• Journal of Korean Home Economics Education Association
• /
• v.23 no.3
• /
• pp.69-90
• /
• 2011

To develop the descriptive evaluation questionnaire with high validity and reliability on the Clothing and Textiles section of the middle school Technology & Hone Economics textbook, apply it to students and analyze its results. We made out a draft for descriptive evaluation questionnaire that was based upon the concrete establishment of the goal and the range of evaluation. We also made a rubric for scoring as well as sample answer-sheets. Finally, we completed a total of twenty three descriptive evaluation questions and we applied it to sixty five 2nd-grade students in two classes in a middle school. Descriptive evaluation questionnaire exhibited the relative high validity on each question. Moreover, three graders gave the same score on each question of descriptive evaluation, suggesting that descriptive evaluation questionnaire has the high inter-grader reliability and the strong correlation. But, low academic achievement was generally observed in the subjects. They had difficulty in describing their knowledge via their own language and drawing up accurate and detailed answers. They recognized the positive aspects of descriptive evaluation questionnaire, but they felt it uncomfortable due to study-burden and description itself. To overcome these limitations, it is required that students should experience various materials related to subject contents in classes as well as textbooks, concentrate themselves on finding solutions for problems, expand their scope, and practice describe them in advance. Therefore, the additional training for description evaluation questionnaire will be necessary for the more efficient and discriminative questionnaire. Also the questionnaire with high validity and reliability should be developed and the aggressive and voluntary participation of teachers will be needed.

• The Journal of Korean Physical Therapy
• /
• v.18 no.4
• /
• pp.61-70
• /
• 2006

Purpose: The purpose of this study was to investigate the activities of daily living (ADL) and instrumental activities of daily living (IADL) of residential home dwelling elderly and home for the aged dwelling elderly. In attempt to address medical professional caring the elderly, this comparative study examines the factors associated with dependence in the ADL and IADL in two samples of elderly people living in two different environments. Methods: The instrument of ADL and IADL widely used Katz ADL and IADL. Katz ADL and IADL was not a perfect fit for Korean. In concern with cultural factors Won developed K(Korean)-ADL and K-IADL scale reflecting Korean's own language expression and cultural factors in year of 2002. The assessment tool of this study was K-ADL and K-IADL. Differences of ADL and IADL were tested for statistical significance using group t-test and x2 test for comparisons between the residential home dwelling elderly and the home for the aged dwelling elderly. Results: Comparison of assessment for K-ADL and K-IADL in two different dwelling types was significant. Performance of ADL and IADL depend upon their living environment such as social status, number of children, income, present illness as well as age group. This study also showed significant differences of performance in some activities of ADL and IADL between the elderly who live in their own home and live in home for the aged. Comparison of performance of ADL and IADL in different dwelling types revealed that only one item of ADL was significant but only one item of IADL was not significant. It means that IADL is more difficult activities in the home for the aged dwelling elderly than the residential home dwelling elderly. The coupled elderly has more independent in some ADL and IADL activities compared with the single elderly. Conclusion: Using K-ADL and K-IADL is more convenient for Korean elderly. Medical professional consider some factors like dwelling style, social status, existing diseases and disabilities in order to care the elderly and train him/her activities of daily living as well as instrumental activities of daily living. Medical professional, especially physical and occupational therapist emphasize the training items which are bathing of ADL and grooming, housework, preparing meals, laundry, traveling, public transportation, shopping, using telephone and taking medicine of IADL based on the result of this study.

• Journal of Intelligence and Information Systems
• /
• v.21 no.2
• /
• pp.131-150
• /
• 2015

