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Disaster alerts are text messages sent by government to people in the area in the event of a disaster. Since the number of disaster alerts has increased, the number of people who block disaster alerts is increasing as many unnecessary disaster alerts are being received. To solve this problem, this study proposes a deep learning model that automatically classifies disaster alerts by disaster type, and allows only necessary disaster alerts to be received according to the recipient. The proposed model embeds disaster alerts via KoBERT and classifies them by disaster type with LSTM. As a result of classifying disaster alerts using 3 combinations of parts of speech: [Noun], [Noun + Adjective + Verb] and [All parts], and 4 classification models: Proposed model, Keyword classification, Word2Vec + 1D-CNN and KoBERT + FFNN, the proposed model achieved the highest performance with 0.988954 accuracy.

This paper shows the applicability of deep learning algorithm in predicting target position and getting correction value of impact point in order to improve the accuracy of naval gun firing. Predicting target position, the proposed model using LSTM model and RN structure is expected to be more accurate than existing method using kalman filter. Getting correction value of impact point, the another proposed model suggests a reinforcement model that manages factors which is related in ballistic calculation as data set, and learns using the data set. The model is expected to reduce error of naval gun firing. Combining two models, a ballistic calculation model for improving accuracy of naval gun firing based on deep learning algorithm was designed.

In this paper, we present a method for effectively predicting traffic volume based on vehicle location data that are collected by using LDP (Local Differential Privacy). The proposed solution in this paper consists of two phases: the process of collecting vehicle location data in a privacy-presering manner and the process of predicting traffic volume using the collected location data. In the first phase, the vehicle’s location data is collected by using LDP to prevent privacy issues that may arise during the data collection process. LDP adds random noise to the original data when collecting data to prevent the data owner’s sensitive information from being exposed to the outside. This allows the collection of vehicle location data, while preserving the driver’s privacy. In the second phase, the traffic volume is predicted by applying deep learning techniques to the data collected in the first stage. Experimental results with real data sets demonstrate that the method proposed in this paper can effectively predict the traffic volume using the location data that are collected in a privacy-preserving manner.

In this paper, a new approach to solve the constrained dynamic programming is proposed by using the constraint programming. While the conventional dynamic programming scheme has the state space augmented with states on constraints, this approach, without state augmentation, represents states of constraints as domains in a contraining programming solver. It has a hybrid computational mechanism in its computation by combining solving the Bellman equation in the dynamic programming framework and exploiting the propagation and inference methods of the constraint programming. In order to portray the differences of the two approaches, this paper solves a simple version of the long-term car rental financing problem. In the conventional scheme, data structures for state on constraints are designed, and a simple inference borrowed from the constraint programming is used to the reduction of violation of constraints because no inference risks failure of a solution. In the hybrid approach, the architecture of interface of the dynamic programming solution method and the constraint programming solution method is shown. It finally discusses the advantages of the proposed method with the conventional method.

In this paper, we propose a prediction model for osteopenia and osteoporosis based on a convolutional neural network(CNN) using computed tomography(CT) images. In a single CT image, CNN had a limitation in utilizing important local features for diagnosis. So we propose a compound model which has two identical structures. As an input, two different texture images are used, which are converted from a single normalized CT image. The two networks train different information by using dissimilarity loss function. As a result, our model trains various features in a single CT image which includes important local features, then we ensemble them to improve the accuracy of predicting osteopenia and osteoporosis. In experiment results, our method shows an accuracy of 77.11% and the feature visualize of this model is confirmed by using Grad-CAM.

