bluuebunny/crossref_metadata_2025_split huggingface dataset:
Progress in the Application of Geostatistics Downscaling Methods in the Field of Public Health
Authors: Liu Xin , Lv Xiao , Zhang Zhijie 
Published: April 2021

AbstractWith the continuous development of the two emerging fields of medical geography and spatial epidemiology, Geostatistical downscaling plays an important role in high-precision disease mapping and incidence prediction in a range of public health application This article combines the application cases of different diseases and lists a series of geostatistical downscaling methods, including Area-to-point and Area-to-Area Kriging, Area-to-point and Area-to-area Poisson Kriging, Area-to-point and Area-to- Area regression Kriging, Area-to-point and Area-to-Area co-kriging, Area-to-point and Area-to-Area binomial kriging, etc. By introducing the application progress of different methods in the field of public health, the advantages and disadvantages are compared. Finally, trends and some basic questions of methods proposed recently are discussed in the conclusion.
CNN Fusion Based Brain Tumor Detection from MRI images using Active Contour Segmentation Techniques
Authors: Rajesh Babu K , Sairam Nadipalli L. S. P. , Sai Tejaswini C. , Bharath Kumar G. , Vasantha P. 
Published: April 2021

AbstractEarly diagnosis of a brain tumor may increase life expectancy. Magnetic resonance imaging (MRI) accompanied by several segmentation algorithms is preferred as a reliable method for assessment. This manuscript presented the performance analysis of clustered based and fusion-based segmentation techniques intended to detect the tumor from human brain MRI images in an efficient manner. Four primary steps are involved in this work such as pre-processing, clustering, segmentation, and fusion techniques. The main clustering methods such as K-means and fuzzy c-means (FCM) were first applied to the pre-processed MRI images, and then, the clustered images were segmented directly using the active contour segmentation techniques such as chan-vese (C-V) and level set method (LSM). Then in the next step, the clustered images were fused by using the non-sub sampled contour transform (NSCT) and convolution neural network (CNN) fusion methods, and then, the fused images were segmented by using the C-V and LSM segmentation methods again. The results of both clustered based and fusion-based segmentation in terms of structural similarity index measure (SSIM), dice coefficient (DC), computational time, sensitivity, precision, and segmentation accuracy revealed that CNN fusion-based C-V segmentation performs better than without fusion (clustered based or direct segmentation) to detect the tumors from the MRI images. The results indicate that C-V performs better with CNN as compared with the LSM. Finally, the fusion-based segmentation is an efficient approach to detect the tumor from the MRI images with minimal information loss and high segmentation accuracy over the clustered based segmentation.
Review on Vibration Signal Analysis of Rotating Machinery Based on Deep Learning
Authors: Sun Yingqian , Feng Tang , Jin Zhenzhen 
Published: April 2021

Abstract
Rotating machinery is widely used in industrial systems, and its operation state is directly related to the working performance of the system. Research on equipment condition monitoring and fault diagnosis based on vibration signal analysis is of great significance to ensure the safe and stable operation of equipment. In recent years, the field of deep learning has developed rapidly, and many researchers have applied it to the vibration signal analysis of rotating machinery equipment. Firstly, the development history of deep learning is reviewed. Then, the principles of deep learning models such as convolutional neural network, deep belief network, stacked auto-encoder and their applications in vibration signal analysis of rotating machinery are introduced. Finally, the future development trend of deep learning is discussed.
The impact of implementing auditory intellectually repetition (air) learning model based on learning community for students’ creative thinking skills
Authors: Hobri  , Sahnawi  , Susanto  , Ridlo Z R 
Published: April 2021

AbstractThe aim of the research is to know the impact of implementing Auditory Intellectually Repetition (AIR) learning model based on learning community for student’s creative thinking skill. The research subjects were Indonesian second-grade students of junior secondary school in class VIIIB as the experimental class and class VIIIA students as the control class. Quasi Experimental is used as quantitative research designs. Data collection methods are observation, pre-test, and post-test. Instrument testing and analysis were carried out before conducting tests to measure discriminatory power, difficulty level of test items, reliability, and validity. Analysis of the data in the affective and psychomotor domains used descriptive techniques, cognitive domains using prerequisite testing, similarity of initial ability averages, and hypothesis testing. The results show that the learning instrument has reached values above 4.0 so that they are declared valid and feasible to be used for the learning process with the aim of exploring students’ creative thinking skills. The application of the AIR learning model based on learning community give a positive effect on the students’ creative thinking skill. The average achievement aspect of student’s creative thinking skills in the experimental class is higher than the control class.
Performance optimization method for distributed storage system based on hybrid storage device
Authors: Wu Chen-han 
Published: April 2021

