bluuebunny/crossref_metadata_2025_split huggingface dataset:
Text mining-based analysis of online comments for skincare e-commerce
Authors: Hong Wen , Wu Yiping , Li Shangze , Wu Ying , Zhou Zehai , Huang Yanbin 
Published: September 2021

Abstract
This article uses online review data of skincare products on e-commerce platforms to summarize the consumer demand characteristics through text clustering analysis and offer common marketing proposals for skincare merchants. From the role of review text clustering analysis, this paper derives two dimensions from e-commerce platforms and skin care product categories, and through feature extraction and lexical item clustering analysis of consumer online review information on different platforms, the focus of attention and characteristic tendencies of consumers on skin care products on different platforms are mined. In turn, the review information can be mined and analyzed to obtain information with business value, and relevant measures can be taken to improve the platform’s services, promote business growth, enhance customer satisfaction, etc. First, develop a reasonable marketing strategy. Second, strengthen product branding.
Research on Image Classification Algorithm Based on Pytorch
Authors: Jiang Lizheng , Zhang Zizhao 
Published: September 2021

Abstract
With the development of artificial intelligence and deep learning, image classification technology has ushered in new opportunities and challenges. The so-called image classification problem is the problem that the user passes in the image, and then the computer sends out the classification and description of the content of the image. Feature description and detection as a traditional image classification method have many drawbacks, such as low accuracy and long time-consuming problems. Among the existing deep learning frameworks, Pytorch is relatively effective. Researchers can use the Pytorch framework to construct convolutional neural network models quickly and easily, and train them on massive data sets. This paper briefly introduces the image classification algorithm, neural network and Pytorch. The classification experiment is carried out on the Fashion MNIST data set, and the accuracy of the model reaches 88.39%. The future research direction is discussed.
Space-Based Information Service Recommendation Algorithm based on Hybrid Strategy
Authors: Wang Yeyin , Wang Shenwei , Yu Jie , Gao Yufeng , Wang Rui , Wang Feng , Li Ying 
Published: September 2021

Abstract
The recommendation technology has been applied to all walks of life, but it is rarely used in space-based information service. Considering the advantages of the recommendation technology and the particularity of space-based information service, a space-based information service recommendation algorithm based on hybrid strategy is proposed. Based on the different stages of the user, the similarity calculation is carried out from three aspects of user attributes, non-common service and service interest. The missing data in the service interest model is predicted by singular value decomposition to solve the problems of cold start and data sparse. Finally, the proposed algorithm is compared with the traditional algorithm, and the accuracy and scalability of the proposed algorithm are improved.
Improved unsupervised domain adaptation network based on category attention
Authors: Fan Longhao , Gao Shouwei , Zhu Fan , Zhu Zhenzhong , Zhou Chaozheng , Peng Yaxin 
Published: September 2021

Abstract
Domain adaptation method can significantly reduce the distribution difference between images in variant domains, which plays an important role in unsupervised medical image segmentation. In this paper, an improved unsupervised domain adaptation framework is proposed based on category attention mechanism. The framework considers both image-level and feature-level alignment, and realizes semantic segmentation in different domains through a shared encoder-decoder. A category attention based classifier is proposed to compute the category attention feature and refine the semantic segmentation prediction. Sufficient experiments on MMWHS-2017 dataset indicate that the proposed method achieves the best segmentation performance among all comparasion algorithms.
Fault detection of pressure sensor of blast furnace fan based on Chaos Sparrow Search Algorithm-Support vector machine regression
Authors: Zhang Ming , Liu Qiong , Deng Jihong , Chen Chun 
Published: September 2021

