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Physics
Computer Science

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Measurement of Information Security Awareness Level: A Case Study of Digital Wallet Users

Awareness of a person is an important factor in maintaining information security and reducing the fraud gap. The existence of various types of digital wallet application fraud requires someone to have an awareness of information security. This study was conducted to measure the information security awareness of digital wallet users. The research was conducted by distributing questionnaires to 156 respondents of digital wallet users. These respondents represented users of digital wallet applications in Indonesia. Information security was measured based on the user’s knowledge-attitude-behavior. The assessment of information security awareness level was calculated using the Analytic Hierarchy Process (AHP) method. The results showed that the score was at the awareness level of good (80.78%). However, several focus areas still showed score at the average level which was one of the reasons why there are still many information security breaches against users of the digital wallet.

Introduction
The development of information technology has led to various innovations in various sectors. These innovations have brought significant changes to the way of living, working and communicating. No wonder these changes have led to a digital lifestyle. The technology-based financial sector or fintech also contributes to changes to a digital lifestyle. Nowadays, the digital wallet is one of the trending technology.
The popularity of digital wallet among the public is also high, this is showed by Dailysocial research that digital wallets (82.7%) were the most popular fintech product category according to respondents, followed by investment (62.4%), pay later (56.7%), and p2p lending that accommodated personal needs (40%). The same source also stated that 81.6% of the considerations for using digital wallets were based on respondents' trust in the product itself. 72.2% of respondents also tend to use it because of its suitability to their needs. Besides, 72.9% of respondents chose that digital wallets have the appropriate functionality. Then, 66.2% of respondents also stated that digital wallets were easy to use and 32.8% of using digital wallets could save time [1].
The continued growth of digital wallets and high popularity among the public has led to the rise of various types of fraud and deception mode on behalf of these service providers. This is evidenced by the Police report which stated that the high number of cases received in 2019 was online fraud. However, the police did not specifically mention the number of online deception figures, especially deception that occurred on digital wallet service. The mode of deception that is carried out can be in the form of IOP Publishing doi: 10.1088/1757-899X/1077/1/012003 2 suspicious messages, phone calls and so on. From these messages or call, deceiver usually asks for OTP from customers, ask for personal data even ask for a balance transfer. Apart from deception, there are also cybercrimes in the form of digital wallet burglary. These cybercriminals usually perform these 3 types: impersonate and take over SIM cards (SIM Swab), Man-in-the-middle (MiTM) attacks and phishing and also malware attack [2].
The rise of various types of fraud and cybercrime modes related to digital wallet service shows the importance of public awareness of security in the cyberspace. In the research conducted by Ogutcu, et al., stated that security issues are not just a technological problem, but also human attitudes and behavior towards the use of information technology [3]. Therefore, public awareness of information security needs to be properly assessed and evaluated. This is useful to find out more about the scope of information security which still needs to be improved as a preventive measure and self-protection towards various types of fraud and data theft.
Kruger and Kearney have developed a model for measuring information security awareness, namely the Knowledge-Attitude-Behavior (KAB) model. This model is the most commonly used as a basic model for measuring information security [4]. Several studies have adopted the KAB model as a basis for measuring information security awareness, such as a study written by Parsons, et al. [5]. However, there is no specific research that discusses measuring the level of information security awareness for digital wallet users.
The purpose of this research is to find out the security awareness of Indonesians in cyberspace by testing their security level when using a digital wallet. The population of digital wallet users in this study represented the majority of Indonesian users.

Information Security
Information security is defined as the protection of information and information systems from unauthorized access, use, disclosure, interference modification, or destruction [6]. The concept of information security according to Andress is known as the CIA Triad, namely Confidentiality, Integrity, and Availability. Confidentiality is an important component of privacy and refers to an organization's ability to protect the data of unauthorized persons. Integrity refers to the ability to prevent to not change illegally. Availability refers to the ability to access data when needed.
Meanwhile, according to Whitman and Mattord, Information Security is an organization that must have several layers of security to protect its operations. The security in question is as follows [7]: x Physical security generally protects physical items or objects and areas from illegal access and abuse x Personnel security generally protects an individual or group of individuals who have the authority to access the organization and its operation x Operation security generally protects the details of a particular operation or series of activities.
x Communication security generally protects communication media, technology and content.
x Network Security generally protects network component, connection, and their contents.
x Information security generally protects confidentiality, integrity and availability of information assets, whether in terms of storage, processing or transmission. This is achieved through the application of policies, education, training and awareness, as well as technology.

Information Security Awareness
Information security awareness is characterized by understanding security policy, procedure and practice so that when security problems occur, the public can make appropriate judgments. Improve information security awareness is like implementing a security policy, taking preventive measures, improving user security behavior patterns, or changing routines so that they can implement habits related to information security [8]. 3 Building a level of information security awareness is important because human behavior is considered to be the biggest threat of information security. Various types of information security threats arise due to human behavior such as carelessness and negligence in safeguarding information, unwise mobile use, access unsafe sites and handle the data that does not comply with security policies. This behavior can be risky internally and externally. It caused cybercrime and deception to steal confidential data [9].

Measurement of Information Security Awareness
Measuring information security awareness is a dynamic process. Risk and threat are constantly changing so that the measurement of information awareness needs to be continuously measured and updated to reflect changes in the risk profile.
The most popular information security awareness measurement model used is the model proposed by Kruger and Kearney. 3 dimensions serve as benchmarks for assessing information security awareness, namely Knowledge, Attitude, Behavior. Knowledge to measure what users know about information security. Having insights related to information security is an initial step in creating information security awareness. Attitude is to measure how users feel or think about the known risk of information security. Behavior is to measure what users do or actions regarding information security [4]. Table 1 is a scale of the level of awareness that is used to help define the level of awareness of the object studied. In the dimensions of Knowledge, Attitude and Behavior, it is focused on the variable focus area which refers to the variable developed by Parsons, namely Human Aspects of Information Security (HAIS-Q) [5]. Each dimension variable is measured to the 7 HAIS-Q variables, namely password management, email usage, use of the internet, use of social media, mobile device, information handling, and incident reporting.

Analytic Hierarchy Process Method
In this study, 3 area dimension variables and 7 focus area variables are used so that need a method that can calculate the validity up to the tolerance level for inconsistencies of various criteria and alternatives chosen by decision-makers that is Analytic Hierarchy Process (AHP). AHP is decision support using a basic approach. This approach is used to select the best number of alternatives that are evaluated based on several criteria [10]. It describes a complex multi-factor or multi-criteria problem into a hierarchy. Hierarchy is defined as a representation of a complex problem in a multilevel structure where the first level is the goal, then the factor level, criteria, sub-criteria, and so on until the last level of alternatives [11]. Data quality from respondents takes precedence over quantity data when applying the AHP method. The sample size in the AHP method does not have a specific formula or standard [12], there is a general consensus that the AHP does not require a very large sample [13]. Figure 1 shows the hierarchy of AHP.

Methodology
This section discussed the research stages, research instruments, data collection, hierarchical structure design, and data analysis techniques. The detail of each point explained below.

Research Stages
This section shows the stages taken in this study. Each stage explained below: x Determine the problem. The first thing to do was to determine the problem that was researched.
In this case, it also looked for facts about the problem and determined the research objectives. x Literature study. At this stage, looking for various things or information that was relevant in the research, including searching for several previous studies that have similar topics or were related to this research. x Make a research design. The research design made was compiling a hierarchical structure for the implementation of the AHP. In this hierarchical structure, the KAB (knowledge, attitude and behavior) model acted as a criterion and the HAIS-Q (human aspect information security questionnaire) acted as an alternative.
x Create questions and distribute questionnaires. After determining the research design, the next step was to create a questionnaire question. The questionnaire refers to two variables, namely the dimensional variable and the focus area variable. Furthermore, the questionnaire was distributed online by having predetermined criteria for respondents.
x Processing data and analysis. The next stage was processing the data from the questionnaire and analysis. The data processing technique used was AHP. After getting the research results, the results were analyzed. x Conclude. In this stage, conclusions can be drawn based on the data that has been collected and analyzed. At this stage, several recommendations were also given that could be made to improve information security awareness.

