bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysis of student interaction with learning objects on blended learning course applying cooperative learning together method on Moodle learning management system
Authors: Nilakusmawati D P E , Suprapti N W S , Darmawan I D M B A , Raharja M A 
Published: January 2021

Abstract
The purpose of this study is to determine the results of implementation of blended learning on Marketing Research Techniques course using cooperative learning together method, and how the interaction between students and learning objects on blended learning course using Learning Management System (LMS) of Moodle. Class activities using cooperative learning together method include group assignments in accordance with the topic set by lecturer, presentation of group work results, inter-group discussions, and continued with improvement of group assignments based on the results of discussion. Student activities in accessing learning objects in the form of URL viewed from student log activities on LMS, showed only 11.42% of students were enrolled from total of 35 students with an average access of 1.47 views. Student interactions with objects in the form of text, accessed by 29.10% of students with an average access of 2.33 views. Presentation slides is accessed by 12.85% of students and images/maps/charts is accessed by 6.66% of students. Unlike only the learning objects in the form of assignments and quizzes, accessed by almost all students with a high average number of accesses. The learning object in the form of quizzes is accessed by 99.44% and assignments accessed by 95.37% of students.
Passive control for a class of T-S fuzzy systems with memory controller
Authors: Wang Yanhua , Han Ying , He Xiqin 
Published: January 2021

Abstract
Taking the advantage of fuzzy systems, a class of T-S fuzzy models used to describe nonlinear systems. Firstly, by designing a memory state feedback controller, we draw the conclude of robust stability and the conditions of passive control. Secondly, using Lyapunov stability and linear matrix inequality technology in MATLAB, we can utilize the gain matrix about the controller of the memory state feedback. Finally, a simulation example is given to illustrate the effectiveness of the proposed method.
A software defect prediction method based on sampling and integration
Authors: Shen Peng , Ding Xiaoming , Mu Xuyun , Xu Jianing 
Published: January 2021

Abstract
This paper mainly analyzes the characteristics of software defect prediction from the perspective of machine learning, and proposes a semi-supervised software defect prediction method based on sampling and integration for the problem of class imbalance in software defect data and the incomplete classification of data sets. SISDP). SISDP firstly constructs a robust KNN marking model by taking a balanced sample of samples to mark a batch of unmarked data, and then iteratively adds the newly marked data to the original data set for the next marking model. , iterate until the data is marked. For the marked data set, the hybrid sampling algorithm is used to obtain the training set, and the integrated classification model composed of the multi-classification algorithm is classified and trained. SISDP not only reduces the interference of a few classes on the marking process, but also improves the generalization ability of the defect prediction model.
The Effect and Research of Environmental Analysis Software in Environmental Early Warning under the Background of Big Data
Authors: Liu Yun , Chen Xin , Jia Liming , Zhao Yue , Zhao Ran , Dong Yingli , Du Yingqiu , Wang Shuai , Jiang Tao , Bai Li 
Published: January 2021

Abstract
Enrichment of Pesticide Organic pollutants in Surface Water by solid Phase extraction, Analysis and characterization by Gas Chromatography-Mass Spectrometry and Total screening Software, semi-quantitative, At the same time, the semi-quantitative results were verified by SIM mode of gas chromatography-mass spectrometry. The results show that the recovery rate of acids, alcohols and ketones in water meets the requirements of monitoring index. The verification results show that, This screening method is based on the absence of external standard materials and targets. Can be used for early warning monitoring of most potential pesticide organic pollutants in water.
Application of Internet of Things Technology in Forest Fire Warning of Jizhou District
Authors: Han Junde 
Published: January 2021

Abstract
With the IoT technology as the entry point, this paper designs the general plan for the IoT-based forest fire warning & monitoring system of Jizhou District, through the analysis of the application of IoT technology in the forest fire prevention of Tianjin; builds the workflow of the IoT-based forest fire warning system; and put forward the suggestions and countermeasures for the development of forest fire warning system in Jizhou District, thus providing theoretical basis and realistic foundation for improving the efficiency of Tianjin forest disaster prevention and mitigation.
Digital protection and reflection on Tibetan ghee sculpture art of intangible cultural heritage in China
Authors: Lu Bin , Zheng Gang 
Published: January 2021

Abstract
China’s Tibetan ghee sculpture art is the first batch of works of art approved by the state council to be included in the national intangible cultural heritage list. This paper mainly studies the origin, digitalization and intelligent protection of the Tibetan butter sculpture art in China, and also explores and attempts to effectively protect the intangible cultural heritage with modern science and technology. We hope that more people will understand, inherit and protect the fine traditional culture of the Chinese nation, and that our cultural confidence will continue to advance to new levels.
A machine learning model for quantifying the effect of lifestyle interventions for patients with type 2 diabetes mellitus
Authors: Bi Suhuan , Ding Xiangqian , Yu Shusong , Guo Baoqi , Mu Liangliang , Wang Bin 
Published: January 2021

