bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on breach prediction for big data through hybrid ensemble learning and logistic regression
Authors: Zhang Xiaohan , Chen Xu 
Published: August 2021

Abstract
The fast-growing online lending sector faces the risk of borrowers defaulting. Network lending data are often unbalanced. On the basis of EasyEnsemble algorithm, we proposed a hybrid ensemble algorithm, which combines the advantages of Bagging and Boosting. By comparing the performance of different learning models, the experimental results show that the hybrid ensemble algorithm has good classification accuracy and generalization ability. Further, we find that social capital has a negative effect on borrower default rate. This paper provides a relatively reliable method for the credit risk assessment of online borrowers, and also provides a new idea for risk monitoring in the field of online lending.
Application of XML and TCP communication on Dynamic customization of the smart home system
Authors: Li Zhihao , Sun Jie , Danzengouzhu  , Lan Hai , Lu Wenbo 
Published: August 2021

Abstract
Aiming at the problem of personalized customization of a smart home system, a scheme of personalized customization of the smart home system based on parsing XML authorization files is proposed. First, develop an XML authorization file generator, and use it to generate personalized XML authorization files; secondly, use the developed app to parse the XML authorization file to realize the legal authorization of the app and the dynamic generation of the home control interface; finally, click buttons on the control interface to trigger the TCP communication between the app and ESP8266, and then with the help of esp8266 to realize the switch of electrical appliances. Experimental results show that dynamic customization of the smart home system based on XML can implement dynamic customization of control interfaces for various home layouts, and can effectively control the home furnishings.
Group Intelligence Perception Applied in the Electromechanical Equipment Fault Intelligent Diagnosis Support System
Authors: Zhou GenRu 
Published: August 2021

Abstract
The paper designs a fault diagnosis and monitoring system for electromechanical equipment based on group intelligence perception technology. The system uses the Internet of Things technology to establish a fault feature selection and fault diagnosis model for electromechanical equipment. At the same time, the system uses micro-electromechanical system sensors to realize real-time monitoring of electromechanical equipment, and realizes the collection and upload of status data and fault data of base electromechanical equipment. The design of this system allows all parties involved in electromechanical equipment such as users, manufacturers, maintainers, and industry experts to break through the constraints of time and space. All relevant parties can communicate in real time on the problems of the equipment, so that the electromechanical equipment can be more efficiently automated and intelligently maintained.
Single Image Dehazing Method Based on Semi-Training Color Stripping
Authors: Wang Ye , Ji Dahai 
Published: August 2021

Abstract
In this paper, a semi training color stripping dehaze-net (STCSDN) is proposed which is a new method of dehazing with strong adaptability. It mainly depends on two important properties of convolution neural network in the process of dehazing. One is that the learning speed of convolutional neural network for contour and shadow information is faster than that for color information. The other is that network is not sensitive to haze concentration. Based on property one, STCSDN extracts the haze free gray image from the hazy color image by using the semi trained generator as the sketch extraction module through the CycleGAN. The gray image only contains the image contour and shadow information, and discards the original color information interfered by haze information. This method has strong adaptability, visibility and authenticity, and can be applied to any scene. Based on property two, STCSDN can process hazy images with different concentrations and get better results. Through the simulation experiments on different types of dehazing data sets, it is proved that the STCSDN proposed in this paper can remove the influence of haze, restore the image details and enhance the visual effect.
Design of On-line Simulation System for Signal Control of Urban Intersections Based on Visual Sensing Technology
Authors: Wang Juanjuan , Wang Yanan , Zhou Hongfang 
Published: August 2021

Abstract
Intersection area is the area with the most serious conflicts and accidents in the whole urban traffic system. Therefore, it is of great significance to combine modern computer technology, sensing technology and communication technology to intelligently control intersections, improve the efficiency of traffic signal control and reduce the delay time in conflict areas. Starting with video detection and using deep learning in the detection module, a video detection mechanism based on restricted Boltzmann machine network is proposed. Based on C # programming language, the intersection traffic signal simulation module is designed through COM interface provided by traffic simulation software VISSIM, which realizes road network import, parameter setting, signal timing change, simulation operation and simulation result output. Numerical experiments show that fuzzy control method with self-correcting factor is superior to timing control method and classical fuzzy control method in average delay time of vehicles.
Solving TSP based on an Improved Ant Colony Optimization Algorithm
Authors: Zhang Hao , Gao Yunfeng 
Published: August 2021

