bluuebunny/crossref_metadata_2025_split huggingface dataset:
Using Machine Learning Algorithms to Predict Efficiency of US’s States Mask Policy During COVID-19
Authors: Cui Jin 
Published: August 2021

Abstract
Over the past year, the COVID-19 outbreak deeply and thoroughly changed the way the world is. However, the control policy’s efficiency is still in dispute. Through the way of machine learning, now we are able to find and to probe into the data about corona-virus spreading patterns in a short period of time, suiting the remedy to the case, to launch targeted prevention policies, and minimize the economic loss under the premise of control the spread of the virus on a large scale. We directly use the LES algorithm and K-means clustering to make a comparison about the data feature. Therefore, the results are much more convincing than using any other recursive analyzing method alone. It is precise because of the ID3 algorithm, which we use for further analysis, to find the reason why those policies work.
Research on hand key point recognition and localization method based on SSD network
Authors: Cheng Xiaoyu , Jiang Tao , Tan Xuewen , Huang Lidong 
Published: August 2021

Abstract
With the development of the times and the progress of society, multimedia presentation has become a necessary skill for people. In the field of education, the application of multimedia presentations for teaching activities has become a necessary and common way. In this paper, we propose to use camera capture for image analysis and wireless data transmission technology for information interaction with computers. Among them, SwiftShow performs image analysis by capturing the gestures of the presenter using the camera. The device provides personalization, and users can choose the mode that suits them through the interaction interface, and even customize the gestures. The device can be controlled by the simple hand gesture recognition overhead, which not only can realize the basic functions such as ppt play, pause, page turn, and focus, but also includes many practical and unique presentation functions, the research aims to completely free the presenter from the computer.
Machine Learning Algorithm for Classification
Authors: Tan Haoyuan 
Published: August 2021

AbstractRecently, machine learning methods have a good performance in the field of classification tasks. Summarizing and comparing the performances of different classifiers in the application of their specific classification tasks has a reference significance. In this paper, five classical machine learning classifiers, including GMM, Random Forest, SVM, XGBoost, and Naive Bayes, are compared to show their computing characteristics. The advantages and disadvantages are analysed in this paper. Based on the different datasets, namely different specific classification tasks, the different classifiers perform similarly. However, the SVM-based classifier has the lowest accuracy while processing the text data to apply the text classification task. This result shows that if the classification task is difficult, the accuracy would not be high. This research summarizes the performances of different machine learning methods in the application of specific classification tasks. And this research has a reference significance for the machine learning-based classifiers.
A method for forecasting alpine area load based on artificial neural network model
Authors: Wu Guoliang , Zu Guangxin , Zheng Jun 
Published: August 2021

Abstract
In order to realize the modernization of power grid management, mid-term power load forecasting has become an important research in power dispatching. Accurate mid-term load forecasting is of great significance to future power system dispatching and safe operation, and is a prerequisite for safe and economical operation of power systems. Based on error back propagation (BP) neural network has the advantages of generalization ability, self-learning ability and self-adapting ability, and simple local calculation, this paper proposes to use the BP neural network model to forecast the power load in the alpine region. Firstly, the historical load data and meteorological data of a certain area in Harbin are studied, analyzed and screened, and they are used as the input data of the network, a mid-term load forecasting model based on BP neural network is established, and then the model is simulated and improved through simulation to predict changes in the future development of electric load demand. The prediction data results and images show that the medium-term load prediction accuracy and training speed based on the BP neural network can reach the target, and the performance is good.
Mobile edge computing scheduling algorithm based on minimum total delay-time
Authors: Zhao Yuancheng 
Published: August 2021

Abstract
As an emerging architecture, mobile edge computing (MEC) extends cloud computing services to network edges close to users through MEC servers, satisfying the application needs of real-time control and real-time data analysis. However, due to the limited computing power of MEC server, the task has a long delay-time. In order to improve the current situation, this paper proposes a scheduling algorithm of minimum total delay-time (MTD), in which the server determines the optimal order of task computation to MTD. In addition, this paper proposes an incentive mechanism for users to submit tasks with reasonable computation and expected completion time, while reducing the number and computation of submitted tasks when server computing resources are insufficient. The results show that the performance of the algorithm is close to that of the traditional scheduling algorithm, and total delay-time and average delay-time have improved by 17% to 200%.
SPECT bone imaging thyroid lesion segmentation
Authors: Mingyang-Luo  , Qiang-Lin  , Tongtong-Li  
Published: August 2021