Research Instrument
The questionnaire was created using Google form and consisted of 55 questions consisting of 51 questions regarding information security and 4 questions which represent the criteria for respondents such as age, latest education, occupation and gender. The 51 questions regarding information security relating to 3-dimensional variables and 7 focus area variables. The questionnaire was translated into the Indonesian to ensure broader coverage. Google form is also set to only accept one response to prevent duplicate respondents. Respondents were asked to fill out the questionnaire using a Likert Scale with a scale of 1 to 5. The Likert Scale was designed to measure attitudes in a scientifically accepted way and validated in 1932 [14]. Table 2 is the Likert Scale table used.

Data Collection
The questionnaires were distributed randomly online using random sampling techniques and met the requirements of using a digital wallet application in Indonesia. According to Arif Rahman, random sampling has several assumptions that the population is known as a whole, the sample is drawn randomly from the population, and each member of the population has an equal chance of being sampled [14]. The respondent's data were then processed using the Analytic Hierarchy Process and the results could be drawn a conclusion and analysis.

Hierarchical Structure Design
AHP has a hierarchical structure which is one of the stages to use in this method. The top hierarchical structure is the purpose of the decisions taken, then the next level is the criteria and the lowest level is the alternative [4]. Figure 2 is the hierarchical structure used in this study.

Figure 2. AHP Hierarchy Structure
The highest level of the structure is knowing the level of information security, the next level is the criteria that is filled with 3-dimensional variables (knowledge, attitude, behavior) and the lowest level is 7 focus area variables (password management, email usage, use of the internet, use of social media, mobile device, information handling, and incident reporting).

Data Analysis Technique
After the respondent's data collected, Kruger and Kearney measure the weight of the dimensions and focus areas using AHP. The value of the dimensional variable weight that is used in this study refers to the weight that has been made by Kruger and Kearney [4]. Table 3 represents the weight that was used in the dimensional variable. Attitude 20 Behavior 50 Determining the weight of the focus area variable requires expert consideration to fill the pairwise of the comparison matrix [10]. An expert in information security from the University of Indonesia was IOP Publishing doi:10.1088/1757-899X/1077/1/012003 6 interviewed to get a priority scale between the focus area variables. Table 4 is the weight of the focus areas used in this study.
The score of each focus area per dimension was calculated and normalized to the sum of one. The total score, v (a), was determined using the formula below [4]: After obtained the value from the awareness level of the digital wallet application users, the value was grouped into 3 categories of information security awareness level [4]. Table 5 represents the value categories that are used and the colors used in the results table. The function of giving color to the table is to make it easier to read the table and provide detailed information so that it can make it easier to take action for each dimension and focus area.

Result and Discussion
The questionnaire was distributed online to the general public but the respondent has used a digital wallet application. The number of respondents was 156, representing users of digital wallet applications in Indonesia. The results of measuring the average information security awareness of digital wallet application users can be seen in Table 6. The total score is 80.78%. The result of the measurement is categorized into the good category according to the metering of the level of information security awareness used by Kruger and Kearney [4]. This showed that the information security awareness of digital wallet application users was satisfactory so that no action is needed to improve information security awareness.
Although the overall showed the good category, several focus areas need attention. The focus area of "use of social media" had the lowest average level of awareness of 73.35%. Some respondents still considered that displaying a phone number on social media was an action that would not interfere with their information security. Although this action is quite risky. When other people know someone's phone number, this will be a fraud gap to be able to log in by getting an OTP number from the victim.
The focus area of "use of internet" and "information handling" also still needs attention because each dimension is categorized into an average level and has the potential to take action to improve information awareness. The lowest value in the focus area of "use of internet" was on the knowledge dimension, which was 70.67%. It may some respondents were not aware that there could be a fake application that IOP Publishing doi:10.1088/1757-899X/1077/1/012003 7 acts on behalf of the digital wallet service so that they provide their data to the fake application. Many respondents also might access digital wallet applications with a free VPN from the application store. Besides, respondents were also not aware that entering personal data on online sites that lured digital wallet balance vouchers that could compromise their personal data. Meanwhile, the lowest value in the focus area of "information handling" was on the behavior dimension, which was 75.32%. Many respondents still stored important information about digital wallet accounts such as PINs on their phones without providing protection. This is quite risky when someone accesses their phone and accidentally opens information about a digital wallet account. The last focus area that needs to be considered was "email usage", because the score of the knowledge and attitude dimensions still showed the category of needing supervision, 71.15% for the knowledge dimension and 77.56% for the attitude dimension. There were still many respondents who deliberately entered their data because they were lured with reward through fake email notifications on behalf of the digital wallet service. They also might don't understand the threat of email containing malware.
Although none of the dimensions and focus areas showed the poor category, digital wallet service providers need to make some improvements in several focus areas with the average category by the following recommendations: x Based on focus area of information handling and email usage that has an average score and needs some improvements, so that digital wallet service providers need to provide education to users periodically. This is important to do because unconsciously users tend to do things that can threaten the security of their digital wallets. This education can be in the form of an appeal not to put a phone number on social media, then not to store important information about digital wallets on phone, and an appeal to only trust the official website or email of a digital wallet service provider. The hopes that digital wallet users always remember to keep their data security.
x Based on focus area use of internet that the score still on average and needs an action such as the digital wallet service providers may add policies for digital wallet users. One of the policies that need is to prohibit users from accessing digital wallet applications using a free VPN from the application store. Cybersecurity observers said that using a free VPN can be risky. The types of risks are also varied, such as VPN traffic can easily spread advertisements or malware, the other risk is robbery and data leaks [16]. So that digital wallet providers need to strictly prohibit their users from accessing digital wallets using a free VPN.

Conclusion
The measurement of information security awareness to digital wallet application users using the KAB model has been successfully delivered. The KAB model assessed 3 dimensions that are Knowledge, Attitude, Behavior and 7 focus areas that are password management, email usage, use of the internet, and use of social media, mobile device, information handling, and incident reporting. The results showed that the total score of the respondents' category of awareness was at the good. However, focus areas such as "use of social media", "use of internet", "information handling", and "email usage" still showed average scores or require supervision. Although there are no dimensions and focus areas that indicate a poor category, these actions are still needed to increase the level of user awareness in several focus areas with an average score. Such as an action to provide education to users regarding appeal not to carelessly display a phone number and reminding them to properly store valuable information, then not easily trusting fake sites and emails. It can also create policies that strictly prohibit the use of free VPN when opening digital wallet applications.


Stress Classification using Deep Learning with 1D Convolutional Neural Networks

https://doi.org/10.17977/um018v4i22021p145-152 ©2021 Knowledge Engineering and Data Science | W : http://journal2.um.ac.id/index.php/keds | E : keds.journal@um.ac.id This is an open access article under the CC BY-SA license (https://creativecommons.org/licenses/by-sa/4.0/) KEDS is Sinta 2 Journal (https://sinta.kemdikbud.go.id/journals/detail?id=6662) accredited by Indonesian Ministry of Education, Culture, Research, and Technology Stress Classification using Deep Learning with 1D Convolutional Neural Networks

