• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1702-1721
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• 2019

A workflow process (or business process) management system helps to define, execute, monitor and manage workflow models deployed on a workflow-supported enterprise, and the system is compartmentalized into a modeling subsystem and an enacting subsystem, in general. The modeling subsystem's functionality is to discover and analyze workflow models via a theoretical modeling methodology like ICN, to graphically define them via a graphical representation notation like BPMN, and to systematically deploy those graphically defined models onto the enacting subsystem by transforming into their textual models represented by a standardized workflow process definition language like XPDL. Before deploying those defined workflow models, it is very important to inspect its syntactical correctness as well as its structural properness to minimize the loss of effectiveness and the depreciation of efficiency in managing the corresponding workflow models. In this paper, we are particularly interested in verifying very large-scale and massively parallel workflow models, and so we need a sophisticated analyzer to automatically analyze those specialized and complex styles of workflow models. One of the sophisticated analyzers devised in this paper is able to analyze not only the structural complexity but also the data-sequence complexity, especially. The structural complexity is based upon combinational usages of those control-structure constructs such as subprocesses, exclusive-OR, parallel-AND and iterative-LOOP primitives with preserving matched pairing and proper nesting properties, whereas the data-sequence complexity is based upon combinational usages of those relevant data repositories such as data definition sequences and data use sequences. Through the devised and implemented analyzer in this paper, we are able eventually to achieve the systematic verifications of the syntactical correctness as well as the effective validation of the structural properness on those complicate and large-scale styles of workflow models. As an experimental study, we apply the implemented analyzer to an exemplary large-scale and massively parallel workflow process model, the Large Bank Transaction Workflow Process Model, and show the structural complexity analysis results via a series of operational screens captured from the implemented analyzer.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.83-91
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• 2021

Health is an outset of psychological, social, financial, and physical state. Several macroeconomic factors are entangled with health and mortality. Infant mortality and life expectancy are two keyguard on demographic research context on last few decades. On the other hand, foreign inflows play an unprecedent role for raising economic circulation and providing more opportunities to build a better society. The study aims to investigate the relationship between foreign direct investment (FDI), economic growth, and Bangladesh's health. This study employs time-series data from 1980 to 2018. Results show, with Auto-regressive Distribute Lag (ARDL) model, that there is significant cointegration among variables. Foreign investment and economic output relate significantly and positively to health. On the contrary, education is quasi-linked with a different sign-on different model. For model validation, pitfalls of time-series multicollinearity, heteroscedasiticy, and autocorrelation are not present. Also, CUSUM and CUSUMSQ tests are validating the model as stable and fit for future prediction. Medical assessment and education need more attention from the government as well as the private sector. FDI can play a catalyst role for improving the health sector, raising opportunity in educating and creating a better lifestyle. In order to optimize foreign investment, the government should implement necessary reforms and policies.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.12 no.6
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• pp.125-135
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• 2002

The electronic payment system based on blind signature is susceptible to the blackmailing attack as opposed to keep the lifestyle of users private. In this paper. we suggest an efficient electronic cash system using a blind XTR-DSA scheme, which improves the method of defeating blackmailing in online electronic cash systems of ［6,9］. In case of blackmailing, to issue the marked coins we use the blind XTR-DSA scheme at withdrawal. In ［6,9］, to cheat the blackmailer who takes the marked coins the decryption key of a user had to be transferred to the Bank. But in our proposed method the delivery of the decryption key is not required. Also, in the most serious attack of blackmailing. kidnapping, we can defeat blackmailing with a relatively high probability of 13/18 compared with 1/2 in ［9］ and 2/3 in ［6］. If an optimal extension field of XTR suggested in ［7］ is used, then we can implement our system more efficiently.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.4008-4023
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• 2022

