bluuebunny/crossref_metadata_2025 huggingface dataset:
Measuring Information Capacity of Volatility Risk Premium Using Quantile Regression
Authors: Ji Yifan
Published: April 2021

Volatility is a very important variable in financial research, and so is the volatility risk premium. This article constructs upward, downward and overall volatility risk premiums based on the daily data of Shanghai 50 ETF options and the 5-minute high-frequency data of the Shanghai 50 Index from February 9, 2015 to February 28, 2020. The quantile regression method is used to predict the return of the Shanghai 50 Index in the next 14 days, 30 days, 60 days, 90 days, 180 days, 270 days, and 360 days. Our study finds that the 0.05 quantile has the best prediction effect, and the overall volatility risk premium is better than the upward and downward volatility risk premiums in most of the time, which shows overall risk premium has more information than others.
Cultivation of International Accounting Talents under the Background of Informatization
Authors: Wang Xiuqing
Published: April 2021

With the rapid development of new technologies such as the internet of things, big data, artificial intelligence, mobile internet, cloud computing and block chain, accounting professional education is facing profound changes and challenges. In the future, finance will transform from financial accounting to management accounting. Under the background of informatization, it is urgent for colleges and universities to keep up with the pace of social development and take industrial development and enterprise transformation and upgrading as the guidance and the support of personnel training and knowledge innovation. It is also necessary to strengthen the training of international accounting personnel from comprehensive, applied, cooperative and innovative aspects.
Current Situation and Promotion Methods of Talent Exchanges and Cooperation between Two Sides of the Taiwan Straits under the Background of Artificial Intelligence
Authors: Lin Yuhan
Published: April 2021

Since 2004, with the support of the central policy, the Fujian Provincial government began to put forward the development strategies of developing the economic zone on the west coast of the Taiwan Straits, and the exchanges and cooperation of talents across the Taiwan Straits has become increasingly frequent. After about 15 years later, the mode of talent exchanges and cooperation in Fujian province has become mature, among which the talent exchange activities in higher education are outstanding. Under the background of artificial intelligence, this article mainly outlines the current situation of the talent exchanges between two sides of the Taiwan straits: universities, teachers and students; and makes analysis and evaluations of the prospect of talent cooperation, and puts forward the corresponding policy recommendations, which can promote the development of college talent exchanges between Fujian and Taiwan, to establish a long-term effective mechanism of talent exchanges and cooperation with the characteristics of cross-strait.
Event Prediction Technology Based on Graph Neural Network
Authors: Fu Qiang , Wei Yongchao
Published: April 2021

With the rapid development of the mobile Internet and the continuous expansion of network scale, the network security situation is becoming increasingly severe, and the endless network security threats have put forward higher requirements for network security performance. Based on the above background, the purpose of this paper is to explore the event prediction technology based on graph neural network. Due to the slow convergence of the network event prediction and evaluation model, the untimely risk assessment and inaccurate safety prediction caused by the incomplete parameter setting of the prediction model have become prominent problems in this field. This paper proposes an event prediction technology based on graph neural network. This method first uses genetic algorithms to optimize the weights in the training process of the graph neural network, which overcomes the blindness of initial weight selection and improves the training efficiency of the graph neural network; the KDDCup99 data set is used to conduct experiments on the above two methods respectively. Verification and analysis. The simulation and comparison experiments respectively verify that the neural network-based network security situation assessment and prediction method proposed in this paper can realize the assessment and prediction of the network situation more efficiently and accurately.
Talking about the Application of VR Technology in Art Derivative Cultural Creation
Authors: Liu Han
Published: April 2021

In the context of the epoch-making development of science and the continuous improvement of the social system, VR technology has quietly emerged. With the rapid development of the Internet industry, various industries are also adapting to receive the collisions brought by new fields. VR is one example. In recent years, art-derived cultural creation has continued to expand. In order to improve the integrity and creativity of cultural and creative product design, some industries use VR technology to realize product design from a full perspective, so as to have a more comprehensive and in-depth understanding of the cultural and historical sense of the product. Through the analysis and discussion of the application status of VR technology, the integration of VR technology and the design of art-derived cultural and creative products will finally show the cultural and creative products in the form of three-dimensional models, which has certain guidance for the inheritance and development of culture significance.
Color Application in Ancient Shu Cultural Landscape Based on Data Quantitative Analysis
Authors: Ye Dingying , Zhao Xian , Li Xue , Feng Yuanyi
Published: April 2021

As an important factor in the landscape experience, color has attracted more and more attention. This paper investigates and collects the color application of the ancient Shu cultural landscape in five sites where the development of ancient Shu culture is relatively mature. Based on the quantitative analysis of the data, it is concluded that there are some problems between landscape’s subject and carrier colors in hue, value and chroma. Meanwhile, the corresponding suggestions are also made.
Design and Analysis of Self-propelled Vegetable Seeder
Authors: Xie Fuxiang , Yu Qingxia , Li Xinyu
Published: April 2021