Stress has been a major problem impacting people in various ways, and it gets serious every day. Identifying whether someone is suffering from stress is crucial before it becomes a severe illness. Artificial Intelligence (AI) interprets external data, learns from such data, and uses the learning to achieve specific goals and tasks. Deep Learning (DL) has created an impact in the field of Artificial Intelligence as it can perform tasks with high accuracy. Therefore, the primary purpose of this paper is to evaluate the performance of 1D Convolutional Neural Networks (1D CNNs) for stress classification. A Psychophysiological stress (PS) dataset is utilized in this paper. The PS dataset consists of twelve features obtained from the expert. The 1D CNNs are trained and tested using 10-fold cross-validation using the PS dataset. The algorithm performance is evaluated based on accuracy and loss matrices. The 1D CNNs outputs 99.7% in stress classification, which outperforms the Backpropagation (BP), only 65.57% in stress classification. Therefore, the findings yield a promising outcome that the 1D CNNs effectively classify stress compared to BP. Further explanation is provided in this paper to prove the efficiency of 1D CNN for the classification of stress. without human intervention [13]. Machine learning provides the ability for machines to train on and accordingly adapt when there is new data input, enables the prediction from known data, and learns from the new unknown data.
One of the machine learning methods that can be used to recognize stress is Artificial Neural Networks (ANNs). ANNs are a non-linear data-driven self-adaptive approach instead of the traditional model-based methods" by Solomatine & Ostfeld [14]. "ANNs can determine and study correlated patterns between input data and corresponding target values. It can use to predict the outcome of new independent input data after training. Neural networks (NNs) "have been used in a wide variety of applications where statistical methods are traditionally employed like classifying problems or predicting outcomes as said by Solomatine & Ostfeld [14].
For a very long period, ANNs had to be limited in size due to difficulties in training. However, Hinton, Osindero, and Teh [15] suggested a procedure that could be used with much larger networks known as Deep Learning (DL). LeCun, Bengio, and Hinton [16] said Deep learning allows computational models composed of multiple processing layers to learn the representation of data with multiple levels of abstraction. Convolutional Neural Networks (CNNs), one of the most well-known DL models, have become the de facto standard for many Computer Vision and Machine Learning operations over the last decade. CNNs are feed-forward Artificial Neural Networks (ANNs) with convolutional and subsampling layers that alternate. When trained on a large visual database with ground-truth labels, deep 2D CNNs with many hidden layers and millions of parameters can learn complex objects and patterns. With proper training, this one-of-a-kind ability transforms them into the primary tool for various engineering applications involving 2D signals such as images and video frames. As an alternative, 1D Convolutional Neural Networks (1D CNNs) have recently been developed as modified 2D CNNs. 1D CNNs have been utilized in several classification problems like heart sound and Soil Texture [17][18] [19]. It is believed that the classification between stress and nonstress will be more accurate with multiple processing layers compared to the ANNs that only consist of a single processing layer. Smets et al. [20] said the previous approach to measure stress does not allow for continuous monitoring and often suffers from biases such as demand effects, response, and memory biases; therefore, the focus has been shifted towards measuring bodily responses as indicators of stress.
This study aims to use stress datasets to anticipate an accurate output; society will use the outcomes. The remaining parts of this paper are organized as follows: methodology and empirical studies are presented in section 2; Findings and the analysis of the results are presented in sections 3 and 4, and finally, section 5 concludes the paper's future works.

II. Methods
This section introduces the research methodology that had been used in this research. The components in this section include Dataset Preparation, Data pre-processing, Research Design, Research Implementation, and Instruments, as in Figure 1. DL has become a growing research interest, and the method has shown certain advantages of learning performance. DL can learn from the past data to solve complex problems and has been widely used in classification. It allows the computational models with various processing layers to learn the data representation with various levels of abstraction. The result of the DL method is compared to one of the neural network standards, the Backpropagation (BP) algorithm, which was used in previous studies related to stress classification [21] [22]. This comparison is to analyze the effectiveness of the DL algorithms in this study. The research focuses on psychophysiological stress, which looks at heart rate variability (HRV), galvanic skin response (GSR), skin temperature (Temp), and respiration.

A. 1D Convolutional Neural Networks (1D CNNs)
1D CNNs is very similar to CNNs where the input of 1D CNNs is dimensional, whereas an ordinary CNNs is two-dimensional. A Convolutional Neural Network (CNN) is very similar to ordinary neural networks as they are made up of neurons that have learnable weight and biases. Each neuron receives some inputs, performs a dot product, and optionally follows it with a non-linearity. Loss function still can be found in the last layer or fully connected layer, and all the tips developed for learning regular neural networks still apply. Figure 1 shows the schematic representation of 1D CNNs. The first hidden layer is always a convolutional layer, followed by a pooling layer. The convolutional and pooling layers can be present multiple times in the network until the final hidden layer is the full-connected layer. The Schematic Representation of 1D Convolutional Neural Networks is as shown in Figure 2.
The convolution layer is where the previous layer's feature maps are folded with learnable kernels and execute the activation function to form the output feature map. Each output map may combine convolutions with multiple input maps. The mathematical formula for constructing the convolution layer is used (1).
where is the selection of input maps, each output map is given an additive bias , for an output map, the input maps will be folded with different kernels. So, if output map and map both sums over input map , the kernels applied to map are different for output maps and .
The pooling layer produces downsampled versions of the input maps. If there are N input maps, there will be exactly N output maps, although the output map is smaller (2).
where ( ℓ−1 ) represents a sub-sampling function. This function will sum over each distinct nby-n block in the input so that the output is n-times smaller along both spatial dimensions. Each output map is given its multiplicative bias and an additive bias .
The fully connected layer will combine features learned by different convolution kernels so that the network can build a global representation of the holistic input. Let Denote the weight given to input map when forming the output map . The output map is (3).
Subject to the constraints (4).
These constraints can be enforced by setting the variables equal to the softmax over a set of unconstrained, underlying weight use (5).
Each set of weights for fixed j are independent of all other such sets for any other , the single map is updated, and the subscript . Each map is updated in the same way except with different indices.

B. Empirical Studies
Several experiments are conducted on the psychophysiological stress dataset. The implementation is multivariate analysis to evaluate 1D CNNs deep learning model. There is a total of twelve features in the raw data given by Dr. Elena Smets [20], which include Skin Conductance / Galvanic Skin Response (SC/GSR), Skin Temperature (Temp), Blood Volume Pulse (BVP), Respiration Changes (RSP), Heart Rate (HR), Electromyography amplitude (EMG amp), Blood Volume Pulse amplitude (BVP amplitude), Respiration Changes rate (RSP-Rate), Respiration Changes amplitude (RSPamplitude), Heart Rate Variability amplitude (HRV amplitude), Respiration Changes + Heart Rate Coherence (RSP+HR Coherence), and Segments. The segments will display the state of the respondents at that moment. For each sensing modality, features have been calculated on a sliding window of 30 s with 29 s overlap. The illustration of the datasets is shown in Figure 3.
The dataset had been separated into two parts: a training set and a testing set. The training set usually consists of input vectors and corresponding output vectors that fit into the model to produce a result compared with the target for each input vector in the training dataset. The testing set is utilized Fig. 3. Psychophysiological stress dataset to provide an unbiased evaluation of a final model fit on the training dataset. The dataset had been normalized using the Z-Score normalization technique to scale the dataset value from 0 to 1. The normalized data then fits into the proposed deep learning model for classification. The classification performance of the deep learning model will be evaluated by utilizing the k-fold cross-validation. Brownlee [23] said that Cross-validation is a resampling procedure used to evaluate a limited data sample of machine learning models. In k-fold cross-validation, the dataset is divided into k subsets of equal size. The model is built fork times, leaving one of the subsets from training and using it as the test set.
Python is a hugely popular general-purpose programming language, so it is beneficial to apply solidly to AI and profound learning principles. Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. TensorFlow serves as the backend engine for the Keras API to handle the low-level operations such as tensor products and convolutions. The machine used is Intel® Core™ i7-4720HQ CPU @ 2.60GHz 2.59GHz with 8.00GB RAM. The proposed method uses the 1D CNNs deep learning model to classify. 1D CNNs works precisely like normal CNNs but with different input dimension. The input for normal CNNs usually be two-dimensional or image datasets, but the input for 1D CNNs is one-dimensional or signal datasets. Table 1 shows the dataset, batch size, epochs, activation function, and optimizer used in this study.
The Batch Size used is 256. Batch Size is the number of training examples utilized in one iteration. The data was run for 15 epochs to see the model's performance. Epoch is one forward pass and one backward pass of all the training examples. The activation functions are ReLU and Softmax. ReLU or Rectified-Linear Unit Layer will provide a threshold at zero. Softmax function or normalized exponential function is used to represent categorical distribution. The optimizer function is SGD. SGD or Stochastic Gradient Descent is a simple yet efficient approach to discriminative learning of linear classifiers under convex loss functions such as Support Vector Machine or Linear Regression. Figure 4 shows the schematic representation of the 1D CNNs model.
The classification accuracy of the output is calculated according to the use of equation (6).   Table 2 shows the results of the 1D CNNs deep learning model with 10-fold cross-validation. In each fold, the data had been divided into ten groups. The first group will be used as testing in the first fold, and the other groups will be used as training. In the second fold, the second group will be used as testing, and the other groups will be used as training, followed by the third group for testing in the third fold. This will recursively continue until the 10 th fold is completed. Besides that, the plotting of accuracy and loss function for each training process and testing process also can be seen in Table 2. The Final accuracy for 1D CNNs is 99.97%.

