• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.835-843
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• 2023

Given the structure of XML data, path and tree pattern matching algorithms play an important role in XML query processing. To facilitate decisions or relationships between nodes, nodes in an XML tree are typically labeled in a way that can quickly establish an ancestor-descendant on relationship between two nodes. However, these techniques have the disadvantage of re-labeling existing nodes or recalculating certain values if insertion occurs due to sequential updates. Therefore, in current labeling techniques, the cost of updating labels is very high. In this paper, we propose a new labeling technique called Fast XML encoding, which supports the update of order-sensitive XML documents without re-labeling or recalculation. It also controls the length of the label by reusing deleted labels at the same location in the XML tree. The proposed reuse algorithm can reduce the length of the label when all deleted labels are inserted in the same location. The proposed technique in the experimental results can efficiently handle order-sensitive queries and updates.

• Korean Journal of Agricultural Science
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• v.51 no.3
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• pp.399-412
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• 2024

This study aimed to analyze consumers' behaviors and reactions to the use-by date labeling system and provide policy implications for its efficient implementation, by utilizing 213 consumers data conducted via an Internet survey using the Google online form. We refer "pure consumption date" as the period that have passed sell-by date yet have not passed use-by date. Consumers' willingness to accept (WTA) for pure consumption date food was surveyed, which means the discount ratio of pure consumption date food compared to the original price by sell-by date. Setting the expected effects of use-by date labeling system as five: food waste reduction (waste), food purchasing cost reduction (cost), and international standardization (standard), etc., Tobit regression result showed waste had the greatest (negative) impact on consumer's WTA, while cost and standard had positive impact on consumer's WTA. The logistic regression result revealed that consumers trying to reduce grocery costs have higher probability to purchase use-by date labeling food, and further expect higher WTA. Also consumers valuing the importance of environmental protection or food quality are more likely to purchase use-by date food. Conversely consumers valuing food safety importance tend to have negative impact on purchasing use-by date food, hence expect higher WTA. It is noteworthy that consumers valuing the importance of promoting the use-by date labeling system have significantly higher probability of purchasing use-by date food. Additionally, consumers' WTA averaged 54.3%, implying that consumers are willing to purchase use-by date food when it is discounted more than 54.3% from the original price, where women expect higher WTA, the aged over 60 expect higher WTA, furthermore single-parent households expect 21.3% higher than the average WTA. However, old-aged, unmarried women, higher educated and higher income groups were negative in purchasing use-by date food. These results suggest that customized sales policy and effective promotion strategies reflecting socio-demographic characteristics of consumers would be necessary to achieve effective implementation of the newly introduced system.

• Journal of the Korea Society of Computer and Information
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• v.29 no.8
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• pp.11-21
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• 2024

This study explores the use of virtual reality (VR) technology to identify and label key segments of the golf swing. To address the limitations of existing VR devices, we developed a platform to collect kinematic data from various VR devices using the OpenVR SDK (Software Development Kit) and SteamVR, and developed a semi-automated labeling technique to identify and label temporal changes in kinematic behavior through LSTM (Long Short-Term Memory)-based time series data analysis. The experiment consisted of 80 participants, 20 from each of the following age groups: teenage, young-adult, middle-aged, and elderly, collecting data from five swings each to build a total of 400 kinematic datasets. The proposed technique achieved consistently high accuracy (≥0.94) and F1 Score (≥0.95) across all age groups for the seven main phases of the golf swing. This work aims to lay the groundwork for segmenting exercise data and precisely assessing athletic performance on a segment-by-segment basis, thereby providing personalized feedback to individual users during future education and training.

• Child Studies in Asia-Pacific Contexts
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• v.4 no.1
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• pp.1-12
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• 2014

Emotion is a central feature of social interactions. In this study, we examined age-related changes in emotion expressiveness and emotion knowledge and how young children's emotion expressiveness and knowledge were related. A total of 300 children attending a daycare center contributed data for the study. Observation and interview data relevant to measures of emotion expressiveness and knowledge were collected and analyzed. Both emotion knowledge and expressed positive affect increased with age. Older preschool children expressed positive affect more frequently than did younger preschoolers. Older preschool children also labeled, recognized, and provided plausible causes mores accurately than did younger preschool children. In addition, we tested whether children's errors on the free labeling component conform to the structural model previously suggested by Bullock and Russell (1986) and found that preschool children were using systematic strategies for labeling emotion states. Relations between emotion expressiveness and emotion knowledge generally were not significant, suggesting that emotional competence is only gradually constructed by the child over the preschool years.

