• Environmental and Resource Economics Review
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• v.28 no.3
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• pp.355-383
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• 2019

This study is to investigate whether averting costs for wearing $anti-PM_{10}$ masks and using air purifiers at home to reduce exposure from $PM_{10}$ are influenced by subjective risk perceptions and/or objective $PM_{10}$ concentration levels, whose estimates will be used to measure the willingness to pay for $PM_{10}$ risk reduction. An empirical analysis was conducted on a sample of 1,224 respondents who participated in the web-based survey in the late October of 2017. As we reflect the potential endogeniety bias in the estimation of averting cost functions of using air purifiers, the coefficients of risk perception were differed by 6~7 times. Respondents. subjective risk perceptions were influenced by individuals' knowledge, attitudes and demographic variables, as well as the levels of $PM_{10}$ concentrations in their residential region. The marginal willingness to pay for risk reductions at the mean levels of their risk perceptions were measured at 1,000 won per month from wearing $anti-PM_{10}$ masks and 6,000 won for using air purifiers respectively.

• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.543-559
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• 2019

Data often include missing values due to various reasons. If the missing data mechanism is not MCAR, analysis based on fully observed cases may an estimation cause bias and decrease the precision of the estimate since partially observed cases are excluded. Especially when data include many variables, missing values cause more serious problems. Many imputation techniques are suggested to overcome this difficulty. However, imputation methods using parametric models may not fit well with real data which do not satisfy model assumptions. In this study, we review imputation methods using nonlinear models such as kernel, resampling, and spline methods which are robust on model assumptions. In addition, we suggest utilizing imputation classes to improve imputation accuracy or adding random errors to correctly estimate the variance of the estimates in nonlinear imputation models. Performances of imputation methods using nonlinear models are compared under various simulated data settings. Simulation results indicate that the performances of imputation methods are different as data settings change. However, imputation based on the kernel regression or the penalized spline performs better in most situations. Utilizing imputation classes or adding random errors improves the performance of imputation methods using nonlinear models.

• Journal of Science and Technology Studies
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• v.18 no.3
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• pp.153-216
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• 2018

The contemporary practices of Big-Data based automated decision making algorithms are widely deployed not just because we expect algorithmic decision making might distribute social resources in a more efficient way but also because we hope algorithms might make fairer decisions than the ones humans make with their prejudice, bias, and arbitrary judgment. However, there are increasingly more claims that algorithmic decision making does not do justice to those who are affected by the outcome. These unfair examples bring about new important questions such as how decision making was translated into processes and which factors should be considered to constitute to fair decision making. This paper attempts to delve into a bunch of research which addressed three areas of algorithmic application: criminal justice, law enforcement, and national security. By doing so, it will address some questions about whether artificial intelligence algorithm discriminates certain groups of humans and what are the criteria of a fair decision making process. Prior to the review, factors in each stage of data mining that could, either deliberately or unintentionally, lead to discriminatory results will be discussed. This paper will conclude with implications of this theoretical and practical analysis for the contemporary Korean society.

• Korean Journal of Forensic Psychology
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• v.11 no.1
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• pp.37-61
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• 2020

Lee, Khogali, Despodova, and Penrod (2019) demonstrated that American participants whose races are different from a defendant and a victim rendered more punitive judgments against the defendant in a same-race crime (e.g., White observer-Black defendant-Black victim) compared to a cross-race crime (e.g., White observer-Black defendant-Hispanic victim). The aim of the current study was to test the replicability of their findings in a different country-South Korea. Study 1a failed to replicate the race-combination effect in South Korea with three new moderators-case strength, defendant's use of violence, and race salience. Study 1b was conducted with the same design of Study 1a in the United States to examine whether the failure of the replication in Study 1a was due to cultural differences between South Korea and the United States. However, Study 1b also failed to replicate the race-combination effect. Study 2 conducted a meta-analytic review of the data from Lee et al.'s (2019) study, along with the data from Study 1a and 1b and revealed that the race-salience manipulation in Study 1a and 1b might have caused the null results. We conclude that when people' races are different from both a defendant and a victim, they are likely to render more punitive judgments against the defendant in a same-race crime than a cross-race crime. However, the race-combination effect is only sustained when race-relevant issues are not salient in the crime.

