ABSTRACT
With the increasing number of social media users in recent years, news in various fields, such as politics, economics, and so on, can be easily accessed by users. However, most news spread through social networks including Twitter, Facebook, and Instagram has unknown sources, thus having a significant impact on news consumers. Fake news on COVID-19, which is affecting the global population, is propagating quickly and causes social disorder. Thus, a lot of research is being conducted on the detection of fake news on COVID-19 but is facing the problem of a lack of datasets. In order to alleviate the problem, we built a dataset on COVID-19 fake news from fact-checking websites in Korea and propose deep learning for detecting fake news on COVID-19 using the datasets. The proposed model is pre-trained with large-scale data and then performs transfer learning through a BiLSTM model. Moreover, we propose a method for initializing the hidden and cell states of the BiLSTM model to a [CLS] token instead of a zero vector. Through experiments, the proposed model showed that the accuracy is 78.8%, which was improved by 8% compared with the linear model as a baseline model, and that transfer learning can be useful with a small amount of data as we know it. A [CLS] token containing sentence information as the initial state of the BiLSTM can contribute to a performance improvement in the model.
ABSTRACT
The purpose of this paper is to study the recognition of ships and their structures to improve the safety of drone operations engaged in shore-to-ship drone delivery service. This study has developed a system that can distinguish between ships and their structures by using a convolutional neural network (CNN). First, the dataset of the Marine Traffic Management Net is described and CNN’s object sensing based on the Detectron2 platform is discussed. There will also be a description of the experiment and performance. In addition, this study has been conducted based on actual drone delivery operations—the first air delivery service by drones in Korea.
ABSTRACT
The goal of the present study is to examine the psychology of working framework/theory with a sample of Korean workers. This study examined the structural model of sociocultural factors (i.e., economic constraints and social marginalization), psychological variables (i.e., work volition and career adaptability), and outcomes of decent work based on the psychology of working framework. This study assumed that decent work helps all workers attain a sense of self-respect, dignity, experience freedom and security in the work environment and provides an opportunity for workers to contribute to society. Data were collected from 420 Korean workers, with an average age of 39.13 years (SD = 9.26). We used a hypothesis model that did not assume a direct path from economic constraints and social marginalization to decent work and work volition and career adaptation to job satisfaction and life satisfaction. We also employed an alternative model that assumed all of its paths and compared the models' goodness of fit based on prior studies. Results indicated that alternative models have higher goodness of fit than hypothesis models. All path coefficients were significant except for the direct path from social marginalization to work volition and career adaptability to life satisfaction. Additionally, work volition and career adaptability mediated both the relationship between social marginalization and job satisfaction and between marginalization and life satisfaction. This study enabled the comprehensive examination of the relevance of various social environments and psychological and occupational characteristics that should be considered when exploring job or life satisfaction in the process of career counseling.