Abstract
Traveling Salesman Problem (TSP) is a typical Problem in combinatorial optimization field in modern times. Most of the problems in reality can be transformed into TSP problems for solving. Such as postal problems, communication network design, etc. Ant colony algorithm, as a heuristic algorithm, has been successfully applied to solving TSP problems. Based on the improved ant colony algorithm, this paper solves the travel agent problem, evaluates the population according to the membership degree, and updates the pheromone in turn, so as to achieve a good balance in solving speed and quality.
Design scheme of ship risk prediction model from the perspective of artificial intelligence
Authors: Ruan Qunsheng , Luo Fang , Li Zhiliang , Shi Manying , Zhang Zhiling 
Published: August 2021

Abstract
Vessels sailing at sea are full of risks, accidents occur frequently, and it is easy to cause heavy casualties and property losses. To this end, this article focuses on how to build a high-precision and rapid ship risk prediction model, and proposes a design plan for a ship risk prediction model based on big data and artificial intelligence. The plan includes a basic data layer, a technical business layer, and an application layer, with a focus on The technical research scheme of the graph representation model construction of the ship risk big data, the dynamic graph matching algorithm and the ship risk prediction is elaborated in detail, and finally the experimental design method of the scheme is given. The design scheme proposed in this paper can provide reference and inspiration for the research and development of marine risk system.
Research on Fatigue Detection Method of Equipment Operators Based on Multi-Source Physiological Signals
Authors: Guo Wen , Di Changan , Long Li 
Published: August 2021

Abstract
In order to effectively detect the fatigue status of equipment operators, this paper proposes a fatigue detection model that integrates multi-source physiological signals to evaluate the fatigue level of equipment operators in order to improve the efficiency of military operations and training. This method selects various physiological signals such as human heart rate, heart rate variability, body temperature, respiratory rate, blood oxygen concentration, etc. according to the working environment and working characteristics of the equipment operator, and establishes the relationship between the characteristic values of various physiological parameters and fatigue levels, And then use the optimized support vector machine to perform fusion judgment on the feature values of multi-source physiological parameters, and obtain the fatigue state of the equipment operator. Experimental results show that this method can make a more accurate judgment on the fatigue state of equipment operators, and is of great significance for ensuring the efficiency of equipment operators’ work and training.
A fusion algorithm model based on KNN-SVM to classify and recognize spam
Authors: Ge Zengle 
Published: August 2021

Abstract
Spam usually has the characteristics of large data dimension and large numbers of samples. At present, the common classification learning algorithms have their own advantages and disadvantages. Aiming at the classification tendency of K nearest neighbor algorithm (KNN) in the face of unbalanced sample data and the long time consuming of support vector machine (SVM) training model, a fusion algorithm of KNN and SVM is proposed. Firstly, the content of spam is extracted, and then the text preprocessing is transformed into sparse matrix. The k nearest neighbor samples of the test sample set are screened out by KNN algorithm, and then the SVM algorithm is used to train these screened samples, establish the separation hyperplane, identify the mail, and judge whether it is spam or not. The results show that the KNN-SVM fusion algorithm can effectively reduce the impact of sample imbalance while maintaining the high classification accuracy of SVM algorithm, and its classification efficiency is slightly lower than that of KNN, algorithm but much higher than that of SVM algorithm.
Summary of Metal Fracture Image Recognition Method
Authors: Lu Yufei , Wang Lin , Pan Dongyu , Chen Xiaoxia 
Published: August 2021

Abstract
Fracture is the metal component in the test or use of the fracture surface after the formation of a matching section. In fracture analysis, the macroscopic and microscopic morphology of fracture provides the most direct information for failure analysis and the most direct evidence for fracture analysis. How to effectively process a series of feature information contained in fracture image and make reasonable use of these feature information is of great significance in engineering practice. At present, metal fatigue failure detection in industrial production mainly relies on human eyes, experience and simple auxiliary tools. However, it is difficult to guarantee the accuracy of detection because of individual differences. To solve this problem, this paper lists three metal fracture image recognition methods, which are Grouplet-RVM recognition method, the empirical Ridgelet-2DPCA method and the empirical Ridgelet-KPCA method. All the three methods aim to extract higher recognition effect so as to compare and optimize the recognition of metal fracture images. The advantages and disadvantages of these methods are discussed in detail in this article for ease of use.
A reversible information hiding algorithm in AMBTC domain based on human vision system
Authors: Zhang Tao , Ding Jiantao , Tian Zhewen 
Published: August 2021

