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In this paper, we propose an NFT(Non-Fungible Token)-based access control method for safely sharing data between users in blockchain environment. Since all data stored in the blockchain can be accessed by anyone due to the nature of the technology, it is necessary to control access except for authorized users when sharing sensitive data. For that, we generate each data as NFT and controls access to the data through the smart contract. In addition, in order to overcome the limitations of single ownership of the existing NFT, we separated the NFT into ownership and use rights, so that data can be safely shared between users. Ownership is represented as an original NFT, use rights is represented as a copied NFT, and all data generated as NFT is encrypted and uploaded, so data can be shared only through the smart contract with access control. To verify this approach, we set up a hypothetical scenario called Building Information Modeling (BIM) data trade, and deployed a smart contract that satisfies 32 function call scenarios that require access control. Also, we evaluated the stability in consideration of the possibility of decryption through brute-force attack. Through our approach, we confirmed that the data can be safely shared between users in blockchain environment.

In this paper, we proposed VW-FedAVG(Validation based Weighted FedAVG) which updates the global model by weighting according to performance verification from the models of each device participating in the training. The first method is designed to validate each local client model through validation dataset before updating the global model with a server side validation structure. The second is a client-side validation structure, which is designed in such a way that the validation data set is evenly distributed to each client and the global model is after validation. MNIST, CIFAR-10 is used, and the IID, Non-IID distribution for image classification obtained higher accuracy than previous studies.

In this study, we propose a language and platform to describe and manage the MLOps(Machine Learning Operations) workflow for time series data anomaly detection. Time series data is collected in many fields, such as IoT sensors, system performance indicators, and user access. In addition, it is used in many applications such as system monitoring and anomaly detection. In order to perform prediction and anomaly detection of time series data, the MLOps platform that can quickly and flexibly apply the analyzed model to the production environment is required. Thus, we developed Python-based AI/ML Modeling Language (AMML) to easily configure and execute MLOps workflows. Python is widely used in data analysis. The proposed MLOps platform can extract and preprocess time series data from various data sources (R-DB, NoSql DB, Log File, etc.) using AMML and predict it through a deep learning model. To verify the applicability of AMML, the workflow for generating a transformer oil temperature prediction deep learning model was configured with AMML and it was confirmed that the training was performed normally.

This paper proposes a system for classifying gait types using an ensemble deep learning network for gait data measured by a smart insole equipped with multi-sensors. The gait type classification system consists of a part for normalizing the data measured by the insole, a part for extracting gait features using a deep learning network, and a part for classifying the gait type by inputting the extracted features. Two kinds of gait feature maps were extracted by independently learning networks based on CNNs and LSTMs with different characteristics. The final ensemble network classification results were obtained by combining the classification results. For the seven types of gait for adults in their 20s and 30s: walking, running, fast walking, going up and down stairs, and going up and down hills, multi-sensor data was classified into a proposed ensemble network. As a result, it was confirmed that the classification rate was higher than 90%.

In this paper, we propose a multi-task model that can simultaneously predict general-purpose tasks such as part-of-speech tagging, lemmatization, and dependency parsing using the UD Korean Kaist v2.3 corpus. The proposed model thus applies the self-attention technique of the BERT model and the graph-based Biaffine attention technique by fine-tuning the multilingual BERT and the two Korean-specific BERTs such as KR-BERT and KoBERT. The performances of the proposed model are compared and analyzed using the multilingual version of BERT and the two Korean-specific BERT language models.

Recently, researches on the prediction of sea water temperature using artificial intelligence models has been actively conducted in Korea. However, most researches in the sea around the Korean peninsula mainly focus on predicting sea surface temperatures. Unlike previous researches, this research predicted the vertical sea water temperature profile of the East Sea, which is very important in submarine operations and anti-submarine warfare, using XBT(eXpendable Bathythermograph) data and machine learning models(RandomForest, XGBoost, LightGBM). The model was trained using XBT data measured from sea surface to depth of 200m in a specific area of the East Sea, and the prediction accuracy was evaluated through MAE(Mean Absolute Error) and vertical sea water temperature profile graphs.

