• KIPS Transactions on Software and Data Engineering
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• v.8 no.9
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• pp.373-378
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• 2019

Since many programming sources are open online, problems with reckless plagiarism and copyrights are occurring. Among them, source codes produced by repeated authors may have unique fingerprints due to their programming characteristics. This paper identifies each author by learning from a Google Code Jam program source using deep neural network. In this case, the original creator's source is to be vectored using a pre-processing instrument such as predictive-based vector or frequency-based approach, TF-IDF, etc. and to identify the original program source by learning by using a deep neural network. In addition a language-independent learning system was constructed using a pre-processing machine and compared with other existing learning methods. Among them, models using TF-IDF and in-depth neural networks were found to perform better than those using other pre-processing or other learning methods.

• Geomechanics and Engineering
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• v.11 no.1
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• pp.77-94
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• 2016

A forked tunnel, as a special complicated underground structure, is composed of big-arch tunnel, multi-arch tunnel, neighborhood tunnels and separate tunnels according to the different distances between two separate tunnels. Due to the complicated process of design and construction, surrounding jointed rock mass stability of the big-arch tunnel which belongs to the forked tunnel during excavation is a hot issue that needs special attentions. In this paper, an elasto-plastic damage constitutive model for jointed rock mass is proposed based on the coupling method considering elasto-plastic and damage theories, and the irreversible thermodynamics theory. Based on this elasto-plastic damage constitutive model, a three dimensional elasto-plastic damage finite element code (D-FEM) is implemented using Visual Fortran language, which can numerically simulate the whole excavation process of underground project and perform the structural stability of the surrounding rock mass. Comparing with a popular commercial computer code, three dimensional fast Lagrangian analysis of continua (FLAC3D), this D-FEM has advantages in terms of rapid computing process, element grouping function and providing more material models. After that, FLAC3D and D-FEM are simultaneously used to perform the structural stability analysis of the surrounding rock mass in the forked tunnel considering three different computing schemes. The final numerical results behave almost consistent using both FLAC3D and D-FEM. But from the point of numerically obtained damage softening areas, the numerical results obtained by D-FEM more closely approach the practical behaviors of in-situ surrounding rock mass.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.179-186
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• 2021

We have been developed the smart tourism app and the Instagram and YouTube contents to provide personalized tourism information and travel product information to individual tourists. In this paper, we develop a rule-based smart tourism chatbot with the khaiii (Kakao Hangul Analyzer III) morphological analyzer and Neo4J graph database. In the proposed chatbot system, we use a morpheme analyzer, a proper noun dictionary including tourist destination names, and a general noun dictionary including containing frequently used words in tourist information search to understand the intention of the user's question. The tourism knowledge base built using the Neo4J graph database provides adequate answers to tourists' questions. In this paper, the nodes of Neo4J are Area based on tourist destination address, Contents with property of tourist information, and Service including service attribute data frequently used for search. A Neo4J query is created based on the result of analyzing the intention of a tourist's question with the property of nodes and relationships in Neo4J database. An answer to the question is made by searching in the tourism knowledge base. In this paper, we create the tourism knowledge base using more than 1300 Jeju tourism information used in the smart tourism app. We plan to develop a multilingual smart tour chatbot using the named entity recognition (NER), intention classification using conditional random field(CRF), and transfer learning using the pretrained language models.

• Translational and Clinical Pharmacology
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• v.26 no.4
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• pp.166-171
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• 2018

Although there are many commercially available training software programs for pharmacokinetics, they lack flexibility and convenience. In this study, we develop simulation software to facilitate pharmacokinetics education. General formulas for time courses of drug concentrations after single and multiple dosing were used to build source code that allows users to simulate situations tailored to their learning objectives. A mathematical relationship for a 1-compartment model was implemented in the form of differential equations. The concept of population pharmacokinetics was also taken into consideration for further applications. The source code was written using R. For the convenience of users, two types of software were developed: a web-based simulator and a standalone-type application. The application was built in the JAVA language. We used the JAVA/R Interface library and the 'eval()' method from JAVA for the R/JAVA interface. The final product has an input window that includes fields for parameter values, dosing regimen, and population pharmacokinetics options. When a simulation is performed, the resulting drug concentration time course is shown in the output window. The simulation results are obtained within 1 minute even if the population pharmacokinetics option is selected and many parameters are considered, and the user can therefore quickly learn a variety of situations. Such software is an excellent candidate for development as an open tool intended for wide use in Korea. Pharmacokinetics experts will be able to use this tool to teach various audiences, including undergraduates.

• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.139-150
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• 2021

Object orientation has become the predominant paradigm for conceptual modeling (e.g., UML), where the notions of class and object form the primitive building blocks of thought. Classes act as templates for objects that have attributes and methods (actions). The modeled systems are not even necessarily software systems: They can be human and artificial systems of many different kinds (e.g., teaching and learning systems). The UML class diagram is described as a central component of model-driven software development. It is the most common diagram in object-oriented models and used to model the static design view of a system. Objects both carry data and execute actions. According to some authorities in modeling, a certain degree of difficulty exists in understanding the semantics of these notions in UML class diagrams. Some researchers claim class diagrams have limited use for conceptual analysis and that they are best used for logical design. Performing conceptual analysis should not concern the ways facts are grouped into structures. Whether a fact will end up in the design as an attribute is not a conceptual issue. UML leads to drilling down into physical design details (e.g., private/public attributes, encapsulated operations, and navigating direction of an association). This paper is a venture to further the understanding of object-orientated concepts as exemplified in UML with the aim of developing a broad comprehension of conceptual modeling fundamentals. Thinging machine (TM) modeling is a new modeling language employed in such an undertaking. TM modeling interlaces structure (components) and actionality where actions infiltrate the attributes as much as the classes. Although space limitations affect some aspects of the class diagram, the concluding assessment of this study reveals the class description is a kind of shorthand for a richer sematic TM construct.

