• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.73-85
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• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.

• Journal of The Korean Association For Science Education
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• v.31 no.5
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• pp.788-800
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• 2011

The purpose of this study was to investigate the elementary school teachers' perception of science writing. In this study, 10 elementary school teachers who have taught in the 3rd or 4th grade science lesson in 2010 were selected. Researchers constructed interview guide in three parts including the teachers' understanding of science writing, the status of science writing teaching and the difficulties of science writing in their classes. For the investigation, semi-structured in-depth interviews with 10 elementary school teachers were conducted individually. The results showed that the elementary school teachers were unfamiliar with the word ‘science writing’ and considered science writing as a writing using science learning contents. Also, they think that teaching science writing in their science lessons was not needed and didn't assess and provide detailed feedback with the students' written works. Most teachers needed teaching materials and assessment tools for science writing. To develop elementary teachers' understanding of the value and use of writing for learning in science, they will need to participate in science writing programs for in-service teachers and various teaching materials and assessment tools should also be developed.

• 대한공업교육학회지
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• v.34 no.2
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• pp.128-160
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• 2009

Narratively I have described interactions between two teachers performing an introduction to the engineering class with various situations such as place, teacher, student and subject. I have specifically illuminated a three-dimensional narrative inquiry space embracing the culture of the university, the college of engineering and the ABEEK(Accreditation Board of Engineering Education of Korea)program. The result of the study is as follows: First, in order to stimulate the students' motivation, the teachers have to make not only their class PowerPoint slides match the size of the classroom, but the content of the slides must be condensed with core concepts. They also should utilized some video clips to empower students' interest in the subject within their classrooms. Second, the teachers should do various class activities in the classroom. Instead of spending most of the class time with his/her explanation, it would be advantageous for the teachers to allow the students to perform a task in class. Third, the teachers should ask their students about assignments which are helping students' understanding of the subject and planning of their future. Lastly, the teachers need to design the mid-term and the final tests inducing the students' motivation. Those tests also must test students' creativity and insight of the subject. Thus, the test should consist of an interpretive exercise and an essay type of item thus reducing the multiple choice types of items. There are several limitations to the study. First it is difficult to generalize what we found here because it is a case study. Second, we could not study in depth the effect of the interaction between the two teachers who were performing the introduction to the engineering course during the academic semester. Third, this study just probed into the difficulties of teaching the course. Hence, we have to understand more by focusing on each issue such as adapting to a new learning environment as a student from abroad, a practical experience boosting the students' interest in the introduction to the engineering course, also a practical experience on process based learning-versus result based learning, and an effective management of the student team presentation etc.

• Tunnel and Underground Space
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• v.31 no.5
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• pp.374-384
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• 2021

In this study, we propose a new approach for automatic fracture detection in CT scan images of rock specimens. This approach is built on top of two-stage object detection deep learning algorithm called Faster R-CNN with a major modification of using rotated bounding box. The use of rotated bounding box plays a key role in the future work to overcome several inherent difficulties of fracture segmentation relating to the heterogeneity of uninterested background (i.e., minerals) and the variation in size and shape of fracture. Comparing to the commonly used bounding box (i.e., axis-align bounding box), rotated bounding box shows a greater adaptability to fit with the elongated shape of fracture, such that minimizing the ratio of background within the bounding box. Besides, an additional benefit of rotated bounding box is that it can provide relative information on the orientation and length of fracture without the further segmentation and measurement step. To validate the applicability of the proposed approach, we train and test our approach with a number of CT image sets of fractured granite specimens with highly heterogeneous background and other rocks such as sandstone and shale. The result demonstrates that our approach can lead to the encouraging results on fracture detection with the mean average precision (mAP) up to 0.89 and also outperform the conventional approach in terms of background-to-object ratio within the bounding box.

