Earticle

Smart IoT over high speed network and high performance smart devices explodes the ubiquitous services and applications. Nearest Neighbor(NN) query is one of the important type of queries that have to be supported for ubiquitous information services. In order to process efficiently NN queries in the wireless broadcast environment, it is important that the clients determine quickly the search space and filter out NN from the candidates containing the search space. In this paper, we propose a hybrid index of Voronoi and grid partition to provide quick search space decision and rapid filtering out NN from the candidates. Grid partition plays the role of helping quick search space decision and Voronoi partition providing the rapid filtering. We show the effectiveness of the proposed index by comparing the existing indexing schemes in the access time and tuning time. The evaluation shows the proposed index scheme makes the two performance parameters improved than the existing schemes.

Recently, IoT technology has been widely used in many industries. Also research on integrating IoT technology with IoT sensors is actively underway. One of the important challenges in IoT is to support low-latency communication. With the development of communication networks and protocols, a variety of protocols are being used, and their performance is improving. In this paper, we compare the performance and analyze the characteristics of some of the major communication protocols in IoT application, namely MQTT, HTTP, and HTTPS. IoT sensors acquired data by connecting an Arduino equipped with ESP8266 and a temperature and humidity sensor (DHT11). The server measured the performance by building servers for each protocol using AWS EC2. We analyzed the packets transmitted between the Arduino and the server during the data transmission. We measured the amount of data and transfer time. The measurement results showed that MQTT had the lowest data transmission time and data amount among the three protocols.

We compared empirically the forecast accuracies of the LSTM model, and the ARIMA model. ARIMA model used auto.arima function. Data used in the model is 100 days. We compared with the forecast results for 50 days. We collected the stock closing prices of the top 4 companies by market capitalization in Korea such as “Samsung Electronics”, and “LG Energy”, “SK Hynix”, “Samsung Bio”. The collection period is from June 17, 2022, to January 20, 2023. The paired t-test is used to compare the accuracy of forecasts by the two methods because conditions are same. The null hypothesis that the accuracy of the two methods for the four stock closing prices were the same were rejected at the significance level of 5%. Graphs and boxplots confirmed the results of the hypothesis tests. The accuracies of ARIMA are higher than those of LSTM for four cases. For closing stock price of Samsung Electronics, the mean difference of error between ARIMA and LSTM is -370.11, which is 0.618% of the average of the closing stock price. For closing stock price of LG Energy, the mean difference is -4143.298 which is 0.809% of the average of the closing stock price. For closing stock price of SK Hynix, the mean difference is -830.7269 which is 1.00% of the average of the closing stock price. For closing stock price of Samsung Bio, the mean difference is -4143.298 which is 0.809% of the average of the closing stock price. The auto.arima function was used to find the ARIMA model, but other methods are worth considering in future studies. And more efforts are needed to find parameters that provide an optimal model in LSTM.

This asymed drone controller is indispensable for two components: Guidance and Controller. In which the Ministry of Guidance will receive waypoints from which to form an orbit then combine the data with the current location of the vessel, thereby calculating and also supplying the controller to drive the vehicle to follow the outlined trajectory. This article will use the Line Of Sight (LOS) algorithm to design the Guidance and Controller sets. The result as well as the effectiveness of the controller will be shown through matlab/SIMULINK simulation.

The purpose of this study is to review the proposed solutions to the Blockchain trilemma put forward by various research scholars and to draw conclusions by comparing the findings of each study. We found that the models so far developed either compromise scalability, decentralization, or security. The first model compromises decentralization. By partially centralizing the network, transaction processing speed can be improved, but security strength is weakened. Examples of this include Algorand and EOS. Because Algorand randomly selects the node that decides the consensus, the security of Algorand is better than EOS, wherein a designated selector decides. The second model recognizes that scalability causes a delay in speed when transactions are included in a block, reducing the system's efficiency. Compromising scalability makes it possible to increase decentralization. Representative examples include Bitcoin and Ethereum. Bitcoin is more vital than Ethereum in terms of security, but in terms of scalability, Ethereum is superior to Bitcoin. In the third model, information is stored and managed through various procedures at the expense of security. The application case is to weaken security by applying a layer 1 or 2 solution that stores and reroutes information. The expected effect of this study is to provide a new perspective on the trilemma debate and to stimulate interest in continued research into the problem.

