• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Journal of Science Education
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• v.33 no.2
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• pp.336-345
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• 2009

The purpose of this study is to analysis ERF components patterns of causal questions generated during the observation of biological phenomenon. First, the system that shows pictures causing causal questions based on biological phenomenon (evoked picture system) was developed in a way of cognitive psychology. The ERF patterns of causal questions based on time-series brain processing was observed using MEG. The evoked picture system was developed by R&D method consisting of scientific education experts and researchers. Tasks were classified into animal (A), microbe (M), and plant (P) tasks according to biological species and into interaction (I), all (A), and part (P) based on the interaction between different species. According to the collaboration with MEG team in the hospital of Seoul National University, the paradigm of MEG task was developed. MEG data about the generation of scientific questions in 5 female graduate student were collected. For examining the unique characteristic of causal question, MEG ERF components were analyzed. As a result, total 100 pictures were produced by evoked picture and 4 ERF components, M1(100~130ms), M2(220~280ms), M3(320~390ms), M4(460~520ms). The present study could guide personalized teaching-learning method through the application and development of scientific question learning program.

• The Korean Journal of Elementary Counseling
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• v.8 no.1
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• pp.33-50
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• 2009

The purpose of this study was to develop a PBL-based career education program and to examine its effectiveness on school children's career maturity. It's specifically meant to prepare a career education program to assist students to get an accurate grip on their aptitude, interest and personality and explore various sorts of occupations in the course of solving authentic and contextual career-related problems. After children's developmental characteristics and needs were analyzed, task analysis was implemented, and the objectives were defined. And then the core of the program, PBL problems were developed, and the validity of the problems were verified Evaluation plans and tools were prepared to assess children's problem-solving process and presentation, and an online learning space was designed. The program that consisted of 10-minute 21 sessions was provided to fifth-grade elementary schoolers for eight weeks. The findings of the study were as follows: The experimental group that participated in the PBL-based career education program showed a more significant improvement than the control group that didn't in career attitude and three career attitude subfactors involving planness, disposition and compromise. And the former made a more significant progress than the latter in career ability and its subfactors including vocational comprehension, self-understanding and decision-making skills as well. As a result of making a content analysis to make up for the survey, the students reported that they were able to get an objective understanding of themselves and acquire diverse and profound knowledge on work and the business world in the middle of solving the given PBL problems related to different areas in group and giving a presentation. In conclusion, a PBL based career education program developed by this researcher encouraged the students to have an objective self-understanding, to have a dynamic interactive discussion with their group members. Therefore the program had a positive impact on boosting the career attitude and career ability of the elementary schoolers. The findings suggested that in the field of elementary career education, autonomous learning attitude and subjecthood are the crucial factors to stimulate school children to explore and create their own future.

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.169-178
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• 2020

The reductions of troops/human resources, and improvement in combat power have made Korean Department of Defense actively adapt 4th Industrial Revolution technology (Artificial Intelligence, Big Data). The defense information system has been developed in various ways according to the task and the uniqueness of each military. In order to take full advantage of the 4th Industrial Revolution technology, it is necessary to improve the closed defense datamanagement system.However, the establishment and usage of data standards in all information systems for the utilization of defense big data and artificial intelligence has limitations due to security issues, business characteristics of each military, anddifficulty in standardizing large-scale systems. Based on the interworking requirements of each system, data sharing is limited through direct linkage through interoperability agreement between systems. In order to implement smart defense using the 4th Industrial Revolution technology, it is urgent to prepare a system that can share defense data and make good use of it. To technically support the defense, it is critical to develop Multi Repository Meta-Data Management (MRMM) that supports systematic standard management of defense data that manages enterprise standard and standard mapping for each system and promotes data interoperability through linkage between standards which obeys the Defense Interoperability Management Development Guidelines. We introduced MRMM, and implemented by using vocabulary similarity using machine learning and statistical approach. Based on MRMM, We expect to simplify the standardization integration of all military databases using artificial intelligence and bigdata. This will lead to huge reduction of defense budget while increasing combat power for implementing smart defense.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.77-88
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• 2017

Recently, with the advent of knowledge based society where information and knowledge make values, patents which are the representative form of intellectual property have become important, and the number of the patents follows growing trends. Thus, it needs to classify the patents depending on the technological topic of the invention appropriately in order to use a vast amount of the patent information effectively. IPC (International Patent Classification) is widely used for this situation. Researches about IPC automatic classification have been studied using data mining and machine learning algorithms to improve current IPC classification task which categorizes patent documents by hand. However, most of the previous researches have focused on applying various existing machine learning methods to the patent documents rather than considering on the characteristics of the data or the structure of patent documents. In this paper, therefore, we propose to use two structural fields, technical field and background, considered as having impacts on the patent classification, where the two field are selected by applying of the characteristics of patent documents and the role of the structural fields. We also construct multi-label classification model to reflect what a patent document could have multiple IPCs. Furthermore, we propose a method to classify patent documents at the IPC subclass level comprised of 630 categories so that we investigate the possibility of applying the IPC multi-label classification model into the real field. The effect of structural fields of patent documents are examined using 564,793 registered patents in Korea, and 87.2% precision is obtained in the case of using title, abstract, claims, technical field and background. From this sequence, we verify that the technical field and background have an important role in improving the precision of IPC multi-label classification in IPC subclass level.

