• Journal of Society of Preventive Korean Medicine
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• v.19 no.3
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• pp.91-102
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• 2015

Objective : The purpose of this study is to find out the possibility of application to herbal medicine's report form for adverse drug reaction (ADR) by reviewing and analyzing Asian countries's ADR report forms. Method : We investigated, compared, and analyzed ADR report forms (ADR-RF) of Asian countries's ADR institutions (ACAI), such as, Korea institute of drug safety & risk management and Dongguk university Ilsan oriental hospital (DUIOH) in Korea, national center for ADR monintoring (NCAM) in China, pharmaceuticals and medical devices agency (PMDA) in Japan, Ministry of Health and Welfare (MOHW) in Taiwan, and drug office, department of health, the government of the Hong Kong special administrative region (GHKSAR) in Hong Kong. Results : ADR-RF for ACAI included common contents, such as, patients information (name(initial), gender, age, weight), adverse event (AE)'s report information (Recognition and report for AE occurrence, first or follow up report, Severe AE), the detailed information of AE (the title of AE, onset & closing date of AE symptoms, the progress & results detailed test of AE), the information of AE's medicine (the types of medicine, product name, ingredient name, suspected or combination drug, single dose & frequency, dosage form, administration route, dealing for AE-suspected medicine), and AE reporter's information (reporter's information, institution's information). Taiwan had ADR-RF and the department exclusively for herbal medicine (HM), but others (except DUIOH) had not only no ADR report form but also contents for HM. Conclusion : ADR-RF for HM have to include the common contents of ACAI at least, as well as HM information related to ADR, such as the title, composition and types of HM, history related to HM's ADR, and the contents of drug-induced liver injury and so on. In addition, the main department of government for HM's ADR will be needed.

• Journal of Digital Contents Society
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• v.18 no.3
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• pp.567-575
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• 2017

Location-based services play an important role in many applications such as disaster warning systems and recommendation systems. These applications often require not only location information (e.g., name, latitude, longitude, etc.) but also the impact of events (e.g., earthquake, typhoon, etc.) on locations. Recently, to provide the impact of an event on a location, how to calculate location trustiness by using multimodal information such as earthquake information and disaster sensor data is researched. In the previous approach, the linear decrement of impact value of an event is applied to obtain the location trustiness of a specific location. In this paper, we propose a new approach to enrich location trustiness, that is, the impact of an event on a location, by using social media information additionally. Firstly, we design a collecting system for earthquake information and social media data. Secondly, we present an approach of location trustiness calculation based on earthquake information. Finally, we propose a new approach to enrich location trustiness by augmenting the trustiness in spatially distributed manner based on social media.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.183-203
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• 2018

News articles are the most suitable medium for examining the events occurring at home and abroad. Especially, as the development of information and communication technology has brought various kinds of online news media, the news about the events occurring in society has increased greatly. So automatically summarizing key events from massive amounts of news data will help users to look at many of the events at a glance. In addition, if we build and provide an event network based on the relevance of events, it will be able to greatly help the reader in understanding the current events. In this study, we propose a method for extracting event networks from large news text data. To this end, we first collected Korean political and social articles from March 2016 to March 2017, and integrated the synonyms by leaving only meaningful words through preprocessing using NPMI and Word2Vec. Latent Dirichlet allocation (LDA) topic modeling was used to calculate the subject distribution by date and to find the peak of the subject distribution and to detect the event. A total of 32 topics were extracted from the topic modeling, and the point of occurrence of the event was deduced by looking at the point at which each subject distribution surged. As a result, a total of 85 events were detected, but the final 16 events were filtered and presented using the Gaussian smoothing technique. We also calculated the relevance score between events detected to construct the event network. Using the cosine coefficient between the co-occurred events, we calculated the relevance between the events and connected the events to construct the event network. Finally, we set up the event network by setting each event to each vertex and the relevance score between events to the vertices connecting the vertices. The event network constructed in our methods helped us to sort out major events in the political and social fields in Korea that occurred in the last one year in chronological order and at the same time identify which events are related to certain events. Our approach differs from existing event detection methods in that LDA topic modeling makes it possible to easily analyze large amounts of data and to identify the relevance of events that were difficult to detect in existing event detection. We applied various text mining techniques and Word2vec technique in the text preprocessing to improve the accuracy of the extraction of proper nouns and synthetic nouns, which have been difficult in analyzing existing Korean texts, can be found. In this study, the detection and network configuration techniques of the event have the following advantages in practical application. First, LDA topic modeling, which is unsupervised learning, can easily analyze subject and topic words and distribution from huge amount of data. Also, by using the date information of the collected news articles, it is possible to express the distribution by topic in a time series. Second, we can find out the connection of events in the form of present and summarized form by calculating relevance score and constructing event network by using simultaneous occurrence of topics that are difficult to grasp in existing event detection. It can be seen from the fact that the inter-event relevance-based event network proposed in this study was actually constructed in order of occurrence time. It is also possible to identify what happened as a starting point for a series of events through the event network. The limitation of this study is that the characteristics of LDA topic modeling have different results according to the initial parameters and the number of subjects, and the subject and event name of the analysis result should be given by the subjective judgment of the researcher. Also, since each topic is assumed to be exclusive and independent, it does not take into account the relevance between themes. Subsequent studies need to calculate the relevance between events that are not covered in this study or those that belong to the same subject.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.2
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• pp.263-279
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• 2024

With the increasing trend of Cloud migration, security threats in the Cloud computing environment have also experienced a significant increase. Consequently, the importance of efficient incident investigation through log data analysis is being emphasized. In Cloud environments, the diversity of services and ease of resource creation generate a large volume of log data. Difficulties remain in determining which events to investigate when an incident occurs, and examining all the extensive log data requires considerable time and effort. Therefore, a systematic approach for efficient data investigation is necessary. CloudTrail, the Amazon Web Services(AWS) logging service, collects logs of all API call events occurring in an account. However, CloudTrail lacks insights into which logs to analyze in the event of an incident. This paper proposes an automated analysis framework that integrates Cloud Matrix and event information for efficient incident investigation. The framework enables simultaneous examination of user behavior log events, event frequency, and attack information. We believe the proposed framework contributes to Cloud incident investigations by efficiently identifying critical events based on the ATT&CK Framework.

