bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mathematical Model of Seat Arrangement in Large Gymnasium
Authors: Sun Shu-E 
Published: May 2020

Abstract
In China, where the population ranks first in the world and the total economic output ranks second in the world, the construction of large-scale stadiums or Olympic sports centers has become a trend. When we arrange the seats on the viewing platform, we should not only consider the rationality, practicability and aesthetic value of the seat distribution, but also consider the safety of evacuation.Therefore, under the comprehensive consideration of the above factors, the mathematical model of periodic jump sequence is established, that is, an arithmetic sequence of periodical arithmetic jump, which provides an effective theoretical basis for engineering designers to use computers to process large data and arrange seats scientifically, and also provides an operable accurate model for later programming calculation and simulation technology research.
A Scientometric Analysis of Self-tracking in Relation to Artificial Intelligence and Big Data
Authors: Wu Xue , Zhao Man , Liao Han-Teng , Sun Si-Pan 
Published: May 2020

Abstract
As an increasing amount of personal data has been gathered by wearable and mobile devices, self-tracking, or the practice that people keep track, has become an important topic in Artificial Intelligence (AI) and big data applications. With the aim to provide a systematic review of the literature on self-tracking, this paper presents a scientometric analysis of 109 articles since 2000 collected from the Web of Science. Based on keyword co-occurrence network analysis, the paper has identified four major clusters: (1) wearables as quantified-self applications; (2) big data and critical theory; (3) data and privacy; (4) personal informatics. The further keywords-in-context (KWIC) analysis of the abstracts of the dataset clarifies the seemingly-interchangeable notions of “self-tracking” and “quantified-self”: While “self-tracking” refers to more general activities, practices, technologies, and applications of keeping tracks, “quantified-self” refers to the more conscious efforts and meaning-making outcomes of the self-tracking activities. Such clarification, along with the keyword network analysis, suggests that self-tracking has become a specific and major type of datafication of human conditions or existence and that quantified-self is the construction of self through such datafication. A more integrated conceptual framework is needed for future research to better understand what amounts to meaningful datafication of human conditions and existence, thereby helping researchers and designers to discern the classic notions of health, wellness, and happiness for better research and design outcomes.
Research on short-term traffic flow prediction model based on RNN-LSTM
Authors: Liu Shuying , Li Zhang , Li Hangfu 
Published: May 2020

Abstract
With the rapid development of economy and the increasing number of cars, it is the first task to effectively predict the traffic flow of road traffic to alleviate road congestion and reduce traffic accidents. Aiming at the uncertainty and nonlinearity of traffic flow data, the traffic flow prediction model based on long-term and short-term memory is designed and compared with the traditional BP model and RNN model. The simulation results show that the LSTM model has a higher prediction accuracy and better prediction effect on traffic flow.
Accounting, Auditing, Accountability: Key Insights from a Multidisciplinary Review of AI and Big Data Literature
Authors: Pan Chung-Lien , Pan Yu-Chun , Xu Zhichao , Chen Zizhen , Yu Lin , Qiu Jingjing , Lin Zhuoshan , Li JiaLong 
Published: May 2020

Abstract
Big Data and Artificial Intelligence (AI) projects have been rapidly applied by the diversity of applications, as well as effectively uncover the secret behind the data. In recent years, Big Data and AI have also had a huge impact on accounting and auditing, such as the launch of intelligent robots. This research puts forward a review of literature on Big Data, AI, and Machine Learning with Accounting, Auditing, and Accountability, based on 259 articles retrieved from Web of Science Database since 1989 to 2019. With the database, the author analyzed occurrences and produced the data contact network with the United States as the largest data resource subject and associated in three directions. Through the comparison of the three figures, it is not difficult to find that the countries with advantages in data resources have more influence in an organizational and sources cluster, and the United States is the most obvious example.
A New Quick View Retrieval Method for GF-6 Remote Sensing Images
Authors: Deng Guang , Peng Zhang , Li Zhiyong , Tian Xin 
Published: May 2020

Abstract
As of November 3, 2019, all seven satellites for project of the high-resolution earth observation system have been launched. We have ushered in an era of massive remote sensing data. When we do large-scale forest land mapping, such as national and provincial scale, we need to retrieve images that meet the image quality requirements, are as cloudless as possible, cover the research area, and have as few images as possible. The conventional practice is that the application unit searches the web page provided by the remote sensing data provider for the remote sensing data it needs, and orders to obtain the data. It is often time-consuming and laborious to query data that meet the needs. This paper puts forward a new selection process method of GF-6, GF-1 and GF-2 satellite data. This is a step-by-step method. First, the cloud coverage, time and area are selected according to the metadatas provided by the data provider to meet the requirements. Then, according to the image quality and cloud amount set by the program, it is selected again. Finally, in this image set, the final selected image is given according to the requirements of maximum coverage of the research area and minimum overlap of the image itself. The method can greatly save the time for manually selecting images and improve the working efficiency. This paper designs a program, which can be used conveniently by users. The paper introduces the idea of program design in detail.
A Localization Algorithm Based on NLOS Identification with Improved VB-AKF
Authors: Chu Hao , Fan Zhibo , Wang Fei , Ji Peng , Wu Chengdong 
Published: May 2020

