bluuebunny/crossref_metadata_2025 huggingface dataset:
The Non-negative Inverse Eigenvalue Problem for Tridiagonal Matrix, Circulant Matrix and Symmetric Matrix
Authors: Zhu Pengyu , Yong Yingjing , Guo Zhifeng , Ren Baicheng
Published: April 2021

The Non-negative Inverse Eigenvalue Problem (NIEP) is a sub-problem extracted from inverse eigenvalue problem with a long history from 1930s determining the sufficient and necessary conditions in order that, σ={
λ

1,…,
λ
n
}to be the spectrum of an entry wise non-negative n x n matrix. There had been many excellent researchers and scholars who contributed to discover many practical theories. The following of this paper would be then separated into two parts to further analyze the NIEP. In the first section of the paper, some important preexisting conclusions would be demonstrated and the groups’ understanding of these indispensable theories would be expressed. In the second section, three special and wildly used matrix, including tridiagongal matrix, circulant matrix, and symmetric matrix would be considered. The solution of the NIEP of these matrices done by the group would be expressed.
Mapping XML to RDF: An algorithm based on element classification and aggregation
Authors: Liu Yuchun , Hong Zhiyu
Published: April 2021

Many data in Web are exchanged and stored in XML. RDF achieves rapid response and reasoning by aggregating a large amount of knowledge. It has been widely used in intelligent field as a way of data organization. Building RDF based on XML is the focus of this article. This paper analyzes the tree structure of XML, divides the XML elements into three sub-models according to the structure, then aggregates and classifies XML elements, and abstracts the aggregates into three abstract structure models, and finally proposes related mapping rules. Based on the optimization of existing methods, this paper proposes an XML-to-RDF mapping algorithm based on element classification and aggregation, which is reflected in three aspects: optimized mapping rules to ensure the completeness and accuracy of data content and semantics; proposed general algorithms for different types of XML; identified equivalent elements of XML and solved the problem of RDF data redundancy caused by such equivalent elements.
Regulatory Approaches of Cross-border Data Flow in the Big Data Era: China’s Choice
Authors: Yang Xi
Published: April 2021

Cross-border data flow is characterized by multi-direction and rapid dynamic gridding in the big data era. Accordingly, these characteristics necessitate effective regulation for crossborder data flows. The US and the European Union (EU) follow their respective regulatory models for cross-border data flows by considering each one’s economic, political, and historical traditions. The “geographically based” policy of data flows, characterized as internal and external, is the basic position adopted by the EU on cross-border data flows. Although the US regulatory path favors (in principle) the free flow of data across borders, this country actually adopts data protectionism for data involving critical technologies and security areas. Given the political system differences among China, the EU, and the US, China can learn from the experiences of the US and the EU, actively seek value balance between data regulation and free flow, construct its own regulatory model, and establish a diversified data management system. Lastly, China should also actively participate in the international regulation competition on cross-border data flows, thereby giving the country a voice in the international arena.
Multi-dimensional QoS Cloud Computing Task Scheduling Strategy Based on Improved Ant Colony Algorithm
Authors: Ge Junwei , Yu Dehua , Fang Yiqiou
Published: April 2021

This paper proposed a multi-dimensional QoS cloud computing task scheduling algorithm based on improved ant colony algorithm, considering QoS demand of users and load balancing of cloud platform comprehensively. First, this paper defines a QoS model composed of the completion time and execution cost of tasks, and defines the cloud platform load balancing constraint function. Secondly, in view of the shortcomings of ant colony algorithm such as slow convergence speed and easy to fall into local optimum, the pheromone update method and expected heuristic function are improved, and the pheromone strength is dynamically changed. Finally, the simulation is carried out in cloudsim and compared with the ACS algorithm and the MMAS algorithm. Experimental results show that the algorithm in this paper is better than these two algorithms in terms of user satisfaction and cloud platform load.
A multi-strategy improved sparrow search algorithm
Authors: Ouyang Chengtian , Qiu Yaxian , Zhu Donglin
Published: April 2021

As a novel algorithm, the sparrow search algorithm has better optimization performance than other intelligent optimization algorithms. However, in complex problems, there is still the possibility of falling into a local optimum and relying on the initial population stage. In response to these shortcomings, a multi-strategy improved sparrow search algorithm (KLSSA) is proposed. First, in the initial population stage, K-means clustering method is used to cluster and differentiate the individual positions of sparrows, which speeds up the work efficiency of the population and gets rid of the influence of randomness. Then, the levy flight mechanism and adaptive local search strategy are respectively introduced in the calculation of the location update of the discoverer and the follower, so that the discoverer can conduct a wide range of searches more flexibly, and the follower has a more detailed search method. Through the 10 standard test functions, it can be seen that the multi-strategy improved sparrow search algorithm has stronger optimization ability and better optimization speed.
Prospect of Voiceprint Recognition Based on Deep Learning
Authors: Shan Shuaijie , Liu Jianbao , Dun Yaowu
Published: April 2021