Abstract
In the era of big data and artificial intelligence, the storage data of data centers is growing rapidly. Big data storage systems with high-performance computing require high-speed data access and greater storage capacity. However, traditional storage technology based on hard disk media (HDDs) has been difficult to meet the current demand for high-performance storage, solid state drives (SSDs)’ cost will be a lot for storing massive amounts of data. Therefore, in response to the above problems, this paper proposes a hierarchical hybrid storage technology for distributed file system, including an adaptive file migration strategy and client caching mechanism. Through experimental verification, the proposed technology fully combines the high I/O throughput characteristics of SSDs and the large capacity characteristics of disks, thereby providing a cost-effective high-performance distributed storage solution, and when processing real-time data, it has higher access performance.
Optimization based on LLVM global instruction selection
Authors: Zhufeng Huang , Jiandong Shang 
Published: April 2021

Abstract
Instruction selection is a key component of code generation. High-quality instruction selection has a great impact on the size and quality of the generated code. The existing instruction selection technology is mostly limited to a single statement or a single basic block, and the global instruction selection based on LLVM degrades the entire function in the form of SSA. Global instruction selection optimization based on LLVM is implemented on Shenwei platform, including global instruction merge optimization based on cost model, register bank selection optimization and instruction locality optimization. Through the test of SPEC CPU2006, Experimental results show that the average speed-up ratio before and after the optimization of global instruction selection based on LLVM is 1.08, and the maximum speed-up ratio is 1.36. In addition, when the quality of the generated code is equivalent, the global instruction selection is compared with the default instruction selection, the LLC compilation speed is increased by an average of 20%, and the entire compilation cycle is increased by an average of 6%-8%.
Research on timbre classification based on BP neural network and MFCC
Authors: Meng Zhihang 
Published: April 2021

Abstract
Music exists in all aspects of life. With the development of deep learning and neural network technology in recent years, music information retrieval has become an emerging field that has received widespread attention. In the past, musicians often relied on manual labeling to classify music. This method is not only time-consuming and labor-intensive, but also inaccurate. Although some researchers have tried to extract music features for automatic classification, it is difficult to extract appropriate audio features due to the interaction between the fundamental wave and harmonics of the music itself. In this paper, the Mel cepstrum coefficient is selected to extract the timbre characteristics, and the improved BP neural network is used to process the characteristics, which overcomes the previous problems and obtains a higher accuracy rate.
Research on IoT Security Defense Based on Honeypot Technology
Authors: Wang Zhenxia , Yu Linlin 
Published: April 2021

Abstract
With the expansion of the Internet and the increase in the number of end users, mobile terminals are vulnerable to hackers, computer viruses and other dangerous attacks. Honeypot technology is a highly deceptive security protection technology, which can run in a variety of networks and terminal systems with characteristic vulnerabilities deliberately. Meanwhile, honeypot technology can induce intruders to launch attacks. Therefore, the source of the attack can be captured to realize security defenses, protecting important system terminals from infringement. Moreover, by inducing the intruder to attack the honeypot terminal, it can not only delay the time to attack the real target, but also timely monitor, collect and analyze the attack behavior to provide support for dealing with unknown threats. So, in order to solve the problems of traditional methods and apply honeypot technology to the process of unknown threat defense of mobile terminal, an intelligent defense method is proposed in the paper for mobile terminal unknown threats based on honeypot technology. The results of comparative experiments prove that the method proposed in the paper has less underreporting of unknown threats, and can fully detect the source information, which can meet the needs of mobile terminals for effective defense against unknown threats.
DRTaint: A Dynamic Taint Analysis Framework Supporting Correlation Analysis Between Data Regions
Authors: Yang Pan , Kang Fei , Zhao Yuntian , Shu Hui 
Published: April 2021

Abstract
Taint analysis is a common technology for software analysis, which is widely used in the region of information security. Aiming at the problem that the existing binary program dynamic taint analysis framework does not support “replay” analysis and the analysis efficiency is low, a taint analysis framework called DRTaint is proposed. DRTaint is a universal taint analysis framework that supports correlation analysis between data regions. Experiments show that DRTaint shows good performance advantages when performing taint analysis many times on one program. On the basis of DRTaint, this paper studies the malicious code behavior extraction method based on taint analysis, and the research finds that DRTaint can provide better support for malicious code behavior extraction.
Synchronous playback technology of airborne network video based on RTP
Authors: An Ying , Hao Zhao , Zhang Guo Wang 
Published: April 2021

