationship between the number of monitoring points and the reliability and sensitivity of the network were obtained.
Speech Emotion Recognition algorithm based on deep learning algorithm fusion of temporal and spatial features
Authors: An Xu Dong , Ruan Zhou 
Published: April 2021

Abstract
In recent years, human-computer interaction systems are gradually entering our lives. As one of the key technologies in human-computer interaction systems, Speech Emotion Recognition(SER) technology can accurately identify emotions and help machines better understand users’ intentions to improve the quality of human-computer interaction, which has received a lot of attention from researchers at home and abroad. With the successful application of deep learning in the fields of image recognition and speech recognition, scholars have started to try to use it in SER and have proposed many deep learning-based SER algorithms. In this paper, we conducted an in-depth study of these algorithms and found that they have problems such as too simple feature extraction methods, low utilization of human-designed features, high model complexity, and low accuracy of recognizing specific emotions. For the data processing, we quadrupled the RAVDESS dataset using additive Gaussian white noise (AWGN) for a total of 5760 audio samples. For the network structure, we build two parallel convolutional neural networks (CNN) to extract spatial features and a transformer encoder network to extract temporal features, classifying emotions from one of 8 classes. Taking advantage of CNN’s advantages in spatial feature representation and sequence encoding conversion, I obtained an accuracy of 80.46% on the hold-out test set of the RAVDESS data set.
Uterine magnetic resonance image segmentation based on deep learning
Authors: Niu Yu’ang , Zhang Yuanyang , Ying Liping , Li Hong , Chen Wenbo , Miao Hanci , Bao Nan 
Published: April 2021

Abstract
Endometrial diseases have always been a common disease among women worldwide. For the prevention of endometrial diseases and the treatment after illness, there is an urgent need to solve the problem of positioning the uterus, which is also one of the difficult problems that plague clinicians. Research in recent years has shown that neural networks based on deep learning are an important tool in the medical field. This article discusses the uterus magnetic resonance image segmentation method based on deep learning. First, we completed the preprocessing of the data based on the hessian matrix, and expanded the data, then inputted the data into the DenseUNet network. In this article, our method obtained 87.60% Dice Similariy Coefficient(DSC), 86.57% precision, 88.11% sensitivity and 99.75% specificity in 13 different test sets. Finally, our method was compared to UNet and UNet++ networks, and achieved better performance. Our method effectively solved the problems of uterus magnetic resonance image automatic segmentation. It can be used in the pre-operation planning of stem cell surgery to repair the endometrium.
An Attention-based Convolutional Neural Network for Melanoma Recognition
Authors: Chen Qi , Wang Lidan , Gan Xiuling , Duan Shukai 
Published: April 2021

Abstract
Early automatic and accurate melanoma recognition is an important method to reduce melanoma deaths. Existing methods are less sensitive to the position of the lesion areas. Network training may be affected by the uncorrelated noisy parts. In light of this circumstance, an end-to-end attention-based network AF-CNN for accurate melanoma recognition is proposed in this paper, which is mainly composed of pre-trained VGG19, attention blocks and a classification layer. Instead of treating each part of the input dermoscopy images equally, our AF-CNN model has strong discriminative ability to focus on the lesion areas. The AF-CNN was evaluated on the ISIC2017 dataset and concluded the proposed single model achieves the state-of-the-art result in melanoma recognition task.
Human fall recognition based on WiFi CSI with dynamic subcarrier extraction of interference index
Authors: Zhou Yichao , Gao Ming , Luo Yunxiao , Fan Xianglin 
Published: April 2021

Abstract
Recently, human fall recognition based on channel state information(CSI) were emerging technologies. However, the widespread deployment of WiFi may cause wireless interference problems for such WiFi CSI recognition, which will reduce the accuracy of human fall activity recognition. So, the paper proposed a dynamic subcarrier extraction method based on interference index (CII-DSE). It used the interference index to divide the interference level and determined the corresponding weak correlation subcarrier combination to reduce the presence of channel interference. Experiment results shown that in the WiFi overlapping channel interference environment, the accuracy of CII-DSE was 95. 8%, which effectively improves human the fall recognition influence.
Rigidity of conformal minimal two-spheres in a compact symmetric space
Authors: Yi Min 
Published: April 2021

Abstract
In this paper we studied rigidity of submanifolds, rigidity is an important property of submanifolds in differential geometry. Then we manily proved that any conformal minimal two-sphere immersed in a compact symmetric space N is determined, up to an isometry of N, Byits first and second fundamental forms.
Man-Machine Translation—Future of Computer-Assisted Translation
Authors: Aihua Zhu 
Published: April 2021

Abstract
The development of artificial intelligence has given birth to machine translation, which greatly improves translation efficiency. However, the quality of machine translation is not satisfactory, especially in terms of proprietary terms, cultural vocabulary, and text logic. Human-machine translation has become an innovative product of computer-assisted translation, which automatically saves corpus modified or stored by the translator and grows together with it, constituting a unique translation library. After that the machine can automatically identify and retrieve the correct translation in subsequent translation practices, maintaining quality while ensuring quantity and improving translation efficiency.
Experimental research on Ferrofluid Combined rotary Sealing of High Power Motor
Authors: Xing Feifei , Ruican Hao , Ji Jun 
Published: April 2021

