bluuebunny/crossref_metadata_2025_split huggingface dataset:
How do age, language, narrative task, language proficiency and exposure affect narrative macrostructure in German-Swedish
bilingual children aged 4 to 6?
Authors: Lindgren Josefin , Bohnacker Ute 
Published: November 2020

Abstract
Previous studies show mixed findings concerning whether higher-order story structure (macrostructure) is similar across
bilinguals’ two languages. It is not known how macrostructure is influenced by general language proficiency and amount of exposure. The
present study investigates these issues in 46 German-Swedish bilingual 4- to 6-year-olds. Narratives were elicited in both languages with
two picture-based tasks from the Multilingual Assessment Instrument for Narratives (MAIN) in the telling mode. We investigate to what extent
the language of elicitation (Swedish vs German) influences bilingual children’s macrostructure (story structure, episodic complexity) and
explore effects of narrative task, age, narrative length, expressive vocabulary and estimated language exposure, both separately and
combined, on macrostructure in the respective language. Results show that macrostructural skills developed measurably with age from 4 to 6
years in both languages, with no task effects. Story structure scores were higher in the majority language Swedish than in German and
developed differently with age. The effect of narrative length on story structure was similar in the two languages. Language exposure did
not have any significant effect. Macrostructure scores were significantly affected by expressive vocabulary in German only. Generally, the
results may be linked to slightly higher language proficiency in Swedish.
RETRACTED: Research on Network Security Vulnerability Detection Method Based on Artificial Intelligence
Authors: Luo Guoyu 
Published: November 2020

Abstract
The Internet is gradually being used in various fields, and people’s lives are increasingly dependent on Internet technology. In network construction, we need to focus on maintaining network security and protecting the legitimate interests of users. In the network security defense system, network security vulnerability detection is an important part. Network attacks and network security vulnerability detection technology updates have shown a confrontational spiral in the field of network information security. One of the key contents of network security protection is how to effectively detect unknown network security vulnerabilities. In terms of network security vulnerability analysis, this paper studies cross-site scripting vulnerability analysis, SSL security vulnerability analysis, and binary vulnerability analysis; in terms of network security vulnerability prediction, it discusses random forest prediction models, convolutional neural network prediction models, and support vector machine predictions. Model; combined with common rules for network security vulnerability detection, comparative analysis of the network security vulnerability detection effects of several typical machine learning methods.
Knowledge distillation and data augmentation for NLP light pre-trained models
Authors: Luo Hanwen , Li Yudong , Wang Xiaodong , Zhang Yuqing 
Published: November 2020

Abstract
Model lightweight aims to solve the problems of various large models for slow training and high resource requirement. Knowledge distillation can be a good solution to these problems. We built a lightweight model that meet the competition requirements and have prominent NLP capabilities. RoBERTa-tiny-clue was used as our backbone model. We tested the effect of soft labels and hard labels on knowledge distillation, made knowledge distillation, fine-tuned this model to get a lighter model with better performance, and then applied it downstream NLP tasks. We also adopted a series of data augmentation methods to improve the performance of the model on downstream tasks, customized different optimization solutions for four tasks. Based on open-source pre-trained model RoBERTa-tiny-clue and public available datasets, we achieved 15 times smaller and 10 times faster than BERT-base, and 95% of BERT-base performance on downstram NLP tasks. Using suitable data augmentation methods for the trained lightweight model, the performance of the model on various downstream tasks reaches or exceeds BERT-base.
Change points estimation for equipment degradation process based on fuzzy c-means clustering
Authors: Zhu Haizhen , Xiao Mingqing , Zhao Xin 
Published: November 2020

