• Title/Summary/Keyword: occurrences

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A Study on Word Sense Disambiguation Using Bidirectional Recurrent Neural Network for Korean Language

  • Min, Jihong;Jeon, Joon-Woo;Song, Kwang-Ho;Kim, Yoo-Sung
    • Journal of the Korea Society of Computer and Information
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    • v.22 no.4
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    • pp.41-49
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    • 2017
  • Word sense disambiguation(WSD) that determines the exact meaning of homonym which can be used in different meanings even in one form is very important to understand the semantical meaning of text document. Many recent researches on WSD have widely used NNLM(Neural Network Language Model) in which neural network is used to represent a document into vectors and to analyze its semantics. Among the previous WSD researches using NNLM, RNN(Recurrent Neural Network) model has better performance than other models because RNN model can reflect the occurrence order of words in addition to the word appearance information in a document. However, since RNN model uses only the forward order of word occurrences in a document, it is not able to reflect natural language's characteristics that later words can affect the meanings of the preceding words. In this paper, we propose a WSD scheme using Bidirectional RNN that can reflect not only the forward order but also the backward order of word occurrences in a document. From the experiments, the accuracy of the proposed model is higher than that of previous method using RNN. Hence, it is confirmed that bidirectional order information of word occurrences is useful for WSD in Korean language.

Prediction of the Number of Food Poisoning Occurrences by Microbes (원인균별 식중독 발생 건수 예측)

  • Yeo, In-Kwon
    • The Korean Journal of Applied Statistics
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    • v.26 no.6
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    • pp.923-932
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    • 2013
  • This paper proposes a method to predict the number of foodborne disease outbreaks by microbes. The weekly data of food poisoning occurrences by microbes in Korea contain many zero-valued observations and have dependency between outbreaks. In order to model both phenomena, the number of food poisonings is predicted by an autoregressive model and the probabilities of food poisoning occurrences by microbes (given the total of food poisonings) are estimated by the baseline category logit model. The predicted number of foodborne disease outbreaks by a microbe is obtained by multiplying the predicted number of foodborne disease outbreaks and the estimated probability of the food poisoning by the corresponding microbe. The mean squared error and the mean absolute value error are evaluated to compare the performances of the proposed method and the zero-inflated model.

Volatility clustering in data breach counts

  • Shim, Hyunoo;Kim, Changki;Choi, Yang Ho
    • Communications for Statistical Applications and Methods
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    • v.27 no.4
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    • pp.487-500
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    • 2020
  • Insurers face increasing demands for cyber liability; entailed in part by a variety of new forms of risk of data breaches. As data breach occurrences develop, our understanding of the volatility in data breach counts has also become important as well as its expected occurrences. Volatility clustering, the tendency of large changes in a random variable to cluster together in time, are frequently observed in many financial asset prices, asset returns, and it is questioned whether the volatility of data breach occurrences are also clustered in time. We now present volatility analysis based on INGARCH models, i.e., integer-valued generalized autoregressive conditional heteroskedasticity time series model for frequency counts due to data breaches. Using the INGARCH(1, 1) model with data breach samples, we show evidence of temporal volatility clustering for data breaches. In addition, we present that the firms' volatilities are correlated between some they belong to and that such a clustering effect remains even after excluding the effect of financial covariates such as the VIX and the stock return of S&P500 that have their own volatility clustering.

CHAIN DEPENDENCE AND STATIONARITY TEST FOR TRANSITION PROBABILITIES OF MARKOV CHAIN UNDER LOGISTIC REGRESSION MODEL

  • Sinha Narayan Chandra;Islam M. Ataharul;Ahmed Kazi Saleh
    • Journal of the Korean Statistical Society
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    • v.35 no.4
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    • pp.355-376
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    • 2006
  • To identify whether the sequence of observations follows a chain dependent process and whether the chain dependent or repeated observations follow stationary process or not, alternative procedures are suggested in this paper. These test procedures are formulated on the basis of logistic regression model under the likelihood ratio test criterion and applied to the daily rainfall occurrence data of Bangladesh for selected stations. These test procedures indicate that the daily rainfall occurrences follow a chain dependent process, and the different types of transition probabilities and overall transition probabilities of Markov chain for the occurrences of rainfall follow a stationary process in the Mymensingh and Rajshahi areas, and non-stationary process in the Chittagong, Faridpur and Satkhira areas.