The design of this paper is to draw lessons from and optimize the advanced design at home and abroad, and design a vegetable seeder that can better adapt to the actual complex sowing situation in China. This design uses air-suction metering device, which can accurately sow seeds and improve the production efficiency. This design improves the overall structure of the seeder to adapt to different sizes and rugged and complex land conditions, whether complex hilly or flat terrain, no matter how well the soil can adapt. The sowing amount, plant spacing and row spacing can be adjusted in a wide range to meet the different needs of sowing. With a small change in the structure, the actual production needs can be met.
Reform and Innovation of Government Public Service Algorithm Model in the Era of Big Data
Authors: Li Mengyuan
Published: April 2021

With the continuous development of information technology, data is being generated and accumulated at an unprecedented speed. Big data policies have gradually extended from the overall plan to major industries and various sub-fields. The development of the industry has risen to the national strategic level. In order to provide big data Development provides a good environment. The government has issued relevant policies and regulations at the national level to conduct top-level design and system deployment of the national big data development strategy. The purpose of this article is to organically integrate public management with the advanced information technology of big data algorithms through interdisciplinary, and provide theoretical basis and technical support for the innovation of government public services in my country. To solve the difficulties and challenges encountered by government public service algorithms in the big data environment and the limitation of the problem scale of government public service algorithms under traditional platforms, the MapReduce model is used to transplant government public service algorithms to the Hadoop platform for research. The government public service model algorithm is MapReduce, and the algorithm of the government public service model under the Hadoop platform is analyzed experimentally from the solution quality and problem scale of the algorithm. The experiment shows that after transplanting the government public service model algorithm to the cloud computing platform, the price of the optimal solution for the public service is calculated to be 1,756,633,690 yuan/1,000 square meters.
Analysis of Automobile Body Structure and Monocoque Body Welding Technology
Authors: Liu Xiang
Published: April 2021

Is the main purpose of the car body by passenger, cargo, safely and reliably protect passengers and cargo from wind, sand, rain, snow, etc., constitute a good air mechanics environment, body should guarantee reasonable structure, beautiful shape, color coordination, and can effectively reduce the car’s fuel consumption, air resistance and ensure the car driving stability and cooling condition of the engine, ensure the car ventilation, etc. The body parts mainly include body shell, surface meal gold, freight car and cab, door, window, frame and so on. The automobile body structure can be divided into three types: non-bearing, semi-bearing and bearing. There are many kinds of welding methods commonly used in body maintenance, and it is of great significance to research and formulate the welding repair process for body.
On the Application of Big Data in National Supervision Work
Authors: Huang Yiwen
Published: April 2021

Big data has inserted scientific and technological wings into national procuratorial supervision, but due to the lag of legislation, system and talents, it has also brought some problems and challenges. Efficient integration and application of big data technology into national procuratorial supervision requires not only strengthening legislation and sharing data resources at the national level, but also attaching great importance to the training of information talents.
Research on the Impacts of Multisensory Marketing on Customer Loyalty Based on Data Analysis
Authors: Chen Jiyang , Zhang Yong , Zhang Lei , Zou Qianqian
Published: April 2021

The last several decades have witnessed the wide application of multisensory marketing techniques in hospitality industry. It is widely acknowledged that sensory experience adds value to brand equity. However, the impacts of multisensory marketing on customer loyalty in luxury hotels still remain unknown. This study examines the relationship between multisensory marketing and customer loyalty in luxury hotels. A structural equation model which reveals the relationship between 10 variables of five-sense marketing and 4 variables of customer loyalty was built by AMOS 22.0. Based on the framework, a questionnaire survey targeting on 300 consumers of luxury hotels was conducted. The result showed that visual, olfactory, auditory, tactile and gustative are able to improve customer loyalty in luxury hotels effectively. Meanwhile, it also revealed that tactile (touch) marketing has the most remarkable impacts on the customer loyalty of luxury hotels, followed by visual, gustative, olfactory and sound marketing.
Application of Data Mining in English Online Learning Platform
Authors: Qu Weiwei
Published: April 2021

English is becoming more and more important in our life and English learning is also conducted anytime and anywhere. With high-tech products more and more popular, learning English through mobile phones and other products is very convenient. There are numerous platforms for English online learning, but they provide a very single learning content. All learners, no matter what their learning purposes are, have the same learning content, thus problems will follow. Based on the analysis of current situation, this paper puts forward solutions, case analysis and conclusion process. The application of data mining technology to English online learning platform provides 80% ideas for the construction of online learning platform. Statistics show that nearly 70 million people study online every year.
Using Various Data in the Internet Age to Study How Private Investment Can Promote High-Quality Economic Growth in Shaanxi
Authors: Luo Cui
Published: April 2021