Abstract
The working equipment would experience a degrading process until it runs to failure. Many works have been done to illustrate and describe this process as accurate as possible. In the real world applications, the monitored data are unlabelled data. In this paper, we aim to automatically assign degrading states to the monitored. To achieve this target, the change points between adjoining states should be evaluated. Instead of using the conventional hard clustering methods, we adopted the fuzzy c-means clustering, where the monitored data can be assigned to two adjoining states, to estimate the change points. Additionally, the degrading process of an intermittent working equipment is utilized to verify the effectiveness the evaluation method. The experiment results show that fuzzy c-means clustering is suitable for assigning state labels to the unlabeled data and evaluating the change point of involved equipment.
Web Service Composition Optimization Based on Adaptive Mutant Beetle Swarm
Authors: Yang Hongwei , Xue Fucheng , Zhu Hongmei , Zhu Leilei , Li Li 
Published: November 2020

Abstract
with the continuous development of cloud computing technology and big data, web services are widely used in intelligent information processing in all walks of life, when facing a large number of services with similar functions and quality, web service composition technology is a crucial technology in web services, which enable common services to be combined and select the optimal service combination to meet the wide range of users’ needs. Aiming at the problems of algorithm accuracy and stability of existing service composition technologies, the penalty function is used to construct the fitness function in this paper, which transforms the constrained optimization problem into an unconstrained optimization problem, and realizes the global optimization. Traditional web service composition models are aimed at QoS attribute perception. This paper establishes a QoE model that focuses on user experience, which can better meet the needs of users. An adaptive mutant beetle herd algorithm was proposed in this paper, the author introduced Beetle Antennae Searching Algorithm into the common particle swarm optimization algorithm, and added an adaptive mutation factor to enhance the searching ability of the algorithm; The use of dynamic learning factors allows the algorithm to have better population diversity and enhanced convergence. The experimental results that the algorithm proposed in this paper has higher algorithm accuracy, comparatively faster convergence speed and stability.
Analysis of Emotional Evolution of Emergent Events Network Based on Spatial Measurement
Authors: Luo Qingyue , Song Yinghua , Liu Shanyao 
Published: November 2020

Abstract
I study the temporal and spatial evolution of network emotions in emergencies, and provide decision support for public opinion guidance. This paper uses python to compile a crawler to obtain the comment information of Weibo, calculates its network sentiment value based on the naive Bayes classifier, uses spatial analysis methods and the SAAR spatial measurement model system, and analyzes the temporal and spatial pattern evolution and spatial agglomeration of network sentiment Characteristics, and further reveals its influencing factors and complex spatial correlation. At the same time, there is a spatial spillover effect of online emotions, and the intensity of comments and public opinion has a promoting effect on it, while geographical distance has an obvious inhibitory effect on it. Online emotions have regional differences. Disaster management departments can use this difference to more efficiently identify emotionally hot areas from social media data, so as to take timely response measures.
Evaluation of Resource Scheduling Ability of Joint Action System of System in Military and Local Government
Authors: Zhen Ziqing 
Published: November 2020

Abstract
Based on the normal process of system evaluation, this paper defined the resource scheduling ability of the Joint Action System of System in Military and Local Government from some aspects such as staff, equipments and materials. In this paper, the quality of task completion was taken as the utility function of resource scheduling for building the evaluation method of resource scheduling ability of Joint Action System of System in Military and Local Government. Moreover, the feasibility of the evaluation method was verified by actual cases, so this paper solved the operational problem of resource scheduling capability evaluation of this system.
Digital Reproduction and Virtual Simulation System Design of Astronomical Clock-tower
Authors: Zhang Tian , Lv De-Sheng 
Published: November 2020

Abstract
The author designed and developed a virtual simulation system to introduce people to the great astronomical installations called astronomical Clock-tower of the Song Dynasty in China. In this paper, the operation data of the replica of the Astronomical Clock-tower was studied and defined, the digital replication and working principle of each part of the astronomical clock tower platform are studied, the dynamic flow physical simulation was carried out by using the Obi fluid plug-in, and the user experience system was improved by the design of virtual reality. Based on the interaction design analysis and usability research analysis, the user learning test table is formulated. The statistical data illustrates the advantages of the system in terms of learning and user experience.
An optical multi-wavelength conversion method for distributed satellite system based on the nonlinearity of SOAs
Authors: Shi Xiaodong , Li Yongjun , Xin Ning , Wang Weilong 
Published: November 2020