Abstract
As a research hotspot, wireless sensor network (WSN) has a wide range of application prospect. The non-line of sight (NLOS) localization is one of the most basic techniques. In order to mitigate the NLOS influence and increase the localization accuracy, we proposes an improved VB-AKF algorithm in this paper. Firstly, the propagation state of the signal is identified. Then, for the LOS and NLOS mix environments, we reduce the NLOS error by changing the attenuation coefficient. And the maximum likelihood (ML) method is used for localization. Finally, the simulation results show that our method can effectively obtain higher localization accuracy.
Exploring different notions of literacy: a literature review analysis of literacy research related to Artificial Intelligence and Big Data application
Authors: Wang Zijia 
Published: May 2020

Abstract
Artificial Intelligence (AI) and Big Data projects have been rapidly applied by organizations and adopted by younger users, leading to an increasing number of various capacity building applications, as well as significant impacts on the well-being of both designers and users. This research presents a review of literacy-related literature on “Big Data”, “Artificial Intelligence”, “Machine Learning”, based on 73 articles retrieved from the Web of Science Database. The author produced a keyword co-occurrence network map highlighting three main domains: digital literacy, information literacy, and data literacy. This paper concluded that data and information literacy share similar semantic networks whereas digital literacy is related more to pedagogy and innovations, while being the more distant node from both data and information literacy. For stakeholders such as educators, academic researchers, and policymakers, the paper provides reference points for understanding technological literacy and the current relationship among key literacy concepts and domains in relation to AI and Big Data
An enhanced artificial bee colony algorithm for numerical function optimization
Authors: Yu Yi , Wu Yonggang , Liu Xinglong 
Published: May 2020

Abstract
In response to the problems of slow convergence, low convergence accuracy and easy to fall in local optimum of the standard artificial bee colony (ABC) algorithm, this paper proposed the following two improvement measures: Firstly, on the basis of using the current optimal solution, the gradient modification strategy was used to reduce the leading role of the current optimal solution, so that the nectar resources could be fully searched to ensure the diversity of the population; Secondly, the crossover and mutation operation in genetic algorithm was used to replace the random search of scout bees, and the prior information was effectively utilized, which not only improved the search efficiency but also ensured the diversity of population to a certain extent. The numerical experiment results show that the proposed algorithm has good convergence accuracy and robustness, and also presents an excellent performance in multi-dimensional optimization.
A Scientometric Analysis of Artificial Intelligence and Big data for well-being and human potential
Authors: Zhang Zonghe , Liao Han-Teng , Wu Xue , Xu Zhichao 
Published: May 2020

Abstract
Technology has a significant impact on our way of life. Research on artificial intelligence and big data and the cross-field of human well-being plays an important role in guiding the application of science and technology to the people-oriented and good application direction. The reviews in this field focused on the achievements of artificial intelligence and big data in a specific field, such as subjective well-being/biobanks/Translational medicine (TM)/connected health (CH) and medical Internet of Things(mIoT). There is no bibliometric study on the use of artificial intelligence and big data to promote human happiness and health. To provide a systematic review of the literature on Positive Artificial Intelligence and Big data, this paper presents the main institutions, authors, funding agencies and disciplines, based on a scientometric analysis of 243 articles collected from Web of Science.
Research on Virtualization Security in Cloud Computing
Authors: Chen Lei , Xian Ming , Liu Jian , Wang Huimei 
Published: May 2020

Abstract
Cloud computing services, with its advantages of flexible configuration, efficient utilization of resources and easy management, have achieved rapid development and wide application, in recent years. However, the security issues in cloud computing are also becoming increasingly prominent, at same time, cloud computing security has become an important factor hindering the development of cloud computing. The virtualization technology is one of the core technologies of cloud computing, meanwhile, virtualization security is the core issue of cloud computing security as well. This article introduces the mainstream virtualization technology, reviews the security threats of virtualization in cloud computing, and puts forward the corresponding countermeasures.
The Science and Technology Innovation Report on the Ocean Big Data Application along the ice silk road
Authors: Lin Bo 
Published: May 2020