Since the reform and opening up, with the gradual advancement of China’s economic system reform, China’s investment and financing system has been adjusted and reformed. With the advancement of investment commercialization, the growing investment sector that is increasingly visible to major players in the private capital market is expanding. Private capital is playing an increasingly important role in China’s economic development. Due to various reasons such as history and geography, the growth rate of private capital investment in Shaanxi Province, which is located on the bridge in the northwest, is significantly behind the eastern coastal provinces. Therefore, it compared the factors affecting private investment in Shaanxi Province and coastal provinces, sought out the gap in the development of private investment in the two provinces, and formulated measures to stimulate private investment enthusiasm in Shaanxi Province. It is not only the urgent need of Shaanxi’s economic development, but also the urgent need of our country. East and West need to develop in a balanced way. On this basis, this article selects Shaanxi and Zhejiang provinces for comparative analysis. Western private investment developed earlier and contained more theories, so this article first combed the theories related to Western private investment, and then briefly reviewed the development of private investment theory since the founding of the People’s Republic of China. Secondly, benchmark and analyze the private investment in the two provinces from three aspects: one is to benchmark and analyze the status quo of private investment in the two provinces. Compare the factors that affect investment. Finally, by consolidating the foundation of private investment development, encouraging private investment to participate in the urbanization of Shaanxi, establishing a diversified modern financial system, creating software and hardware suitable for private investment development, and relaxing investment fields to implement a fair market access system. Made policy recommendations. Through the research of this article, it is estimated that the economic growth rate of Shaanxi will reach 13%.
Toxicity of essential oils from orange (Citrus sinesis L. Obbeck) and lemongrass (Cymbopogon nardus L. Rendle) on Aedes aegypti a vector of Dengue Hemorrhagic Fever (DHF)
Authors: Kurniasih N , Nuryadin W , Harahap M N , Supriadin A , Kinasih I
Published: April 2021

Aedes aegypti mosquito is one of common vectors for the pathogens of different diseases like dengue hemmorrhagic fever. The most common approach to vector-borne disease is by chemical control, mostly through the use of insecticides. As compared to synthetic pesticides, essential oils (EOs) are ecologically safe, have no mammalian toxicity or the chances of development of resistance are reasonable and highly popular with the organic growers. This research has studied the larvacidal activity of essential oil from orange (Citrus sinesis L. Obbeck) and lemongrass (Cymbopogon nardus L. Rendle). Orange and lemongrass cleaned and cut into small pieces, dried and then blended to obtain crude drug samples. Samples distilled with the Stahl distillation method for 4 hours. Furthermore, terpenoids phytochemical test and test against larvae of Aedes aegypti larvicides to obtain the LC50 value. The yield of essential oil of orange (Citrus sinensis L Obbeck) obtained is 6.84%, while the essential oils of lemongrass (Cymbopogon nardus L. Rendle) was 0.96%. In the phytochemical test essential oils either of orange peel and lemongrass showed terpenoids. From the GCMS data, an essential oil from orange and lemongrass contain citronellal and limonene. Citronellal compound acts as an insecticide ingredient that works as an antifeedant and repellent. An essential oil from orange and lemongrass are toxic, with LC50 values obtained from lemongrass 35.133 ppm is smaller than the orange is 790.935 ppm. The essential oil of lemongrass more toxic than the essential oil of orange. Both of essential oils can be used as a green pesticide candidates.
Utilization of rabbit manure and biochar chicken manure and its effect on the growth and yield of pakchoy plants
Authors: Mesa I M , Situmeang Y P , Wirajaya A A N M , Udayana I G B , Yuliartini M S
Published: April 2021

This study aims to determine the effect of rabbit manure, biochar from chicken manure, and its interactions on the growth and yield of pakchoy plants. This study uses a randomized block design (RBD) with factorial patterns (2 factors). The first factor is the dose of rabbit manure which consists of 4 levels (0, 10, 20, and 30 tons ha−1). The second factor is the biochar dose of chicken manure consisting of 4 levels (0, 5, 10, and 15 tons ha−1). The results showed that the treatment of biochar dose of chicken manure and the treatment of interactions between the dose of rabbit manure and the biochar dose of chicken manure had no significant effect on all observed variables. The treatment of rabbit droppings did not significantly affect all observed variables, except that the fresh weight of the roots showed a real effect. Fresh weight of economic results in the application of rabbit manure 30 tons ha−1, gives a higher yield or an increase of 17.08% when compared to without rabbit manure. While the fresh weight of economic results on the application of biochar from chicken manure 30 tons ha−1 gave higher yields or increased by 23.86% compared to without biochar.
Antibacterial activity of Vitex trifolia methanol extract against pathogenic bacteria
Authors: Zulkifli L , Basri M H , Syukur A
Published: April 2021