Abstract
It was designed a ferrofluid combined rotary sealing structure for a high power motor in this paper. In addition, it was calculated the magnetic field distribution of the structure by Ansys at different gaps, the result showed that the magnetic field distribution trend was generally the same. In the sealing experiment, it was concluded that when the maximum gap was 0.6mm, the single-stage sealing pressure capacity reached at 36.7KPa, and the sealing capacity remained stable with speed increasing. The experiments showed that the design was suitable for sealing high-power motors in corrosive environments.
Application of Microelectronics Computer Auxiliary Function under the Internet of Things Platform
Authors: Xiaojiang Li 
Published: April 2021

Abstract
With the continuous deepening of economic globalization, the pressure of market competition faced by all walks of life is increasing day by day. If it occupies a place in the fierce market competition, it must continuously improve technological level and technical content. Nowadays, science and technology have become an important standard to measure a country’s comprehensive strength and development status, and the improvement of science and technology has also risen to the level of national development strategy. As the core of all high-tech technologies, microelectronics technology can turn some unreachable technological products into reality. Combining it with the Internet of Things, it will help lay a solid foundation for technological innovation. Therefore, this article analyzes the concepts of Internet of Things and microelectronic computers, and deeply studies the meaning of the combination of the two. At the same time, it expounds the application of microelectronic computer auxiliary functions under the Internet of Things platform, hoping to provide references for professionals.
Numerical calculation for effectively simulating the electric field in electroporated tissue
Authors: Xu Xu , Yi Zhang , Yang Lei 
Published: April 2021

Abstract
At present, cell membrane perforation technology has been widely used in biology and clinical medicine. Therefore, it is important to master the change pattern of perforated cell membrane potential to further improve the research and application of cell perforation technology. Among them, numerical simulation is an important tool to noninvasively study the effects of tissue properties, electrode arrangement and pulse transmission scheme during electroporation. In this paper, an improved Newton’s method is used to predict pulse voltages and to verify the fit of the method used in this paper for different tissue parameters in a numerical computational model developed specifically for electroporation. The purpose is to improve the reference for researchers to provide faster and more accurate iterative algorithms when studying the electroporation mechanism. The comparative analysis of simulation results between this paper and COMSOL software shows that the method can improve the accuracy of predicting the electric field distribution, reduce the number of iterations, and the effect is well fitted under different tissue parameters. We believe that this knowledge will contribute to a better understanding of in vivo electroporation kinetics and improve electroporation treatment planning techniques.
Non-invasive electric arc fault detection based on sliding approximate entropy
Authors: Liu Zhao , Cao Xuewei , Wang Ziyang , Li Jinshun , Chen Xiaokai 
Published: April 2021

Abstract
In recent years, electrical fires have occurred frequently and have become the first cause of various fires. Among them, the electric arc is one of the important causes of electrical fires. In this paper, the research on non-invasive electric arc fault detection is carried out for low-voltage users, which can discover and detect the electric arc only by analysing the aggregated current measurements from outdoors. In order to achieve this goal, firstly, the abrupt change of the current mode is discovered and detected with sliding window from the total load current signal based on the approximate entropy. Then the current signature samples are extracted around the abrupt change. Finally, according to the pre-set electric arc signature classifier, it is judged whether the abrupt change is caused by the electric arc fault. In addition, under laboratory conditions, an electric arc simulation experiment is carried out for common appliances of actual low-voltage users. The results show the effectiveness of the proposed non-invasive electric arc fault detection method based on approximate entropy.
Effective Computer-assisted Terminology Management Through SDL MultiTerm
Authors: Yang Zhengang 
Published: April 2021

Abstract
Long term changes have been brought about by information and computer technology to translation activity in the information age. Meanwhile, technical translation service demand is increasing, so new strategies and methods are needed to adapt to the change in translation activity. Computer-assisted terminology management tool plays an increasing important role in translation industry, attracting more and more research focus. The paper expounds the forms and significance of terminology management, with special emphasis on SDL MultiTerm. The paper argues that terminology work, also a form of collaborative team work interlinked with the whole translation process, will be further facilitated by computer and information technology to ensure quality translation service.
The Conception of Application of Computer Virtual Reality Technology in Sports Training
Authors: Ren Yue , Li Jianchen 
Published: April 2021

Abstract
In recent years, with the rapid development of the economy and the rapid progress of science and technology, computer technology has also made great progress, and the computing power of the computer has been improved unprecedentedly. The progress of computer technology has also brought technological innovation and new human-computer interaction experience, and a large number of frontier technology has emerged in the field of computer. Virtual reality technology is one of the outstanding representatives. At present, virtual reality technology has been applied in many industries and has played an important role. Athletic sports will also be promoted by the spirit of “science and technology to serve the Olympic Games” of the State Sports Administration. Among them, all kinds of advanced sports science and technology, including virtual reality technology, will also penetrate the daily training and scientific research of competitive sports, and provide scientific and technological support for coaches and athletes in various sports to create better results. Therefore, this paper will focus on the advantages of virtual reality technology, and study the possibility of combining virtual reality technology with the training of many competitive sports events. So as to promote the development of the physical education industry, bring fundamental changes, and promote the progress of sports in China.