• Journal of the Korean Home Economics Association
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• v.33 no.2
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• pp.159-166
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• 1995

The purpose of the present study is to investigate how closely care labels comply with the 1984 version of the Care Labeling Rule, as well as the change in degree of compliance prior to and after the 1988 IFI care label campaign. Label information was analyzed on the basis of country of origin. The information was also divided into two sets. The basis for dividing the data into two sets was the beginning of the IFI care label campaign in 1988 The data were obtained from 1147 checklists. The information for 1147 samples in six clothing categories were collected from department, specialty, and discount stores. Chi-square analyses were conducted to test hypotheses. While there was no significant difference in the number of incorrect labels on domestically produced garments compared to imported garments in set 1, there was a significant difference in set 2. Also, there was a significnat differnece in the number of incorrect labels between in set 1 and in set 2.

• Annual Conference of KIPS
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• 2023.11a
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• pp.410-413
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• 2023

레이블링 작업은 데이터 분석 시 필요한 사전 작업중 하나이다. 모든 데이터들에 대해 레이블링 작업은 시간/인적 자원을 필요로 하기에, 해당 작업을 보완할 방법이 존재한다면 요구되는 리소스를 줄여 효율성을 크게 향상시킬 수 있다. 본 논문에서는 통신회사에서 적재된 데이터 셋에 대하여 레이블이 없는 데이터(Unlabeled-data)에 대해 의사 레이블링(Pseudo-labeling), SMOTE 를 통한 데이터 증강을 활용하여 기존에 활용되지 못한 데이터를 추가하여 모델에 학습시킨다. 실험을 통해 의사 레이블을 통한 모델 학습 방법이 기존 도메인 지식의 레이블 방법보다 효율적이고 성능이 우수함을 확인하였다.

• Culinary science and hospitality research
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• v.22 no.4
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• pp.37-50
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• 2016

This study aims to examine the effect that the degree of knowledge about country-of-origin labeling on country-of-origin labeling work performance, which is a culinary staff task. This study is also intended to analyze differences in knowledge depending on hours of origin labeling education, and the moderating effect of education hours in the relationship between knowledge and performance. This study targeted culinary staff members working in ten five-star hotels in Seoul. A total of 205 self-administered questionnaires were distributed from November 14th to 27th, 2014, and 240 questionnaires(98.4%) were used for analysis after the exclusion of 4 with unreliable responses. Based on the data collected, frequency analysis, reliability test, exploratory factor analysis, simple regression analysis, t-test and moderating regression analysis were conducted using SPSS 18.0 program. The study findings are as follows. Culinary staff knowledge of origin labeling had a significantly positive effect on job performance and the degree of knowledge was higher in the group that attended one to two-hour periods of education. This suggests a differences in knowledge depending on the hours of education, which then had a moderating effect on the relationship between knowledge and performance. In conclusion, in order to improve knowledge of country-of-origin labeling and the level of performance, there is a need to increase education hours and enable culinary staff memebers to learn more knowledge and apply it to actual tasks. Based on these results, the limitation of the study and the direction of future research were also discussed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.338-340
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• 2019

Recently, the speed and size of data accumulation are increasing due to the development of networks. There are many difficulties in classifying these data. One of the difficulties is the difficulty of labeling. Labeling is usually done by people, but it is very difficult for everyone to understand the data in the same way and it is very difficult to label them on the same basis. In order to solve this problem, we implemented GAN to generate new image based on input image and to learn input data indirectly by using it for learning. This suggests that the accuracy of classification can be increased by increasing the number of learning data.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.223-224
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• 2021

The process before the model learning stage in AI R&D can be subdivided into data collection/cleansing-data purification-data labeling. After that, according to the purpose of development, it goes through a stage of verifying the model by performing learning by using the algorithm of the artificial intelligence model. Several studies describe an important part of AI research as the learning stage, and try to increase the accuracy by changing the structure and layer of the AI model. However, if the refinement and labeling process of the learning data is tailored only to the model format and is not made for the purpose of development, the desired AI model cannot be obtained. The latest research reveals that most AI research failures are the failure of the learning data rather than the structure of the AI model. analyzed.

• Smart Media Journal
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• v.11 no.4
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• pp.38-45
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• 2022

Object detection models have become the current tool of choice for plant disease detection in precision agriculture. Most existing research improves the performance by ameliorating networks and optimizing the loss function. However, the data-centric part of a whole project also needs more investigation. In this paper, we proposed a systematic strategy with three different annotation methods for plant disease detection: local, semi-global, and global label. Experimental results on our paprika disease dataset show that a single class annotation with semi-global boxes may improve accuracy. In addition, we also studied the noise factor during the labeling process. An ablation study shows that annotation noise within 10% is acceptable for keeping good performance. Overall, this data-centric numerical analysis helps us to understand the significance of annotation methods, which provides practitioners a way to obtain higher performance and reduce annotation costs on plant disease detection tasks. Our work encourages researchers to pay more attention to label quality and the essential issues of labeling methods.