III. Results and Discussions
The dataset is tested in a second experiment with The Backpropagation (BP) algorithm for further proof. BP is fit into the same data to compare the performance of both models. In Table 2, it is clearly stated that the training classification performance of 1D CNNs is 100% and the testing classification performance of 1D CNNs is 99.97%. However, the training classification performance of BP is 65.85%, and the testing classification performance of BP is 65.57%. Figure 5 clearly shows that the 1D CNNs deep learning model outperforms the BP algorithms. Moreover, our algorithm achieved higher accuracy than the methods mentioned in [24]. Table 3 compares the implementation of the CNN and other recent works based on different machine and deep learning algorithms. As shown in Table 3, our work achieves better accuracy than most of the other recent works (which have been reported in this paper) except that of [20], which achieved 99.8% using the physiological signals dataset. However, our work's accuracy gains better results than the same dataset.
The datasets must be structured appropriately to fit perfectly into the model and provide a good analysis of the results. Every step, from loading the dataset from a text file or CSV file to evaluating the trained model's performance, is critical because simple errors can cause errors when the code is run. The parameters used to build the model must be tested repeatedly to find the best parameter. For example, a sigmoid may be better when ReLU is a terrible choice and vice versa. Building the best model requires a lot of trial and error. The deep learning model is well-known for its high accuracy, regardless of whether it can solve a classification problem or a regression problem.  [25] Physiological signals 99.80% ANN [26] WESAD dataset 95.21% SVM [20] Psychophysiological Stress 93.4 ± 3.2% Convolutional Neural Network (CNN) [27] The heart rate signal 98.69 ± 0.45% Machine learning [28] The mind-brain-body dataset 81.33% Machine learning [29] Questionnaire (DASS 21) Between 0.628 -0.798 Hybrid deep learning technique [30] Employee Data 96.2 % This work Psychophysiological Stress 99.7%

IV. Conclusion
In this study, the 1D CNNs model was utilized to classify stress and compared with BP and various techniques from the Literature review. A psychophysiological stress dataset was used in this study. The performance of the developed model is evaluated by comparing it with the BP model and other works. The result obtained in this study shows that the 1D CNNs model is reliable in classifying stress with low lost function, which is 0.001, and high accuracy of 99.97%. More datasets are required to enhance the result of stress classification in the future. Skill and experience are essential in assisting the person in selecting and checking the best method in the modeling process. Spiking Neural Network as the latest generation of neural network and other areas of study will be applied to do stress classification.


Research on the Protection Predicament and Digital Breakthrough Path of Rural Architecture in Shandong

. Rural architecture, as an important carrier of rural areas, faces the huge impact of modern life style and construction mode, and there is a crisis of cognitive degradation and loss of skills. With the continuous promotion of rural revitalization, the crisis of its protection and inheritance is increasingly serious, and the disadvantages of the single inheritance mode of oral transmission and personal transmission are also gradually revealed. In this predicament, how to innovate the inheritance of traditional skills has become an urgent problem to be solved. In the era when digital technology has penetrated into every field of modern life and become a technology booster, this paper intends to use digital technology and take the construction technology of rural architecture in Shandong province as the entry point to solve the dilemma faced by rural architecture and study the effective way of its digital inheritance and development.

Introduction
Currently, under the promotion of national policies, rural construction activities in Shandong have increased. As an important carrier of countryside, rural architecture is in an embarrassing situation as it cannot stand alone in the flood of construction: skills are losing, forms are unfounded, literature researches are lacking, some local governments are forced to imitate the Hui-style with horse-head wallstyle buildings in rural construction. The main reason is that domestic scholars have relatively insufficient researches on Shandong's rural construction, leading to the lack of relevant literature and data. And due to a single skill inheritance, the number of craftsmen who master traditional construction and repairing techniques is decreasing year by year. When implementing the project of rural construction protecting and repairing, local governments and architects have no evidence and no human resources to turn to. While part of the new buildings in rural areas are directly adapting the southern styles, completely losing the regional expression of rural construction and the inheritance of the rural context. After the proposal of realizing the great revival of Chinese traditional culture in 2025, and the Shandong government's proposal to build a rural architecture system of "Lu Style" that highlights the traditional characteristics of the province in 2025, this research applies the digital method to construct the new inheritance system. Taking the construction techniques of Shandong's rural buildings as starting point, this research uses a digital technique to carry out research on the inheritance system and utilization approaches, to solve the problems of protection and inheritance of rural buildings, to dock the actual problems in rural construction, and finally to inspire rural construction and revival in the background of China's new urbanization [1].

Research status abroad
Amos Rapoport's (1969) work "House Form and Culture" officially marked that the study of rural construction became a discipline in the Western. And then researches and protection on rural construction became increasingly active in the field of international cultural heritage protection, and multidisciplinary research has been maintained for long. Representative scholar Paulo Liver (1997) wrote the book "Encyclopedia of Vernacular Architecture of the World", which involves a multidisciplinary research perspective and accelerates the general development of the research. And later, more and more scholars have studies on construction techniques from the perspectives of architecture, art and ecology. In terms of digital inheritance of vernacular architecture, in 1996, Japan included rural architecture in the "Cultural Property Protection Law", protecting it as cultural heritage and digital protection work are tried to carry out. Takeo Ojika (2007) used digital restoration to protect the ancient village. Eugene (2019) thought that in the digital era, cultural heritage can be passed on and shared digitally.

Research status at home
Researches on domestic rural architecture. In our country, researches on rural architectures started from traditional dwellings. Liu Dunzhen's "Introduction to Chinese Housing" (1953) discusses the historical development process of our country's residential architecture. Taking village investigation as a typical method, Chen Zhihua and Lou Qingxi (1989) introduced theories of different disciplines into the perspective of rural architecture (Pan Xi, 2014), promoting the development of research work. Shan Jixiang(2010) pointed out the current problems of rural architecture. Many scholars have studied and traced southern rural architecture from different perspectives such as woodcarving decoration, oil painting and wooden construction (Yang Jing, 2015; Pei Qiangqiang, 2016), Li Zhen (2016) proposed to establish a genealogy and database of construction techniques. Gao Qi (2019) pointed out that traditional techniques need innovation. Pan Lusheng suggested to build a comprehensive framework based on computers and to achieve protection work by using digital method (2006). Jia Xiuqing pointed out that digital methods should move from basic protection to inheritance research (2012). Some scholars researched on the digital communication channels and target groups of traditional brick carving techniques, believing that the inheritance model should conform to the development of the times (Li Yujie, 2019).

A review of the current status of Shandong rural architecture
Scholars at home and abroad have conducted researches on rural architecture from different perspectives and methods, and it shows that there are many achievements but still some shortcomings: First, the existing research areas in China are more focused on the south and less on Shandong. Second, the existing researches are mostly carried out from a single perspective such as architecture form and techniques, but less researches on the genealogy of construction techniques and inheritance system. Third, in the traditional Chinese architecture construction system, "oral teaching" has always been the main way to pass on the traditional techniques of construction [2], craftsmen form systematic construction formulas through learning, coping, practicing [3]. However, with the development of technologies, the disadvantages of this single inheritance model are gradually exposed. In the background of digital era and big data, digital inheritance, communication and utilization of Shandong's rural architecture are relatively lagging, and the innovation is insufficient. Therefore, this article takes the construction techniques as starting point, pedigree as the focus, database as the carrier, inheritance as the goal and makes the research with digital methods.