Abstract
It is highly necessary to share information between satellites in optics distributed satellite system. The signals usually need to be multi-casted to several nodes. The initial signals can be copied to several wavelengths directly by multi-wavelength converting technology. So it is an efficient way to solve the issue of optics multicast in distributed satellite network. To realize the optics multicast in distributed satellite system, an optical multi-wavelength conversion method based on the nonlinearity of semiconductor optical amplifier (SOA) is put forward. Firstly, by making use of the SOA’s property of spectrum broadening combined with offset filter, the initial signal is filtered by blue-shifted filter and red-shifted. The pre-conversion and detecting signals are produced respectively. Then the multi-wavelength conversion is realized based on the four-wave mixing (FWM) in another SOA. As is obtained by dividing the initial signals, the pre-conversion and detecting signals can be synchronized more easily. The results show that the output Q factor for each channel is promoted and varies with wavelength and signal input power. The power improvement is more than 15dB and the maximum value is 27dB.
Research on Dynamic Political Sentiment Polarity Analysis of Specific Group Twitter Based on Deep Learning Method
Authors: Chang Chengyang , Wang Xiaodong 
Published: November 2020

AbstractThe article introduce a method to analyzing the dynamic political sentiment polarity of American politicians’ tweets within a fixed period of time to assist analysts in judging the direction of American politics and the future trend of China-US relations. This research is applied in the field of think tanks or intelligence analysis. The service targets are intelligent analysts, and the data is the tweet text data sent by a specific group in a fixed time period. We propose an architecture that combines multiple deep learning models and use a dedicated tweet data set to construct a specific group to obtain an sentiment polarity multi-classifier, and then introduce the time characteristics of tweets, and finally obtain the dynamic political sentiment polarity of politicians. The US politician tweets data set proposed in this article verifies that the proposed comprehensive architecture is better than traditional deep learning methods in this task, and the accuracy of the classifier verification set reaches 80.66%. According to the sentiment polarity judgments of 20 US governors and senators, the success rate is 75%. The analysis of individual dynamic political sentiment polarity can provide effective help and intelligence support for analysts. The method in this paper effectively uses a variety of deep learning techniques to assist analysts to obtain more accurate dynamic political sentiment polarity from massive Twitter text data.
Research on Rayleigh Scattering and Brillouin Scattering of Bidirectional Wavelength Division Multiplexing System
Authors: Wu Qiwu , Zhou Yang 
Published: November 2020

Abstract
In order to study the influence of Rayleigh and Brillouin scattering on the performance of bidirectional Wavelength Division Multiplexing (WDM) system. On the VPI simulation platform, a two-way WDM simulation system was built. Rayleigh scattering and Brillouin scattering were generated by setting the parameters of the optical fiber module and system parameters such as Brillouin Scattering, Rayleigh scattering, Rayleigh Distortion, and Rayleigh Distortions. Then, the frequency spectrum of the received signal and the bit error rate were observed and analysed. Experimental results show that Rayleigh and Brillouin scattering will cause the performance of the two-way WDM system to degrade. By separating the back propagation channel, the impact of these two effects on the performance of the two-way WDM system can be reduced.
Real-time and accurate object detection on edge device with TensorFlow Lite
Authors: Dai Junyan 
Published: November 2020

