bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Real-time Algorithm for Regional Crowd Counting Based on Survival Analysis Theory
Authors: Wang Jun , Fu Yongfeng , Ma Lianhang , Yang Xiaokang , Dong Jiangping 
Published: September 2019

Abstract
This paper proposes a real-time algorithm for regional crowd counting based on survival analysis theory. Our algorithm can accurately calculate regional people flow in real time over a long period, which considers tourists’ stopping time as a type of survival time, and discretizes its continuous distribution probability as a proportion of stopping people in a monitoring region. Compared to other crowd counting methods, our algorithm calculates regional population in real time using a mathematical statistical model, which greatly reduces the dependence on hardware devices. Experiments in two real scenarios verify that the average accuracy of our algorithm is over 91% in comparison with 60% of sensing devices.
Research on a Novel Improved KMP Fuzzy Query Algorithm
Authors: Ni Bowen , Hu Xiaomei , Chai Jianfei , Qu Hewei , Yu Tao 
Published: September 2019

Abstract
The rapid development of computer technology has led to an increasing demand for database management information systems. Most of the data queries existing in the current system use accurate query methods, which leads to inefficient query and cannot solve the fuzzy matching problem between strings. Based on the Knuth-Morris-Pratt algorithm, this paper introduces the concept of ambiguity and proposes an improved KMP fuzzy query algorithm, which is applied to the disease query system to verify the feasibility of the algorithm. The improved KMP fuzzy query algorithm not only has a high matching speed between strings, but also satisfies the fuzzy matching between strings. Compared with the traditional BF algorithm, KMP algorithm and other algorithms, the improved KMP fuzzy query algorithm has superiority in terms of fuzzy matching.
Research on Feature Recognition of UAV Acoustic Signal Based on SVM
Authors: Jin Hengkang , Zhang Yiwen 
Published: September 2019

Abstract
At present, the analysis of UAV flight acoustic signals is mainly based on traditional speech signal processing methods, and has not been analyzed in depth. According to the flight signal of UAV, combined with the aerodynamic characteristics of UAV, the characteristics of UAV’s acoustic signal are analyzed. The three feature extraction algorithms of pitch period, FFT and Mel Cepstral Coefficient (MFCC) are analyzed and compared. Feature extraction is performed, and a support vector machine (SVM) classification algorithm is applied to perform multi-classification model recognition. The measured and experimental results show that based on SVM classification and recognition, the three feature recognition methods all realize the classification of the model. The comprehensive FFT is the best, and the MFCC is the second. The pitch period is not suitable as the feature extraction method alone.
RETRACTED: Application Analysis Based on Computer Software Technology in Big Data Era
Authors: Zhang Peijun 
Published: September 2019

Abstract
With the rapid development of the economy and the continuous development of Internet technology, China has entered the era of big data. In the era of big data, the application of computer software technology is more extensive and in-depth. This article is mainly from the following four aspects. First, a brief introduction to computer software technology and the era of big data; Second, the application of computer software technology in the next generation of big data; Third, the challenges that computer software technology needs to face in the era of big data; Fourth, in the era of big data considerations for using computer software technology. This paper briefly introduces and analyzes the application of computer software technology in the era of big data.
Incentive Mechanism of Medical Data Sharing Based On Information Entropy in Blockchain Environment
Authors: Liang Xiaoyan , Chen Wanghu , Li Jing , Mu Yuxiang , Tian Zhen 
Published: September 2019

Abstract
With the rapid development and wide applications of information technology in the medical field, the data sharing of medical information has become one of the focus that has been received much attention by most researchers in recent years. At present, the scheme of data sharing based on blockchain has been becoming more and more mature, and it has the features of decentralized, secure and tamper-resistant to address the problem of data security in the process of data sharing, thereby improving the quality of medical service of citizens, reducing medical cost and cutting down medical risk. Obviously, information technology is not the main factor to hinder data sharing between medical institutions. Actually, the low enthusiasm of medical institutions was due to lack a comprehensive incentive mechanism of medical data sharing. Combining the medical field, this paper proposes an incentive mechanism of data sharing based on information entropy, which can effectively promote more medical institutions to participate in data sharing and enhance the enthusiasm of medical data sharing. Analyses show that the approach is efficient and effective.
Multi-layer Network Attack and Defense Model Based on Entropy Method to Measure Network Security
Authors: Zhao Xiaolin , Guo Jiong , Jiang Xiaoyi , Zhao Jingjing , Xu Hao 
Published: September 2019

