Earticle

미국 수정헌법 제1조가 천명하고 있는 표현의 자유는 강력한 보호를 받는다. 미국의 표현의 자유를 이해하려면, 특히 미국 연방대법원의 오 랜 논의를 거쳐 판례법으로 수립한 ‘명백ㆍ현존 위험 원칙’이라는 표현 의 자유 보호 기준을 통해 어떻게 표현의 자유를 최대한 보호하고 필요한 상황에서는 제한의 기준으로 사용하고 있는지를 살펴야 한다. 연방대 법원이 인류 역사상 처음으로 언론의 자유를 헌법적 가치로 정립하고 이 를 제한할 수 있는 기준으로 제시한 이 원칙은 미국 법학계와 실무에서 오랜 시간 동안 중심적인 역할을 수행해 왔다. 명백ㆍ현존 위험 원칙이 처음 등장한 것은 1919년 Schenck 사건이며 1969년 Brandenburg 판결에서 연방대법원은 기존 원칙을 폐기하고, 주창(advocacy)과 선동 (incitement)을 구분하며 의도(intent), 급박성(imminence), 실현 가능 성(likelihood)의 세 가지 요건을 갖춘 새로운 기준을 제시하였다. 물론 미국의 명백ㆍ현존 위험 원칙은 표현 규제의 최소 요건으로 기능하고 있 으나, 이 원칙이 실제로 미국에서 어떻게 적용되어 왔는지에 대한 재판 실무적ㆍ실증적 분석이 필요하다. 9ㆍ11 테러 이후 미국은 테러리즘 관 련 표현을 광범위하게 규제하면서, 이 원칙을 직접 적용하지 않는 사례 들이 많다. 따라서 미국 재판 실무에서 드러난 명백ㆍ현존 위험 원칙의 운영 실태를 분석하여, 명백ㆍ현존 위험 원칙의 역사적 변천과 현대적 의미를 고찰하고자 한다. 그에 더하여 명백ㆍ현존 위험 원칙을 계승하여 명백ㆍ현존 위험 원칙의 현재 모습이라고 일컫는 ‘Brandenburg 기준’ 의 적용 범위에 대해서 살펴보고, 나아가 ‘Brandenburg 기준’의 구조적 한계 등을 비판적으로 검토하고자 한다.

Freedom of expression, as declared by the First Amendment of the United States, is strongly protected. To understand the freedom of expression in the United States, it is necessary to examine how freedom of expression is protected as much as possible and used as a standard for restrictions when necessary through the freedom of expression protection standard called the 'clear and present danger principle' established by case law after long discussions by the U.S. Supreme Court. For the first time in human history, this principle, which the Supreme Court has established freedom of speech as a constitutional value and presented as a criterion for limiting it, has played a central role in the American legal profession and practice for a long time. The first clear and present danger principle appeared in the 1919 Schenck case, and in the 1969 Brandenburg ruling, the Supreme Court abolished the existing principle, distinguished between advocation and incitement, and presented a new standard with three requirements: intent, imminence, and likelihood. Of course, the clear and present danger principle in the United States functions as the minimum requirement for expression regulation, but a trial practical and empirical analysis of how this principle has actually been applied in the United States is needed. Since the 9/11 terrorist attacks, the United States has widely regulated expressions related to terrorism, and there are many cases in which this principle is not directly applied. Therefore, by analyzing the operational status of the clear and present danger principle revealed in the practice of the US trial, the historical transition and modern meaning of the clear and present danger principle will be examined. In addition, the scope of application of the 'Brandenburg standard', which is referred to as the current appearance of the clear and present danger principle by inheriting the clear and present danger principle, will be examined critically, such as the structural limitations of the 'Brandenburg standard'.

According to statistics from the National Forensic Service (NFS), the number of telephone financial fraud cases has more than doubled since onset of COVID-19 compared to the previous year. Telephone financial fraud, including false emergency calls to numbers 112 or 119, involves using telecommunication networks to threaten victims or extort money and valuables. The amount lost to voice phishing, in particular, has been steadily increasing annually posing a significant social problem. In such cases of telephone financial fraud, voice recordings serve as a crucial piece of evidence. This paper researches the forensic application of the latest deep learning-based speaker recognition technology for analyzing an individual from voice evidence. It explains the operational principles and usage of VOCALISE, a commercial speaker recognition engine developed by Oxford Wave Research. Notably, VOCALISE, widely used in forensic analysis organizations worldwide, is adept at creating sample groups from speaker datasets, measuring their statistical characteristics, and analyzing specific voice signals. To evaluate performance on korean speech data, we used 736 voice files collected from 159 individuals involved in actual voice crime cases to calculate the Equal Error Rate (EER) and Cost of Log-Likelihood Ratio (CLLR). Based on these real audio recordings when actual voice evidence is submitted by investigative agencies the paper explains how to use VOCALISE for a globally accepted likelihood ratio-based verbal expression method.

