bluuebunny/crossref_metadata_2025_split huggingface dataset:
Classification and Evaluation for Microblog Popularity Prediction
Authors: Ji Gang , Zhu Yuxuan , Niu Yukai , Hu Kai 
Published: April 2021

Abstract
In recent years, with the rapid development of the Internet, especially the mobile Internet, social networks have entered the stage of vigorous development and become one of the main sources of information. User-generated contents (UGC) on social platforms can spread information along social networks at an astonishing speed. Existing literature has proposed many prediction methods for the popularity prediction on social networks. This paper presents a classification and establishes a unified evaluation framework of popularity prediction methods for microblogs. More specifically, we divide these mainstream prediction methods into four types: feature based methods, time series methods, collaborative filtering methods and deep learning methods and conduct experiments on the real-world weibo data using these methods to predict. Finally, according to four indicators, including accuracy, efficiency, robustness and bias, we evaluate and compare the methods. Based on the prediction and evaluation results, this paper summarizes and draws the following research conclusions:(1) The deep learning method has the characteristics of high accuracy, high robustness and low bias. The DeepFM method, one of the deep learning methods, performs better than the other three prediction methods when using temporal data as its input. (2) The feature based methods only using temporal features are basically consistent with those using all available features, indicating that the temporal feature has strong prediction power. Therefore, the ‘peeking’ strategy that monitors the early response of users in the initial period after the items are posted is effective. Additionally, the predictive power of temporary features can be further amplified in time series methods and deep learning methods. (3) Due to the sparse user-item interaction in social networks, the accuracy and efficiency of collaborative filtering methods are low, which makes it impossible to predict the popularity of items in social networks well.
Research on high reliability in redundant link aggregation mode
Authors: Yin Xiaogui , Gan Weimin 
Published: April 2021

Abstract
Now, with the rapid development of the Internet, the requirement of bandwidth and reliability of backbone link is higher. In traditional Ethernet networks, it needs to pay a high price to increase the network bandwidth, if you use high-speed equipment, so not only the cost is high, but also the network is not flexible enough. In order to ensure the stability and reliability of Ethernet network, this paper proposes a redundant link aggregation technique, without replacing the equipment, and bundling multiple physical interfaces into a logical interface to increase the bandwidth of the link, dynamic and effective backup links can also be used to improve the reliability of links between devices. Redundant link aggregation can not only improve network bandwidth resources, but also solve the problem of network congestion.
Study on face detection based on an improved Gaussian skin color model
Authors: Tan Lingli 
Published: April 2021

Abstract
An improved Gaussian skin color model is proposed for face detection applications. It overcomes the issue that the traditional models become ineffective in extracting the skin color region in the presence of diverse skin colors and illuminations. It is a two-dimensional gaussian mixture model based on H-SV (i.e., an improved HSV) and YCbCr color spaces. Experimental results from several face image databases show that the detection accuracy is improved by 35.8% and 6.3%, respectively, compared to a traditional gaussian skin model and Adaboost algorithm.
Adaptive algorithm for feature selection of speech emotion recognition based on genetic algorithm and SVM
Authors: Chen Hong , Zhang Bo 
Published: April 2021

Abstract
As a key carrier for people to obtain information in daily life, speech not only provides text signals, but also contains emotional signals. As an important factor of semantics, emotion signals are the focus of improving the intelligent level of human-computer interaction. Therefore, it is important to improving the accuracy of speech emotion recognition. This paper proposes to incorporate derivative feature parameters into the voice emotion recognition feature selection, and to improve the feature selection algorithm based on genetic algorithm and SVM. This article may provide technical support to promote the development of artificial intelligence.
TDA 3D Data Model and Feature Simplification Analysis
Authors: Li Ou 
Published: April 2021

Abstract
Topology data analysis (TDA) is a combination of topology and data science. The analysis process will not cause information loss. It has been widely used in various industries in recent years. However, how to display TDA data with simpler data structure is still under exploration. This paper designs and simulates a method of TDA point cloud data segmentation and classification data topology information simplification. Firstly, the point cloud data is segmented based on the region growing method. Secondly, the idea of “topological compression” is used to express the richer data information of each segmentation part with a simpler data structure; in the experimental model, the experiment is completed on the platform of MATLAB 2016 (b), and the simple and complex representation of segmented vehicle targets completely includes all the pixel positions and gray 3D data information of the vehicle.
Research on NSGA-II Flexible Job Shop Scheduling Based on Variable Length Chromosomes
Authors: Hao Yuanfeng , Ji Chunlei , Xiao Wei 
Published: April 2021