In this paper, we propose a BERGPT-chatbot, a domestic AI chatbot that can alleviate negative emotions based on text input such as ‘Replika’. We made BERGPT-chatbot into a chatbot capable of mitigating negative emotions by pipelined two models, KR-BERT and KoGPT2-chatbot. We applied a creative method of giving emotions to unrefined everyday datasets through KR-BERT, and learning additional datasets through KoGPT2-chatbot. The development background of BERGPT-chatbot is as follows. Currently, the number of people with depression is increasing all over the world. This phenomenon is emerging as a more serious problem due to COVID-19, which causes people to increase long-term indoor living or limit interpersonal relationships. Overseas artificial intelligence chatbots aimed at relieving negative emotions or taking care of mental health care, have increased in use due to the pandemic. In Korea, Psychological diagnosis chatbots similar to those of overseas cases are being operated. However, as the domestic chatbot is a system that outputs a button-based answer rather than a text input-based answer, when compared to overseas chatbots, domestic chatbots remain at a low level of diagnosing human psychology. Therefore, we proposed a chatbot that helps mitigating negative emotions through BERGPT-chatbot. Finally, we compared BERGPT-chatbot and KoGPT2-chatbot through ‘Perplexity’, an internal evaluation metric for evaluating language models, and showed the superity of BERGPT-chatbot.

In this paper, we propose a method of extracting and recognizing related information for research from images of the unstructured pulmonary function test papers using character detection and recognition techniques. Also, we develop a post-processing method to reduce the character recognition error rate. The proposed structuring method uses a character detection model for the pulmonary function test paper images to detect all characters in the test paper and passes the detected character image through the character recognition model to obtain a string. The obtained string is reviewed for validity using string matching and structuring is completed. We confirm that our proposed structuring system is a more efficient and stable method than the structuring method through manual work of professionals because our system’s error rate is within about 1% and the processing speed per pulmonary function test paper is within 2 seconds.

Recently, deep learning, which is the most used in the field of artificial intelligence, has a structure that is gradually becoming larger and more complex. As the deep learning model grows, a large amount of data is required to learn it, but there are cases in which it is difficult to integrate and learn the data because the data is distributed among several owners and security issues. In that situation we conducted parallel learning for each users that own data and then studied how to integrate it. For this, distributed learning was performed for each owner assuming the security situation as V-environment and H-environment, and the results of distributed learning were integrated using Average, Max, and AbsMax. As a result of applying this to the mnist-fashion data, it was confirmed that there was no significant difference from the results obtained by integrating the data in the V-environment in terms of accuracy. In the H-environment, although there was a difference, meaningful results were obtained.

In this paper, we propose a system that uses image data and beacon data to classify authorized and unauthorized perosn who are allowed to enter a group facility. The image data collected through the IP camera uses YOLOv4 to extract a person object, and collects beacon signal data (UUID, RSSI) through an application to compose a fingerprinting-based radio map. Beacon extracts user location data after CNN-LSTM-based learning in order to improve location accuracy by supplementing signal instability. As a result of this paper, it showed an accuracy of 93.47%. In the future, it can be expected to fusion with the access authentication process such as QR code that has been used due to the COVID-19, track people who haven't through the authentication process.

In this paper, in order to support rapid and effective decision-making and response in disaster situations, we identified the author's organization of academic research papers and conducted a collaborative relationship analysis study based on this. For this purpose, 2,308 papers in 69 Korean academic journals classified by disaster and safety type were selected for analysis and experimental data were constructed based on the Korea Science Citation Database (KSCD) and institutional identification data provided by KISTI. Collaborative relationship analysis was conducted for each of the four units (Institution, Institution type, Institution region and University department type). First, statistical status such as frequency of appearance was compared, and basic properties and main centrality index of each co-occurrence network were calculated and analyzed using Social Network Analysis Method. In addition, a visualization map was created and presented for each network so that the collaborative relationship could be viewed and understood as a whole. The results of this study are expected to contribute to the search activities of institutions and cooperative groups that support effective disaster response and to lay the foundation for the information service system.

Blockchain technology is a distributed ledger technology that shares the ledger between multiple nodes connected to a distributed network. The data managed through the existing central server is managed through the blockchain, and the transparency, accuracy, and integrity of the transaction data is increased, and the need for data management through the blockchain is increasing. In this paper, recognizing the need for trust-based data sharing between trust-based institutions in the financial industry, this paper describes the process of selecting leader nodes in Raft, a private blockchain consensus algorithm, as a way to increase data management efficiency through blockchain. A modified consensus algorithm is presented. The performance of the modified consensus algorithm and the general Raft consensus algorithm presented in this paper was compared and analyzed based on the transaction processing time, and it was confirmed that the efficiency of the consensus process was increased by applying the proposed consensus algorithm.