In this paper, we propose a legal document search method that uses the Sentence-BERT model. The general public who wants to use the legal search service has difficulty searching for relevant precedents due to a lack of understanding of legal terms and structures. In addition, the existing keyword and text mining-based legal search methods have their limits in yielding quality search results for two reasons: they lack information on the context of the judgment, and they fail to discern homonyms and polysemies. As a result, the accuracy of the legal document search results is often unsatisfactory or skeptical. To this end, This paper aims to improve the efficacy of the general public's legal search in the Supreme Court precedent and Legal Aid Counseling case database. The Sentence-BERT model embeds contextual information on precedents and counseling data, which better preserves the integrity of relevant meaning in phrases or sentences. Our initial research has shown that the Sentence-BERT search method yields higher accuracy than the Doc2Vec or TF-IDF search methods.

In this paper, we propose a system which automatically generates the ShEx schemas to describe and validate RDF knowledge graphs constructed by RML mapping. ShEx schemas consist of constraints. The proposed system generates most of the constraints by converting the RML mapping rules. The schemas consisting only of constraints obtained from mapping rules can help users to figure out the structure of the graphs generated by RML mapping, but they are not sufficient for sophisticated validation purposes. For users who need a schema for validation, the proposed system is also able to provide the schema with added constraints generated from metadata extracted from the input data sources for RML mapping. The proposed system has the ability to handle CSV, XML, JSON or RDBMS as input data sources. Testing results from 297 cases show that the proposed system can be applied for RDF graph validation in various practical cases.

In this paper, we propose diffraction-based sound control method to improve sound immersion in a virtual environment. The proposed technique can express the wave and flow of sound in a physical environment and a pattern similar to diffraction in real-time. Our approach determines whether there is an obstacle from the location of the sound source and then calculates the position of the new sound reflected and diffracted by the obstacle. Based on ray tracing, it determines whether or not it collides with an obstacle, and predicts the sound level of the agent behind the obstacle by using the vector reflected and refraction by the collision. In this process, the sound attenuation according to the distance/material is modeled by attenuating the size of the sound according to the number of reflected/refracted rays. As a result, the diffraction pattern expressed in the physics-based approach was expressed in real time, and it shows that the diffraction pattern also changes as the position of the obstacle is changed, thereby showing the result of naturally spreading the size of the sound. The proposed method restores the diffusion and diffraction characteristics of sound expressed in real life almost similarly.

Using the extended technology acceptance model, this study aims to quantitatively analyze the user experience with the <Prayer for Life Metaverse>. In the case of Perceived Usefulness and Perceived Ease of Use, only limited detailed factors have significant impacts on the degree of satisfaction. The degree of satisfaction has positive correlations with the degree of immersion and the variables of Behavioral Intention to Use Metaverse. Although <Prayer of Life Metaverse> sophisticatedly remediates <Prayer of Life Special Exhibition> using digital technology to provide visitors with the sameness of exhibits, exhibition circulation and interaction method, the metaverse exhibition does not acquire the same value of the exhibition. In conclusion, <Prayer of Life Metaverse> cannot replace <Prayer of Life Special Exhibition>, however, it has the potential to offer learning usefulness to visitors with low accessibility to the art museum.

In this paper, we proposes digital content management technology in a cloud computing environment. proposes digital content management technology in a cloud computing environment. Computing services using networks are basic infrastructure services that cannot be missed in the era of the 4th Industrial Revolution. Financial services, digital content services, and industrial and home network services using smartphones are changing from services in the local area to a cloud service environment where the entire service is possible. Therefore, this study proposed a system to safely support digital content services suitable for cloud computing environments. The proposed system provides convenience and safety for users to access the system, protects the copyright of digital content authors, and provides a secure digital content distribution and management system. The purpose of this study is to stabilize and revitalize the digital content market by providing a digital content distribution structure suitable for the cloud computing environment.

In this paper, proposes a delivery robot that can be autonomous driving learning. The proposed robot is designed to be used in park-type apartments without ground parking facilities. Compared to the existing apartments with complex ground and underground routes, park-type apartments have a standardized movement path, allowing the robot to run stably, making it suitable for students' initial education environment. The delivery robot is configured to enable delivery of parcels through machine learning technology for route learning and autonomous driving using cameras and LiDAR sensors. In addition, the control MCU was designed by separating it into three parts to enable learning by level, and it was confirmed that it can be used as a delivery robot for learning through operation tests such as autonomous driving and obstacle recognition. In the future, we plan to develop it into an educational delivery robot for various delivery services by linking with the precision indoor location information recognition technology and the public technology platform of the apartment.