As a biometric technology, voiceprint recognition is as unique as human fingerprint and pupil, so voiceprint recognition has great potential in practical application. Aiming at the feature extraction method and voiceprint recognition method, this paper first introduces the principle of voiceprint recognition, traditional MFCC, LPDA and other feature parameters and their performance; secondly, the traditional voiceprint recognition methods such as GMM and GMM-SVM are introduced, as well as their shortcomings and improvement schemes. Aiming at the shortcomings of voiceprint recognition system based on traditional algorithms in accuracy, robustness and real-time, this paper introduces the role of deep learning neural network in different stages of voiceprint recognition, and introduces the characteristics and network structure of some typical algorithms based on deep learning. Finally, according to the advantages and disadvantages of deep learning in voiceprint recognition, the development prospect and challenges of voiceprint recognition technology are analysed.
Improved Fault Location Algorithm for Radial Distribution Network Based on Power Failure Information
Authors: Han Yi , Qian Zhongmin , Shao Danlu , Lin Qiujia , Tang Zhexuan , Chen Minyu , Ai Qian
Published: April 2021

As the scale of the distribution network continues to increase, the importance of distribution network is getting higher and higher. Therefore, it is necessary to achieve fault location and restore it as soon as possible after the fault occurs. This paper proposes a new fault location algorithm for the radial distribution network, which is improved on the basis of traditional matrix algorithm. First, the power failure incidence matrix (PFIM) and power interruption information matrix (PIIM) is constructed based on the topological connection and fault information at each node. The fault location matrix (FLM) is then obtained through PFLM and PIIM to realize the fault location. Second, the accuracy of the proposed algorithm is verified by mathematical derivation. Finally, an 11-node radial distribution network is illustrated to testify the proposed algorithm. Results show that the improved fault location matrix algorithm proposed in this paper can effectively achieve fault location in radial distribution network.
Using the Markov chain to predict the outcome of an election in a given situation
Authors: Wan Yajie
Published: April 2021

This paper used Markov Chain, a mathematical model that is often utilized in predicting, to forecast the result of the election process of a person in a specific circumstance. In the way of modeling, starting from the tree diagram to the model of Markov Chain and related transition matrix, this paper aimed at the election result, which relates the object of study, a specific candidate. The research data are collected from the prompt of the research question and the calculation of coding based on the transition matrix. The result is, when it is the 64th year of that specific candidate running for the reelection campaign, he will be likely to retire from politics.
Structured information extraction for bone scan image report texts
Authors: Huang Mengge , Zhang Jingyi , Deng Tao , Man Zhengxing , Lin Qiang
Published: April 2021

Extracting structured information from the bone scan image report text plays a crucial role in supporting clinical analysis and research. This study summarized the structure and characteristics of 3608 bone scan image report text using dictionary-based information extraction method, including data cleaning, entity recognition, building dictionary and extraction rules. This method was used to obtain the structured data of bone scan image report text required for clinical research, and the effect evaluation was carried out on 1000 randomly selected report texts, with the precision rate and recall rate higher than 90%. The method proposed in this study is practical and could have good effect on structured results for bone scan imaging report text.
An Automatic Abstractive Text Summarization Model based on Hybrid Attention Mechanism
Authors: Wang Zhe
Published: April 2021

Attentional sequence-to-sequence models based on RNN have achieved promising performances in the automatic abstractive summarization technology. However, there are still some shortcomings, including the inaccuracies and lack of key information. In this paper, an abstractive text summarization model based on the hybrid attention mechanism is presented. The model aims at adopting sentence-level attention mechanism to guide the word-level attention distribution. Moreover, this model modulates the weight of sentence-level attention value to alleviate the adverse effect of high variance on word-level attention distribution for short documents. The experimental results on the LCSTS dataset show that the presented model can effectively improve the ROUGE scores and can better summarize the source document while retaining the important information. An example in this paper shows that the model can generate summaries that are similar to human-written ones.
Research on Entity Recognition in Aerospace Engine Fields Based on Conditional Random Fields
Authors: Gao Wenchao , Zhao Shanshan , Zhu Shiying , Ren Shengbo
Published: April 2021

With Entity recognition is an important basic tool for many natural language processing tasks such as information extraction, question answering systems, and syntactic analysis. Entity recognition is divided into general field and specific field. There were different recognition methods for different fields. At present, one of the more common entity recognition technologies is a rule-based method, and the other is a statistical method. Due to data sets scarcity in the aerospace engine field, there is a lack of corresponding entity recognition research in the aerospace engine field. This paper proposes a statistical-based entity recognition method to identify entities in a specific field based on conditional random fields. This method constructs a training set in the aerospace engine field through manual annotation, extracts entities in the aerospace engine field, uses words, word frequency, and part-of-speech features respectively, and conducts experiments. This method can more accurately identify entities in the aerospace engine field, and finally make the accuracy rate, Recall rate and F value reach 92.7%, 87.1%, 89.8%.
Application of computed tomography angiography in thoracic aortic aneurysm
Authors: Yuan Biying , Liu Jianhua , Tian Jiale , Xu Nannan
Published: April 2021