Abstract
Objection detection is of vital importance to many fields, such as autonomous driving, outdoor robotics, and computer vision. Existing approaches on object detection can hardly run on the resource-constrained edge devices. In order to mitigate this dilemma, we propose to apply TensorFlow Lite to convert Float32 neural network model to unit8 neural network with subtle or even no accuracy loss. Two advantages are here for conversion. First, it reduces the model size to a quarter so that it fits for devices with limited storage. Second, it achieves much faster inference time. I conduct an experiment on MSCOCO dataset. Experimental results show that our proposed method achieves mAP 72.1 and FPS 23 on edge device.
Baby-Follower: a Child-Care Robot System Based on OpenMV and IOT
Authors: Guo Xingyue , Wu Jiahe , Fang Jing 
Published: November 2020

Abstract
Frequent accidents of children at home have caused disastrous injury and economic loss. However, the traditional child-care robots mainly focus on human-computer interaction and emphasize educational function, which cannot be used specifically to solve children’s safety problems. A child-care robot system based on OpenMV and IOT is proposed in this paper. The system is a smart vehicle-type robot, which recognizes and locates the target with the AprilTag label attached to children’s legs and hazard points by the machine vision module, follows the children’s movement, monitors the surrounding environment through sensors, uploads data to OneNET cloud platform and provides audible alarms of the emergency to the guardians in real time. Furthermore, it also provides video real-time transmission function, manual control function and data viewing function through browser and App to protect children from danger and assist guardians to accompany children’s growth.
RDSSD: Refinement Deconvolutional Single Shot Detector
Authors: Guo Ke 
Published: November 2020

Abstract
The main contribution of this paper is a refinement for deconvolutional single shot detector. Our model is called refinement deconvolutional single shot detector (RDSSD). In the field of object detection, most two-stage detectors are more accurate than one-stage detectors. We use its advantages to optimize deconvolutional single shot detector. Compared with DSSD, RDSSD mainly changes two parts: the anchor fine-tuning module and the specific detection module. The anchor fine-tuning module adjusts the scale and size of the anchors. The optimized anchors are used as the input of specific detection module and accurately detect the objects. By adding these two structures, a large number of negative samples are filtered out before the specific identification, and the size of the anchors is roughly adjusted, which helps detect confusing objects. RDSSD adds the loss of the fine-tuning anchor module and the loss of the specific detection module to get the total loss. This makes our method end-to-end. We use Pascal VOC2007 and Pascal VOC 2012 datasets to train the model. Our RDSSD with 320×320 input achieves 79.3 mAP on VOC 2007.
Automatic correction system of elementary arithmetic images based on deep learning
Authors: Liu Mingtao , Xia Xinyu 
Published: November 2020

Abstract
The elementary arithmetic exercise is corrected by manual traditionally, which is time consuming and inefficient. With the development of machine learning technology, the accuracy of image recognition is gradually improved, which can assist the manual correcting task. In this paper, a convolutional neural network model is built and trained to realize the recognition of numbers and operators in images based on Keras framework. After the training process, an automatic correction system for elementary arithmetic images is developed using the Django framework. The trained model is used to identify and correct the elementary arithmetic images automatically. The user uploads the elementary arithmetic images by the browser without installing any other application software, which can be used to help teachers in practice after some optimizations.
Semi Supervised Image Segmentation Based on Markov Random Field and Kernel K-means
Authors: Liu Xianfeng , Gao Zhiliang , Chen Ming , Luo Dandan 
Published: November 2020

Abstract
Semi supervised image segmentation uses a small amount of supervised information to improve the performance of image segmentation. In the first part of this paper, we construct a submodular regularization term based on Markov random field (MRF). The regularization term is combined with kernel k-means (KKM), and a semi supervised kernel k-means algorithm (SKKM) is designed based on graph cut technique. In the second part of this paper, we combine SKKM with smooth regularizer to get MRF & SKKM. The experimental results show that, compared with the original SKKM, the segmentation results of MRF & SKKM achieve better results in common evaluation indicators; moreover, compared with the binary image of segmentation results, MRF & SKKM has higher and smoother edge fiting.
Handwritten digit recognition based on corner detection and convolutional neural network
Authors: Zhong Chen , Wang Yuhang , Zhang Decheng , Wang Ke 
Published: November 2020

