The purpose of this study is to verify the influence relationship of digitally converted services on consumers' intention to use since traditional services are being converted to digital services due to technological development and increase in non-face-to-face services. The study consisted of a program development procedure and a program effectiveness verification procedure, and bootstrapping was performed to verify the mediating effect adjusted along with multiple regression analysis. The subjects of this study were 323 university (graduate) students and the general public residing in Korea. Results. First, it was found that the three perceived interaction factors (perceived communication, perceived control, and perceived reactivity) of digital transformed services had a positive effect on perceived usefulness and perceived ease of use, respectively. Second, the relationship of influence of technology acceptance intention was verified. Third, it was confirmed that the effect of the three perceived interaction factors of digital transformed services on intention to use was mediated by perceived usefulness and perceived ease of use. Fourth, the mediating effect mediated by digital disparity was confirmed. As a result, it was confirmed that the three perceived interaction factors of the digitally converted service are important factors in the intention to use the digitally converted service. This suggests that efforts are needed to minimize the digital divide.
The purpose of this study is to find a way that companies can use social media trust effectively to increase brand trust and intelligently increase it by linking with brand authenticity. Therefore, the purpose of this study is as follows. First, a conceptual definition of the research variables of trust in social media, trust in information sources, and trust in messages is established, Second, the casual relationship between two conceptually defined independent variables and mediator variables of perceived risk and self-efficacy was verified. Third, the casual relationship was verified on whether it affects brand authenticity and brand trust through perceived risk and self-efficacy, which are mediators of social media trust. Fourth, through the verification result of the casual relationship between the research variables constituting the research model of this study, it was intended to present academic and practical implications on how social media trust can effectively enhance brand trust.
In 1991, the United Nations World Tourism Organization (UNWTO) declared "tourism accessible for all" recommending the practice of the right to enjoy tourism. According to the Ministry of Health and Welfare of Korea (2019), the disabled, who are the most vulnerable in tourism, accounted for 5.1% of the total population, and the number of the elderly over 65 is expected to increase to 20.3% by 2025. In particular, the need for customized policies has been raised as the proportion of disabled people among the elderly aged 65 and over continues to increase. Thus, this study identified the spatial characteristics of Universal Design (UD) tourist destinations considering the tourism vulnerable groups. Administrative units (425 dongs) in Seoul were used as spatial units for analysis. As a research method, first, a spatial model was specified through LM verification, and then spatial regression analysis was performed. As a result of the analysis, the spatial characteristics of UD tourist destinations were found to have positive (+) effects on the number of universally certified businesses, the number of restaurants, and the number of bus stops that were available to the vulnerable. It was confirmed that there are a large number of universal certified businesses, restaurants, and bus stops in dongs with UD tourist destinations. The findings will provide policy implications when promoting the right to enjoy tourism in the future and improving Korean universal design quality.
Journal of Korea Entertainment Industry Association
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v.15
no.4
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pp.349-357
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2021
Recently, drones are rapidly becoming common and expanding. There is a great need for diversity in whether drone flight data can be used as entertainment technology analysis data. In particular, it is necessary to check whether it is possible to analyze and utilize the flight and operation process of entertainment drones, which are developing through autonomous and intelligent methods, through data analysis and machine learning. In this paper, it was confirmed whether it can be used as a machine learning technology by using FC data in the evaluation of drones for entertainment. As a result, FC data from DJI and Parrot such as Mavic2 and Anafi were unable to analyze machine learning for entertainment. It is because data is collected at intervals of 0.1 second or more, so that it is impossible to find correlation with other data with GCS. On the other hand, it was found that machine learning technologies can be applied in the case of Fixhawk, which used an ARM processor and operates with the Nuttx OS. In the future, it is necessary to develop technologies capable of analyzing the characteristics of entertainment by dividing fixed-wing and rotary-wing flight information. For this, a model shoud be developed, and systematic big data collection and research should be conducted.
Anomaly detection is a method to detect and block abnormal data flows in general users' data sets. The previously known method is a method of detecting and defending an attack based on a signature using the signature of an already known attack. This has the advantage of a low false positive rate, but the problem is that it is very vulnerable to a zero-day vulnerability attack or a modified attack. However, in the case of anomaly detection, there is a disadvantage that the false positive rate is high, but it has the advantage of being able to identify, detect, and block zero-day vulnerability attacks or modified attacks, so related studies are being actively conducted. In this study, we want to deal with these anomaly detection mechanisms, and we propose a new mechanism that performs both anomaly detection and classification while supplementing the high false positive rate mentioned above. In this study, the experiment was conducted with five configurations considering the characteristics of various algorithms. As a result, the model showing the best accuracy was proposed as the result of this study. After detecting an attack by applying the Extra Tree and Three-layer ANN at the same time, the attack type is classified using the Extra Tree for the classified attack data. In this study, verification was performed on the NSL-KDD data set, and the accuracy was 99.8%, 99.1%, 98.9%, 98.7%, and 97.9% for Normal, Dos, Probe, U2R, and R2L, respectively. This configuration showed superior performance compared to other models.
