bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deep Learning based Privacy Information Identification approach for Unstructured Text
Authors: Ning Yichen , Wang Na , Liu Aodi , du Xuehui 
Published: April 2021

Abstract
Data sharing sometimes brings the privacy disclosure risk. Anonymization methods such as k-anonymity, l-diversity prevent privacy disclosure, but such methods are suitable for structured text. There are a lot of unstructured texts in people’s lives (such as social network texts, clinical texts), and identifying and structuring the private information(PI) of unstructured texts is a problem. Based on this, we propose a deep learning-based unstructured text PI identification approach, which can extract PI in unstructured text, associate the PI with the corresponding subject, and organize it into structured data, to support follow-up anonymization. This approach is divided into two tasks: PI identification and PI association. we respectively propose a sequence labeling model based on the RoBERTa-BiLSTM-CRF hybrid neural network and a PI association method based on the RoBERTa-HCR hybrid neural network to identify PI and organize it into structured data. The experimental results show that, compared with the benchmark model, RoBEERTa-BiLSTM-CRF has better performance; compared with the current Chinese coreference resolution model, the average F1-score value of RoBERTa-HCR is increased by 6%.
A Closed-Form Localization Algorithm in Scan-Based Sonar
Authors: Sun Ping , Rong Yingjiao 
Published: April 2021

Abstract
Multi-station TDOA positioning is a more accurate positioning method, which can locate the acoustic emitter by processing the arrival time of signals collected by three or more measuring stations. This paper presents a closed-form localization algorithm using the angle-of-arrival and difference time of scan time measurements from the scan-based sonar (SBS).The basic principle of multi station TDOA passive location is proposed. Location scheme based on the combination of different platforms is used. The basic principle of TDOA passive location is discussed, as well as the realization scheme of the main problems such as the synchronization of observation station and acoustic target, the selection of ground observation station, error correction and so on.
Domain Adaptive Chinese Word Segmentation Method for power data security classification
Authors: Dong Yang , Zheng Yang , Zhang Xu , Gao Jing , Fan Zheng 
Published: April 2021

Abstract
Chinese word segmentation (CWS) is an important task for Chinese NLP, and also an essential pre-processing step to establish a word-root database for security classification of power data, covering different domains such as laws & regulations, power. It is impracticable to label a large number of training corpus for each domain, which brings great challenge to the supervised statistical learning method to carry out effective CWS. Therefore, a Chinese word segmentation approach based on dictionary and semi-supervised conditional random field (SS-CRF ) is presented. At first, a CRF model for CWS is trained with self-training and active learning algorithms and used to conduct CWS task. Then the dictionary features are introduced to correct the result of CRF based segmentation by adopting RMM algorithm. Experiments on a cross domain segmentation task show that the proposed method can effectively improve the domain-adaptive performance of CWS.
Feasibility of applying computed tomography angiography in diagnosing and classifying aortic intramural hematoma
Authors: Yuan Biying , Liu Jianhua , Tian Jiale , Xu Nannan 
Published: April 2021

Abstract
IMH is a common manifestation of aortic disease, which is also a common lesion in acute aortic syndromes. It is often considered as a precursor of AD. Paying close attention to the development of IMH is important for the prognosis of patients. CTA can effectively determine the location and range of IMH and whether it is associated with AD. At present, post-processing techniques of computed tomography angiography include MPR, CPR, VR. These methods are commonly used to evaluate IMH.
Study on Dynamic Monitoring Technology of Soil and Water Conservation in Construction Projects using Multi-source Remote Sensing Information
Authors: Xitao Huang , Siyu Chen , Yu Zhang , Han Zhang , Xiaofeng Yang , Pengtao Jiao , Liyuan Xiao 
Published: April 2021

Abstract
It is the legal duty of water administrative departments to monitor and supervise construction projects, but the single image source and interpretation technology have stunted supervision efficiency improvement. By utilizing the characteristics and advantages of multi-source remote sensing information, we constructed dynamic monitoring technology of soil and water conservation in construction projects based on multi-source remote sensing data, and used Mi Zhi County and Zi Zhou County in Yu Lin City as the experimental area to extract new disturbance maps from 2017 to 2018. The results show that coordinated application of multi-source remote sensing information can effectively improve the monitoring frequency up 64%, and the accuracy up 25%, which can provide reference for the informatization of soil and water conservation supervision in construction projects.
An inner full-state feedback control model for traffic flow system
Authors: Zhang Lidong , Li Xiaowei , Wang Jian 
Published: April 2021

