bluuebunny/crossref_metadata_2025_split huggingface dataset:
Residual Fusion Network Based Attention for Aspect Level Sentiment Analysis
Authors: Wang Long , Chen Lijiang , Cai Linghan , Mao Xia 
Published: November 2020

Abstract
Aspect level sentiment analysis is a more fine-grain task compared to document level and sentence level. The aim of the task is to identify the sentiment polarity expressed to the given aspect in the text. In this paper, we propose a residual aspect fusion network with attention for the aspect level sentiment classification. In this network, bidirectional GRU is used to collect semantic information of both sentence words and aspect words, and then attention mechanism is applied to construct aspect vectors for each word in the sentence. In the residual fusion module, the model fuses the aspect information and sentence information together by residual connection structure, in which position weight is applied to utilize the position information of aspect words. At the end of the model, Transformer encode layer is used to extract global feature and fully connected layers are used as the classifier. We train and test the network on SemEval2014 task 4 (Restaurant and Laptop) and Twitter datasets, and the experiments demonstrate that our model performs better than previous methods.
Research on Security Issues of Docker and Container Monitoring System in Edge Computing System
Authors: Xu Xinchen , Xu Aidong , Jiang Yixin , Wang Zhen , Wang Qianru , Zhang Yunan , Wen Hong 
Published: November 2020

Abstract
With the development of the 5G technology, edge computing will sink the computing center to the edge of the network close to the user, with low latency, high security, and lightweight features. The development of edge computing also puts forward a series of requirements for security, standardization, and unification. Docker container technology highly meets the existing needs of edge computing due to its lightweight, standardization, convenience, and security isolation. This article mainly analyzes the vulnerability of Docker containers and summarizes the security problems faced by the application of Docker container technology under edge computing systems. It also introduces a container monitoring software Prometheus and proposes a feasible edge computing risk monitoring model based on the Docker engine and Prometheus monitoring software.
Research on Movable Bolt Pre-tightening Equipment for Underground Roadway in Coal Mine
Authors: Xu Suogeng , Min Fusong 
Published: November 2020

Abstract
Research and develop a crawler-moving bolt pre-tightening equipment to be used in bolt support instead of manual pre-tightening. The device adopts a micro hydraulic drive crawler chassis to realize displacement, and has a good parking function to meet the ramp operation; the positioning mechanism has a fast positioning function, realizing rapid movement of the mechanical arm and accurately locating the anchor pre-tightening position; It adopts the principle of “first tensioning and then tightening” to achieve high pre-tightening force of the anchor rod, and the tensile force is adjustable, which is safe and reliable. The research of this equipment can significantly enhance the roadway support effect, lower the roadway support cost, reduce the labor intensity of workers, and create good conditions for the rapid advancement of coal mining face.
Research on Material Allocation Path Based on Hopfield Neural Network and Simulated Annealing Hybrid Algorithm
Authors: Xi Junpeng , Zhao Xudong , Zhu Yingshuang , Xiao Baoqiang , Chen Shun 
Published: November 2020

Abstract
When a natural disaster occurs, in order to ensure the basic material needs of the affected people, the delivery of emergency materials after the disaster occupies a very important position. Aiming at the defects that the Hopfield neural network algorithm is easy to fall into local optimum and the simulated annealing algorithm convergence speed is too slow, a hybrid neural network algorithm (SA-HNN) is proposed. Combining the advantages of Hopfield neural network and simulated annealing algorithm, the simulated annealing algorithm is the mechanism of receiving a poor solution with a certain probability is applied to the Hopfield neural network algorithm, which overcomes the defect that the neural network is easy to fall into the local optimal. Based on this hybrid algorithm, the vehicle distribution path optimization problem is solved. Compared with the traditional neural network algorithm, the algorithm is improved Calculation efficiency and accuracy.
Application of deep neural networks in classification of medium resolution remote sensing image
Authors: Dian-lai Wang , Ai-xia Su , Wen-ping Liu 
Published: November 2020

