• Journal of Digital Convergence
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• v.13 no.9
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• pp.49-56
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• 2015

Korea government has established clear cost estimation standard based on a survey of e-learning contents development cost and presented 'e-Learning Contents Development Cost Estimation Guidelines' that reflect the characteristics of the e-learning industry. However, if there is no institutional support, this guideline and system fails to achieve the purposes and objectives. And it is likely to be facing a dead document. Therefore, the policy foundation is required. This study suggested the following policy; stepwise activation of cost estimation standard, enact announcement and periodically adjustment of cost estimation standard, installation and operation of cost estimation standard operational committee, conjunction with the e-learning industry survey, cultural diffusion of co-owned copyright, systematic monitoring of the e-learning contents development process, research on activating policy of cost estimation standard, conjunction with the standard contract for enhancing policy effectiveness.

• The Journal of Information Technology
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• v.6 no.3
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• pp.137-150
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• 2003

Recently, developing information technology and increasing internet usage, e-Learning service industry is rapidly growing. However institutions and regulations related e-Learning service are insufficient. Users of e-Learning service were lower grade in school relatively, spent average 40,000won/month and used other private education service. Users answered that they were generally satisfied at e-Learning service but were not satisfied at e-Learning 'fee'and 'the contract process'. Specially, the result suggest that consumer's satisfaction is affect by experience of demage and complains related e-Learning usage.

• Journal of Information Technology Services
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• v.20 no.1
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• pp.131-143
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• 2021

Commercial transactions, one of the pillars of the capitalist economy, are occurring countless times every day, especially small and medium-sized businesses. However, small and medium-sized enterprises are bound to be the legal underdogs in contracts for commercial transactions and do not receive legal support for contracts for fair and legitimate commercial transactions. When subcontracting contracts are concluded among small and medium-sized enterprises, 58.2% of them do not apply standard contracts and sign contracts that have not undergone legal review. In order to support small and medium-sized enterprises' fair and legitimate contracts, small and medium-sized enterprises can be protected from legal threats if they can reduce the risk of signing contracts by analyzing various risks in the contract and analyzing and informing them of toxic clauses and omitted contracts in advance. We propose a risk prediction model for the machine reading-based legal contract to minimize legal damage to small and medium-sized business owners in the legal blind spots. We have established our own set of legal questions and answers based on the legal data disclosed for the purpose of building a model specialized in legal contracts. Quantitative verification was carried out through indicators such as EM and F1 Score by applying pine tuning and hostile learning to pre-learned machine reading models. The highest F1 score was 87.93, with an EM value of 72.41.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.12
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• pp.321-332
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• 2020

Decentralized approaches are extensively researched by academia and industry in order to cover up the flaws of existing systems in terms of data privacy. Blockchain and decentralized learning are prominent representatives of a deconcentrated approach. Blockchain is secure by design since the data record is irrevocable, tamper-resistant, consensus-based decision making, and inexpensive of overall transactions. On the other hand, decentralized learning empowers a number of devices collectively in improving a deep learning model without exposing the dataset publicly. To motivate participants to use their resources in building models, a decent and proportional incentive system is a necessity. A centralized incentive mechanism is likely inconvenient to be adopted in decentralized learning since it relies on the middleman that still suffers from bottleneck issues. Therefore, we design an incentive model for decentralized learning applications by leveraging the Ethereum smart contract. The simulation results satisfy the design goals. We also outline the concerns in implementing the presented scheme for sensitive data regarding privacy and data leakage.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.231-238
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• 2023

As the demand for distance education increases, interest in the management of learners' rights is increasing. Blockchain technology is a technology that guarantees the integrity of the learner's learning history, and enables learner-led learning control, data security, and sharing of learning resources. In this paper, we proposed a blockchain technology-based learning management system based on Hyperledger Fabric that can be verified through permission between nodes among blockchain platforms. Learning resources can be shared differentially according to the learning progress. Also the percentage of individual learners that can be managed. As a result of the study, the superiority of the platform in terms of convenience compared to the existing platform was demonstrated. As a result of the performance evaluation for the research in this paper, it was confirmed that the convenience was improved by more than 5%, and the performance was 4-5% superior to the existing platform in terms of learner satisfaction.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.588-592
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• 2015

Many studies have affirmed a negative influence of fragmentation on learning and knowledge sharing in construction projects. However, the literature overlooked enablers of learning within this context. The purpose of this paper is to explore the factors that facilitate project learning and ways to negate any unbecoming effects of fragmentation. Qualitative study used to explore the enablers through interviews administered to 11 top management individuals working in different construction projects in Malaysia. The findings revealed the following factors: participation, relationships, togetherness, and roles of project leader and coordinator. The role of boundary objects was also highlighted including information technology (IT), contract and procedures, drawings, specifications, and reports. The outcome of this paper initiates the development of a model for better knowledge creation and sharing in construction projects. The significance of this model stems from its ability to connection both the characteristics of construction project and project learning theories using the enablers. It is envisaged that future work will be to confirm the model in a quantitative study.