The study interprets each of three classification models based on Bath-Tub Failure Rate (BTFR), Extreme Value Distribution (EVD) and Conjugate Bayesian Distribution (CBD). The classification model based on BTFR is analyzed by three failure patterns of decreasing, constant, or increasing which utilize systematic management strategies for reliability of time. Distribution model based on BTFR is identified using individual factors for each of three corresponding cases. First, in case of using shape parameter, the distribution based on BTFR is analyzed with a factor of component or part number. In case of using scale parameter, the distribution model based on BTFR is analyzed with a factor of time precision. Meanwhile, in case of using location parameter, the distribution model based on BTFR is analyzed with a factor of guarantee time. The classification model based on EVD is assorted into long-tailed distribution, medium-tailed distribution, and short-tailed distribution by the length of right-tail in distribution, and depended on asymptotic reliability property which signifies skewness and kurtosis of distribution curve. Furthermore, the classification model based on CBD is relied upon conjugate distribution relations between prior function, likelihood function and posterior function for dimension reduction and easy tractability under the occasion of Bayesian posterior updating.

We consider the problem of designing independently operating local quantizers at nodes in distributed estimation systems, where many spatially distributed sensor nodes measure a parameter of interest, quantize these measurements, and send the quantized data to a fusion node, which conducts the parameter estimation. Motivated by the discussion that the estimation accuracy can be improved by using the quantized data with a high probability of occurrence, we propose an iterative algorithm with a simple design rule that produces quantizers by searching boundary values with an increased likelihood. We prove that this design rule generates a considerably reduced interval for finding the next boundary values, yielding a low design complexity. We demonstrate through extensive simulations that the proposed algorithm achieves a significant performance gain with respect to traditional quantizer designs. A comparison with the recently published novel algorithms further illustrates the benefit of the proposed technique in terms of performance and design complexity.

Maximum likelihood is a criterion often used to derive localization algorithms. In particular, in this paper we focus on a distance-based algorithm for the localization of nodes in static wireless networks. Assuming that Ultra Wide Band (UWB) signals are used for inter-node communications, we investigate the ill-conditioning of the Two-Stage Maximum-Likelihood (TSML) Time of Arrival (ToA) localization algorithm as the Anchor Nodes (ANs) positions change. We analytically derive novel lower and upper bounds for the localization error and we evaluate them in some localization scenarios as functions of the ANs’ positions. We show that particular ANs’ configurations intrinsically lead to ill-conditioning of the localization problem, making the TSML-ToA inapplicable. For comparison purposes, we also show, through some examples, that a Particle Swarm Optimization (PSO)-based algorithm guarantees accurate positioning also when the localization problem embedded in the TSML-ToA algorithm is ill-conditioned.

A ()-ary QAM scheme maps a binary symbol into two-consecutive signal points (signal vector). It provides very lower PAPR than the conventional QAM because it removes corner signal points. For the scheme, this paper discusses an exact analysis of log-likelihood ratio calculation of the signal vector and signal point, and an approximated method to reduce hardware complexity for the channel coding. Numerical results show that the approximated method provides lower hardware complexity and small performance gap compared to exact one. Also, the PAPR and error performances of ()-ary QAM are compared with the one of conventional QAM.

Maximum likelihood (ML) is the best estimator asymptotically as the number of training samples approaches infinity. This paper deduces an adaptive algorithm for blind signal processing problem based on gradient optimization criterion. A parametric density model is introduced through a parameterized generalized distribution family in ML framework. After specifying a limited number of parameters, the density of specific original signal can be approximated automatically by the constructed density function. Consequently, signal separation can be conducted without any prior information about the probability density of the desired original signal. Simulations on classical biomedical signals confirm the performance of the deduced technique.

