bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mathematical modeling of elastic waves radiation during screw dislocation segment oscillations
Authors: Dezhin V V 
Published: February 2021

Abstract
In this paper, we consider elastic waves radiation (acoustic emission) during oscillations of screw dislocation segment under action of external influences. The dislocation oscillations braking by medium was not taken into account. Elements of generalized susceptibility inverse matrix for the screw dislocation segment in nondissipative crystal are written. The energy emitted by the dislocation oscillator has been found. Using numerical calculations of the generalized susceptibility matrix elements for the screw dislocation segment, estimate of the radiated energy is obtained.
Astrophysical magnetohydrodynamical outflows in the extragalactic binary system LMC X-1
Authors: Papavasileiou Th V , Papadopoulos D A , Kosmas T S 
Published: February 2021

Abstract
In this work, at first we present a model of studying astrophysical flows of binary systems and microquasars based on the laws of relativistic magnetohydrodynamics. Then, by solving the time independent transfer equation, we estimate the primary and secondary particle distributions within the hadronic astrophysical jets as well as the emissivities of high energy neutrinos and γ-rays. One of our main goals is, by taking into consideration the various energy-losses of particles into the hadronic jets, to determine through the transport equation the respective particle distributions focusing on relativistic hadronic jets of binary systems. As a concrete example we examine the extragalactic binary system LMC X-1 located in the Large Magellanic Cloud, a satellite galaxy of our Milky Way Galaxy.
Research of Water Body Turbidity Classification Model for Aquiculture Based on Transfer Learning
Authors: Zheng Jianhua , Yang Gaolin , Huang Yanxuan , Liu Leian , Hong Guihuang , Qiu Zitao , Liu Shuangyin 
Published: February 2021

Abstract
Fodder, fish manure, and pond sludge will seriously affect the turbidity of the water body in aquaculture. How to quickly and online judge the turbidity of the water body is very important for realizing efficient, low-cost, and accurate control of aquaculture. In view of the shortcomings of traditional detection methods, a transfer learning method based on ResNet deep learning network model is proposed to realize water body turbidity classification, and two transfer learning methods of parameter partially frozen and completely unfrozen are designed based on ResNet18.Subsequently, the turbidity data set of aquaculture water quality was constructed, and the data set was enhanced by image cropping, image flipping, random scaling, and other methods. The experimental results show that when all parameters are not frozen, the transfer learning method can achieve the best class effect, and the accuracy rate is 0.9686, which can provide an effective method for online detection of aquaculture water body turbidity.
Research on document similarity calculation and detection based on deep learning
Authors: Xing Cui , Yang Yan , Luo Jian 
Published: February 2021

Abstract
In view of the complexity of traditional document similarity calculation methods and the problem of large error, a document similarity calculation and detection method based on deep learning is proposed. The effective subtree matching method is used to calculate the similarity of document feature sequences, and the frequency of feature items is obtained. In order to reduce the complexity of document similarity detection, a deep learning method is used to classify documents. The keywords in the document are extracted, and the similarity calculation and detection results are obtained by calculating the similarity between keywords. The experimental results show that the calculation error of the proposed method is low and the results are reliable, which fully shows that the method is effective and feasible.
Fog Removal Algorithm for Geographic Images Using Generative Adversarial Nets
Authors: Li Dong , Nie Ying , Liu Yang Cheng 
Published: February 2021

Abstract
In order to improve the imaging effect of geographic images in a complex weather environment. This paper designs a geographic image defogging algorithm by using the generation countermeasure network to improve the clarity of the image. Firstly, the imaging mode and degradation process of the foggy image is studied in this paper, and the atmospheric scattering model is established to simulate the degradation of the image. Secondly, according to the data characteristics of image defogging, an optimized image defogging model is generated based on Conditional Generation Adversarial Nets (CGAN), so that the image output by the model is closer to the real image. The experimental results show that the image defogging model designed in this paper has good contrast and color richness, which can effectively solve the problem of image degradation in fog. Moreover, the reduction of paired data sets will reduce the measurement index of the geographic image defogging model. This paper provides a reference for the study of geographic image defogging.
Evaluation Grade Prediction Method with Limited Information from Experts
Authors: Wei Xiao , You Hongliang , Cheng Jiajun , Gao Qiang , Tang Shanhong 
Published: February 2021