Abstract
The sensor diagnosis system of the blast furnace axial fan is very important to the safety of the blast furnace fan control system. In view of the fact that the sensor fault detection of the blast furnace fan is not considered in the existing research, this paper proposes to use SVR to establish a fault detection model for the blast furnace fan outlet pressure sensor, and then use the improved chaotic sparrow algorithm to optimize the selection of the SVR penalty parameters and kernel function parameters Find the optimal parameters. By comparing the model prediction value and the residual error of the diagnostic sensor output value, the sensor fault diagnosis is realized. When the fault is judged, the model prediction value is used instead of the fault sensor output value to be used by the blast furnace fan control system to realize the fault-tolerant control of the blast furnace fan control system. The simulation results show that this method realizes the fault detection of the pressure sensor of the blast furnace fan and improves the safety of the blast furnace production.
Edge detection algorithm based on valley lines and watershed
Authors: Wang Bo , Yang Zhiying 
Published: September 2021

Abstract
The purpose of edge detection algorithm is to highlight the edge in the image, which is also the premise and key of image segmentation. However, the traditional edge detection algorithm will amplify the influence of noise in the process of derivative operation, and the noisy edges will often appear, which is not conducive to the accurate extraction of edge. To solve this problem, we propose an edge detection algorithm combining valley lines and watershed algorithm. In this algorithm, the bilateral filter is used to replace the Gaussian filter to achieve edge preserving and denoising. The rewritten Canny operator is used to calculate the low value points and get the valley lines of the image. This process can achieve the first denoising. Then, the valley lines are used to replace the discrete points of water injection and the watershed algorithm is used to calculate the segmentation results of the image. At this time, there are many pseudo edges around the main boundary, and the pseudo edges are deleted by setting the threshold, so as to achieve the effect of secondary denoising. The experimental results show that our method has good performance in noise resistance and edge continuity compared with other and traditional edge detection algorithms.
Research on Robotic Process Automation Application in Electricity Charge Settlement for Distributed Photovoltaic Power Generation
Authors: Ye Ming , Zhao Qian , Zhao Zhiwei , Yu Qiao , Xu Yong 
Published: September 2021

Abstract
Distributed photovoltaic power generation(DPPG) directly supplies power to users nearby, which is conductive to expanding the scale of renewable energy utilization, and is helpful to achieve the goal of “carbon peak and carbon neutrality”. DPPG electricity charge settlement, with the characteristics of large business volume, strong regularity, high repetition, cumbersome manual operation and high error rate, is the typical business scenario for introducing modern information technology to carry out management innovation. Facing the whole process of DPPG electricity charge settlement, starting from business process analysis and optimization and redesigning, the paper introduces robotic process automation (RPA) to build a settlement model of “intelligent robot + personnel + information system”, and provides inspiration and reference for improving the efficiency of electricity charge settlement.
Traffic Clustering Method Based on Adaptive Density Grid
Authors: Sun Haichuan , Gu Liang , Meng Yaning , Xin Haoguang , Dong Chenni , Ma Xin , Zhao Xixi , Wang Liguo , Duan Xiaorong , Gong Xin , Bi Sheng 
Published: September 2021

Abstract
At present, the scale of internal network of some enterprises is expanding with the development of enterprises. The terminal of enterprises is becoming more and more diversified in terms of the number of types and the scope of application. The form of terminal security protection is becoming more and more severe. In this paper, based on adaptive adjust the density of the grid flow clustering method, by aiming at characteristics of the grid density value difference reflect the characteristics of the data of uncertainty on the influence of density threshold, adjustment of adaptive threshold density, determine the key characteristic vector, and the flow characteristics and the agreement that certain learning training, finally realizes the flow clustering.
A multi-step attack scenario mining method based on alert correlation
Authors: Liu Jianyi , Hu Wei , Wang Chan , Zhang Jingwen , Zhang Yahao 
Published: September 2021

