• The KIPS Transactions:PartD
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• v.16D no.6
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• pp.851-858
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• 2009

Mobbing is not restricted to problem of young people but the bigger recent problem occurs in workspaces. According to reports of ILO and domestic case mobbing in the workplace is increasing more and more numerically from 9.1%('03) to 30.7%('08). These mobbing brings personal and social losses. The proposed algorithm makes it possible to grasp not only current mobbing victims but also potential mobbing victims through user profile and contribute to efficient personnel management. This paper extracts user profile related to mobbing, in a way of selecting seven factors and fifty attributes that are related to this matter. Next, expressing extracting factors as '1' if they are related me or not '0'. And apply similarity function to attributes summation included in factors to calculate similarity between the users. Third, calculate optimizing weight choosing factors included attributes by applying neural network algorithm of SPSS Clementine and through this summation Mobbing-Value(MV) can be calculated . Finally by mapping MV of online social network users to G2 mobbing propensity classification model(4 Groups; Ideal Group of the online social network, Bullies, Aggressive victims, Victims) which is designed in this paper, can grasp mobbing propensity of users, which will contribute to efficient personnel management.

• Journal of Digital Contents Society
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• v.13 no.1
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• pp.101-110
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• 2012

Recently, social network sites are very popular with the enhancement of mobile device function and distribution. This gives rise to the registrations of the people on the social network sites and the usage of services on the social sites is also getting active. However, social network sites' venders do not provide services enough compared to the demand of users' to share contents from diverse roots by users effectively. In addition, the personal information can be revealed improperly in processes sharing policies and it is obvious that it raises a privacy invasion problem when users access the contents created from diverse devices according to the relationship by policies. However, the existing methods for the integration management of social network are weak to solve this problem. Thus, we propose a model to preserve user privacy, categorize contents efficiently, and give the access control permissions at the same time. In this paper, we encrypt policies and the trusted third party classifies the encrypted policies when the social network sites share the generated contents by users. In addition, the proposed model uses the RCBAC model to manage the contents generated by various devices and measures the similarity between relationships after encrypting when the user policies are shared. So, this paper can contribute to preserve user policies and contents from malicious attackers.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.164-171
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• 2006

Recently Digital multimedia broadcasting (DMB) has been available as a commercial service. The users sometimes have difficulty in finding their preferred multimedia contents and need to spend a lot of searching time finding them. They are even very likely to miss their preferred contents while searching for them. In order to solve the problem, we need a method for recommendation users preferred only minimum information. We propose an algorithm and a system for recommending users' preferred contents using preference transition probability from user's usage history. The system includes four agents: a client manager agent, a monitoring agent, a learning agent, and a recommendation agent. The client manager agent interacts and coordinates with the other modules, the monitoring agent gathers usage data for analyzing the user's preference of the contents, the learning agent cleans the gathered usage data and modeling with state transition matrix over time, and the recommendation agent recommends the user's preferred contents by analyzing the cleaned usage data. In the recommendation agent, we developed the recommendation algorithm using a user's preference transition probability for the contents. The prototype of the proposed system is designed and implemented on the WIPI(Wireless Internet Platform for Interoperability). The experimental results show that the recommendation algorithm using a user's preference transition probability can provide better performances than a conventional method.

• The Journal of Bigdata
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• v.8 no.2
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• pp.125-131
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• 2023

Globally, drug side effects rank among the top causes of death. To effectively respond to these adverse drug reactions, a shift towards an active real-time monitoring system, along with the standardization and quality improvement of data, is necessary. Integrating individual institutional data and utilizing large-scale data to enhance the accuracy of drug side effect predictions is critical. However, data sharing between institutions poses privacy concerns and involves varying data standards. To address this issue, our research adopts a federated learning approach, where data is not shared directly in compliance with privacy regulations, but rather the results of the model's learning are shared. We employ the Common Data Model (CDM) to standardize different data formats, ensuring accuracy and consistency of data. Additionally, we propose a drug monitoring system that enhances security and scalability management through a cloud-based federated learning environment. This system allows for effective monitoring and prediction of drug side effects while protecting the privacy of data shared between hospitals. The goal is to reduce mortality due to drug side effects and cut medical costs, exploring various technical approaches and methodologies to achieve this.