Vitex trifolia (Legundi plants) is a plant that is widely used as an ingredient of traditional medicine in Lombok Island for a variety of microbial infections. In this study, a qualitative test of V. trifolia methanol extract was shown to contain secondary metabolites, such as steroids, saponins, alkaloids, flavonoids, terpenoids, and tannins. The inhibitory test of methanol extract of roots, stems and leaves of V. trifolia against pathogenic bacteria (Staphylococcus aureus, Staphylococcus epidermidis, Escherichia coli, and Klebsiella pneumoniae) has been carried out. The experimental design used was a Completely Randomized Design. The treatment factor was the concentration of methanol extract with 4 levels (C1 = 7.5%, C2 = 15%, C3 = 30%, C4 = 60%). The extraction method used was methanol maceration, and the well diffusion method was used in antibacterial activity assay. Data were analyzed by using Anova followed by DMRT test. Anova analysis results showed that differences in the concentration of methanol extract of leaf have a significant effect in inhibiting the growth of all tested bacteria. The DMRT showed that the best treatment was with 60% methanol extract. In this study, S. aureus was the most sensitive bacterium to V. trifolia methanol extract, compared to other test bacteria. It can be concluded that the methanol extract of V. trifolia is an effective antibacterial source, especially against S. aureus.
Photogrammetric stereo image rectification
Authors: Tjahjadi M E , Handoko F
Published: April 2021

Today, off-the-shelf digital cameras are hand on tools for conducting visual inspections in surveying and mapping related activities or in structural inspection of industrial objects. Dense point clouds of the inspected object surface are of typical data required and their accuracies are also become more demanding. Deriving these data are off particular interest of this paper instead we work on overlapping images for generating those ones. An imaging geometry of stereo images is exploited further to reveal some drawbacks of the normalized stereo imaging configurations. We utilize a photogrammetric collinearity condition to model physical reality of the imaging process. A sequence of coordinate axes rotations on both images is rearranged as well as preserving normalized images resolutions to be equal to the source ones. Also based upon the prescribed resolutions, an efficient indirect resample is performed to interpolate grey values of the warped images. To ascertain reliability of the mathematically developed method, C++ programming codes were written and implemented in the solution. It reveals that a nuisance such as y-parallax is completely eliminated. Therefore, by using our method, the normalized images can be used in the dense point clouds production in a high quality and efficiency.
Digitization of Takepan Sasak manuscipt based on the OPF flipbook
Authors: Samsu L M , Saiful M , Fathurrahman I , Bahtiar H
Published: April 2021

This study aims to preserve the Takepan Sasak manuscript, studied and known by the public. This study uses evaluative methods and Kvisoft Flipbook Maker software. The process stages consist of data collection, analysis, digitization, testing and experimentation, and evaluation. The results of this study are in the form of a Sasak Takepan Manuscript flipbook which is expected to be accessed by the public digitally in a 3-dimensional model.
Rapid identification of urban green space using Planetscope satellite image and artificial intelligence
Authors: Adhiwibawa M A S , Limantara L , Brotosudarmo T H P
Published: April 2021

Urban green open space is areas in a city or town filled with vegetation to support socio-ecological functions. These areas have increasingly threatened as a result of being converted to urban infrastructures. As an essential feature of city infrastructure, urban green space should be monitored according to the spatial plan of the city area. However, the space that has been assigned to the urban green space is not a match for its current use. One of the problems that caused urban green space usage mismatch is difficulties in identifying urban green space changes. Planetscope satellite imagery is a high-resolution satellite image that can be used to identify open green spaces in urban areas. In this research, we used an artificial intelligence method to develop a pixel classification process for accurate and efficient identification of the green open space. The results showed that Planetscope satellite imagery and artificial intelligence methods had 99% accuracy in monitoring green open spaces. The use of this technology can assist in the early detection of green open space changes effectively and efficiently.
The design of a letter archiving application using the Model View Controller (MVC) concept
Authors: Arianti B D D , Kuswanto H , Januari H A , Jamaluddin J
Published: April 2021

Letters are an essential means of communication in an institution or organization, and they are closely related to the times’ progress that cannot be abandoned, including today’s digital era. Based on observations, letter archiving at Hamzanwadi University is still conventional and not digitally recorded. So this research aims to design a letter archiving application by applying the Model View Controller (MVC) concept with a development model using ADDIE (Analysis, Design, Development, Implementation, and Evaluation). This Design consists of system architecture, flowchart, data flow diagram, and ERD.
Mathematic connections through scientific prezi and lectora inspire
Authors: Susilawati W , Nuraida I
Published: April 2021