Abstract
Nuclear medicine SPECT is the main functional imaging modality, which plays an important role in the diagnosis and treatment of thyroid diseases. Hyperthyroidism is a common thyroid disease with symptoms such as exophthalmos, eyelid edema, and vision loss. In order to accurately segment thyroid lesions from SPECT thyroid data, a U-net-based thyroid lesion segmentation model was constructed. First, mirror, rotate, and translate the existing data. At the same time, use the generative adversarial network to expand the data. Then build a segmentation model based on U-net. Finally, perform experimental evaluation and analysis based on a set of real SPECT thyroid data. The results show that: in the traditional data expansion, the IoU value of the improved RS-U-net model is 66.83.
Ant Colony Algorithm for Container-based Microservice Scheduling in Hybrid Cloud
Authors: Lin Miao , Xi Jianqing , Bai Weihua , Wu Jiayin 
Published: August 2021

Abstract
The microservice model divides the application into a group of loosely coupled and collaborative fine-grained services, which results in the workflow scheduling problem, especially in the complex environment of resource diversity such as hybrid cloud. To solve this problem, this paper establishes a workflow scheduling model of microservice based on multiple container instances and proposes an ant colony optimization (ACO) algorithm to optimize finish time and resource cost. The algorithm proposed optimizes update strategies of multi-objective heuristic information to improve the selection probability of the dominant path. The comparative experiments show that the proposed optimization algorithm achieves better results in scheduling objectives in the hybrid cloud.
A Deep Neural Network for Stock Price Prediction
Authors: Dai Hengwei , Wang Wenfei , Cao Jiashang , Wu Haotian 
Published: August 2021

Abstract
The stock market is a large, complex system that consists of many human factors. Due to the human action’s randomness to the market environment, the market behavior is usually erratic. In this case, many previous works were trying to find out an automatic prediction model that can capture relationships between stock market prices and the surroundings of the stock markets. Recently, a simple LSTM-attention mechanism was proposed to advance the prediction accuracy of previous works. However, the simple LSTM with attention mechanism only demonstrates how the past data influence the next. This forward-only relationship does not reflect a backward relationship, which is how the future data is related to the past data. To enforce the backward relationship on the attention-LSTM model, a multilayer bidirectional LSTM model with an attention mechanism is proposed. A bidirectional LSTM layer that can encode the data relationship in both directions is leveraged. Then attention mechanism is applied to introduce the model with the ability to focus on critical content and ignore the disturbance. Experimental result shows that the attention-BiLSTM model has a coefficient of the determinant of 0.9940, which is better than the baselines.
Research on the state detection of the secondary panel of the switchgear based on the YOLOv5 network model
Authors: Chen Wang , Liqiang Zhang , Tianpeng Yan , Tao Jiang , Yijing Jia , Zhihao Lai 
Published: August 2021

Abstract
In recent years, with the development of artificial intelligence algorithms, deep learning algorithms are widely used in target detection. The switchgear in the power system plays a key role in the safe operation of the system and there are a large number of them. In order to improve the work efficiency of testers and reduce manual misjudgment, it is proposed to apply the deep learning YOLOv5 algorithm to detect the status of the switchgear secondary panel in real time. The algorithm uses the configuration environment, The data set training and target test obtain the light state information, and the experimental results prove the effectiveness of the algorithm, which provides technical support for the field of switchgear electrical test robots.
Multi-Label Classification of Research Papers Using Multi-Label K-Nearest Neighbour Algorithm
Authors: Li Shurui , Ou Jiechen 
Published: August 2021

Abstract
With the frequent interaction and cooperation between different disciplines in recent years, the number of research papers associated with multiple subjects increased. Correspondingly, some of the existing literatures belong to a single discipline, while others may simultaneously involve more than 2 subjects. At this time, the traditional single-label text classification is not conducive to people obtaining comprehensive and cutting-edge research papers in real life. Thus, it’s of great importance to conduct a multi-label classification of research papers effectively. This paper tests the performance of multi-label learning tasks with text data obtained from the Kaggle website. Firstly, lemmatization and Term Frequency-Inverse Document Frequency (TF-IDF) are used for feature extraction in the pre-processing part. The critical information of text content is statistically analysed, and text content is converted into numerical and high-dimensional vector space. As the traditional single-label classification algorithm is not suitable for the above problem, this paper adopts the Multi-Label K-Nearest Neighbour (ML-KNN) algorithm framework for classification. Experimental results report that the ML-KNN algorithm has achieved better results in multi-label text classification problems than a traditional multi-label algorithm, which proves the effectiveness of the ML-KNN algorithm for text data prediction with multiple subjects. Moreover, the work in this paper is analysed and summarized.
Experimental Investigation of Lensless Fourier Transform Hologram Multiple Images
Authors: Gao Zhiqiang , Fan Qi 
Published: August 2021

