Earticle

This study is focused on the problem of interpretation of the Temptation Test Dialogues between Jesus and Devil (Matthew 4:1-11), which are expressed as an indirect speech act. These dialogues have a structure which can be interpreted differently by readers, because the points of the test questions and answers are out of line. Dostoevsky evaluated these dialogues as having influenced the discussion of Russian religious philosophy through the issue of bread and freedom in the <The Brothers Karamazov>. In his work he gave many criticisms to Jesus, but there were not any questions about the ambiguous attitude of Jesus’ answers in the dialogues. And some researchers of dialogue text have studied this conversation in terms of dialogue strategy, but did not pay any attention to Jesus' response attitude. They did not point out the problem of violation of Grice's the cooperative principle, even though these dialogues have unnatural forms of responses. The inference method for these dialogues is based on Searle's(1981) inference algorithm of the Inference theory, Relevance theory, Cooperative principle and the Speech act theory, through which the dialogues were expressed as indirect speech act with conversation implicature.

이론과 실천의 독립적 관계는 아리스토텔레스의 이론지와 실천지의 구분에서 명백히 나타나며, 합리성의 실천적 측면은 상황에 합당한 행위와 판단을 지향한다. 추론 형식과 형식 계산만으로는 실제 판단에서 논증의 합당함을 평가할 수 없다. 아리스토텔레스는 이 점을 분명히 했으며, 그가 말한 실천적 그리고 수사학적 삼단논법은 상황에 합당한 개연적 판단이다. 개연성은 판단 유형에 따라 다 측면으로 나타난다. 근대 확실성 추구의 시대정신 속에서 개연성의 몇 측면은 추론 및 계산 형식으로 대체되고, 상황에 합당함으로서 개연적 판단은 합리성의 맥락에서 배제된다. 하지만, 그러한 개연적 판단은 현실세계에서 실천 영역의 합리성에 속한다. 윤리학과 수사학에서 아리스토텔레스의 중요한 문제는 상황에 합당한 개연적 판단의 도덕적 차원을 논하는 것이다.상황 및 행위자 보편화를 추구하는 규범윤리의 이론은 하나가 아니다.攀규범윤리의 전통과 분류는 다음에서 다뤘다. 이상하(2005).攀攀 갈등하는 상황에 규범윤리의 이론들이 개입할 때 결과는 중재에 의한 실천적 해결이 아니라 윤리적 딜레마로 끝나는 경우가 많다. 윤리적 딜레마는 다양한 윤리 이론들의 모순을 보여주는 창이 된다. 이론 다양성에 의한 윤리적 딜레마의 인식은 ‘이론 의존 위험성’의 인식으로 이어졌고, 이 점은 현대 실천윤리의 부활에 결정적 동기가 된다. 이론 의존성의 위험을 인식할 때 이론과 실천의 두 관계가 주제로 떠오른다. 사후 정당화 혹은 평가에서 이론의 간접적 역할을 강조하거나 혹은 실천적 문제 해결에서 이론의 개선을 지향하는 이론과 실천의 상보적 관계와 이론과 실천의 독립적 관계가 그 두 가지다. 아리스토텔레스의 강한 실천 정신은 이론과 실천의 독립적 관계의 지향 속에서 나타난다. 규범윤리와의 대비 속에서 실천윤리 정신을 과거 전통에서 찾고, 다원화된 가치체계 속에서 실천윤리가 극복해야 할 한계를 지적하는 것이 이 글의 작은 목적이다.

The Independence relation between theory and practice is apparent in Aristotle's distinction of moral virtues form intellectual virtues. The practical aspect of rationality bears a close relation to reasonable judgements and action appropriate for situation. The reasonableness of an argument in substantial judgement and action cannot be evaluated only by formal structure of inference and calculus. This is clear in Aristotle's conception of practice. His practical and rhetorical syllogism is a kind of probable judgement in which situational factors cannot be ignored. The several aspects of being probable are inherent in various types of judgement. Some of those aspects have been substituted for formal inference and probability calculus. Since the modern Zeitgeist represented by the 'Pursuit of Certainty', probabilism in judgement doesn't belong to the context of rationality. But probabilism in judgement cannot be excluded in human rationality in the real world. Good judgement is a probable judgement supportable by a reasonable argument in which situational factors are considered.