Thoracic aortic aneurysm (TAA) is the most common thoracic aortic dilatation. Computed tomography angiography can effectively assess the shape, location and degree of dilatation of TAA. It can also show the blood vessels involved in TAA, including left subclavian artery, double renal artery and abdominal aorta. At present, post-processing techniques of computed tomography angiography include MPR, CPR, VR. These methods are commonly used to evaluate TAA.
Model predictive control of microbial fuel cell based on Kalman state estimation
Authors: Wang Mimmin , An Aimin , Zhao Yingying
Published: April 2021

Aiming at the constraints and undetectable interference in the microbial fuel cell system, a microbial fuel cell model predictive control method based on state estimation is proposed. According to the principles and actual requirements of the microbial fuel cell system, a state space model with input constraints is established. By introducing the model predictive controller, the performance of constrained optimization control is improved. Combined with the Kalman filter estimator, the impact of unmeasured interference on the predictive controller is compensated, and the control accuracy and robustness of the system are improved. The simulation experiment finally indicate that model predictive control based on kalman state estimation makes the output voltage of system reach the desired value, the input flow meets the actual demand and the cost is optimal. In addition, it has a good ability to deal with interference.
Aspect Based Sentiment Analysis of catering field reviews via RoBERTa-AOA model
Authors: Li Bo , Pan Fang , Shou Zhaoyu , Zhang Huibing
Published: April 2021

The emotional information included in catering field reviews has a great influence on the ordering behaviour of users. The traditional Aspect Sentiment Analysis Model based on Neural Network often has the problem of neglecting Aspect Words. This paper proposes an aspect sentiment analysis model that combines RoBERTa and Attention-over-Attention (AOA). Firstly, the model uses RoBERTa to model sentences and aspects respectively. Then, the deep semantic features of sentence and aspect were extracted by Bi-LSTM. Finally, AOA is adopted to learn the weight of aspect to sentence and sentence to aspect respectively, and the results are input into SoftMax layer for classified output. Experimental results on real Meituan catering data sets show that the accuracy of RoBERTa-AOA is about 1.7%-3.7% higher than that of other models.
Inner synchronization of Boolean networks with time delays
Authors: Liang Yi , Hu Haihui , Alimjan Gulnaz
Published: April 2021

The model of Boolean networks with time delays is proposed, whose delays are multiple state delays for each node. Inner synchronization problem of the Boolean networks is investigated, and necessary and sufficient condition of synchronization is established based on the semi-tensor product of matrices. Moreover, illustrative examples show the efficiency of the proposed results.
A Loan risk assessment model with consumption features for online finance
Authors: Li Li , Huang Ding
Published: April 2021

While online finance is rapidly growing, it is a big challenge to evaluate the loan risk of users based on the data on the Internet. In this study, we used basic user information and added the user’s consumption features to construct a loan risk assessment model that integrates features. We extract consumption features from two aspects: firstly, construct consumption portrait features through statistical analysis and clustering; secondly, combine convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) networks to extract sequence features in consumption, and add attention mechanisms to improve the evaluation effect. Finally, the features are combined and fed into the fully connected layer, and the probability of default loan is calculated through the activation function. Our data set comes from an online finance company. The experimental results show that the loan assessment model combined with consumption features does improve the accuracy of default loans under the AUC and KS indicators.
Attention Relational Graph Convolution Networks for Relation Prediction in Knowledge Graphs
Authors: Wang Shuo , Zhong Yi , Wang Chengpeng
Published: April 2021

Recently, the tasks based on knowledge graph (KG), such as question answering, information retrieval, are more and more widely used. However, due to the incompleteness of the relations (links) between entities, it is very important to study the relations prediction based on KG to complete the missing relations between entities. In recent years, graph convolution networks (GCNs) have been a new method to solve the reasoning of knowledge graph. However, the existing knowledge graph relation prediction model based on GCNs fails to consider the importance between nodes. In order to obtain more abundant relations information between entities (nodes), inspired by the graph attention network (GAT), we propose an attention weighted relational graph convolutional network (denoted as AWR-GCN), which is used as an encoder of the encoder-decoder model for relationship prediction, and the decoder is a linear model. Compared with the advanced methods in the commonly used relational prediction data sets, our model has a certain performance improvement and reached advanced level.
Face Manipulation with Generative Adversarial Network
Authors: Xu Longpeng
Published: April 2021

Generative adversarial network has appeared as an effective image manipulation tool in recent years and has been widely used. The GAN-based manipulation of face images is also possible and tools including DeepFake are already misused. In this paper, we discuss the pros and cons of face manipulation with generative adversarial network. We find that this technique can be very useful for recovering masked face and further improving face recognition accuracy.
Named entity recognition method of Chinese EMR based on BERT-BiLSTM-CRF
Authors: Gao Wenchao , Zheng Xiaohui , Zhao Shanshan
Published: April 2021