The low quality of students in mathematical connections requires learning processes using technology-based. The purpose of the study was to analyze the ability of mathematical connections between students learning scientific based on prezi, lectora-inspire and conventional learning. This study used a quasi-experimental method and a nonequivalent control group design. Involving a sample of 84 students from three senior high school classes which were divided into an experimental group and a control group as a test instrument for mathematical connection ability. The data analysis uses independent T-test, one way ANOVA, and post hoc least square differences test. The finding is that there are differences in the improvement and achievement of mathematical connection skills between students who carry out prezi-based scientific learning, better than students who have lectora-inspiration based learning and conventional learning. Prezi and lectora-inspire scientific learning facilitates students interaction processes to construct concepts, find problems, apply media, and reflect processes so that it can improve students mathematical connection abilities.
Equivalence between categories of supermodules
Authors: Hadi I , Mahatma Y
Published: April 2021

Let R be commutative algebra with unit element. Given any R-module U, there exists a category CU
whose objects are all pairs (N,M) where N is direct sum of U and M contains U. It has been shown that if W is a direct summand of U then there exists a functor from CU
to CW. Further, if W and U are isomorphic then there exists category equivalence between CU
and C W
.
Modeling of West Java inflation with Spatial Durbin Model (SDM)
Authors: Santi V M , Djuaraidah A , Sumertajaya I M , Fitrianto A , Rahyu W
Published: April 2021

Inflation in Indonesia, in general, can be caused by a combination of various factors, both in terms of demand, supply, and expectations. The contribution of each element that influences inflation is not always the same from time to time. Gordon (1997) explains the determinants of inflation from these three sides, namely demand, supply, and expectations. The importance of expectations in influencing increase was also stated earlier by Chopra (1985). In this study, spatial Durbin for panel data with fixed effects in modeling inflation in the West Java province. The results show that Minimum Work Wage, percentage of poor population, Gross Regional Domestic Product (GRDP), spatial lag of poor population, and spatial lag of GRDP have a significant influence on the Durbins panel spatial model. There is an adjacent regional effect on inflation in the West Java province based on the value of the lambda obtained. Based on the criteria R-square of 94.29%, it can be concluded that the SDM panel data model has an excellent performance in explaining the diversity of data. The AIC and BIC criteria also show that the SDM panel data model well applied in modeling Regency/City inflation in West Java province.
Distance and displacement concept: Comprehension shifting of students on learning process
Authors: Jufriadi A , Ayu H D , Sholikhan S , Muttaqin A , Budiyono A , Sundaygara C , Hudha M N
Published: April 2021

This study aims to investigate comprehension shifting students of distance and displacement concept on learning process. A mixed methods approach using embedded experiments has been used to analyze the comprehension shifting of students. Quantitative and qualitative data obtained from responses to multiple-choice conceptual questions and the reasons put forward during the discussion in the learning process. The results of the analysis have shown that initially all students were not able to distinguish distances and displacements correctly. After the initial verbal discussion, about 10% of students were able to correctly distinguish distance and displacement. The discussion was continued with lecturer intervention by asking students to visualize the problem through pictures or graphics, so that all students (100%) were able to distinguish distances and displacements correctly. Furthermore, the results of the analysis of changes in student understanding can be used as a basis for educators to design learning strategies for the topics of distance and displacement in particular and kinematics in general.
Development of learning material in the form of a smartphone application on the subject of temperature and heat by inquiry-based learning (IBL) for physics high school subject
Authors: Raihanati R , Nasbey H , Astra I M , Rahma A
Published: April 2021

An smartphone application which are used as learning material on the subject of temperature and heat have been developed. The development process is carried out using research and development methods with reference to the Analysis-Design-Development-Development-Implementation-Evaluation development model. The novelty of learning material that was developed was to use inquiry-based learning in the writing system. In addition, the learning material which developed also includes questions that refer to higher-order thinking. The results of the validity tests show the adequacy of the learning materials with the level of difficulty of the learning materials in high school. The learning material has been tested in SMA 16 Jakarta. The test results show an increase in learning outcomes shown by the gain score of 0.4.
Characteristics of neutron diffusion coefficient as a function of energy group in the one-dimensional multi-group diffusion equation of finite slab reactor core
Authors: Shafii M A , Zakiya I , Fitriyani D , Tongkukut S H J , Abdullah A G
Published: April 2021

