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Modern society is deepening people's interest in healthy life. Therefore, the development of research and service related to health is proceeding. In the IT field, healthcare systems using information in the medical field are being developed. Most systems utilize PC and smart TV or use smart phones to provide users with information and movement-related information in the healthcare field. However, algorithms and systems that accurately analyze user 's body information are in development stage. To solve this problem, this paper proposes a system that provides information to the user by analyzing the BMI (Body Mass Index) index and BMR (Basal Metabolic Rate) of the user. The information provided to the user suggests appropriate exercise intensity, appropriate level of exercise, and fitness equipment that are appropriate for the user's body, unlike conventional fitness systems that detect motion and show the calories consumed. In addition, the user can perform efficient exercise based on the recommended information. [ABSTRACT FROM AUTHOR]

In the metaverse environment, avatars perform various roles as alternative representations of individuals. Avatars can be expressed from the perspectives of enhancing immersion and providing flexible security measures within the metaverse space. This study aims to adjust the representation of avatar details based on the avatar's role in the virtual space. By tuning the hyper-parameters(such as Random Seed, Style Embedding Vector, Batch Size, Number and size of layers in the generator network, Number and size of layers in the discriminator network) of the styleGAN algorithm, a system is designed where the level of immersion, privacy preservation, and data exchange on the network can be controlled to automatically adapt the level of detail in the avatar. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to identify high-risk groups for Long-Term Care Insurance (LTCI) in older people not approved for LTCI and to examine the characteristics of each high-risk group. Methods: This study was a retrospective study using the National Health Insurance Service database and included 7,724,101 older A decision- tree model was used to predict the high-risk groups for LTCI. The dependent variable was defined as LTCI eligibility. As independent variables, 78 variables consisting of personal factors, environmental factors, health status, and physical and cognitive abilities were used. Results: The prediction model to identify high-risk groups for LTCI was developed as the decision-tree model consisting of 19 end nodes with 10 risk factors. Eleven groups were identified as high-risk groups. The results showed the model could predict about 72% of the older people at high risk for LTCI needs using the NHIS database without the assessment of LTCI eligibility. Conclusion: The findings might be useful for the development of evidence-based preventative services and can contribute to preemptively discovering those who need preventive services in older adults. [ABSTRACT FROM AUTHOR]

This paper describes a design of crypto-processor that supports multiple block cipher algorithms of PRESENT, ARIA, and AES. The crypto-processor integrates three cores that are PRmo (PRESENT with mode of operation), AR_AS (ARIA_AES), and AES-16b. The PRmo core implementing 64-bit block cipher PRESENT supports key length 80-bit and 128-bit, and four modes of operation including ECB, CBC, OFB, and CTR. The AR_AS core supporting key length 128-bit and 256-bit integrates two 128-bit block ciphers ARIA and AES into a single data-path by utilizing resource sharing technique. The AES-16b core supporting key length 128-bit implements AES with a reduced data-path of 16-bit for minimizing hardware. Each crypto-core contains its own on-the-fly key scheduler, and consecutive blocks of plaintext/ciphertext can be processed without reloading key. The crypto-processor was verified by FPGA implementation. The crypto-processor implemented with a 0.18㎛ CMOS cell library occupies 54,500 gate equivalents (GEs), and it can operate with 55 MHz clock frequency. [ABSTRACT FROM AUTHOR]

The news articles collected with keyword “non faceto-face” were analyzed through topic modeling applied with LDA algorithm. In this study, collected articles were divided into two periods, period 1(the beginning of COVID-19 spread) and period 2(the end of COVID-19 spread), according to issued date of the articles. The articles of period 1 showed support for non-faceto-face treatment, smart library, the beginning of the online financial era, non-face-to-face entrance exam and employment, stock investment for main topic words. And the articles of period 2 showed conversion to non face-to-face classes, increasing unmanned stores, online finance, education industry, home treatment for main topic words. Also, further issues were discussed through visualization of topic words. These results provide evidence that education and unmanned business in non-face-to-face industries are growing. [ABSTRACT FROM AUTHOR]

In order to overcome the limitations of the instructor-centered lecture-style teaching method, recently, flipped learning, a learner-centered teaching method, has been widely introduced. However, despite the many advantages of flipped learning, there is a problem that students cannot solve questions that arise during prior learning in real time. Therefore, in order to solve this problem, we developed DBbot, an assistant chatbot for database design course managed in the flipped learning method. The DBBot is composed of a chatbot app for learners and a chatbot management app for instructors. Also, it’s implemented so that questions that instructors can anticipate in advance, such as questions related to class operation and every semester repeated questions related to learning content, can be answered using Google's DialogFlow. It’s implemented so that questions that the instructor cannot predict in advance, such as questions related to team projects, can be answered using the question/answer DB and the BM25 algorithm, which is a similarity comparison algorithm. [ABSTRACT FROM AUTHOR]

We study the problem of sampling a subset of nodes of graphs for bandlimited graph signals such that the signal values on the sampled nodes provide the most information in order to reconstruct the original graph signal. Instead of directly minimizing the reconstruction error, we focus on minimizing the upper bound of the reconstruction error to reduce the complexity of the selection process. We further simplify the upper bound by applying useful approximations to propose a low-weight greedy selection process that is iteratively conducted to find a suboptimal sampling set. Through the extensive experiments for various graphs, we inspect the performance of the proposed algorithm by comparing with different sampling set selection methods and show that the proposed technique runs fast while preserving a competitive reconstruction performance, yielding a practical solution to real-time applications. [ABSTRACT FROM AUTHOR]