Abstract
Aiming at the problems of redundant alerts, a multi-step attack scenario mining method is designed based on alert correlation, which has three steps: alerts preprocessing, clustering based on attribute similarity and clustering the attack graph set to mine attack scenario. This algorithm can discover new attack model without a large amount of graph features by analyzing and studying the characteristics and intrinsic relationship of the alerts. Therefore, the full attack scenario can be fully displayed, the efficiency of security managers can be improved.
Recognition and Application of Tunnel Water Accumulation Based on Computer Vision
Authors: Chen Yuan , Wang Yongwei , Li Kunyao 
Published: September 2021

Abstract
Water accumulation in tunnel threatens the safety of driving and the tunnel itself. In order to detect the tunnel waterlogging in time, a method based on computer vision is proposed. This method utilizes Laplace transform for image preprocessing to remove fuzzy image, uses the network MobileNetV2 to build a tunnel waterlogging recognition model, and then smooths the prediction results. Based on this method, a tunnel waterlogging recognition and early warning platform is developed which applied to a city video surveillance system successfully. The results show that: the tunnel waterlogging recognition method based on computer vision has high accuracy, tiny computation and low cost. The existing video monitoring system can be upgraded intelligently without installing any hardware, so as to realize the active recognition and early warning of tunnel water accumulation.
Focal Rank Loss Function With Encoder-decoder Network For Skin Lesion Segmentation
Authors: Wang Lingdu , Zhang Lei , Shu Xin 
Published: September 2021

Abstract
Skin cancer is a major health problem as melanoma is one of the deadliest types of skin cancer and causes thousands of deaths worldwide each year. Automatic segmentation of skin lesions is considered an essential step in computer-aided diagnosis (CAD) systems for melanoma detection, which help the specialists better examine pigmented skin lesions. However, the segmentation is a challenging task due to the low contrast between each lesion and its surrounding skin tissue which makes the boundary pixels hard to classify. Besides, the class imbalance problem exists in skin lesion segmentation problem. Thus, a new hybrid loss function based on focal loss and rank loss is proposed to alleviate the class imbalance problem and the hard-easy classified pixels problem in skin lesion segmentation problem. The proposed method is evaluated on the ISIC 2017 dataset using the proposed encoder-decoder network, and the result shows the loss outperforms most methods in terms of the dice coefficient and especially the sensitivity.
RMKnet: A novel and parameter-efficient architecture for robust r-peak detection
Authors: Lu Qi 
Published: September 2021

Abstract
The Electrocardiogram (ECG) has become the most important diagnostic criteria in the non-invasive diagnosis of heart disease. The automatic detection of r-peaks in an electrocardiogram signal is also crucial in a all kinds of applications about heart disease. Recently, lots of algorithms have been proposed to automatically detect r-peaks in ECG signals. Traditional detection methods have high detection accuracy on high quality ECG signals which don’t have so much noise, but they perform poorly on low quality ECG signal. Most of the deep learning methods are more robust and have higher accuracy than the traditional methods, but they are usually computationally expensive and difficult to implement and customize. In this paper, we proposed a novel and robust model architecture based on Unet[17] to detect r-peaks. After training, we use re-parameterization greatly accelerating the inference process and reducing the inference memory utilization. The proposed network is evaluated on two open-access ECG databases: CPSC-DB, MIT-BIH and achieves high F1 scores which showing good generalization on different databases comparing to all competing methods.
A malicious code family classification method based on self-attention mechanism
Authors: Zhang Ru , Zhao Xinjian , Li Jiaqi , Zhang Song , Shang Zhijie 
Published: September 2021

Abstract
Malicious code families have become a major threat to network security. Many current methods convert malicious code into images and use deep learning to classify malicious code families. However, the family classification method based on deep learning incorporates the overall characteristics of the malicious code into the classification model, which may cause interference with redundant information in the malicious code. This paper proposes a malicious code family classification method based on a self-attention mechanism. When analysing a noisy data structure such as malicious code images, the attention mechanism is introduced to filter the interference information in the malicious code. The experimental results show that the classification accuracy rate of this method for malicious code families is 99.56%, and the recall rate is 98.06%. Rigorous theoretical analyses and numerous experiments prove our method is efficient and reliable.
The balanced quaternary sequence of odd period with low autocorrelation
Authors: Ke Meijian 
Published: September 2021

