• Journal of the Korean Academy of Child and Adolescent Psychiatry
• /
• v.32 no.4
• /
• pp.154-160
• /
• 2021

Objectives: This study identified the association between excessive exposure to screen media and behavioral and emotional problems in elementary school students. Methods: A total of 331 parents of children aged 7-10 years were recruited from "The Kids Cohort for Understanding of Internet Addiction Risk Factors in Early Childhood (K-CURE)" study. Children's demographics, household media ownership, screen time, and behavioral/emotional problems were assessed using a parental questionnaire. Children's behavior/emotional problems were measured using the Korean version the of Child Behavior Checklist (K-CBCL) score. Results: The total K-CBCL score in the screen overuse group was 51.18±9.55, significantly higher than 47.28±10.09 in the control group (t=2.14, p=0.05). For each subscale, the externalization score (51.65±10.14, 48.33±8.97, respectively; t=2.02, p<0.05), social problem score (55.41±6.11, 53.24±5.19, respectively; t=2.27, p<0.05), and rule breaking behavior score (55.71±6.11, 53.24±5.19, respectively; t=2.27, p<0.05) were significantly higher in the screen overuse group than in the control group. In addition, the screen overuse group also had a significantly higher usage rate than the control group, even if limited to smartphones, not only on weekdays (3.56±2.08, 1.87±2.02, respectively; t=-4.597, p<0.001) but also weekends (1.62±0.74, 1.19±0.83, respectively; t=-3.14, p=0.003). Conclusion: The study suggested that screen media overuse patterns in children in Korea are particularly relevant to the excessive use of smartphones and are related to higher risks of emotional and behavioral problems.

• Journal of KIBIM
• /
• v.12 no.4
• /
• pp.61-69
• /
• 2022

Earthmoving operations account for approximately 25% of construction cost, generally executed prior to the construction of buildings and structures with heavy equipment. For the successful completion of earthwork projects, it is crucial to constantly monitor earthwork equipment (e.g., trucks), estimate productivity, and optimize the construction process and equipment on a construction site. Traditional methods however require time-consuming and painstaking tasks for the manual observations of the ongoing field operations. This study proposed the use of a GPS sensor embedded in a smartphone for the tracking and visualization of equipment locations, which are in turn used for the estimation and simulation of cycle times and production rates of ongoing earthwork. This approach is implemented into a digital platform enabling real-time data collection and simulation, particularly in a 2D (e.g., maps) or 3D (e.g., point clouds) virtual environment where the spatial and temporal flows of trucks are visualized. In the case study, the digital platform is applied for an earthmoving operation at the site development work of commercial factories. The results demonstrate that the production rates of various equipment usage scenarios (e.g., the different numbers of trucks) can be estimated through simulation, and then, the optimal number of tucks for the equipment fleet can be determined, thus supporting the practical potential of real-time sensing and simulation for onsite equipment management.

• Journal of Information Processing Systems
• /
• v.19 no.3
• /
• pp.289-301
• /
• 2023

Due to the development and dissemination of modern technology, anyone can easily communicate using services such as social network service (SNS) through a personal computer (PC) or smartphone. The development of these technologies has caused many beneficial effects. At the same time, bad effects also occurred, one of which was the spam problem. Spam refers to unwanted or rejected information received by unspecified users. The continuous exposure of such information to service users creates inconvenience in the user's use of the service, and if filtering is not performed correctly, the quality of service deteriorates. Recently, spammers are creating more malicious spam by distorting the image of spam text so that optical character recognition (OCR)-based spam filters cannot easily detect it. Fortunately, the level of transformation of image spam circulated on social media is not serious yet. However, in the mail system, spammers (the person who sends spam) showed various modifications to the spam image for neutralizing OCR, and therefore, the same situation can happen with spam images on social media. Spammers have been shown to interfere with OCR reading through geometric transformations such as image distortion, noise addition, and blurring. Various techniques have been studied to filter image spam, but at the same time, methods of interfering with image spam identification using obfuscated images are also continuously developing. In this paper, we propose a deep learning-based spam image detection model to improve the existing OCR-based spam image detection performance and compensate for vulnerabilities. The proposed model extracts text features and image features from the image using four sub-models. First, the OCR-based text model extracts the text-related features, whether the image contains spam words, and the word embedding vector from the input image. Then, the convolution neural network-based image model extracts image obfuscation and image feature vectors from the input image. The extracted feature is determined whether it is a spam image by the final spam image classifier. As a result of evaluating the F1-score of the proposed model, the performance was about 14 points higher than the OCR-based spam image detection performance.

• The Journal of Bigdata
• /
• v.7 no.1
• /
• pp.193-212
• /
• 2022

The Bass Diffusion Model is one of the most successful models in marketing research, and management science in general. Since its publication in 1969, it has guided marketing research on diffusion. This paper illustrates the usage of the Bass diffusion model, using mobile cellular subscription diffusion as a context. We fit the bass diffusion model to three large developed markets, South Korea, Japan, and China, and the emerging markets of Vietnam, Thailand, Kazakhstan, and Mongolia. We estimate the parameters of the bass diffusion model using the nonlinear least square method. The diffusion of mobile cellular subscriptions does follow an S-curve in every case. After acquiring m, p, and q parameters we use k-Means Cluster Analysis for grouping countries into three groups. By clustering countries, we suggest that diffusion rates and patterns are similar, where countries with emerging markets can follow in the footsteps of countries with developed markets. The purpose was to predict the timing and the magnitude of the market maturity and to determine whether the data follow the typical diffusion curve of innovations from the Bass model.