• Smart Media Journal
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• v.10 no.1
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• pp.39-46
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• 2021

This study is about a method of extracting a summary from a news article in consideration of the importance of each sentence constituting the article. We propose a method of calculating sentence importance by extracting the probabilities of topic sentence, similarity with article title and other sentences, and sentence position as characteristics that affect sentence importance. At this time, a hypothesis is established that the Topic Sentence will have a characteristic distinct from the general sentence, and a deep learning-based classification model is trained to obtain a topic sentence probability value for the input sentence. Also, using the pre-learned ELMo language model, the similarity between sentences is calculated based on the sentence vector value reflecting the context information and extracted as sentence characteristics. The topic sentence classification performance of the LSTM and BERT models was 93% accurate, 96.22% recall, and 89.5% precision, resulting in high analysis results. As a result of calculating the importance of each sentence by combining the extracted sentence characteristics, it was confirmed that the performance of extracting the topic sentence was improved by about 10% compared to the existing TextRank algorithm.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.155-164
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• 2022

The review of known decisions showed that currently there are no systems and technologies for supporting the decision about the possibility of concluding the civil law agreements for medical, therapeutic and dental services. The paper models the decision-making support process on the possibility of concluding the civil law agreements for medical, therapeutic and dental services, which is the theoretical basis for the development of rules, methods and system for supporting the decision about the possibility of concluding the civil law agreements for medical, therapeutic and dental services. The paper also developed the system for supporting the decision about the possibility of concluding the civil law agreements for medical, therapeutic and dental services, which automatically and free determines the possibility or impossibility of concluding the corresponding civil law agreement for the provision of a corresponding medical service. In the case of formation of a conclusion about the possibility of concluding the agreement, further conclusion and signing of the corresponding agreement takes place. In the case of forming a conclusion about the impossibility of concluding the agreement, a request is made for finalizing the relevant agreement for the provision of the relevant medical service, indicating the reasons for the impossibility of concluding the agreement - missing essential conditions in the agreement. After finalization, the agreement can be analyzed again by the developed system for supporting the decision.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.307-315
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• 2022

The review of known decision-making support systems and technologies regarding the possibility of donation and transplantation showed that currently there are no systems and technologies of decision-making support regarding the possibility of donation and transplantation considering civil law. The paper models the decision-making support process regarding the possibility of donation and transplantation, which is a theoretical basis for the development of rules, methods and technology of decision-making support regarding the possibility of donation and transplantation considering civil law. The paper also developed the technology of decision-making support regarding the possibility of donation and transplantation considering civil law as a component of the Unified State Information System for Organ and Tissue Transplantation, which automatically and free of charge determines the possibility/impossibility of donation and transplantation. In the case of the possibility of donation, the admissible type of donation is also determined - over-life or after-life donation - and data about potential donor is entered in the relevant Donor Register. In the case of the possibility of transplantation, if the recipient needs a transplant of one of the paired organs or a part of the organ/tissue, then data about potential recipient are entered in the Transplantation List from both over-life and after-life donor, otherwise, if the recipient needs a transplant of a non-paired organ or both paired organs, then data about potential recipient are entered only in the Transplantation List from after-life donor.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.3991-4010
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• 2021

The Question Similarity Measurement of Chinese Crop Diseases and Insect Pests (QSM-CCD&IP) aims to judge the user's tendency to ask questions regarding input problems. The measurement is the basis of the Agricultural Knowledge Question and Answering (Q & A) system, information retrieval, and other tasks. However, the corpus and measurement methods available in this field have some deficiencies. In addition, error propagation may occur when the word boundary features and local context information are ignored when the general method embeds sentences. Hence, these factors make the task challenging. To solve the above problems and tackle the Question Similarity Measurement task in this work, a corpus on Chinese crop diseases and insect pests(CCDIP), which contains 13 categories, was established. Then, taking the CCDIP as the research object, this study proposes a Chinese agricultural text similarity matching model, namely, the AgrCQS. This model is based on mixed information extraction. Specifically, the hybrid embedding layer can enrich character information and improve the recognition ability of the model on the word boundary. The multi-scale local information can be extracted by multi-core convolutional neural network based on multi-weight (MM-CNN). The self-attention mechanism can enhance the fusion ability of the model on global information. In this research, the performance of the AgrCQS on the CCDIP is verified, and three benchmark datasets, namely, AFQMC, LCQMC, and BQ, are used. The accuracy rates are 93.92%, 74.42%, 86.35%, and 83.05%, respectively, which are higher than that of baseline systems without using any external knowledge. Additionally, the proposed method module can be extracted separately and applied to other models, thus providing reference for related research.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.1
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• pp.99-107
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• 2022

In the current technological society, where various technologies are converged into one and being transformed into new technologies, new cyber attacks are being made just as they keep pace with the changes in society. In particular, due to the convergence of various attacks into one, it is difficult to protect the system with only the existing security system. A lot of information is being generated to respond to such cyber attacks. However, recklessly generated vulnerability information can induce confusion by providing unnecessary information to administrators. Therefore, this paper proposes a mechanism to assist in the analysis of emerging cyberattack convergence technologies by providing differentiated vulnerability information to managers by learning documents using deep learning-based language learning models, extracting vulnerability information and classifying them according to the MITRE ATT&CK framework.