In this paper, we design and implement an Android mobile application that helps hearing impaired people communicate based on STT(Speech-to-Text) and TTS(Text-to-Speech) APIs and accelerometer sensor of a smartphone. This application provides the ability to record what the hearing-Impairment person's interlocutor is saying with a microphone, convert it to text using the STT API, and display it to the hearing-Impairment person. In addition. In addition, when a hearing-impaired person inputs a text using the TTS API, it is converted into voice and told to the interlocutor. When a hearing-impaired person shakes their smartphone, an accelerometer based background service function is provided to run the application. The application implemented in this paper provides a function that allows hearing impaired people to communicate easily with other people when communicating with others without using sign language as a video call.

As the main carrier of information, digital image is becoming more and more important. However, with the popularity of image acquisition equipment and the rapid development of image editing software, in recent years, digital image counterfeiting incidents have emerged one after another, which not only reduces the credibility of images, but also brings great negative impacts to society and individuals. Image copy-paste tampering is one of the most common types of image tampering, which is easy to operate and effective, and is often used to change the semantic information of digital images. In this paper, a method to protect the authenticity and integrity of image content by studying the tamper detection method of image copy and paste was proposed. In view of the excellent learning and analysis ability of deep learning, two tamper detection methods based on deep learning were proposed, which use the traces left by image processing operations to distinguish the tampered area from the original area in the image. A series of experimental results verified the rationality of the theoretical basis, the accuracy of tampering detection, location and classification.

Malicious activities of Botnets are responsible for huge financial losses to Internet Service Providers, companies, governments and even home users. In this paper, we try to confirm the possibility of detecting botnet traffic by applying the deep learning model Convolutional Neural Network (CNN) using the CTU-13 botnet traffic dataset. In particular, we classify three classes, such as the C&C traffic between bots and C&C servers to detect C&C servers, traffic generated by bots other than C&C communication to detect bots, and normal traffic. Performance metrics were presented by accuracy, precision, recall, and F1 score on classifying both known and unknown botnet traffic. Moreover, we propose a stackable botnet detection system that can load modules for each botnet type considering scalability and operability on the real field.

Travel pattern recognition is widely used in many aspects such as user trajectory query, user behavior prediction, interest recommendation based on user location, user privacy protection and municipal transportation planning. Because the current recognition accuracy cannot meet the application requirements, the study of travel pattern recognition is the focus of trajectory data research. With the popularization of GPS navigation technology and intelligent mobile devices, a large amount of user mobile data information can be obtained from it, and many meaningful researches can be carried out based on this information. In the current travel pattern research method, the feature extraction of trajectory is limited to the basic attributes of trajectory (speed, angle, acceleration, etc.). In this paper, permutation entropy was used as an eigenvalue of trajectory to participate in the research of trajectory classification, and also used as an attribute to measure the complexity of time series. Velocity permutation entropy and angle permutation entropy were used as characteristics of trajectory to participate in the classification of travel patterns, and the accuracy of attribute classification based on permutation entropy used in this paper reached 81.47%.

In the recent power industry, a change is taking place from manual meter reading to remote meter reading using AMI(Advanced Metering Infrastructure). If such the power data generated from the AMI is recorded on the blockchain, integrity is guaranteed by preventing forgery and tampering. As data sharing becomes transparent, new business can be created. However, Ethereum blockchain is not suitable for processing large amounts of transactions due to the limitation of processing speed. As a solution to overcome such the limitation, various On/Off-Chain methods are being investigated. In this paper, we propose a interface server using data sharding as a solution for storing large amounts of power data in Etherium blockchain environments. Experimental results show that our power-data processing system with sharding method lessen the data omission rate to 0% that occurs when the transactions are transmitted to Ethereum and enhance the processing speed approximately 9 times.