Abstract
Aiming at the problem that high misclassification rate of remote sensing image occurs due to the phenomenon of “same spectrum different matter” and “same object different spectrum”, the deep neural networks(DNN) is proposed to the classification of medium resolution remote sensing image. The deep neural networkstake advantage of the neural network with multiple hidden layers to learn the characteristics that can describe the essential attributes of data, and has achieved high accuracy in land cover classification in Miyun District, Beijing city. The experimental results indicate that the classification accuracy of this algorithm is the highest compared with SVM and BP neural network, the classification accuracy reaches 96.53%, and the classification accuracy is 10.39% higher than that of SVM. The kappa coefficient of DNN is 0.95, which is also the highest among the comparison algorithm. So, the DNN can apply to the classification of moderate resolution remote sensing image.
Comparison of Convolutional Sparse Coding Network and Convolutional Neural Network for Pavement Crack Classification: A Validation Study
Authors: Tang Haitong , Shi Jun , Lu Xia , Yin Zhichao , Huang Lixue , Jia Dongbao , Wang Nizhuan 
Published: November 2020

Abstract
Accurate and effective identification of pavement cracks can provide a reference for pavement performance evaluation and prediction. Inspired by recent outstanding performance of convolutional sparse coding theory in various research fields, we envision whether convolutional sparse coding can be beneficial for crack detection in complex environments. Therefore, based on the multi-layer convolutional sparse coding (ML-CSC) model, this paper combines multi-layer iterative soft threshold algorithm (ML-ISTA) and convolutional neural network (CNN) into recurrent neural networks (RNN) to identify crack images across the simulated experiments. And in different noise environments, different training cycles and different epochs, the ML-ISTA is compared with traditional CNN and the layered basis pursuit (LBP), which is another popular algorithm for ML-CSC. Experimental results showed that under the different training conditions with the same parameter setting, the stability and accuracy of the ML-ISTA is better than CNN and LBP. The ML-ISTA can achieve crack identification accuracy of 99.36% efficiently, which demonstrates the effectiveness of convolutional sparse coding in crack detection.
Helmet Detection Algorithm Based on Single Pixel Zoom
Authors: Zhou Yongheng , Jiang Lifen , Liang Yan , Ma Chunmei , Sun Huazhi , Nie ShuaiBao , Zuo Ying 
Published: November 2020

Abstract
The automatic detection technology of helmet wearing is of great significance to the safety monitoring of construction site. It is difficult to detect a helmet when the object is small, overlapped, or unclear. Therefore, we propose SPZ-Det model to improve the performance of detect small targets in helmet detection task. We redesign the backbone network layers and proposes a Single Pixel Zoom (SPZ) module. SPZ-Det introduces the lower-level feature map, while for the same scale feature map, we select the higher-level feature map. Then the important elements of each feature map layer are amplified by SPZ module to solve the problem of small object feature fading. In the attention module of SPZ, the residual mechanism is introduced to weaken the influence of the attention. The proposed model improves the detection accuracy and keeps the detection speed. On the Safety-Helmet-Wearing dataset, the mAP of SPZ-Det is 27.9% higher than Efficientdet-d0 and gets an 8.8% increase compares with YOLOv3.
DisCANTree: A Distributed Algorithm for Incremental Frequent Itemset Mining based on MapReduce
Authors: Xiao Wen , Hu Juan 
Published: November 2020

Abstract
Frequent itemset mining is one of the most important data mining tasks. Classical frequent itemset mining algorithms need to store data in a centralized way and run in a batch way, which cannot meet the requirements of fast updating big data mining. In this paper, we propose a distributed incremental frequent itemset mining algorithm, DisCANTree, which uses CANTree to store the conditional database, achieves the load balance between nodes by grouping all items, updates the new transaction to the existing CANTree to avoid the load of tree reconstruction, and uses the efficient FPGrowth algorithm to mine CANTree to generate frequent itemsets. The popular distributed programming model MapReduce and its open source system Hadoop are used to implement the DisCANTree algorithm. The experimental results show that the DisCANTree algorithm has more advantages than the most popular PFP algorithm in performance as well as the number of transferred records between nodes, and especially suits for the fast updating sparse big data.
Intelligent Test Paper Generation Based on Dynamic Programming Algorithm
Authors: Wang YiFei , Wang SuRong 
Published: November 2020