Abstract
Type 2 diabetes is the most common type of diabetes. The cornerstone of type 2 diabetes treatment is healthy lifestyle. This paper proposes a machine learning model for quantifying the effect of lifestyle interventions for patients. In the proposed incremental intervention model, the original physical indicators and the lifestyle interventions were taken as input vectors separately and transformed through different nonlinear functions. We evaluated our method with the dataset of 12,318 patients from a national funding project and compared with MLP and SVR. The experimental results (R

2
=0.85, RMSE= 0.51, MAE=0.35) indicated that the model outperformed those prediction models. Besides, the machine learning based method is cost-effective and time-saving. The proposed method provides new insights into prevention and treatment of chronic diseases.
Facilities Layout and Location Analysis based on Police Department Position in Dekalb, IL
Authors: Zhang Ouzheng , Ding Mengda 
Published: January 2021

Abstract
The officers of Dekalb Police Department were aware of the high crime rate and they’re considering whether set up a new office or relocate the existing department to shorten the distance, also, ensure to reach the crime scene in super speedy. To select a more reasonable location, an essential assumption is that the current office will no longer associated with. Besides the existed office, geo-policing is not a necessary factor neither. Therefore, the police officers will work from the new location that we inferred and answer the incoming calls there. First thing first is to model it as a single facility location problem, and then, calculate by the Minisum model with rectangular distances as well as the Minimax model with tchebychev distances.
Similarity study of clinical data
Authors: Yu Wenjun , Wang Kai , Hu Bin , Huang Yu 
Published: January 2021

Abstract
The classification of patients is very important for accurately determining the patient’s disease and subsequent drug use. More and more clinical data provide a clue for the classification of various diseases. We use related software to generate simulation data based on ICD10 and propose a specific algorithm to classify patients, and find patients belonging to other categories in the classified patient group, which provides a theoretical basis for the improvement of various classification algorithms for diseases.
Research of Recommendation System Based on Deep Interest Network
Authors: Wang Shan-Shan , Pan Yuan-Yuan , Yang Xiao 
Published: January 2021

Abstract
With the development of e-commerce, it provides users with many choices. But how to quickly and accurately recommend goods to users is an important topic in this field. Matrix factorization recommendation model based on scoring data is widely used, but data sparsity affects the recommendation quality of the model. In the paper, a product recommendation algorithm based on deep interest network is proposed. First, the user’s purchased goods and the user’s search are embedded coded, and the sparse features are transformed into low-dimensional dense features.Then, the feature vectors of the purchased goods and the search text vectors are joined together to input the deep interest network as the feature to predict the user’s interest.Finally, the effectiveness of the model is validated by using record data. The validity of the model is verified by comparing with other models.
Research on Named Entity Recognition Technology for Chinese Titles
Authors: Zhang Kong , Qian Gang 
Published: January 2021

Abstract
Aiming at the problem that the feature extraction of Chinese title data is insufficient in the process of named entity recognition and the marking data is difficult to obtain, this paper presents a CNN-BiLSTM-CRF model. The model trains large-scale corpus to generate word vectors with semantic information and trains the character vectors in words through CNN to generate character vectors containing character features, the word vector and the character vectors are merged as the input of BiLSTM network. Through the BiLSTM network, the front and back texts features are fully extracted, and CRF is used to restrict the relevance of tags. In addition, this paper proposes an algorithm based on active learning. This algorithm uses the proposed model construction committee to select the samples with large amount of information to mark, which effectively reduces the amount of data annotation.
Improvement of Winding Gait for Snake Robot
Authors: Xiao Wenyu , Wei Wu , Gao Yong 
Published: January 2021

Abstract
In order to solve the problem that the image information is difficult to handle due to the swing of the snake robot’s head during its serpentine locomotion, this paper improves the control function of the general winding gait for the orthogonal joint-connected snake-like robot, and the useful image information is obtained by reducing the swing of the camera-mounted snake’s head based on the improved function. Combining the Sigmoid function with the general function to control the rotation of the yaw joints and applying the Gaussian function to the pitch joints to lift the snake head off the ground, this new control function presented in this paper not only enables the joints to rotate smoothly, but also decreases the swing range of the snake head. Additionally, it can also fasten the robot’s movement. Experiments based on MATLAB and GAZEBO platform demonstrate the superiority and effectiveness of the improved winding gait.
A Study on Different Functionalities and Performances among Different Activation Functions across Different ANNs for Image Classification
Authors: Zhang Xia , Chang Di , Qi Weimin , Zhan Zhiming 
Published: January 2021