The aim of the present work was to evaluate the association between coffee consumption and the development of left ventricular systolic dysfunction (LVSD) in patients who had had an acute coronary syndrome. During 2006.2007, 144 male ($65\;{\times}\;14$ years) and 50 female ($71\;{\times}\;12$ years) post-acute coronary syndrome patients who developed LVSD (ejecti fraction 40%) after the cardiac event and 129 male ($64\;{\times}\;12$ years) and 51 female ($67\;{\times}\;10$ years) post-acute coronary syndrome patients without LVSD (ejection fraction >50%) were included in the study. Participants were consequently selected. Detailed information regarding their medical records, sociodemographic and anthropometric data, and various psychological and lifestyle characteristics (physical activity, smoking habits, etc.) were recorded. In particular, nutritional habits, including coffee consumption, were evaluated using a semiquantitative food-frequency questionnaire. Multi-adjusted analysis revealed that in normotensive patients coffee consumption of 1-2 cups/day was associated with 88% (95% confidence interval, 0.02-0.84) lower likelihood of developing LVSD and consumption of >3 cups/day with 90% (95% confidence interval, 0.01-0.88) lower likelihood for LVSD, compared with no history of consumption of coffee and after adjusting for various confounders. In contrast, in hypertensive patients coffee consumption of >3 cups/day was associated with 4.5-fold higher likelihood for developing LVSD (95% confidence interval, 0.89-22.58) as compared with no history of coffee consumption. Coffee consumption has opposite effects on the likelihood of developing LVSD in post-acute coronary syndrome patients depending on their blood pressure levels.

With the advent of the information society, image restoration technology has aroused considerable interest. Guided image filtering is more effective in suppressing noise in homogeneous regions, but its edge-preserving property is poor. As such, the critical part of guided filtering lies in the selection of the guided image. The result of the Expected Patch Log Likelihood (EPLL) method maintains a good structure, but it is easy to produce the ladder effect in homogeneous areas. According to the complementarity of EPLL with guided filtering, we propose a method of coupling EPLL and guided filtering for image de-noising. The EPLL model is adopted to construct the guided image for the guided filtering, which can provide better structural information for the guided filtering. Meanwhile, with the secondary smoothing of guided image filtering in image homogenization areas, we can improve the noise suppression effect in those areas while reducing the ladder effect brought about by the EPLL. The experimental results show that it not only retains the excellent performance of EPLL, but also produces better visual effects and a higher peak signal-to-noise ratio by adopting the proposed method.

In this paper, we propose a new three-dimensional (3D) photon-counting integral imaging reconstruction method using a merging reconstruction process and maximum likelihood estimation (MLE). The conventional 3D photon-counting reconstruction method extracts photons from elemental images using a Poisson random process and estimates the scene using statistical methods such as MLE. However, it can reduce the photon levels because of an average overlapping calculation. Thus, it may not visualize 3D objects in severely low light environments. In addition, it may not generate high-quality reconstructed 3D images when the number of elemental images is insufficient. To solve these problems, we propose a new 3D photon-counting merging reconstruction method using MLE. It can visualize 3D objects without photon-level loss through a proposed overlapping calculation during the reconstruction process. We confirmed the image quality of our proposed method by performing optical experiments.

This study examines the factors influencing misinformation correction through fact-checking news using the elaboration likelihood model (ELM). A pretest-posttest experimental design was conducted with 502 Korean participants exposed to fact-checking news on a critical public health issue. By focusing on a non-Western context, this study expands fact-checking research beyond Western settings. To enhance ecological validity, a real-world stimulus?a fact-checking report aired on a major TV channel?was utilized. The study explores how motivational factors (need for cognition, issue involvement) and ability factors (news literacy, daily news consumption) influence elaborative processing and how elaboration mediates misinformation correction through attitude change. Additionally, it investigates the moderating effects of media-source credibility and political alignment between individual and the media source. Findings indicate that elaboration significantly mediates the relationship between not only issue involvement (motivational factor) but also news literacy (ability factor) and misinformation correction. However, these mediation effects weaken as political alignment diverges, reducing the efficacy of elaboration. These results contribute to a deeper understanding of the theoretical implications of the ELM in the context of misinformation correction through fact-checking news and suggest practical strategies to enhance the effectiveness of fact-checking interventions in politically polarized environments.