One of the most straightforward approaches that widely used to solve the neutron transport equation is the diffusion equation approach. The diffusion equation describes the individual behavior of the average neutron trajectory when interacting with matter. Usually, the neutron diffusion equation is obtained under the assumption that scattering is isotropic in the laboratory system of coordinates, and neutrons have the same energy and region is homogeneous; this called a one-speed diffusion equation. It leads to the diffusion coefficient to be independent of the spatial position; the volume of the reactor is constant, and the number density of the fuel atoms is also relatively constant. In this study, multi-group neutron diffusion characteristics were introduced in two ways. First, they vary with energy in the finite slab reactor core using a one-dimensional multi-group diffusion equation with the Gauss-Seidel iteration method. Second, using Fick’s law directly. The study used macroscopic cross-sections in the U-PuN fuel cell level as initial input for 70 energy groups. The data library used is JFS-3-J33 for 70 energy groups, which is the library data of SLAROM computer codes from JAEA Japan. The first way indicates the diffusion coefficient characteristics of U-235 and Pu-239 fuel isotopes firmly have the same pattern in each group energy. They have fluctuations throughout the fast, intermediate, and thermal energy group regions because both isotopes are fertile material. On the other hand, the diffusion coefficient of U-238 fuels isotope tends to be stable in each energy group. This event occurs because the isotope of U-238 is natural uranium, which is included as a fertile material. The second way shows the diffusion coefficients characteristics of the nuclear fuel isotopes firmly have the same pattern in each energy group, especially in the fast and thermal energy group region. They have fluctuations only throughout the intermediate energy group regions because, in this area, there is a resonant region.
A comprehensive comparison on current deep learning approaches for plant image classification
Authors: Zhou Cheng-Li , Ge Lin-Mei , Guo Yan-Bu , Zhou Dong-Ming , Cun Yu-Peng
Published: April 2021

Plant identification and classification play a key role in understanding, protecting and conserving biodiversity. Traditional plant taxonomy needs long time intensive training and experience, which limited others to identify plant categories. With the development of automated image-based classification, machine learning (ML) is becoming a popular tool. Image classification, especially plant images taxonomy, has achieved great improvement in these years by deep learning (DL) methods. In this study, we first reviewed current deep learning applications in the field of plant image classification, and then we tested six deep learning methods in four public plant image datasets. In order to test the classification power of DL methods at cultivar level, we prepared a Camellia sasanqua Thunb. dataset, which is called Camellia@clab, for assessing classification performance of the six DL methods. These DL models’ classification performance all exceeded 70% in the four public plant image datasets, and LeNet and DenseNet had stable good performance, with median prediction accuracy of the LeNet was over 87.29% and that of DenseNet was over 93.8% in the four public datasets at species level. At cultivar level, the lowest median prediction accuracy of those DL methods decreased to 62%, but LeNet and DenseNet still performed very well. The prediction accuracy of LeNet and DenseNet was 82.3% and 100% in the Camellia@clab dataset, respectively. DenseNet model showed a stable best classification performance among the five datasets. To our knowledge, this is the first study that provides a comprehensive review and comparison on applying current DL methods to plant image classification. This study will provide guidance for DL applications in plant image classification, and point out the protentional DL research direction for modeling improvement.
Infrared small target detection based on robust principal component analysis joint directional derivative penalty
Authors: Zhang Tiancheng , Wu Yiquan , Zhou Fei
Published: April 2021

Many robust principal component analysis based methods have been proposed for infrared small target detection recently. However, due to the lack of local prior information, these methods show poor performance under complex backgrounds. In this paper, a novel infrared patch-image model joint the directional derivative saliency feature is proposed. First, Laplace norm minimization is used instead of nuclear norm minimization to approximate low rank background can automatically capture the inherent rank information, which has higher matching with the real rank. Then, considering that the l
1-norm constrains target patch-image inexactly, we construct a saliency map by using the directional derivative feature of the target and incorporate it into the l
1-norm to enhance target and suppress background clutter simultaneously. Extensive experiments show the superiority of the proposed method to the other state-of-the-art methods in robustness and efficiency.
Gesture Recognition Method Based on Attention Mechanism for Complex Background
Authors: Jianchun Geng , Jiannuan Geng , Lili Wen
Published: April 2021

Human-computer interaction technology is still an unaccomplished goal due to complex background and changeable lighting conditions. A two-stage gesture recognition method is proposed to improve the accuracy of gesture recognition. The first stage combines attention mechanism and atrous spatial pyramid pooling (ASPP) which aims to segment gesture from complex background. The second stage uses a two stream convolutional neural network, combines features from the original picture and the output of the first stage, aims to realize the classification of gestures. It can recognize gestures more accurately compared with other gesture recognition methods.
End-to-End Encrypted Messaging Based on PGP with Forward Secrecy
Authors: Shen Yiming
Published: April 2021

In the era of big data, more and more users prefer messaging applications with end-to-end encryption to protect their privacy. However, if a hacker gets the password or encrypting key, it is possible to login into the account, or intercept the network packets and get the original messages. This paper introduces a new decentralized online messaging system that combines PGP and Diffie–Hellman key exchange and gives forward secrecy to end-to-end encrypted messaging. It is not possible for the hacker to decrypt the intercepted messages once the temporary session is ended, even if the hacker gets the PGP private keys.
A multi-point crosstalk attack detection and localization scheme based on hierarchical PCE in multi-domain optical networks
Authors: Qiwu Wu , Liu Xueyue , Jiang Lingzhi , Zheng Mingzhu
Published: April 2021

