• KIPS Transactions on Computer and Communication Systems
• /
• v.11 no.8
• /
• pp.249-258
• /
• 2022

Since the advent of Bitcoin, various virtual assets have been actively traded through virtual asset services of virtual asset exchanges. Recently, security accidents have frequently occurred in virtual asset exchanges, so the government is obligated to obtain information security management system (ISMS) certification to strengthen information protection of virtual asset exchanges, and 56 additional specialized items have been established. In this paper, we compared the domain importance of ISMS and CryptoCurrency Security Standard (CCSS) which is a set of requirements for all information systems that make use of cryptocurrencies, and analyzed the results after mapping them to gain insight into the characteristics of each certification system. Improvements for 4 items of High Level were derived by classifying the priorities for improvement items into 3 stages: High, Medium, and Low. These results can provide priority for virtual asset and information system security, support method and systematic decision-making on improvement of certified items, and contribute to vitalization of virtual asset transactions by enhancing the reliability and safety of virtual asset services.

• Journal of Internet Computing and Services
• /
• v.24 no.4
• /
• pp.127-135
• /
• 2023

This research paper explores the application of FinBERT, a variational BERT-based model pre-trained on financial domain, for sentiment analysis in the financial domain while focusing on the process of identifying suitable training data and hyperparameters. Our goal is to offer a comprehensive guide on effectively utilizing the FinBERT model for accurate sentiment analysis by employing various datasets and fine-tuning hyperparameters. We outline the architecture and workflow of the proposed approach for fine-tuning the FinBERT model in this study, emphasizing the performance of various datasets and hyperparameters for sentiment analysis tasks. Additionally, we verify the reliability of GPT-3 as a suitable annotator by using it for sentiment labeling tasks. Our results show that the fine-tuned FinBERT model excels across a range of datasets and that the optimal combination is a learning rate of 5e-5 and a batch size of 64, which perform consistently well across all datasets. Furthermore, based on the significant performance improvement of the FinBERT model with our Twitter data in general domain compared to our news data in general domain, we also express uncertainty about the model being further pre-trained only on financial news data. We simplify the complex process of determining the optimal approach to the FinBERT model and provide guidelines for selecting additional training datasets and hyperparameters within the fine-tuning process of financial sentiment analysis models.

• The Journal of Society for e-Business Studies
• /
• v.27 no.2
• /
• pp.65-77
• /
• 2022

Due to the recent development in electronic financial services, transactions of electronic prepayment are rapidly growing, leading to growing fraud attempts. This paper proposes a methodology that can effectively detect fraud transactions in electronic prepayment by machine learning algorithms, including support vector machines, decision trees, and artificial neural networks. Actual transaction data of electronic prepayment services were collected and preprocessed to extract the most relevant variables from raw data. Two different approaches were explored in the paper. One is a transaction-based approach, and the other is a user ID-based approach. For the transaction-based approach, the first model is primarily based on raw data features, while the second model uses extra features in addition to the first model. The user ID-based approach also used feature engineering to extract and transform the most relevant features. Overall, the user ID-based approach showed a better performance than the transaction-based approach, where the artificial neural networks showed the best performance. The proposed method could be used to reduce the damage caused by financial accidents by detecting and blocking fraud attempts.

• Investigative Magnetic Resonance Imaging
• /
• v.2 no.1
• /
• pp.58-66
• /
• 1998

Purpose : This paper introduces a new three dimensional magnetic Resonance Image classification which is based on Mar kov Random Field-Gibbs Random Field with a line model. Material and Methods : The performance of the Gibbs Classifier over a statistically heterogeneous image can be improved if the local stationary regions in the image are disassociated from each other through the mechanism of the interaction parameters defined at the local neighborhood level. This usually involves the construction of a line model for the image. In this paper we construct a line model for multisignature images based on the differential of the image which can provide an a priori estimate of the unobservable line field, which may lie in regions with significantly different statistics. the line model estimated from the original image data can in turn be used to alter the values of the interaction parameters of the Gibbs Classifier. Results : MRF-Gibbs classifier for volumetric MR images is developed under the condition that the domain of the image classification is $E^{3}$ space rather thatn the conventional $E^{2}$ space. Compared to context free classification, MRF-Gibbs classifier performed better in homogeneous and along boundaries since contextual information is used during the classification. Conclusion : We construct a line model for multisignature, multidimensional image and derive the interaction parameter for determining the energy function of MRF-Gibbs classifier.

