• Journal of the Korea Society of Computer and Information
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• v.20 no.5
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• pp.73-81
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• 2015

Most of the software fault prediction studies focused on the binary classification model that predicts whether an input entity has faults or not. However the ability to predict entity fault-proneness in various severity categories is more useful because not all faults have the same severity. In this paper, we propose fault prediction models at different severity levels of faults using traditional size and complexity metrics. They are ternary classification models and use four machine learning algorithms for their training. Empirical analysis is performed using two NASA public data sets and a performance measure, accuracy. The evaluation results show that backpropagation neural network model outperforms other models on both data sets, with about 81% and 88% in terms of accuracy score respectively.

• Journal of Digital Contents Society
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• v.8 no.4
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• pp.615-622
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• 2007

A game engine should have the architecture that can manage various interactions between entities in a game for offering users various game experience. For this purpose, the game engine architecture based on message system is used. But only by this message based system, it is difficult to change game world continuously according to some storylines. The reason of this is event-driven system like message based system is appropriate for processing individual message but is not appropriate for processing more bigger logical work unit. For this purpose this paper proposes the storyline entity. The storyline entity has logical flows for a storyline and is called by engine continuously. By this proposed game engine architecture he or she can maintain free game experience by message based system and can add some progressing of storylines.

• Journal of Korean Society of Archives and Records Management
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• v.5 no.2
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• pp.49-72
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• 2005

This study aims to analyze the structures of electronic records classification and suggest managing requirements for electronic folder as basic entity for ERM. This present study begins with analyzing the various types of electronic folders based on the Requirements for Electronic Records Management Systems: Functional Requirements of U.K. It designs some examples of classification structures for clarifying the meaning of the electronic folders, components and markers. Finally, it analyses some implications for korean environments including application of electronic folder concept, principles of folder open and closure, and introduction of electronic part entity for efficient folder management.

• Journal of the Korean Society for Railway
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• v.7 no.1
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• pp.20-25
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• 2004

Railroad geotechnical information management system was developed by using Web GIS and DB in this study. The standardization of railroad geotechnical information is progressed by classifying three groups as like basic informations, vibration informations along railway lines and design drawings. The basic informations consisted of basic and dynamic properties of soils, geophysical exploration and seismic survey/exploration. And the specification for 'human exposure to whole-body vibration' was adopted to construct the vibration informations along railway lines. The informations as like drawings and photographs were saved by changing to graphic files in the standardization of design drawings. In the case of standardization of geographical information, the topographical maps(NGIS, 1:5000) were primarily used as digital maps. Another digital maps(KRRI, 1:5000) and their geographical DB based on NGI code system were added on this maps. The standardized informations were used to construct their database. And railroad information management system was developed using Entity-Relation(ER) model which had a good feasibility for expansion and transition to other system in designing stage of database. This system consisted of layer selection, search and analysis of geotechnical informations and Zeus DB was adopted for GIS operating and user interface. This system could be a good tool for saving, searching and analyzing the geotechnical and geophysical informations. These DB systems would offered the basic informations to plans, design and construction of railroad lines etc. in practical use.

• Journal of Arbitration Studies
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• v.29 no.1
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• pp.71-89
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• 2019

When the host states do not comply with the investor-state arbitral awards voluntarily, it is difficult for the successful claimants to seek the enforcement of arbitral awards against the host state because of the doctrine of state immunity. This raises a question whether the investors might be able to seize the assets of the state-owned enterprises, as well as those of the host states. The investors might consider the properties held by state-owned enterprises as an attractive target especially when it has been established that the host state is responsible for the act of its state-owned enterprise. In such case, the investor might argue that the close relationship between the state-owned enterprise and the host state has already been recognized so that the commercial assets of the state-owned enterprise could be subject to attachment. On the other hand, the host state might argue that the state-owned entity exists separately from the state, and thus its assets cannot be equated with those of the host state. Moreover, even if this argument is not accepted and, as a result, the properties of the state-owned entity is equated with those of the host state, the host state might still be able to argue that non-commercial assets of the state-owned enterprise are immune from execution.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.283-284
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• 2019

규칙 기반의 챗봇(Chatbot)은 개발자가 미리 지정한 키워드와 패턴을 통해 사용자의 의도(Intent)를 파악하기 때문에, 챗봇을 응용한 어플리케이션에서는 제한적인 활용도를 보인다. 본 논문에서는 위 문제를 해결하기 위해, 프레임워크 기반의 한글 자연어 처리 챗봇 성능 향상을 위한 점진 학습(Incremental Learning)을 제안한다. DialogFlow는 규칙 기반의 챗봇 프레임워크로서, 사용자 질의 패턴에 대한 사전 학습이 치명적이다. 제안하는 점진 학습 기법은 사용자 질의가 미리 학습되어 있지 않은 경우에도, 유사도 기반으로 질의의 의도를 결정할 수 있다. 이때 entity 조합과 기존에 학습된 질의들과의 유사도를 통해 의도를 결정하여, 프레임워크를 점진적으로 학습한다. 이를 적용하여 연세대학교 정보들을 제공하는 챗봇을 개발하고, 실험을 통해 제안된 점진 학습 기법은 기존 시스템보다 다양한 종류의 질의 처리가 가능하고, 더욱 빠른 응답 속도를 나타내는 것을 확인하였다. 또한 사용자가 증가함에 따라 점진 학습을 통해 성능이 더욱 증가하는 자가 학습 모형으로서의 우수함을 확인하였다.