Abstract
In order to discover the compound effect of traffic system’s state variables on its stability properties and inner mechanism, we presented a type of inner full-state feedback control strategy based on optimal velocity model of traffic flow system. We took the car’s velocity and headway as the inner feedback control input parameters and car’s acceleration as the system’s output parameter. With Lyapunov’s direct method, we deduced the new state space equation and its stability condition. Under its stable conditions, we studied the effect of full-state feedback control strategy on traffic flow and the jamming transition by use of numerical and analytical methods. The new system’s controllability and observability were also studied. Numerical simulations verified our analytical results’ correctness.
Overall structure design of model management and data protection system based on STL
Authors: Zhang Changhao , HuLi  , Yu Tianbiao , Shi Xiaolei 
Published: April 2021

Abstract
This paper realizes the development of STL model management and data protection system for 3D printing cloud platform. Firstly, the 3D printing cloud platform is briefly introduced, and the system requirements analysis, system technical architecture construction, system flow and system database design are completed. Then, based on ASP.NET, database and other technologies, combined with C# language, Web development is carried out. Finally, from the perspective of tourist users, registered users and background administrators, the functions of their respective modules are realized.
Longitudinal aerodynamic coefficients modeling from simulated flight data of fixed wing aircraft based on three methods
Authors: Wang Zhigang , Li Aijun , Wang Lihao , Ma Shichao 
Published: April 2021

Abstract
The equation error method, output error method, and neural network method are used to model the aircraft longitudinal aerodynamic coefficients from simulated flight data. The equation error method and the output error method are based on postulated mathematical models of the longitudinal aerodynamic coefficients, and the least squared principle and the maximum likelihood principle are used to estimate the model parameters respectively. The neural network method uses the BP network output to fit the aerodynamic coefficients without assumptions of mathematical model of aerodynamic coefficients. Two sets of simulation tests were conducted, one set of data is used for model identification, and the other set is used for model validation.
An ensemble approach for art face recognition problem
Authors: Wang Cunzheng 
Published: April 2021

Abstract
In recent years, with the development of the digitalization of cultural relics, many cultural relic restoration models based on different calculation methods have been proposed. Although the models for feature classification (such as VGG16, Xception) or ensemble models (such as SVM and ANN) are relatively mature, multiple classification problems and accuracy problems are still far from ideal. This paper proposes an ensemble method for artistic face recognition based on multiple models. Six convolutional neural network models are used as basic models to compare their performance in gender and status classification. Based on the KaoKore data set, after training them to obtain the corresponding characteristics, the ensemble model is used for weight combination to obtain a more accurate model. It has achieved 98.10% and 90.13% accuracy on gender and status classification respectively, which is the best in the current literature.
Credit risk assessment in bank based on SMEs using PCA
Authors: Huang Zhou , Yan Zifeng , Zhao Qingzhu , Ma Keke 
Published: April 2021

Abstract
In recent years, small and medium-sized enterprises (SMEs) have gradually become the backbone of China’s national economic development due to their strong innovation ability and high operational efficiency. However, such enterprises have poor anti risk ability in the early stage of development and need banks to lend to them. In order to help banks quickly and accurately evaluate the ability of small and medium-sized enterprises to determine the amount of loans, this paper proposes a comprehensive ability evaluation model based on PCA algorithm. Considering the strong impact of the new COVID-19 on SMEs in 2020, we introduce this sudden factor into the model to improve the adaptability of the model and help banks to accurately evaluate the enterprise capacity under the COVID-19.
Countermeasures of Computer Security Storage and Cloud Computing Technology
Authors: Jin Wei 
Published: April 2021

Abstract
With the development and application of the Internet, it has greatly changed the way people live, produce and learn, and has been widely used in all fields of society. In the process of computer operation, massive data information is generated, which provides important technical support for computer storage for the effective processing of these data information. The Internet is a double-edged sword, which provides convenience, and is threatened by computer virus, illegal access, hardware damage and so on at the same time in the process of computer information storage, which leads to the collapse of computer system to bring huge economic losses for enterprises and individuals. This paper first expounds the security problems of computer security storage and the characteristics of cloud computing technology, and combines the characteristics of cloud computing technology to explain the application of cloud computing technology in computer security storage.
Collaborative Attention Network for Person Re-identification
Authors: Li Wenpeng , Sun Yongli , Wang Jinjun , Cao Junliang , Xu Han , Yang Xiangru , Sun Guangze , Ma Yangyang , Long Yilin 
Published: April 2021