With the widespread use of Chinese electronic medical records, the extraction of medical named entities and the resolution of the polysemous term of entities are of great significance to the analysis and processing of patient information and disease diagnosis. This paper uses a BERT Chinese pre-training vector that does not rely on manual feature selection, combines BiLSTM and CRF Chinese named entity recognition algorithm model, and applies it to the processing of the CCKS2020 electronic medical record data set. This paper conducts experimental tests and comparisons between the BERT-BiLSTM-CRF model and other models. The results show that the results of this model are better than other models, and it can accurately identify multiple entity categories and has a good application prospect.
Research on Interior Scene Design Based on Cinema4d Technology
Authors: Meng FeiYan , Zhang Ziyan
Published: April 2021

This paper completes the 3D virtual interior scene modeling and design based on Cinema4d software technology. Through a full range of simulation analysis, to select environmentally friendly materials, match reasonable space structures, make full use of the space, and improve the utilization of indoor space. By making models of various parts of the interior design and building walls and windows, the entire interior space is designed. Finally, the interior design was rendered through lighting to ensure the expected design effect. Experiments show that the realism of 3D indoor scene design is higher than other similar methods, and it is suitable for 3D virtual scene design. It is hoped that the research in this article can provide some reference on feasibility for interior scene design.
Research on Facial Expression Recognition Method Based on Bilinear Convolutional Neural Network
Authors: Zhang Yong , Yi Weikang
Published: April 2021

Facial expressions express different emotions through different degrees of distortion of facial muscles, and the expression images spontaneously generated by people in real scenes are susceptible to interference from different illumination angles and postures. An extraction based on bilinear convolutional neural network is proposed. This is an end-to-end method for emotion classification based on second-order features of expression images. This method optimizes the network structure by adding a batch normalization layer, increasing the maximum pooling core, and replacing the fully connected layer with a global average pooling layer. Finally, the bilinear normalization method is used to convert one-dimensional features into two the dimensional features complete the final classification. Through testing on the FER2013 dataset, this method obtains an accuracy of 73.2%, which is 0.5% higher than the current state-of-the-art method, and proves that extracting image second-order features is more conducive to expression classification than first-order features. In addition, compared with other mainstream methods, the proposed improved model has higher recognition accuracy.
Traffic sign detection based on Haar and adaBoost classifier
Authors: Wang Wenhao , Gao Hui , Chen Xiaobing , Yu Zhenyang , Jiang Mingxing
Published: April 2021

Intelligent transportation system is a hot issue in the field of computer vision. Traffic sign detection is an important part of intelligent transportation system. A traffic sign detection algorithm based on Haar feature and AdaBoost algorithm is proposed in this paper. Firstly, Haar features are extracted from the positive and negative samples of the training set, and then these features are used to train the AdaBoost cascade classifier. Finally, the Haar features are extracted from the test image, and the possible traffic signs in the image are detected, the experimental results show that the method can achieve good detection effect, and provide a new idea for the detection of traffic signs.
Research on The Numerical Accuracy of Network Public Opinion Propagation Model Based on Runge-Kutta Method
Authors: Fan Lidan , Zhao Sifan , Han Fengqing
Published: April 2021

In a SIsIoR public opinion transmission model with discussion mechanism and emotional tendency of public opinion, government intervention is added to list the corresponding dynamic differential equation. It is found that there is no exact solution to the equation. Theoretically, the equation satisfies Routh-Hurwitz stability and Lipschitz continuity, and finally runs to the local equilibrium point. Explicit Euler method and 4th order Runge-Kutta method are used for numerical simulation, and the results are real and effective. By comparing the two methods, it is found that the numerical results of the fourth-order Runge-Kutta method are more accurate, and the iteration step size is shorter, which is more consistent with the theoretical expectation. This method is more suitable for the simulation of network public opinion dissemination, and has the advantages of stronger verifiability and higher numerical accuracy of the model. The simulation of network public opinion transmission based on Runge-Kutta method can provide reliable numerical basis for the guidance of public opinion under the environment of netizens’ emotional tendency, and provide scientific decision-making basis for the government to intervene in network public opinion transmission.
Prediction model of physical activity level and diabetes mellitus based on artificial neural network
Authors: yu Wang , xiaofei Liu , pei Zhang , jinying Li , yiting Wang
Published: April 2021