Abstract
Activation functions are very essential in artificial neural networks (ANNs), since they are non-linear functions and they have been proved necessary to implement deep learning. Recently, ReLU is one of the most well-known activation functions; however, several competitors – e.g. LReLU and SWISH – have nowadays been proposed or ‘discovered’. In this paper, the authors perform a detailed comparison of five activation functions over two image classification datasets. We found the overall performances of accuracy rates ranked from the best GELU, RELU, SWISH, SELU, down to Sigmoid. Such the observation would result in the improvement of future image classification via designing new state-of-the-art activation functions.
A Study on Machine-Learning-Based Prediction for Bitcoin’s Price via Using LSTM and SVR
Authors: Zhang Xia , Qi Weimin , Zhan Zhiming 
Published: January 2021

Abstract
Bitcoin plays an increasingly crucial role currently, whose of prices are challenged to be predicted when compared to the index of any traditional stock markets. With the accent of machine learning and deep learning, it is urgent to find out whether a machine learning model can or cannot see the way that the changes of Bitcoin’s price or volume interact over some period of time leads to a price increase or decrease within next few days. Testing results indicated that, the proposed two machine learning models built by LSTM and SVR were capable to capture longer-range dependencies and performed accurately by producing state-of-the-art results.
BeiDou Navigation Satellite System III Application in The Power Industry
Authors: Wang Zhiqiang , Yang Hongzhen , He Chen , Shao Weiping 
Published: January 2021

Abstract
As an important lifeline industry of the national economy, electric power has great demand for navigation and positioning, short message communication and timing of the BeiDou system. With the BeiDou III put into operation, the BeiDou system will be more widely used and deepened in power. This paper first introduces the main application of BeiDou system in China’s power system, and then introduces the construction situation and various performance indicators of BeiDou III in detail. From the three aspects of precise positioning, short message communication and timing function, the performance index of BeiDou III and its specific application in power system are expounded. In addition, the future application and development of BeiDou system in energy industry are prospected.
Gas sensor fault diagnosis for imbalanced data based on generative adversarial networks
Authors: Sun Yongyi , Liu Shuxia , Yu Ying , Zhao Tingting , Zou Zhihui , Zhang Jinghua , Zhang Shuang , Zhang Hongquan 
Published: January 2021

Abstract
Gas sensors play a key role in gas detection. Gas sensors are prone to failure, so they are of great significance to classify gas sensor fault. Traditional gas sensor fault diagnosis methods do not consider the condition of the imbalance of fault data. In this paper, the generative adversarial networks (GAN) method is used to generate small sample sensor fault data, balance various types of sensor fault data, and then the random forest is used for fault classification. Finally, the sensor fault data is obtained by self-made experimental system to verify the proposed method. The experimental results show that the proposed method could effectively improve the accuracy of gas sensor fault diagnosis.
RETRACTED: Intelligent traffic strategy based on 5G auto Autonomous driving
Authors: Cheng Bihui 
Published: January 2021

Abstract
In this paper, a complete mathematical model is established for the traffic efficiency of autonomous vehicles under high traffic light. Under the background of the mature automatic driving technology, a model to calculate the traffic efficiency is established to calculate the time consumed by passing a certain vehicle. In the case of setting traffic lights, we first block off intersections that interfere with the operation of fire trucks, and then make dynamic planning for the traffic lights after the fire trucks pass, so as to finally restore traffic order. Based on the tabu search algorithm scheduling strategy under traffic lights, the scheduling mechanism of special vehicles under general conditions is obtained.
Research for aircraft design process optimization based on design structure matrix
Authors: Jiang Yueqing , Yin Bo , Jia Dongning 
Published: January 2021

Abstract
The interaction of information during the structural design of aircraft is complex. In order to optimize the design process and improve the efficiency in research and development, this paper performs clustering calculation on the structural design matrix of a certain type of trainer and optimizes it through genetic algorithm. In addition, a specific method for calculating the amount of information through information connection weight is given. The simulation results show that the optimized matrix has strengthened the information interaction within the cluster, reduced the information interaction between clusters, and reduced the frequency of information feedback during the design process, which plays a role in improving the efficiency of research and development.
Fault Diagnosis of Rolling Bearing Based on Convolutional Neural Network of Convolutional Block Attention Module
Authors: Wang Chaoqun , Li Binbin , Jiao Bin 
Published: January 2021

Abstract
Aiming at the problem of insufficient comprehensive feature extraction in the fault diagnosis of rolling bearings, this paper proposes a fault diagnosis model of convolutional neural network (CNN) based on Convolutional Block Attention Module (CBAM). The model uses CBAM instead of pooling layer to join the CNN, and then exports to the full connected layer to realize the fault type identification and complete the fault diagnosis of rolling bearing. Experiments show that the accuracy of this method is 99.94%, and comparison with some other methods proves the effectiveness of this method.
Computer Vision-based Intelligent Bookshelf System
Authors: Yu Hao 
Published: January 2021