Abstract
The quality of visual feature representation has always been a key factor in many computer vision tasks. In the person re-identification (Re-ID) problem, combining global and local features to improve model performance is becoming a popular method, because previous works only used global features alone, which is very limited at extracting discriminative local patterns from the obtained representation. Some existing works try to collect local patterns explicitly slice the global feature into several local pieces in a handcrafted way. By adopting the slicing and duplication operation, models can achieve relatively higher accuracy but we argue that it still does not take full advantage of partial patterns because the rule and strategy local slices are defined. In this paper, we show that by firstly over-segmenting the global region by the proposed multi-branch structure, and then by learning to combine local features from neighbourhood regions using the proposed Collaborative Attention Network (CAN), the final feature representation for Re-ID can be further improved. The experiment results on several widely-used public datasets prove that our method outperforms many existing state-of-the-art methods.
Research on the Recognition of Abnormal Behaviors in the Elderly Based on Wi-Fi Signals
Authors: Li YiFan , Cao YongChun , Lin Qiang , Wang WeiQiong 
Published: April 2021

Abstract
Wandering behaviour is an important diagnostic indicator for patients with Alzheimer’s disease, and falls are the main threat to the health of the elderly. Effective detection of these two types of abnormal behaviours of the elderly and timely intervention are important for improving the quality of life of patients with Alzheimer’s disease and related diseases significance. This study proposes and studies a method for detecting and identifying abnormal behaviours of the elderly based on Wi-Fi signals. First, the correlation between Wi-Fi signal changes and abnormal behaviours of the elderly was analyzed; secondly, this study applied general Wi-Fi equipment to obtain the status data of the abnormal behaviours of the elderly and preprocess the data through Hampel filtering; then, the effective features of the CSI sequence fragments were designed and extracted, a data set of 2243 samples including 7 types of abnormal behaviours of the elderly was constructed; finally, a BiLSTM-Based abnormal behaviour recognition model for the elderly was constructed, and the average classification accuracy reached 95.95%, which proved this experiment is feasible and effective.
Research on Weapon Equipment Storage Structure Optimization Model Based on Improved ABC Classification Method
Authors: Li Xiaosong , Li Zenghua , Peng Xinran , Xiao Zhenhua 
Published: April 2021

Abstract
Weapon equipment storage is an important part of weapon equipment supply support. In this paper, we analysed the related concepts of weapon equipment storage, introduced the ABC classification method, proposed the steps to construct the optimization model of the weapon equipment storage structure, studied the influencing factors of the weapon equipment storage structure, and constructed a storage structure optimization model based on the improved ABC classification. The optimal weapon equipment storage structure and strategy were obtained, and a case analysis was carried out. The research conclusions can provide reference for the practical work of weapon equipment storage.
Analysis of Cheating in Exams Based on Difference Equation Model
Authors: Wu Chaolin , Maalla Allam 
Published: April 2021

Abstract
The problem of cheating in exams is a serious social problem, and cheaters have done huge damage to the social public order. How to reduce or even eliminate cheaters? Researchers have analyzed from different angles, most of the research is from the perspective of game theory or evolutionary game theory. However, game theory and evolutionary game theory have the disadvantage of not being able to quantitatively examine the changes in the number of cheaters and supervision. Aiming at the deficiencies of game theory and evolutionary game theory, this paper establishes the evolution model of the cheating problem of the differential equation model, analyzes the balance point and characteristics of the coexistence evolution model of different actors of cheaters and regulators, and examines the behavior of cheaters along with supervision. Changes in the number; the influence of different factors on the changes in the number of cheaters is analyzed, and on this basis, a comparison of anti-cheating strategies is made. After comparing the effects of the two different anti-cheating strategies of ‘blocking’ and ‘blocking’, it is concluded that ‘blocking’ can reduce cheaters more than ‘blocking’, and corresponding policy recommendations are given. Considering that it is difficult to obtain the data of cheaters and supervisory behaviors, the model was simulated using MATLAB, and the simulation results verified the correctness of the analysis.
Extraction and construction algorithm of traffic road network model based on OSM file
Authors: Wang Hangong , Wang Jiakang , Yu Peiyao , Chen Xudong , Wang Ziyang 
Published: April 2021

Abstract
In order to solve the problems that are not conducive to the automatic analysis and processing of related applications in the initial data of the road network, the main data is extracted from it to construct a traffic road network model. We designed an algorithm for extracting and constructing a traffic network model based on OSM files. The algorithm is mainly divided into preprocessing of OSM geographic data, road network extraction, road recognition, intersection recognition and merging, providing data basis for traffic simulation needs.
GDC block: gradient-guided direction-aware convolution block for image classification
Authors: Peng Jinye , Liu Maomei , Tang Lei , Zhong Sheng , Luo Hangzai 
Published: April 2021