Academic value
Rural architecture is an important carrier for highlighting rural vitality and rural culture. There are many great rural architecture, construction techniques and architecture forms in Shandong area, but due to the difference in the depth of research results between the north and the south by the ancestors, and the limitation of objective research conditions at the time, resulting in the situation that the traditional architecture history emphasizes on the official style rather than the rural local, on the form and style rather than the techniques and craftsmanship [4]. And with the single inheritance model becoming more and more prominent with the changes of the time, the traditional techniques are facing the danger of losing, the traditional system are facing the danger of departing. This research can make up for the above shortcomings, strengthen the academic basic research, make up for the insufficiency of researches on Shandong's architecture history, and push forward the breadth and depth of the Chinese architecture history research through the research on the inheritance system context and the research on the genealogy of the construction techniques.

Realistic value
a) Digital method expands the breadth of inheritance and the dimension of communication, which rescues and protects the construction techniques that are on the edge of loss.
b) The establishment of database and the digital application can not only provide scholars with professional academic data inquiries, but also provide a platform for popularization of science for the public, improve cultural cognition, enhance cultural selfconfidence and realize cultural power. At the same time, it helps the locals to make appropriate rural architecture protection guidance, and will provide strong data resource support for the provincial government's proposal to create "Lu Style Architecture with Regional Characteristics in Shandong" in 2025, thereby enriching the composition of Chinese architecture style system.

The problems of rural architecture in Shandong
Rural constructions in different regions in Shandong have different forms, construction modes, architecture cultures and humanistic cultures. The construction mode of "sea grass house" along the coast and the stones houses in the middle mountain areas of Shandong are very different. Such regional traditional construction techniques are "spontaneous", its generation and development are derived from actual use needs. To make clear of the construction technologies, techniques and humanistic composition, it is necessary to research thoroughly on regional village constructions in Shandong, such as the layout, street and lane scale, building forms, construction techniques and building materials, and conduct data collection and classification. By combining and excavating the rural architecture in Shandong and integrating the existing research results, this study analyzes the deficiencies and problems in the protection, inheritance, and utilization of the rural architecture in Shandong, laying the foundation for the next step of research.

Genealogy research with the goal of inheritance
A large number of rural architectures in our country show the colorful traditional style and regional features, and are rich in the cultural "genes" of local buildings. However, the realistic situation of weak locality of protection theory has caused great dilemma to heritage protection. To rescue these rural architecture heritages, it is necessary to firstly sort out the pedigree of rural architecture from a macro perspective.
Through field survey, mapping, interviews and image collection, the collected documents, mapping data and image data are archived according to regions and building types, and their wooden techniques, oil painting techniques, construction customs are classified and studied; this paper further analyzes the differentiated construction modes of different regions horizontally, forming the regional division and genealogical framework of techniques. And then it conducts a longitudinal research on the inheritance and evolution of techniques, thereby forming the rural architecture genealogy from construction techniques, building forms, materials and other modules.

Digitization of the crafting process with the goal of inheritance
Establish a database of rural architecture in Shandong, digitally store the forms, colors and constructing materials by classification and regions; at the same time, perform 3D animation simulation and decomposition of the traditional technique process; realize 3D architecture roaming, VR virtual reality immersive experience to classic rural architectures; and finally achieve digitization of data collection, data analysis, architecture drawing, modeling, technique processing and experiencing.

Research on the development, dissemination and utilization of application platforms with the goal of digital inheritance
Complete the application platform construction through the establishment, design, development of the back-end database, intermediate business logic layer and front-end application layer. Establish an audience-centered communication and utilization system by analyzing the needs of different audiences in the process of information searching and acceptance. To fully utilize the dissemination role of digital method by accessing cloud platforms, Wechat App, etc. to make it serve the scholars, designers, governments and the public.

Thoughts of the research
Firstly, this paper classifies and sorts out the rural architectures by region; then through field surveys and interviews, typical rural architecture mapping and digital collection of its construction techniques, the paper focuses on the digital inheritance and summarizes the technical genealogy, and establishes a database. Finally, from the perspective of digital utilization, animation is used to stimulate the technical process, application is developed to serve the public.

Discussion on research methods
1)Field research method: field research method will run through all the stages of this project. At the initial stage, the distribution of rural architectures in Shandong will be the research scope; after grasping certain documents, firsthanded materials will be recorded by scientific methods, such as communicating with villagers, questionnaires, mapping the typical architecture cases according to different architecture types.
2)Typological method: classify the collected data into typological first-class classification according to regions, ages, architecture types, etc.; make second-class classification according to wood construction techniques, oil painting techniques, stone and soil stacking techniques, construction customs, etc.; then make typological thirdclass classification according to videos and audios, images, literature and expert database, etc.; and lastly establish a complete framework system.

Key points of the research
Analysis on the construction techniques and its genealogy of Shandong's rural architecture. The study of construction techniques can break away from the onesided opinion that identifies buildings by forms and colors. The establishing process of construction techniques and its genealogy is the result of specific historical times, natural environment, humanities and other multiple factors, it is not only the main theme of rural architectures, but also includes the changes and inheritance of regional culture and social structure. So this content is the research theme of the project inheritance content, also an important method to clarify the types, causes and inheritance path of rural architecture in Shandong.
Research on the construction of application platforms and the creative use of application methods. China is at the stage of rapid development of digitization and big data application. Digital application and relevant product forms are gradually being widely used by the public. New media represented by Web 3.0, mobile App and mobile catalysts become an important part in constructing and disseminating the national soft power. Its advantages of maximizing interpersonal communication and multidimensional dissemination of technology and culture will benefit the integration of the latest technology and communication channels, making the inheritance and expression of rural culture heritages more vivid. And they are the effective platforms for spreading the national culture in the context of new media. This content is an important carrier and approach of realizing the research on project inheritance content.

Main arguments
In the background of information and big data, it is necessary to establish a protection strategy centered on inheritance, and an application strategy that takes creative utilization as the bond, to digitally inherit and creatively use the traditional techniques. Digital information acquisition and processing technologies can better collect, organize and preserve rural architecture data and information. Digitization can break through the fidelity effect, inheritance effect and the scope of inheritance that cannot be achieved by traditional technologies. Cultural heritage protection should not only focus on protection, but also on passing it to the next generation. Only through effective communication, positive publicity, can more people know about cultural heritages, recognize cultural heritages and then actively participate in the protection of cultural heritages, so that cultural heritage can gain lasting vitality. This is also the great chance for the revival of traditional construction techniques

Conclusion
This paper discusses the predicaments faced by rural architectures in Shandong Province, and theoretically suggests to solve the problem with digital method, and to establish the concept of Shandong rural architecture database. The process of building up the database does not only systematically sort out the rural architectures in Shandong area, but also uses the digital method for data collection, storage, images and animation of traditional techniques to imitate the process of rural architecture construction. It does not only solve the problem of single inheritance, consolidates the data of rural architecture in Shandong area, make up for the insufficiency of research on rural architecture in Shandong's constructional history, but also contributes to the protection and supervision of great rural buildings, and have a positive guiding significance for the protection and inheritance of rural architecture and the construction of new village in Shandong.


extfieldsofstudy:
Computer Science
Physics

text:
Sentiment analysis on micro-blog data using machine learning techniques - A Review

The user generated content in social networks, websites, ratings and reviews about product, people, places have become very beneficial for various fields to improve its growth. Sentiment analysis, which studies user generated content and find the correct sentiment polarity. Data collection, feature extraction, sentiment classification and decision making are the steps involved in this process. Machine Learning and Deep learning Methods are used to predict the sentiments. Large Input dataset from websites or microblog is taken as input and processed. There are some challenges in identifying accurate decisions. This Review Paper focus on recent studies about learning models such as Support vector Machines, Maximum Entropy, deep learning models and various methods which are used in solving sentiment classification, product review analysis, Text Processing and visual analysis etc.