Abstract
Over the past twenty years, the National Marine Environmental Forecasting Center (NMEFC) has carried out safety and security tasks for a total of 9 batches of Arctic science and technology navigations and 22 voyages of merchant ships along the North East Arctic waterway. The paper summarizes their successful experience and technology innovation achievements of applying on big data, focusing on the infrastructure innovation construction of the ocean forecast big data: creating an autonomous Arctic observation system, exploring the Arctic international cooperation model, participating in the global regional observation plan ; scientific and technological business innovation: research method innovation, security technology innovation, super-computing innovation; as well as science innovation achievement: science research achievements, application technology achievements, computer technology achievements. Finally, a brief prospect for the future of the Arctic is given from the perspective of prediction.
Robust Multi-View Feature Selection Method
Authors: Liu Yufang , Xu Sibin , Huang He , Zhao Fei , Wang Linyu , Liang Xin , Tang Ruixin 
Published: May 2020

Abstract
Recently, Multi-view learning has attracted widely attention. Inspired by the idea of RFS [6], we propose to utilize the Multi-View features to develop a Multi-View feature selection method. In our work, the weight of each view can be adaptively adjusted. An efficient algorithm is designed to solve the proposed method. Numerical study confirms the effectiveness of our proposed method.
Research on Development Strategy of News App under the Background of Artificial Intelligence
Authors: Guo Wei , Zhang Bo 
Published: May 2020

Abstract
With the rapid development of the mobile Internet, a large number of Internet users have access to current news through the mobile phone. The market performance of the media Applictipon(referred to as App) is strong, and it has become increasingly prominent in the public opinion. This article analyzes the present situation of the development of media APP by consulting literature, market research, comparative research and other methods, aiming at exploring and studying the future development trend of media APP and its development countermeasures.
Research on Legal Protection System of Personal Information in Big Data Era
Authors: Deng Xianghua , Zhang Bo 
Published: May 2020

Abstract
In the era of big data, the problem of leakage of personal information is becoming more and more serious. With the citizens’ increasing legal awareness, people pay more and more attention to the protection of personal information. In the context of big data, this article is based on the impact of big data on the protection of personal information. By studying the current status of the domestic and international legal protection of personal information, this paper puts forward some proposals for the protection of information, combined with the current status and existing problems of the legal protection of personal information,
The Visualization Analysis of Artificial Intelligence Research in Recent 10 Years in China
Authors: Zhang Wei , Li Liling , Liu Jialiang 
Published: May 2020

Abstract
Based on the research literature of artificial intelligence in 2010-2019 in CNKI database, this paper uses the econometric analysis method and CiteSpace software to draw the knowledge map and chart of literature distribution, authors, institutions, discipline distribution, keywords and other dimensions. The paper analyzes the current situation and process of artificial intelligence research in China in the past decade from the perspective of visualization, and abstracts six research topics of artificial intelligence.
Modelling and Simulation of Facility Planning Problem Based on Improved SLP Method
Authors: Zhang Bo , Wang Xianlin 
Published: May 2020

Abstract
Facility planning plays a critical role in various fields. An excellent facility layout can minimize the losses caused by the connection between facility units in production operations. In this paper, we improved the traditional Systematic Layout Planning (SLP) method, combine it with the multi-objective decision optimization model. The Improved SLP (ISLP) method is described in detail in this paper, including its principles, process of building model, and calculation methods. To make the ISLP more suitable for research and practical problems, we established a mathematical model for it. This optimization model was also applied to the existing facility layout problem to realistically validate the effectiveness of the ISLP and the model. Results on the established model were found first, then the Flexsim simulation software was used to simulate the layout of the facility before and after the improvement. Finally, the results of the experiment were analysed and compared.
Human Posture Recognition Method Based On Skeleton Vector With Depth Sensor
Authors: Wang Jian , Liu Xue Hua 
Published: May 2020

Abstract
This paper proposes a human body gesture recognition method. The method collects bone information of the human body through the Kinect depth sensor, and then uses the direction cosine method for feature extraction.Finally, the feature vector is sent to the BP neural network for training and recognition. In the case of less sample training, the system can still accurately identify the five postures of standing, sitting, leaning forward, leaning backward and underarm. The input is composed of low-dimensional bone information, so the speed and recognition response speed during network training is better than the conventional RGB image recognition method. Experiments show that the recognition rate and real-time performance of the system can meet the needs of practical applications.
Exploring research fronts and topics of Big Data and Artificial Intelligence application for cultural heritage and museum research
Authors: Zhao Man , Wu Xue , Liao Han-Teng , Liu Yu 
Published: May 2020