With the fast development of artificial intelligence day by day, users are demanding explanations about the results of algorithms and want to know what parameters influence the results. In this paper, we propose a model for bankruptcy prediction with interpretability using the SHAP framework. SHAP (SHAPley Additive exPlanations) is framework that gives a visualized result that can be used for explanation and interpretation of machine learning models. As a result, we can describe which features are important for the result of our deep learning model. SHAP framework Force plot result gives us top features which are mainly reflecting overall model score. Even though Fully Connected Neural Networks are a “black box” model, Shapley values help us to alleviate the “black box” problem. FCNNs perform well with complex dataset with more than 60 financial ratios. Combined with SHAP framework, we create an effective model with understandable interpretation. Bankruptcy is a rare event, then we avoid imbalanced dataset problem with the help of SMOTE. SMOTE is one of the oversampling technique that resulting synthetic samples are generated for the minority class. It uses K-nearest neighbors algorithm for line connecting method in order to producing examples. We expect our model results assist financial analysts who are interested in forecasting bankruptcy prediction of companies in detail.

We create a malware detector classification method with using the Sequential Probability Ratio Test (SPRT) in IoT. More specifically, we adapt the SPRT to classify malware detectors into two categories of basic and advanced in line with malware detection capability. We perform evaluation of our scheme through simulation. Our simulation results show that the number of advanced detectors is changed in line with threshold for fraction of advanced malware information, which is used to judge advanced detectors in the SPRT.

We devise a collaboration construction method based on the SPRT (Sequential Probability Ratio Test) for malware detection in IoT. In our method, high-end IoT nodes having capable of detecting malware and generating malware signatures harness the SPRT to give a reward of malware signatures to low-end IoT nodes providing useful data for malware detection in IoT. We evaluate our proposed method through simulation. Our simulation results indicate that the number of malware signatures provided for collaboration is varied in accordance with the threshold for fraction of useful data.

With the recent proliferation of memory-intensive workloads such as deep learning, analyzing memory access characteristics for efficient memory management is becoming increasingly important. Since read and write operations in memory access have different characteristics, an efficient memory management policy should take into account the characteristics of these two operations separately. Although some previous studies have considered the different characteristics of reads and writes, they require a modified hardware architecture supporting read bits and write bits. Unlike previous approaches, we propose a software-based management policy under the existing memory architecture for considering read/write characteristics. The proposed policy logically partitions memory space into the read/write area and the write area by making use of reference bits and dirty bits provided in modern paging systems. Simulation experiments with memory access traces show that our approach performs better than the CLOCK algorithm by 23% on average, and the effect is similar to the previous policy with hardware support.

Journaling file systems are widely used to keep file systems in a consistent state against crash situations. As traditional journaling file systems are designed for block I/O devices like hard disks, they are not efficient for emerging byte-addressable NVM (non-volatile memory) media. In this article, we present a new in-memory journaling file system for NVM that is different from traditional journaling file systems in two respects. First, our file system journals only modified portions of metadata instead of whole blocks based on the byteaddressable I/O feature of NVM. Second, our file system bypasses the heavy software I/O stack while journaling by making use of an in-memory file system interface. Measurement studies using the IOzone benchmark show that the proposed file system performs 64.7% better than Ext4 on average.

In 2020 when the COVID-19 pandemic began in full-scale, the WHO Director-General warned of the dangers of an infodemic. The infodemic is a phenomenon in which false information spreads rapidly like an epidemic and causes chaos, and it was noted that the COVID-19 pandemic is not just limited to health problems, but also linked to a variety of issues such as human rights, economic inequality, various discrimination, hate speech, fake news, global governance etc. In the field of education, it is necessary to think about how to connect this global situation with school classes. Accordingly, this study suggested the direction for global citizenship education by analyzing how the infodemic spreads on Korean social media with the case of the recent global COVID-19 pandemic. According to the research results, the rate of negative emotions was higher than positive ones in the emotions that generate infodemic, while anxiety and anger were focused among negative emotions. In addition, the infodemic tended to spread widely with the feelings of anger rather than anxiety, and the feelings of anger led to advocating aggressive policies against certain country and regions. Therefore, global citizenship education is required to focus on a sense of duty and responsibility as a citizen, not on the level of national identity based on an exclusive sense of belonging. Furthermore, global citizenship education needs to lead to presenting a blueprint for education in a way that can enhance the awareness of the global community for joint response to global challenges and realize common prosperity based on sustainability and justice.

Wireless communication technology is becoming increasingly prevalent in smart factories, but the rise in the number of wireless devices can lead to interference in the ISM band and obstacles like metal blocks within the factory can weaken communication signals, creating radio shadow areas that impede information exchange. Consequently, accurately determining the radio communication coverage range is crucial. To address this issue, a Radio Environment Map (REM) can be used to provide information about the radio environment in a specific area. In this paper, a technique for estimating an indoor REM using a mobile robot and machine learning methods is introduced. The mobile robot first collects and processes data, including the Received Signal Strength Indicator (RSSI) and location estimation. This data is then used to implement the REM through machine learning regression algorithms such as Extra Tree Regressor, Random Forest Regressor, and Decision Tree Regressor. Furthermore, the numerical and visual performance of REM for each model can be assessed in terms of R2 and Root Mean Square Error (RMSE).

