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Influencer advertising, which has gained significant attention in academia and industry, is widely adopted as a digital marketing strategy. This study empirically analyzes the impact of perceived influencer channel attributes and ad attributes on the suitability of advertisements and their effects on consumers' positive and negative advertising behaviors. The research aims to identify various factors that can enhance the effectiveness of influencer advertising. The results reveal that among the influencer channel attributes, informativeness and intimacy have a positive impact on ad suitability, while ad clutter has a negative impact. Additionally, ad-influencer fit positively affect ad attention and negatively influences ad avoidance. Based on these findings, companies can enhance the effectiveness of influencer advertising by first selecting influencers who align well with the advertisement and emphasizing informativeness and emotional bonding to improve ad suitability. Moreover, the study suggests that influencer advertising strategies can be effective as long as they avoid excessive ad clutter, as it diminishes ad suitability. Marketing practitioners and advertising planners can utilize these insights to formulate more effective influencer advertising strategies.

Given the surge in the elderly population, and increasing in dementia cases, there is a growing interest in digital therapies that facilitate steady remote treatment. However, in the cognitive assessment of digital therapies through clinical trials, the absence of log data as an essential evaluation factor is a significant issue. To address this, we propose a solution of utilizing weighted derived variables based on high-importance variables' accuracy in log data utilization as an indirect cognitive assessment factor for digital therapies. We have validated the effectiveness of this approach using machine learning techniques such as XGBoost, LGBM, and CatBoost. Thus, we suggest the use of log data as a rapid and indirect cognitive evaluation factor for digital therapy users.

The purpose of this study is to evaluate the impact of intermediate features on FER performance. To achieve this objective, intermediate features were extracted from the input images at specific layers (FM1~FM4) of the pre-trained network (Resnet-18). These extracted intermediate features and original images were used as inputs to the vision transformer (ViT), and the FER performance was compared. As a result, when using a single image as input, using intermediate features extracted from FM2 yielded the best performance (training accuracy: 94.35%, testing accuracy: 75.51%). When using the original image as input, the training accuracy was 91.32% and the testing accuracy was 74.68%. However, when combining the original image with intermediate features as input, the best FER performance was achieved by combining the original image with FM2, FM3, and FM4 (training accuracy: 97.88%, testing accuracy: 79.21%). These results imply that incorporating intermediate features alongside the original image can lead to superior performance. The findings can be referenced and utilized when designing the preprocessing stages of a deep learning model in FER. By considering the effectiveness of using intermediate features, practitioners can make informed decisions to enhance the performance of FER systems.

A growing number of large scale knowledge graphs raises several issues how knowledge graph data can be organized, discovered, and integrated efficiently. We present a novel semantic-based mashup platform for contents convergence which consists of acquisition, RDF storage, ontology learning, and mashup subsystems. This platform servers a basis for developing other more sophisticated applications required in the area of knowledge big data. Moreover, this paper proposes an entity matching method using graph convolutional network techniques as a preliminary work for automatic classification and discovery on knowledge big data. Using real DBP15K and SRPRS datasets, the performance of our method is compared with some existing entity matching methods. The experimental results show that the proposed method outperforms existing methods due to its ability to increase accuracy and reduce training time.

The automotive industry is undergoing a paradigm shift due to the convergence of IT and rapid digital transformation. Various components, including embedded structures and systems with complex architectures that incorporate IC semiconductors, are being integrated and modularized. As a result, there has been a significant increase in vehicle defects, raising expectations for the quality of automotive parts. As more and more data is being accumulated, there is an active effort to go beyond traditional reliability analysis methods and apply machine learning models based on the accumulated big data. However, there are still not many cases where machine learning is used in product development to identify factors of defects in performance and durability of products and incorporate feedback into the design to improve product quality. In this paper, we applied a prediction algorithm to the defects of automotive door devices equipped with automatic responsive sensors, which are commonly installed in recent electric and hydrogen vehicles. To do so, we selected test items, built a measurement emulation system for data acquisition, and conducted comparative evaluations by applying different machine learning algorithms to the measured data. The results in terms of R2 score were as follows: Ordinary multiple regression 0.96, Ridge regression 0.95, Lasso regression 0.89, Elastic regression 0.91.