Objective: clinical practice shows that the control and treatment of chronic diseases, in addition to drugs, exercise health management is more significant, but also the cheapest, no side effects, thorough intervention. It has been proved many times that exercise can control and prevent chronic diseases. Through the related research of sports intervention, because the researcher selects the experimental object and the movement type difference, thus obtains the different research conclusion. Different types of people use different types of exercise, to achieve different exercise results, especially in the group of chronic patients. Therefore, to discuss the establishment of evaluation and selection system of exercise mode in chronic disease exercise management is the time requirement of health promotion for the aged patients. Methods 1242 patients with different degrees of diabetes in Henan Province were selected and their informed consent was obtained. Using the physical activity scale (IPAQ) and the long form questionnaire, the questionnaire was issued and collected from July 1,2020 to December 31,2020 by the uniformly trained physical examination physician, the data were analyzed by artificial neural network. Results: this study has found a certain correlation between diabetes and physical activity level whose data is incomplete, but has showed an overall positive correlation.
Traffic sign detection and recognition based on CNN-ELM
Authors: Li Wenju , Na Xinyuan , Su Pan , Zhang Qing
Published: April 2021

Aiming at the problem of low traffic sign recognition rate and slow speed, a traffic sign recognition algorithm combining CNN and Extreme Learning Machine is proposed. First, the ResNet50 network is used to extract image features, and then the Region Proposal Network (RPN) is used to generate proposals from the extracted image feature maps. Finally, the extreme learning machine is used to classify the generated proposals, and the fully connected layer is used for regression prediction. The experiment shows that compared with the Faster R-CNN model, the CNN+ELM improves the recognition accuracy on the TT-100K dataset 7.7% and reduces the training time per epoch by 32 seconds.
Study on reflection separation based on orthogonal polarization images
Authors: Zhang Jinghua , Zhang Yan , Shi Zhiguang , Li Biao , Zhang Yu , Ling Feng , Suo Yuchang , Liu Di
Published: April 2021

In this paper, a deep learning method based on polarization image is proposed. Firstly, the polarization characteristics of reflected light and transmitted light are analyzed by using the Fresnel’s law. Then, based on the polarization characteristics that the reflected light is polarized in the perpendicular direction and the transmitted light is polarized in the parallel direction, the polarization images obtained from the parallel direction and the perpendicular direction are input into the Encoder-Decoder net at the same time, and the net is trained by L1 loss. The experimental results show that by combining the polarization information and deep learning method, the reflected component and transmitted component can be effectively separated.
Screw hole location for laptop based on improved RCF
Authors: Huang Yafei , Hu Jiwei , Lou Ping
Published: April 2021

The recent application of computer vision to disassembly and remanufacturing has pushed forward this field significantly. In disassembling the laptop, we find that the existing positioning screw methods such as hough transform and template matching were not suitable for black laptops, so we propose a new method to solve this problem. We use RCF network to locate the screw hole contour, and make two improvements according to the characteristics of small screw hole size: 1. A feature integration module is added to RCF network to increase multi-scale features.2. A modulating factor is added to the loss function to enhance the attention to the indistinguishable pixels. Our approach has shown good results on black laptops and has higher ODS and OIS than the original RCF network and the existing method.
Tibetan text classification based on RNN
Authors: Yan Wei , Cao Hui , Cui Zeyu
Published: April 2021

In this paper, a deep learning RNN model is used to classify Tibetan texts. The core idea is to first preprocess the Tibetan news corpus, and then use Tibetan syllables to construct a Tibetan syllable table based on the lexical and grammatical structure of Tibetan, embed the syllables in the sentence, and represent each syllable as a fixed Numerical vector. Secondly, the RNN cyclic neural network model is constructed. First, the text of different lengths is filled or truncated into a sequence length of uniform length. For each input text, the vector representation of text syllables is input on each time step of RNN to train the RNN model. The test samples were then used to evaluate the accuracy of model classification by introducing recall rate, precision rate and F-test. Finally, compared with traditional machine learning Logistic algorithm, polynomial naive Bayes algorithm and KNN algorithm, the results show that RNN model has better classification effect.
MQSD: A Scheduling Mechanism of Mixed Queue in the Integrated Intelligent Network
Authors: Zheng Jiangshan , Yang Li , Pan Chengsheng , Shi Huaifeng
Published: April 2021

In the integrative intelligent network, the burst and heterogeneous characteristics of the aggregation node traffic lead to increased queuing delay and frame loss rate of the business flow. It is difficult to meet the quality of service (QoS) requirements of the differentiated business flow. In response to the above problems, a mixed queue scheduling based on demand service (MQSD) is proposed for the integrated intelligent network of heaven and earth. Different scheduling algorithms are used to schedule different business flows, which distinguishes the different needs of the business. The weight of business flow is calculated using the construction judgment matrix method, and is determined to be acceptable through a consistency test. By comparing the available service volume with the demanded service volume, the available service volume to the business flow will be dynamically adjusted. Establish a simulation experiment platform to schedule different types of randomly generated data streams. Simulation results show that the MQSD mechanism reduces queuing delay and frame loss rate caused by burstiness while meeting QoS requirements.
Simulation Analysis of Harmonic Suppression for AC Charging Pile of Electric Vehicle
Authors: Zhou Z W , Yan X H
Published: April 2021