Machine tools are widely used in mechanical manufacturing corporations, as well as in engineering courses at universities in Vietnam. The PINACHO S-90/200 lathe is particularly popular. This paper aims to research and select an optimal cutting depth to minimize power costs and ensure surface roughness quality when machining upper plain cylindrical turning products with the PINACHO S-90/200 lathe on C45 steel material.

Instatoon is a type of webtoons distributed through Instagram, but in the process of advancement of the webtoon industry, it shows the possibility of diversifying into a new genre that meets creativity, and diversity beyond uniform commercial webtoons. Since it does not go through a large platform, the content is freely composed. In addition, it is differentiated from other types of webtoons because in many cases artists create instatoon by adding entertaining elements into their real-life stories rather than drawing fictional ones. Furthermore, instatoons are free to enjoy, satisfaction level is high, but in most cases, even if there is a complaint, readers just enjoy webtoons without expressing it. We aimed to find out how the service quality characteristics of these instatoons affect behavioral intentions through the coexistence of satisfaction and dissatisfaction of readers, and meaninful results were found through empirical analysis. We discusses factors affecting the spread of Instatoons, including readers' satisfaction and dissatisfaction, the importance of relatability, good directing, and informativeness. The study emphasizes the need to approach maintaining existing readers and attracting new ones separately, and to manage readers effectively in the future.

Perspective taking, one’s knowledge of their own mental and emotional states and inferences about others’ mental and emotional states, is an important higher order cognitive skill required in successful writing. However, there has not been much research on the identification and examiantion of the psychometrics of perspective taking. To fill in this gap, I reviewed the psychological and cognitive frameworks of perspective taking including theory of mind, audience awareness, development of epistemological understanding, and argumentation schema. I also reviewed various methods of examining the psychometric properties of perspective taking in written composition, including both manual and computational approaches. The review of literature yielded suggestions on the development of manual coding scheme for perspective taking as well as the selection of indexes to draw from natural language processing tools. Challenges and affordances of combining the manual and computational approach are discussed along with future research directions to advance the field of psycholinguistics.

With the increasing popularity of computer-generated virtual influencers, the trend is rising especially on social media. Famous virtual influencer characters Lil Miquela and Imma were all created by CGI graphics workflows. The process is typically a linear affair. Iteration is challenging and costly. Development efforts are frequently siloed off from one another. Moreover, it does not provide a real-time interactive experience. In the previous study, a real-time graphic workflow was proposed for the Digital Actor Hologram project while the output graphic quality is less than the results obtained from the CGI graphic workflow. Therefore, a real-time engine graphic workflow for Virtual Influencers is proposed in this paper to facilitate the creation of real-time interactive functions and realistic graphic quality. The real-time graphic workflow is obtained from four processes: Facial Modeling, Facial Texture, Material Shader, and Look-Development. The analysis of performance with real-time graphical workflow for Digital Actor Hologram demonstrates the usefulness of this research result. Our research will be efficient in producing virtual influencers.

With the OTT media market growing rapidly, the significance of trustworthy data verification and certification cannot be emphasized enough. This study delves into the crucial need for such measures in South Korea, exploring the steps involved, the technological and policy-related considerations, and the challenges that may arise once these measures are put into place. Drawing on in-depth interviews and the analytical hierarchy process (AHP), this study surveyed various stakeholder groups, both directly and indirectly related to OTT data authentication and certification. By assessing the severity of OTT data-related issues and identifying the requirements for reliability-improvement policies, participants shared their valuable insights and opinions on this pressing matter. The survey results clearly indicate a divided opinion among stakeholders and industry experts on the reliability of OTT data, with some expressing trust while others remain skeptical. However, there was a consensus that advertising-based AVOD is more reliable than SVOD. By analyzing the priorities of authentication and verification, this study paves the way for the establishment and operation of a Korean MRC (KMRC), centered on the OTT media industry. The KMRC will serve as a vital platform for ensuring the authenticity and accuracy of OTT data in South Korea, providing businesses and industry players with a reliable source of information for informed decision-making. This study highlights the pressing need for reliable data authentication and certification in the rapidly growing OTT media market, and provides a persuasive case for the establishment of a KMRC in South Korea to meet this critical need.