In surface mount technology (SMT), the screen printer, which is an equipment for applying solder cream, has a lot of poor coating as the pad becomes smaller. To solve this problem, a jet printer is being used recently. However, if the nozzle at the end of the valve applied to the jet printer head is not cleaned, solder cream remains or an error occurs. To prevent this, the nozzles should be cleaned periodically. In this paper, a more stable cleaning method than the existing technology is presented for the stable application of solder cream on a jet printer. In this method, cut a 35mm wide mujin cloth, wrap it in a roll, and rotate it with a DC geared motor on the other side to clean it. As a result, it was confirmed that the solder paste was not left on the nozzle surface and was well wiped when cleaning with about 2,000 dotting cycles.

Recently, studies on the detection and classification of Android malware based on API Call sequence have been actively carried out. However, API Call sequence based malware classification has serious limitations such as excessive time and resource consumption in terms of malware analysis and learning model construction due to the vast amount of data and high-dimensional characteristic of features. In this study, we analyzed various classification models such as LightGBM, Random Forest, and k-Nearest Neighbors after significantly reducing the dimension of features using PCA(Principal Component Analysis) for CICAndMal2020 dataset containing vast API Call information. The experimental result shows that PCA significantly reduces the dimension of features while maintaining the characteristics of the original data and achieves efficient malware classification performance. Both binary classification and multi-class classification achieve higher levels of accuracy than previous studies, even if the data characteristics were reduced to less than 1% of the total size.

Git's commit message is closely related to the project life cycle, and by this characteristic, it can greatly contribute to cost reduction and improvement of work efficiency by identifying risk factors and project status of project operation activities. Among these related fields, there are many studies that classify commit messages as types of software maintenance, and the maximum accuracy among the studies is 87%. In this paper, the purpose of using a solution using the commit classification model is to design and implement a complex classification model that combines several models to increase the accuracy of the previously published models and increase the reliability of the model. In this paper, a dataset was constructed by extracting automated labeling and source changes and trained using the DistillBERT model. As a result of verification, reliability was secured by obtaining an F1 score of 95%, which is 8% higher than the maximum of 87% reported in previous studies. Using the results of this study, it is expected that the reliability of the model will be increased and it will be possible to apply it to solutions such as software and project management.

As products that consumers want become more diverse, the types of automation equipment are becoming more diverse and advanced to produce competitive products. In general, equipment is manufactured with built-in interface devices (HMI) for users so that operators can efficiently monitor and operate equipment quickly. Because HMI devices are connected to various industrial controllers, elements such as communication protocols of various controllers must be understood and set up in the design stage. Non-experts not only have difficulty choosing compatible items among various protocols, but also have limitations in integrating and operating on one device because screens and settings are statically assigned. This paper proposes a model that can scan information such as equipment ID and communication protocol with QR code using a mobile device, access industrial controller, and remotely operate the displayed equipment screen. The proposed model is expected to increase efficiency in inspection and management of automated equipment as it can easily set, monitor, and operate the communication environment of various automated equipment using one mobile device.

In this study, we developed a system to dynamically balance a daily stock portfolio and performed trading simulations using gradient boosting and genetic algorithms. We collected various stock market data from stocks listed on the KOSPI and KOSDAQ markets, including investor-specific transaction data. Subsequently, we indexed the data as a preprocessing step, and used feature engineering to modify and generate variables for training. First, we experimentally compared the performance of three popular gradient boosting algorithms in terms of accuracy, precision, recall, and F1-score, including XGBoost, LightGBM, and CatBoost. Based on the results, in a second experiment, we used a LightGBM model trained on the collected data along with genetic algorithms to predict and select stocks with a high daily probability of profit. We also conducted simulations of trading during the period of the testing data to analyze the performance of the proposed approach compared with the KOSPI and KOSDAQ indices in terms of the CAGR (Compound Annual Growth Rate), MDD (Maximum Draw Down), Sharpe ratio, and volatility. The results showed that the proposed strategies outperformed those employed by the Korean stock market in terms of all performance metrics. Moreover, our proposed LightGBM model with a genetic algorithm exhibited competitive performance in predicting stock price movements.

In this paper, we propose to develop a Bluetooth Health Device Profile (HDP)-based smartphone system to utilize it for early detection of urinary tracts diseases that occur a lot in cats. Therefore, based on Bluetooth HDP, we developed a device and mobile application system (Mycatner®) that can monitor cat activity, toilet usage, urinary disease, and health status, and evaluated its availability to monitor cat health status. The specific feature of this system is that it can measure the number of cat urination frequencies to identify abnormal conditions suspected of urinary tract diseases early, and second, it can be tested with urine test paper and shared with animal hospitals, reducing time and cost. As a result, it is evaluated that the developed device capable of wireless monitoring the urinary system health status of cats is the first in our knowledge.