Abstract
Network security is also facing major challenges. On the one hand, network security assessment can dynamically reflect the state of network security and reveal potential threats. On the other hand, network security assessment is the basis of network security management, it is also the prerequisite for building a reliable and secure network. This paper proposes a multi-level network security measurement model, which divides the network into a network environment layer and a network attack and defense layer. The network environment layer considers the network hardware security and the reliability of a network topology. The network attack and defense layer mainly examines the security status of the network from the perspective of network attack and defense. The model comprehensively evaluates and measures the network from different layers of the network, and conducts experiments. The result of experiment proves that the model can effectively assess the security of the network.
Channel Pruning for Efficient Convolution Neural Networks
Authors: Wang Wei , Zhu Liqiang 
Published: September 2019

Abstract
In this paper, we propose a novel pruning method using feature maps Ln-norm to compress deep convolutional neural networks (CNNs) with large model size and long inference time. Different from existing pruning methods, which are mainly based on convolution kernels L1-norm, the proposed method uses output feature maps Ln-norm, where n is a variable. This method can accurately identify the redundant convolution filters and has good compression performance. The experiments on ImageNet show that the proposed method outperforms previous methods and is generally suitable for any CNNs models.
A Novel Strategy for Short-Term Prediction of Fading Channel
Authors: Sui Yongbo , He Yigang , Shi Luqiang , Huang Yuang , Cheng Tongtong , Wu Yuting 
Published: September 2019

Abstract
In this paper, a novel prediction strategy based on firefly algorithm, echo state network and Savitzky-Golay filter for short-term prediction of fading channel is proposed and introduced in detail. This hybrid strategy includes two parts, that is, optimization part and repairing part. In the former, parameters of echo state network are optimized by firefly algorithm so that an optimal prediction echo state network is obtained, then in order to improve performances in prediction strategy, prediction sample of echo state network is repaired by repairing strategy based on Savitzky-Golay filter. Finally, the classic Rayleigh channel is tested and performances are analysed and compared with classic AR, which verified the validity and correctness of our proposed prediction strategy.
Moving Vehicle Detection with Shadow Elimination Based on Improved ViBe Algorithm
Authors: Xia Xue , Lu Xiaobo , Cao Yichao , Xia Shudong , Fu Changqing 
Published: September 2019

Abstract
Moving vehicle detection based on video processing has been widely used in intelligent transportation system recently. However there are also many problems, such as dynamic background, ghost region, and shadow of moving objects. This paper proposes an improved ViBe object detection algorithm. First, an accurate background image is obtained by using the multi-frame averaging method, and then the background model is initialized by this accurate background image, thus effectively reducing the generation of ghost region. Whenever there is no moving object for a fixed number of consecutive frames in the video, this frame is updated to the background image. Conservative update strategy and foreground point counting method are adopted to update the background and reduce the impact of dynamic background on the foreground detection. Next, the foreground image detected by improved ViBe algorithm is input into the shadow elimination method proposed in this paper. Shadows in foreground pixels are detected in RGB color space, and then the pixels determined as shadows are eliminated. Finally, accurate moving vehicles are obtained. Our algorithm can effectively eliminate the shadows of moving vehicles, quickly adapt to background changes and illumination mutation, and get accurate moving objects, which is helpful for vehicle contour extraction and subsequent image processing.
Analytic Model and Assessment Framework for Data Quality Evaluation in State Grid
Authors: Li Zhe , Wu Sai , Zhou Hongwei , Zou Sheng , Dong Tingting 
Published: September 2019

Abstract
With the construction of information infrastructure and the smart grid development, big data platform become an important component to support the construction of smart grid, as a consequence, the quality of the data contain in the big data platform become an important part of the process of big data platform construction. In this paper, a discussion about the current data quality analysis model was presented and also the typical data quality problem appears in the process of information infrastructure construction of the State Grid Corporation is analyzed. On the basis of this, the data quality assessment for the information system in the whole life cycle of the State Grid Corporation is designed. As a result, data quality can be checked by the analysis model in order to ensure the data quality level in the whole life cycle of the information system, finally a case was carried out for the preliminary analysis by using the mentioned framework, the result show that this framework can be effective in embedded into the workflow and can be helpful for the data quality assessment.
A Fraudulent Transactions Simulation Method Based on Genetic Algorithm
Authors: Wang Xiaoguo , Li Yuanxiu , Zhao Ran 
Published: September 2019

Abstract
When using machine learning or other methods to construct the fraud detection models, the banking industry faces such problems: the number of fraud transactions data is too small, which affect the training of anti-fraud model and the detection effect of fraud transaction. This paper proposed a data simulation algorithm based on genetic algorithm (GA-DS). By studying the feature of real fraudulent transactions, we designed the crossover mutation and other genetic operators, explored the suitable fitness function that can evaluate the quality of simulated data, and generated simulated data satisfying the characteristics of the original transaction. The experiment result shows that mixing the simulated data and the original data into the training can improve the detection ability of anti-fraud model.
A Refined Classification Method for Transformer Fault Diagnosis
Authors: Luan Le , Mo Wenxiong , Wang Hongbin , Kong Lingming , Zhou Kai 
Published: September 2019