• Journal of Information Technology Services
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• v.21 no.4
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• pp.91-103
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• 2022

This study proposes a model for predicting contracted power using electric power data collected in real time from convenience stores nationwide. By optimizing the prediction model using machine learning, it will be possible to predict the contracted power required to renew the contract of the existing convenience store. Contracted power is predicted through the XGBoost regression model. For the learning of XGBoost model, the electric power data collected for 16 months through a real-time monitoring system for convenience stores nationwide were used. The hyperparameters of the XGBoost model were tuned using the GridesearchCV, and the main features of the prediction model were identified using the xgb.importance function. In addition, it was also confirmed whether the preprocessing method of missing values and outliers affects the prediction of reduced power. As a result of hyperparameter tuning, an optimal model with improved predictive performance was obtained. It was found that the features of power.2020.09, power.2021.02, area, and operating time had an effect on the prediction of contracted power. As a result of the analysis, it was found that the preprocessing policy of missing values and outliers did not affect the prediction result. The proposed XGBoost regression model showed high predictive performance for contract power. Even if the preprocessing method for missing values and outliers was changed, there was no significant difference in the prediction results through hyperparameters tuning.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.272-275
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• 2022

The federated learning can be utilized in conjunction with the blockchain technology to provide good privacy protection and reward distribution mechanism in the field of intelligent IOT in edge computing scenarios. Nonetheless, the synchronous federated learning ignores the waiting delay due to the heterogeneity of edge devices (different computing power, communication bandwidth, and dataset size). Moreover, the potential of smart contracts was not fully explored to do some flexible design. This paper investigates the fusion application based on the FLchain, which is the combination of asynchronous federated learning and blockchain, discusses the communication optimization, and explores the feasible design of smart contract to solve some problems.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.11
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• pp.471-480
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• 2023

The Korean construction order volume in South Korea grew significantly from 91.3 trillion won in public orders in 2013 to a total of 212 trillion won in 2021, particularly in the private sector. As the size of the domestic and overseas markets grew, the scale and complexity of EPC (Engineering, Procurement, Construction) projects increased, and risk management of project management and ITB (Invitation to Bid) documents became a critical issue. The time granted to actual construction companies in the bidding process following the EPC project award is not only limited, but also extremely challenging to review all the risk terms in the ITB document due to manpower and cost issues. Previous research attempted to categorize the risk terms in EPC contract documents and detect them based on AI, but there were limitations to practical use due to problems related to data, such as the limit of labeled data utilization and class imbalance. Therefore, this study aims to develop an AI model that can categorize the contract terms based on the FIDIC Yellow 2017(Federation Internationale Des Ingenieurs-Conseils Contract terms) standard in detail, rather than defining and classifying risk terms like previous research. A multi-text classification function is necessary because the contract terms that need to be reviewed in detail may vary depending on the scale and type of the project. To enhance the performance of the multi-text classification model, we developed the ELECTRA PLM (Pre-trained Language Model) capable of efficiently learning the context of text data from the pre-training stage, and conducted a four-step experiment to validate the performance of the model. As a result, the ensemble version of the self-developed ITB-ELECTRA model and Legal-BERT achieved the best performance with a weighted average F1-Score of 76% in the classification of 57 contract terms.

• The Journal of Bigdata
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• v.5 no.1
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• pp.89-96
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• 2020

Deep learning using an artificial neural network has been recently researched and developed in various fields such as image recognition, big data and data analysis. However, federated learning has emerged to solve issues of data privacy invasion and problems that increase the cost and time required to learn. Federated learning presented learning techniques that would bring the benefits of distributed processing system while solving the problems of existing deep learning, but there were still problems with server-client system and motivations for providing learning data. So, we replaced the role of the server with a blockchain system in federated learning, and conducted research to solve the privacy and security problems that are associated with federated learning. In addition, we have implemented a blockchain-based system that motivates users by paying compensation for data provided by users, and requires less maintenance costs while maintaining the same accuracy as existing learning. In this paper, we present the experimental results to show the validity of the blockchain-based system, and compare the results of the existing federated learning with the blockchain-based federated learning. In addition, as a future study, we ended the thesis by presenting solutions to security problems and applicable business fields.