• International Journal of Computer Science & Network Security
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• v.23 no.2
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• pp.1-12
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• 2023

The article examines the axiological psycho-philosophical understanding of the phenomenon of money and its value role in modern society. The traditional and virtual context of the representation of the money phenomenon is considered.Following the ideas of G. Simmel, the authors consider money not only as a purely economic, but also a psycho-philosophical, cultural and social phenomenon. Money appears as a result of cultural development of the world and gradually forms a monetary culture as a space of economic and social interaction of people. Under the influence of the monetary culture of one or another historical period, the character of a person's economic activity, values and life orientations are formed. Modern money culture is often called financial civilization. Peculiarities of modern monetary culture are studied, its main features and problems are determined in the article. The problem of the peculiarities of the constructive and destructive attitude of the individual towards money is identified; a psycho-philosophical and cultural-identification typology of people is described, which is based on clinical observations and interpreted through the prism of psychoanalytic theory. The concept of money is highlighted from the standpoint of a social-psychological approach. The theoretical foundations of money's influence on the decision-making process and human behavior are also revealed.

• KNOM Review
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• v.22 no.3
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• pp.20-24
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• 2019

Blockchain is a distributed ledger-based technology where all nodes participating in the blockchain network are connected to the P2P network. When a transaction is created in the blockchain network, the transaction is propagated and validated by the blockchain nodes. The verified transaction is sent to peers connected to each node through P2P network, and the peers keep the transaction in the memory pool. Due to the nature of P2P networks, the number and type of transactions delivered by a blockchain node is different for each node. As a result, all nodes do not have the same memory pool. Research is needed to solve problems such as attack detection. In this paper, we analyze transactions in the memory pool before solving problems such as transaction fee manipulation, double payment problem, and DDos attack detection. Therefore, this study collects transactions stored in each node memory pool of Bitcoin and Ethereum, a cryptocurrency system based on blockchain technology, and analyzes how much common transactions they have using jacquard similarity.

• The Korean Journal of Applied Statistics
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• v.36 no.1
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• pp.85-100
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• 2023

The stochastic volatility (SV) model is one of the main methods of modeling time-varying volatility. In particular, SV model is actively used in estimation and prediction of financial market volatility and option pricing. This paper attempts to model the time-varying volatility of the bitcoin market price using SV model. Hidden Markov model (HMM) is combined with the SV model to capture characteristics of regime switching of the market. The HMM is useful for recognizing patterns of time series to divide the regime of market volatility. This study estimated the volatility of bitcoin by using data from Upbit, a cryptocurrency trading site, and analyzed it by dividing the volatility regime of the market to improve the performance of the SV model. The MCMC technique is used to estimate the parameters of the SV model, and the performance of the model is verified through evaluation criteria such as MAPE and MSE.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.9-17
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• 2023

Research on virtual assets has been mainly interested in policy preparation or legislation for the introduction of virtual assets, or virtual asset operation technology, but this study presents investment factors that most asset investors consider important when making investment decisions. By doing so, we came up with research results that are practically helpful to virtual asset investors. According to the research results, (1) virtual asset investors consider business models such as marketability and competitive advantage of virtual assets as the most important factors, and (2) are highly interested in factors that can be objectively judged when investing in virtual assets. The results of this study suggest that a virtual asset trading market environment that can provide objective investment information and discover various judgment factors that enable virtual asset investors to objectively judge virtual assets should be prepared, and that virtual asset businesses using core technologies will continue to grow. This suggests that a variety of policy support is needed to enable this.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.1
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• pp.17-28
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• 2019

In the financial investment management strategy, the distributed investment selecting and combining various financial assets is called portfolio management theory. In recent years, the blockchain based financial assets, such as cryptocurrencies, have been traded on several well-known exchanges, and an efficient portfolio management approach is required in order for investors to steadily raise their return on investment in cryptocurrencies. On the other hand, deep learning has shown remarkable results in various fields, and research on application of deep reinforcement learning algorithm to portfolio management has begun. In this paper, we propose an efficient financial portfolio investment management method based on Asynchronous Advantage Actor-Critic (A3C), which is a representative asynchronous reinforcement learning algorithm. In addition, since the conventional cross-entropy function can not be applied to portfolio management, we propose a proper method where the existing cross-entropy is modified to fit the portfolio investment method. Finally, we compare the proposed A3C model with the existing reinforcement learning based cryptography portfolio investment algorithm, and prove that the performance of the proposed A3C model is better than the existing one.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.377-388
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• 2022

Metaverse, realistic virtual space technology has become a hot topic. However, due to the lack of an institutional system to the metaverse environment, concerns are rising over the leakage of industrial confidentiality, including digital assets produced, stored, processed, and transferred within the metaverse. Digital forensics, a technology to defend against hacking attacks in cyberspace, cannot be used in metaverse space, and there is no basis for calculating the extent of damage and tracking responsibility, making it difficult to respond to human resources leakage and cyberhacking effectively. In this paper, we define the scope of industrial confidentiality information and leakage scenario and propose policy and institutional measures based on problems in each metaverse scenario. As a result of the study, it was necessary to prepare a standardized law on Extra-territorial search and seizure issues and a system for collecting cryptocurrency evidence to respond to industrial confidentiality leaks in the metaverse. The study expects to contribute to industrial technology development by preparing in advance for problems that may arise in metaverse technology.

