bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Comparative Analysis of Three Supervised Learning Algorithms in Stock Selection
Authors: Sun Zhen , Zhao Shangmei 
Published: March 2020

Abstract
In this paper, our goal is to judge which algorithm is the best, through comparing the classification accuracy of the three supervised machine learning algorithms, using the data of four financial factors which can reflect the intrinsic value of corporate stock. Our empirical results show that Support Vector Machine got the extremely high classification accuracy in the test both inside and outside the samples; Random Forest achieved the highest classification accuracy in the test within the samples, but it’s accuracy was not as good as Support Vector Machine in the test outside the samples, it means Random Forest was prone to over-fitting; The classification accuracy of Naive Bayes was very low in the test both inside and outside the samples. According to the Bayesian hypothesis, we can see that financial factors are not independent of each other. As a result, the optimal algorithm is Support Vector Machine, followed by the Random Forest, and it is not advisable to use the Naive Bayes, when we selecting the stocks using financial factors data.
Research on Augmented Reality Method Based on Improved ORB Algorithm
Authors: Xiaokang Ren , Danling Cao , Jie Ren , Bianli Du 
Published: March 2020

Abstract
Aiming at the problems of time-consuming three-dimensional registration algorithm in augmented reality system, low accuracy of feature point matching and poor registration effect, an improved ORB (S-ORB) algorithm is proposed and applied to the Majiayao painted pottery augmented reality system. Firstly, the scale space is established by Gaussian filtering and downsampling, and the feature points are extracted by FAST corner detection. Secondly, the descriptors of feature points are calculated by rBFEF algorithm. Finally, the feature matching set is obtained by hamming distance and the mismatching point is eliminated by RANSAC algorithm to complete the three-dimensional registration. Make the virtual information stack correctly added to the real scene. The experimental results show that the S-ORB algorithm is better than the ORB, SURF and BRISK algorithms in time and accuracy when the target position is rotated and scaled. It has better real-time and robustness in the AR system of the painted pottery.
Performance analysis of LDPC decoding algorithm
Authors: Zhou Fan , Niu Linlin , Tian Binghe 
Published: March 2020

Abstract
The LDPC code is the guarantee of reliable information transmission channel coding technology, compared with other coding technology, LDPC code has excellent performance, LDPC decoding algorithm performance is a key, in order to find out the performance of decoding algorithm, compare the BF algorithm and WBF algorithm, BP algorithm and BP algorithm LLR, MS algorithm, NMS algorithm performance, the results show that NMS algorithm has lower complexity and better performance.
Third-party Evaluation of the Flight Control Software
Authors: Yuan Li , Sun Xueyang , Liu Xingyu 
Published: March 2020

Abstract
Flight control software which is the core of missile flight control system guides and controls missile flight along program trajectory. Effective testing of flight control software is an important means to ensure the quality of flight control system. Software testing and evaluation is the comprehensive measurement and assessment of the software quality in according with relevant standards taken by the independent third-party testing and evalution organizations according to the requirements of entrustors. This paper mainly tests the configuration items of flight control software. Firstly, the characteristics of the software under test are introduced. Then, the testing strategies, methods and environment are given. Finally, the test results are analyzed.
The Lattice Sieve Field Selection of Cado-NFS
Authors: Xue Ping , Chen Shaozhen , Fu Zhixin 
Published: March 2020

Abstract
The software application of the discrete logarithms on the “large” finite field is studied. The most effective algorithm for the problem is the general number field sieve (GNFS). Focusing on the theory of GNFS of solving software Cado-NFS and lattice sieves, we find the selection of lattice sieve which improve efficiency of CADO-NFS. This is the first parameter modification suggest. The efficiency of our selection is better than the current by experiment.
Multimodal data fusion algorithm applied to robots
Authors: Zhang Xin , Feng Zhiquan , Tian Jinglan , Yang Xiaohui 
Published: March 2020

AbstractIn recent years, the use of multimodal human-computer interaction technology to achieve the enhancement of human intelligence has become a new topic in human-computer interaction research. When the robot can’t react correctly in a single mode, it is necessary to realize multimodal fusion. To this end, this paper proposes a multimodal fusion algorithm that applies the data obtained by the CNN feature layer to the decision-level. The speech recognition text is semantically matched with the text in the text library, and the similar probability vector is returned. At the same time, the similarity probability vector of the gesture recognition is obtained, and the data is filtered by the threshold, and the set of high probability data codes is assigned to the two modes. The intersection operation, and the final instruction is sent to the robot. The experimental results show that the influence of environmental factors on the single channel result is reduced, and the single mode ambiguity problem is eliminated. The multi-channel fusion algorithm with additional weight is more accurate than the common multi-channel fusion algorithm. At the same time, it has also been well received by many test users.
Tibetan-Chinese cross-lingual word embeddings based on MUSE
Authors: Ma Wei , Yu Hongzhi , Zhao Kun , Zhao Deshun , Yang Jun 
Published: March 2020