Abstract
In the field of inspection and testing, it is necessary to manually fill a large number of test record forms, and the digital information accounts for a large proportion in the record forms. For the purpose of automatic and accurate recognition of handwritten form data, this paper proposes a handwritten digit recognition method based on Harris corner detection algorithm and convolutional neural network(CNN). The Harris corner detection algorithm is used to identify the positioning marks in the form image, which determine the possible fill-in positions of handwritten digits. Through a 14-layer CNN model, and using the ReLu function as the excitation function, the handwritten digit features of the target area in the image are recognized. The experimental results show that the average recognition accuracy rate of this method on the test set can reach 98.14%. So a technical solution for intelligent recognition of record forms is thus provided.
A Fine-grained Sentiment Analysis Method Based on Dependency Tree and Graph Attention Network
Authors: Tan Yejin , Guo Wangshu , He Jiawei , Liu Jian , Xian Ming 
Published: November 2020

Abstract
With the rapid development of the Internet, e-commerce and social media have continued to develop and grow. Merchants tend to better understand users’ attitudes and emotional tendencies through comments. As the number of user reviews increases, the previous sentiment analysis methods have highlighted the problems of high cost and high error rate. How to use more advanced methods to analyse comments has become an urgent problem to be solved. In order to solve the above problems, in this article, we propose a fine-grained sentiment analysis method based on dependency tree and graph neural network, which can help businesses and social network platforms to identify users’ sentiment tendencies and can be subsequently used in recommendation systems and public opinion analysis systems. The experimental results show that our scheme has achieved the best results.
Digital zenith camera accuracy analysis scheme based on digital image processing
Authors: Yang Deguo , Cui Li , Liang Congxia 
Published: November 2020

Abstract
This article uses a digital zenith camera to perform field measurements to obtain high-precision astronomical longitude and latitude data. To identify the photographed stellar markers, we utilize FFT and digital image processing methods to do CCD star image matching, the establishment of a numerical fit model for star matching, and then the astronomical coordinates of the station against the table decomposition, and finally the use of astronomical geodesy to calculate the vertical deviation of the station and the error in cyclic observation, to test the instrument in the Sensitivity in both directions, astronomical longitude and astronomical latitude.
Local corner smoothing of linear toolpath based on B-spline fitting and its piecewise interpolation
Authors: Song Dening , Zhou Chao , Zhong Yuguang , Yao Jianjun , Ma Jianwei 
Published: November 2020

Abstract
Line segment toolpaths are commonly utilized in machining of complex parts with curved surfaces. However, the first-order discontinuity of the linear toolpath results in frequent variation of the feedrate, which is bad to the machining quality. To smooth the sharp corners of linear toolpath thus avoiding above problem, this paper presents a local corner smoothing approach. Compared with existing methods, the smoothed toolpath using the proposed the is one integral B-spline, instead of alternative straight-line segments and spline segments, which make the continuity more easily to ensured. Additionally, the integral long B-spline is interpolated through a piecewise feedrate scheduling method, thus the computational burden can be released. Effectiveness of the presented method in corner smoothing and kinematic constraining is demonstrated by illustration test.
Research on Manufacturing Process for Cross-Braced End of Bogie
Authors: Deng Yonghong , Liu Jian , Li Dong 
Published: November 2020

Abstract
To overcome the manufacturing defects and improve the manufacturing quality of the end of the cross-braced of Bogie, through the analysis of a rough and a formed structure of the end of the cross-braced, the stress situation in the key tapping process of the inner hole is calculated, the tapping process and the corresponding parameters are determined, and the corresponding control strategies are put forward for other key processes in the manufacturing. Through a large number of production practice, the manufacturing process can significantly improve processing quality, improve production efficiency and reduce production costs of the end of the cross-braced of Bogie.