Since machine learning was introduced into cross-site scripting (XSS) attack detection, many researchers have conducted related studies and achieved significant results, such as saving time and labor costs by not maintaining a rule database, which is required by traditional XSS attack detection methods. However, this topic came across some problems, such as poor generalization ability, significant false negative rate (FNR) and false positive rate (FPR). Moreover, the automatic clustering property of graph convolutional networks (GCN) has attracted the attention of researchers. In the field of natural language process (NLP), the results of graph embedding based on GCN are automatically clustered in space without any training, which means that text data can be classified just by the embedding process based on GCN. Previously, other methods required training with the help of labeled data after embedding to complete data classification. With the help of the GCN auto-clustering feature and labeled data, this research proposes an approach to detect XSS attacks (called GCNXSS) to mine the dependencies between the units that constitute an XSS payload. First, GCNXSS transforms a URL into a word homogeneous graph based on word co-occurrence relationships. Then, GCNXSS inputs the graph into the GCN model for graph embedding and gets the classification results. Experimental results show that GCNXSS achieved successful results with accuracy, precision, recall, F1-score, FNR, FPR, and predicted time scores of 99.97%, 99.75%, 99.97%, 99.86%, 0.03%, 0.03%, and 0.0461ms. Compared with existing methods, GCNXSS has a lower FNR and FPR with stronger generalization ability.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.2
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• pp.133-148
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• 2005

Since understanding of similarities and differences among protein structures is very important for the study of the relationship between structure and function, many protein structure comparison systems have been developed. Hut, unfortunately, these systems introduce their own protein data derived from the PDB(Protein Data Bank), which are needed in their algorithms for comparing protein structures. In addition, according to the rapid increase in the size of PDB, these systems require much more computation to search for common substructures in their databases. In this paper, we introduce a protein structure comparison system named WS4E(A Web-Based Searching Substructures of Secondary Structure Elements) based on a PSAML database which stores PSAML documents using the eXist open XML DBMS. PSAML(Protein Structure Abstraction Markup Language) is an XML representation of protein data, describing a protein structure as the secondary structures of the protein and their relationships. Using the PSAML database, the WS4E provides web services searching for common substructures among proteins represented in PSAML. In addition, to reduce the number of candidate protein structures to be compared in the PSAML database, we used topology strings which contain the spatial information of secondary structures in a protein.

• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.87-99
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• 2013

As Internet advances, websites with simpel HTML pages are changing to complex web application systems with enormous contents and various services. With this trend, it is noted that situations where Web accessibility of the old and the handicapped is inhibited are growing. To solve this problem, The Disability Discrimination Act has been enacted since April 2013. This act triggers massive website reorganization efforts. However, in order for the huge and sophisticated web applications and web sites to ensure a web accessibility, a framework is required to throughout the web site development. Based on thorough review of website development methodologies, web accessibility compliance standards, and various web accessibility issues related to website characteristics, this study proposes a practice oriented "Web Accessibility Compliance Framework". The current study also examines the usefulness and value of this framework by applying it to the internet banking development project of W bank and receiving a certificate for high quality website complying web accessibility standards.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.1-23
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• 2019

Big data refers to data that is difficult to store, manage, and analyze by existing software. As the lifestyle changes of consumers increase the size and types of needs that consumers desire, they are investing a lot of time and money to understand the needs of consumers. Companies in various industries utilize Big Data to improve their products and services to meet their needs, analyze unstructured data, and respond to real-time responses to products and services. The financial industry operates a decision support system that uses financial data to develop financial products and manage customer risks. The use of big data by financial institutions can effectively create added value of the value chain, and it is possible to develop a more advanced customer relationship management strategy. Financial institutions can utilize the purchase data and unstructured data generated by the credit card, and it becomes possible to confirm and satisfy the customer's desire. CRM has a granular process that can be measured in real time as it grows with information knowledge systems. With the development of information service and CRM, the platform has change and it has become possible to meet consumer needs in various environments. Recently, as the needs of consumers have diversified, more companies are providing systematic marketing services using data mining and advanced CRM (Customer Relationship Management) techniques. KB Kookmin Card, which started as a credit card business in 1980, introduced early stabilization of processes and computer systems, and actively participated in introducing new technologies and systems. In 2011, the bank and credit card companies separated, leading the 'Hye-dam Card' and 'One Card' markets, which were deviated from the existing concept. In 2017, the total use of domestic credit cards and check cards grew by 5.6% year-on-year to 886 trillion won. In 2018, we received a long-term rating of AA + as a result of our credit card evaluation. We confirmed that our credit rating was at the top of the list through effective marketing strategies and services. At present, Kookmin Card emphasizes strategies to meet the individual needs of customers and to maximize the lifetime value of consumers by utilizing payment data of customers. KB Kookmin Card combines internal and external big data and conducts marketing in real time or builds a system for monitoring. KB Kookmin Card has built a marketing system that detects realtime behavior using big data such as visiting the homepage and purchasing history by using the customer card information. It is designed to enable customers to capture action events in real time and execute marketing by utilizing the stores, locations, amounts, usage pattern, etc. of the card transactions. We have created more than 280 different scenarios based on the customer's life cycle and are conducting marketing plans to accommodate various customer groups in real time. We operate a smart offering system, which is a highly efficient marketing management system that detects customers' card usage, customer behavior, and location information in real time, and provides further refinement services by combining with various apps. This study aims to identify the traditional CRM to the current CRM strategy through the process of changing the CRM strategy. Finally, I will confirm the current CRM strategy through KB Kookmin card's big data utilization strategy and marketing activities and propose a marketing plan for KB Kookmin card's future CRM strategy. KB Kookmin Card should invest in securing ICT technology and human resources, which are becoming more sophisticated for the success and continuous growth of smart offering system. It is necessary to establish a strategy for securing profit from a long-term perspective and systematically proceed. Especially, in the current situation where privacy violation and personal information leakage issues are being addressed, efforts should be made to induce customers' recognition of marketing using customer information and to form corporate image emphasizing security.