• Tunnel and Underground Space
• /
• v.30 no.6
• /
• pp.573-590
• /
• 2020

This study presents the current status of DECOVALEX-2023 project Task G and our research results so far. Task G, named 'Safety ImplicAtions of Fluid Flow, Shear, Thermal and Reaction Processes within Crystalline Rock Fracture NETworks (SAFENET)' aims at developing a numerical method to simulate the fracture creation and propagation, and the coupled thermohydro-mechanical processes in fracture in crystalline rocks. The first research step of Task G is a benchmark simulation, which is designed for research teams to make their modelling codes more robust and verify whether the models can represent an analytical solution for displacements of a single rock fracture. We reproduced the mechanical behavior of rock and embedded single fracture using a three-dimensional grain-based distinct element model for the simulations. In this method, the structure of the rock was represented by an assembly of rigid tetrahedral grains moving independently of each other, and the mechanical interactions at the grains and their contacts were calculated using 3DEC. The simulation results revealed that the stresses induced along the embedded fracture in the model were relatively low compared to those calculated by stress analysis due to stress redistribution and constrained fracture displacements. The fracture normal and shear displacements of the numerical model showed good agreement with the analytical solutions. The numerical model will be enhanced by continuing collaboration and interaction with other research teams of DECOVALEX-2023 Task G and validated using various experiments in a further study.

• Korean Journal of Microbiology
• /
• v.49 no.3
• /
• pp.215-220
• /
• 2013

Against environmental stresses, many bacteria utilize the alternate sigma factor RpoS that induces transcription of the specific set of genes helpful in promoting bacterial survival. Intracellular levels of RpoS are determined mainly by its turnover through proteolysis of ClpXP protease. Delivery of RpoS to ClpXP strictly requires the adaptor protein RssB. The two-component-type response regulator RssB constantly interacts with RpoS, but diverse environmental changes inhibit this interaction through modification of RssB activity, which increases RpoS levels in bacteria. This review discusses and summarizes recent findings on regulatory factors in RssB-RpoS interactions, including IraD, IraM, IraP anti-adaptor proteins of RssB and phosphorylation of N-terminal receiver domain of RssB. New information shows that the coordinated regulation of RssB activity in controlling RpoS turnover confers efficient bacterial defense against stresses.

• Journal of KIISE
• /
• v.42 no.3
• /
• pp.341-360
• /
• 2015

While architectural patterns provide software development solutions by providing schemas for structural organizations of software systems based on empirical knowledge, Jackson's problem frames provide a method of analyzing software problems. Problem frames are useful to understanding the software development problem, by putting emphasis on the problem domain, rather than on the solution space. Research exists that relates problem frames and software architecture, but most of this research uses problem frames only to understand given problems. Moreover, none of the existing research derives architectural patterns by considering both problem frames and quality attributes. In this paper, we propose a software architecture design method for pattern-based architecture design, by matching problem frames and architectural patterns. To that end, our approach first develops the problem model based on the problem frames approach, and then uses it to match with candidate architectural patterns, from the perspectives of both functionality, and quality attributes. Functional matching uses the problem frame diagram to match the problem model of an architectural pattern. We conduct a case study to show that our approach can systematically decide the right architectural patterns, and provide a basis for fine-grained software architecture design.

• Journal of KIISE:Software and Applications
• /
• v.35 no.3
• /
• pp.189-196
• /
• 2008

To build autonomous agents who can make a decision on behalf of humans in time-critical complex environments, the formulation of operational knowledge base could be essential. This paper proposes the methodology of how to formulate the knowledge base and evaluates it in a practical application domain. We analyze threat data received from the multiple sensors of Aircraft Survivability Equipment(ASE) for Korean helicopters, and integrate the threat data into the inductive model through compilation technique which extracts features of the threat data and relations among them. The compiled protocols of state-action rules can be implemented as the brain of the ASE. They can reduce the amounts of reasoning, and endow the autonomous agents with reactivity and flexibility. We report experimental results that demonstrate the distinctive and predictive patterns of threats in simulated battlefield settings, and show the potential of compilation methods for the successful detection of threat systems.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.11
• /
• pp.2167-2175
• /
• 2017

In recent years, an IoT is dramatically developing according to the enhancement of AI, the increase of connected devices, and the high-performance cloud systems. Huge data produced by many devices and sensors is expanding the scope of services, such as an intelligent diagnostics, a recommendation service, as well as a smart monitoring service. The studies of edge computing are limited as a role of small server system with high quality HW resources. However, there are specialized requirements in a smart factory domain needed edge computing. The edges are needed to pre-process containing tiny filtering, pre-formatting, as well as merging of group contexts and manage the regional rules. So, in this paper, we extract the features and requirements in a scope of efficiency and robustness. Our edge offers to decrease a network resource consumption and update rules and learning models. Moreover, we propose architecture of edge computing based on learning factors sharing with a cloud system in a smart factory.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.15 no.6
• /
• pp.701-708
• /
• 2005

Structuring mechanism, important to serve messages in post-office box structure, is to construct the hierarchy of classes according to the contents of message objects. This Paper Proposes $\alpha$-cut based genealogy grouping method to cluster a lot of structured objects in application domain. The proposed method decides the relationship first by semantic similarity relation and fuzzy relation, and then performs the grouping by operations of search( ), insert() and hierarchy(). This hierarchy structure makes it easy to process group-related processing tasks such as answering queries, discriminating objects, finding similarities among objects, etc. The proposed post-office box structure may be efficiently used to serve and manage message objects by the creation of groups. The Proposed method is tested for 5500 message objects and compared with other methods such as non-grouping, BGM, RGM, OGM.