• Journal of the Ergonomics Society of Korea
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• v.33 no.5
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• pp.355-363
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• 2014

Objective:The purpose of this study is to design a three-set type (Sebulsik) keyboard that is to input Korean text with no shifted keys and also compatible with the standard Korean computer keyboard or ANSI keyboard. Background: The KS computer keyboard is two-set type (Dubulsik). Existing and proposed designs of three-set type of past studies are not compatible with KS or ANSI keyboard and are complex with many redundant letters. Method: The number of Korean letters for 3-set type is analyzed. Then Korean letters are arranged with normality and with spatial compatibility to the KS Korean keyboard, and symbols were arranged to same positions with ANSI keyboard. Results: Initial consonants of 14 numbers and 6 vowels are arranged as exactly same positions of KS keyboard, and other vowels are arranged with spatial compatibility. Symbols are arranged to the same positions with ANSI keyboard, and 10 digits are confirmed and has compatibility to International standard. Conclusion: A 38-key model, 38K, is designed to require minimal keys to input Korean text with no shifted keys, increased the compatibility to the KS Korean computer keyboard. Application: Using the proposed 38-key model, 38K, it can be taken into account for keyboards in industrial production. It is applicable to user group of 3-set type Korean keyboard with more easy than past keyboards.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1263-1279
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• 2020

Activating appropriate roles for a session in the role-based access control (RBAC) model has become challenging because of the so-called role explosion. In this paper, factors and issues related to user-driven role management are analysed, and a session role activation (SRA) problem based on reasonable assumptions is proposed to describe the problem of such role management. To solve the SRA problem, we propose an extended RBAC model with context-based role filtering. When a session is created, context conditions are used to filter roles that do not need to be activated for the session. This significantly reduces the candidate roles that need to be reviewed by the user, and aids the user in rapidly activating the appropriate roles. Simulations are carried out, and the results show that the extended RBAC model is effective in filtering the roles that are unnecessary for a session by using predefined context conditions. The extended RBAC model is also implemented in the Apache Shiro framework, and the modifications to Shiro are described in detail.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.2
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• pp.101-107
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• 2015

Modern football has transformed into a scientific football based on data. With this trend, various methods for tactics studies and outcome prediction have been developed on the perspective of data analysis. In this paper, we propose a structural equation model for football game. We analyze critical factors that affect to the winning of game except psychological parts and the causal relationship between latent variables and observed variables is statistically verified through the proposed structural equation model. The results show that the Passing ability and the Ball possession affect to the Attack ability, and consequently it has a positive impact on the winning of game.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.914-927
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• 2017

Weighted network link prediction is a challenge issue in complex network analysis. Unsupervised methods based on local structure are widely used to handle the predictive task. However, the results are still far from satisfied as major literatures neglect two important points: common neighbors produce different influence on potential links; weighted values associated with links in local structure are also different. In this paper, we adapt an effective link prediction model-local naive Bayes model into a weighted scenario to address this issue. Correspondingly, we propose a weighted local naive Bayes (WLNB) probabilistic link prediction framework. The main contribution here is that a weighted cluster coefficient has been incorporated, allowing our model to inference the weighted contribution in the predicting stage. In addition, WLNB can extensively be applied to several classic similarity metrics. We evaluate WLNB on different kinds of real-world weighted datasets. Experimental results show that our proposed approach performs better (by AUC and Prec) than several alternative methods for link prediction in weighted complex networks.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.707-720
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• 2021

To improve the accuracy of the recommendation system, multi-criteria recommendation systems have been widely researched. However, it is highly complicated to extract the preferred features of users and items from the data. To this end, subjective indicators, which indicate a user's priorities for personalized recommendations, should be derived. In this study, we propose a method for generating recommendation candidates by predicting multi-criteria ratings from reviews and using them to derive user priorities. Using a deep learning model based on convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM), multi-criteria prediction ratings were derived from reviews. These ratings were then aggregated to form a linear regression model to predict the overall rating. This model not only predicts the overall rating but also uses the training weights from the layers of the model as the user's priority. Based on this, a new score matrix for recommendation is derived by calculating the similarity between the user and the item according to the criteria, and an item suitable for the user is proposed. The experiment was conducted by collecting the actual "TripAdvisor" dataset. For performance evaluation, the proposed method was compared with a general recommendation system based on singular value decomposition. The results of the experiments demonstrate the high performance of the proposed method.

• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.32-44
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• 2023

Information security breach is a major risk in e-learning. This study presents the potential information security disruptions in Learning Management Systems (LMS) from the perspective of users. We use the Technology Acceptance Model approach as a user perception model of information security, and the results of a questionnaire comprising 44 questions for instructors and students across Indonesia to verify the model. The results of the data analysis and model testing reveals that lecturers and students perceive the level of information security in the LMS differently. In general, the information security aspects of LMSs affect the perceptions of trust of student users, whereas such a correlation is not found among lecturers. In addition, lecturers perceive information security aspect on Moodle is and Google Classroom differently. Based on this finding, we recommend that institutions make more intense efforts to increase awareness of information security and to run different information security programs.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.354-357
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• 2021

Infants communicate their feelings and needs to the outside world through non-verbal methods such as crying and displaying diverse facial expressions. However, inexperienced parents tend to decode these non-verbal messages incorrectly and take inappropriate actions, which might affect the bonding they build with their babies and the cognitive development of the newborns. In this paper, we propose an aural-visual two-stream based infant cry recognition system to help parents comprehend the feelings and needs of crying babies. The proposed system first extracts the features from the pre-processed audio and video data by using the VGGish model and 3D-CNN model respectively, fuses the extracted features using a fully connected layer, and finally applies a SoftMax function to classify the fused features and recognize the corresponding type of cry. The experimental results show that the proposed system classification exceeds 0.92 in F1-score, which is 0.08 and 0.10 higher than the single-stream aural model and single-stream visual model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.10
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• pp.2676-2683
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• 2009

The objective of this thesis is to improve the quality of a software product based on the enhancement of a software design quality using a better error detecting method. Also, this thesis is based on a software design method called as MOA(Methodology for Object to Agents) which uses an ontology based ODES(A Method based on Ontology for Detecting Errors in the Software Design) model as a common information model. At this thesis, a new format of error detecting method was defined. The method is implemented during a transformation process from UML model to ODES model using a ODES model, a Inter-View Inconsistency Detection technique and a combination of ontologic property of consistency framework and related rules. Transformation process to ODES model includes lexicon analysis and meaning analysis of a software design using of multiple mapping table at algorithm for the generation of ODES model instance.

• Journal of The Korean Association of Information Education
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• v.9 no.2
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• pp.339-348
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• 2005

The trend of the computer education is shifting from problem-solving in the real world and using functions for it to behavioristic perspectives which encourage people to acquire the functions simply according to the applying programs of well-known companies. Thus, this research studies the computer instructional model which emphasizes the basic computer education and connections to the real world at the same time. In the constructivistic perspectives, this model emphasizes the learners activities, their using of meta-cognitive strategies to reflect their level of the lesson and collaborative-reflective learning of problem-solving. The research applied in the real computer skill education and according to the result of the research, I could find the experimental group got high level of learning achievement and this benefits to the high level group rather than low and middle group.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.95-100
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• 2023

Due to Traffic accidents people faces health and economical casualties around the world. As the population increases vehicles on road increase which leads to congestion in cities. Congestion can lead to increasing accident risks due to the expansion in transportation systems. Modern cities are adopting various technologies to minimize traffic accidents by predicting mathematically. Traffic accidents cause economical casualties and potential death. Therefore, to ensure people's safety, the concept of the smart city makes sense. In a smart city, traffic accident factors like road condition, light condition, weather condition etcetera are important to consider to predict traffic accident severity. Several machine learning models can significantly be employed to determine and predict traffic accident severity. This research paper illustrated the performance of a hybridized neural network and compared it with other machine learning models in order to measure the accuracy of predicting traffic accident severity. Dataset of city Leeds, UK is being used to train and test the model. Then the results are being compared with each other. Particle Swarm optimization with artificial neural network (PSO-ANN) gave promising results compared to other machine learning models like Random Forest, Naïve Bayes, Nearest Centroid, K Nearest Neighbor Classification. PSO- ANN model can be adopted in the transportation system to counter traffic accident issues. The nearest centroid model gave the lowest accuracy score whereas PSO-ANN gave the highest accuracy score. All the test results and findings obtained in our study can provide valuable information on reducing traffic accidents.