• Journal of Korean Elementary Science Education
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• v.41 no.4
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• pp.658-672
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• 2022

The unique teaching and learning difficulties of speed-related units in elementary school science are mainly due to the student's lack of mathematical thinking ability and procedural knowledge on speed measurement, and curriculums and textbooks must be constructed with these in mind. To identify the implications of composing a new science curriculum and relevant textbooks, this study reviewed the structure and contents of the speed-related units of three curriculums from the 2007 revised curriculum to the 2015 revised curriculum and the resulting textbooks and examined their relevance in light of the literature. Results showed that the current content carries the risk of making students calculate only the speed of an object through a mechanical algorithm by memorization rather than grasp the multifaceted relation between traveled distance, duration time, and speed. Findings also highlighted the need to reorganize the curriculum and textbooks to offer students the opportunity to learn the meaning of speed step-by-step by visualizing materials such as double number lines and dealing with simple numbers that are easy to calculate and understand intuitively. In addition, this paper discussed the urgency of improving inquiry performance such as process skills by observing and measuring an actual object's movement, displaying it as a graph, and interpreting it rather than conducting data interpretation through investigation. Lastly, although the current curriculum and textbooks emphasize the connection with daily life in their application aspects, they also deal with dynamics-related content somewhat differently from kinematics, which is the main learning content of the unit. Hence, it is necessary to reorganize the contents focusing on cases related to speed so that students can grasp the concept of speed and use it in their everyday lives. With regard to the new curriculum and textbooks, this study proposes that students be provided the opportunity to systematically and deeply study core topics rather than exclude content that is difficult to learn and challenging to teach so that students realize the value of science and enjoy learning it.

• Journal of the Korean Society of Radiology
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• v.16 no.4
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• pp.491-497
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• 2022

In this study, we tried to secure basic data to create an environment necessary for distance learning through a survey on professor demand. Among the 184 full-time faculty members of the university, 73 (39.89%) respondents in 2021 and 87 (47.28%) in 2022 were included. As a result of the research on professor demand, in the 2021 school year, 27 people (37%) were classified as LMS improvement items when checking attendance, 38 people (23.3%) were pin-mics as content development support items, and 26 people (35.6%), 33 people (45.2%) of GOM Mix and 25 people (34.2%) of the distance education support center wanted to learn video editing program as the item of video editing program they are currently using. In the 2022 school year, 27 people (31.03%) said mobile upgrade as an LMS improvement item, 52 people (59.8%) of pin-microphone as a content development support item, 33 people (37.9%), but currently using the content creation intention using a studio. As for the video editing program they are working on, 47 people (54%) of GOM Mix Pro and 23 people (26.4%) of the distance education support center want to learn content creation method. In addition, the intention to produce content using the studio for the 2021 and 2022 academic year and the desired educational topic of the distance education support center in the future appeared insignificant (p > 0.05). In this distance education support center, we are working to solve the class of LMS attendance, upgrade mobile, and plan to distribute pin microphones. We are planning to increase the usability of the studio and provide training on how to use video editing programs and how to create video content. In order for a smooth class to take place in a university distance class, the university authorities should seek ways to support the instructor so that he/she does not have difficulties in performing his/her role as a teaching designer, such as setting learning goals, organizing and organizing content, motivating learning, and establishing effective class participation plans. there is a need

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.121-130
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• 2024

As internet and communication technology (ICT) is improved exponentially, types and amount of available data also increase. Even though data analysis including statistics is significant to utilize this large amount of data, there are inevitable limits to process various and complex data in general way. Meanwhile, there are many attempts to apply machine learning (ML) in various fields to solve the problems according to the enhancement in computational performance and increase in demands for autonomous systems. Especially, data processing for the model input and designing the model to solve the objective function are critical to achieve the model performance. Data processing methods according to the type and property have been presented through many studies and the performance of ML highly varies depending on the methods. Nevertheless, there are difficulties in deciding which data processing method for data analysis since the types and characteristics of data have become more diverse. Specifically, multi-variate data processing is essential for solving non-linear problem based on ML. In this paper, we present a multi-variate tabular data processing scheme for ML-aided data analysis by using Titanic dataset from Kaggle including various kinds of data. We present the methods like input variable filtering applying statistical analysis and normalization according to the data property. In addition, we analyze the data structure using visualization. Lastly, we design an ML model and train the model by applying the proposed multi-variate data process. After that, we analyze the passenger's survival prediction performance of the trained model. We expect that the proposed multi-variate data processing and visualization can be extended to various environments for ML based analysis.