• Journal of the Korea Society for Simulation
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• v.5 no.2
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• pp.25-40
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• 1996

Modeling of systems for intensive knowledge-based processing requires a modeling methodology that makes efficient access to the information in huge data base models. The proposed level access mothodology is a modeling approach applicable to systems where data is stored in a hierarchical and modular modules of active memory cells(processor/memory pairs). It significantly reduces the effort required to create discrete event simulation models constructed in hierarchical, modular fashion for above application. Level access mothodology achieves parallel access to models within the modular, hierarchical modules(clusters) by broadcasting the desired operations(e.g. querying information, storing data and so on) to all the cells below a certain desired hierarchical level. Level access methodology exploits the capabilities of object-oriented programming to provide a flexible communication paradigm that combines port-to-port coupling with name-directed massaging. Several examples are given to illustrate the utility of the methodology.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.23 no.4
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• pp.25-44
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• 2012

This study analyzed the current condition of authority control, introduced the MADS(Metadata Authority Description Schema) for authority data. MADS supports encoding an authority description for a agent(person, organization), event, term(topic, temporal entity, genre, geographic entity, hierarchical geographic entity, occupation) and defines main elements, subelements, and attributes using the XML schema language. Lastly, using the MADS's characteristics, this study proposed the effective use plans of interoperability of authority data.

• Journal of the Korea Society for Simulation
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• v.8 no.1
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• pp.113-138
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• 1999

Driven by the explosive expansion and acceptance of the Internet and its multimedia front-end, the Web, a new generation of the modeling and simulation tools have come up with the name of Web-Based Simulation (WBS). Most of WBS libraries inherit its powerful advantages from Java. However, there are cases where explicit specification of models or interface objects is more desirable than the black-box programs. This paper presents an XML-based DEVS (Discrete Event System Specification) markup language for sharing simulation models on the Web. DEVS provides a system-theoretic formalism for the language while XML supports platform-independent data access. This paper focuses on the design of such a language.

• International Journal of Industrial Entomology and Biomaterials
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• v.9 no.2
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• pp.155-165
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• 2004

In Korean Peninsula including neighboring islands and Japanese Islands identical firefly species or the species belonging to same genera occur together in both territories. These geographic firefly species, nonetheless, have never been subject to taxonomic consideration together until recently, lacking clear species status and phylogenetic relationships. A recent serial study of these fireflies using luciferase gene and/or portions of mitochondrial DNA sequences provided some insight into these populations in terms of validity of species name, phylogenetic relationships, and speciation event. In this article, thus, we have reviewed the recent progress on phylogenetic and/or population genetic aspects of these species, i.e., Hotaria-group fireflies, Luciola lateralis, and Pyrocoelia rufa to better understand the firefly species in these regions.

• Annual Conference of KIPS
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• 2015.10a
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• pp.1095-1097
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• 2015

본 논문에서는 트위터 기반 이벤트 탐지에서의 기계학습을 통한 지명 노이즈제거 방식을 제안한다. 이벤트 탐지 시스템은 트위터 사용자 개개인을 이벤트 탐지의 센서로 이용하여 특정 지명에서 발생하는 이벤트를 탐지하였다. 그러나 지명과 동형이의어 관계의 단어가 탐지되어 이벤트 탐지의 정확도를 낮추는 요인이 된다. 이에 본 논문에서는 먼저 노이즈 관련 데이터베이스 구축을 이용하여 제거 필터링을 진행한 후에 기계학습을 이용해서 지명 유무를 결정하였다. 실험결과 본 논문에서 제시하는 예측기법은 89.6%의 신뢰도로 노이즈제거 기법의 필요성을 보였다.

• The Journal of Bigdata
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• v.7 no.2
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• pp.1-9
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• 2022

In the global era, the importance of imported food safety management is increasing. Address information of overseas food companies is key information for imported food safety management, and must be verified for prompt response and follow-up management in the event of a food risk. However, because each country's address system is different, one verification system cannot verify the addresses of all countries. Also, the purpose of address verification may be different depending on the field used. In this paper, we deal with the problem of classifying a given overseas food business address into the administrative district level of the country. This is because, in the event of harm to imported food, it is necessary to find the administrative district level from the address of the relevant company, and based on this trace the food distribution route or take measures to ban imports. However, in some countries the administrative district level name is omitted from the address, and the same place name is used repeatedly in several administrative district levels, so it is not easy to accurately classify the administrative district level from the address. In this study we propose a deep learning-based administrative district level classification model suitable for this case, and verify the actual address data of overseas food companies. Specifically, a method of training using a label powerset in a multi-label classification model is used. To verify the proposed method, the accuracy was verified for the addresses of overseas manufacturing companies in Ecuador and Vietnam registered with the Ministry of Food and Drug Safety, and the accuracy was improved by 28.1% and 13%, respectively, compared to the existing classification model.