Internet of Things (IoT) technology, which provides services by connecting various objects in the real world and objects in the virtual world based on the Internet, is emerging as a technology that enables a hyper-connected society in the era of the 4th industrial revolution. Since IoT technology is a convergence technology that encompasses devices, networks, platforms, and services, various studies are being conducted. Among these studies, studies on measures that can measure service quality provided by IoT software are still insufficient. IoT software has hardware parts of the Internet of Things, technologies based on them, features of embedded software, and network features. These features are used as elements defining IoT software quality measurement metrics. However, these features are considered in the metrics related to IoT software quality measurement so far. Therefore, this paper presents a metric for reusability measurement among various quality factors of IoT software in consideration of these factors. In particular, since IoT software is used through IoT devices, services in IoT software must be designed to be changed, replaced, or expanded, and metrics that can measure this are very necessary. In this paper, we propose three metrics: changeability, replaceability, and scalability that can measure and evaluate the reusability of IoT software services were presented, and the metrics presented through case studies were verified. It is expected that the service quality verification of IoT software will be carried out through the metrics presented in this paper, thereby contributing to the improvement of users' service satisfaction.

Building Information Modeling (BIM) data is a digitized construction design by worldwide construction design stands rules. Some research are being conducted to utilize blockchain for safe sharing and trade of BIM data, but there is no way to store BIM data directly in the blockchain due to the size of BIM data and technical limitation of the blockchain. In this paper, we propose a method of storing BIM data by combining a distributed file system and a blockchain. We propose two network overlays for storing BIM data, and we also propose generating the Level of Detail (LOD)-based merkle tree for efficient verification of BIM data. In addition, this paper proposes a system design for distributed storage of BIM data by using blockchain besu client and IPFS client. Our system design has a result that the processing speed stably increased despite the increase in data size.

Based on the technostress theory, this study aims to explore the effect of technostress caused by the use of social media during or after work hours on job turnover intention. This study conducted an online survey targeting 341 Chinese WeChat users. According to the results of the structural model analysis, role overload, role conflict, and work invasion that occur during work affect technostress, and social interaction overload, invasion of private life, and Fear of Missing Out (FoMO) that occur after work have a effect on technostress as well. Technostress occurring during work did not appear to have an effect on turnover intention, but technostress occurring after work was found to have a positive effect on turnover intention. It is expected that this study will be used as a basic data for the correct use of social media within an organization.

Private security has the common job characteristics of the police and crime prevention, and is responsible for the safety of our society. However, the hiring process for private security is very different from that of the police. Therefore, in this study, the problems of the private security recruitment process were identified through the police recruitment process and improvement points were suggested. As a result of comparing and examining the recruitment process of the police, the recruitment of private security guards is carried out through education and training, and problems such as the training process and physical strength verification required for security work were investigated. In order to improve the problems in the private security recruitment process, the curriculum of criminal law and criminology, physical examination such as 100m running and left and right grip strength, and practical cases of security work should be added. It is hoped that this study will serve as a basic data for the development of the private security industry along with the recruitment of excellent security guards.

The purpose of this study was to analyze the responses of 5,824 adults(2,574 males and 3,250 females over the age of 19 years) using raw data from the 7th period of the National Health and Nutrition Examination Survey to investigate the relationship between systemic disease activity restriction and oral health. There were many systemic disease activity restrictions in adults with oral chewing and speaking problems, and it was statistically significant(p<.001). Factors influencing activity restriction due to systemic disease include age(odds ratio 1.03), Male(odds ratio 0.84), education level(odds ratio 0.57, 0.45, 0.31), drinking(odds ratio 1.38), chewing(odds ratio 1.86) and speaking(odds ratio 1.84) problems. There was a higher probability of activity restriction due to systemic disease when they received treatment for periodontal disease(odds ratio 1.27) and broken teeth(odds ratio 2.1). Also, it was statistically significant that the quality of life decreased when there was chewing and speaking problems.