Abstract
In order to solve the problem of multiple reconstructed images in digital hologram, the lensless fourier transform hologram is taken as an example for comparative experiment. When the same experimental light path and recording conditions are used and the only variable is the sensitive element, the reconstructed image of CCD does not appear multiple images, while the reconstructed image of CMOS appears multiple images. In order to determine the reliability of the experiment, two correlated beams were imaged with the same optical path. The experimental phenomenon is still that there are multiple images in CMOS, but not in CCD. The working principle of CCD and CMOS in digital holography experiment is different.
Research on image segmentation and edge detection technology based on computer vision
Authors: Qi Ji , Yang HaiTao 
Published: August 2021

Abstract
With the continuous development of computer vision technology, image segmentation technology and edge detection technology are becoming more and more mature. Firstly, this paper simulates the fixed threshold method and adaptive threshold method in image segmentation, and concludes that the fixed threshold method is applicable to images with obvious targets and backgrounds, and the adaptive threshold method is applicable to images with uneven distribution of light and dark, and secondly, the comparative experiment and result evaluation of edge detection of images by Sobel operator and Canny operator are used to conclude that the Sobel algorithm is more accurate in positioning edges, while Canny algorithm image edge information is more continuous and single edge response is better. Its edge detection is also better.
Piecewise Linear map enabled Harris Hawk optimization algorithm
Authors: Zhao Juan , Gao Zheng-Ming , Zhang Yu-Jun 
Published: August 2021

Abstract
Chaotic maps were usually introduced to improve the original swarm-based nature-inspired algorithms. Due to their chaotic characteristics, the chaotic maps were introduced to replace the pseudo random numbers in computer engineering and consequently better performance would be achieved. In this paper, we introduce another chaotic improvement to the Harris hawk optimization (HHO) algorithm with Piecewise Linear map. Nevertheless, the chaos would be introduced to improve the randomness of the controlling parameter which was used to balance the ratio of exploration and exploitation. Monte Carlo simulation experiments were carried out and results confirmed this kind of improvements would significantly raise the capability in optimization.;
A Dynamic Scheduling Workflow Algorithm Based On Critical Path
Authors: Tongyue Zhu , Wenhui Yang 
Published: August 2021

Abstract
With the explosion in the size of scientific workflows, local workflows can no longer satisfy existing computing needs, and cloud computing platforms have become the first choice for scientific workflows. Compared with traditional local computing, cloud computing platform not only needs to consider the loss of scheduling time and transmission time, but also involves the cost of cloud resources. Therefore, whether the scheduling scheme is reasonable becomes the decisive factor of workflow efficiency. Aiming at how to achieve efficient scheduling under cost constraints, a dynamic scheduling algorithm based on critical path is proposed. The algorithm uses the optimized Dijkstra algorithm to classify the task nodes, guarantees the completion time of key nodes in the scheduling process, adjusts the priority of nodes dynamically, and selects the best resources according to the loss weight. Experiments show that the optimization rate increases with the increase of the number of task nodes. The algorithm proposed in this paper is suitable for large-scale workflow operation and can effectively reduce the scheduling time.
Predicting and Evaluating the Online News Popularity based on Random Forest
Authors: Zhang Yuchao , Lin Kun 
Published: August 2021

Abstract
With the rapid development of science and technology, the internet has become a large media of information spread. There is a large quantity message on this platform. And online articles are the main form of information propagation. If the press can know what kind of articles will be more popular, they can construct an article that can help them spread the information they want to spread. Therefore, it’s very important to predict the popularity of these articles. Some models in machine learning could be applied to this problem. In this paper, it will introduce an approach based on Random Forest. To avoid too much calculation, the experiment first uses PCA to make dimension reduction. Then the model evaluation uses the ROC area values to assess the accuracy of the model. Its performance is better than CART and C4.5.
An improved hybrid recommendation algorithm based on feature preference
Authors: XU Jiwen 
Published: August 2021

Abstract
In order to solve the problem of data matrix sparsity and cold start in the era of big data. This paper designs a collaborative filtering algorithm, which is an improved hybrid recommendation algorithm based on feature preference analysis. It combines the analysis of user feature preference and item feature, and then uses the traditional collaborative filtering idea to recommend the best scoring object to users. The experimental results show that the accuracy of the proposed algorithm is improved obviously.
Ultrafine particles, blood pressure and adult hypertension: a population-based survey in Northeast China
Authors: Lin Li-Zi , Gao Meng , Xiao Xiang , Knibbs Luke D , Morawska Lidia , Dharmage Shyamali C , Heinrich Joachim , Jalaludin Bin , Lin Shao , Guo Yuming , Xu Shu-Li , Wu Qi-Zhen , Chen Gongbo , Yang Bo-Yi , Zeng Xiao-Wen , Yu Yunjiang , Hu Li-Wen , Dong Guang-Hui 
Published: August 2021