예술적 상황은 그것이 가져오는 이해가능성에 그 힘을 의존하며, 그런 이해가능성은 그 자체가 변형의 도구인 ‘정확하고’ 구체적인 양식을 통해 설명된다. 이와 관련하여 우리는 마힘바타(7세기 인도 이론가)와 아누마나와 관련된 그의 이론화 (추론)를 주목할 필요가 있다. 그에 따르면, 첫째, 자연 속에서 계속되는 모순되지 않 는 보편적 관계(vyapti)에 의해 객체와 주체가 함께 결합될 때, 이 둘은 변모한다. 둘 째, 마힘바타는 연역법칙에 의해 필요에 따라 일상적인 것에서 의미 있는 것으로 발전 한다는 사실을 고려할 때만 예술성이 보편화된다고 주장한다. 일상적인 것에서 의미 있는 것으로의 예술적 경험의 이 두 가지 탁월하게 빛나는 전제를 바탕으로 예술성의 동시성과 전시를 낳는 것이 가능해진다. 마힘바타의 추론 이론에서 발전된 개념적 구 조를 바탕으로 W. B. 예이츠와 T. S. 엘리엇의 시를 읽을 때 새로운 관점을 제공한 다. 예이츠의 시에서 육체와 영혼은 각각 작은 것과 큰 것이라는 의미를 내포하는 두 용어로 나타나며, 둘 다 퇴폐와 해방에 의해 결합되어 궁극적으로 보편적인 병존 (vyapti)을 하게 된다. 마찬가지로 엘리엇의 시에서 고통, 불임, 죽음은 육체와 관련되 는 용어이고 진리와 지식라는 용어는 영혼과 관련된다. 낮은 수준에 몸이 있고 높은 수준에 영혼의 내재가 있다. 하지만 그 사이에는 병존(모순되지 않은 보편적 경험)이 있는 반면 몸은 영혼으로 발전한다. 모든 사실은 필연적으로 마힘바타의 아누마나 (Anumana)에서 확립된 추론에 귀속된다.

Artistic situation owes its strength to the comprehensibility it brings about and in turn such a comprehensibility is illustrated through ‘precise’ and concrete modals which themselves are the instruments of transformation. In this regard we can make mention of Mahimbhatta (7th century Indian theoretician) and his theorization in relation to Anumana (Inference) It is his particular opinion and a great extent part of the general truth that the object and subject enter into transformation when they are joined together by the uncontradicted universal relationship (vyapti) which remains continuous in nature. In the second place, Mahimbhatta makes out that the artistry becomes universal only in view of the fact that it progresses from ordinary to significant as necessitated by the law of deduction which, in turn, puts up a definition for all the different stages of the evolution of an artistic experience from ordinary to significant. Based on these two extraordinarily brilliant premises, it becomes possible to bring about concomitance and exposition of ‘qua’ artistry. The poetry of W.B. Yeats and T.S. Eliot would merit elucidation by offering them to the conceptual structures developed in Mahimbhatta’s Anumana theory. In W.B. Yeats for example, it is found that the body and soul emerge as two terms entailing significance on being minor and the other being major respectively and both are brought together by decadence and liberation which ultimately become Universal Concomitance (vyapti). Similarly in Eliot, it is the suffering, infertility and death which bear upon body, and truth and knowledge are terms Eliot would employ to mean the soul. It could be seen that at the lower level there is the body and at the higher level there is the inherence of soul. Yet in between, one understands that there is the vyapti (uncontradicted universal experience) while body evolves into soul. It is, of necessity, that entire fact yields to deduction established in Anumana of Mahimbhatta.

In 2017, Philippine Statistics Authority reported that tourism contributed 12.2 percent to the country’s gross domestic product amounting to PHP1.929 trillion. The study assessed hospitality and tourism students’ ability to logically analyze assumptions, arguments, deductions, inferences, and interpret information in various scenarios. The study concluded that among the various critical thinking indicators, Inference, Interpretation, and Argument are positively moderate to strongly associated with the students’ total scores. The factor Deduction and Assumptions are positively correlated with the dependent variable only slightly moderate. Self-Rating association with critical thinking did not establish strong correlation. Comparison of means showed that Gender did not significantly differentiate the scores on all indicators. On the basis of Course, analysis of variance showed that critical scores were differentiated excepting the variable Inference. Total scores indicated differences given the different Year Level. BSTM students showed a higher critical thinking scores in almost all indicators except Deductions.