Seafarers play an important role in shipping and logistics. However, seafarers are relatively vulnerable to mental illness because they have to board ships for a considerable period of time and work in isolation. In particular, in the pandemic situation caused by COVID-19, the crew change is delayed due to the closure of many ports around the world, increasing the mental burden on seafarers. The mental health management of the crew is important because these mental problems can lead to major accidents of lives and ships. This paper identified the necessity of mental health management of seafarers through a survey and identified problems with the currently operated mental health management program and curriculum. Especially, this study proposed VR-based programs to help crews receive mentally counseling treatment in a timely manner and to reduce the mental burden on them by preventing sensitive personal information exposure. Through this, it is expected to contribute to the stable development of the logistics industry by establishing a safe seafarers working environment.

This study attempted to compare the pattern with the absorption layer by analyzing the pattern of commercially available urinary incontinence panty products. Through this, it tried to obtain basic data necessary for the development of functional urinary incontinence panty for active seniors. Twelve commercially available products were decomposed to analyze size and patterns, and appearance and clothing pressure were evaluated through 3D simulation. As a result of comparing the size and pattern of urinary incontinence panty, it was analyzed that the size difference between parts was large even though the product was called the same. Products from the same brand also showed a big difference depending on design and absorption. As a result of the appearance evaluation for the 3D simulation, it was found that there were significant differences between products in all items such as the front, side, and back. Product No. 9 was analyzed to be the best except for the waist fit on the side. In the evaluation of clothing pressure, most of them were marked in red except for products 1, 2, and 8 due to the nature of the panty product. In the future, it is thought that actual wearing experiments and size standardization studies on urinary incontinence pants should be conducted.

As the coronavirus disease(COVID-19) pandemic is declared and the number of confirmed cases and deaths increases in countries around the world, the world is gripped with fear. Therefore, in this study, psychological factors of infection prevention behaviors of firefighters and maritime police officers were analyzed based on the Health Belief Model. Although there was no significant difference in the COVID-19 prevention behaviors between the two groups, there was a significant positive correlation between the perceived disease infection possibility, perceived severity, and perceived benefits in the general characteristics and the COVID-19 prevention behaviors. There was no significant difference with perceived obstacles. This study is of great significance in that it is the first analysis of firefighters and maritime police officers as a health belief model, and can be used as basic data for the implementation of new infectious disease prevention actions.

This study was examined the effect and the continuity of music therapy for reduce on post-traumatic stress (PTS) in 119 emergency medical technicians (EMTs). The subjects of the study were 42 EMTs in the C area, and the study was conducted from November 25, 2019 to March 1, 2020. The experimental group conducted a total of ten music therapy programs twice a day for five days. The session-specific program was conducted by two music therapists, including the early, mid, late, and closed stages. Each step applied intervention techniques necessary for goals such as improvisation, rhythm making, Nanta, and couple physical activities. The control group was required to take a free break (TV viewing, cell phone games, sleep, exercise, etc.) at the same time as the experimental group's program. The study found that the control group had no difference in PTS before and after the application of music therapy, but the experimental group had a significant decrease in PTS immediately after the application of music therapy and a gradual increase in PTS after 4 and 12 weeks (p<.05). Repetitive music therapy is judged to be an effective way to mitigate the PTS leve for EMTs.

The purpose of this study is to investigate the characteristics of childhood abuse experience, analyze factors affecting gender role attitudes, find out the moderating effect of social support, and suggest preventive measures and alternatives for childhood abuse experiences. proceeded. This study was conducted by the Ministry of Gender Equality and Family, through a domestic violence survey conducted every three years in accordance with the 「Act on the Prevention of Domestic Violence and Victim Protection, etc.」 Analysis was performed on 4,546 people and 4,514 men. As a result, it was found that childhood abuse experience had a negative (-) effect on gender role attitudes, and the analysis of the moderating effect of social support also showed a negative (-) effect. In the future, it is necessary to strengthen awareness of childhood abuse prevention and child protection from the beginning of life, and it is suggested that the level of children's parental education and social support and sufficient measures be prepared for future research.