The nonlinear load of electric vehicle AC charging pile brings harmonic pollution to the power system, which seriously affects the safe and stable operation of the power system. In this paper, the AC charging pile of an electric vehicle is taken as the research object, and the system simulation and harmonic analysis are carried out. And APF is used to compensate and suppress the harmonic of the simulation model of electric vehicle charging. The simulation results show that the charger mainly increases the 5th and 7th harmonics to the power grid. After adding APF to the power grid side, the harmonics of the power grid side are suppressed, and the influence of the charging pile on the DC power grid is greatly reduced.
RETRACTED: Research on oil painting creation mode based on multimedia technology in big data Era
Authors: Wang Lin
Published: April 2021

With the continuous development of social economy, people’s living standards have been significantly improved. Under the impact of the information age, it has caused an indelible impact on the industry. In today’s era, with the continuous promotion of the development of the field, the art industry also has a lot of innovation and improvement. Due to the continuous expansion and simplicity of multimedia technology, we have a better experience of the visual effect experience of daily life, and promote the development of the art industry. In today’s information society, data-based situation, now the painting creation is also welcome to its brand-new side, it is moving towards the direction of diversified development and constantly forward exploration. The diversified forms shown in the era of big data have turned the visual art of painting into a kind of data symbolization, which meets the requirements of the development of multimedia in today’s society and has an impact on today’s social painting art to a certain extent. In the era background of today’s big data society, it also has some significant impact on the thoughts of many artists, and then has an impact on the social art development.
RETRACTED: Innovation of Cross-border E-commerce Practice Teaching Based on Big Data
Authors: Deng Jianghua , Qing Haohua
Published: April 2021

With the development of the times, cross-border e-commerce has gradually become an important course, but the current teaching methods are difficult to provide students with good teaching. Based on the background of big data, this article hopes to practice cross-border e-commerce in schools The teaching finds a practical form with good feasibility, and summarizes from it that can reflect the universally applicable internal laws, so that the school’s e-commerce teaching can make more students realize the role of big data. Studies have shown that cross-border e-commerce practice teaching under the background of big data is more popular among students than the current traditional teaching model, with a favorable rate of over 90%. And in terms of teaching efficiency, the efficiency of teaching based on big data reaches 0.835, which is much higher than traditional teaching methods. This shows that practical teaching of cross-border e-commerce based on big data can play an important role.
Design and Analysis of Enterprise Management System Framework Based on Blockchain Technology
Authors: Jiang Leilei , Dong Ke
Published: April 2021

With the advent of the data age and the rapid development of Internet of things technology, blockchain technology has been proposed. Since the advent of blockchain, it has been widely concerned. Its tamper proof, traceability, anonymity and openness have attracted more and more people’s interest. Many researchers and practitioners have stepped into this field. In this paper, the related technical concepts of blockchain are described, and the blockchain technology is integrated into enterprise management innovation to provide new ideas for enterprise management. This paper designs an enterprise management system based on blockchain technology according to the requirements, and carries out bug monitoring and pressure test on the system, and the results show that the system can be used well.
Application of Artificial Intelligence Technology in Traffic Flow Forecast
Authors: Li Yunxiang , Liu Guochang , Cheng Yingying , Wu Jifei , Xiong Yongyi , Ma Ruosen , Wang Yuhang
Published: April 2021

In recent years, China’s urbanization process has been accelerating, the number of motor vehicles has been increasing, and the problems of traffic congestion, traffic noise and environmental pollution have become increasingly prominent. The application of artificial intelligence system in the field of transportation provides a good idea to solve the above problems. Traffic flow prediction is to estimate the traffic flow in a period of time according to historical traffic data. This technology can provide decision-making basis for traffic guidance and path planning. In this paper, the deep learning theory of artificial intelligence technology is used to predict the short-term traffic flow, and the LSTM prediction model is constructed. On the basis of preprocessing the original data, through correlation analysis, compression matrix construction and other steps, a more accurate short-term traffic flow prediction is realized. According to the model, this paper also discriminates the actual road congestion, and the prediction results are basically consistent with the actual situation.
Thoughts on the Reform of Higher Vocational Dance Teaching Based on Big Data
Authors: Shang Meiling
Published: April 2021

In the fast-developing information age, the current teaching level can no longer meet the current students’ demand for knowledge. Based on the current teaching system and platform, this paper uses modern programming and big data information database as the basis, and combines some shortcomings of current college education methods and levels to design a set of remote dance information teaching system. On the basis of the B/S network structure, a new online and offline interactive teaching model for the three roles of teachers, students and administrators established by using big data has successfully enriched the diversity of higher vocational education methods.
Clove leaf ethanol extract (Syzygium aromaticum L. Merr. and Perr) is formulated as antiseptic liquid soap
Authors: Maryam S , Hartono K , Widiyawati I E
Published: April 2021