Abstract
This paper describes the problem of intelligent paper grouping and its mathematical model. By optimizing and improving the traditional dynamic programming algorithm, its space complexity is reduced from O(nb) to O(b). At the same time, the flexibility of dynamic programming algorithm is increased by using marker function and tracking algorithm, and the result composition is tracked to obtain the optimal solution. Finally, through several experiments, the improved dynamic programming algorithm is compared with the greedy algorithm and brute force algorithm, and it is found that the improved dynamic programming algorithm has a very good result and is with high efficiency when applied to the simple test paper. It is the most recommended algorithm among the two algorithms compared in this paper.
A Knowledge Graph Based Framework in Relationship Modelling and Real-time Monitoring of Market Participants
Authors: Cheng Wenliang , Guo Kaihao , Jiang Tianpei , Zhang Haipeng 
Published: November 2020

Abstract
Various types of relationships among participants exist in the foreign exchange market, such as transaction, credit relations inside the market and subcompany, investing and shareholding relations outside the market. In the market with complex relationships, an incident happened in one institution may have “butterfly effect”. To monitor systemic financial risks, we integrate various types of data and establish a Knowledge Graph describing the relationships between market participants. Based on the Knowledge Graph, we design a framework to measure the impact of various events on the entire market by connecting the internal market relationships to external market related incidents. By using the BiLSTM-CRF model, we extract the relevant entities in the market news with high accuracy and measure the impact on the market with graph related indicators such as closeness and betweenness centralities. In addition, to use external unstructured data, we use a document-level event extraction method to analyse these unstructured datasets and extract new events and relationships from them to dynamically update the Knowledge Graph.
Construction of a campus Q&A platform based on big data and microservice architecture
Authors: Du Zhixiong , Li Jun , Xiao Zihan , Yu Chenhao , Tang lu , Chen Guangxue , Luo Wanyu 
Published: November 2020

Abstract
As an indispensable part of the network education system, the network answering system has become a research hotspot in the computer field. Based on the current campus platform construction, this paper proposes a campus Q&A platform based on big data and microservice architecture. The microservice framework uses Springcloud and its related components, and the big data component is a combination of components using ElasticSearch and Kafka and Storm. The platform has been put into operation for the construction of the majority of college students and has good practicability.
A Competency Mining Method Based on Latent Dirichlet Allocation (LDA) Model
Authors: Wu Jiying , Son Guandong , Wang Sihui 
Published: November 2020

Abstract
A text mining approach based on latent Dirichlet allocation (LDA) is proposed to analyze the competency characteristics. First, we briefly introduce the principle and hypothesis of latent Dirichlet allocation (LDA) model. Second, we elaborate the idea of using LDA topic model to extract competency, Then, we use Chinese text materials such as biographies and interviews of Chinese scientific and technological talents as data sources to conduct experiments, and obtain four competency topics including knowledge, attitude, quality and values. The research results preliminarily verify the effectiveness of latent Dirichlet allocation (LDA) model in competency research, but there are still many details to be improved in the future.
Multi-convLSTM neural network for sensor-based human activity recognition
Authors: Li Zili , Liu Yixin , Guo Xuerong , Zhang Ji 
Published: November 2020