Abstract
The toxicity of ultrafine particles (UFPs) on blood pressure (BP) has not been well studied. We aimed to evaluate the associations of long-term UFP exposure with different components of BP and the risk of prehypertension/hypertension. We included a total of 24 845 Chinese adults (18–74 years old) in a cross-sectional survey (Liaoning province, China). The 4 year (2006–2009) average concentrations of UFP was estimated using a chemical transport model. We measured systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP) and pulse pressure (PP), prehypertension and hypertension. We used a generalized linear mixed model to examine the associations while controlling for important individual covariates. One unit (1 μg m−3) increase in UFP was associated with an increase in SBP of 1.52 mm Hg [95% confidence interval (CI): 0.48–2.55], DBP of 0.55 mm Hg (95% CI: 0.01–1.08) in DBP, MAP of 0.92 mm Hg (95% CI: 0.23–1.61) and PP of 0.62 mm Hg (95% CI: 0.05–1.20). The UFP-BP associations were stronger in women than in men. One unit increase in UFP was associated with an increased risk of hypertension and prehypertension (OR = 1.23, 95% CI: 1.09–1.38; OR = 1.12, 95% CI: 1.04–1.21) compared to normotension group, and these associations were stronger in overweight/obese participants. Our study showed that long-term exposure to UFP was associated with elevated BP and higher odds of hypertension. These findings suggest that strategies to monitor and reduce UFPs, which are not a regulated pollutant, could have beneficial cardiovascular effects.
Surface coatings analysis and their effects on reduction of tribological properties of coated aluminum under motion with ML approach
Authors: Chowdhury Mohammad Asaduzzaman , Hossain Nayem , Masum Abdullah Al , Islam Md Sakibul , Shahin Mohammad , Hossain Md Imran , Shuvho Md Bengir Ahmed , Ali Md Ramjan , Ahmed A K M Foysal , Nandee Mr Rajib 
Published: August 2021

Abstract
The popularity of coated aluminum is gaining significant attention in numerous sectors in the industry due to its specific strength, corrosion resistance, and recyclability. However, because of friction, its lifetime reduces which causes a billion-dollar loss every year to our property. Many types of research are going around the world on how friction and wear loss can be reduced. This research focuses on the tribological study of coated aluminum in different conditions in the experiments, lubricant is used to find its efficiency, and coating materials have also its self-lubricating properties. Both reciprocating motion of pin and simultaneous motion of pin and disc applied. The combined effects of lubrication and motions are correlated with the reduction of tribological properties to a certain extent. The velocity of both pin and disc is also varied. Applied loads are changed in different experiments as well. Roughness analysis has also been done to observe the effect of lubricant, motion, and applied load on the surface of the specimens. SEM, EDX, XRD, and FTIR tests are also performed to check the morphology of the specimens. The experiments show that comparatively less friction and wear are in at lubricated, reciprocating, and less velocity of pin and disc conditions. Less coefficient of friction is observed at higher applied load but less wear is produced at lower applied load. The Machine Learning (ML) approach is used to detect patterns automatically in datasets and create models to predict future data or other outcomes.
Stacking fault energy and fcc→hcp transformation driving force in Fe-Mn-C-Cr-Si high manganese steels and experimental investigation
Authors: Lang Dong , Li Qiangguo , Huang Xuefei , Huang Weigang 
Published: August 2021

Abstract
In order to improve the strength and work hardening capacity of high manganese steel, the effects of alloying elements on the stacking fault energy (SFE) and driving force of fcc→hcp transformation in Fe-Mn-C-Cr-Si high manganese steels were explored in detail. Based on the thermodynamic calculations, the Fe-0.6C-15Mn-(4,6)Cr-(0,3)Si (wt.%) steels were prepared to investigate the microstructure and mechanical properties. The calculated results show that the Cr reduces the SFE of Fe-C-Mn-Cr high Mn steels linearly and the reduction rate of SFE is greater as the C content increasing. With increasing the Si concentration, the SFE of Fe-C-Mn-Si steels decreases when the Mn content is higher than 18 wt.%. However, when carbon content is less than 1 wt.% and Mn content less than 18 wt.%, the SFE reaches to a maximum value and then declines with the increase of Si content. The combined effect of Cr and Si on the SFE of Fe-C-Mn-Si-Cr steels appears the similar behaviors to that of Si. Generally, the value of driving force,

Δ

G

γ
→
ε

,

of fcc→hcp transformation in Fe-Mn-C-2Cr-Si (wt.%) steels increases with the increase of C and Mn and decreases with the Si content increasing. However, for 0.6 wt.% C and 7 wt.% Mn steels, the value of

Δ

G

γ
→
ε

first increases and then decreases with the increase of Si content. The results of tensile test indicated that the Fe-15Mn-0.6C-6Cr-3Si (wt.%) high manganese steel demonstrates a better combination of the ultimate strength of 987 MPa, yield strength of 470 MPa, fracture elongation of 41.5% and high work hardening capacity because of the ε-martensitic transformation caused by the great

Δ

G

γ
→
ε

(large negative value) and low SFE under stress.