Introduction
Nowadays, enormous amount of information is presented in webpages, databases, and blogs as online document.The unstructured form of data from the internet required to be changed well-structured data, toanalyze further. The Analysis process used in many fields such as Consumer information, websites, and Social Media [1].The goal of sentiment analysis is study user feedback and predict the sentiments. Usually, It defines opinion polarity, i.e., if somebody has positive, negative, or neutral opinion towards something. For example, (i) if a person plan for a trip to a particular place, he simply refer the online feedback of previous visitors for making the conclusion. (ii) If a person want to purchase a product, he reads online ratings and comments first, and take a decision to buy the product or not. The need of sentiment analysis is raised because requirement of analyzing and formatting the unstructured data in social media or websites . [2].The idea of sentimentanalysis is , without human intervention it determines the way of user reviews [3].  In social media, Twitter has made a big revolution, which is connecting millions of people worldwide through micro blogging service. The registered user of twitter can post short messages about anything, these short messages are called Tweets, which can have character-set length upto 280 .In the recent years researchers applied SA techniques with twitter comments and the results helpful in decision making.Machine learning techniques are applied by extracting the sentences and features. The polarity score has been assigned to the lexicons and sum of the polarity score is calculated from the overall values. [4]

1.1.Sentiment analysis with product review
The human's opinions, sentiments towards a product are being analyzed by the machine and the results encourage the purchasing decision of the customers .product review analysis is an entirely recognized field, where the customers can easily decide their product needs. Initially the product reviews will be taken as input text, and preprocessing is done .In this phase the input file is normalized, which ready for training process. This file is mapped with the saved features for training. After mapping the data will be fed into the different models and the performance of the models are analyzed as results. Several Machine Learning and are proposed in the past .Deep learning models, a division of Machine learning well suited for sentiment analysis with large dataset. The following is the basic procedure in sentiment analysis for product review.

Procedure for SA process
Step 1: Input text paragraph from dataset Step 2: Preprocessing the text. It includes removing stop words, removing numbers , expanding acronyms, replacing negation and removing URLs .
Step3 :Feature Extraction phase: Extracting words corresponds to adjective, adverb and verb.
Step 4:Selection of essential features for training the input data. Mapping the input data with selected features.
Step 5:The dataset can be divided 70% training and 30% testing.
Step 6: Training dataset to Machine learning algorithms or rain the model with Deep learning techniques Step 7: Test the model with different values.
Step 8: Result analysis with performance of the model.

Literature Review
In this section various studies about sentiment analysis by using MachineLearning and Deep Learning models are explained. Naive Bayes, Maximum Entropy, Ensemble methods, SVM,Decision trees, Stochastic Gradient descent are some of the familiar machine learning methods used in recent researches. .

Machine Learning Techniques
The Author [5,MonishaKanakaraj], analyzed Navies Bayes, MaximumEntropy, Support vector Machines algorithms in his work. Twitter dataset 7086 reviews from election dataset, 25000 reviews from movie dataset were taken as input for sentiment analysis. Randomized Trees classification method produces better outcomes . Author [6,Ana Valdivia ], applied sentiment analysis with Trip advisor; it is an American travel website company that provides user opinions about their hotels, restaurant and monuments. The analysis task categorized into three steps, polarity detection, Aspect selection, and classification. The author applied SAMs , SentiStrength, Bing,Syuzhet, and CoreNLP methods to analyze the user opinios.The result shows that Syuzhet detect at best 57.48 and 45.10 percent of negative feedback.In this paper [7, (Geetika Gautam)] Author proposed machine learning techniques for sentence classification and taken product reviews as input dataset from twitter data. The naïve byes method provides better result than maximum entrophy.SVM provides improved results in unigram model. The accuracy level is gained in semantic analysis word net to to 89.9% from 88.2%.

2.2.Deep Learning Techniques
In deep learning, data representation is referred by increasing the abstraction levels . [16].In the previous papers, there are various deep learning Architectures are Discussed,. Deep learning is used effectively in both supervised and unsupervised algorithms.Author [17,Islam.J], proposed a CNN framework for predicting the sentiments from social images. This is implemented by using s using Caffe and Python on a Linux platform. This model can be used in multimedia content to know user feedback, predictive modeling and advertising. A twitter dataset with 1269 images is taken as input and back propagation method is applied.The proposed model achieved better performance than existing systems. AlexNet was used in earlier systems and GoogleNet provides nearly 9% improved performance.
Theauthors[18, A. Severyn]implemented deep learning methods with twitter data. To initialize the word embedding neural language is used.Embedded words and parameters are applied to initialize the network, and training phase done by using supervised corpus Semeval-2015. Sentence matrix pooling, activations, convolutional layers and softmaxare the components used in proposed system. Deep learning technique is implemented in messagelevel, phrase level to predict polarity and better outcomes achieved.
The researcher [19, L. Yanmei] has explained about sentiment analysis with user opinions from a microblog. The user feedback on hot events analyses by using CNN . The corpus which has 1000 micro-blog comment divided into three labels. 274 for neutral , 300 for negative and 426 for positive emotions. The proposed work is compared with earlier models CRF, SVM, other traditional methods and performances analysed.The proposed model achieved the good performance in emotion analysis.

Conclusion
The need of normalizing the online data increases researches in field of sentiment analysis. It extracts the unstructured data from social media and examines the user opinions. Machine Learning and Deep Learning Techniques play vital role in pre-processing the texts, classification, training, testing the models and result analysis. The outcomes of the research used to know the user expectations and feedback about a product, place, or any common thing. This analysis provides the way for development in all the fields. In this paper various studies have been discussed about machine Learning and Deep Learning Models which are used in sentiment classification.

Analysis with future work
This paper explained about some recent researches in sentiment analysis . There are so many models established by using Machine learning and Deep learning Methods. NaiveBayes, Support Vector Machines. Maximum Entropy, Ensemble methods, Decision trees, Stochastic Gradient descent are most used machine learning algorithms. Convolutional neural networks-CNN, DBN,Recursive neural network -RNN,DNN,RNN are frequently used Deep learning Algorothms. Since Deep Learning models have more hidden layers, performs better than other Techniques. These models provide efficient training in supervised and unsupervised ways and also it performs automatic feature extraction. These models have some challenges, it requires large input dataset for better performance and it is costly IOP Publishing doi:10.1088/1757-899X/1049/1/012012 8 to train. The performance of the algorithm varies depend on the problem and the input dataset.As a future work product review analysis can be done by using Deep learning Methods and the accuracy of the outcomes can be shown better than previous models.


3D Reconstruction Method of Rapeseed Plants in the Whole Growth Period Using RGB-D Camera

The 3D reconstruction method using RGB-D camera has a good balance in hardware cost, point cloud quality and automation. However, due to the limitation of inherent structure and imaging principle, the acquired point cloud has problems such as a lot of noise and difficult registration. This paper proposes a three-dimensional reconstruction method using Azure Kinect to solve these inherent problems. Shoot color map, depth map and near-infrared image of the target from six perspectives by Azure Kinect sensor. Multiply the 8-bit infrared image binarization with the general RGB-D image alignment result provided by Microsoft to remove ghost images and most of the background noise. In order to filter the floating point and outlier noise of the point cloud, a neighborhood maximum filtering method is proposed to filter out the abrupt points in the depth map. The floating points in the point cloud are removed before generating the point cloud, and then using the through filter filters out outlier noise. Aiming at the shortcomings of the classic ICP algorithm, an improved method is proposed. By continuously reducing the size of the down-sampling grid and the distance threshold between the corresponding points, the point clouds of each view are continuously registered three times, until get the complete color point cloud. A large number of experimental results on rape plants show that the point cloud accuracy obtained by this method is 0.739mm, a complete scan time is 338.4 seconds, and the color reduction is high. Compared with a laser scanner, the proposed method has considerable reconstruction accuracy and a significantly ahead of the reconstruction speed, but the hardware cost is much lower and it is easy to automate the scanning system. This research shows a low-cost, high-precision 3D reconstruction technology, which has the potential to be widely used for non-destructive measurement of crop phenotype.