Abstract
With the development of artificial intelligence, using machine learning methods to build evaluation models has attracted more and more attention. However, training anevaluation model often needs a lot of labeling samples annotated by experts. It is very difficult to get enough labeling data with a limited group of experts. This paper proposes a method to learn the evaluation model with limited information from experts. This method has two stages. In the first stage, we build a large training set with ordinary people by comparing every two samples. After that, we train a Siamese Network with the paired comparison data set to get a score for each sample. In the second stage, we map the scores to evaluation grades with the help of experts. In the experiments, we use the UCI wine quality data set to evaluate our method. Experimental results demonstrate that we get a basically equivalent accuracy (0.5% decrease) with only1.45% samples labeled byexperts.
Application Development of Dance Pose Recognition Based on Embedded Artificial Intelligence Equipment
Authors: Zhou Qian , Wang Jun , Wu Peng , Qi Yue 
Published: February 2021

Abstract
With the development and popularization of artificial intelligence and machine vision, human posture recognition has become one of the most important research directions in the field of machine vision, but its application research in dance movement recognition is in its infancy, especially in the field of Chinese classical dance. In view of this situation, the application of artificial intelligence in basic hand position recognition of Chinese classical dance. Including analysis of basic hand position of Chinese classical dance, comparison of human posture recognition models, recognition and comparison of basic hand position of Chinese classical dance on NVIDIA embedded devices using TensorFlow deep learning framework, and combination of traditional culture and artificial intelligence technology.
ACGANs-CNN: A Novel Intrusion Detection Method
Authors: Zhou Qi , Tan Minsheng , Xi Hewen 
Published: February 2021

Abstract
In this paper, an intrusion detection model (ACGANs-CNN) method based on GAN and CNN fusion is proposed for the reasons that unknown attack sample data cannot be provided in training samples, the number of training samples is limited, and known attack sample types account for less such small sample data. The model converts network traffic data into grayscale images, generates the same proportion of attack samples by generating the counter network, ensures the uniform distribution of attack samples in the training set, and introduces the gradient penalty function to improve the stability of the training model. Secondly, CNN is used to better extract sample features. In order to prevent overfitting, the nonlinear activation function Relu and Dropout method are introduced. At the same time, the convergence speed of the model is accelerated, and the detection efficiency of the model is improved. Attention is introduced to highlight the key features and to classify samples based on these key features. In this paper, the KDDCUP99 data set is used for model evaluation. Experimental results show that this algorithm (ACGANs-CNN) has stronger model training stability, higher quality of generated fake samples, and better feature extraction effect in small sample data. Its detection rate and accuracy of attack types are significantly higher than that of traditional machine learning algorithms such as SVM, KNN, RF, and other CNN models.
Research on key Technology of Classroom Teaching Evaluation Based on Artificial Intelligence
Authors: Zhou Huatao , Lu Jie , Huang Yulan , Chen Yu 
Published: February 2021

AbstractThere are many defects in current classroom teaching evaluation, which is not conducive to the implementation of quality education. Therefore, the AI-based classroom teaching evaluation technology is proposed, using artificial intelligence theory, from the perspective of serving quality education, analyzing the problems in teaching evaluation, exploring effective classroom teaching evaluation, and ultimately promoting the development o f students. Starting from the theory of artificial intelligence, it discusses the necessity of constructing a multi-evaluation system for the teaching process, which improves students’ language application ability and humanistic quality, and applies artificial intelligence theory to construct teaching from the aspects of evaluation methods, evaluation methods, evaluation objects, and evaluation subjects. Evaluation System. Experimental results show that this technology can accurately evaluate teaching effects and improve teaching quality
Implementation of Artificial Intelligence Algorithm In Embedded System
Authors: Lu Jie , Zhou Huatao 
Published: February 2021

Abstract
The development of embedded technology directly affects the development of Internet of Things, and the development of artificial intelligence brings great convenience to people’s life. Based on this, the implementation method of artificial intelligence algorithm in embedded system is studied. Based on embedded system hardware configuration and the embedded system software running algorithm is optimized, simplifying the operation steps of the embedded system, improve the effect of embedded system, to strengthen the research of embedded technology, and increase investment in manpower and material resources in embedded system research, learning western advanced technology, and the embedded technology is applied in the Internet of things, to improve the effect of artificial intelligence algorithm in the application of the embedded system. The experimental results show that the artificial intelligence algorithm is applied to the embedded system to improve the system performance effectively.
Construction of Foreign Language Curriculum System in Engineering majors in the Age of Artificial Intelligence
Authors: Deng Chaoqun 
Published: February 2021

Abstract
China has a wide range of engineering students sources and high quality. but because of the low level of internationalization of education, it is unable to meet the needs of international talents in economic globalization. Artificial intelligence has stepped into the education industry, bringing opportunities for foreign language teaching to cultivate engineering talents with an international perspective. In the era of artificial intelligence, the construction of foreign language curriculum system in engineering majors should set higher-level curriculum teaching objectives, develop innovative teaching system and improve the teaching assessment of high-challenge courses guided by OBE education concept in order to improve the quality of personnel training.
Ship Classification by the Fusion of Panchromatic Image and Multi-spectral Image Based on Pseudo Siamese LightweightNetwork
Authors: Li Mengyang , Sun Weiwei , Du Xuan , Zhang Xiaohan , Yao Libo 
Published: February 2021