Abstract
This paper is devoted to the research of an intelligent bookshelf system. By introducing the current status of reading and purchasing books by people in various countries around the world, the value of the existence of the intelligent bookshelf is analyzed and proved. By introducing the main related technologies and algorithms used in the intelligent bookshelf system, this paper brings out the introduction to the principle and mode of operation, and an analysis of the system.
Application of Deep Reinforcement Learning in Control of Drawing Robots
Authors: Liu Cheng , Zhu Liucun , Ji Xinyu , Zheng Xiaodong 
Published: January 2021

Abstract
In this paper, the drawing robot studied is after the system extracts the edge of the image, convert its pixels to actual physical coordinates, then control the robot to track these coordinate values and realize image drawing. In many cases, the edges extracted by the edge detection algorithm have a lot of unnecessary details and edge lines of many branch points. Here, some techniques are used to remove these redundant and branch points. When the robot is tracking the trajectory, the quality of its control algorithm directly determines its performance. In this paper, the control system is designed using the deep deterministic strategy gradient (DDPG) algorithm in reinforcement learning, focusing on the setting of its reward function. Here we propose a reward function. After simulation testing, the reward function setting meets the system requirements, the controller can achieve high-precision and high-speed trajectory tracking effect, and it can also suppress chattering caused by external interference and model errors.
Ray tracing collision detection based on GPU pipeline reorganization
Authors: Cheng Sining , Qu Huiyan , Chen Xianjun 
Published: January 2021

Abstract
This paper proposes a parallel collision detection algorithm based on ray tracing to improve GPU performance. The algorithm uses parallel ray tracing pipeline technology to improve the performance of the graphics processing unit (GPU). When the GPU combines multiple ray tracing, it will cause the work group threads to diverge, resulting in too many idle threads, which will reduce the performance of the system. In order to avoid thread divergence, a new collision detection technology for the detailed detection stage of ray tracing using a pipeline structure is proposed. By optimizing the addition of buffers, the ray tracing is divided into three stages, and the pipeline is designed to integrate multiple rays tracking to avoid unnecessary synchronization issues between the CPU and GPU, thereby improving system performance. The comparison experiment results show that the proposed algorithm improves the system performance by 2.7 times.
A Novel Aircraft Refueling Behavior Detection Model based on Deep Learning
Authors: He Ran , Wang Jianxing , Zhang Zhengning 
Published: January 2021

Abstract
How to efficiently and accurately monitor the sparse aircraft Refueling behaviors from a large amount of video streams is of great significant for improving the level of management and refueling efficiency of aviation fuel stations. Due to the COVID-19 virus epidemic, the number of flights has dropped severely, the collection of image samples for refueling behaviors from large airport becomes difficult, which hinders the real-time detection of Refueling behaviors and reduces the efficiency of aviation fuel station. Therefore, automatically detecting the refueling behaviors of each station in time and accurately from a large number of aviation refuel stations still keeps challenging. To address this challenge, we propose a novel aircraft refueling behavior detection model based on deep learning, to quickly and accurately determine the refueling behaviors through analysing the video stream collected from the massive cameras deployed in the airport. Our proposed model adopts Inception v3 architecture of ImageNet to realize the model capability of transfer learning, the data augmentation to address the issue of overfitting, and the mAP (mean Average Precision) to test the performance. Our proposed model is also applied in the detection of refueling behaviors in China National Aviation Fuel Group, LTD (CNAF). The practical application results show better performance than other existing methods. Our work will promote the updating of related industry standard.
A parallel construction method based on linear hierarchical bounding box without stack
Authors: Wang Mingquan , Qu Huiyan , Liu Yi 
Published: January 2021

Abstract
The intersect operation of geometry is a common method of collision detection. To achieve a fast intersect operation, a hierarchical bounding box must be quickly constructed. This paper proposes an effective space division method for constructing high-quality linear hierarchical bounding boxes through ray tracing. This method can generate more regular axial bounding boxes into a complete binary tree. This structure can be completely parallel on the GPU, and effective parallelization can ensure the fastest tree-building time. The algorithm in this paper uses a non-stack ray tracing method to increase the frame rate of the 3D model rendering in the graphics processing unit and reduce the construction time of the bounding box. The experimental analysis of construction time, frame rate, average number of intersections and other performance indicators, the experimental results show that the use of stack-free midpoint splitting method and surface heuristic splitting method to ensure rapid construction time and efficient ray traversal performance, improve the object’s light traversal efficiency.