DETECTION OF LANDSLIDE AREAS USING UNSUPERVISED CHANGE DETECTION WITH HIGH-RESOLUTION REMOTE SENSING IMAGES

  • Park No-Wook;Chi Kwang-Hoon
    • Proceedings of the KSRS Conference
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    • 2005.10a
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    • pp.233-235
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    • 2005
  • This paper presents an unsupervised change detection methodology designed for the detection of landslide areas. The proposed methodology consists of two analytical steps: one for multi-temporal segmentation and the other for automatic selection of thresholding values. By considering the conditions of landslide occurrences and the spectral behavior of multi-temporal remote sensing images, some specific procedures are included in the analytical steps mentioned above. The effectiveness and applicability of the methodology proposed here were illustrated by a case study of the Gangneung area, Korea. The case study demonstrated that the proposed methodology could detect about $83\%$ of landslide occurrences.

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Finding approximate occurrence of a pattern that contains gaps by the bit-vector approach

  • Lee, In-Bok;Park, Kun-Soo
    • Proceedings of the Korean Society for Bioinformatics Conference
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    • 2003.10a
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    • pp.193-199
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    • 2003
  • The application of finding occurrences of a pattern that contains gaps includes information retrieval, data mining, and computational biology. As the biological sequences may contain errors, it is important to find not only the exact occurrences of a pattern but also approximate ones. In this paper we present an O(mnk$_{max}$/w) time algorithm for the approximate gapped pattern matching problem, where m is the length of the text, H is the length of the pattern, w is the word size of the target machine, and k$_{max}$ is the greatest error bound for subpatterns.

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Algorithm for Finding the Vertex Location of Triangle & Rectangle using the Number of Occurrences of Chain Codes (Chain Code 발생빈도를 이용한 삼각형 및 사각형의 꼭지점 인식 알고리즘)

  • Kim, K.S.;Son, J.R.;Park, C.W.
    • Proceedings of the KIEE Conference
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    • 1987.07b
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    • pp.1343-1346
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    • 1987
  • This paper proposes a new algorithm for finding the vertex location of triangle and rectangle. The algorithm accumulates the number of occurrences of chain codes which range from 0 to 7 and computes the location of vertices using the accumulated value. Hardware and software system were constructed using IBM-PC/AT and VAX-11/780 for the experiment.

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The Occurrences of Allergic Diseases and Accidents within Housing and Residents' Consciousness

  • Kim, Sung-Hwa;Lee, Jae-Hoon
    • Architectural research
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    • v.16 no.1
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    • pp.9-16
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    • 2014
  • Allergic diseases as the major symptoms of Sick Building Syndrome have significantly increased because of the indoor pollutants resulted from the enhanced energy conservation performances of residential buildings. Following traffic accidents, domestic accidents are known as the second most frequent accident type. This study analyzes the occurrences of allergic diseases and home accidents caused by housing conditions, together with the residents' consciousness of the diseases and accidents. The findings of this study are expected to help develop the design guidelines and new housing types conducive to the healthy housing environment. For this study, a questionnaire survey was conducted in two rounds which include face-to-face questionnaire survey and online survey, collecting 200 responses and 1000 responses respectively. The data based on the valid 1011 responses were analyzed by Frequency Analysis and T-test.

Stakeholders' Perception of the Causes and Effects of Construction Delays on Project Delivery

  • Akinsiku, Olusegun Emmanuel;Akinsulire, Akintunde
    • Journal of Construction Engineering and Project Management
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    • v.2 no.4
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    • pp.25-31
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    • 2012
  • The growing rate of delays is adversely affecting the timely delivery of construction projects. This study therefore assesses construction stakeholders' perception of the causes of delays and its effects on project delivery in a bid to proffer solution in minimizing the occurrences of delays. Questionnaire was used to elicit responses from construction stakeholders; a total of thirty three causes of delays, seventeen resultant effects of delays and fifteen methods of minimizing construction delays were identified for the study based on literature reviews. The results suggest that client's cash flow related problems are the main causes of delays while time and cost overruns are the major identifiable effects of delays in construction projects. However, adequate project planning and budgeting were suggested as possible ways of minimizing the occurrences of delays.