• Journal of The Korean Society of Integrative Medicine
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• v.11 no.2
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• pp.23-35
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• 2023

Purpose : Lung cancer induces a decrease in physical activity and a deterioration of respiratory ability. Exercise is an effective treatment to reduce side effects of anti-cancer treatments, also influence the survival and successful rehabilitation in lung cancer patients. However, there is insufficient evidence to show which period is the most effective to apply exercise for lung cancer patients. Therefore, this study was conducted to evaluate the efficacy of exercise-based interventions before and after surgery. Methods : Clinical trials (CTs) and randomized controlled trials (RCTs) reported in PubMed database were investigated. The trials investigated in this study were published for 10 years before August 20, 2022. The risk of bias was judged according to the Cochrane guideline. The materials included in this meta-analysis were 6-minute walk test (6MWT), pulmonary function, and quality of life (QOL). Results : 1 CT and 9 RCTs were selected in current study. In the meta-analysis, exercise increased 6MWT in preoperation (mean difference [MD] 29.49; 95 % confidence interval [CI] .99 to 57.99; p=.04; I²=0 %), 3 months postoperation (MD 54.97; 95 % CI 31.85 to 78.09; p<.001; I²=45 %) and 6 months postoperation (MD 85.59; 95 % CI 45.06 to 126.12; p<.001; I²=47 %). Exercise, also enhanced the lung function such as FEV₁/FVC (%) in postoperation (MD 7.64; 95 % CI 6.26 to 9.02; p<.001; I²=19 %). Additionally, exercise improved QOL, such as preoperative EORTC-QLQ-C30-LC13 in mental function (MD 3.21; 95 % CI .64 to 5.79; p=.01; I²=0 %) and postoperative SF-36 in mental component summary (MD 9.24; 95 % CI 4.94 to 13.54; p<.001; I²=0%). Conclusion : These results indicate that exercise-based intervention can elevate the ability to exercise and the mental componentof QOL within 3 months.

• Therapeutic Science for Rehabilitation
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• v.12 no.2
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• pp.55-68
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• 2023

Objective : This study aims to analyze the effects of visual perception intervention by systematically reviewing the studies that applied visual perception intervention to children with cerebral palsy. Methods : The databases used were PubMed, EMbase, Science Direct, ProQuest, Koreanstudies Information Service System (KISS), Research Information Sharing Service (RISS), and the National Assembly Library. The keywords used were cerebral palsy, CP, and visual perception. According to the PRISMA flowchart, 10 studies were selected from among studies published from January 1, 2012 to March 30, 2022. The quality level of the selected studies, the demographic characteristics of study participants, the effectiveness of interventions, area and strategies of intervention, assessment tools to measure the effectiveness of interventions, and risk of bias were analyzed. Results : All selected studies confirmed that visual perception intervention was effective in improving visual perception function. In addition, positive results were shown in upper extremity function, activities of daily living, posture control, goal achievement, and psychosocial areas as well as visual perception function. The eye-hand coordination area was intervened in all studies. Conclusion : In visual perception intervention, It is necessary to evaluate the visual perception function by area, and apply systematically graded customized interventions for each individual.

• Korean Journal of Culture and Social Issue
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• v.30 no.1
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• pp.55-79
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• 2024

The purpose of this study is to conduct an exploratory investigation into Korean psychopathy by synthesizing the results of prior domestic research on the validation of psychopathy assessment tools. Domestic research on the validation of psychopathy scales has been constrained by limited research methodologies, bias toward male subjects, and the application of inappropriate factor structures. Furthermore, although discrepancies between the original scale and the Korean scale were identified regarding the factor structures through in construct validity research, discussion on the concept of Korean psychopathy has been limited. As a result, this study compared 16 domestic papers on the validation of psychopathy assessment tools along with 9 international foreign papers that addressed the factor structure of each original scale. By comparing the derived factor structures, items assigned to each factor, and omitted items from each study, the characteristics of Korean psychopathy were explored. The findings revealed that Korean psychopathy is recognizable from materialism, machiavellianism, and antisocial behavior and impulsivity. This study holds significance in synthesizing the outcomes of current domestic psychopathy validation research and offers a conceptual foundation to help understand Korean psychopathy.

• Information Systems Review
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• v.20 no.3
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• pp.51-71
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• 2018

Many studies in the field of information security reveal the need to increase awareness. However, although awareness of information security has been raised to a considerable extent, actual security behavior has been shown to fall short of that. Therefore, we wanted to identify the role of psychological factors in making information security decisions by conducting a experimental study. The results show that there are differences in perception of information security risks according to the probabilistic distance and the degree of relative optimism due to social distance. In relation to their relative optimism and intention of information security, they reduced the level of perceived risk compared to those close to them and found that their influence varied according to their probabilistic distance. This study has made valuable attempt in terms of methodology and it is meaningful that the psychological factor is taken into consideration for the information protection behavior, so that the range of relative optimism that actually affects the perception of risk is narrowed. It is expected to contribute to the improvement of information security level of information technology users and protection of information assets by empirically identifying necessity of various approaches to decision making process for information security.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.12
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• pp.7543-7550
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• 2013