• Journal of the Korean Chemical Society
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• v.60 no.3
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• pp.203-214
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• 2016

The purpose of this study was to investigate brain activation pattern and functional connectivity during convergence thinking based creative problem solving and chemistry problem solving to identify characteristic convergence thinking that is backbone of creative problem solving using functional magnetic resonance imaging(fMRI). A fMRI paradaigm inducing convergence thinking and chemistry problem solving was developed and adjusted on 17 highschool students, and brain activation image during task was analyzed. According to the results, superior frontal gyrus, middle frontal gyrus, inferior frontal gyrus, medial frontal gyrus, cingulate gyrus, precuneus and caudate nucleus body in left hemisphere and cuneus and caudate nucleus body in right hemisphere were significantly activated during convergence thinking. The other hand, middle frontal gyrus, medial frontal gyrus and caudate nucleus in left hemisphere and middle frontal gyrus, lingual gyrus, caudate nucleus, thalamus and culmen of cerebellum in right hemisphere were significantly activated during chemistry problem solving. As results of analysis functional connectivity, all of areas activated during convergence thinking were functionaly connected, whereas scanty connectivity of chemistry problem solving between right middle frontal gyrus, bilateral nucleus caudate tail and culmen. The results show that logical thinking, working memory, planning, imaging, languge based thinking and learning motivation were induced during convergence thinking and these functions and regions were synchronized intimately. Whereas, logical thinking and inducing learning motivation functioning during chemistry problem solving were not synchronized. These results provide concrete information about convergence thinking.

• Journal of Korean Home Economics Education Association
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• v.30 no.3
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• pp.43-66
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• 2018

The purpose of this study was to conduct the instruction for 'Family Life Planning' based on backward design and measured the learners' understanding and satisfaction for testing validity. In short, the result of this study are as follows: In this study, first of all, the students could explain significant concepts, knowledge, and principles for the planning of family life; they could interpret and apply them; they have perspectives on them; they could empathize them; and they could have self-knowledge. The students could also accomplish high achievements for important concepts related to the field of family life planning. In conclusion, this study showed that the developed instruction was very effective for the students to achieve fruitful results, accelerating the learners' persistent understanding. Second, the learners had high satisfaction on the instruction of Family Life Planning based on backward design with the average score of 3.68 out of the perfect score 4. The students could be satisfied with the developed instruction since they could have high interest in the class thanks to diverse learning materials, and they could take an active part in the learning tasks based on group activities and questions. Also they could apply the contents that they learned through task performances to new situation and context. Therefore, this study proved that the developed instruction enhanced the learners' satisfaction on class.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.191-206
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• 2022

Recently, it is a de-facto approach to utilize a pre-trained language model(PLM) to achieve the state-of-the-art performance for various natural language tasks(called downstream tasks) such as sentiment analysis and question answering. However, similar to any other machine learning method, PLM tends to depend on the data distribution seen during the training phase and shows worse performance on the unseen (Out-of-Distribution) domain. Due to the aforementioned reason, there have been many efforts to develop domain-specified PLM for various fields such as medical and legal industries. In this paper, we discuss the training of a finance domain-specified PLM for the Korean language and its applications. Our finance domain-specified PLM, KB-BERT, is trained on a carefully curated financial corpus that includes domain-specific documents such as financial reports. We provide extensive performance evaluation results on three natural language tasks, topic classification, sentiment analysis, and question answering. Compared to the state-of-the-art Korean PLM models such as KoELECTRA and KLUE-RoBERTa, KB-BERT shows comparable performance on general datasets based on common corpora like Wikipedia and news articles. Moreover, KB-BERT outperforms compared models on finance domain datasets that require finance-specific knowledge to solve given problems.