In order to analyze the factors influencing career maturity of general high school students, this study verified the moderated mediating effect of career exploration efficacy by the frequency of conversation with parents in the process of self-awareness and class attitude affecting career maturity. For the study of the second year of general high school, the results of verifying the moderated mediating effect using SPSS 22 and Model 7 of PROCESS macro from the results of the first year survey of the Korea Education and Employment Panel (KEEP) II are as follows. First, positive self-awareness and class attitude had a positive effect on career maturity. Second, career exploration efficacy mediates self-awareness, class attitude, and career maturity. Third, in the relationship between self-awareness and career maturity, there is no moderated mediating effect on career exploration efficacy by the frequency of conversation with parents. Fourth, in the relationship between class attitude and career maturity, a confrontation-moderated mediating effect on career exploration efficacy by conversation frequency with parents appears. Based on these studies, implications and future tasks for career maturity of general high school students were proposed.
In order to improve formal presentation attitudes such as presentation of job interviews and presentation of project results at the company, there are few automated methods other than observation by colleagues or professors. In previous studies, it was reported that the speaker's stable speech and gaze processing affect the delivery power in the presentation. Also, there are studies that show that proper feedback on one's presentation has the effect of increasing the presenter's ability to present. In this paper, considering the positive aspects of correction, we developed a program that intelligently corrects the wrong presentation habits and attitudes of college students through facial analysis of videos and analyzed the proposed program's performance. The proposed program was developed through web-based verification of the use of redundant words and facial recognition and textualization of the presentation contents. To this end, an artificial intelligence model for classification was developed, and after extracting the video object, facial feature points were recognized based on the coordinates. Then, using 4000 facial data, the performance of the algorithm in this paper was compared and analyzed with the case of facial recognition using a Teachable Machine. Use the program to help presenters by correcting their presentation attitude.
KIPS Transactions on Computer and Communication Systems
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v.11
no.10
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pp.363-372
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2022
Since the recent COVID-19 Pandemic, the ransomware fandom has intensified along with the expansion of remote work. Currently, anti-virus vaccine companies are trying to respond to ransomware, but traditional file signature-based static analysis can be neutralized in the face of diversification, obfuscation, variants, or the emergence of new ransomware. Various studies are being conducted for such ransomware detection, and detection studies using signature-based static analysis and behavior-based dynamic analysis can be seen as the main research type at present. In this paper, the frequency of ".text Section" Opcode and the Native API used in practice was extracted, and the association between feature information selected using K-means Clustering algorithm, Cosine Similarity, and Pearson correlation coefficient was analyzed. In addition, Through experiments to classify and detect worms among other malware types and Cerber-type ransomware, it was verified that the selected feature information was specialized in detecting specific ransomware (Cerber). As a result of combining the finally selected feature information through the above verification and applying it to machine learning and performing hyper parameter optimization, the detection rate was up to 93.3%.
is a project for the senior generation with humanistic knowledge to become a mentor and communicate with them to present the wisdom and direction of life to the new generations of mentees based on various life experiences. has been expanding since 2015, starting with the pilot operation in 2014. In general, projects such as these are assessed to establish effectiveness indicators to verify effectiveness and to establish project management and development strategies. However, most of the evaluations have been conducted quantitatively and qualitatively based on the short-term duration of the project. Therefore, in the case of continuous projects such as , especially in the field of culture and arts where long-term effectiveness verification is required, the short-term evaluation is difficult to predict and judge the actual meaningful effects. In this regard, tried to examine the qualitative change of key participants in this project through the 2017 and 2018 image tracking survey. For this purpose, we adopted qualitative research methodology through interview video shooting, field shooting, and value coding as a research method suitable for the research subject. To analyze the results, first, the interview images were transcribed, keywords were extracted, value encoding works were matched with human psychological values, and the theoretical method was used to identify changes and to derive the meaning. In fact, despite the fact that the study conducted in this study was a follow-up survey, it remained a limitation that it analyzed the changed pattern in a rather short time of 2 years. However, this study systemized the specific methodology that researchers should conduct for follow-up and provided the flow of research at the present time when there is hardly a model for follow-up in the field of culture and arts education business in Korea as well as abroad. Significance can be derived from this point. In addition, it can be said that it has great significance in preparing the detailed system and case of comparative analysis methodology through value coding.
This study examined the patterns of coping strategies among Koreans during the early stage of the COVID-19 pandemic, explored the influence of demographic information (gender, age, economic level, household type), along with the unusual experiences due to COVID-19 (fear, stress of COVID, constraints of routine, income risk) on the classification of subclasses, and analyzed the latent profile differences in psychological wellbeing (life satisfaction, depression, and anxiety). An online survey was conducted among Korean Adults(n=600) between April 13, 2020 and 21, when WHO declared COVID-19 a global pandemic and Daegu as well as Gyeongsangbuk-do was nominated as a special disaster zone. First, Latent Profile Analysis (LPA) was used to identify subclasses of coping strategies and results suggested that the 4-class model had the best fit. Second, Class memberships were predicted by gender, age, economic level, as well as fear, stress, constraints of routine, and income risk, among the unusual experiences due to COVID-19. Finally, there are differences in psychological wellbeing among latent profiles. 'High level of adaptive coping group 3' showed the highest level of life satisfaction, 'Adaptive-maladaptive coping group 4' showed the highest level of depression, anxiety. Implications and suggestions are discussed based on the study results.
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