Abstract
In this paper, we construct some balanced quaternary sequences of odd period p with low autocorrelation from two types of Legendre sequences and its cyclic shift or complement and inverse Gray mapping, where p is odd prime. Firstly, we choose a Legendre sequence pair of period p, which include p ≡ 1(mod 4) and p ≡ 3(mod 4), then we get a new Legendre sequence pair of period p by cyclic shift or complement, finally, we can get a quaternary sequence of period p by utilizing inverse Gray mapping into the new Legendre sequence pair.
Facial Target Detection and Keypoints Location Study Using MTCNN Model
Authors: Chai Yuan , Liu Jing , Li Yang 
Published: September 2021

Abstract
With the extremely rapid development of AI, facial recognition technology in the field of computer vision, which has the advantages of non-contact and easier to collect information, has become a hot topic in all walks of life. Facial target detection and keypoints location are two core parts of facial recognition technology. According to the analysis of the existing facial detection and keypoints location methods, by comparing their advantages and disadvantages, we find the existing problems. Based on the existing research methods, this paper proposes a high-precision facial detection and keypoints location method based on MTCNN model, which highlights the good accuracy of this method compared with the basic CNN or traditional algorithms, so as to improve the accuracy and effectiveness of facial recognition. It has more prominent advantages in this field, and is worthy of scientific research.
Research on Time Series Forecast of Container Throughput Based on Selective Deep-Ensemble Model
Authors: Jin Xiao , Bo Liu , Jing Huang 
Published: September 2021

Abstract
Accurate forecast of container throughput is an important basis for reasonably planning port construction, making port operation plan and adjusting port development direction. Aiming at the complex nonlinear characteristics of port container throughput, a hybrid model based on selective deep-ensemble for container throughput forecasting (HMSD) is presented in this paper. Firstly, this model decomposes the original container throughput time series into several intrinsic mode functions and a residual by empirical mode decomposition (EMD). Considering highly nonlinear characteristics of each intrinsic mode function, the proposed model constructs three deep neural networks, namely, long short term memory (LSTM), gated recurrent unit (GRU) and convolutional neural network (CNN), as base learners to predict intrinsic mode functions. Then, this model establishes selective deep-ensemble forecasting model by improved group method of data handling (GMDH) on intrinsic mode functions and obtains their ensemble forecasting results. Furthermore, this model uses an autoregressive integrated moving average model to predict the linear residual. Finally, the forecasting result of the total container throughput is obtained by integrating the forecasting results of all intrinsic mode functions and the residual. The empirical analysis of container throughput data of Xiamen port and Shanghai port in China shows that the HMSD model has better forecasting effect than other models.
Research on Diabetes Prediction Analysis Based on Improved ANN Algorithm
Authors: Wang Yinggui , Wang Ben , Di Jiazhi , Sun Jiehao , Wang Xiya 
Published: September 2021

Abstract
Diabetes mellitus is a common chronic disease with a long phase of asymptomatic. This paper focuses on five classification algorithms in machine learning, which are MLP, SVM, KNN, DT and the improved algorithm of ANN. By adjusting appropriate parameters for mining and analysing diabetes data, classifier effect is analysed according to the performance indicators of accuracy, precision, recall, and F1-score. The suitable algorithm is researched for diabetes prediction, and provides ideas for mining and analysing other disease data from current medical industry.
Multiple Granularity Network and Dynamic Label for Domain Adaptive Person Re-identification
Authors: Wang Xile , Zhang Sihan , Song Junyu , Zhang Miaohui 
Published: September 2021