The goal of deep learning is to extract complex features from multidimensional data use the features to create models that connect input and output. Deep learning is a process of learning nonlinear features and functions from complex data, and the user data that is employed to train deep learning models has become the focus of privacy concerns. Companies that collect user’s sensitive personal information, such as users’ images and voices, own this data for indefinite period of times. Users cannot delete their personal information, and they cannot limit the purposes for which the data is used. The study has designed a deep learning method that employs privacy protection technology that uses distributed collaborative learning so that multiple participants can use neural network models collaboratively without sharing the input datasets. To prevent direct leaks of personal information, participants are not shown the training datasets during the model training process, unlike traditional deep learning so that the personal information in the data can be protected. The study used a method that can selectively share subsets via an optimization algorithm that is based on modified distributed stochastic gradient descent, and the result showed that it was possible to learn with improved learning accuracy while protecting personal information.

Recently, many studies have been conducted on ways to utilize AI technology in NIDS (Network Intrusion Detection System). In particular, CNN-based NIDS generally shows excellent performance. CNN is basically a method of using correlation between pixels existing in an image. Therefore, the method of generating an image is very important in CNN. In this paper, the performance comparison of CNN-based NIDS according to the image generation method was performed. The image generation methods used in the experiment are a direct conversion method and a one-hot encoding based method. As a result of the experiment, the performance of NIDS was different depending on the image generation method. In particular, it was confirmed that the method combining the direct conversion method and the one-hot encoding based method proposed in this paper showed the best performance.

Squatting of agricultural work can cause musculoskeletal disorders due to excessive pressure and rotational force on the knee joint In order to improve the assistive chair used in squatting agricultural work so that it can be used in a narrow groove, it is intended to improve the musculoskeletal harm of squatting work by attaching a spring on the assistive chair. Therefore, in the presenty study, 3D drawing was done using Pro- Engineer (3D), and a mock-up was produced and tested. Using pro-Engineer, it was judged that it was rare for plastic to be broken by a spring, so the analysis was conducted with a focus on springs. It was found that the structure that can absorb the shock according to the rigidity of the tape spring and balance the body is that the power to withstand the load of the weight is distributed as a whole when five springs are used. Electromyography was measured using ME600 (Mega Electronics, Finland) Measuring equipment attached to the waist, thighs, calves, and shins. EMG values were measured and compared with the prototype in two ways, when the worker did not wear the product and when he wore an existing product on the market. As a result of the experiment when using the prototype, the maximum EMG value for each part is considered to be helpful in preventing musculoskeletal diseases as the amount of muscle used is reduced in the waist, thighs, calves, and shins. .

In recent years, with the breakthrough of Artificial Intelligence (AI) technology in deep learning algorithms such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAE), AI generation technology has rapidly expanded in various sub-sectors in the art field. 2022 as the explosive year of AI-generated art, especially in the creation of AI-generated art creative design, many excellent works have been born, which has improved the work efficiency of art design. This study analyzed the application design characteristics of AI generation technology in two sub fields of artistic creative design of AI painting and AI animation production , and compares the differences between traditional painting and AI painting in the field of painting. Through the research of this paper, the advantages and problems in the process of AI creative design are summarized. Although AI art designs are affected by technical limitations, there are still flaws in artworks and practical problems such as copyright and income, but it provides a strong technical guarantee in the expansion of subdivisions of artistic innovation and technology integration, and has extremely high research value.