Introduction
Traditional measurement of agronomic traits adopts manual methods, which has disadvantages such as low efficiency, strong subjectivity, poor repeatability, damage to plants, and incomplete measurement , etc. Computer vision technology can analyze complete phenotype parameters such as plant structure, shape, color and texture t one time, making it possible to quantitatively study the growth laws of crops. Threedimensional reconstruction is an important type of computer vision technology. It performs digital modeling of crops in a computer, stores the three-dimensional geometry and color of plant shapes and organs in the computer to achieve rapid, lowcost, fast agronomic traits of crops and accurate non-destructive measurement [1] . 3D reconstruction technologies which are currently used in agriculture mainly include laser scanners, binocular vision, motion recovery structures and RGB-D cameras, etc.
(1)Three-dimensional laser scanner. It is a high-precisio n point cloud acquisition instrument. For example, Katrine et al. used a laser scanner to measure the daily growth ch anges of 10 genotype of Brassica naps, explored how differ ent stresses affect plant phenotype, and better understood t he interaction between plant responses and energetic regula tion [2] . Ana et al. used terrestrial laser scanner (TLS) and m obile scanning system (MMS) to obtain the point cloud of t he vine trunk and calculated the volume of the vine trunk through a grape-shaped artificial object (VSAO) calibration method, and proposed a skeleton based on internal measur ement. The algorithm of modeling a cylinder with a certain height and diameter effectively solved the problem of volu me estimation [3] . Zhang et al. used the airborne lidar syste m to obtain the three-dimensional point cloud of forest tree s, and extracted the ground and building point clouds thro ugh the irregular triangulation method and plane fitting filt ering algorithm, which provided a basis for high-precision quantitative estimation of forest canopy biomass [4] . Su et al. used laser scanners to collect lidar data at six growth stage s of 20 maize varieties under drought stress, and calculated three drought-related phenotypic data such as plant height, plant area index and predicted leaf area, in order to ident ify the effects of drought stress. The effect of the phenotyp e in each growth stage provides a reference [5] . Indu et al. u sed ground lidar to track a cylinder along a single branch layer by layer to achieve three-dimensional reconstruction o f a single tree, and used super-body clustering method and multiple regression technology to achieve automatic calcula tion of tree parameters such as leaf area [6] .
(2)The structural method of exercise recovery (SFM). It is a technology that can automatically recover camera parameters and the three-dimensional structure of the scene from multiple image sequences. Wei et al. proposed a high-throughput wheat phenotype measurement method based on volume reconstruction and estimated the fresh weight of wheat. This method has greatly improved in plant reconstruction, model processing and feature extraction [7] . Zhang et al. collected multiple multi-spectral images of four-leaf-age rapeseed and obtained the three-dimensional point cloud of rapeseed through the motion restoration structure algorithm. After denosing the point cloud, the control points and control length were evaluated to study plant nutrition and pest stress. The spatial distribution is of great significance [8] . Liang  (3)Binocular vision method. It generates parallax through different viewing angles of two cameras, and then obtains the distance information of the object through depth calculation. centroid of the smallest bounding rectangle of litchi fruits, and calculated the swing angles of litchi fruits under three disturbance states using the principle of pendulum, which solved the visual positioning problem of picking robots in the natural environment [13] .
(4)RGB-D camera method. The RGB-D camera adds depth measurement to the function of the RGB ordinary camera. The mainstream solutions include structured light and TOF (time of flight). It has simple operation, low cost and high efficiency, it always has great potential in the field of 3D reconstruction. Hu et al. proposed a Kinect-based automatic measurement system for leafy vegetable crops, which reconstructed plant point clouds from multiple perspectives, and simultaneously measured key growth parameters such as relative height and absolute height, projected area and volume, and the obtained data are all expressed a good degree of fit (R 2 = 0.9457-0.9914) [14] . Xu et al. used the Kinect V2 to take color images and depth images of rape branches under four viewing angles, and performed image preprocessing, point cloud registration and point cloud filtering to obtain the color point cloud of rape branches, and then used European clustering to identify the angle the phenotypic parameters of siliques were measured and the overall accuracy was better [15] . Xu et al. proposed a three-dimensional scanning reconstruction system with multiple RGB-D Cameras (Xtion sensors). The initial pose of the camera was obtained through a pre-calibrated image acquisition platform. The reconstruction system can obtain high-precision three-dimensional models of various complex large scenes [16] . Martin et al. used Kinect V2 to collect images of field corn plants, and measured plant height after three-dimensional reconstruction. The measurement accuracy exceeded the measurement results of three-dimensional laser scanners [17] . Manuel et al. used high-resolution threedimensional images, used ICP registration algorithm for registration, and used random sampling consensus algorithm to remove the incoming soil point cloud. By comparing the true seedling position on the ground, it was shown that the method obtained the location of the corn is close to the ground distribution [18] . Feng [19] . Liu et al. proposed a threedimensional reconstruction algorithm based on depth information segmentation and clustering of strawberry canopy morphology. The color image and intensity image were registered to reconstruct the strawberry canopy structure morphology with color information [20] . Yu et al. placed Kinect V2 on the cart, took the depth image directly above the cotton crop in the field, calculated the plant height in the stitched depth image, and realized the non-destructive and rapid measurement of the cotton plant height in the field [21] . Efi et al. proposed a sweet pepper detection algorithm that combines adaptive threshold segmentation and sensors. The image was divided into rectangular sub-images with approximately uniform lighting conditions, and the RGB image was converted into a 3D natural difference index image. Threshold adaptive calculation and application, the algorithm was robust to the selected threshold and the noise from the camera itself [22] .
In summary, the point cloud obtained by the laser scanner method has the highest accuracy, but it is expensive, cumbersome to operate, and time-consuming, making it difficult to realize an automated system. The SFM method has higher accuracy and the lowest cost, but high-precision threedimensional reconstruction requires a large number of images, which leads to the most serious consumption of computing resources [23] . Binocular vision has the fastest reconstruction speed and is easy to automate, but its high-precision point cloud requires extremely high-performance image sensors and a stable imaging environment. In contrast, the RGB-D camera represented by Kinect is more balanced in three aspects: point cloud accuracy, processing speed and automatability. At the same time, this method has the highest requirements for image processing algorithms, mainly due to two factors. The first one requires a registration algorithm to unify the point clouds from different coordinate systems into the same coordinate system [24] . Other outliers and floating point noise were removed based on the normals of the viewpoint and the surface. This method can effectively remove outliers and improve the quality of point clouds [28] .
Aiming at the needs of multi-scale, non-destructive, highthroughput and high-precision measurement of rape phenotypic traits, this paper proposes a three-dimensional reconstruction method using Azure Kinect. This method collects color maps, depth maps and infrared maps from 6 viewing angles, improves the color map-depth map alignment method provided by Microsoft to remove ghost images, and proposes a neighborhood maximum filter method to filter out floating points in the depth map, and then use the improved ICP registration algorithm [29] to fuse the local point cloud into a complete point cloud, and finally realize the accurate three-dimensional color modeling of a single rapeseed during the whole growth period. The threedimensional reconstruction method has low cost, high accuracy, fast speed and easy automation. It will provide important basic data for the non-destructive measurement of rape phenotype, growth law research, selection and breeding, and design of agricultural machinery, etc., also can be popularized and applied to other crops.

Experimental Setup and Data Acquisition
In 2019 and 2020, rapeseed was planted at Huazhong Agricultural University for two consecutive years, with varieties

The complete color point cloud calculation method of rape moss
Taking rapeseed at the first flowering stage as an example,   determine, the robustness is poor, and it is difficult to find a parameter that has a good effect on the two noise point clouds.

3D point cloud registration
By registering and fusing point clouds from multiple angles into a whole, the complete 3D shape of the rape plant can be Repeatedly using this method for multiple viewing angles, a complete point cloud of a rape plant can be obtained.