• Knowledge Management Research
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• v.23 no.3
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• pp.23-44
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• 2022

With the development of blockchain technology, play-to-earn (P2E) games, one of the decentralized applications (dApps), are receiving great social attention. P2E games are positively evaluated as areas with high growth potential based on blockchain technology, and at the same time, they are negatively evaluated as speculative as people can cash P2E game items in the form of cryptocurrency. In this situation, the purpose of this study is to investigate factors affecting the intention to use P2E games. Along with the discussion of hedonic system adoption, we consider the factors with perceived enjoyment, economic incentive, and social influence. In order to verify our research model, data were collected from 350 adults with P2E game experience or recognition, and a structural equation model was carried out. The analysis results find that perceived enjoyment and subjective norm have a significant positive effect on the intention to use P2E games, and economic incentive does not have a significant effect. In addition, peer influence and external influence have a significant positive effect on subjective norm. Drawing on these findings, we present several academic and practical implications for future research.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.99-107
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• 2021

We live in a myriad of data. Various data are created in all situations in which we work, and we discover the meaning of data through big data technology. Many efforts are underway to find meaningful data. This paper introduces an analysis technique that enables humans to make better choices through the trend and prediction of time series data as a principal component analysis technique. Principal component analysis constructs covariance through the input data and presents eigenvectors and eigenvalues that can infer the direction of the data. The proposed method computes a reference axis in a time series data set having a similar directionality. It predicts the directionality of data in the next section through the angle between the directionality of each time series data constituting the data set and the reference axis. In this paper, we compare and verify the accuracy of the proposed algorithm with LSTM (Long Short-Term Memory) through cryptocurrency trends. As a result of comparative verification, the proposed method recorded relatively few transactions and high returns(112%) compared to LSTM in data with high volatility. It can mean that the signal was analyzed and predicted relatively accurately, and it is expected that better results can be derived through a more accurate threshold setting.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.675-683
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• 2022

Blockchain, which is the basis of virtual assets such as cryptocurrency, is receiving great attention as one of the cornerstone technologies of the 4th industrial revolution. Blockchain is a technology that can fundamentally change our lives not only in finance, but also in politics, logistics, and culture. However, it shows lower-than-expected usability because it is complicated to learn and is continuously being developed. In this study, we tried to investigate whether the Technology Acceptance Model(TAM) of virtual assets can be changed through education on the underlying technology, blockchain. A video-based online experiment was conducted with a total of 103 participants and examined how the type of training(positive, negative) and measurement timing(before, after) affect perceived usefulness, perceived ease of use, acceptance, which are TAM variables, and trust and security, which are related to blockchain characteristics. As a result of the experiment, interactions were found in all dependent variables according to the type of education and measurement timing. Specifically, groups that received negative education had no difference in all variables before and after, but it was found that groups that received positive education showed an increase afterwards. Through this, it can be seen that the effect of education based on the anchoring effect is also shown in the intention to use virtual assets using block chain technology, suggesting that the intention to use blockchain related technology can be increased through positive education.

• Smart Media Journal
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• v.13 no.1
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• pp.36-44
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• 2024

Metaverse is attracting attention as the development of virtual environment technology and the emergence of untact culture due to the COVID-19 pandemic. In this study, by analyzing users' reviews on the "Zepeto" application, which has recently attracted attention as a metaverse service, we tried to confirm changes in the requirements for the metaverse after the COVID-19 pandemic. To this end, 109,662 reviews of "Zepeto" applications written on the Google Play Store from September 2018 to March 2023 were collected, topics were extracted using LDA topic modeling technique, and topics were analyzed using the Causal Impact technique to examine how topics changed before and after based on "March 11, 2020" when the COVID-19 pandemic was declared. As a result of the analysis, five topics were extracted: application functional problems (topic1), security problems (topic 2), complaints about cryptocurrency (Zem) in the application (topic 3), application performance (topic 4), and personal information-related problems (topic 5). Among them, it was confirmed that security problems (topic 2) were most affected by the COVID-19 pandemic.