Abstract
In recent years, human activity recognition (HAR) has attracted a lot of attention due to its wide application, such as indoor positioning and navigation. This paper proposes a MconvLSTM to construct a multi-unit deep network structure,which can effectively improve the accuracy of HAR. Firstly, the input data is dimensionally expanded. Secondly, multiple convLSTM module are used to input data from different sensors to achieve partial weight sharing. Multiple outputs are merged finally. The experimental results show that the partial weight sharing mechanism and dimension expansion effectively improve the extraction of single sensor features, aiming to improve the activity recognition rate. Using public UCI datasets for testing, the accuracy is significantly improved compared to traditional convLSTM network results.
Identification of Anoectochilusroxburghibased on transfer learning
Authors: Liu Yansong , Chai Qinqin , Wang Wu , Zhang Xun 
Published: November 2020

Abstract
Different varieties of Anoectochilusroxburghi(A. roxburghi) have different medicinal values. However, the identification and the clinical efficacy of A. roxburghi was seriously affe cted by the phenomena of doping and adulteration. Thus, accurate identification of different va rieties of A.roxburghiis important. However, traditional identification methods for A.roxburghii dentification are usually time-consuming, laborious or have low accuracy. Thus, an A.roxburgh i leaf image recognition method based on transfer learning is proposed. The model use convolu tion neural network to extract leaf features of A.roxburghautomatically. Then, the knowledge, l earned by a model from a large-scale image data set ImageNet, is transferred to realize the task of A.roxburghi image recognition with the help of transfer learning.Low training cost and high precision classification of leaf strains is realized in this way. Finally, 368 leaf images of 6 speci es of A.roxburghi are collected for recognition, including dayuanye, xiaoyuanye,and etc.. To te st the transfer effect, the Resnet50 transfer learning model is compared with the the other four models. The comparison results show that the accuracy of Resnet50 transfer learning model rea ches 100% in a quicker speed, which illustrates that the model has good robustness and better p erformance of realizing the accurate recognition and classification of the leaves of A.roxburghi. Moreover, data augmentation helps to reduce the over-fit phenomenon of the transfer learning model.
Effect of continuous annealing process on microstructure, texture and properties of cold rolled dual phase steel
Authors: Wei-wei Wang , Li Guang-ying , Liu Liu 
Published: November 2020

Abstract
In order to improve the strength and plasticity of cold rolled dual phase steel DP980, the relationship between annealing process and properties were systematically studied by adjusting continuous annealing process parameters with 800-840?. The primary ferrite, the secondary ferrite, recrystallized ferrite grains, island martensite distribution,precipitate and texture of different phases at different annealing temperatures were analyzed by methods with OM, SEM, TEM and EBSD. The effect of continuous annealing temperature on the strength and plasticity of dual phase steel DP980 were researched by mechanical tensile test in addition. The results show that recrystallized ferrite grains and the secondary ferrite can be refined by optimizing and adjusting the continuous annealing process, at same time, a small amount of retained austenite is distributed beside the island martensite on the microstructure of DP980. The precipitates NbC particle size is about 50∼150 nm. Finally, the fine uniform microstructure, the dispersed martensite and fine precipitates are beneficial to the improvement of the strength and plasticity which increase the strength plasticity balance to a level of 15 -18GPa%.
Increasing body size and fecundity in a salamander over four decades, possibly due to global warming
Authors: Okamiya Hisanori , Hayase Nagatoshi , Kusano Tamotsu 
Published: November 2020

AbstractRecent climate change has been shown to affect phenotypic traits, such as body size and fecundity, in some animals. It is important to assess the response of a species to climate change for predicting a population’s future. We compared historic and contemporary body size and clutch size measurements in the lentic breeding salamander, Hynobius tokyoensis, collected from a wide range of latitudes in its geographical range and concluded that the species has gone through significant increases in body size and clutch size over the last four decades. Although a decrease in body size due to climate change is well documented for other species, reports of an increase in body size are rare. In addition, we found that increases in temperature and precipitation were constant regardless of latitude, but that the ratios of increase in body size and clutch size were greater in high-latitude populations. Our results suggest that, even within a species, the magnitude of the response to climate change depends on the geography of the population.
IgA vasculitis (Henoch–Schönlein purpura): refractory and relapsing disease course in the adult population
Authors: Van de Perre Els , Jones Rachel B , Jayne David R W 
Published: November 2020

Abstract

Background
The disease course of adult immunoglobulin A (IgA) vasculitis (IgAV; Henoch–Schönlein purpura) has not been well defined.