Abstract
The domain adaptive person re-identification (Re-ID) has be more popular among researchers. Because it can save a lot of resources as it only exploits the source domain knowledge and does not need the complex annotation efforts in target domain. It aims to extend a model trained on a labeled dataset to another dataset which is unlabeled. Many works reduce feature distribution gap between two different datasets to solve the problem. However, these works ignore the problem which is the variations within an unlabeled dataset. In the paper, we propose a domain adaptive person Re-ID framework based on multiple granularity network and dynamic label (MGDL). Specifically, we send the images of two different datasets into multiple granularity network at the same time for joint training to reduce feature distribution gap which is between the two different datasets. The network is trained by two different kinds of pseudo labels, namely, conservative label and radical label. The two kinds of pseudo labels are used to alternating pull and push the feature distribution in the target domain to reduce the variations within an unlabeled dataset. Experiments have shown that the MGDL achieves competitive performance in person Re-ID which is under the cross-domain setting.
MSPPNet: A Lightweight Network for Real-time Semantic Image Segmentation
Authors: Liang Yuting , Hang Tangtian , Chen Jie , Liu Lei 
Published: September 2021

Abstract
Real-time semantic segmentation is widely applied in many fields. However, current state-of-the-art methods ignore the inference speed, while some other models that have short run-times produce coarse segmentation results. To balance the inference speed and segmentation accuracy, we propose a Multi-scale Spatial Pyramid Pooling Network (MSPPNet), a lightweight and efficient network for real-time semantic segmentation. Here, we adopt modified Xception to obtain high-level and low-level feature maps, which fundamentally reduces computational complexity and the number of parameters. Besides, we design the Multi-scale Spatial Pyramid Pooling module (MSPP) to aggregate context information from high-level feature maps, which effectively improves segmentation accuracy. Furthermore, the spatial attention mechanism is employed to enrich the details of segmentation and recover object boundaries. Experiments on the Cityscapes dataset show that MSPPNet has less than 1M parameters, and achieves 64.55% mean IoU with a speed of 121 fps. It is demonstrated that MSPPNet achieves a balance between speed and accuracy.
Computational Simulation and Prediction to the Protonated Uranyl-Salophen Coordination with Cyclohexenone
Authors: Lan Wenbo , Meng Yanbin , Zeng Huiwen , Wang Xiaofeng , He Liping , Tan Bin , Li Jianhuang , Wang Liping , He Ying , Zeng Hui , Wang Lei 
Published: September 2021

Abstract
In this study, computer simulation prediction technology was used to establish an interaction model between cyclohexene and protonated small molecules modified uranyl-Salophen. Theoretical simulation research the compounds using Uranyl-Salophen modified by methyl, vinyl and carboxyl-propenyl, with the cyclohexene complexes in vacuum condition, were performed by using density functional theory (DFT) at level with the B3LYP/6-311G*basis set. All of the combination structure is carried out under the same base group, in optimization, infrared spectrum, the binding energy, and the calculation of molecular orbital energy level. The results showed that the capacity and stability of complexes whose Uranyl-Salophen modified by carboxyl-propenyl was strongest, the vinyl followed, and the methyl weakest, and methyl modified even reduce the compounds combining ability.
Research on the Water Quality Intelligent Monitoring System of River Gushing Based on Federal Learning
Authors: Zhu Jiadong 
Published: September 2021

Abstract
Intelligent video surveillance technology is increasingly widely used in social production and life and plays an important role in economic development and environmental protection. This paper focuses on the field of water quality monitoring, and aims at the problems of traditional river gushing including difficulty of water quality monitoring, poor timeliness and insufficient automation, and proposes the intelligent water quality monitoring method from the perspective of intelligent image treatment. A federated learning system combined with a NVIDIA Jetson TX2 edge nodes is constructed by HD cameras deployed at various monitoring points of the gushing. On this basis, based on the convolutional neural network technology, the identification and monitoring of the river gushing water quality can effectively reduce the operation amount and water quality monitoring automation degree under the case of ensuring the accuracy.