Secondary metabolite compound is compound that have medicinal properties, cosmetics, dyes, fragrances. Secondary metabolite compound can be modified into one form of pharmaceutical dosage, namely cosmetic products. One of the cosmetic products is liquid soap preparation. Soap that can kill bacteria is known as antiseptic soap. Clove leaves (Syzygium aromaticum L. Merr. And Perr) contain eugenol compounds which have antiseptic properties. This study aims to determine the effectiveness of the ethanol extract of clove leaves as an antiseptic liquid soap preparation. The formulation of clove leaf extract with concentrations of 5%, 10% and 15% with HPMC concentrations of 1 gr, 2 gr and 3 gr. The liquid soap formulation of clove leaf ethanol extract was tested for organoleptic, pH, specific gravity, high foam and viscosity. The results of testing the quality of all liquid soap formulations meet the requirements according to the standards set by SNI. Based on the evaluation of this liquid soap preparation, the ethanol extraction preparation can be formulated into liquid soap preparations. After being tested for its antibacterial activity, Sedian Liquid Soap from Ethanol Extract of Clove Leaves has effectiveness as an antiseptic.
Targeted discovery of glycoside hydrolases from enzymes reservoir of digestive gland Achatina fulica through functional metagenomic strategies
Authors: Kurniawati M , Yunus A , Ayu H D , Subandi S , Suharti S
Published: April 2021

Digestive gland Achatina fulica is source of bioprospecting of glycoside hydrolases enzymes for many biotechnological and industrial processes. Nevertheless, there is limitation to discover novel enzymes. The functional-based approach analyzes the metagenomic library based on the genomic function of an organism used to look for new enzyme-producing genes. The aim of this study was to determine the chances of obtaining the novel genes for glycoside hydrolases from enzymes reservoir of digestive gland A. fulica through functional metagenomic method. The results showed that the total RNA concentration isolated from the digestive gland of A. fulica was 2,343.2 ng/uL. A total of 2 uL of total RNA has been used to construct the metagenomic library, so there are 4.69. 1010 - 1.87. 1011 transcribed molecules from 1.17. 109 - 4.68. 109 genes. This is a great number of chances to acquire the novel glycoside hydrolase genes.
Variation in recombinant protein production volume of Fim-C-salmonella typhi as a raw material for typhoid detection kit at laboratory scale
Authors: Nurjayadi M , Jinan S F , Setiyoto T , Hardianto D , Sulfianti A , Agustini K , Sukmawati D , El-Enshasy H A
Published: April 2021

Salmonella typhi is gram-negative bacteria that caused typhoid fever in humans; prevention of the disease is currently through vaccination. The development of disease detection tools is also being carried out so that the detection process is faster and more accurate. In line with the development of typhoid detection devices, prior studies have managed to find factors that influence the production of Fim-C S. typhi protein on a small scale as raw material for typhoid detection kits. The purpose of this research is to apply the results of previous studies in the production of recombinant Fim-C-S. typhi proteins with volume variations of 50mL-300mL, as a foundation for large-scale production. The results of protein production were characterized by Sodium Dodecyl Sulfate Polyacrilamide Gel Electrophoresis (SDS-PAGE) and protein concentration measurements using the Bicinchoninic Acid (BCA) Assay at a wavelength of 562 nm. The results showed an amount of that protein increase along with gradually production volumes, mainly the protein in the form of inclusion bodies. According to the results obtained can be concluded production of the recombinant protein Fim-C-S. typhi at 50-300 mL volume variation on a laboratory scale has been successfully performed with consistent results, which is expected to be basic in production at pilot scale and large scale.
Chemical analysis of commercial quail laying ration with substitution of fermentation waste bean sprouts
Authors: Mardewi N K , S Rejeki I G A D , Rukmini N K S
Published: April 2021

Japanese quail (Coturnix-coturnix japonica) has great potential to be developed as an alternative source of cheap animal protein. Generally, quail breeders use commercial rations. The problem is that commercial rations are expensive. From an economic perspective, the cost of the ration is very high, reaching 70% of the total cost of production. To reduce the cost of ration and to maintain the nutritional quality, it is tried to substitute commercial rations using feed ingredients that have good nutritional content and the price is cheap by using waste bean sprouts. The purpose of this study was to determine the chemical quality of commercial quail laying rations substituted with fermented waste bean sprouts. The study used a completely randomized design (CRD). The treatment consisted of substituting 0%, 5%, 10% and 15% waste bean sprouts in the commercial quail laying ration. From the research results obtained that the substitution of fermented waste bean sprouts in commercial quail laying rations has a very significant effect (P<0.01) on dry matter, water content, ash content and organic matter. Substitution of waste bean sprouts in quail laying rations up to the level of 10% produces the lowest ash content and water content and meets the SNI standard, as well as the highest organic matter content of the ration
Application of yeasts isolated from fermented cocoa beans for biocontrol of pathogenic mold in chocolate fruit
Authors: Sukmawati D , Arman Z , Hasanah R , Balqis M , Setiawan A , Tafrijiyyah F , Sari R , Supiyani A , Prihantini N B , Husna S N A , El Enshasy H A , Dailin D J
Published: April 2021