Abstract
The idea of word embedding is based on the semantic distribution hypothesis of linguist Harris (1954), who believes that words with the same semantics are distributed in similar contexts. The learning of word embedding is a crucial technology in natural language processing. In recent years, cross-language word vectors have received more and more attention. Cross-language Word vectors can transfer knowledge between different languages. Most importantly, this transfer can occur between rich-resource and low-resource languages. This paper uses the Tibetan-Chinese Wikipedia corpus to train monolingual word vectors. Based on the Tibetan-Chinese bilingual translations, we use the supervised method in the MUSE library to train the Tibetan-Chinese bilingual cross-language word embeddings. In the experiment, we evaluate the result of word representation on the standard lexical semantic evaluation task. The results show that the method has a certain improvement in the semantic representation of the word embedding.
A Propagation Condition Identification and Non-Line of Sight Error Mitigation Algorithm in Wireless Sensor Network
Authors: chu Hao , Lian Siyi , Yang Hao , Wu Rongguang 
Published: March 2020

Abstract
Node localization is one of key technologies in wireless sensor networks (WSN). In practice, the occlusion of obstacles often leads to large non-line-of-sight (NLOS) error. Therefore, the misidentification of LOS (line-of-sight)/NLOS conditions can seriously reduce the accuracy of localization algorithms in WSN. In this paper, we propose a novel localization algorithm based on hypothesis testing method, which combines the NLOS condition identification algorithm and the extended Kalman filter method to mitigate the NLOS error. Moreover, we introduce the false alarm rate fa to fuse the estimated values in the LOS/NLOS condition to obtain more accurate results. The simulations demonstrate that the proposed algorithm can effectively reduce the influence of NLOS error and improve the localization accuracy.
Field-aware User Influence Recommendation Model Based on Trust Relationship
Authors: Yun Bai , Wandong Cai 
Published: March 2020

Abstract
In current determining of user influence based on network structure, the overall importance of user node in network usually attracts great attention. However, the significance of the users in specific field has not been fully studied, which result in low accuracy and reliability in user influence measurement. In order to resolve these problems, this research proposes a field-aware user influence model, which constructs user influence for specific fields, and analyses the global influence of users in the whole network on a network structure basis. The model, assisted by historical behaviour data, not only considers the spread of user influence, but also takes the relationship between user influence and fields into account. Referring to the global influence, the model is able to optimize the composition of user influence, and further improve the accuracy of recommendation. The experiments based on real data sets conducted in this research unexceptionally proved that the field-aware user influence model proposed in this paper, compared with other methods, can effectively improve the recommendation accuracy.
Semantic Dependency Graph Parsing of Financial Domain Questions Based on Deep Learning
Authors: Xin Peng , Qiujun Li 
Published: March 2020

Abstract
In order to effectively solve the problems of limited accuracy of semantic parsing and ambiguous semantic recognition in the current Question-Answer System based on Chinese Knowledge Map in intention recognition. This paper presents a method about Dependency Reduction in language dependency analysis(DR-BLSTM-CRF) based on Semantic Dependency Graph Parsing(SDGP), Bidirectional Long Short-term Memory(BLSTM) model and Conditional Random Field(CRF) of Xunfei Open Platform. The semantic dependency graph dependency reduction method is as follows:1) Semantic Dependency Graph Parsing of interrogative sentences based on Web API of Xunfei Open Platform to obtain a sentence representation containing semantic dependency information. 2) Named Entity Recognition (NER) algorithm combined with BLSTM and CRF is used to identify the named entity of the interrogative sentence to gain a sequence containing character label information, and then combine the analysis results of semantic dependency graph to obtain a more accurate semantic dependency graph through dependency reduction. The experimental results show that the precision, recall and F1 value of the model proposed in this paper are 33.4%, 33.9% and 34.2% higher than those of LTP in terms of the semantic dependency analysis on the 140,000 self-built data sets of financial domain questions. The model can effectively analyze the semantic dependency of financial domain questions.
Understanding the future of Deep Reinforcement Learning from the perspective of Game Theory
Authors: Gao Ziyi 
Published: March 2020