Abstract
Compression is a common processing method of digital image, and the compression of carrier image usually affects the performance of hidden information. Therefore, it is necessary to research on the preprocessing method to improve some performance of hidden information in the compressed domain. Aiming at the problem of tampering attack on compressed domain image and the security of secret information preprocessing in the previous information hiding algorithms, we propose a reversible information hiding algorithm in AMBTC domain based on human vision system. Firstly, RGB color image is converted to YCbCr image, AMBTC coding is used to generate high and low average value sequence in brightness component. Secondly, the secret information is compressed by run length coding and scrambled. Thirdly, the histogram shift technique is used to embed the information into the mean sequence and generate the reserved information at the same time. Fourth, the color component is divided into normal block and absolute smooth fast, the specific position of the bit plane of the normal block is deleted, and the reserved information is embedded. The experimental results show that, compared with other algorithms, the proposed algorithm has better invisibility and robustness, and after a certain attack, the extracted secret information still has high recognizability.
Greenhouse Intelligent Monitoring System Based on MCU
Authors: Zhu Yingying , Fang Yueyun 
Published: August 2021

Abstract
Modern greenhouse cultivation of crops yield and quality is affected by temperature and humidity is very big, this paper designed a greenhouse intelligent monitoring system based on single chip microcomputer, STC89C52 as control core, using composite was carried out on the temperature and humidity sensor DHT11 real-time monitor and record, use buzzer and LED lights to alert users to temperature and humidity changes and respond in a timely manner.
Research on the method of underwater image enhancement
Authors: Li Tiansong , Yang Jian , Zhou Haiyan , Lian Tao 
Published: August 2021

Abstract
A multi-angle image enhancement method based on underwater imaging was proposed to solve the problems of color bias, geometric distortion and low contrast of the observed object in the complex and changeable underwater environment. Firstly, the gray world algorithm is used to preprocess the underwater image. Then an image optimization denoising algorithm based on K-SVD dictionary learning is used to keep the structure and texture information of the original image. Finally, combining with the underwater optical imaging model, the blue-green channel enhancement algorithm is used to realize the underwater imaging defog processing, which makes the restored image closer to the reality. The results show that this method can solve the problems of noise interference, fuzzy underwater image and low contrast.
RETRACTED: Research on Algorithms of University Course Multimedia Assistant System Based on Scientific Human-Computer Interaction
Authors: Luo Yang 
Published: August 2021

Abstract
With the rapid advancement of artificial intelligence theory, this paper adopts the human-computer interaction method and derives the mathematical XXX. In combination with the optimization of the university course content, the performance is improved through the use of multimedia auxiliary systems and the use of B/S network model experience Formula, it consists of a client layer, a Web service layer and a database service layer. The physical architecture of the system is deployed. Finally, the statistical comparative analysis of mathematical science proves that the multimedia auxiliary algorithm has significant effects, and the artificial intelligence technology plays an active role in the multimedia auxiliary system.
Predictive Classification Model Based on Vespa Comprehensive Detection System
Authors: Gong Ci , Deng Gefeng , Shan Haicheng , Deng Changxin 
Published: August 2021

Abstract
Aiming at the problem that it is difficult to distinguish the authenticity of a large number of eyewitness reports when Asian giant hornet invaded, a multi-scale comprehensive detection model of Asian giant hornet was designed. The model uses cellular automata to process geographical information, YOLOv3 and improved Resnet101 to process image information, TF-IDF and Linear SVC to process textual information, and fuses the three kinds of information with a certain weight to obtain the final detection probability of Asian giant hornet. Compared with a single detection algorithm, this model makes use of multi-scale and multi-class information, and has higher classification accuracy and better robustness.
Object detection based on self feature distillation
Authors: Wu Zhuguanyu , Hu Zixiao 
Published: August 2021

Abstract
For object detection, traditional feature distillation methods need to select features from the teacher model to supervise student model training. But what kind of teacher model to choose and what kind of feature transformation to do are two significant problems. In this work, we put forward a self-supervised target detection approach, a simple and effective teacher-free feature distillation method. Specifically, we utilize more semantic features in-depth to distill shallow features, and in the channel dimension, we utilize salient features to monitor the remaining features. We use L2 loss to achieve feature minimization, with complex feature transformation and loss definition, because there is a smaller semantic gap between features from the same model and features from the same model. We prove theoretically that such self feature distillation is equivalent to adding feature perturbation and self feature of different subnetworks. For the first time, Self Feature Distillation (SFD) extends feature distillation from teacher model avatar into a general feature regularization method. Our experiments show that SFD is superior to the current feature regularization method. At the same time, self achieves 1.5 times of the training acceleration of SOTA’s teacher-based distillation method because it does not need additional training parameters as the teacher model.
ACDNet with ASPP for Camouflaged Object Detection
Authors: Zhu Qihui 
Published: August 2021