This paper proposes a method to derive the time points for helpful intervention based on conclusions drawn from a previous study which collected data during barista training performed by students with developmental disabilities. To identify this intervention time, this study utilized distance information between the hand of a student and each barista tool in a virtual reality space. If the cumulative distance from a student’s hand to the key barista tool is relatively longer than the cumulative distance to other barista tools, it is highly likely that the student is not using the key barista tool, which must be used to successfully complete the given task. In other words, the distance information signals the task is not being properly performed and represents the intervention time to help the student. As a result of verifying a hypothesis through statistical analysis, this study found that the relationship between the success or failure of a student’s training and the cumulative distance between the student’s right hand and the key barista tool was statistically significant, but not for the left hand.

This study was conducted to provide basic data for the development of oral health education programs by examining oral health knowledge and oral health behaviors, and by identifying factors affecting oral health knowledge and scaling experience of Chinese domestic students. From March to May, a self-reported questionnaire was administered to 194 in the G area. The data were analyzed frequency analysis and independent t-test, multiple regression analysis, logistic regression analysis by using SPSS/WIN 26.0 program. The overall average of oral health knowledge was 13.83 out of 24, and knowledge of periodontal disease and scaling was the highest at 3.75, and knowledge of oral hygiene products was the lowest at 1.38. Oral health knowledge was significantly higher in those who had scaling experience for the past year, brushed teeth for more than 3 minutes at a time, used oral hygiene products, and had oral health education experience. As a result of multiple regression analysis, oral health education experience was the most important factor on oral health knowledge. As a result of logistic regression analysis, oral health knowledge was the most influential factor on scaling experience. It is necessary to expand education on the importance of oral care and actively introduce oral health management program for Chinese domestic students.

In this paper, we use a LiDAR sensor and an image camera to detect a left-turning waiting vehicle in two ways, unlike the existing image-type or loop-type left-turn detection system, and a left-turn traffic signal corresponding to the waiting length of the left-turning lane. A system that can efficiently assign a system is introduced. For the LiDAR signal transmitted and received by the LiDAR sensor, the left-turn waiting vehicle is detected in real time, and the image by the video camera is analyzed in real time or at regular intervals, thereby reducing unnecessary computational processing and enabling real-time sensitive processing. As a result of performing a performance test for 5 hours every day for one week with an intersection simulation using an actual signal processor, a detection rate of 99.9%, which was improved by 3% to 5% compared to the existing method, was recorded. The advantage is that 99.9% of vehicles waiting to turn left are detected by the LiDAR sensor, and even if an intentional omission of detection occurs, an immediate response is possible through self-correction using the video, so the excessive waiting time of vehicles waiting to turn left is controlled by all lanes in the intersection. was able to guide the flow of traffic smoothly. In addition, when applied to an intersection in the outskirts of which left-turning vehicles are rare, service reliability and efficiency can be improved by reducing unnecessary signal costs.

The most basic thing to measure the condition of a companion animal is to check the breathing and pulse. There are several methods to measure the breathing and pulse of a companion animal, and the PPG method is generally used to measure the oxygen saturation (SpO2) in a companion animal. However, since the input PPG signal is inputted with various information as well as oxygen saturation, it is necessary to separate and extract oxygen saturation information from the PPG signal in order to measure the oxygen saturation. Therefore, in this paper, a wearable measuring device for companion animals that can be measured by applying the PPG method was designed and implemented, and an algorithm for separating oxygen saturation information from the PPG signal input through the wearable measuring device was proposed.

This study was conducted with the purpose of presenting data for preparing elderly work policies and mental health policies by comparing and analyzing the effects of working ability on depression of urban and rural elderly people. Data from the 16th year of the Korea Welfare Panel (2021) were used for the analysis data for this study, and data from 4,838 elderly people aged 65 and over were extracted and used for analysis. For the analysis method, descriptive statistics and regression analysis were performed using SPSS 22.0. As a result of the study, it was found that the depression decreased when the elderly were able to work. It was found that health status and household type had an effect on urban elderly, and health status and education level of rural elderly affected. Therefore, it is necessary to provide local social welfare services that are appropriate for the local characteristics, suitable for the resources and environment of urban and rural areas.