Omission of noun phrases for obligatory cases is a common phenomenon in sentences of Korean and Japanese, which is not observed in English. When an argument of a predicate can be filled with a noun phrase co-referential with the title, the argument is more easily omitted in Encyclopedia texts. The omitted noun phrase is called a zero anaphor or zero pronoun. Encyclopedias like Wikipedia are major source for information extraction by intelligent application systems such as information retrieval and question answering systems. However, omission of noun phrases makes the quality of information extraction poor. This paper deals with the problem of developing a system that can restore omitted noun phrases in encyclopedia documents. The problem that our system deals with is almost similar to zero anaphora resolution which is one of the important problems in natural language processing. A noun phrase existing in the text that can be used for restoration is called an antecedent. An antecedent must be co-referential with the zero anaphor. While the candidates for the antecedent are only noun phrases in the same text in case of zero anaphora resolution, the title is also a candidate in our problem. In our system, the first stage is in charge of detecting the zero anaphor. In the second stage, antecedent search is carried out by considering the candidates. If antecedent search fails, an attempt made, in the third stage, to use the title as the antecedent. The main characteristic of our system is to make use of a structural SVM for finding the antecedent. The noun phrases in the text that appear before the position of zero anaphor comprise the search space. The main technique used in the methods proposed in previous research works is to perform binary classification for all the noun phrases in the search space. The noun phrase classified to be an antecedent with highest confidence is selected as the antecedent. However, we propose in this paper that antecedent search is viewed as the problem of assigning the antecedent indicator labels to a sequence of noun phrases. In other words, sequence labeling is employed in antecedent search in the text. We are the first to suggest this idea. To perform sequence labeling, we suggest to use a structural SVM which receives a sequence of noun phrases as input and returns the sequence of labels as output. An output label takes one of two values: one indicating that the corresponding noun phrase is the antecedent and the other indicating that it is not. The structural SVM we used is based on the modified Pegasos algorithm which exploits a subgradient descent methodology used for optimization problems. To train and test our system we selected a set of Wikipedia texts and constructed the annotated corpus in which gold-standard answers are provided such as zero anaphors and their possible antecedents. Training examples are prepared using the annotated corpus and used to train the SVMs and test the system. For zero anaphor detection, sentences are parsed by a syntactic analyzer and subject or object cases omitted are identified. Thus performance of our system is dependent on that of the syntactic analyzer, which is a limitation of our system. When an antecedent is not found in the text, our system tries to use the title to restore the zero anaphor. This is based on binary classification using the regular SVM. The experiment showed that our system's performance is F1 = 68.58%. This means that state-of-the-art system can be developed with our technique. It is expected that future work that enables the system to utilize semantic information can lead to a significant performance improvement.

• Journal of Intelligence and Information Systems
• /
• v.24 no.3
• /
• pp.221-241
• /
• 2018

As we enter the knowledge society, the importance of information as a new form of capital is being emphasized. The importance of information classification is also increasing for efficient management of digital information produced exponentially. In this study, we tried to automatically classify and provide tailored information that can help companies decide to make technology commercialization. Therefore, we propose a method to classify information based on Korea Standard Industry Classification (KSIC), which indicates the business characteristics of enterprises. The classification of information or documents has been largely based on machine learning, but there is not enough training data categorized on the basis of KSIC. Therefore, this study applied the method of calculating similarity between documents. Specifically, a method and a model for presenting the most appropriate KSIC code are proposed by collecting explanatory texts of each code of KSIC and calculating the similarity with the classification object document using the vector space model. The IPC data were collected and classified by KSIC. And then verified the methodology by comparing it with the KSIC-IPC concordance table provided by the Korean Intellectual Property Office. As a result of the verification, the highest agreement was obtained when the LT method, which is a kind of TF-IDF calculation formula, was applied. At this time, the degree of match of the first rank matching KSIC was 53% and the cumulative match of the fifth ranking was 76%. Through this, it can be confirmed that KSIC classification of technology, industry, and market information that SMEs need more quantitatively and objectively is possible. In addition, it is considered that the methods and results provided in this study can be used as a basic data to help the qualitative judgment of experts in creating a linkage table between heterogeneous classification systems.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.26 no.4
• /
• pp.366-373
• /
• 2020

According to Article 77 of the Ship Safety Act and Article 97(2) of the Enforcement Ordinance of the Ministry, the Recognized Organization (RO) personnel (ship surveyors) responsible for statutory survey shall have educational qualifications and experience in a specific field or obtain a license under the National Technical Qualifications Act. However, graduates from maritime high schools and those who completed the short-term course of the Ocean Polytec did not satisfy the qualification standards for the RO personnel since they did not graduate from the departments of maritime/fisheries or shipbuilding. Major shipping countries such as the United Kingdom, the United States, and Canada use the IACS (International Association of Classification Societies) regulations, and the Ship Safety Act in Japan has eliminated the qualification requirements for ship surveyors. In particular, under the IMO (International Maritime Organization) and IACS regulations, the RO personnel shall have as a minimum the following formal educational background: a degree or equivalent qualification from a tertiary institution recognized within a relevant field of engineering or physical science (minimum two years' program); or a relevant qualification from a marine or nautical institution and relevant sea-going experience as a certified ship officer; and competency in the English language commensurate with their future work. Considering that Article 17 of the Enforcement Decree on Public Officials Appointment Examinations prohibits educational restrictions and there are no educational restrictions on the qualifications of British and Japanese surveyors, if the maritime high school graduates have sufficient sea-going experience, education, and training, they could be recognized as meeting the qualification requirements. Moreover, those who completed the short-term course of the Ocean Polytec could also be recognized as meeting the qualification requirements because they are required to have at least a professional bachelor's degree (in the case of a third-class CoC (Certificate of Competancy)) and some sea-going experience after completion.

• Journal of Intelligence and Information Systems
• /
• v.25 no.1
• /
• pp.43-61
• /
• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

• Journal of Intelligence and Information Systems
• /
• v.27 no.1
• /
• pp.65-82
• /
• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.