Abstract
The current rapid development of the remote sensing satellite industry provides a large amount of image data for ship classification tasks. Aiming at the problem of insufficient feature extraction of single source image, this paper designs a lightweight ship classification model based on the fusion of panchromatic image and multispectral image of pseudo Siamese network to extract image features more fully. First, establish a multi-source remote sensing image ship target classification dataset MPFS (MS and PAN Ship image Fusion Classification Dataset); secondly, send panchromatic images and multispectral images to the network through different convolutional layers, thendesign a multi-level feature extraction network for panchromatic images and an adaptive feature extraction network for spectral imagesrespectively; finally, concatenate the features along the channel dimension and send them to the classification network.
Multi-Turn Response Selection in Retrieval Based Chatbots with Hierarchical Residual Matching Network
Authors: Zhang Zhuo , Zheng Danyang , Gong Ping 
Published: February 2021

AbstractResponse selection in retrieval-based chatbot aims to find the most relevant response in a candidate repository given the conversation context. A key technique to this task lies in how to measure the matching degree between conversation context and response at rich semantic information. In this paper, we propose a hierarchical residual matching network (HRMN) to fully extract and make use of the rich semantic information in the conversation history and response for themulti-turn response selection task. We empirically verify HRMN on two benchmark data sets and compare against advanced approaches. Evaluation results demonstrate that HRMN outperforms strong baselines and has a distinct improvement in response selection.
Translation between High- and Low-frequency SAR Images using Cycle-Consistent Conditional Adversarial Network
Authors: He Daquan , Jin Tian , Song Yongkun , Wu Chen 
Published: February 2021

Abstract
The bi-frequency (high- and low) synthetic aperture radar (SAR) images cannot be directly compared due to their distinct statistical properties. To diminish their statistical difference, we manage to translate the bi-frequency SAR images into one another. Therefore, we propose a cycle-consistent conditional adversarial network to achieve the goal. The cycle-consistency criteria in the Cycle GAN and the conditional generation adversarial networks in the Pix2Pix are integrated to construct the cycle-consistent conditional adversarial network. Experiments on Ku-band and P-band SAR images validate that our method outperforms Cycle GAN and Pix2Pix.
Goal-Directed Navigation with A Hybrid Planning Strategy Through Learning from Vision
Authors: Zhou Xiaomao , Du Bing , Huang Tao 
Published: February 2021

Abstract
The existing hippocampal modeling approaches rarely span the wide functionality range from processing raw sensory signals to planning and action. This paper presents a goal-directed navigation system consisting of two planning strategies. The first one is a biologically inspired neural planning and navigation model that is related to learned representations of place and HD cells. It is responsible for generating spatial trajectories leading to the neighboring area of the target. The place and HD cells are trained unsupervisedly from visual images using a modified slow feature analysis (SFA) algorithm. To interpret their functional role in navigation, a planning network is trained to predict the neural activities of place and HD cell representations given selected action signals. Recursive prediction and optimization of the action signals generate goal-directed activation sequences, in which the continuous states and action spaces are represented by the population of place-, HD- and motor neuron activities. Furthermore, a second planning strategy relying on visual recognition is proposed and performs target-driven reaching on a local scale for finer accuracy. Experimental results show the effectiveness of the proposed system.
Knowledge Graph Completion Based on Graph Representation and Probability Model
Authors: Liu Luwei , Zhu Cui , Zhu Wenjun 
Published: February 2021

Abstract
Knowledge Graph provides an effective scheme for unstructured knowledge on the Internet. but, to a large extent, the lack of information in the knowledge graph restricts the application of the knowledge graph. Therefore, knowledge graph completion has been an attractive research area. In recent years, many researchers apply graph convolutional neural networks to knowledge graph embedding so as to solve this problem, including R-GCN, SACN et al. The current state-of-the-art SACN uses graph convolutional neural as an encoder to make more accurate embeddings of graph nodes, and uses a convolutional network as a decoder, leading a good performance of link prediction task. In this work, we produce a novel graph representation model based on SACN---property graph convolutional network called PGCN. PGCN treats the knowledge graph as a property graph, regarding the initial embedding vector of entities and relations as the property of nodes and edges. What makes the model different is that it introduces the node clustering before convolving the nodes, so that the graph takes into account the importance of neighbors when aggregating the neighbors of nodes. We adopt the probability model Conv-TransE proposed by SACN for the modeling of relation of graph, Conv-TransE takes advantages of ConvE and use probability method which greatly improves the efficiency of the experiment and avoids the construction of the corrupted triplet. We conduct experiments on the standard datasets WN18RR and FB15k-237, and demonstrate that our new model achieves better performance.
A Method For Selecting Command Nodes Oriented To Succession Command Scenarios
Authors: Li Yawen , Dong Baoliang 
Published: February 2021