Methods
In a retrospective survey, we studied 85 adult IgAV patients with extended follow-up (median 43 months) for 67 patients.

Results
Only 33 of 67 (49%) achieved complete remission. Ongoing renal disease was the most common persistent organ manifestation, but extra-renal disease activity was also present in &gt;50% of patients not achieving complete remission. Twenty-nine of 67 (43%) had relapsing disease, with 18/67 (27%) experiencing several relapses. Skin disease was the most common feature in relapsing patients, followed by nephritis. At 4 years of follow-up, 6 of 29 (21%) experienced progressive disease and 10/29 (34%) relapsing disease. Five of 67 (7%) developed nephritis after diagnosis, within the first 6 months of follow-up. At final follow-up, 10 of 67 (15%) had chronic kidney disease Stage ≥G3a, 18 (27%) haematuria and 13 (19%) proteinuria. No therapy appeared particularly effective and only 6/17 patients treated with mycophenolate mofetil experienced a good response.

Conclusions
The disease course of adult IgAV is different from that seen in children, with higher frequency of persisting and relapsing disease. Renal disease is the main determinant of ongoing disease activity, but extra-renal features were seen in &gt;50% of patients with chronic disease activity. No clear conclusions on use or choice of immunosuppressive agent could be made based on our experience.
The additive prognostic value of B-lines and heart rate reserve during “kindergarten” stress echocardiography
Authors: Scali M.A , Ciampi Q , Zagatina A , Prota C , Cortigiani L , Borguezan-Daros C , Wierzbowska-Drabik K , Djordjevic-Dikic A , Simova I , Boshchenko A , Gaibazzi N , Torres M.A , Carpeggiani C , Picano E 
Published: November 2020

Abstract

Background
B-lines by lung ultrasound (LUS) were added to stress echo (SE) as a direct sign of pulmonary congestion useful to establish an objective link between dyspnoea symptoms and acute heart failure. They are feasible with “kindergarten” training of few hours and pocket size instruments.

Aim
To assess the prognostic value of “kindergarten SE” only based on B-lines and imaging-independent heart rate reserve (HRR).

Methods
We enrolled 2,149 patients (age 63±16 yrs, 831 women, 39%) with known or suspected coronary artery diseasereferred for exercise (n=1,015), dipyridamole (n=1,039), adenosine (n=16) or dobutamine (n=79) SE. By LUS, we adopted the 4-site simplified scan, each site scored from 0=normal A-lines, to 10=coalescing B-lines. HRR was assessed as peak/rest ratio of heart rate. All patients were followed-up.

Results
Interpretable HRR and LUS data were obtained in all patients (feasibility=100%). Abnormal B-lines (≥2) at peak stress were present in 756 patients (35%). Abnormal HRR (≤1.80 for exercise and dobutamine and ≤1.22 for vasodilator) was found in 986 patients (46%), both positivity in 388 patients (18%). During a median follow-up time of 15 months, 137 spontaneous events occurred in 120 patients: 38 deaths, 28 myocardial infarctions, 60 acute heart failures, 11 strokes. B-lines ≥2 and/or reduced HRR were independently associated with adverse outcome (see figure). At multivariable analysis, a three-fold increased risk of death was observed when both B-lines and HRR were abnormal (Hazard ratio: 3.097, 95% Confidence Intervals 1.095–8.754, p=0.03).

Conclusions
A super-simplified stress test (“SE without SE”) with simple heart rate assessment by EKG and LUS for B-lines evaluates key variables such as chronotropic incompetence (due to reduced sympathetic reserve) and pulmonary congestion (due to backward acute heart failure) and allows an accurate prediction of outcome.
Figure 1

Funding Acknowledgement
Type of funding source: None