Purpose: This study investigated the associations of awareness of blood pressure or blood sugar levels and subjective body weight perception with weight loss attempts in overweight and obese adults. Methods: For this cross-sectional, descriptive, survey-based study, data were obtained from the 2021 Community Health Survey conducted by the Korea Disease Control and Prevention Agency. The participants (N=6,571) were adult residents (≥19 years old) of northern Gyeonggi Province in Korea with body mass indexes of 23 kg/m2 or greater. Results: The average age of the participants was 53.11±16.56 years, with a range of 19 to 100 years. Among the participants, 73.2% responded that they had tried to maintain or lose weight in the past year. The prevalence of weight loss attempts was higher among participants with certain demographic characteristics-women (who comprised 39.2% of the study sample), younger individuals, and those with higher education levels-than among their counterparts. Conclusion: Participants who were aware of their blood pressure or blood sugar levels and those with a perception of normal or obese body weight were more likely to attempt weight control than participants without these characteristics. Therefore, encouraging individuals to become aware of their blood pressure or blood sugar levels and to maintain an accurate perception of body weight may motivate them to attempt weight management.

정확한 인식률을 보이고 있는 상업적인 음성인식 시스템은 화자종속 고립데이터로부터 학습 모델을 사용한다. 그러 나 잡음 환경에서 데이터양에 따라 음성인식의 성능이 저하되는 문제점이 있다. 본 논문에서는 가우시안 분포에서 Maximum Log Likelihood를 이용한 벡터 양자화 기반 음성 인식 성능 향상을 제안한다. 제안하는 방법은 음성에 대한 특징 을 가지고 벡터 양자화와 Maximum Log Likelihood 음성 특징 추출 방법을 이용하여 유사 음성에 대한 음성 인식의 정확성 을 높이는 최적 학습 모델 구성 방법이다. 이를 위해 HMM을 기반으로 음성 특징을 추출하는 방법을 사용한다. 제안하는 방법을 사용하여 기존 시스템에서 생성되어 사용되는 음성 모델에 대한 부정확한 음성 모델에 대한 정확성을 향상시킬 수 있으므로 음성 인식에 강인한 모델을 구성할 수 있다. 제안하는 방법은 음성 인식 시스템에서 향상된 인식의 정확도를 보인다.

Commercialized speech recognition systems that have an accuracy recognition rates are used a learning model from a type of speaker dependent isolated data. However, it has a problem that shows a decrease in the speech recognition performance according to the quantity of data in noise environments. In this paper, we proposed the vector quantization based speech recognition performance improvement using maximum log likelihood in Gaussian distribution. The proposed method is the best learning model configuration method for increasing the accuracy of speech recognition for similar speech using the vector quantization and Maximum Log Likelihood with speech characteristic extraction method. It is used a method of extracting a speech feature based on the hidden markov model. It can improve the accuracy of inaccurate speech model for speech models been produced at the existing system with the use of the proposed system may constitute a robust model for speech recognition. The proposed method shows the improved recognition accuracy in a speech recognition system.

A sample with special missing pattern called a monotone sample or a monotone missing data pattern occurs in many applications. The statistical methods for analyzing a monotone sample have been developed by several analysts. The purpose of this paper is to introduce the maximum likelihood estimation of parameters when there is a multivariate normal sample with 2-step monotone pattern, and to present the maximum likelihood estimates of parameters in closed forms. These estimates can be obtained by maximizing the overall likelihood function which is expressed in terms of the marginal and conditional likelihood functions with respect to parameters. The maximum likelihood estimates presented are expected to he used to develop statistical methods for analyzing a multivariate normal sample with 2-step monotone pattern in applications.

산업의 발달로 전 세계적인 지구온난화가 진행되었고, 이로 인한 급격한 기후변화가 세계 각처에서 일어나고 있다. 하지만 현재 기후변화 문제의 심각성에도 불구하고 기후변화 리스크 관리에 관한 연구는 거의 이루어지지 않고 있다. 따라서 본 연구에서는 기후변화 리스크의 효율적 관리를 위해 발생가능성 및 발생영향의 특성을 분석하고자 한다. 이를 위해 국내외 문헌조사를 통해 리스크 목록 을 작성하고 전문가 설문을 실시하여 각각의 리스크에 대한 발생가능성 및 영향을 평가한 후 SPSS 를 통해 요인분석을 실시하였다. 분석결과, 각각의 리스크 부문에서 중요하게 평가된 요인 특성을 도출하였고, 발생가능성과 발생영향간의 특성차이도 확인하였다. 이를 통해 기후변화 리스크를 효 율적으로 관리하기 위해 예방적 측면을 집중적으로 관리해야 하는 리스크와 대응적 측면을 집중적 으로 관리해야 하는 리스크를 구분할 수 있었다.