Abstract
As the core of enterprise production management, production scheduling plays a pivotal role in the optimal allocation of resources and scientific operation. A more optimized workpiece scheduling method can greatly reduce the staying time of workpieces in the workshop and improve production efficiency. Most of the existing scheduling algorithms are coded according to the length of the number of workpieces and machines, and the number of workpieces in each batch of the factory is often different. This scheduling method has certain limitations. How to perform different batches and varying numbers of workpieces? Production scheduling has become an urgent problem for modern enterprises. Based on this, this paper proposes a variable-length chromosome encoding method, which can perform production scheduling on different batches and different numbers of workpieces, greatly improving the scope of application of scheduling scenarios. At the same time, because of the shortcomings of low population diversity and slow operation speed in the non-dominated sorting genetic algorithm, an adaptive layering strategy is proposed. Simulation experiments show that the improved algorithm can better complete scheduling tasks and improve production efficiency.
The Research on Single Page Application Front-end development Based on Vue
Authors: Li Nian , Zhang Bo 
Published: April 2021

Abstract
‘Vuejs’ is a popular front-end framework, which uses MVVM (Model View View-Model) design pattern to support data-driven and component-based development. This paper discusses the basic working principle and development mode of Vue, and on this basis, uses SPA (Single Page Application) to design and implement a mobile mall front-end page based on Vue. This research fully demonstrates the advantages of front-end and back-end separation technology in Web development, and provides a powerful front-end support for full stack development.
Data assimilation model based on machine learning
Authors: Lang Jialin , Qiu Feng , Wu Pin 
Published: April 2021

Abstract
Data assimilation(DA) is a method mainly absorbs the observation data into the simulation model, integrates the errors of observation and simulation, and provides a more accurate state so as to reduce the forecast error. Data assimilation has been widely used in the fields of atmosphere and ocean. However, traditional data assimilation methods require a lot of computing resources and consume a long time. Machine learning is a data analysis method with strong learning ability and rapid prediction ability. Long Short-Term Memory network (LSTM) is a widely used machine learning model, which has good effect on time series prediction. In this paper, we use the historical data of data assimilation to train LSTM model and get the prediction model. The experimental results show that the LSTM model can learn the latent law from the historical data, the results of the model fit the real data well, and the calculation speed is greatly improved compared with the original data assimilation algorithm.
A Time-Based Online Customer Behaviors Analysis Model
Authors: Qin Zeqing , Wang Zesong 
Published: April 2021

Abstract
With the advent of the big data era, reviews are common when shopping online. To better understand the connection between reviews and the quality of the real product, we have to use mathematical language to simplify the problem. This relationship can help both the buyer to get what they want and the firms to make better products with attractive features. To help us understand how the indicators like star ratings can reflect the product’s popularity, we construct a series of models to illustrate them. We first use the NLP method to process text type data, such as product review information, and convert these text data into sentiment scores, with the help of the k-means++ algorithm, we cluster the data into 3 categories. These categories can help the company to track the performance of their products by simply calculating which category the product belongs to. Next, a Time-based Reputation Judgment Model combined with AHP and Markov Chain Model was established to describe the change of reputation over time. In this way, a company can easily predict the future trend of their merchandise sales.
Prediction of Corrosion Resistance of B10 Copper-Nickel Alloy Based on Optimal Convolutional Neural Network
Authors: Dong Yuehua , Hu Donghai , Wang Zhigang 
Published: April 2021

Abstract
Aiming at the defects of insufficient number of corrosion resistance prediction models and simple feature extraction of B10 copper-nickel alloy, a corrosion resistance prediction model of B10 copper-nickel alloy based on optimized convolutional neural network is proposed. The convolutional neural network architecture for grain boundary image characteristics is proposed to conclude a step-by-step convolution operation by analysing the traditional convolution operation process, and it proves theoretically that such a step-by-step convolution operation can reduce the consumption of additional parameters; learning pool proposed single-channel operation can reduce the loss of feature information; the multi-layer feature fusion learning strategy makes the expressive force of deep network extraction features diversified. The results of multiple experiments demonstrate that the improved algorithm introduced can improve the prediction accuracy of the model in image classification comparing with the traditional convolutional neural network model, and the improved convolutional neural network model can better achieve the prediction of corrosion resistance of B10 copper-nickel alloy.
Research on Logistics Distribution Path Planning Based on Fish Swarm Algorithm
Authors: Xiong Chuanyu , Xu Youhua 
Published: April 2021