• Journal of The Korean Association For Science Education
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• v.15 no.2
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• pp.173-184
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• 1995

The purpose of this study was to analyse the problems of 'Science Inquiry Experiment Contest(SIEC)' which was one of 8 programs of 'The 2nd Student Science Inquiry Olympic Meet(SSIOM)'. The results and conclusions of this study were as follows: 1. It needs to reconsider the role of practical work within science experiment because practical work skills form one of the mainstays in current science. But the assessment of students' laboratory skills in the contest was made little account of. It is necessary to remind of what it means to be 'good at science'. There are two aspects: knowing and doing. Both are important and, in certain respects, quite distinct. Doing science is more of a craft activity, relying more on craft skill and tacit knowledge than on the conscious application of explicit knowledge. Doing science is also divided into two aspects, 'process' and 'skill' by many science educators. 2. The report's and checklist's assessment items were overlapped. Therefore it was suggested that the checklist assessment items were set limit to the students' acts which can't be found in reports. It is important to identify those activities which produce a permanent assessable product, and those which do not. Skills connected with recording and reporting are likely to produce permanent evidence which can be evaluated after the experiment. Those connected with manipulative skills involving processes are more ephemeral and need to be assessed as they occur. The division of student's experimental skills will contribute to the accurate assess of student's scientific inquiry experimental ability. 3. There was a wide difference among the scores of one participant recorded by three evaluators. This means that there was no concrete discussion among the evaluators before the contest. Despite the items of the checklists were set by preparers of the contest experiments, the concrete discussions before the contest were necessary because students' experimental acts were very diverse. There is a variety of scientific skills. So it is necessary to assess the performance of individual students in a range of skills. But the most of the difficulties in the assessment of skills arise from the interaction between measurement and the use. To overcome the difficulties, not only must the mark needed for each skill be recorded, something which all examination groups obviously need, but also a description of the work that the student did when the skill was assessed must also be given, and not all groups need this. Fuller details must also be available for the purposes of moderation. This is a requirement for all students that there must be provision for samples of any end-product or other tangible form of evidence of candidates' work to be submitted for inspection. This is rather important if one is to be as fair as possible to students because, not only can this work be made available to moderators if necessary, but also it can be used to help in arriving at common standards among several evaluators, and in ensuring consistent standards from one evaluator over the assessment period. This need arises because there are problems associated with assessing different students on the same skill in different activities. 4. Most of the students' reports were assessed intuitively by the evaluators despite the assessment items were established concretely by preparers of the experiment. This result means that the evaluators were new to grasp the essence of the established assessment items of the experiment report and that the students' assessment scores were short of objectivity. Lastly, there are suggestions from the results and the conclusions. The students' experimental acts which were difficult to observe because they occur in a flash and which can be easily imitated should be excluded from the assessment items. Evaluators are likely to miss the time to observe the acts, and the students who are assessed later have more opportunity to practise the skill which is being assessed. It is necessary to be aware of these problems and try to reduce their influence or remove them. The skills and processes analysis has made a very useful checklist for scientific inquiry experiment assessment. But in itself it is of little value. It must be seen alongside the other vital attributes needed in the making of a good scientist, the affective aspects of commitment and confidence, the personal insights which come both through formal and informal learning, and the tacit knowledge that comes through experience, both structured and acquired in play. These four aspects must be continually interacting, in a flexible and individualistic way, throughout the scientific education of students. An increasing ability to be good at science, to be good at doing investigational practical work, will be gained through continually, successively, but often unpredictably, developing more experience, developing more insights, developing more skills, and producing more confidence and commitment.

• The Journal of Korean Academy of Sensory Integration
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• v.21 no.2
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• pp.45-57
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• 2023

Objective : The purpose of this study was to investigate the concurrent validity of the self- and proxy-report versions of the KIDSCREEN-10 quality of life questionnaire. Methods : A total of nine children and nine parents were selected to represent a cohort registered for a school-based wellness program. Two versions of the KIDSCREEN-10 questionnaire (self- and proxy reports) were administered to the children and their parents. The Rasch rating scale model was applied to determine the dimensionality and item difficulty of the two versions of the questionnaire. Moreover, the item-person matching map and Spearman's rho were compared to confirm the concurrent validity of the two versions. Results : All items, except four items (i.e., autonomy, home life, concentration/learning, and peers/social support), fit the Rasch rating scale model of the children's self-report version of the questionnaire. With regard to the parent's proxy-report version, two items misfit the model. While the items of the self- and proxy-report versions showed similar item difficulties, the parents had a tendency to be more severe in their ratings than the children. The correlation between the two versions was relatively low (Spearman's rho = .533, p > .05). The scatterplots between the two versions showed differences in the item difficulties of the physical and psychological well-being and self-perception items. Conclusion : These findings suggest that the three identified items should be taken into consideration when measuring children's health-related quality of life using the KIDSCREEN-10 questionnaire.