With the rapid development of deep learning and artificial intelligence, generative models have achieved remarkable success in the field of image generation. By combining the stable diffusion method with Web UI technology, a novel solution is provided for the application of AI painting generation. The application prospects of this technology are very broad and can be applied to multiple fields, such as digital art, concept design, game development, and more. Furthermore, the platform based on Web UI facilitates user operations, making the technology more easily applicable to practical scenarios. This paper introduces the basic principles of Stable Diffusion Web UI technology. This technique utilizes the stability of diffusion processes to improve the output quality of generative models. By gradually introducing noise during the generation process, the model can generate smoother and more coherent images. Additionally, the analysis of different model types and applications within Stable Diffusion Web UI provides creators with a more comprehensive understanding, offering valuable insights for fields such as artistic creation and design.

Digital art creation has become an indispensable part of today's society, but traditional digital art production methods have been difficult to meet the growing creative needs of artists. Therefore, this study takes the cloud series works of artist Morten Lasskogen as an example and explores the application value of 3D Max and Unreal Engine in digital art created by analyzing the lighting effects in the works of art. This research aims to form reference materials through actual case analysis and provide artists with more efficient ideas for digital art creation.

Chinese shadow puppetry is an ancient form of drama with a long history. Known as the "mother of Chinese folk opera", it has rich cultural connotations and artistic value. At the same time, as a classic video game genre, side-scrolling games have many loyal fans around the world. However, in recent years, some previous entertainment cultures such as shadow puppetry are slowly disappearing. In contrast, video games play an increasingly important role in people's entertainment. Combining Chinese traditional culture with video games can be a great way to promote the preservation of these cultures. By making traditional culture more accessible and engaging, developers can help to ensure that these cultures continue to be enjoyed by future generations.The side-scrolling game is a classic game type, and it has many similarities with shadow puppetry. This paper will analyze the similarities and differences between Chinese shadow puppetry and horizontal version games, and try to explore how to organically integrate the two, so as to promote the inheritance and development of traditional culture, and promote cultural innovation and the development of creative industries.

Smart home and artificial intelligence technologies are developing rapidly, and various smart home systems associated with artificial intelligence (AI) improved the quality of living for people. In the present research, we examine the role of individuals’ motivational system in their responses to the application of AI in smart homes. In particular, this research focuses on individuals’ prevention motivational system and investigates whether individuals’ attitudes toward the application of AI in smart homes differ according to their level of prevention motivation. Specifically, it is hypothesized that individuals with strong (vs. weak) prevention motivation will have more favorable attitudes toward the application of AI in smart homes. Consistent with the hypothesis, the results reveal that the respondents in the strong (vs. weak) prevention motivation reported significantly more favorable attitudes toward the six types of AI-based application in smart homes (e.g., AI-based AR/VR games, AI pet care system, AI robots, etc.). Our findings suggest that individuals’ prevention motivational system may be an effective market segmentation tool in facilitating their positive responses to the application of AI in smart homes.

To improve the overall performance and realism of your game, it is important to calculate the trajectory of a projectile accurately and quickly. One way to increase realism is to use a ballistic model that takes into account factors such as air resistance, density, and wind when calculating a projectile's trajectory. However, the more these factors are taken into account, the more computationally time-consuming and expensive it becomes, creating a trade-off between overall performance and efficiency. Therefore, we present an optimal solution to find a balance between ballistic model accuracy and computation time. We perform ballistic calculations using numerical methods such as Euler, Velocity Verlet, RK2, RK4, and Akima interpolation, and measure and compare the computation time, memory usage (RSS, Resident Set Size), and accuracy of each method. We show developers how to implement more accurate and efficient ballistic models and help them choose the right computational method for their numerical applications.