The industrial revolution has led to global warming worldwide, Problems are occurring around the world. Despite the seriousness of the current climate change problem, little research has been done on climate change risk management. Therefore, we need to study risk, a key element in responding to climate change. The purpose of this study is to analyze the likelihood and impacts for the effective management of climate change risks. To do this, we employed expert questionnaire survey on likelihood and impact of climate change risks and conducted a factor analysis. As a result of the analysis, this study identified the characteristics of the factors that were important in the risk and confirmed the difference between likelihood and impact of climate change risks. Finally, in terms of climate change risk management, it possible to effectively manage climate change by classifying the risks that need to be focused on preventive aspects and the risks that need to focus on responsive aspects.

Background/Objectives: This study was designed to compare the Varimax analysis of Principal Component Analysis(PCA) with the Exploratory Factor Analysis(EFA) method using Maximum Likelihood Estimation(MLE) and oblique rotation(Direct Oblimin) for the multicultural experience of university students. multicultural experience variables of college students were analyzed as two factors through Maximum Likelihood Estimation(MLE). Methods/Statistical analysis Q1, Q2, Q3 are multicultural direct experiences, and Q4, Q5, Q6, Q7 are multicultural indirect experiences. Findings: As a result of this study, it is suggested to use Maximum Likelihood Estimation(MLE) and oblique rotation(Direct Oblimin). As a prerequisite for applying the Maximum Likelihood Estimation(MLE), it is thought that Improvements/Applications it is necessary to increase the accuracy of the response rate by using the face-toface interview method when conducting a survey.

본 연구는 재범자의 운전자 관련 특성을 기반으로 음주운전 재발 가능성에 영향을 미치는 유의한 요인을 규명하고자 하였다. 음주운전 재발이 발생할 때까지의 기간의 차이를 확인하기 위해 생존 모델이, 음주운전 재발 가능성에 영향을 미치는 요인을 규명하기 위해 Cox 비례위험 모델이 사용되 었다. 분석을 위해 도로교통공단 의정부지청에서 음주운전으로 기소된 운전자 320명을 대상으로 음주운전 특별교통안전교육 프로그램을 이수한 데이터를 수집하고, 경찰청 데이터베이스 시스템의 음주운전 관련 자료와 결합했다. 생존 분석 결과, 첫 번째 위반에서 두 번째 위반까지의 소요되는 중위수 기간은 약 347.8주인 것으로 분석되었으며, 음주운전 재발 가능성에 영향을 미치는 위험 요인 으로 연령, 성별, 직업, 교육 및 혈중 알코올 농도가 유의한 것으로 나타났다. 분석결과로부터 알 수 있듯이 운전자 인적요인별로 차별화된 교육과 관리를 위한 대책 수립이 필요한 것으로 판단된다.

This study attempted to identify significant factors affecting the likelihood of Driving Under the Influence of Alcohol (DUIA) recurrence based on the drivers’ related characteristics of recidivists. A survival model was used to identify the differences in time period taken until the DUIA recurrence occurs, and a Cox proportional hazard model was employed to investigate which factors are more likely to affect the likelihood of DUIA recurrence. For the analysis, data were collected through a survey of 320 drivers who had been prosecuted as DUIA and to complete the DUIA special traffic safety education program at the Uijeongbu branch of Road Traffic Authority, and then combined with DUIA-related data stored at the National Police Agency database system. The results of survival analysis indicated that the median period from the first offence to the second one was about 347.8 weeks, and that age, gender, occupation, education, and Blood Alcohol Concentration (BAC) were significantly found as risk factors affecting the likelihood of DUIA recurrence. From the analytical results, it seems to establish a countermeasure for differentiated education and management for each driver factor.

People have adopted Social Networking sites (SNSs) as a part of daily lives. While using the SNSs, people easily collect, distribute or write others’ personal information without the information owner’s acknowledgement. Thus, privacy intrusion has been paid attention by both academia and industries. This research focuses on the process where people have their privacy intrusion intention. According to Elaboration Likelihood Model (ELM), there are largely two routes in persuasion process. If a person has low involving context in privacy intrusion such as gossip and curiosity in using SNSs (peripheral route), (s)he may intrude others’ privacies with low motivation and ability. On the other hand, if a person has high involving context in privacy intrusion such as social justice control and power in using SNSs (Central route), (s)he may intrude other’s personal information with high motivation and ability. Using survey data collected from 400 Korean SNS users, we find that gossip and social justice control are effectively associated with privacy intrusion intention through curiosity and power as mediators. This research indicated that privacy intrusion intention in SNSs can be explained by ELM process. It implies privacy intruders could be distinguished by their level of elaboration likelihood. This result can help privacy researchers, policy makers and practitioners to understand intrusion intention.