• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.147-156
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• 2020

This study aims to analyze the effect of public spending, macroeconomic variables, and BPK opinion on economic growth. This study is motivated by the inequality of fiscal policy effectiveness between regions in Indonesia in influencing the economic growth of different regions, the ability of local governments to attract foreign investors, and the transparency of regional financial management in designing development programs to encourage regional economic growth. The analytical tool in this study is a dynamic panel regression model with data from 2008 to 2017. The results of this study show that, in the short term, the population affects regional economic growth, while in the long term, the economic growth is affected by the number of people, the poor, General Allocation Fund, health budget, foreign investment and BPK opinion. The findings of this study are that in the long term the General Allocation Fund becomes an obstacle to economic growth, this is because the general allocation funds is widely used to cover the lack of funds for routine regional activities, thereby reducing activities for development programs. Another research finding is that fiscal policies carried out by local governments make a small and ineffective contribution to promoting economic growth.

• Journal of Arbitration Studies
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• v.28 no.2
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• pp.165-178
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• 2018

Exchanging information with a person without credentials over the Internet does not pose any problems. A decentralized system based on blockchain technology enables the user to exchange new value(currency) with other uncredited users. The blockchain technology creates a new paradigm in which the distribution system can be founded on trust. Various applied distribution systems are being developed based on this paradigm. This study analyzed the problems between an institute's grading system and the central administration system. The limitations of an institute's current central management system were presented through actual cases. To improve the problem, a decentralized system based on block chain technology was presented in order to overcome the fundamental limitations by utilizing blockchain technology, peer-to-peer network, and the distribution system. In the central system, a malicious moderator could create a malicious edit that becomes the cause of a dispute, but in a decentralized system, a problem cannot be created even if there were to be a malicious moderator. However, it is difficult for a single college institute to create a distribution system in order to actualize an effective system. Comparatively, it would be possible to create a decentralized system in which all educational institutes in Korea (elementary schools, middle schools, high schools, colleges) took part in. The application of a decentralized system would improve the public transparency and reliability of educational institutes.

• Korean Journal of Agricultural Science
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• v.47 no.2
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• pp.283-290
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• 2020

In 2018, 647 thousand tons of eggs were produced and consumed. However, the issue of pesticides used for egg in 2017 made Korean consumers worry about the food safety of eggs, and the volume of egg consumption decreased. The Korean egg industry also has another problem due to an unclear and inefficient marketing structure at the farm level. This marketing situation of eggs at the farm level in Korea needs a large-scale restructuring of the market structure, including introducing an EPC (egg processing center). Especially, the introduction of an EPC has been discussed by government officers and specialists, but the social benefit of an EPC, which will be the driving point for approving an EPC, has not been measured yet. The purpose of this study was to measure the effect of introducing an EPC in Korea. Through an analysis using EDM (equilibrium displacement model), a few findings are presented. First, the introduction of an EPC may increase the transparency of price discovery and decrease the transaction cost. And thus, it results in a higher producer price, lower consumer price, and larger quantity at market equilibrium. Second, an EPC will improve the level of food safety of eggs, which can increase the satisfaction of domestic producers and consumers. Third, the introduction of an EPC may create new consumption of eggs. Based on these three effects, the new social benefits in monetary terms from the introduction of an EPC in Korea could be 23.9 - 35.2 billion won.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.587-608
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• 2022

The European Union recently established the General Data Protection Regulation (GDPR) for secure data use and personal information protection. Inspired by this, South Korea revised their Personal Information Protection Act, the Act on Promotion of Information and Communications Network Utilization and Information Protection, and the Credit Information Use and Protection Act, collectively known as the "Three Data Bills," which prescribe safe personal information use based on pseudonymous data processing. Based on these bills, the personal data store (PDS) has received attention because it utilizes the MyData service, which actively manages and controls personal information based on the approval of individuals, and it practically ensures their rights to informational self-determination. Various types of PDS models have been developed by several countries (e.g., the US, Europe, and Japan) and global platform firms. The South Korean government has now initiated MyData service projects for personal information use in the financial field, focusing on personal credit information management. There is also a need to verify the efficacy of this service in diverse fields (e.g., medical). However, despite the increased attention, existing MyData models and frameworks do not satisfy security requirements of ensured traceability, transparency, and distributed authentication for personal information use. This study analyzes primary PDS models and compares them to an internationally standardized framework for personal information security with guidelines on MyData so that a proper PDS model can be proposed for South Korea.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.251-261
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• 2022

As AI has a wide range of influence on human social life, issues of transparency and ethics of AI are emerging. In particular, it is widely known that due to the existence of historical bias in data against ethics or regulatory frameworks for fairness, trained AI models based on such biased data could also impose bias or unfairness against a certain sensitive group (e.g., non-white, women). Demographic disparities due to AI, which refer to socially unacceptable bias that an AI model favors certain groups (e.g., white, men) over other groups (e.g., black, women), have been observed frequently in many applications of AI and many studies have been done recently to develop AI algorithms which remove or alleviate such demographic disparities in trained AI models. In this paper, we consider a problem of using the information in the sensitive variable for fair prediction when using the sensitive variable as a part of input variables is prohibitive by laws or regulations to avoid unfairness. As a way of reflecting the information in the sensitive variable to prediction, we consider a two-stage procedure. First, the sensitive variable is fully included in the learning phase to have a prediction model depending on the sensitive variable, and then an imputed sensitive variable is used in the prediction phase. The aim of this paper is to evaluate this procedure by analyzing several benchmark datasets. We illustrate that using an imputed sensitive variable is helpful to improve prediction accuracies without hampering the degree of fairness much.