컨텍스트 인식 환경에서 개인화 추천 서비스를 제공하기 위해서는 수집된 컨텍스트 정보를 빠르게 분석하고, 효과적으로 사용자의 목적을 추론할 수 있어야 한다. 그러나 모바일 장비에서 수집되는 컨텍스트는 환경에 따라 데이터의 차이가 발생함으로 인해 기존의 추론 알고리즘을 그대로 적용하기에는 적합하지 않고 모바일 환경에 적합한 효율적인 알고리즘이 필요하다. 본 연구에서는 정보의 누락이나 오류 등으로 인한 손실을 최소화하기 위해 나이브 베이즈 분류기를 사용하여 행동 패턴을 분류하였다. 또한 사용자의 성향을 효과적으로 학습하고 행동 목적을 추론하기 위하여 패턴 매칭 기법을 사용하였다. 제안한 개인화 추천 서비스 시스템을 스마트폰에서 어플리케이션을 추천하는 서비스를 적용하여 정확도를 평가하였다.

In order to provide with personalized recommendation service in context-awareness environment, the collected context data should be analyzed fast and the objective of user should be able to inferred effectively. But, the context collected from the mobile devices is not suitable for applying the existing inference algorithms as they are due to the omission or uncertainty of information and the efficient algorithms are required for mobile environment. In this paper, the behavior pattern was classified using naive bayes classification for minimize the loss caused by the omission or error of information. And pattern matching was used to effectively learn of the users inclination and infer the behavior purpose. The accuracy of the suggested inference model was evaluated by applying to the application recommendation service in the smart phones.

Environment indoor quality (EIQ) is a significant aspect of the built environment to maintain occupant health, comfort, prosperity and productivity. One of the most critical issues on EIQ is the environment of indoor air quality (EIAQ). Indoor air pollution has a big impact on the degradation quality of human life due to harmful chemicals and other toxic materials that it is worsened by ten times than the outdoor air pollution. Environment indoor air quality index (EIAQI) does a crucial role in determining the EIAQ that is good for a healthy human life by combining indoor air quality index (IAQI) and thermal comfort index (TCI). This research presents an EIAQ monitoring and controlling system based on fuzzy logic controller (FLC) to identify, classify and calculate the EIAQI value expressed in four categories: excellent, good, bad and worst. Additionally, the clustering technique is used to categorize the air pollutants according to the similarities characteristics and human health impact. EIAQI values are used as index references to set the control system automatically. The control system is used as a system that can notify the status level and reduce indoor air and thermal comfort pollutants and is in the form of fans, inlet?outlet exhaust, and buzzer and LED. Therefore, these models are an appropriate tool for identifying, classifying, assessing, providing guidance to increase the quality of human life.

"This study proposes DropBlock, a structured regularization technique, to mitigate Membership Inference Attacks (MIAs). Unlike traditional dropout, DropBlock masks contiguous regions of feature maps during training, disrupting spatial correlations and preventing data memorization. Evaluated on MNIST, CIFAR-10, and CIFAR-100, DropBlock significantly outperforms baseline defenses, reducing MIA success rates by up to 12.8% on CIFAR-10 and 12.1% on CIFAR-100. Furthermore, it suppresses the attack True Positive Rate by over 40% at low false alarm rates while improving classification accuracy by 2?3%. These findings confirm that DropBlock provides a superior and practical defense without requiring noise injection. 2018 The Korean Institute of Communications and Information Sciences. Publishing Services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)."

Conventional split computing approaches for AI models that generate large outputs suffer from long transmission and inference times. Due to the limited resources of the edge server and selfish MDs, some MDs cannot offload their tasks and sacrifice their performance. To address these issues, we formulate an optimization problem to determine one or two split points that minimize inference latency while ensuring fair offloading among MDs. Additionally, we devise a low-complexity heuristic algorithm called fast and fair split computing (F2SC). Evaluation results demonstrate that F2SC reduces inference time by 3.8%~20.1% compared to the conventional approaches while maintaining fairness.

Lexical inference is an important vocabulary learning method, but it is still unknown whether an explicit instruction of inference strategies would improve inference accuracy. In this study, it is investigated whether an explicit instruction of word-meaning inference strategies in class contributes to the success of meaning inference while reading. Two reading texts materials containing 10 pseudowords each were created (Hamada, 2013). Thirty four Korean EFL university students participated in the task experiments. The participants’ meaning inference accuracy rate was tested two times (pretest and posttest after treatment). It was found that the experiment group was significantly more advanced than the control group in word-meaning inference rate. The results indicated that an explicit instruction of word-meaning inference strategies was conducive to successful inference of unknown vocabulary. It is suggested that careful sections of inference strategies for instruction with consideration of learner’s proficiency levels would be necessary, that continuous training of inference strategies with practical application for reading comprehension and vocabulary learning is recommended, and that global strategies rather than local strategies would be necessary for learners to become successful in word-meaning inference.