This study sought to measure the absorption of commercially available urinary incontinence panty by applying a method of measuring the maximum saline amount of adult diapers in an international standard. The subject was 15 products from 3 brands on the market, and the SPSS 27.0 program was used for analysis. As a result of measuring the maximum absorption of commercial incontinence panty, it was found that the maximum absorption of product B was the highest and the maximum absorption of product C was the lowest. Even though the product was marked with the same absorption amount, it showed differences in the same product of the same brand, and it also showed differences depending on the brand. The reflux amount of the injected physiological saline and the urinary incontinence panty showed a positive (+) correlation, and as the amount of the injected physiological saline increased, the reflux amount of the urinary incontinence panty increased. It was analyzed that the time when the leakage begins and the amount of leakage have a negative (-) correlation. In future research, it is believed that the development of functional pads that can increase absorption and reduce reflux and leakage should be carried out.

This study is to evaluate the intervention effect using a flowmeter in bag-valve mask skill education. The purpose of this study was to compare the accuracy of bag-valve mask skill between intervention group with flowmeter and control group without flowmeter, understand the improvement effect of skill education of bag-valve mask, and provide basic data to suggest the method of skill education. The total number of subjects of this study was 60, with 30 intervention group and 30 control group. In comparison of the optimal number of normal tidal volume range at pre-test and post-test, the normal range percentages of the intervention group before and after education were 32.8% and 86.7%, respectively, and there was a significant difference(p<0.01). The normal range percentages of the control group before and after education were 20.0% and 34.7%, respectively, and there was a significant difference(p<0.05). To evaluate the factors associated with good performance of bag-valve mask skill of the subjects including the normal range of tidal volume, the logistic regression analysis has been performed, and the significant influential factors were gender(10.305, 1.20-87.98), educational experience of field practice(31.674, 1.25-805.16), and intervention(92.750, 4.58-1879.69). Through this study, it was confirmed that the intervention using flowmeter for the skill education of bag-valve mask was effective, and it is necessary to consider reflecting it in the education of students majoring in emergency medical technology in the future.

The labor director system to be introduced into the company law, the labor director must be explained in the Korean company law and the inevitability of its introduction must be persuaded. Conflicts with shareholders' right to appoint institutions are also a task that must be resolved. Management has absolute meaning for shareholders who receive dividends from operating profit. On the other hand, for workers who are guaranteed the right to collective action and are paid for their labor according to the contract law and the labor law, the management must be considered as a partner in labor-management cooperation, so the labor director system may cause confusion. There are growing calls to create a system that can form a 'relationship of understanding, participation, and cooperation', away from the existing 'control and command'-centered manpower management that causes labor-management confrontation and the system can also serve as an opportunity to reduce harmful effects of high-handed personnel administration in public institutions.

The purpose of this study is to analyze and verify the effects of multicultural children's psychosocial adaptation, bicultural experience, parental support and parenting attitudes, and school activities on the development of Korean language ability using data from the second stage of the MAPS(Multicultural Adolescents Panel Study) using a structural equation model. The subjects of this study were 396 children from foreign families and mid-immigrant families, multicultural children who were enrolled in the fourth grade of elementary school in 2019. As a result of the study, it was found that psychosocial adaptation, bicultural experience, and school activities directly or indirectly significantly affect the ability of multicultural children to speak and understand Korean. In particular, it was found that school activities have a direct effect on the improvement of the Korean language ability of multicultural children, so it was understood that the support of friends and teachers should be treated as very important parts of educational activities in the educational field. These results mean that the most ideal educational environment that affects the development of Korean language skills must be necessarily reflected in the Korean language education policy for multicultural children.

In this paper, we propose the effects of coordinative locomotor training in body alignment of high school baseball players. Coordinative locomotor training was applied to 20 subjects in the experimental group for 30 minutes, 5timess aweek for 4 weeks. Body alignmen was measured using a formertic, and variables representing body alignment included trunk inclination, trunk imbalance, pelvic tilt, pelvic torson, kyphotic angle and lordotic angle. The results of this study were as follows: As for the Body alignment, there were significantly increased in kyphotic angle and lordotic angle in the experimental group. From the above results, it seems that coordinative locomotor training has a positive effects on the body alignment of high school baseball players. The coordinative locomotor training was able to produce confirmation that body alignment change in the case of effective exercise interventions in high school baseball players. Coordinative locomotor training is thought to be effective in preventing physical imbalance in high school baseball.

This paper analyzes the effect of profit incentives within the setting of bounty open source project. A simple decision-making model based on classical utility maximization is presented for open source developers that includes income effects from the bounty prize. We then simulate the decisions of multiple developers to assess the effect from the bounty prize. Our result shows that learning costs can greatly reduce the software quality improvement benefit from bounty project. It also suggests that open source projects can benefit more when they have multiple small bounty projects than a single large bounty project since it reduces the learning cost and the opportunity cost for the open source developers.