Existing studies on this topic have focused on the effect of online content quality on consumer attitudes and behavior, with very few illustrating the effect of influencer image on consumer attitudes and behavior. The purpose of this study intents to reveal how influencer image affect consumer behavior. We have developed an image transfer theory-based research model to reveal how influencers transfer their image to endorsed products to influence consumer behavior. The results show that influencer image positively affects satisfaction, which in turn affects the product’s cognitive and affective images in the vlog. Moreover, it was found that a product's cognitive image and affective image influence consumer behavior intention. Furthermore, purchase experience exhibits significant differences in its path. Based on these results, the social media-related research theoretical implication will be offered, and managerial implications will be provided for foreign brand promotion strategies

Commercialization of research results plays an important role in the socio-economic development of a country based on science, technology and innovation. However, in recent years, the commercialization of research results by universities in Vietnam remains ineffective because scientists often carry out research on issues they find interesting, and don’t listen to enterprises to find out what the latter need. In the other hand, the process of commercializing research results in universities encounters difficulties, including the issue of establishing intellectual property rights the limited quality of research results, and the limited access to capital resources to develop inventions. This paper focuses on analyzing the current challenges of commercialization of research results in universities and propose solutions to promote commercialization of research results from the universities, suitable to the conditions of Vietnam.

This study confirmed the effect of manager's communication type perceived by organizational members on innovative behavior and the mediating effect of organizational culture. In other words, the manager's communication type was statistically positive for organizational members' innovation behavior (Hypothesis 1) and organizational culture (Hypothesis 2). Organizational culture improved innovative behavior of organizational members (Hypothesis 3). In addition, organizational culture played a mediating role between the manager's communication type and organizational members' innovative behavior (Hypothesis 4). In other words, the importance of improving the innovative behavior of organizational members, which is a key factor for advancing organizational sustainability management, was confirmed. It was confirmed that the manager's communication type and organizational culture act as a key factor for the advancement of innovative behavior. Therefore, executives and managers confirmed the importance of systematic managers' communication-type competency development and organizational culture revitalization plans and the necessity of implementing them in order to improve innovative behavior.

Most agriculture plantings are based on traditional farming and demand a lot of human work processes. In order to improve the efficiency as well as the productivity of their farms, modern agricultural technology was proven to be better than traditional practices. Internet of Things (IoT) is usually related in modern agriculture which provides the farmer with a real-time monitoring condition of their farm from anywhere and anytime. Therefore, the application of IoT with a sensor to measure and monitors the humidity and the temperature in the mushroom farm that can overcome this problem. This paper proposes an IoT based monitoring system for indoor Ganoderma lucidum cultivation at a minimal cost in terms of hardware resources and practicality. The results show that the data of temperature and humidity are changing depending on the weather and the preliminary experimental results demonstrated that all parameters of the system were optimized and successful to achieve the objective. In addition, the analysis results show that the quality of Ganoderma lucidum produced on the research method conforms to regulations in Vietnam.

Due to the aging workforce in the construction industry in South Korea, the accident rate has been increasing. The cognitive abilities of older workers are closely related to both safety incidents and labor productivity. Therefore, there is a need to improve cognitive abilities through personalized training based on cognitive assessment results, using cognitive training content, in order to enable safe performance in laborintensive environments. The provided cognitive training content includes concentration, memory, oreintation, attention, and executive functions. Difficulty levels were applied to each content to enhance user engagement and interest. To stimulate interest and encourage active participation of the participants, the difficulty level was automatically adjusted based on feedback from the MMSE-DS results and content measurement data. Based on the accumulated data, individual training scenarios have been set differently to intensively improve insufficient cognitive skills, and cognitive training programs will be developed to reduce safety accidents at construction sites through measured data and research. Through such simple cognitive training, it is expected that the reduction of accidents in the aging construction workforce can lead to a decrease in the social costs associated with prolonged construction periods caused by accidents.

In this study, the absolute and relative strength of six upper extremity resistance exercises were measured by classifying resistance exercise experts and non-experts. As a result, the skilled group showed higher absolute and relative muscle strength than the unskilled group in the 6 upper extremity resistance exercises. These results are judged to be the hypertrophy of fast-twith muscles, the mobilization of motor units, and the increase in the speed of nerve conduction while the skilled person consistently performs resistance exercise. Experts use intermuscular coordination efficiently to stably perform the load according to the movement and exercise intensity performed during exercise, whereas the inexperienced person uses relatively large muscle groups rather than efficiently using intermuscular coordination. It is considered that exercise motion and load were performed by mobilizing. In addition, as a result of comparing the absolute and relative muscle strength between the 6 types of upper limb resistance exercises, there was a difference between the 6 types of upper limb resistance exercises in the two groups. It can be judged that greater muscle strength and endurance were created through liver coordination.