• The Journal of Society for e-Business Studies
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• v.23 no.3
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• pp.207-224
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• 2018

In this article, an empirical study was conducted by using public dataset from Lending Club Corporation, the largest online peer-to-peer (P2P) lending in the world. We explore significant predictor variables related to P2P lending default that housing situation, length of employment, average current balance, debt-to-income ratio, loan amount, loan purpose, interest rate, public records, number of finance trades, total credit/credit limit, number of delinquent accounts, number of mortgage accounts, and number of bank card accounts are significant factors to loan funded successful on Lending Club platform. We developed online P2P lending default prediction models using discriminant analysis, logistic regression, neural networks, and decision trees (i.e., CART and C5.0) in order to predict P2P loan default. To verify the feasibility and effectiveness of P2P lending default prediction models, borrower loan data and credit data used in this study. Empirical results indicated that neural networks outperforms other classifiers such as discriminant analysis, logistic regression, CART, and C5.0. Neural networks always outperforms other classifiers in P2P loan default prediction.

• Animal cells and systems
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• v.8 no.3
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• pp.231-233
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• 2004

Sequences of cytochrome b gene and control region of mitochondrial DNA from Korean common otters (Lutra lutra lutra L.) were examined to provide the genetic information for the conservation of this subspecies. Two haplotypes and one haplotype were revealed in cytochrome b gene and control region, respectively. The available sequences of European common otter (L. l. lutra) from GenBank were compared together with those of Korean common otter in order to determine the degree of sequence variation between them. In cytochrome b gene sequences, two haplotypes from Korea and two haplotypes of Europe showed differences in 12 of 1,045 sites. The Tamura-Nei nucleotide distances between two European haplotypes was 0.10% and those between two Korean haplotypes was also 0.10%, but those between Korean haplotypes and European ones ranged from 0.96% to 1.16%. In the control region, one Korean haplotype and seven European ones showed differences in seven of 300 sites; the Tamura-Nei distances among seven European haplotypes were 0.34% to 1.01%, but those between Korean haplotype and European ones ranged from 1.01% to 1.69%. Although further molecular and morphological studies with specimens from eastern Asia including Amur region and northeast China are needed, it is possible that the Korean common otter might be closer or identical to the far-eastern Asian common otter, L. l. amurensis Dybowski.

• Journal of Navigation and Port Research
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• v.29 no.6 s.102
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• pp.567-572
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• 2005

Trade between nations has been globalized since establishing the WTO(World Trade Organization). By lowering trading barriers under the WTO's system, trade in goods has been gradually increased It requires global logistic system that transports goods in between nations. To save cost of product, cargo of product is containerized and container ships to carry container cargo is going to be bigger: In the market, there are many vendors to provide artificial intelligent modules to operate container terminal. In order to integrate automated container terminal system easily and successfully, this thesis proposes high-level XML/ JMS( eXtensive Markup Language/Java Message Service) communication model and multi-agent based system architecture to share knowledges, solve problems, and active objectives by cooperating between autonomous and intelligent agents that are developed by 3rd party companies in the market. This thesis analyzed current situation of advanced automated container terminal with case studies on implemented systems and difficulties to develop automated container terminal system, reviewed technologies of intelligent agent, communication and automation that unmaned automated container terminal is required.