Abstract
Dissolved gas analysis is one of the most effective methods for diagnosing transformer faults. The traditional method for oil-immersed transformer fault diagnosis can only recognize several types of defects and has a low accuracy rate. In order to improve the classification effectiveness, a refined classification method for transformer fault diagnosis is proposed. It can detect more types of faults with a higher accuracy rate. The proposed method is based on the probability-output relevance vector machine, and a three-layer four-classifier model is constructed to analyse the different diagnostic results of different kinds of input data. In this model, a binomial tree is used to transfer the multi-classification problem to four binary classification problems; each classifier is a binary classifier used to distinguish the transformer type between two types of error. The proposed method is employed for analysis of 100 DGA samples consisting of characteristic gas content. The experimental result shows that this method has a high diagnostic rate and can diagnose 11 kinds of operation state.
Application Research of Public Participation Platform for Urban Planning Management Based on Panoramic Technology
Authors: Dan Chenglong , Gong Junhong , Chen Xiyue , Jiang Daiyin 
Published: September 2019

Abstract
The creation of a public participation platform for urban planning based on panoramic technology by combining CSS and THREEJS on the Web end provides an effective channel for the public to participate in urban planning. Panoramic technology is a new type of streaming media, it highly restores the planned content through the virtual simulation, panoramic technology could deepen the public understanding of the content of the plan, it also reduced the learning cost of public participation in planning due to the professionalism of planning. Through the mongo DB-based database, the planning and comment data can be effectively managed for the auxiliary decision-making of urban planning, and the democratic and scientific nature of urban planning can be improved.
Improved TF-IDF for We Media Article Keywords Extraction
Authors: Guan Xinxin , Li Yeli , Gong Hechen 
Published: September 2019

Abstract
Keyword extraction is one of the work of computer text topic mining, and it is also the basis of text analysis and public opinion analysis. The keywords extracted by the traditional TF-IDF algorithm are mainly calculated based on the word frequency. The importance of other feature words with fewer occurrences and the comments of readers below the article are not considered. Aiming at the above problems, this paper improves the traditional TF-IDF algorithm, adds the part of speech and the reader’s comment as the impact factor, and recalculates the weight of TF-IDF, so that the accuracy of the algorithm is improved. This paper uses the Python language programming to crawl from the media article and implement the improvement of the algorithm. Experiments show that the improved TF-IDF algorithm has significantly improved compared with the traditional TF-IDF, in terms of accuracy, recall rate, F1, MacAvg_P, MacAvg_R and MacAvg_F1.
Robot Indoor Text Contents Recognition Based on Visual SLAM
Authors: Hu Lan 
Published: September 2019

Abstract
Although research on indoor mobile robots has evolved over the years and has made good progress in many areas, few researchers have noticed their combination in text/character recognition and navigation applications. In fact, robots navigate and move with obstacles and various visual disturbances without prior knowledge, and people and objects are possible obstacles. Indoor robots may need to recognize text/characters to help them determine their location and move on. In order to enable mobile robots to navigate and locate more accurately, we improved the visual text content recognition system based on Faster RCNN. This method uses the attention mechanism to replace the original re-sharp method in Faster RCNN, and the visual attention mechanism can be adopted for the anchor. Reduce the number and error, and reduce the interference of non-target information on the identification system. This method can reduce the problem of large system consumption time. Experiments on the indoor robot dataset show that the method has certain advantages in accuracy and cost time.
A Method of Cylindrical Panoramic Video Stitching
Authors: Chen Jie , Li Xiaoyu , Yu Xiao 
Published: September 2019

Abstract
In this paper, we propose a cylindrical stitching method which based on template frame image to realize 360° panoramic video stitching. Firstly, we selected template frame to determine the camera parameters and distortion coefficient. And, removing the image distortion. After that, extracting the template frame feature and making rough matching with the feature points, and then eliminating the feature points of mismatching by the improved feature point screening method. Then, feature points are projected on the cylinder in advance for subsequent video image fusion. Finally, image fusion is carried out after the cylinder projection of the video image. The speed was greatly improved in the subsequent video splicing by using camera parameters, distortion coefficient and transformation matrix of template frame image. In addition, the CUDA platform was adopted for GPU parallel processing in order to further improve the speed to meet the real-time requirements. Finally, we realize real-time video stitching with 30 frames per second.
A Deep Clustering Algorithm based on Gaussian Mixture Model
Authors: Lin Xianghong , Yang Xiaofei , Li Ying 
Published: September 2019