• Journal of Christian Education in Korea
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• v.71
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• pp.121-154
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• 2022

This study is to summarize the relationship between youth in terms of developmental psychology, university education, faith, and spirituality in order to form and improve relationships, which are major developmental tasks of youth, and to suggest Christian youth education by the elements of education. Relationships are formed when you are connected to another person and community, feel interested in each other, feel a sense of bond and belonging, and maintain a stable and satisfactory relationship. This is not skill or technology, but is related to life attitude and value, and continuous learning and training are required. Various developmental tasks in youth have something in common with relationships. Relationships positively affect the lives of young people, such as satisfaction with college life in the early stages of youth, adaptation to college life, personality, and career decision. Relationships are also very important in faith because human existence and faith are defined and formed through relationships. The relationship between the community and others plays an important role in spiritual development for the meaning of life and inner growth. In the aspects of learners and educational environment, it was suggested to understand learners with desire for relationships, the generation they live in, and the educational environment in which the relationship between young people occurs. In terms of teachers, teachers have to try to change their roles such as facilitators, guides, managers, and mentors. For the educational purpose and content, it was suggested that relationships should be the ultimate purpose and the educational content for this was presented in three different types of relationships and each main contents to be dealt with. In terms of educational method, it was proposed to select a learner-centered group learning method that induces communication and active participation of learners to cause interaction by considering other elements of education according to the content of the relationship in the cognitive, emotional, and behavioral dimensions. In the aspects of educational results and evaluation, it was proposed to confirm that what was considered during the educational planning stage was effectively carried out in actual education, to evaluate various evaluation methods, various aspects, and to summarize the evaluation results for the specific application.

• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.53-69
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• 2011

Vision and voice-based technologies are commonly utilized for human-robot interaction. But it is widely recognized that the performance of vision and voice-based interaction systems is deteriorated by a large margin in the real-world situations due to environmental and user variances. Human users need to be very cooperative to get reasonable performance, which significantly limits the usability of the vision and voice-based human-robot interaction technologies. As a result, touch screens are still the major medium of human-robot interaction for the real-world applications. To empower the usability of robots for various services, alternative interaction technologies should be developed to complement the problems of vision and voice-based technologies. In this paper, we propose the use of accelerometer-based gesture interface as one of the alternative technologies, because accelerometers are effective in detecting the movements of human body, while their performance is not limited by environmental contexts such as lighting conditions or camera's field-of-view. Moreover, accelerometers are widely available nowadays in many mobile devices. We tackle the problem of classifying acceleration signal patterns of 26 English alphabets, which is one of the essential repertoires for the realization of education services based on robots. Recognizing 26 English handwriting patterns based on accelerometers is a very difficult task to take over because of its large scale of pattern classes and the complexity of each pattern. The most difficult problem that has been undertaken which is similar to our problem was recognizing acceleration signal patterns of 10 handwritten digits. Most previous studies dealt with pattern sets of 8~10 simple and easily distinguishable gestures that are useful for controlling home appliances, computer applications, robots etc. Good features are essential for the success of pattern recognition. To promote the discriminative power upon complex English alphabet patterns, we extracted 'motion trajectories' out of input acceleration signal and used them as the main feature. Investigative experiments showed that classifiers based on trajectory performed 3%~5% better than those with raw features e.g. acceleration signal itself or statistical figures. To minimize the distortion of trajectories, we applied a simple but effective set of smoothing filters and band-pass filters. It is well known that acceleration patterns for the same gesture is very different among different performers. To tackle the problem, online incremental learning is applied for our system to make it adaptive to the users' distinctive motion properties. Our system is based on instance-based learning (IBL) where each training sample is memorized as a reference pattern. Brute-force incremental learning in IBL continuously accumulates reference patterns, which is a problem because it not only slows down the classification but also downgrades the recall performance. Regarding the latter phenomenon, we observed a tendency that as the number of reference patterns grows, some reference patterns contribute more to the false positive classification. Thus, we devised an algorithm for optimizing the reference pattern set based on the positive and negative contribution of each reference pattern. The algorithm is performed periodically to remove reference patterns that have a very low positive contribution or a high negative contribution. Experiments were performed on 6500 gesture patterns collected from 50 adults of 30~50 years old. Each alphabet was performed 5 times per participant using $Nintendo{(R)}$ $Wii^{TM}$ remote. Acceleration signal was sampled in 100hz on 3 axes. Mean recall rate for all the alphabets was 95.48%. Some alphabets recorded very low recall rate and exhibited very high pairwise confusion rate. Major confusion pairs are D(88%) and P(74%), I(81%) and U(75%), N(88%) and W(100%). Though W was recalled perfectly, it contributed much to the false positive classification of N. By comparison with major previous results from VTT (96% for 8 control gestures), CMU (97% for 10 control gestures) and Samsung Electronics(97% for 10 digits and a control gesture), we could find that the performance of our system is superior regarding the number of pattern classes and the complexity of patterns. Using our gesture interaction system, we conducted 2 case studies of robot-based edutainment services. The services were implemented on various robot platforms and mobile devices including $iPhone^{TM}$. The participating children exhibited improved concentration and active reaction on the service with our gesture interface. To prove the effectiveness of our gesture interface, a test was taken by the children after experiencing an English teaching service. The test result showed that those who played with the gesture interface-based robot content marked 10% better score than those with conventional teaching. We conclude that the accelerometer-based gesture interface is a promising technology for flourishing real-world robot-based services and content by complementing the limits of today's conventional interfaces e.g. touch screen, vision and voice.