• Journal of the Ergonomics Society of Korea
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• v.19 no.1
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• pp.1-9
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• 2000

This study was performed to investigate some characteristics on human error proneness in the computerized work environment. Our concerning theme was on human error likelihood according to personal temperament. Two experiments were performed. The first experiment was to study the effect of field- independence/dependence on error likelihood. The second experiment was on error proneness. These experiments were performed in information search task. which was most frequent task in computerized work environment such as the control room of nuclear power plant. Ten subjects were participated in this study. Analyzed results are as follows. Field-independence/dependence had a significant effect in both information search time and error frequency. Error proneness had a significant effect in both factors, too. And, a positive correlation was found between error frequency and information search time. These results will be utilized as a basis to study operator's error proneness in the computerized control room of nuclear power plant. later on.

• Toxicological Research
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• v.33 no.4
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• pp.265-272
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• 2017

Aryl hydrocarbons such as 3-nitrobenzanthrone (NBA), 4-aminobiphenyl (ABP), acetylaminofluorene (AAF), benzo(a)pyrene (BaP), and 1-nitropyrene (NP) form bulky DNA adducts when absorbed by mammalian cells. These chemicals are metabolically activated to reactive forms in mammalian cells and preferentially get attached covalently to the $N^2$ or C8 positions of guanine or the $N^6$ position of adenine. The proportion of $N^2$ and C8 guanine adducts in DNA differs among chemicals. Although these adducts block DNA replication, cells have a mechanism allowing to continue replication by bypassing these adducts: translesion DNA synthesis (TLS). TLS is performed by translesion DNA polymerases-Pol ${\eta}$, ${\kappa}$, ${\iota}$, and ${\zeta}$ and Rev1-in an error-free or error-prone manner. Regarding the NBA adducts, namely, 2-(2'-deoxyguanosin-$N^2$-yl)-3-aminobenzanthrone (dG-$N^2$-ABA) and N-(2'-deoxyguanosin-8-yl)-3-aminobenzanthrone (dG-C8-ABA), dG-$N^2$-ABA is produced more often than dG-C8-ABA, whereas dG-C8-ABA blocks DNA replication more strongly than dG-$N^2$-ABA. dG-$N^2$-ABA allows for a less error-prone bypass than dG-C8-ABA does. Pol ${\eta}$ and ${\kappa}$ are stronger contributors to TLS over dG-C8-ABA, and Pol ${\kappa}$ bypasses dG-C8-ABA in an error-prone manner. TLS efficiency and error-proneness are affected by the sequences surrounding the adduct, as demonstrated in our previous study on an ABP adduct, N-(2'-deoxyguanosine-8-yl)-4-aminobiphenyl (dG-C8-ABP). Elucidation of the general mechanisms determining efficiency, error-proneness, and the polymerases involved in TLS over various adducts is the next step in the research on TLS. These TLS studies will clarify the mechanisms underlying aryl hydrocarbon mutagenesis and carcinogenesis in more detail.

• Journal of Information Technology Services
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• v.11 no.1
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• pp.211-221
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• 2012

Many metric-based classification models have been proposed to predict fault-proneness of software module. This paper presents two prediction models using Bayesian classifier which is one of the most popular modern classification algorithms. Bayesian model based on Bayesian probability theory can be a promising technique for software quality prediction. This is due to the ability to represent uncertainty using probabilities and the ability to partly incorporate expert's knowledge into training data. The two models, Na$\ddot{i}$veBayes(NB) and Bayesian Belief Network(BBN), are constructed and dimensionality reduction of training data and test data are performed before model evaluation. Prediction accuracy of the model is evaluated using two prediction error measures, Type I error and Type II error, and compared with well-known prediction models, backpropagation neural network model and support vector machine model. The results show that the prediction performance of BBN model is slightly better than that of NB. For the data set with ambiguity, although the BBN model's prediction accuracy is not as good as the compared models, it achieves better performance than the compared models for the data set without ambiguity.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.2
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• pp.57-64
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• 2014

Most previous studies of software fault prediction model which determines the fault-proneness of input modules have focused on supervised learning model using training data set. However, Unsupervised learning model is needed in case supervised learning model cannot be applied: either past training data set is not present or even though there exists data set, current project type is changed. Building an unsupervised learning model is extremely difficult that is why only a few studies exist. In this paper, we build unsupervised models using representative clustering algorithms, EM and DBSCAN, that have not been used in prior studies and compare these models with the previous model using K-means algorithm. The results of our study show that the EM model performs slightly better than the K-means model in terms of error rate and these two models significantly outperform the DBSCAN model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1566-1582
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• 2019

Silent Data Corruptions (SDCs) is a serious reliability issue in many domains of computer system. The identification and protection of the program instructions that cause SDCs is one of the research hotspots in computer reliability field at present. A lot of solutions have already been proposed to solve this problem. However, many of them are hard to be applied widely due to time-consuming and expensive costs. This paper proposes an intelligent approach named SDCPredictor to identify the instructions that cause SDCs. SDCPredictor identifies SDC-causing Instructions depending on analyzing the static and dynamic features of instructions rather than fault injections. The experimental results demonstrate that SDCPredictor is highly accurate in predicting the SDCs proneness. It can achieve higher fault coverage than previous similar techniques in a moderate time cost.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.1
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• pp.180-194
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• 2015

On roads vehicles are the bossy, while pedestrians are the frangible. The current paper has a purpose for contributing to pedestrian's safety. First, it studies the association between demographic parameters and TPB(Theory of Planned Behavior)'s variables(pedestrian's attitude toward crossing behavior in roads, personal norm, etc.) and his/her crossing intention, perceived risk while crossing, and the experience of past traffic accidents. Its sample comes from a specific population(college students). Further, the present study endeavors to explain the portion of human cognitive failure/error, impulsiveness, time perspective, and probabilistic/math-logical judgment ability in pedestrian's riky crossings on roads. Research results found that TPB variables and such a few human characteristics have some significant association with the risky crossing intention on the road. Considering the human psychological portion in pedestrian accidents would help us prevent the accidents and reduce unhappiness of the accidents and, further, economic loss and insurance expenditure related with the accidents.