Abstract
Convolutional Neural Network (CNN) has achieved great success in visual applications. In the field of image classification, researchers usually customize CNN models to meet the needs of different real-world applications. It consumes a lot of human labor and computing resources but only achieves slight performance improvement. Besides, some recent works try to integrate prior knowledge into classic CNN models to improve their accuracy, but they are usually only effective for special applications. In this paper, we propose a gradient-guided direction-aware convolution (GDC) block. It can be used to replace low-level convolutions of existing CNN without changing the off-the-shelf architecture. The gradient priors provide object shapes that CNN’s low-level convolution requires. And the direction-aware mechanism expands the receptive field size. This scheme is a trade-off between model size and model accuracy. Experimental results show it can moderately reduce the size of any CNN models while enhancing their performance.
A New Speech Enhancement Method Based on Nonnegative Low-rank and Sparse Decomposition
Authors: Sun Jiayi , Sun Chengli , Hong Yi 
Published: April 2021

Abstract
Enhancement of speech degraded by strong noises is a highly difficult task. In this paper, a nonnegative low-rank and sparse matrix decomposition (NLSMD) based speech enhancement method is given to address this problem. The proposed method is motivated with assumptions that in time-frequency (T-F) domain, since power spectrum of many types of noise with different frame are often correlative, noise can be assumed with a low-rank structure, while speeches are often sparse in T-F units. Based on these assumptions, we formulate the speech enhancement as a NLSMD problem, and design an objective function to recover speech component. Compared with traditional methods, the NLSMD-based method does not require a speech activity detector for noise density estimation. Experimental results show the proposed method can achieve better performance over many traditional methods in strong noise conditions, in terms of yielding less residual noise and lower speech distortion.
Heterogeneous Image Template Matching Based on Region Proposal Network
Authors: Yue Sha , Wang Shengzhe , Cui Yuyong , Guan Wei , Gao Xinyi 
Published: April 2021

Abstract
Heterogeneous template matching is important to disaster relief. The method of traditional heterogeneous template matching in real-time performance is so weak and usually has a lower accuracy than deep learning method. A heterogeneous template matching method based on region proposal network is proposed in this paper. Specifically, deep features between the optical images and SAR images are extracted by using residual network, and MRPN, a suitable region proposal network for matching work, is proposed to complete precise positioning of the template region. Finally, the channel fusion is performed on the matching results to get a better result and confidence. It is shown that the real-time performance and the accuracy performance have been greatly improved, which is reaching 97%.
An improved model of document retrieval efficiency based on information theory
Authors: Xiaoli Li , Xiaokai Yin , Kan Li 
Published: April 2021

Abstract
Literature retrieval is an important auxiliary work of scientific research. The form of literature retrieval is becoming more and more abundant, the number of literature is huge, and the storage set is complex. The algorithm of retrieval tools directly affects the accuracy and efficiency of retrieval. Based on the analysis of the problems existing in various literature retrieval technologies, an improved TF-IDF literature retrieval efficiency model is proposed. It provides a new idea for the design and implementation of literature retrieval system.
Prediction model of physical activity level and hypertension based on artificial neural network
Authors: Hua Liang , Yu Wang , Jinying Li , Wanjun Zhang , Yiting Wang 
Published: April 2021

Abstract
Purpose: Taking the population of Henan Provincial People’s Hospital Chronic Disease Control Center from July 1, 2020 to December 31, 2020 who came to the hospital for physical examination as the research object, analyze and explore the role and contribution of physical activity level in the prediction of hypertension by using the LSTM network model, which can provide references for the clinical diagnosis of hypertension. Methods: Randomly select 2000 physical examination data, remove missing and invalid data, and preprocess them. Finally, select factors such as gender, age, body mass index, weight grade, waist-to-hip ratio, physical activity level, body fat percentage and other factors to establish a neural network prediction model. Then test and study the model, focusing on exploring the contribution of physical activity level to prediction. Result: The level of physical activity has certain advantages in predicting the prevalence of hypertension, but the predictive ability in the later stage is insufficient
Handwritten dynamics classification of Parkinson’s disease through support vector machine and principal component analysis
Authors: Xu Zhifei , Zhu Zhaohui 
Published: April 2021

Abstract
Parkinson’s disease is a chronic neurodegenerative disease that affects the daily lives of tens of thousands of middle-aged and elderly people. The intelligent classification method of Parkinson’s disease has received extensive attention in recent years. The paper proposes a new auxiliary classification model of Parkinson’s disease based on principal component analysis and support vector machine. The model first samples and preprocesses the collected handwritten sensor data, then performs dimensionality reduction by principal component analysis, and finally inputs the dimensionality reduction data into a linear kernel support vector machine for Parkinson’s disease classification and prediction. The experiment uses 5-fold cross-validation for dataset segmentation and performance verification. The average performance results obtained on the Meander r dataset are: accuracy is 70.86%, specificity is 67.23%, sensitivity is 75.98%, and F1-Score is 69.72%, and the average performance results obtained on the Spiral dataset are: accuracy is 77.45%, specificity is 70.26%, sensitivity is 85.58%, and F1-Score is 77.10%.