Abstract
Logistics distribution related to the transportation cost and the competitiveness of logistics enterprises. How to effectively determine the shortest path of logistics distribution has become a key link for logistics enterprises to reduce costs, improve efficiency and enhance competitiveness. This paper proposes a method of using Fish Swarm Algorithm to optimize the logistics distribution path. Experiments show that the method is effective. The algorithm can quickly jump out of the local optimal solution and converge on the shortest path. The optimized path length is less than half of the initial length.
Design and Implementation of Occlusion Image Recognition Algorithm Based on Deep Convolution Generative Adversarial Network
Authors: Liu Zhijun , Cai Feng , Pan Xuefeng 
Published: April 2021

Abstract
In the image recognition process, the accuracy of image recognition is affected due to partial coverage or light problems. This paper proposes a method to solve the problem of target occlusion based on a deep convolution generation confrontation network. The method actively occludes the feature map after the feature is extracted to generate the confrontation sample, and generates a map and a mask from the input image. At the same time, a method is proposed. This new loss function applies the algorithm to handwriting recognition and trains the network through large-scale sample data sets. Experiments show that this method significantly improves the accuracy of image extraction.
Research and implementation of convolution k-means algorithm based on unsupervised learning
Authors: Suyu Huang 
Published: April 2021

Abstract
This paper proposes a framework combining the advantages of unsupervised learning, convolutional neural network and K-means clustering algorithm. When there are few label data available, convolution k-means algorithm is formed by combining k-means algorithm with convolution neural network. Through the learning of convolutional neural network and clustering analysis, as well as the continuous optimization and adjustment of the network, the convolutional k-means algorithm is used to train the deep convolutional network, and the hierarchical function of unsupervised learning technology is used to reduce the dependence on a large number of labeled data. Through the analysis of the experimental results, compared with the supervised learning convolution k-means algorithm, the unsupervised learning convolution k-means algorithm can better represent the data clustering with the increase of the number of filters, and can improve the accuracy of test classification. The unsupervised learning convolution k-means algorithm is better than other unsupervised learning filter methods.
Analysis on Development Tendency of Computer Vision and Graphics based on Bibliometrics
Authors: Ci Yutong , Jiao Fangyu , Tu Wenyan , Fang Ji 
Published: April 2021

Abstract
In this paper, the bibliography included in Web of Science is taken as the data sample. This paper analyzes articles and reviews published from 2010 to 2020 in the field of computer vision and graphics to reveal the major countries or regions. We use leiden-type community algorithm to identify research topics. Besides, we explore the research fronts in the field of computer vision and graphics based on highly cited papers. The results show that the research on computer vision and graphics in the United States started earlier. The United States has always been in the leading position. China has developed rapidly in recent years. The United Kingdom, South Korea and India are also prominent. Deep learning, face recognition, image retrieval and volume rendering are the hottest research topics in the field of computer vision and graphics. Convolutional neural network and deep learning are the most emerging research fronts in the field of computer vision and graphics.
Design and Implementation of Crop Automatic Diagnosis and Treatment System Based on Internet of Things
Authors: Pan Aimin , Wang Nan 
Published: April 2021

Abstract
This paper designs a smart agriculture Internet of Things system which combines crop disease detection with automatic diagnosis and treatment, and provides support for subsequent research on crop disease detection and control. The smart agricultural Internet of Things system uses TCS3200 colour sensor to identify leaf disease characteristics; colour sensor sends the data back; This system compares it with the recorded plant identification database, and carries out small-scale sprinkler irrigation diagnosis and treatment through valve irrigation system. The Internet of Things system consisting of STM32L431 single chip microcomputer, MG811 CO2sensor, BH1750 illumination sensor and DHT11 temperature and humidity sensor monitors environmental data, which is uploaded to the cloud by BC35 module through NB-IoT protocol and can be viewed by users on the display screen. Users can adjust the exhaust system, lighting system and humidification system in the system through the cloud or mobile phone to improve the environment and alleviate crop diseases. The experimental results show that the intelligent agricultural Internet of Things system designed in this paper has achieved the initial goal, and can roughly judge the disease types of plants by leaf colour, which has great commercial application value.
Research on the Technology Realization of Network Mass Information Processing Platform
Authors: Zhao Qiang , Liu Sha 
Published: April 2021

Abstract
In recent years, With the rapid spread and development of the Internet, especially the huge reserve of information and convenient search function, it attracts more and more of the community to the Internet users ranks. But these need supports of high-performance computer technology. Therefore, in order to enhance the capabilities of Internet vast amounts of information intelligent parallel processing, and promote the comprehensive development of the overall system of information storage, management, integration and intelligent processing, data mining, information retrieval and utilization functions, I proposed a massive information processing platform based on high-performance data mining. The one hand, it analyzes the key technologies to achieve the platform; the other hand, it gives a brief introduction to its hotspot applications. Therefore, it provides a reference and guidance with real-world applications for the technology of high performance and highly efficient network of mass information processing platform.
Clustering analysis method of power grid company based on K-means
Authors: Wang Wenting , Ma Qiang , Liu Yong , Yao Ning , Liu Jing , Wang Zhaoxuan , Li HongLin 
Published: April 2021