With the limited number of detection devices and the uncertainty of crosstalk attack source localization, how to propose an effective detection and localization scheme for multipoint crosstalk attack problem in multi-domain optical networks is an important problem that needs to be solved. A new multipoint crosstalk attack detection and location scheme HP-CADL (Hierarchical PCE-Crosstalk Attack Detection and Location) for multi-domain optical networks based on hierarchical PCE is proposed by using the monitoring point selection strategy of local immune algorithm and distributed localization considering continuous parametric analysis. VPI simulation results show that the scheme not only achieves low-cost monitoring and detection and accurate localization of multiple crosstalk attack sources, but also achieves higher accuracy and better performance than other similar schemes in the same multi-domain environment.
Research on QoS Centralized Routing of Software-Defined Aeronautic Swarm Network
Authors: Zhang Hao , Li HaiTao
Published: April 2021

To address the problem of unstable data transmission caused by frequent topology changes in Aeronautic Swarm Network (ASNET), a centralized routing algorithm for Quality of Service(QoS) of the aeronautic swarm network based on Software Defined Network(SDN) is studied. First, this paper designs a Software Defined Aeronautic Swarm Network (SD-ASNET) model. Then, taking advantage of the SDN controller’s ability to obtain global link-state information, we propose a Multiple Link-state Awareness routing (MLSA) based on the shortest path algorithm, taking into account link bandwidth, transmission delay and other factors. The simulation results show that the proposed centralized routing protocol makes full use of the centralized decision-making capability of the SDN controller, and effectively optimizes the link bandwidth utilization and transmission delay by using link parameter information.
Research On Visual Question Answering Based On Deep Stacked Attention Network
Authors: Xiaoqing Zhu , Junjun Han
Published: April 2021

Aiming at the problem that the existing visual question answering model has a language bias in high-level logical reasoning, the model describes images or answers questions with low accuracy. A visual question answering model based on deep stacked attention networks is proposed. At the same time, the attention mechanism is introduced to help the model fully mine the image information; secondly, the problem attention mechanism is introduced to pay attention to the image and the question information at the same time when the problem feature is extracted; finally, the features of the image and the question are integrated and the answer is selected in the classifier. Taking the Visual Genome and VQA2.0 data sets as examples for empirical analysis, the results show that the accuracy of predicting answers is 1.13% higher than the existing best model, which proves the effectiveness and applicability of the model.
Improved MOMVO algorithm to solve the multi-objective HFSP considering machine shutdown
Authors: Huang Hui , Luo Bin , Wang Zhongning , Yu Hao
Published: April 2021

Machine shutdown is a common phenomenon in production workshops, and it is also an important issue that many workshops should consider when scheduling. Based on the multivariety characteristics of hybrid flow shop scheduling problem(HFSP), this paper fully considers the machine shutdown constraint, establishes an HFSP model with dual goals of maximum completion time and machine load balance, and improves the multi-objective multi-verse optimizer(MOMVO) to calculate an example based on actual production data assumptions. And the research results showed that the scheduling scheme obtained by this model and algorithm meets the actual needs and can provide an effective scheduling scheme for the actual production of the enterprise.
A New Heuristic Reinforcement Learning for Container Relocation Problem
Authors: Jiang Tiecheng , Zeng Bo , Wang Yong , Yan Wei
Published: April 2021

Many research efforts on reinforcement learning (RL) is dedicated to solving challenging combinatorial optimization problems, including the container relocation problem (CRP) arising from maritime industry. In CRP, large and heavy containers are stacked on top of each other, those above the target container should be relocated elsewhere before retrieving it. This problem, which minimizes the number of relocations in the retrieval process, is NP-hard, while existing RL studies do not provide satisfactory results yet. We study the structure of CRP problem and design a new heuristic RL framework. On a set of commonly used examples, our RL demonstrates a performance almost equivalent to the most famous results.
Traffic flow prediction based on GRU-BP combined neural network
Authors: Zhang Xijun , Li Jiwen
Published: April 2021