• Journal of the Korean Society for information Management
• /
• v.39 no.2
• /
• pp.111-129
• /
• 2022

Insomnia is a chronic disease in modern society, with the number of new patients increasing by more than 20% in the last 5 years. Insomnia is a serious disease that requires diagnosis and treatment because the individual and social problems that occur when there is a lack of sleep are serious and the triggers of insomnia are complex. This study collected 5,699 data from 'insomnia', a community on 'Reddit', a social media that freely expresses opinions. Based on the International Classification of Sleep Disorders ICSD-3 standard and the guidelines with the help of experts, the insomnia corpus was constructed by tagging them as insomnia tendency documents and non-insomnia tendency documents. Five deep learning language models (BERT, RoBERTa, ALBERT, ELECTRA, XLNet) were trained using the constructed insomnia corpus as training data. As a result of performance evaluation, RoBERTa showed the highest performance with an accuracy of 81.33%. In order to in-depth analysis of insomnia social data, topic modeling was performed using the newly emerged BERTopic method by supplementing the weaknesses of LDA, which is widely used in the past. As a result of the analysis, 8 subject groups ('Negative emotions', 'Advice and help and gratitude', 'Insomnia-related diseases', 'Sleeping pills', 'Exercise and eating habits', 'Physical characteristics', 'Activity characteristics', 'Environmental characteristics') could be confirmed. Users expressed negative emotions and sought help and advice from the Reddit insomnia community. In addition, they mentioned diseases related to insomnia, shared discourse on the use of sleeping pills, and expressed interest in exercise and eating habits. As insomnia-related characteristics, we found physical characteristics such as breathing, pregnancy, and heart, active characteristics such as zombies, hypnic jerk, and groggy, and environmental characteristics such as sunlight, blankets, temperature, and naps.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.8 no.12
• /
• pp.425-435
• /
• 2018

The purpose of this study is to investigate the cultural value as well as business value of Juk-Bang-Ryeum(fishing instrument made by bamboo weir) by the investigation of remains in Gyeongnam Sacheon area and reviewing various historical literatures. The research will contribute to make back data necessary for the registration of World Heritage(UNESCO) and Globally Important Agricultural Heritage Systems(FAO). Fisheries, along with agriculture, have been great significance in human history. In particular, the Fisheries has been considered very important industry due to the geopolitical characteristics of our country surrounded by the sea. We can imagine may types of fishing practices and instruments at the agricultural age. Nonetheless, there are a few fishery heritages such as collecting and hunting tools that remains today. Fortunately, there are many Juk-Bang-Ryeum which is actually operate now from the past 500 years ago at the The Sacheon and Namhae areas. We could found some literature records about it in the historical ancient literatures. We could also infer that Juk-Bang-Ryeum was an important fishery resource of the country for a long time. It was built on the basis of scientific principles to capture fishes using the rapid tide of the natural geological straits, and it prove the wisdom of our ancestors. We also could found some unique cultural heritages that was important to the local community. Naturally, it has been managed as an important asset for the residents. In addition to such historical and humanistic values, it also has business and educational value. It can be useful to understand scientific fishery principles as well as fishery experience field. It has business value as an important tourism resource in the region in connection with historical relics and geological environment resources. In conclusion, it is a valuable asset to be handed down as a valuable cultural heritage.

• The Korean Society of Law and Medicine
• /
• v.24 no.4
• /
• pp.67-101
• /
• 2023

The utilization of generative AI in the medical field is also being rapidly researched. Access to vast data sets reduces the time and energy spent in selecting information. However, as the effort put into content creation decreases, there is a greater likelihood of associated issues arising. For example, with generative AI, users must discern the accuracy of results themselves, as these AIs learn from data within a set period and generate outcomes. While the answers may appear plausible, their sources are often unclear, making it challenging to determine their veracity. Additionally, the possibility of presenting results from a biased or distorted perspective cannot be discounted at present on ethical grounds. Despite these concerns, the field of generative AI is continually advancing, with an increasing number of users leveraging it in various sectors, including biomedical and life sciences. This raises important legal considerations regarding who bears responsibility and to what extent for any damages caused by these high-performance AI algorithms. A general overview of issues with generative AI includes those discussed above, but another perspective arises from its fundamental nature as a large-scale language model ('LLM') AI. There is a civil law concern regarding "the memorization of training data within artificial neural networks and its subsequent reproduction". Medical data, by nature, often reflects personal characteristics of patients, potentially leading to issues such as the regeneration of personal information. The extensive application of generative AI in scenarios beyond traditional AI brings forth the possibility of legal challenges that cannot be ignored. Upon examining the technical characteristics of generative AI and focusing on legal issues, especially concerning the protection of personal information, it's evident that current laws regarding personal information protection, particularly in the context of health and medical data utilization, are inadequate. These laws provide processes for anonymizing and de-identification, specific personal information but fall short when generative AI is applied as software in medical devices. To address the functionalities of generative AI in clinical software, a reevaluation and adjustment of existing laws for the protection of personal information are imperative.