OOXML-based MS-Office digital files are extensively utilized by businesses and organizations worldwide. However, OOXML-based MS-Office digital files are vulnerable to forgery and corruption attack by including hidden suspicious information, which can lead to activating malware or shell code being hidden in the file. Such malicious code can cause a computer system to malfunction or become infected with ransomware. To prevent such attacks, it is necessary to analyze and detect the corruption of OOXML-based MS-Office files. In this paper, we examine the weaknesses of the existing OOXML-based MS-Office file structure and analyzes how concealment and forgery are performed on MS-Office digital files. As a result, we propose a system to detect hidden data effectively and proactively respond to ransomware attacks exploiting MS-Office security vulnerabilities. Proposed system is designed to provide reliable and efficient detection of hidden data in OOXML-based MS-Office files, which can help organizations protect against potential security threats.

The current research aims to examine the moderating effect of consumers’ mindset on their product purchase intention in the multi-promotion offers containing both a bonus pack and a price discount (i.e., BP + PD offers). That is, this research investigates whether consumers’ product purchase intention in the BP + PD offers varies depending on their mindset (growth mindset vs. fixed mindset). Specifically, it is predicted that consumers with a fixed mindset will have higher product purchase intention in the offers containing the high PD but low extra amount of BP (LBP HPD) than in the offers with a high extra amount of BP but low PD (HBP LPD), whereas consumers with a growth mindset will have higher product purchase intention in the HBP LPD offers than in the LBP HPD offers. An experiment was conducted to test the prediction. Consistent with the prediction, it was found that participants’ mindset moderates their product purchase intention in multi-promotion offers. The findings imply that marketers can evoke more positive consumer purchase intention toward BP and PD offers, considering consumer mindset.

In this study, we identify the recognition of ‘The First Slam Dunk’, which is gaining popularity as a sports-based cartoon through big data analysis of social media channels, and provide basic data for the development and development of various contents in the sports industry. Social media channels collected detailed social big data from news provided on Naver and Google sites. Data were collected from January 1, 2023 to February 15, 2023, referring to the release date of ‘The First Slam Dunk’ in Korea. The collected data were 2,106 Naver news data, and 1,019 Google news data were collected. TF and TF-IDF were analyzed through text mining for these data. Through this, semantic network analysis was conducted for 60 keywords. Big data analysis programs such as Textom and UCINET were used for social big data analysis, and NetDraw was used for visualization. As a result of the study, the keyword with the high frequency in relation to the subject in consideration of TF and TF-IDF appeared 4,079 times as ‘The First Slam Dunk’ was the keyword with the high frequency among the frequent keywords. Next are ‘Slam Dunk’, ‘Movie’, ‘Premiere’, ‘Animation’, ‘Audience’, and ‘Box-Office’. Based on these results, 60 high-frequency appearing keywords were extracted. After that, semantic metrics and centrality analysis were conducted. Finally, a total of 6 clusters(competing movie, cartoon, passion, premiere, attention, Box-Office) were formed through CONCOR analysis. Based on this analysis of the semantic network of ‘The First Slam Dunk’, basic data on the development plan of sports content were provided.

Recently, local governments that provide mobile-based public delivery app services have been increasing a lot. The purpose of this study is to analyze whether the restaurant self-employed's public delivery app selection attributes and public institutions' perception of support, which are under economic pressure due to the high delivery fee burden of private delivery apps, affect the intention to use public delivery app services. In this study, the degree of perception of each factor was measured using the Likert 5-point scale, and it was verified through statistical analysis using SPSS. The effect of the selection attribute of public delivery apps and the perception of self-employed people in the restaurant industry on the intention to use the service was empirically analyzed, and the hypothesis was verified using regression analysis. As a result of this study, it was confirmed that convenience, economy, and public interest had a significant effect among the factors of public app selection attributes, and educational support had a significant effect on public institution support.

The purpose of this study was to compare and analyze the body composition, muscle circumference, and muscle activity of men in their 30s and 50s when the resistance exercise program was applied for 12 weeks, and to provide basic data for the preparation of the resistance exercise program for middle-aged men before entering old age. Nine men in their 30s and eleven 11 men in their 50s participated in the moderate intensity resistance exercise program for 12 weeks. Two weeks before the experiment, body composition, muscle circumference, and EMG were pre-tested, and then body composition and muscle circumference were additionally measured at the 8th week. Body composition, muscle circumference, and EMG were measured within 2 weeks after the 12 week exercise program. The measured data were compared and analyzed by Mixed design two-way ANOVA, and the following results were obtained. The body composition showed a significant difference only in the skeletal muscle mass, and it increased in the 30s group at 8 weeks. Muscle circumference did not show significant difference according to group and time. Muscle activity showed a significant difference according to group and time, and pectoralis major and triceps brachii showed a significant decrease to 30s group. The pectoralis major was higher in the 30s group at all times. In summary, when applying a 12-week resistance exercise program for young men and middle-aged men, the effect of improving muscle strength may appear similar, but they are not consistent, and it is thought that gradual changes in training variables are needed to improve muscle mass.