Abstract
In the flight test, the airborne video data is an important part of the flight test data. In order to solve the problem of synchronous playback of multiple channels of airborne network video data, a synchronous playback scheme of multiple channels of airborne network video data based on the RTP/RTCP protocol is proposed. The network video data streaming and preprocessing mechanism are used in this paper. Firstly, by identifying the data packets, the network video data packets are extracted from the data recorded on the airborne. Then, after unpacking and time calculation of the RTP-encapsulated H.264 video data, it uses FFmpeg’s good decoding capabilities to decode a frame of image, and uses SDL’s excellent video performance to display YUV data. The synchronization mechanism based on timestamp is adopted to realize the fast synchronized playback of multiple airborne network videos. The software adopts modular and multi-threaded design ideas to improve processing efficiency and ensure functional scalability. At present, the system has been successfully applied to the simultaneous playback of multi-channel airborne network video. The actual application effect shows that the system works stably and can effectively improve the efficiency of model flight tests.
RETRACTED: Opportunities and challenges of digital media art in the era of big data
Authors: Li Jie , Yao Li Juan 
Published: April 2021

Abstract
In the era of big data, science and technology continue to infiltrate into art, providing more new means of expression for art, and expanding the performance space for digital media art. The statistics and analysis of big data can also more clearly analyze the needs of users, which is both an opportunity and a challenge for the development of digital media art.
Research on Cybersecurity Risk Prevention and Control of New Infrastructure
Authors: Zhu Lina , Ma Zhentao , Huang Haibo , Yan Min 
Published: April 2021

Abstract
Currently, the new infrastructure has entered the stage of intensive deployment. While driving the integration of innovative applications of new generation information and communication technologies such as 5G, and accelerating the process of digital transformation, new infrastructure is also facing the challenges of complex and diverse cyber attacks and increasingly severe security risks. This paper systematically analyzes the new infrastructure cybersecurity risks, summarizes the new challenges faced by new infrastructure, and proposes a risk prevention and control model METAD which combines monitoring and early warning, threat intelligence sharing, risk assessment, and intelligent defense.
Flow Identification of Intelligent Wireless Communication Network Based on Optimal Symbol Output Control
Authors: Yu Wenwu , Liu Ruijie , Zhai Yue , Wei Dan 
Published: April 2021

Abstract
Markov decision model is the most suitable decision algorithm for random dynamic system based on Markov process theory. Through its decision set at each node, the path that meets the requirements is selected as the allowed decision set for data forwarding, and it does not completely depend on the historical moment of the entire system. Since multiple paths may be included in the allowed decision set, further optimization is required to select the optimal path. In the paper, Markov routing decision model is applied to comprehensively consider the communication distance matrix, node transfer probability, and the number of node neighbors in the network. Equipped with different weights according to actual application scenarios, the nodes within the communication range are evaluated, and the evaluation results are used as the basis for the optimal symbol output to control the optimal path, thereby improving the identification of wireless communication network traffic, extending the network life cycle, and achieving global routing optimal strategy selection.
Teaching Design and Implementation of Design Mode in the Course of Java Programming in Higher Vocational Colleges
Authors: Zhang Jun 
Published: April 2021

Abstract
Java programming is one of the important courses for computer majors such as software technology in higher vocational colleges. The course has strong logic and abstraction, and is rich in content and practical. When studying this course, students should not only master the Java language and object-oriented programming ideas and methods, and cultivate programming habits, but also learn to use design patterns to efficiently solve real-world problems. In order to achieve the expected teaching goals in the limited class hours, universities must carry out teaching design work. This article will put forward some views on how to optimize the software design model teaching design and teaching activities based on the analysis of related concepts and teaching status.
Surface Roughness Prediction with Machine Learning
Authors: Zhang Wenhe 
Published: April 2021

Abstract
Surface roughness is an exceedingly essential index to measure the quality of materials. For manufacturing and production, it is very significant yet difficult to precisely measure the surface roughness of materials. Based on input features including the rational speed, feed and depth of cutting, we managed to predict the surface roughness with machine learning models, based on a real-world dataset collected in the laboratory. The process and results are discussed in this paper. We demonstrate that the neural network can achieve a better prediction performance, compared with other baseline models. This insight will be of great help to the manufacture industry.
Modulation Recognition of Communication Signals Based on Deep Learning Joint Model
Authors: Li Zongyu 
Published: April 2021