Registration Optimization Based on ICP Method
(1) Point cloud registration from two perspectives the Euclidean distance, as showed in formula (2).
In order to minimize ( ), the center of gravity of the point cloud P and the point cloud X are calculated separately, as in formula (3).
Use the center of gravity to obtain the cross-covariance matrix of the two point clouds, as in formula (4).
Based on the classic ICP algorithm, this paper proposes an improved method, as shown in Figure 4

Results and analysis
The experiments were carried out on raw data obtained from 40 pots of rapeseed, in which 10 pots in each growth period.
For each pot of plant, 6 frames of data from different views, which cover 360° and these data were processed by the proposed method to show the performance and robustness of the proposed method.

Point-Cloud Noise Removal
We tested and compared two filtering methods: the traditional method of through filtering + statistical filtering, as shown in Figure 6 (a); the recommended method of neighborhood maximum filtering + through filtering, as shown in Figure 6   The neighborhood optimal filtering method needs to set an appropriate neighborhood size and threshold. The size of the neighborhood is too small to judge the sudden change of the depth information, which leads to the deletion of too many valid points. However, too many points will participate in the judgment if the setting is too large, so that the deep mutation information is inaccurate and the mutation point cannot be found.
Through the statistics of the gentle point cloud of a certain part of rape, it is found that the maximum depth difference in the local neighborhood is not obvious, and the difference is basically 5 points. This paper compares the size of different neighborhoods and the size of the threshold to eliminate the effect of floating point noise, and selects the final parameters. Figure 7 shows the filtering results of different parameters, the neighborhood sizes are 3*3, 5*5, and 7*7, and the thresholds range from 3, 5 to 7.
Through comparison, it is found that when the neighborhood size is 3*3 and the threshold is 5. The effect of removing the noise point cloud is the best.

Point cloud registration experiment
We tested and compared the performance of the classic ICP method and the method in this paper for the two-view point cloud registration, which is shown in Figure 8 and Table 3. For any rape plant, the method in this paper undergoes 3 iterations of registration, and finally merges into a point cloud precisely. and (d) are the same type of test results of rape seedlings.   The color 3D point cloud obtained is compared with the method in this paper. Figure 9 seconds, while that of the laser scanner is 991.6 seconds. The former is 1/3 of the latter. If an automated image acquisition device is used, the scanning method of Azure Kinect will be faster.
In general, the reconstruction accuracy of this method is close to that of a laser scanner, but the speed is 3 times faster. This is mainly due to the fact that the data collection speed of the laser   used the whole growing period of rapeseed plants to test.

Conclusion
The overall effect is good, which is helpful to the advance ment and development of rapeseed phenotype nondestructi ve testing technology. It has also achieved good effects on crops such as fruit seedlings, corn, soybeans, and cotton. In general, the research results of this manuscript provide a useful example for RGB-D 3D reconstruction.

Data Availability:
The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest:
The authors declare that they have no conflicts of interest.


s2fieldsofstudy:
Computer Science
Environmental Science

text:
The development and implementation of a multidimensional relational database of fire danger metrological data

The study designs and implements a multi-dimensional relational database of fire danger metrological information, as well as areas with vegetation. During conceptualization, OLAP algorithms are taken into account as applied to the selection of long-term meteodata and their mathematical processing presented by multidimensional hypercubes. When designing the SQL structure, a layered architecture of geographic information system (GIS) is used, including attribute and geographical characters. The data according to the technology is tested in the Jewish Autonomous Region. The database contains metrological information from five weather stations from 1960 to 2020 and five branches of local forest districts from 1997 to 2020. At present, data is being filled in for the Omsk Region, Primorsky Krai of the Russian Federation and the Osh Region of the Kyrgyz Republic.

Introduction
Vegetation fires are the main natural risks to the infrastructure in urban and rural areas. Understanding the processes of forecast risk and developing the best methods for the strategy of fire spreading control, especially their forecast according to various criteria, weather condition parameters, and the state of plant combustible materials [1], demands to use a large amount of statistical data for the land and remote monitoring/sensing [2]. Modeling plant combustible materials burning is considered in different areal extent [3], regional data include daily data on the distribution of meteorological elements around the hydro weather station [4], and global data affect weather forecast models based on the previous observation of a variety of parameters (vegetation state, climatic conditions, topography, a local landscape, the territory development) [5,6]. The consideration of all of them describes the territory fire danger fully. The software development for the map data representation of the simulation result, initialization and online testing procedures is impossible without using multidimensional databases and data aggregation tools upon spatial-temporal requests [7]. A time slice of data available provides the construction of operational schemes for monitoring the territory and controlling key indicators [8,9], managing interregional interaction and force distribution during the elimination of fire hazards. The aim of the study is to design and implement a multidimensional relational database of fire danger metrological information.

Methods
Daily monitoring of fire danger on the territory of the Russian Federation is carried out in the ISDM-Rosleshoz system, and forest fire indicators modeled by the Nesterov method are used for calculations [10]. This method is fixed in all-Union State Standard 22.1.09-99 of 2000.
The research studies Forest Fire Danger Meter Weather Indices [11] and Canadian Fire Weather Index [12] to assess fire danger and drawing up scenarios of evolving situations and fire spread [13], while additionally including forest fuels moisture content, temperature and atmospheric moisture capacity, surface slope, wind speed. They determine the behavior of forest fireguard forces to prevent and extinct vegetation fire, in particular, the patrol schedule and the number of active landing teams, aircraft flights and transportation of firefighting equipment.
When designing a conceptual database model, a system analysis of weather indices is carried out, and flowcharts for processing On-Line Analytical Processing (OLAP) of multi-dimensional cubes (hypercube) are constructed [14]. A hypercube consists of a set of time values of space (cells), while the requirements for normalization of relational databases are not met, but a logical space of computed values (aggregates) is composed according to the specified fire hazard assessment methodology.
When developing the SQL structure of the meteodatabase, the Relation OLAP (ROLAP) organization scheme is taken. In the ROLAP scheme, the concept contains an intermediate layer of meta-data, dimension tables, and facts that are interconnected. The MapInfo software system is used to implement the database.

Results and discussion
To describe the aggregates, a hypercube is made up of three faces: the day/week/month/year of the fire season, weather stations/local forest areas, weather parameters and indices The Star Scheme concept diagram contains nine dimension tables and one fact table. The fact table is controlled by a multiple topic key [15]. The measurement table contains descriptive information, while the fact table contains meteodata for the fire season of each meteodata monitoring point.
The multidimensional relational database contains the following values: the duration of the fire season (during snowfall and snowmelt), the number of days with a high class of fire danger (class IV-V) in the season, thunderstorm activity (keraunic level) (the number of days with thunderstorms), population size (population density), values of the territory fire frequency (the number of fires per unit area of the territory) (figure 2).   Figure 3 shows an example of the location of weather stations for the data collection on the territory of the Jewish Autonomous Region ( figure 3).
The database is tested in the IT system modules for assessing fire seasons illustrated by the territory of branches of local forest districts of the Jewish Autonomous Region. The database consists of the settlements/planning quarters of five branches of the local forest districts, which correspond to the forestry management plans of the Forestry Department of the Jewish Autonomous Region.
At the moment, information on plots/units of local forest districts is being collected and filled in in the system modules: • Module #1 is for calculating fire hazard indicators in the area where the weather station is located. Constructing a 30 km. radius buffer zone/field and Thiessen polygons, as well as determining whether vegetation areas are among these zones. • Module #2 is for restoring fire danger indicators in the settlements of local forest districts that are far from the weather station. The use of the GIS overlay analysis tools is for finding out if vegetation areas are in the affected zone of the station.  At present, data is being filled in for the Omsk Region, Primorsky Krai of the Russian Federation and the Osh Region of the Kyrgyz Republic.

Conclusion
In conclusion, the suggested database of metrology information on weather elements in vegetation areas can be used to estimate fire danger, make short -and long-term forecasts, identify the territorial dynamics of fire danger in different periods, and develop optimal fire monitoring schemes.