Abstract
Succession command is a command method to change command in the face of uncertain factors or irresistible damage. Alternative command nodes need to complete command transfer to achieve uninterrupted command. Facing the alternative scenarios of multiple command nodes, this article proposes a method for selecting the replacement command nodes, researches the command and control capability requirements, analyzes the command capability of the command organization, extracts the key performance indicators of the command and control system to construct an evaluation index system, and uses the analytic hierarchy process to determine the index rights. Based on the value, a hybrid TOPSIS method and grey relational analysis evaluation model is proposed to evaluate and sort the candidate command nodes. Experiments are carried out through the simulation data of the command system. The experimental results prove that the method can effectively select the replacement command nodes and provide preparation for the subsequent replacement command.
Application of Meta Learning in Face Recognition
Authors: Peng Sisi , Zheng Kaining 
Published: February 2021

Abstract
Now is the information age, artificial intelligence rises under the background of this era, but in the field of artificial intelligence, deep learning stands out in machine learning with its excellent learning ability and is rapidly applied to face recognition engineering. However, the success of mainstream deep learning often depends on a lot of training data and training time. This paper proposes to use meta-learning technology to realize face recognition. metalearning is to use the prior knowledge and experience to guide the learning of new tasks and has the ability to learn so that it can avoid the phenomenon of overfitting in the case of only a small number of samples. We regard the data set as a task, that is, meta-train task and meta-test task, using the MAML algorithm [1] fine-tuning gradient model based on prior knowledge to obtain optimization parameters to realize face recognition. Experiments show that deep learning neural networks using meta-learning technology can achieve higher accuracy and rate than ordinary face recognition neural networks. In the experiment, the recognition rate of the face can reach more than 92.6%. It has more intelligence and has made a contribution to the development of face recognition technology.
Research on The Innovation of Higher Vocational English Teaching Mode based on OBE Concept under The Background of Big Data
Authors: Li Hui , Jiao Li 
Published: February 2021

Abstract
This essay makes a series of explorations on Vocational school’s English teaching mode on the basis of OBE concept and on the age of big data, analyzes its research significance, research contents and main innovations, Therefore, it puts forward the innovation path of Higher Vocational English teaching mode based on OBE concept. The final conclusion is that the h igher vocational English teaching mode based on the OBE concept under the background of big data can promote college English teaching, and can promote vocational schools to be employment-oriented by using big data
Neural Image Completion Based on Label Differentiation
Authors: Zhu Xianyi , Ye Zixiao , Tan Guanghua , Duan Sheng , Liu Yaohui , Liu Dong 
Published: February 2021

Abstract
Deep learning based image completion methods are generally based on three technologies, namely the auto encoder based method, the generation of adversarial networks based method and the recurrent network based method. However, many methods have very single results. In order to obtain the diversity of the completion results, this paper proposes a neural image completion method based on label differentiation, called LD-PICNet (Label Differentiation PICNet). This method can not only generate a complete image with clear and good semantic information, but also actively edit the tags on the generated results to maximize the diversity of the output. Specifically, this paper introduces an auxiliary classifier, which uses a single label of the image ground truth to reconstruct the image, and uses the label to actively increase the difference of the latent vectors during image completion to achieve variability of output. In addition, this paper also introduces a depth-weighted loss function based on information entropy. Different data sets are used to conduct experiments. The model’s ability to complete different types of targets is tested. The results show that this method has the ability to generate diversified results, and it has higher clarity than the other methods.
Detection of sleep apnea syndrome by CNN based on ECG
Authors: Bai Yunxiang , Zhang Luqiao , Wan Dechao , Xie Yu , Deng Hanghang 
Published: February 2021

Abstract
Sleep apnea syndrome is a sleep disease that may lead to sudden death. Long term apnea syndrome can cause chronic cerebral hypoxia, hypertension, cardiovascular and cerebrovascular complications. At present, PSG is the most reliable method for diagnosis. But the diagnosis of PSG is complex and expensive. Electrocardiograph(ECG) and portable medical equipment have been widely used nowadays, which makes the acquisition of ECG signal more and more popular and convenient. In this paper, a convolution neural network based on ECG signal is proposed to predict apnea syndrome, the accuracy and sensitivity of this CNN model for apnea syndrome classification are 94% and 88% respectively. The results show that this method has the advantages of low cost and low complexity.