Abstract
In modern communication, it is often required for a non-cooperative party to identify the modulation mode when no prior knowledge is given to facilitate the subsequent demodulation and analysis. However, the traditional modulation recognition process requires cumbersome and uncertain manual signal-feature extraction, making it inapplicable to the complex communication environment. In order to overcome this limitation, this paper proposes a communication-signal modulation recognition model based on the dense connection network (DenseNet) and residual connection network (ResNet). The convolutional block attention mechanism (CBAM) is introduced into the DenseNet and ResNet structures, significantly enhancing the modulation recognition accuracy of the proposed global network model. Besides DenseNet and ResNet, the long short-time memory (LSTM) network is also adopted. The experimental results show the performance of the proposed model.
Research on Application of Computer Aided Mapping in Real Estate Surveying and Mapping
Authors: Li Benli 
Published: April 2021

Abstract
This article analyzes and studies the application advantages of computer-aided drawing. The research content of this paper includes improving the efficiency of drawing, optimizing manual drawing, more perfect graphical output, more convenient storage and management. The author studied the basic data collection, clarifying the principle of layering of drawing elements, determining the form of drawing, editing and processing of collected data, checking of output shape data, graphic plate making and printing and other technical application points. This paper studies the optimization of database content, the improvement of network information sharing, and the optimization of operating systems. The purpose of this article is to optimize the results of real estate surveying and mapping and improve the effect of computer-aided mapping.
Application Analysis of Internet of Things Technology in Large Chain Stores
Authors: Huo Xia 
Published: April 2021

Abstract
The application of the Internet of Things technology in large chain stores can not only accurately locate the location of the goods, understand the information of the goods, and improve the business efficiency of mall operators, but also help consumers quickly find the items they need. At the same time, this can also reduce unnecessary cashier queuing time and help increase the attractiveness and competitiveness of the store. Based on this, this article will analyze how to use the Internet of Things to realize the operation and management of intelligent large-scale chain stores based on the analysis of related concepts. This article studies how to use the Internet of Things business management system to improve business levels and improve operating conditions.
Adaptive Modulation Scheme for Satellite Communication Channel Based on RLNN
Authors: Yu Yongxian , Zhang Luyong 
Published: April 2021

Abstract
This paper discusses the potential applications of reinforcement learning in communication systems. In order to improve the spectral efficiency of wireless communication channels, we propose an adaptive modulation scheme based on reinforcement learning and deep neural network (RLNN) with a average network exploration(AE) strategy. Although the application of machine learning technology in link adaptation has attracted widespread attention, most of the solutions currently proposed are based on offline training algorithms, which are not suitable for real-time operations. Classical reinforcement learning is only effective in discrete actions and states. The proposed technical solution does not depend on the offline training mode. The proposed technical solution uses AE strategy to drive exploration, which can improve the exploration efficiency of the agent. The simulation result shows that we can increase the data transmission rate under limited bandwidth and independently improves link availability, throughput and the efficiency of the wireless communication system.
Network security situation prediction method based on strengthened LSTM neural network
Authors: Dong Zhaowei , Su Xiaoyu , Sun Lihui , Xu Kuikui 
Published: April 2021

Abstract
As an important part of network security situation awareness, network security situation prediction describes the dynamic changes of security situation over time, and predicts future situation values based on historical situation values. In order to improve the accuracy of network security situation prediction, a long- and short-term memory network security situation prediction model based on the Sigmoid weighted reinforcement mechanism is proposed. Firstly, LSTM neural network is used to mine the temporal correlation of network security situation data. Sigmoid weighted linear element is introduced to deal with the gradient problem in the back propagation, and the input value is multiplied by Sigmoid activation function, so as to strengthen the structure of LSTM neural network and improve the accuracy of prediction.Then, the cuckoo search algorithm was used to optimize the super parameters to improve the training time. Finally, the public data set CICIDS2017 was used to verify the model. The simulation experiment results show that the model has a faster convergence rate and smaller errors.
Infrared and visible image fusion based on two-scale decomposition and improved saliency detection
Authors: An Ying , Hao Zhao , Huo Zhaohui 
Published: April 2021

Abstract
Aiming at the problems of poor target saliency, loss of background information and time-consuming in image fusion, a fast image fusion algorithm combining two-scale decomposition and improved saliency detection is proposed. Mean filtering is used to decompose the source image into a base layer and a detail layer. The maximum symmetric surround (MSS) saliency detection algorithm is improved to obtain the dim suppressed MSS algorithm. dim suppressed MSS saliency detection and guided filtering is used to generate fusion rules for each layer. The inverse transformation of two-scale decomposition is used for the fusion sub-image of the base layer and the detail layer to obtain the final fusion result. Experimental results show that the algorithm consumes less time and has better fusion quality, which reflects the feasibility of the proposed algorithm.