Abstract
Clustering autonomously learns the implicit cluster structure in the original data without prior knowledge. The effect of ordinary clustering algorithms is not good to cluster high-dimensional data. In this paper, we propose a deep clustering algorithm based on Gaussian mixture model, which combines two models of stacked auto-encoder and Gaussian mixture model. This algorithm uses the expectation maximization algorithm of reducing dimension data feature to train Gaussian mixture and updates the data cluster so that the data is clustered in the feature space. The experimental results demonstrate that the proposed algorithm improves the clustering accuracy, and verifies the effectiveness of the algorithm.
Multi-scale Feature Mapping Method Based on Clustering Convolution
Authors: Jiang Yun , Ren Junping , Xiao Jize , Zhang Hai , Chen Xiufang 
Published: September 2019

Abstract
In recent years, convolutional neural networks have attracted much attention due to their powerful image feature learning capabilities. However, in many cases, the network exhibits shortcomings such as difficulty in training and adjustment, and time-consuming training. This paper combines clustering and constrained Boltzmann machines to propose a multi-scale feature mapping method based on clustering convolution. Experiments were carried out on Cifar-10 and lung X-ray images. This method not only shortened the time but also improved the accuracy. Therefore, the proposed method is superior to the traditional feature extraction method.
Algebraic Fundamentals in Artificial Intelligence for the Purpose of Undergraduate Education and Training
Authors: Zhang Silan , Xia Jingbo 
Published: September 2019

AbstractCollege algebra syllabus setting is of utmost importance for the higher education developments and students education and training, especially for the newly opened bachelor majors. In this research, we investigated the traditional syllabus of college algebra in a nationwide manner, and discussed the algebraic fundamentals in artificial intelligence. Three case studies were proposed to address the fundamentals of abstract logic thinking, data observation and knowledge inference, and data-driven thinking. Eventually, a suggested syllabus for “Advanced Algebra and its Applications in AI” was introduced subsequently for the new major of “artificial intelligence”.
An Improved K2 Algorithm for Symptom-Disease Bayesian Network
Authors: Yu Ying , Yang Wenwen , Huang Hao , Yang Zheng , Lu Jianye 
Published: September 2019

Abstract
This paper establishes a Bayesian network for pre-diagnosis based on symptom-disease knowledge from clinical data. In order to solve the local optimal problem, the idea of simulated annealing algorithm is introduced into an improved K2 algorithm, and the BDE scoring function is used to determine the optimal network structure of the Bayesian network. performance evaluation indexes are used to evaluate the performance of the network. The experimental results show that the improved K2 algorithm outperformed the K2 algorithm.
Research on Performance Evaluation of Wargame System Based on Deep Reinforcement Learning
Authors: Shang Tongfei , Wu Jinyun , Ma Jianfeng 
Published: September 2019

Abstract
Deep reinforcement learning combines the advantages of deep learning and reinforcement learning to make end-to-end perception decisions in complex high-dimensional state action spaces. The paper proposes a deep reinforcement learning method for the deduction of the wargame system, which can better assist the combat commander in wartime decision-making. The simulation proves the effectiveness of the method.
A Fingerprint Image Enhancement Algorithm Based on Stretching Transfer Function
Authors: Zhou Jun , Wang Chao , Wang Fang , Ma Jinhai , Yang Mei 
Published: September 2019

Abstract
In this paper, a fingerprint image enhancement algorithm based on the stretching transfer function is proposed. The Otsu method is used to generate the threshold, which divides the fingerprint image grayscale roughly into two parts. The width of grayscale distribution can be controlled by adjusting the parameter of the stretching transfer function. The contrast of the input fingerprint image will be enhanced by the function, highlight image layers and make the ridge and valley lines clearer. The function could also retain the fingerprint image information in transformation process, thus lays a solid foundation for the accuracy and reliability of the fingerprint characteristic value extraction in the next step. The treatment effect of the algorithm is obviously better than that of histogram equalization algorithm; the superiority also lies in the smaller amount of calculation and the faster processing speed which effectively avoids manual intervention. It can be used for the occasion with limited computing resources and high real-time requirements. Besides, the ability to control the contrast of output image by only changing a single parameter provides users with regulations and choices in biometric.
Study on Influences of Artificial Intelligence Era on Early Childhood Family Education in China
Authors: Jin Ling 
Published: September 2019

Abstract
The artificial intelligence era will have important influences on the future society. This paper studies the influences of the artificial intelligence era on the early childhood family education in China. First, the problems in the early childhood family education in China are investigated, then the influences and opportunities of the artificial intelligence era on early childhood family education are analyzed, and finally some applications of artificial intelligence in the early childhood family education are discussed. The study shows that the artificial intelligence era has the potential to provide better family education for children.
Transferring Colorization with Smaller Samples
Authors: zhao Yangze , You Fucheng 
Published: September 2019

Abstract
This paper applies transferring learning to colorization and the main body is based on DNN. However, considering the less complexity of the data set and the equipment constraints. We extract global features from vgg pre-trained model instead of Inception-resnet-v2. And we initialize the other parts weights by fix vgg weights. The result shows that we use less training time and sample and get comparable performance.