A Study on the Development of Game-based Mind Wandering Judgment Model in Video Lecture-based Education

Although video lecture materials are very efficient learning materials, they are likely to be unilateral learning materials by the lecturer. It is easily degraded to be one-sided learning, which has been considered as a problem of online education, and it is difficult to judge whether learners are actually learning. Therefore, in this paper, a minimum learning activity judgment model that can automatically determine if they actually learn through mind wandering judgment was proposed to overcome the limitations of previous learning materials, and educational effect verification experiment was performed. Experiment results show that the video lecture class using the minimum learning activity judgment system was effective in improving the academic achievement

Development of Intelligent Information System for Digital Cultural Contents

A significant amount of digital cultural contents is shared online, but learners do not know where subject matter content is or how to find it. Therefore, there is a need for a service to improve educational quality by effectively providing relevant information in response to searches for content that is useful to learners. This study developed and tested the usability and utility of an intelligent information system that effectively searches and visualizes digital cultural contents. The system collects data on digital cultural contents, automatically classifies them, and creates content triple data to automatically display the results with a 3D timeline, knowledge network map, and keyword relation network map through content search, triple search, and keyword search. We also conducted a survey and in-depth interviews to verify users’ satisfaction with respect to the use and utility of the system. For the experiment, we developed survey questions to measure user satisfaction and conducted in-depth interviews regarding the system’s utility with a total of 65 subjects. The results show that the response for satisfaction with regard to the use and utility was generally “satisfied”. In addition, the system stability was evaluated as “high”

Verification of a Dataset for Korean Machine Reading Comprehension with Numerical Discrete Reasoning over Paragraphs

Numerical reasoning in machine reading comprehension (MRC) has demonstrated significant performance improvements in the past few years. However, due to the process being restricted to specific languages, low-resource languages are not considered, and MRC studies on such languages are limited. In addition, the methods that rely on existing information extracted within the span of a paragraph have limitations in responding to questions requiring actual reasoning. To overcome these shortcomings, this study establishes a dataset for learning Korean Question and Answering (QA) models that not only answer within the span of passages but also perform numerical reasoning on passages and questions. Its efficacy was verified by training the model. We recruited eight annotators to tag the ground truth label, and they annotated datasets with 920, 115, and 115 passages in the train, dev, and test, respectively. A simple yet sophisticated automatic inter-annotation tool was created by effectively reducing the possibility of inaccuracy and error entailed by humans in the data construction process. This tool used common KoBERT and KoELECTRA. We defined four general conditions, and six conditions humans must inspect and fine-tune the pre-trained language models with numerically aware architecture. The KoELECTRA and NumNet+ with KoELECTRA were fine-tuned, and experiments in identical hyperparameter settings showed that compared with other models, the performance of NumNet+ with KoELECTRA was higher by more than 1.3 points. Our research contributes to the Korean MRC research and suggests potential and insight into MRC models capable of numerical reasoning

Remote Heart Rate Estimation Using Attention-targeted Self-Supervised Learning Methods

Heart rate measurement is a crucial factor for assessing the overall health status of an individual. Abnormal heart rates, whether lower or higher than baseline, can indicate potential pathological or physiological abnormalities. As a result, it is necessary to have reliable technology for monitoring heart rates in various fields, including medicine, biotechnology, and healthcare. With recent advancements in deep learning research, it is now possible to monitor heart rate conveniently and hygienically without specialized equipment, using facial video photo volume measurement. This new technology employs a deep learning-based video analysis method that requires a large data set to achieve high performance. However, collecting and labeling a vast amount of data is often impractical and costly. Therefore, researchers have been searching for alternative ways to achieve high performance with smaller datasets. This paper proposes a novel self-supervised learning approach suitable to the face video process. Our proposed method can effectively acquire a deep latent expression from a face image sequence and apply it to a target task through transfer learning. Using this method, we aim to improve the remote heart rate estimation performance in a limited-size dataset. Our proposed method is specialized for facial image sequences and focuses on the color change of the face to achieve high performance in existing attention-based deep learning models. The proposed self-supervised learning method has several advantages. First, it can learn useful features from unlabeled data, reducing the reliance on annotated datasets. Second, it can help overcome the problem of insufficient labeled data in specific domains, such as medical image analysis. Third, the proposed method can improve the performance of the target task using pre-trained models on different datasets. Finally, our approach improves the remote heart rate estimation performance by extracting useful features from facial images