• Journal of Climate Change Research
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• v.8 no.1
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• pp.1-10
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• 2017

Over the past centuries, industrialization in developed and developing countries has had a negative impact on global warming, releasing $CO_2$ emissions into the Earth's atmosphere. In recent years, the transportation sector, which emits one-third of total $CO_2$ emissions in the United States, has adapted by implementing a climate change action plan to reduce $CO_2$ emissions. Having an environmental policy might be an essential factor in mitigating the man-made global warming threats to protect public health and the coexistent needs of current and future generations; however, to my best knowledge, no research has been conducted in such a context with appropriate statistical validation process to evaluate the effects of climate change policy on $CO_2$ emission reduction in recent years in the U.S. transportation. The empirical findings using an entity fixed-effects model with valid statistical tests show the positive effects of climate change policy on $CO_2$ emission reduction in a state. With all the 49 states joining the climate change action plans, the U.S. transportation sector is expected to reduce its $CO_2$ emissions by 20.2 MMT per year, and for the next 10 years, the cumulated $CO_2$ emission reduction is projected to reach 202.3 MMT, which is almost equivalent to the $CO_2$ emissions from the transportation sector produced in 2012 by California, the largest $CO_2$ emission state in the nation.

• Cross-Cultural Studies
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• v.28
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• pp.67-86
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• 2012

Metaphors were conceived of as a figure of speech whose role consisted in merely ornamenting the language. However, with their seminal book Metaphors we live by (1980), Lakoff and Johnson have revolutionized the conception of metaphors by placing them as central to human language, thought and action. Cognitive linguists argue that humans tend to conceptualize abstract concepts, such as time, through more experiential and tangible concepts. For instance, it has been observed that the abstract concept of time is conceptualized as space in several unrelated languages. According to the Conceptual Metaphor Theory (CMT), TIME AS SPACE metaphor covers two more specific metaphors: (1) The MOVING TIME metaphor wherein the observer is conceived as a stationary entity, as in The end of the academic year is getting closer; and (2) The TIME AS A LOCATION metaphor wherein times are conceived as stationary points and the observer is conceived as moving relative to these locations, as in We are first approaching the end of the year. This paper aims at probing the validity of the CMT representations of time on the basis of an analysis of time metaphors in two languages: English and French. This analysis is conducted within the framework of CMT. The results corroborate the CMT representations of time, suggesting that in both languages the abstract concept of time is expressed in spatial terms. In English, as in French, time is conceptualized as a moving entity and as having extension in space. In both languages, time can be seen as bounded; therefore, one can perform actions within defined limits of time.

• Journal of Information Science Theory and Practice
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• v.6 no.4
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• pp.28-38
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• 2018

Wikipedia is composed of millions of articles, each of which explains a particular entity with various languages in the real world. Since the articles are contributed and edited by a large population of diverse experts with no specific authority, Wikipedia can be seen as a naturally occurring body of human knowledge. In this paper, we propose a method to automatically identify key entities and relations in Wikipedia articles, which can be used for automatic ontology construction. Compared to previous approaches to entity and relation extraction and/or identification from text, our goal is to capture naturally occurring entities and relations from Wikipedia while minimizing artificiality often introduced at the stages of constructing training and testing data. The titles of the articles and anchored phrases in their text are regarded as entities, and their types are automatically classified with minimal training. We attempt to automatically detect and identify possible relations among the entities based on clustering without training data, as opposed to the relation extraction approach that focuses on improvement of accuracy in selecting one of the several target relations for a given pair of entities. While the relation extraction approach with supervised learning requires a significant amount of annotation efforts for a predefined set of relations, our approach attempts to discover relations as they occur naturally. Unlike other unsupervised relation identification work where evaluation of automatically identified relations is done with the correct relations determined a priori by human judges, we attempted to evaluate appropriateness of the naturally occurring clusters of relations involving person-artifact and person-organization entities and their relation names.

• Genomics & Informatics
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• v.17 no.2
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• pp.15.1-15.7
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• 2019

Automatically detecting mentions of pharmaceutical drugs and chemical substances is key for the subsequent extraction of relations of chemicals with other biomedical entities such as genes, proteins, diseases, adverse reactions or symptoms. The identification of drug mentions is also a prior step for complex event types such as drug dosage recognition, duration of medical treatments or drug repurposing. Formally, this task is known as named entity recognition (NER), meaning automatically identifying mentions of predefined entities of interest in running text. In the domain of medical texts, for chemical entity recognition (CER), techniques based on hand-crafted rules and graph-based models can provide adequate performance. In the recent years, the field of natural language processing has mainly pivoted to deep learning and state-of-the-art results for most tasks involving natural language are usually obtained with artificial neural networks. Competitive resources for drug name recognition in English medical texts are already available and heavily used, while for other languages such as Spanish these tools, although clearly needed were missing. In this work, we adapt an existing neural NER system, NeuroNER, to the particular domain of Spanish clinical case texts, and extend the neural network to be able to take into account additional features apart from the plain text. NeuroNER can be considered a competitive baseline system for Spanish drug and CER promoted by the Spanish national plan for the advancement of language technologies (Plan TL).