• Informatization Policy
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• v.27 no.2
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• pp.3-19
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• 2020

Artificial intelligence(AI) and blockchain are developing as the core technology leading the Fourth Industrial Revolution. However, AI is still showing limitations in securing and verifying data and explaining the evidence for the results, and blockchain also has some drawbacks such as excessive energy consumption and lack of flexibility in data management. This study analyzed technological limitations of AI and blockchain and convergence trends to overcome them, and finally suggested ways to improve Korea's related policies. Specifically, in terms of R&D reinforcement, we proposed 1) mid- and long-term AI /blockchain convergence research at the national level and 2) blockchain-based AI data platform development. In terms of creating an innovative ecosystem, we also suggested 3) development of AI/blockchain convergence applications by industry, and 4) Start-up support for developing AI/blockchain convergence business models. Lastly, in terms of improving the legal system, we insisted that 5) widening the application of regulatory sandboxes and 6) improving regulations related to privacy protection is necessary.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.8
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• pp.1783-1789
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• 2004

It is evaluated that there is infinit capability of creating new e business using P2P program. but the research for the method to protect the copyright of digital contents is urgent even for development of the p2p service because the problem of copyright protection for digital contents is not solved. Though this article, it can be induced that reliable contents sharing use to a flow fund by secure settlement architecture, user authentication and contents encryption and then it as the problem of copyright fee is solved, it is able to discontinue which trouble with a creation work for copyright fee and protection it's once again as growth of p2p market, p2p protocal is will be grow into a important protocal of advanced network. In this article, When users send digital contants to each other in internet, we proposed the P2P DRM algorism to offer a security function which using the technology of copyright management to use a AES Algorithm based on PKI.

• Knowledge Management Research
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• v.22 no.2
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• pp.119-144
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• 2021

Recently, as competition in the market evolves from the competition among companies to the competition among their supply chains, companies are struggling to enhance their supply chain management (hereinafter SCM). In particular, as blockchain technology with various technical advantages is combined with SCM, a lot of domestic manufacturing and distribution companies are considering the adoption of blockchain-oriented SCM (BOSCM) services today. Thus, it is an important academic topic to examine the factors affecting the use of blockchain-oriented SCM. However, most prior studies on blockchain and SCMs have designed their research models based on Technology Acceptance Model (TAM) or the Unified Theory of Acceptance and Use of Technology (UTAUT), which are suitable for explaining individual's acceptance of information technology rather than companies'. Under this background, this study presents a novel model of blockchain-oriented SCM acceptance model based on the Technology-Organization-Environment (TOE) framework to consider companies as the unit of analysis. In addition, Value-based Adoption Model (VAM) is applied to the research model in order to consider the benefits and the sacrifices caused by a new information system comprehensively. To validate the proposed research model, a survey of 126 companies were collected. Among them, by applying PLS-SEM (Partial Least Squares Structural Equation Modeling) with data of 122 companies, the research model was verified. As a result, 'business innovation', 'tracking and tracing', 'security enhancement' and 'cost' from technology viewpoint are found to significantly affect 'perceived value', which in turn affects 'intention to use blockchain-oriented SCM'. Also, 'organization readiness' is found to affect 'intention to use' with statistical significance. However, it is found that 'complexity' and 'regulation environment' have little impact on 'perceived value' and 'intention to use', respectively. It is expected that the findings of this study contribute to preparing practical and policy alternatives for facilitating blockchain-oriented SCM adoption in Korean firms.

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

• Journal of Convergence for Information Technology
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• v.11 no.2
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• pp.31-40
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• 2021

Blockchain technology is also actively studied in the real estate transaction field, and real estate transactions have various ways. In this paper, we propose a model that simplifies the authentication procedure of auction systems using Ethereum's Ether to solve the problem of offline court auctions. The proposed model is written in Ethereum's Solidity language, the court registers the sale date and the sale date with the DApp browser, and the bidder accesses the address of the individual's wallet created through Metamask's private key. The bidder then selects the desired sale and enters the bid price amount to participate in the auction. The bidder's record of the highest bid price for the sale he wants is written on the Ethereum test network as a smart contract. and creates a block. Finally, smart contracts written on the network are distributed by the court auction manager to all nodes in the blockchain network, and each node in the blockchain network can be viewed and contract verified. As a result of analyzing the smart contracts of the proposed model and the performance of the system, there are fees incurred due to the creation and use of Ether on platforms using Ethereum, and participation. Ether's changes in value affect the price of the sale, resulting in inconsistent fees in smart contracts each time. However, in future work, we issue our own tokens to solve the market volatility problem and commission problem with the value change of Ether, and refine complex court auction systems.

• Journal of the Korea Society for Simulation
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• v.30 no.1
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• pp.103-112
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• 2021

In recent years, public bicycle systems are widely spread over the world according to the development of ICT technology. Since the public bicycle systems in large cities need to secure both publicity and convenience for citizens, analysis of various their issues from introduction to operation is required. In addition, it is also necessary to prepare for various scenarios for coexistence with the PM business, which is recently in the spotlight as a last mile means and normally managed privately. This paper introduces modeling and simulation for efficient operations of public bicycle systems. In particular, the proposed method was developed in a form that can be easily used in other cities by modeling the general structure and behavior of the public bicycle system, and it was developed to facilitate modification and expansion of the future model with a component-based model configuration. This paper provides a case study of the propose method, which is the public bicycle simulation in Sejong City. The simulation results were derived by applying the data from the public bicycle system of Sejong City, and they were verified with the associated real data of Sejong City. Using the verified model, it is expected that it can be used as a tool to design and analyze various services on the public bicycle systems, which were especially suitable for Sejong City.