The rapid aging of the veterans has reached a level that cannot handle the demand for veterans care through the existing veterans care infrastructure. Therefore, it is urgent to improve the quality of the overall service of veterans due to the deterioration of the quality of nursing services for veterans with various underlying diseases compared to general patients and the long-term waiting for admission to the veterans care center. In this situation, about 640,000 people are admitted to veterans care institutions, but only about 5% of them can enter the veterans care center smoothly. As of June 2020, the number of people waiting to enter the veterans care center exceeds 1,000, including 520 at Suwon Veterans Nursing Home, 1 at Gwangju Veterans Nursing Home, 47 at Gimhae Veterans Nursing Home, 39 at Daegu Veterans Nursing Home, 86 at Namyangju Veterans Nursing Home.. Therefore, in order to predict those who want to enter the Veterans Nursing Home and wait for admission, and to find an important basis for resolving the long-term atmosphere, the ratio of future care providers is predicted in 2022-2050 and 2022-2024 to establish a cooperative system. As a result, 6,988 people in 2022, 6,797 people in 2023, and 6,606 people in 2024 can be admitted when 'preferred linkage', and 12,057 people in 2022 when 'expanded linkage'. It was found that 11,837 people in 2023 and 11,618 people in 2024 could be admitted. This was derived by estimating the percentage of people who wish to enter the Veterans Nursing Home when linking private nursing homes, and eventually "additional acceptance" of 22.5% in 2022, 20.9% in 2023, 19.4% in 2024, and 38.8% in 2023, 36.3% in 2023, and 34.1% in 2024 are most efficiently available.

Trend Impact Analysis is a prominent hybrid method has been used in future studies with a modified surprise-free forecast. It considers experts' perceptions about how future events may change the surprise-free forecast. It is an advanced forecasting tool used in futures studies for identifying, understanding and analyzing the consequences of unprecedented events on future trends. In this paper, we propose an advanced mechanism to generate more justifiable estimates to the probability of occurrence of an unprecedented event as a function of time with different degrees of severity using adaptive neuro-fuzzy inference system (ANFIS). The key idea of the paper is to enhance the generic process of reasoning with fuzzy logic and neural network by adding the additional step of attributes simulation, as unprecedented events do not occur all of a sudden but rather their occurrence is affected by change in the values of a set of attributes. An ANFIS approach is used to identify the occurrence and severity of an event, depending on the values of its trigger attributes.

Recently, as mega-urbanization progresses, urban logistics centered on large cities is growing rapidly, and logistics transportation is increasing due to the lack of logistics infrastructure and the operation of a delivery system using cargo trucks in the city center. being cited as a cause. In order to solve this problem, domestic researchers are conducting research on the development of “ Urban Underground railway Logistics System” that minimizes the initial infrastructure construction cost by utilizing the existing urban railway facilities in the city. Therefore, this paper analyzed the usage environment of the actual urban railway station to which the system will be applied in order to derive candidates for the test bed selection of “Urban Underground railway Logistics System”. The evaluation criteria for test bed candidate role selection were established, and the candidate group derived from the Brown & Gibson model was used to evaluate the candidate group and derive the candidate role. A review of the results was conducted to contribute to the establishment of a test bed for the “ Urban Underground railway Logistics System” under development.

The development of logistics technology due to the 4th Industrial Revolution and logistics 4.0 has changed the logistics environment from offline to online, and the growth of the online market has accelerated due to the recent increase in non-face-to-face delivery due to COVID-19. Such growth in the online market has expanded the logistics market for delivery, and contributes to creating an advanced logistics ecosystem as last mile services and logistics technologies begin to grow together. As traffic and environmental problems arise as a result, interest in transportation means of cargo increases, and changes to cargo electric vehicles and delivery cycles are being considered. However, since mass transportation of transportation focused on terminal delivery such as cargo electric vehicles and delivery cycles is limited in handling large quantities in urban areas, methods for efficiently transporting cargo while considering the environment are being devised. In order to solve such environmental problems and increase the efficiency of logistics delivery in urban areas, technologies using underground spaces based on existing urban railroads are being developed. In this study, a study was conducted on the establishment of evaluation criteria for selecting a test bed, which is a space for applying the system.