Abstract
With the development of network and the expansion of power grid business, the power grid traffic becomes more and more complex and huge. It is more and more difficult to identify the power grid business through the traffic, and the identification accuracy also needs to be improved. In order to solve this problem, this paper proposes a clustering scheme based on K-means to classify the power grid business through the grid traffic, using elkan K-means algorithm improves the efficiency of the algorithm and finds the most suitable K value for power grid business data.
Test Case Generation Method based on Generative Adversarial Network
Authors: Nie Jiaojiao , Fan Xianglin , Wu Yushuang 
Published: April 2021

Abstract
Traditional fuzzing tools generate low diversity of test cases and low vulnerability detection efficiency. This paper uses a test case generation model based on a generative confrontation network. The model uses LSTM as the generation network to generate data. The model uses fully connected network to construct a discriminant network for classification, and the test cases with the same protocol format are automatically generated after training. Finally the model uses the generated test cases to attack the protocol system to detect vulnerabilities. This method is compared with the traditional tools AFL, Peach and Sulley on the Modbus protocol, and the validity of the method is verified.
A method of terminology alignment in Historical Books based on the correspondence between Pivot language and position
Authors: Xie Xingchen , Wu Xiaoting , Wei Xiaopeng , Che Chao 
Published: April 2021

Abstract
In view of the most difficult problem of terminology translation in the translation of historical books, this paper extracts term translation pairs from bilingual parallel corpus as a reference to improve the quality and efficiency of the translation of historical books. However, the small size of the corpus of historical books leads to poor word alignment, which brings a large number of errors to term alignment. At the same time, the special grammar of ancient Chinese will also affect term alignment. In order to solve the above two problems, this paper proposes a method of term alignment of historical books based on pivot language and position correspondence. This method introduces modern Chinese as the pivot language, makes use of the position correspondence between modern Chinese and English terms in sentences, and uses the sliding window method to generate candidate term pairs, which avoids the influence of word alignment errors on term alignment. In addition, if we first align to modern Chinese, the ancient Chinese terminology will complete the omitted characters in modern Chinese and replace the false characters, so as to avoid the influence of the special usage in ancient Chinese on the term alignment. The experimental results in Historical Records show that the accuracy of this method is slightly better than that of the method based on word alignment, which verifies the effectiveness of this method without word alignment.
Infrared Small Target Detection Based on Multi-Scale Local Contrast Network
Authors: Liu Yuanteng , Wang Yuehuan 
Published: April 2021

Abstract
To mitigate the challenge of the lack of the intrinsic features of the small target which possesses only few pixels, we designed a multi-scale local contrast network which combines features extracted from different scales and layers for single-frame infrared small target detection in this paper. We extract the multi-scale contrasts features in the same layer by multi-scale local contrast module and fuse the cross-layer contrast features by local contrast fusion module. Moreover, we embed these modules at each stage as a depth-wise nonlinear layer into an end-to-end convolutional network to predict the small target. We conduct extensive experiments and comparison with other SOTA methods to verify the performance of our method. The results show that our method outperforms many of its competitors, and when taking computation cost into consideration, it will be more outstanding.
Classification Problem Using Imbalanced Ratio Based Random Forest Method
Authors: Wang Wan , Guo Junlin , Chan Wai Kin Victor 
Published: April 2021

Abstract
In recent decades, as machine learning is getting more and more popular in many fields, multiple classification methods have been proposed and applied to various applications for intelligent decision making. Most of the classification methods are just work on a shallow machine learning classifier. And many of these algorithms are already existed in the machine learning package. In this study, a new Imbalanced Ratio Based Random Forest(IR-RF) method was proposed to make classification and prediction. We focused on dealing with the imbalanced data. So we use several imbalanced datasets and put processed features into our IR-RF model. Three datasets we used are from the UCI machine learning database where RF and IR-RF are compared in the final results. The former RF method indicates the traditional random forest algorithm and the latter IR-RF method is our newly written one. Comparing to other classification model, IR-RF is an algorithm-level method, which is not limited by existing parameters. We can generate more new parameters within the algorithm and train our own classifier. Results show that proposed method has higher accuracy than traditional random forest method after our ratio compared and it can take better account of imbalanced condition. So proposed method not only performs well in high accuracy but also can be applied to especially imbalanced condition which is a new window for practice.