Abstract
This paper proposes a camouflage identification network ACDNet, which can perform end-to-end search and identification simply and efficiently. Use ASPP (Atrous Spatial Pyramid Pooling) to enhance the image features of camouflaged object, and then connect the Parallel Decoder (PD) module to get the global map, and then use the Group-Reversal Block (GRB) module to connect sequentially, refine the details, and finally perform deep supervision learning got result. Compared with the current 13 methods, ACDNet has a significant performance improvement in accuracy and similarity, and it is one of the potential solutions for camouflage identification.
Research on the Theory and Application of Deep Interactive Learning
Authors: Wang Ziyuan , Guo Fan 
Published: August 2021

Abstract
Knowledge distillation (KD), in which a small network (students) is trained to mimic a larger one(teachers), with high precision, has been widely used in various fields. However, the interaction between teachers and students is still weak. It is found in this study that most existing methods, such as Deep Mutual Learning (DML), mainly construct loss function through soft weight indexes. Few researchers pay attention to the sharing of hard and heavy ones. As an improvement of DML, a new online learning distillation method, namely, Deep Interactive Learning (hereinafter DIL), was proposed in this research, which has deeper interaction than DML. We not only output the features of layers, but also disclose the features of hidden layers. We transfer the features to other models to obtain the corresponding softer distribution or features for distillation. Extensive experiments on various data sets show that the accuracy of our method is improved by almost 3% in CIFAR and 2% in ImageNet, which proves the validity of our method.
Brand Loyalty Measurement Model Based on Machine Learning Clustering Algorithm
Authors: Li Yueqiu , You Chunming 
Published: August 2021

Abstract
In the “new economy” represented by the Internet economy background, online consumption as the representative of the new forms of consumption is gradually changing people’s consumption idea and way, online brand loyalty has the extremely important status in the field of online consumption, to stimulate consumption and achieve accurate enterprise marketing, risk control and decision support, improve efficiency and product design business model, business forms, and even change of business thinking, improve enterprise competitiveness is of great significance in the field of online. It is urgent and necessary to apply scientific and effective machine learning method to systematically analyse and study online brand loyalty. In the big data environment, facing the massive data information provided by online consumption, traditional technology methods have gradually failed to meet the competitive needs of enterprises to create and maintain brand loyalty. The traditional random sampling method is difficult to locate the consumer groups with high brand loyalty. At the same time, the traditional data processing technology can not deal with the online consumption behavior with the characteristics of massive, mixed and unstructured data. Traditional approaches have limitations when it comes to the sheer volume of online data and how to use it in real time to target the needs of a brand’s consumer group. The purpose of this study is to build an online consumption of the era of big data model of artificial intelligence, machine learning model, by machine learning method, branded goods purchase behavior of consumers online clustering, building an online brand loyalty measurement model, achieve similar loyalty user clustering, at the same time realize the online measurement of brand loyalty. Among them, focus on machine learning path, machine learning algorithm, model construction methods, and model verification and optimization methods.
Analysis of executive transaction characteristics based on machine learning cross-validation
Authors: Li Yueqiu , You Chunming 
Published: August 2021

Abstract
Executive trading is a kind of investment strategy that analyses and models the financial market with mathematical methods. Machine learning requires computer programs to improve their performance at specific tasks by learning on data sets. Both require the extraction of information from data, so with the success of machine learning in recent years, there has been a strong interest in the combination of executive trading and machine learning methods in both industry and academia. In this paper, based on machine learning is complete, a more complete model of data processing - build - generated forecast - trading strategy - back-test analysis “research framework, and discusses the structures of branching and merging model structure set up, based on the value relevance of the output value and supervision model selection criteria, independently of the back and trading strategy model assessment method and a series of important issues. This paper focuses on the innovative exploration of the application of the loss function, and puts forward several loss functions based on the portfolio point of view, among which the negative cosine loss function shows significant advantages and strong practicability in stability and gain. Corresponding empirical research also reveals the application value and limitation of other loss functions.
Research on Feature Extraction Technology of Japanese Corpus Resources Based on Rule Matching
Authors: Zhang Lele 
Published: August 2021

Abstract
Existing methods of relation extraction can be divided into pattern matching-based methods, dictionary-driven methods and machine learning-based methods, among which machine learning-based methods are the mainstream methods of relation extraction at present. Based on the analysis of Snort’s new features and existing rule matching methods, this paper effectively combines Snort algorithm with Japanese language features, and corrects keyword deviation caused by word frequency dependence by introducing multiple eigenvalues such as position factor and part-of-speech factor. Experimental results show that the introduction of part-of-speech factor and position factor is simple and efficient, and can effectively improve the extraction effect of Japanese keywords, which is suitable for short article keywords.