A company or organization introduces various management techniques and systems to achieve performance in a situation where it does not provide or fails to provide motivating factors such as the provision of parts required or required by employees to achieve desired results. It puts a burden on the employees mentally and physically. If this situation continues for a long time, it can be called job burnout. Job burnout is due to long-term exposure to increase in production based on new management techniques and new technologies implemented by companies or organizations for survival and to achieve excessive work demands through overtime and holiday work to achieve this. It is defined as a state of emotional and mental exhaustion. Therefore, this study investigated how mental and work-related bullying affects job burnout and job commitment, and how they affect turnover intention. As a result, workplace bullying has a positive effect on job burnout and has a direct effect on turnover intention. Therefore, in the case of dental clinics, it is judged that a lot of effort should be put into workplace bullying and job burnout in order to prevent the turnover of excellent talents.

The purpose of this study is to analyze and verify the effect of multicultural adolescents parental support and parenting attitudes on school life and social competency by using the primary data of the second period of the MAPS(Multicultural Adolescents Panel Study). The subject of this study in 2019 was 2,246 multicultural adolescents who were in the fourth grade of elementary school at the time. As a result of the study, it was found that the more positive and positive parental support and parenting attitude were, the higher the degree of school life and social competence of multicultural adolescents. On the other hand, the degree of school life and social competence varies according to gender, and it was found that female students were higher than male students. There was a significant difference in the degree of school life according to the mother's educational background, but there was no significant difference in the degree of social competence. These results suggest that parental support and parenting attitudes are major environmental variables in the educational ecosystem that affect the school life and social competence of multicultural adolescents.

The purpose of this study is to define the concept of value-added services on online music platforms, put forward a model of users' willingness to use value-added services on online music platforms, and explore the way its influencing factors affect users' willingness to use. This study is based on the stimulus-response model, combined with the characteristics of online music service platforms, using information value, social value, and entertainment value as independent variables, using flow experience as a mediating variable, and self-awareness as a moderator variable, and then researches on the use of users. influence of will. Empirical analysis was carried out in this study using SPSS and AMOS statistical analysis tools. It is verified that the value-added services of online music platforms have a positive and positive impact on consumers' flow experience and usage intention. This research can fill in the theoretical defects of online music platform service, which has positive and important significance for music platform operators to improve their service capabilities and improve their income.

The purpose of this study is to investigate the influencing factors and mechanism of the characteristics of the live broadcast e-commerce platform and the characteristics of the host on the impulse purchase intention of live broadcast viewers. Based on the ELM model, this study complements existing research content. This study adopts the form of questionnaire survey and conducts empirical analysis using SPSS, AMOS, Mplus and other analysis software for online live broadcast users. The results show that the characteristics of live broadcast platforms have a positive impact on consumers' flow experience and satisfaction; the personal characteristics of anchors have a positive impact on consumers' flow experience and satisfaction; consumers' flow experience and satisfaction have a positive impact on impulse Purchase intention has a positive impact, and flow experience and satisfaction have a mediating effect on the characteristics of the live broadcast platform and the personal characteristics of the host.

In this paper, we propose a "social mentality" of primary and secondary school teachers in Anshan City, China. To conduct the study, 298 teachers in Ansan City were selected and a survey was conducted. This study adopted the method of combining literature research and questionnaire survey. The results showed that the overall level of “social mentality” of primary and secondary school teachers in Anshan was relatively high. Teachers’ social support levels differed significantly from educational background, teaching section, administrative position, and school location. The path model shows the interaction mechanism between variables. It appeared that general self-efficacy and job involvement play an important role in the mechanism.

In this paper, we propose a python education program that applies computational thinking for non-majors and programming beginners. In this study, we focus on the basics of program logic, breaking away from the difficult grammar and memorization-oriented programming education. And by applying the problem-solving procedure of computational thinking, we propose an educational program that allows non-majors and programming beginners to learn programming easily. In this paper, an 8-week educational program was applied to middle school students with little text coding experience. and through a post-satisfaction survey, it was found that their confidence in programming increased, and they were able to apply computational thinking could be applied to life and other subjects. Although the importance of programming education is being emphasized, it is expected that it will be used as a useful educational program when composing program education for non-majors and beginners in programming for learners who still find it difficult to learn programming.