Enhancing Code Similarity with Augmented Data Filtering and Ensemble Strategies

Although COVID-19 has severely affected the global economy, information technology (IT) employees managed to perform most of their work from home. Telecommuting and remote work have promoted a demand for IT services in various market sectors, including retail, entertainment, education, and healthcare. Consequently, computer and information experts are also in demand. However, producing IT, experts is difficult during a pandemic owing to limitations, such as the reduced enrollment of international students. Therefore, researching increasing software productivity is essential; this study proposes a code similarity determination model that utilizes augmented data filtering and ensemble strategies. This algorithm is the first automated development system for increasing software productivity that addresses the current situation—a worldwide shortage of software dramatically improves performance in various downstream natural language processing tasks (NLP). Unlike general-purpose pre-trained language models (PLMs), CodeBERT and GraphCodeBERT are PLMs that have learned both natural and programming languages. Hence, they are suitable as code similarity determination models. The data filtering process consists of three steps: (1) deduplication of data, (2) deletion of intersection, and (3) an exhaustive search. The best mating (BM) 25 and length normalization of BM25 (BM25L) algorithms were used to construct positive and negative pairs. The performance of the model was evaluated using the 5-fold cross-validation ensemble technique. Experiments demonstrate the effectiveness of the proposed method quantitatively. Moreover, we expect this method to be optimal for increasing software productivity in various NLP tasks

Sentiment-Target Word Pair Extraction Model Using Statistical Analysis of Sentence Structures

Product information has been propagated online via forums and social media. Lots of merchandise are recommended via an expert system method and is considered for purchase by online comments or product reviews. For predicting people’s opinions on products, studying people’s thoughts via extracting information in documents is referred to as sentiment analysis. Finding sentiment-target word pairs is an important sentiment mining research issue. With the Korean language, as the predicate appears at the very end, it is not easy to find the exact word pairs without first identifying the syntactic structure of the sentence. In this study, we propose a model that parses sentence structures and extracts sentiment-target word pairs from the parse tree. The proposed model extracts the sentiment-target word pairs that appear in the sentence by using parsing and statistical methods. For extracting sentiment-target word pairs, this model uses a sentiment word extractor and a target word extractor. After testing data from 4000 movie reviews, the applicable model showed high performance in both accuracy 93.25 (+14.45) and F1-score 82.29 (+3.31) compared with others. However, improvements in the recall rate (−0.35) are needed and computational costs must be reduced

Sentiment-Target Word Pair Extraction Model Using Statistical Analysis of Sentence Structures

Product information has been propagated online via forums and social media. Lots of merchandise are recommended via an expert system method and is considered for purchase by online comments or product reviews. For predicting people’s opinions on products, studying people’s thoughts via extracting information in documents is referred to as sentiment analysis. Finding sentiment-target word pairs is an important sentiment mining research issue. With the Korean language, as the predicate appears at the very end, it is not easy to find the exact word pairs without first identifying the syntactic structure of the sentence. In this study, we propose a model that parses sentence structures and extracts sentiment-target word pairs from the parse tree. The proposed model extracts the sentiment-target word pairs that appear in the sentence by using parsing and statistical methods. For extracting sentiment-target word pairs, this model uses a sentiment word extractor and a target word extractor. After testing data from 4000 movie reviews, the applicable model showed high performance in both accuracy 93.25 (+14.45) and F1-score 82.29 (+3.31) compared with others. However, improvements in the recall rate (−0.35) are needed and computational costs must be reduced