• Journal of the Semiconductor & Display Technology
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• v.22 no.2
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• pp.45-49
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• 2023

When blockchain technology, which shows various applicability, is utilized as a component of a database system, the characteristics of open verification and integrity/transparency of blockchain technology can bring new functionalities or enhanced results to the existing database system. However, when applying this blockchain technology to a database system, the cost versus expected effect in various performance perspectives must be evaluated. These costs include execution time and required storage space, and the performance of the converged system may vary in analysis method depending on the configuration method of the characteristics of the blockchain. This paper aims to propose an analysis model for the entire architecture by considering aspects that are not considered in the performance analysis models of database systems and the unique characteristics of blockchains. In doing so, we are trying to build a theoretical framework as an important conceptual technique that should be considered in the evaluation process of the performance results that can be obtained through the utilization of blockchain components in database systems. What we hope is that this work is expected to provide a useful foundation for researchers interested in the convergence of database systems and blockchain technology in order to construct a system with new future functionalities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.284-310
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• 2024

Identifying clinical pathways for disease diagnosis and treatment process recommendations are seriously decision-intensive tasks for health care practitioners. It requires them to rely on their expertise and experience to analyze various categories of health parameters from a health record to arrive at a decision in order to provide an accurate diagnosis and treatment recommendations to the end user (patient). Technological adaptation in the area of medical diagnosis using AI is dispensable; using expert systems to assist health care practitioners in decision-making is becoming increasingly popular. Our work architects a novel knowledge-based recommender system model, an expert system that can bring adaptability and transparency in usage, provide in-depth analysis of a patient's medical record, and prescribe diagnostic results and treatment process recommendations to them. The proposed system uses a set of parallel discrete fuzzy rule-based classifier systems, with each of them providing recommended sub-outcomes of discrete medical conditions. A novel knowledge-based combiner unit extracts significant relationships between the sub-outcomes of discrete fuzzy rule-based classifier systems to provide holistic outcomes and solutions for clinical decision support. The work establishes a model to address disease diagnosis and treatment recommendations for primary lung disease issues. In this paper, we provide some samples to demonstrate the usage of the system, and the results from the system show excellent correlation with expert assessments.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.4
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• pp.89-100
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• 2020

In order to reduce the time and cost used in insurance processing, studies have been actively carried out to apply blockchain smart contract technology to car insurance. However, by using traffic data that is insufficient to prove accidents, existing studies are being exposed to the risk of insurance fraud, such as forgery and overstated damage by malicious insurers. To solve this problem, we propose an accident data-based reliability estimation model by using both various types of data through sensors, RSUs, and IoT devices embedded in automobiles and smart contracts. In particular, the regression model was applied in consideration of the weight estimation according to the type of traffic accident data and the reliability estimation model trained according to various accident situations. The proposed model is expected to effectively reduce fraud and insurance litigation while providing transparency in the insurance process and streamlining it is well.

• The Journal of the Acoustical Society of Korea
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• v.23 no.5
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• pp.390-399
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• 2004

In this paper, we design and implement a multiple-view VoiceXML editor to improve editing efficiency of the VoiceXML. The VoiceXML multiple-view Editor uses a MVC framework to support multiple views and paradigm. Our multiple-view editor consists of Model. View and Controller using MVC framework. A model, core data structure. is constructed of abstract syntax tree and abstract grammar. A view. user interface. is formalized in unparsing rules and unparser. A controller. to control model and view. is made of command interpreter and tree handler. The VoiceXML multiple-view editor overcomes a drawbacks of existing XML editors by showing document structure and context concurrently. as well as document flows. Our VoiceXML multiple-view editor. which MVC framework has been applied, provides various editing views concurrently to users. Thereby. it supports efficient and convenient editing environments for voice-web documents to users and it guarantees transparency of editors. as various views have a same consistent model.

• Journal of KIISE:Software and Applications
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• v.28 no.11
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• pp.842-854
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• 2001

Today\`s first generation shopping agent is limited to comparing merchant offerings usually on price instead of their full range of attributes. Even in the full range comparison, there is not a good model which considers the overall features in the negotiation process. Therefore, the negotiation model needs to be extended to include negotiations over the more attributes. In this paper, we propose a negotiation model in the agent-mediated electronic commerce to negotiate over prices, product features, warranties and service policies based on utility theory and simple heuristics. We will describe a prototype agent-mediated electronic commerce framework called Pmart. This framework provides the software reuse and the extensibility based on the object-oriented technology. It is implemented on Windows-based platforms using Java and CORBA for the network transparency and platform independence.