This study investigated the Antimicrobial activity of Rutaceae fruit ethanol extracts against microorganisms-induced skin inflammation in cosmetic materials. Rutaceae fruits were separated in two parts of whole fruit (pulp, pulp fegment membrane, peel) and peel, and extracted with 70% ehtanol. The results demonstrated that Rutaceae fruit ethanol extracts showed antimicrobial activity in 5 strains except Staphylococcus aureus. In particular, the antimicrobial activity against Staphylococcus epidermidis was the best in fresh lemons whole fruit. The antimicrobial activity against Escherichia coli was shown only in fresh lemon peel and fresh trifoliate peel. Additionally, antimicrobial activity against Propionibacterium acnes was shown only in the dried lemon peel. In the results of antimicrobial activity against Pityrosporum ovale, in the case of fresh fruits, citron whole fruits showed the highest effect, followed by lemon whole fruits and mandarin orange peel. And in the case of dried fruits, orange peel showed the highest effect, followed by trifoliate peel, mandarin orange peel and lemon peel. Therefore, it is considered that lemon, which shows antimicrobial activity against all skin inflammation-causing microorganisms, can be used as a natural material for improving skin inflammation in cosmetics.

As COVID-19 spread, non-face-to-face activities were required, and the use of service robots is gradually increasing. This paper analyzed the relationship between the increasing trend of service robots before and after COVID-19 through keyword search containing the keyword 'service robot AND non-face-to-face' over the past three years (2018.10-20219) using BigKines, a news big data analysis system. As a result, there were 0 cases in the first period (2018.10~2019.9), 52 cases in the second period (2019.10~2020.9) and 112 cases in the third period (2020.10~2021.9), an increase of 115% compared to the second period. The keywords commonly mentioned in the analysis of related words in the second and third periods were COVID-19, AI, the Ministry of Trade, Industry, and Energy, and LG Electronics, and the weight of COVID-19 was the largest, confirming that the analysis keyword. Due to the spread of Corona 19, non-face-to-face is required, and with the development of information and communication technology, the field of application of service robots is rapidly increasing. Accordingly, for the commercialization of service robots that will lead the non-face-to-face economy, there is an urgent need to nurture human resources that require standardization and expertise in safety and performance fields.

In this paper, we propose operations and maintenance (O&M) planning approach for floating offshore wind farm using the mathematical optimization. To be specific, we present a MILP (Mixed Integer Linear Programming that suggests the composition of vessels, technicians, and maintenance works on a weekly basis. We reflect accessibility to wind turbines based on weather data and loss of power generation using the Jensen wake model to identify downtime cost that vary from time to time. This paper also includes a description of two-stage approach for maintenance planning & detailed scheduling and numeric analysis of the number of vessels and technicians on the O&M cost. Finally, the MILP model could be utilized in order to establish the suitable and effective maintenance planning reflecting domestic situation.

The purpose of this study is to make practical suggestions to help multicultural adolescents’ academic adaptation. For this purpose, changes in the academic adaptation of multicultural adolescents were identified, and longitudinal changes in self-esteem were explored in the relationship between bicultural acceptance attitude and academic adaptation. The data from the panel survey of multicultural youth of the Korea Youth Policy Institute was used for the analysis. The result of analyzing the longitudinal mediating effects of self-esteem between bicultural acceptance and academic adaptation through the multivariate potential growth model, the mediating effects of self-esteem was confirmed. Based on these results, the practical implications for enhancing the bicultural acceptance attitude, academic adaptation, and self-esteem of multicultural youth were presented.

In this paper, we propose a ways how YouTube Mukbang content attributes affect favorability, satisfaction, and brand selection, and suggest to use YouTube Mukbang contents, and to propose a strategic marketing plan using YouTube at the food franchise. This study conducted survey on 218 people who had watched chicken Mukbang among YouTube Mukbang contents. Through previous studies, YouTube content attributes were classified into informativity, entertainment, reliability, and attractiveness. To verify the hypothesis of the study, single regression and multiple regression analysis were conducted for verifying the relationship between variables. Key results of the study are as follows. First, it was found that YouTube Mukbang content attributes had a positive relationship with favorability. Second, it was found that YouTube Mukbang content attributes had a positive relationship with satisfaction. Third, it was found that favorability had an effect on satisfaction. Fourth, it was found that favorability influenced brand selection. Fifth, it was found that satisfaction did not affect brand selection. Based on these findings, a strategic approach will be needed to increase users' favorability by providing attractive and accurate information through YouTube Mukbang contents and to continuously improve brand choices through continuous favorability to revitalizing YouTube marketing at the food franchise.