Objective: To evaluate associations between tea consumption, alcohol drinking and physical activity and breast cancer risk among Chinese females. Methods: Three English databases (PubMed, ScienceDirect and Wiley) and three Chinese databases (CNKI, WanFang and VIP) were independently searched by 2 reviewers up to December 2012, complemented by manual searches. The quality of included studies was assessed with the Newcastle-Ottawa Scale items. Random-effects models were used to estimate the pooled odds ratios (ORs) and 95% confidence intervals (CIs). Potential publication bias was estimated through Egger's and Begg's tests. Heterogeneity between studies was evaluated with $I^2$ statistics. Results: Thirty-nine studies involving 13,204 breast cancer cases and 87,248 controls were identified. Compared with non-drinkers, regular tea drinkers had decreased risk (OR=0.79, 95%CIs: 0.65-0.95; $I^2$=84.9%; N=16). An inverse association was also found between regular physical activity and breast cancer risk (OR=0.73, 95%CIs: 0.63-0.85; $I^2$=77.3%; N=15). However, there was no significant association between alcohol drinking and breast cancer risk (OR=0.85, 95%CIs: 0.72-1.02; $I^2$=63.8%; N=26). Most of the results from the subgroup analysis were consistent with the main results. Conclusion: Tea consumption and physical activity are significantly associated with a decreased risk of breast cancer in Chinese females. However, alcohol drinking may not be associated with any elevation of risk.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.141-166
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• 2019

Recently, channels like social media and SNS create enormous amount of data. In all kinds of data, portions of unstructured data which represented as text data has increased geometrically. But there are some difficulties to check all text data, so it is important to access those data rapidly and grasp key points of text. Due to needs of efficient understanding, many studies about text summarization for handling and using tremendous amounts of text data have been proposed. Especially, a lot of summarization methods using machine learning and artificial intelligence algorithms have been proposed lately to generate summary objectively and effectively which called "automatic summarization". However almost text summarization methods proposed up to date construct summary focused on frequency of contents in original documents. Those summaries have a limitation for contain small-weight subjects that mentioned less in original text. If summaries include contents with only major subject, bias occurs and it causes loss of information so that it is hard to ascertain every subject documents have. To avoid those bias, it is possible to summarize in point of balance between topics document have so all subject in document can be ascertained, but still unbalance of distribution between those subjects remains. To retain balance of subjects in summary, it is necessary to consider proportion of every subject documents originally have and also allocate the portion of subjects equally so that even sentences of minor subjects can be included in summary sufficiently. In this study, we propose "subject-balanced" text summarization method that procure balance between all subjects and minimize omission of low-frequency subjects. For subject-balanced summary, we use two concept of summary evaluation metrics "completeness" and "succinctness". Completeness is the feature that summary should include contents of original documents fully and succinctness means summary has minimum duplication with contents in itself. Proposed method has 3-phases for summarization. First phase is constructing subject term dictionaries. Topic modeling is used for calculating topic-term weight which indicates degrees that each terms are related to each topic. From derived weight, it is possible to figure out highly related terms for every topic and subjects of documents can be found from various topic composed similar meaning terms. And then, few terms are selected which represent subject well. In this method, it is called "seed terms". However, those terms are too small to explain each subject enough, so sufficient similar terms with seed terms are needed for well-constructed subject dictionary. Word2Vec is used for word expansion, finds similar terms with seed terms. Word vectors are created after Word2Vec modeling, and from those vectors, similarity between all terms can be derived by using cosine-similarity. Higher cosine similarity between two terms calculated, higher relationship between two terms defined. So terms that have high similarity values with seed terms for each subjects are selected and filtering those expanded terms subject dictionary is finally constructed. Next phase is allocating subjects to every sentences which original documents have. To grasp contents of all sentences first, frequency analysis is conducted with specific terms that subject dictionaries compose. TF-IDF weight of each subjects are calculated after frequency analysis, and it is possible to figure out how much sentences are explaining about each subjects. However, TF-IDF weight has limitation that the weight can be increased infinitely, so by normalizing TF-IDF weights for every subject sentences have, all values are changed to 0 to 1 values. Then allocating subject for every sentences with maximum TF-IDF weight between all subjects, sentence group are constructed for each subjects finally. Last phase is summary generation parts. Sen2Vec is used to figure out similarity between subject-sentences, and similarity matrix can be formed. By repetitive sentences selecting, it is possible to generate summary that include contents of original documents fully and minimize duplication in summary itself. For evaluation of proposed method, 50,000 reviews of TripAdvisor are used for constructing subject dictionaries and 23,087 reviews are used for generating summary. Also comparison between proposed method summary and frequency-based summary is performed and as a result, it is verified that summary from proposed method can retain balance of all subject more which documents originally have.