Abstract
This paper is to discuss the development of Deep Reinforcement Learning and the future of it from the perspective of Game Theory. The relationship and potential interaction between these two areas are also introduced, especially the optimization method. This paper discusses about the situations both under non-cooperative and cooperative game. Recently, Artificial Intelligence (AI) and Machine Learning (ML) have grasp sufficient attention from various research areas. Deep Reinforcement Learning, as one of the most promising ML methods, enlighten more researchers to devote themselves in this area. However, even such accomplishment could not belie that, for most kinds of real-life problems, DRL is still unable to provide with an optimal strategy. Because unlike the well-adjusted environment in laboratory, real life problems are not always able to be converted into mathematical problems. Under such circumstances, most of real-life problems have no nominal “optimal solution”. Game Theory provides potential solutions to covert “real issues” into “mathematical problems”, then it is easier for researchers to handle.
Guidance of Computer-aided Self-teaching and Practice of English Pronunciation for Chinese Adult English Learners
Authors: Zhou Qian , Li Yonghong , Guo Lei 
Published: March 2020

Abstract
As the difference of pronunciation between English and Chinese are huge, it is difficult for Adult English Learners to correct their pronunciation and get better communication results. This passage has provided very basic but necessary phonetic knowledge, the methods of practice aided by computer and some software, which are all very helpful for the adult English learner who want to improve their pronunciation.
Sliding Mode Steering Control of Spherical Underwater Robot
Authors: Liu Zhimin , Zhou Yiyang 
Published: March 2020

Abstract
From the point of view of motion control, considering the effects of inertia mass, additional mass, secondary resistance, gravity and buoyancy on the robot, a relatively complete dynamic model of the spherical underwater vehicle is established. On this basis, the steering controller of the spherical underwater vehicle is designed based on the principle of sliding mode exponential reaching law. The control performance is compared with that of ordinary PID control algorithm. From the simulation results, it can be seen that the steering control of the spherical underwater vehicle with the designed sliding mode control method can make the robot have faster steering ability and stronger anti-jamming ability.
Research and Application of Label Defect Detection Method Based on Machine Vision
Authors: Ren Xiaokang , Wang Wenqiao , Ren Jie , Mao Xuetao , Zhang Mai 
Published: March 2020

Abstract
For the problems of scratches and false detections such as wrinkles and other defects in the printing process, the HALCON software is used for programming, and the product label defects are detected in real time based on the photometric stereo vision technology. Firstly, by dimming the product labels of four different orientations, different orientation maps are obtained, the illumination map is calculated to obtain the albedo map, and then the four-source photometric stereo technology is used on the basis of the traditional photometric stereo technology. After obtaining the surface gradient information of the tag to be tested, the measurement image is subjected to morphological processing using the gradient information to obtain a curvature image. The curvature image is then image processed. Finally, the defective portion is filtered to effectively segment the surface defect area of the label. This method is implemented using HALCON software and is implemented in conjunction with C# mixed programming.
The integration design of artificial intelligence and normal students’ Education
Authors: Yang Shuai , Bai Haicheng 
Published: March 2020

Abstract
With the coming of the era of intelligence, the deep integration of artificial intelligence and normal education will become an important trend of education development. Normal students, as future teachers, are the key force to cultivate innovative talents in the era of intelligence. It is the current work and research focus to actively use artificial intelligence to improve the comprehensive quality of normal students. However, in the current rapid development of education informatization and intelligence, the development of normal education in China still faces four practical problems: the characteristics of normal students are not fully considered in teaching, and the quality of normal education needs to be improved; teachers’ level of education informatization and intelligence need to be strengthened; lack of comprehensive understanding of students’ personalized characteristics, and personalized teaching still needs efforts. Therefore, we try to build a deep integration framework of artificial intelligence and normal education, and explore the idea of using artificial intelligence technology to solve the four major problems of normal education development. Finally, combined with the design idea of the integration of artificial intelligence and normal education, this paper reflects on the problems that may be faced in the process of practice, and puts forward the corresponding solutions.
A Research on Dynamic Target Tracking with Camera Based on Kalman Filter
Authors: Ma Yimeng 
Published: March 2020

Abstract
With the popularization of the camera, all kinds of video network communication technologies have developed towards civil use and become increasingly popular. For household application, a common PC is used to obtain the frame data from the home network, while the constant frame is set as the background. The update function is used to update background in real time, and thereby forming an inter-frame relation matrix. Based on the matrix, Kalman filter can match and track targets. This method can effectively complete the calculation on the PC, perform precise detection and deal with the complexity of the household application. Finally, with the test results, this paper further proves that the method is feasible and can be used in actual situations.
Background Spatial Correlation Filter in Multi-Channel Object Tracking
Authors: Lyu Jiayi , He Ning , Sun Xin , Xue Jian 
Published: March 2020