Contamination by pathogenic mold in postharvest cocoa beans becomes a significant concern by most Indonesian farmers. Pathogenic mold can cause damage to cocoa beans by such as rotting diseases in fruit. One alternative that can be used to control pathogenic mold is using biological agents such as yeasts. Some group of yeasts can produce cellulase enzyme that can degrade cellulose, and it can possibly break the cell wall with of mold which composed of semi-crystalline chitin, p-need, and cellulose. This study aims to determine the yeast originated from fermented cocoa beans which can produce cellulase enzymes and their potential ability as a biocontrol for pathogenic molds in chocolate fruit. This study includes yeast isolation from fermented beans, screening of yeast isolates that produce cellulase enzymes, and in-vitro antagonistic testing against pathogenic molds on chocolate fruit. The results showed that there were 21 yeast isolates from fermented cocoa beans, and among all, there were five isolates which can produce cellulase enzymes, namely isolate C4.-3.3, C4.-3.13, C4.-4.9, C4.-4.10, and C4.-5.9. Yeast isolate C4.-4.10 can produce cellulase enzymes with an index of 0.32 U/mL. This research showed that the 5 yeast isolates have the low category of cellulase enzyme, and further study is needed to be done to confirm their ability to act as a biocontrol agent.
The effect of brewing time on pH values, polyphenols content, and antioxidant activities of coffee husk tea (cascara tea)
Authors: Maharani S , Mustikawati I , Nailufhar L , Istiqomah S
Published: April 2021

Coffee husk tea, well-known as cascara tea, is a by-product of coffee processing. It contains phenol compound that has antioxidant activity. Total phenol compound is extracted by brewing process. It is commonly called polyphenol compounds. Polyphenols extraction process is deeply connected to brewing method, especially the time. The purpose of this study was to determine the effect of brewing time on pH, polyphenols content, and antioxidants activity of cascara tea. This study was carried out in Completely Randomized Design with variations of brewing time in 5 levels from 5 minutes, 7 minutes, 10 minutes, 12 minutes, and 15 minutes. The results showed that the variation of brewing time significantly affected the levels of pH values, polyphenols content, and antioxidant activity of cascara tea. The brewing time of cascara tea for 10 minutes has the highest level of polyphenols content and antioxidant activity. Meanwhile pH values was decreased during brewing process.
Code-mixed sentiment analysis of Indonesian language and Javanese language using Lexicon based approach
Authors: Tho C , Heryadi Y , Lukas L , Wibowo A
Published: April 2021

Nowadays mixing one language with another language either in spoken or written communication has become a common practice for bilingual speakers in daily conversation as well as in social media. Lexicon based approach is one of the approaches in extracting the sentiment analysis. This study is aimed to compare two lexicon models which are SentiNetWord and VADER in extracting the polarity of the code-mixed sentences in Indonesian language and Javanese language. 3,963 tweets were gathered from two accounts that provide code-mixed tweets. Pre-processing such as removing duplicates, translating to English, filter special characters, transform lower case and filter stop words were conducted on the tweets. Positive and negative word score from lexicon model was then calculated using simple mathematic formula in order to classify the polarity. By comparing with the manual labelling, the result showed that SentiNetWord perform better than VADER in negative sentiments. However, both of the lexicon model did not perform well in neutral and positive sentiments. On overall performance, VADER showed better performance than SentiNetWord. This study showed that the reason for the misclassified was that most of Indonesian language and Javanese language consist of words that were considered as positive in both Lexicon model.
Predicting tuberculosis drug resistance using machine learning based on DNA sequencing data
Authors: Hadikurniawati W , Anwar M T , Marlina D , Kusumo H
Published: April 2021

Tuberculosis is a serious infectious disease caused by Mycobacterium tuberculosis (MTB) that primarily affects the lungs. It is known that several strains of MTB are resistant to drugs used in the treatment. This situation calls for the importance to detect and prevent further drug resistance and thus reducing the mortality rate. The conventional molecular diagnostic test is costly, requires a long time to conduct, and has low prediction ability. This research aims to explore the Machine Learning approach to accurately predict drug resistance which offers a much faster and cheaper solution than the conventional one. Experiments were carried out on 3393 isolates of MTB using several Machine Learning algorithms including C4.5, Random Forest, and Logitboost. Multiple drugs evaluated in this study include rifampicin (RIF), isoniazid (INH), pyrazinamide (PZA), and ethambutol (EMB). By using 10-fold cross-validation, the result had demonstrated that the model can accurately predict drug resistance with an accuracy of 99% and with Area Under Curve (AUC) reaching (near) 1. This result suggests that Machine Learning approach has a promising result in predicting Tuberculosis drug resistance.