Abstract
Modern technologies have supported the preservation, curation and reuse of human cultural heritage assets and resources, the latest development in Artificial Intelligence (AI) and Big Data technologies are expected to have great potentials in the datafication, digitization, and reuse of such resources. With the aim to reveal the research fronts and topics of such potential development, the paper conducted a bibliometric analysis based on 206 related articles from the Web of Science (WoS) database. The paper presents the findings on publication trends, main publication outlets and disciplines, main keyword clusters and research gaps. The paper also finds that the current research scope of AI and Big Data in cultural heritage and museum applications is still in the early stage of storage, calculation and presentation of Big Data. The paper help researchers, funding agencies and practitioners to understand the current status of AI and Big Data applications in cultural heritage and museum.
Deep Metric Learning for text data based on Triplet Network
Authors: Deng Xin , Wu Wei , Wang Fei 
Published: May 2020

Abstract
Text data has not only the characteristics of high dimension and serialization, but also most of the data are not labeled. This makes it difficult for the traditional method to achieve good results only relying on the training class mark to solve the problem of text classification. Later, some people put forward the method of metric learning to solve the problem of text classification. Its principle is to learn the measurement distance function for a specific task according to different tasks, that is to say, samples are classified by “distance measurement”. The triplet network used in this paper is an algorithm based on “distance measurement”. By optimizing the original triplet network, we can learn a non-linear measurement space with stronger data representation ability, so as to achieve better results than the traditional metric learning. Finally, we analyze the performance and efficiency of the method on IMDB movie review data set, and prove the superiority of the method for text classification.
External Sorting Algorithm: State-of-the-Art and Future Directions
Authors: Chen Wenhan , Liu Yang , Chen Zhiguang , Liu Fang , Xiao Nong 
Published: May 2020

Abstract
The advent of the era of big data provides new opportunities and more challenges to sorting algorithms. The traditional internal sorting algorithm cannot adapt to the explosive growth of data, and the memory cannot accommodate all the data for sorting, so the external sorting algorithm arises at the historic moment. Because of the different application scenarios, storage devices and improvement strategies, there are many kinds of external sorting algorithms. Traditional main memory architecture based on DRAM faces the problems of capacity, energy consumption and reliability. Emerging nonvolatile memory technologies are non-volatile, high-density, byte-addressable, low-power, so they can replace persistent storage, main memory or storage class memory. Though NVM devices provide new choices to the revolution of traditional memory and storage system, traditional external sorting algorithms cannot achieve its performance. This paper first sorts out the development of external sorting algorithm, and summarizes it into four kinds of external sorting algorithm based on HDD, embedded device, SSD and NVM. In addition, the classical external sorting algorithms based on different storage devices are listed, and our opinions are put forward. Finally, this paper proposes three problems that need to be solved urgently in the future development of external
Parameter adaptive LS-SVR based on multi-stages division and cluster sampling for remaining useful life prediction
Authors: Yan Jian-wen , Zhong Xiao-hu , Guo San-min , Fan Yu , Zhang Qiang 
Published: May 2020

Abstract
Remaining useful life (RUL) prediction is an advanced technology to manage life cycles of equipment and to reduce maintenance cost. Support vector regressive (SVR) is one of the frequently-used data driven method in the field of prognostics, and is much suitable for analyzing small samples and multi-dimensional data. However, there is a limitation of traditional SVR model that the linearity will increase as the training data increase. In order to overcome it, this paper proposes the parameter adaptive least square support vector regressive (LS-SVR) based on multi-stages division and cluster sampling. It divides the degradation process of the equipment into several stages according to the value of degradation data and selects the model parameters stage by stage. The optimal model parameters of each stage are calculated through the novel testing data, which are obtained through appending the cluster sampling data to the original testing data. Therefore, the optimal model parameters can be selected adaptively online as the degradation evolves. Finally, the proposed method is verified systematically by a simulated dataset of fatigue crack growth and a real-world degradation dataset of GaAs-based lasers. It is shown that the proposed method is of better effectiveness, reliability and robustness.
Palm-vein verification based on U-Net
Authors: Wang Peng , Qin Huafeng 
Published: May 2020

Abstract
As one of the biometric characteristics, palm-vein have received more and more attention in recent years. However, in practical applications, the vein image capturing is affected by various factors, so many low quality images are produced in recognition system. Generally, the vein networks extracted from such low quality images contains many noises, which degrades the recognition accuracy. To address these problems, this paper proposes an end-to-end convolutional neural network to extract vein feature for verification. Firstly, we label the palm-vein pixel by the combination of some handcraft-based palm-vein image segmentation methods and build a training dataset. Secondly, a U-Net network is trained based on the resulting dataset and its outputs are the probability of pixels to belong to vein pattern. Thirdly, we propose a scheme to encode the outputs of U-Net to obtain the vein network patterns. The experiment results on the public CASIA palm-vein dataset implies the effectiveness of our proposed method.