The purpose of this paper is to provide basic data for related studies by comparing and analyzing crimes committed by security guards through criminal statistical analysis, and to contribute to the sound development of the private security industry by strengthening the professional ethics of security guards and reducing guard crimes. The purpose. As a results of a comparative analysis of the number of crimes by security guards and the crime rate are as follows. Although the total number of crimes committed in Korea and the number of crimes committed by security guards decreased every year, the crime rate of security guards was higher than the average crime rate. felonious crimes, violent crimes, customs crimes, and special economic crimes were consistently high. As a countermeasure against the results, first, interest in security guard crimes as perpetrators rather than as victims, second, reinforcement of professional ethics education through new training and job training, third, academic development and systematic It appeared as a specification of the definition and current status of security guards for the study.

This study was conducted with the aim of providing help in finding ways to utilize marketing for small business owners closely related to our daily lives by utilizing SNS marketing. To achive these research purpose, the study conducted various statistical analyses using questionnaire of SNS users. The results and implications of this study are as follows. The characteristics of SNS, such as information provision, interactivity and convenience, all showed positive positive effects on the intention of purchase, and the size of the influence was shown in the order of convenience, information provision and interactivity, confirming that the convenience had the greatest impact on the intention of purchase. Based on the impact of SNS characteristics on purchasing intentions, the results of this study confirmed that all three characteristics of SNS, namely interactivity, information provision and convenience, are important variables in predicting consumers' purchasing intentions. Communication with consumers and providing useful information can be seen as essential factors in conducting SNS marketing for small business owners, and if they focus on the benefits that make it easy to access SNS, they will not only be able to increase their purchasing intentions but also to develop strategic ways to lead to the execution of purchases.

As the COVID-19 pandemic spread around the world, more and more universities are conducting real-time distance video lectures using ZOOM, Webex, and MS Teams. This study attempts to identify the factors influencing learner satisfaction of real-time distance video lectures. Based on the existing research, it was composed of five elements (system factor, content quality, interaction, self-direction, and learning motivation) as learner satisfaction elements of real-time distance video lectures. As a result of analyzing the structural equation model of 160 effective questionnaires by conducting a survey of college students in the metropolitan and Chungcheong areas, it was found that three factors (interaction, self-direction, and learning motivation) influence learner satisfaction. Real-time distance video lectures are expected to continue to expand in the future. Therefore, universities should continuously increase learner satisfaction through the development and evaluation of real-time distance video lecture satisfaction models.

In this study, we proposed a hierarchy process analysis model for Kiosk known as technology-based self-service, In addition, we deduced the selection factors based on the literature review and calculated the weight of the factors. To estimate the priorities of selection factors, we used the data collected from consumers who have used Kiosks at cafe or fast food restaurant. The results of the study showed that the convenience among the factors in the first stage, consisted of safety, design, convenience, informativity, and responsiveness, revealed the most important factor in both cafe and fast food restaurant. Synthetic calculation of the first stage factors and the second stage factors showed that simple procedure was the most important factor. The results of comparing the priorities between cafe and fast food restaurant showed that consumers assessed their priorities differently in simple procedures, nutritional information, and fast menu provision.

In this paper, a study was conducted to understand how students' attitudes and tendencies toward reading affect the newly emerging coding education. Relevant data were collected by dividing it into three areas (reading, coding, and leisure). In the reading area, data on preference books, preferred types of reading and etc were collected. In the coding area, prior learning of coding, main tasks using a computer, time used for learning and etc were collected. In the leisure area, main leisure activities and hours of spent leisure time per one week were collected. Using the collected data, we classified and analyzed the data based on the preferred reading method to identify the problems of non-major students who have difficulties in coding education. In coding education, the excerpts reading student group showed the best achievement (average 60.1), and the extensive reading group showed the lowest achievement (average 48.4). The students who read extensively spent more time in coding study than the group of students who preferred other reading methods, but showed the lowest achievement.