Data collection is an essential element in the construction and operation of a smart factory. The quality of data collection is greatly influenced by network conditions, and existing wireless network systems for IoT inevitably lose data due to wireless signal strength. This data loss has contributed to increased system instability due to misinformation based on incorrect data. In this study, I designed a distributed MQTT IoT smart sensor and gateway structure that supports wireless multicasting for smooth sensor data collection. Through this, it was possible to derive significant results in the service latency and data loss rate of packets even in a wireless environment, unlike the MQTT QoS-based system. Therefore, through this study, it will be possible to implement a data collection management system optimized for the domestic smart factory manufacturing environment that can prevent data loss and delay due to abnormal data generation and minimize the input of management personnel.

In this study, we measured the power and temperature of the floating horizontal solar cell in a coastal lagoon and compared with those of ground solar cell and water platform solar cell. Because the bottom surface of the floating horizontal solar cell was contacting the water, cooling effect was expected stronger than other cells. As a result of the measurement, the power of floating horizontal cell was 11.7% higher than that of the ground cell and 15% higher than that of the water platform cell. During the measurement, it was observed that water waves were continuously flowed on the top surface of floating horizontal cell by the wind, and it could be assumed that the cooling effect occurred not only on the bottom surface of the cell but also on the top surface. In order to analyze the cooling effect and power increasing of the horizontal cell in the wave situation, we measured power and temperature of the cell while generating artificial waves in a laboratory equipped with Zenon lamp as a solar simulator. At the height of the water surface, the power of the cell with waves was 3.7% higher than without waves and temperature was 4.6℃ lower. At 1 cm and 2 cm below the water surface, power of the cell with waves was decreased by 14% and 11% than without waves while temperature was same . At 3 cm below the water surface, there was no effect of waves.

Currently, our society is showing many changes due to the influence of information and communication technology (ICT). At the center of these information and communication technologies are software, intelligence, and sensing technologies. The software-related industry is steadily developing due to various software development policies implemented by the government and related organizations. Software development is desirable, but on the other hand, some negative aspects are also appearing. In this study, we proposed an objective way to infer the progress of software development for reasonable resolution of cases when a dispute related to the progress of development occurred during the software development process. The proposed solution was based on the waterfall model. The outputs generated in each process of the waterfall model are contents excluded from subjectivity. Therefore, it can be used as an objective method for calculating software development progress.

This paper selects 100 IT companies listed on the Shenzhen Stock Exchange from 2016 to 2020, and the public announcement in Hwajung collects ESG integrated ratings and grades for each sector and empirically verifies the relationship between ESG ratings and stock returns. Huazheng ESG level data and QIANZHAN database Using corporate financial data, a total of 500 samples were selected through correlation analysis and linear regression analysis with SPSS23 to analyze the effect of ESG on Return. As a result of the analysis, first, the impact on stock returns was found to be a significant positive (+) value for ESG integrated ratings and ratings by E (environment), S (social), and G (governance) sectors, confirming that ESG ratings have a positive mold of corporate stock returns. Currently, the world's major economies have proposed sustainable development strategies and "carbon neutral" goals. Development strategies are very consistent with ESG concepts, and companies that agree and execute ESG concepts may have higher ratings than other companies in the same industry, resulting in certain evaluation premiums. In addition, capital market performance in recent years shows that companies with ESG concepts or "carbon neutrality" concepts are generally considered to have higher growth potential and stronger anti-risk capabilities in the market. For listed companies, they should focus on ESG investment, improve ESG performance, and actively disclose related information to investors. Improving ESG performance should deliver positive information to society, enhance corporate image, increase market confidence in the future development of listed companies, and positively improve corporate value to actively increase financial, financial, trading, and other aspects of negotiation.