Abstract
Traditional correlation filters have high tracking efficiency, but because of the negative samples in the correlation filter that are obtained by moving the object patch itself, the real background information is not included in the sample composition. In this paper, the method for obtaining and detecting the background spatial information in multiple channel is given. A method that combines the background spatial information is integrated into the framework of correlation filter, and an experiment was carried out on existing publicly available datasets. The experimental results show that the tracking effect in some complicated scenes (e.g., illumination changes, fast motion, scale changes, and occlusion) is improved. This approach can effectively improve the success rate and accuracy of object tracking in complicated scenes without affecting the frame rate, obtaining a performance that is superior to that of other correlation filters.
NAO Robot Walking Control System Based on Motor Imagery
Authors: Chen Kun , Ai Qingsong , Chen Jiaxi , Zhou Shaokun , Zhao Yuyang 
Published: March 2020

Abstract
Brain-computer interface (BCI) technology refers to the spontaneous imagination of a certain mode of movement in the brain to communicate with the computer or control external equipment. This technology helps the limb rehabilitation of paralysed and stroke patients. BCI system will fix electrodes on the experimenter’s brain. This paper designs a brain-computer interface system based on motor imagery, and uses it to control the movement of NAO robot. The system is mainly divided into three modules: signal acquisition, signal processing and robot control. In the aspect of signal acquisition, EEG cap, conductive paste, EEG amplifier and the designed upper computer software system are used to realize the design. Computer storage and processing of data. In signal processing, this paper designs a signal feature method based on Common Spatial Pattern (CSP) and Local Characteristic Scale Decomposition (LCD), and then classifies the features into commands to control NAO robots. In the aspect of communication control, the instruction coding is set in advance, and the classification result is set as the instruction to control the different actions of the robot. Socket communication is carried out by client-server mode using UDP protocol. The experimental results of the study also obtained a high accuracy.
Interlocking Train Control Integrated System Modeling Design Based on AADL
Authors: Song Zeyu , Li Guoning 
Published: March 2020

Abstract
In view of the characteristics of high-speed railway signal system due to its huge structure and complex fault phenomenon, the structure and function of the interlocking train control integrated system was designed by analyzing the important components of the computer interlocking and train control center. The computer interlocking, train control center and interlocking train control integrated system are designed and modeled by architecture analysis and design language (AADL). According to the specific requirements of high-speed railway signal system, reasonable AADL components are adopted in the modeling.
Semi-automatic road lane marking detection based on point-cloud data for mapping
Authors: Kang Zhen , Zhang Qiao 
Published: March 2020

Abstract
Traditional manual method for mapping is time consuming and has low precision. In order to increase the efficiency, this paper proposes a semi-automatic road lane marking detection based on point-cloud data. This approach requires simple interface interaction, and since point cloud data is memory intensive, we convert it to RGB images using the intensity and height of the 3D point cloud. Then, in the interface, the user needs to click a point around the detection line and generate image regions of interest. Finally, the image is filtered using a specific filter to obtain a convolved value for each point, and the position with the largest convolution value is the point at the position of the reticle. In this way, we get a point on the road marking, and multiple point connections generate the road markings on the map. Multiple experiments were performed on data from different routes obtained by different sensors, such as veledyne-64, veledyne-16, to verify the effectiveness of road lane marking detection based on point cloud data for mapping..
Based on Polarized Light Navigation: According to The Analysis of Bias of Polarized Angle Measurement
Authors: Shen Xiaofang , Xu Jun , Zhou Zhanrong , Gao Yang , Xu Jiating 
Published: March 2020

Abstract
Polarized light navigation is a interesting research hotspot in the world today. People study the mechanism of polarized light navigation for a long time. However, less progress has been done on the navigation precision. This article discussed on azimuth calculation and polarized angle measurement. Polarized angle measuring system is designed to detect polarized light information. The bias of polarized angle measurement is analyzed based on the system, from qualitative and quantitative point of view. The quantitative influence on polarized angle measurement and polarized navigation are achieved.
Fire Video Image Detection Based on a Convolutional Neural Network
Authors: Wang Wen , Li Tianping 
Published: March 2020

Abstract
Computer vision-based and real-time flame detection is very important to inmodern surveillance system. At present, convolutional neural network (CNN) has become a topic discussed by more and more researchers because of its high recognition accuracy and wide application. The preprocessind process of the traditional image processing method is complicated and the false positive rate is high. So, in this paper we proposed an algorithm for detecting flame in real time using CNN technology. Firstly, to improve the accuracy of detection, we proposed a suspicious target regions segmentation for disposing the suspected flame regions. This algorithm could locate the target area and segment the target area to improve the flame detection and recognition accuracy. Then, we designed a model based on CNN to classify the extracted feature maps of candidate areas. Finally, we could get the detection of flame according to the the classification results. The experimental results show that the approach has high recognition accuracy.