• Journal of the Korean Society of School Health
• /
• v.36 no.3
• /
• pp.113-125
• /
• 2023

Purpose: The purpose of this study is to develop a model for predicting mental health risk among adolescents based on health behavior information by employing a hybrid machine learning method. Methods: The study analyzed data of 51,850 domestic middle and high school students from 2022 Youth Health Behavior Survey conducted by the Korea Disease Control and Prevention Agency. Firstly, mental health risk levels (stress perception, suicidal thoughts, suicide attempts, suicide plans, experiences of sadness and despair, loneliness, and generalized anxiety disorder) were classified using the k-mean unsupervised learning technique. Secondly, demographic factors (family economic status, gender, age), academic performance, physical health (body mass index, moderate-intensity exercise, subjective health perception, oral health perception), daily life habits (sleep time, wake-up time, smartphone use time, difficulty recovering from fatigue), eating habits (consumption of high-caffeine drinks, sweet drinks, late-night snacks), violence victimization, and deviance (drinking, smoking experience) data were input to develop a random forest model predicting mental health risk, using logistic and XGBoosting. The model and its prediction performance were compared. Results: First, the subjects were classified into two mental health groups using k-mean unsupervised learning, with the high mental health risk group constituting 26.45% of the total sample (13,712 adolescents). This mental health risk group included most of the adolescents who had made suicide plans (95.1%) or attempted suicide (96.7%). Second, the predictive performance of the random forest model for classifying mental health risk groups significantly outperformed that of the reference model (AUC=.94). Predictors of high importance were 'difficulty recovering from daytime fatigue' and 'subjective health perception'. Conclusion: Based on an understanding of adolescent health behavior information, it is possible to predict the mental health risk levels of adolescents and make interventions in advance.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.16 no.6
• /
• pp.540-546
• /
• 2023

The scope of IoT use has expanded due to the development of related technologies, and various sensors have been developed and distributed to meet the demand for implementing various services. Measuring alcohol concentration using a sensor can be used to prevent drunk driving, and to make this possible, accurate alcohol concentration must be measured and safe transmission from the smartphone to the server must be guaranteed. Additionally, a process of converting the measured alcohol concentration value into a standard value for determining the level of drinking is necessary. In this paper, we propose and implement a system. Security with remote servers applies SSL at the network layer to ensure data integrity and confidentiality, and the server encrypts the received information and stores it in the database to provide additional security. As a result of analyzing the accuracy of alcohol concentration measurement and communication efficiency, it was confirmed that the measurement and transmission were within the error tolerance.

• Journal of Practical Engineering Education
• /
• v.16 no.2
• /
• pp.185-193
• /
• 2024

With the advent of smartphones, people can access various information through the internet anytime and anywhere. Even in the vehicle environment, users can use the internet. Users interact with web and applications every day and get information. However, as the amount of data to be processed by the program increases, users inevitably receive a message to wait. User waiting is an inconvenient experience, but minimizing user waiting is the best way because there is time required for data processing. However, if the service processing time exceeds the expected time, users experience more severe boredom and pain. Therefore, various methods and researches are being conducted to alleviate the boredom of user waiting. The most commonly used method to alleviate user waiting boredom is loading. In this study, we investigated the effect of skeleton loading, the latest loading technique, on user waiting experience, and how attractive it is as a design technique in terms of UI compared to other loading techniques.

• Journal of Advanced Technology Convergence
• /
• v.3 no.2
• /
• pp.9-15
• /
• 2024

Due to the high inflation rate of dining out, along with changes in group meals or cafeteria services, office workers are increasingly using workplace cafeterias to reduce their meal expenses even slightly. With the recent development of ICT technology, various fields are realizing that not only are smartphones becoming more popular, but they are also becoming an integration of the latest technologies. In this paper, we analyze the current status of cafeterias with a large number of customers and propose ways to improve problems or difficulties. Since most people always carry their smartphones for urgent communication or work tasks, we aim to develop a cafeteria management system that utilizes the NFC function of smartphones. By presenting the process from customer entry to menu selection, it will enable more efficient use of the cafeteria.

• Science of Emotion and Sensibility
• /
• v.17 no.4
• /
• pp.79-88
• /
• 2014

Smart-phones have become a vital part of our lives, paying a bill online, shopping using applications, using email and office applications. Therefore, the risk of the leakage of personal informations and the misuse of them becomes high, for the cost of loosing smart-phone. Many types of smart-phone security features such as password, slide-lock, and pattern lock have been introduced. However, those security locks make users not to easily access and use a smart-phone. There is tradeoff between security on one hand, and usability and cost on the other. This paper propose Self-Concealment to solve the tradeoff problem and demonstrate the effect through the experiment. In sum, Self-Concealment lowers smart-phone experience; however increases smart-phone use intension. This paper has implication for proposing new User Experience (UX) construct to resolve the trade-off between security and usability.

• Journal of the HCI Society of Korea
• /
• v.11 no.3
• /
• pp.5-12
• /
• 2016

The number of visually-impaired people using a smartphone is currently increasing with the help Text-to-Speech(TTS). TTS converts text data in a mobile application into sound data, and it only allows sequential search. For this reason, the location of buttons and contents inside an application should be determined carefully. However, little attention has been made on TTS service environment during the development of mobile newspaper application. This makes visually-impaired people difficult to use these applications. Furthermore, a mobile application interface which also reflects the desire of the low vision is necessary. Therefore, this paper presents a mobile newspaper interface which considers the accessibility and the desire of various visually impaired people. To this end, the proposed interface locates buttons with the consideration of TTS service environment and provides search functionality. The proposed interface also enables visually impaired people to use the application smoothly by filtering out the words that are pronounced improperly and providing the proper explanation for every button. Finally, several functionalities such as increasing font size and color reversal are implemented for the low vision. Simulation results show that the proposed interface achieves better performance than other applications in terms of search speed and usability.