With the rapid growth of transportation, all kinds of traffic data show explosive growth, and accurate and timely traffic forecasting is also particularly important. Among them, traffic flow prediction is the basis for realizing reasonable traffic guidance and control, and it is also a prerequisite for intelligent transportation. Traffic flow data belongs to a typical chaotic time series with strong non-linearity. In the application of neural network, a two-layer neural network can theoretically approach any continuous function infinitely, so as to achieve very accurate prediction. This paper proposes a predictive traffic flow model based on the combination of GRU network and BP neural network, and uses the US Twin Cities traffic data experiment to verify that the model is feasible in traffic flow prediction. The experimental results show that the combined model has high prediction accuracy and can capture the volatility of traffic flow at rush hour. At the same time, the model has strong robustness.
MultiAdapt: A Neural Network Adaptation For Pruning Filters Base on Multi-layers Group
Authors: Yang Jie , Xie Zhihong , Li Ping
Published: April 2021

Deep convolutional neural networks have been widely used in various AI applications. The most advanced neural networks are becoming deeper and wider, which has caused some large convolutional neural networks to exceed the size limit of the server or application. The pruning algorithm provides a way to reduce the size of the neural network while keeping the accuracy as high as possible. The automatic progressive pruning algorithm is one of the widely used pruning algorithms. The progressive pruning algorithm prunes a certain layer of the network in each iteration to reduce the sparsity while preserving the accuracy as much as possible. In this article, we design a new automatic progressive pruning algorithm named MultiAdapt. MultiAdapt combines the combination method and the greedy algorithm. This multi-layers progressive pruning method greatly increases the search space of the greedy algorithm, making it possible to obtain a better pruning network. We use MultiAdapt to prune large neural networks VGG-16 and ResNet. The experimental results show that the MultiAdapt algorithm is better than other mainstream methods in the balance of neural network model size and accuracy. For image classification tasks on the ImageNet dataset, our method achieved 88.72% and 90.55% TOP-5 accuracy on the 50% sparsity VGG-16 and ResNet, while obtaining nearly 2×reduction in parameters and floating point numbers. The operation is reduced, and the reduction is higher than the recent popular method.
Application of Artificial Neural Network(ANN) and Feature Selection Algorithm(FSA) on the ATLAS Experiment Data to Identify Higgs Boson
Authors: Tang Haozhan
Published: April 2021

From the 1950s, physicists started to smash particles together to temporarily form new smaller particles for observing and studying. With further development and enormous experiments, physicists had successfully built the Large Hadron Collider, which became the key hardware for proton colliding. The historical research and experiment in this field have provided us a number of referring data from the LHC experiment and the detailed information of the LHC upgrade to LH-LHC during the previous several years. The Higgs Boson detected by ATLAS Detector in 2013 was significant to scientific research, and its associated dataset could form a new hypothesis to predict the universe rule for small particles. In this study, both Artificial Neural Network(ANN) and Feature Selection Algorithm(FSA) are applied to the ATLAS experiment data to identify Higgs Boson, including the characteristics and fitness for the current model of nature. The study is based on the experiment result of head-on collisions of protons of extraordinarily high energy. The study is aimed to explore the potential of both methods of ANN and FSA to improve the significance of the experiment study and discovery. It will benefit further study to develop the full potential and the enormous scope of physics opportunities given by LHC.
K-means Clustering Algorithm and Its Improvement Research
Authors: Zhao YanPing , Zhou XiaoLai
Published: April 2021

Clustering is a typical unsupervised learning method, and it is also very important in natural language processing. K-means is one of the classical algorithms in clustering. In k-means algorithm, the processing mode of abnormal data and the similarity calculation method will affect the clustering division. Aiming at the defect of K-means, this paper proposes a new similarity calculation method, that is, a similarity calculation method based on weighted and Euclidean distance. Experiments show that the new algorithm is superior to k-means algorithm in efficiency, correctness and stability.
Detailed Description of Data Analysis Process of Internet of Things System
Authors: He XiaoPing
Published: April 2021

The purpose of data analysis of Internet of things system is to concentrate and extract the information hidden in a large number of seemingly disordered data, and find out the internal laws and useful value of the data object. In practical application, data analysis can help people make judgments and take appropriate actions. The data analysis of the Internet of things is a process of organizing and purposefully collecting data, processing data and analyzing data to form information. The analysis process is the support and execution process of the algorithm. In the data processing cycle, data analysis should be used properly in every link from data acquisition in the field perception layer to data transmission in the communication layer to data processing in the application layer, so as to improve the effectiveness and rationality of data processing.
An immune system-based defence system of robot network security
Authors: He Hua , Zhu Zhiming , Zhang Jie
Published: April 2021

After millions of years of evolution, as the representative of the biological world, human beings have adapted well to the earth’s environment, including a wide variety of bacteria, chlamydia and viruses. Among them, the immune system plays a big role. But the immune system is also inadequate. For example, when dealing with old viruses, antibodies will gradually decrease or even disappear completely; what’s more, in the face of new viruses, the quantity of antibody production is not ideal, maybe even the virus engulfed immune cells. In view of this, this article draws on the human immune system and proposes an immune system-based defence system of robot network security taking advantage of machine learning.