온라인 뉴스는 기존의 신문을 대체하였고, 우리가 정보에 접근하고 공유하는 방법에 큰 변화를 가져왔다. 뉴스 웹사이트들은 사용자가 댓글을 남길 수 있는 기능을 오랜 시간동안 제공하였고, 그 중 몇몇 뉴스 웹사이트에서는 뉴스 기사들에 대한 사용자의 반응들을 크라우드소싱(crowdsource)하기 시작했다. 감정분석 분야에서는 텍스트에 반영된 감정과 반응들을 컴퓨팅적으로 모델링하기 위한 시도를 하고 있다. 본 연구에서는 뉴스 기사에 대한 반응들이 뉴스 본문과 수학적인 상관관계를 갖는지 밝히기 위해, 사용자로부터 생성된 다섯 가지의 감정 라벨(label)을 사용하여 10가지 카테고리(category)에 해당하는 100,000개 이상의 뉴스 기사들을 분석한다. 본 연구에서는 전처리과정이 최소한으로 필요하고 기계학습이 적용하지 않아도 되는 간단한 감정 분석 알고리즘(algorithm)을 제안한다. 우리는 이 모델이 한국어와 같은 형태론적으로 복잡한 언어에도 효과적이라는 것을 증명한다.

Online news has replaced the traditional newspaper and has brought about a profound transformation in the way we access and share information. News websites have had the ability for users to post comments for quite some time, and some have also begun to crowdsource reactions to news articles. The field of sentiment analysis seeks to computationally model the emotions and reactions experienced when presented with text. In this work, we analyze more than 100,000 news articles over ten categories with five user-generated emotional annotations to determine whether or not these reactions have a mathematical correlation to the news body text and propose a simple sentiment analysis algorithm that requires minimal preprocessing and no machine learning. We show that it is effective even for a morphologically complex language like Korean.

Road torments are ample cause of deterioration of pavements structure. As they are not assessed and recovered on time. Progress can be easily assessed by the strong infrastructure of roads. But this infrastructure requires a lot of effort not only in construction but also in maintenance. In this paper our cause to detect main cause of road destruction because road cracks are the main cause of roads destruction. These cracks are assessed both manually and automatically. On a wide level, manual method of crack detection is used. These cracks are differentiated according to their shapes and severity. This is the crucial part where it is necessary to have a system for the assessment of the crack type. So, the processing of that image is done according to the nature of the crack. As different techniques are applicable to different cracks to assess the severity and nature. On the basis of which precautionary measures can be taken. In this paper I used fuzzy inference system here to process the image through segmentation. So that the most accurate and quick observation can be done on the basis of appropriate segmentation technique applied on the certain image.

An automatic speech recognition system is one of the popular research problems. There are many research groups working in this field for different language including Japanese. Japanese vowel recognition is one of important parts in the Japanese speech recognition system. The vowel classification system with the Mamdani fuzzy inference system was developed in this research. We tested our system on the blind test data set collected from one male native Japanese speaker and four male non-native Japanese speakers. All subjects in the blind test data set were not the same subjects in the training data set. We found out that the classification rate from the training data set is 95.0 %. In the speaker-independent experiments, the classification rate from the native speaker is around 70.0 %, whereas that from the non-native speakers is around 80.5 %.

The main purpose of this study was to investigate the effectiveness of vocabulary knowledge on the learners’ reading comprehension of the Korean EFL students. This study has been carried out upon the hypothesis that there is a significant relationship among vocabulary, ability to infer and reading comprehension skills. After the pre-test designed to choose the target group, twenty six participants selected as intermediate level were required to take Test I and II. Test I, where twenty words are underlined in the passage, aims to reveal correlational relationship between vocabulary and reading comprehension ability. The result showed that there is correlational relationship. Test II intends to reveal the relationship among vocabulary knowledge, ability to infer and reading comprehension. To this end, twenty words were provided in the blank form in the reading passage and participants were asked to select the correct answers describing the meaning of passages. Although those words were not exposed in the test, students with high score in pre-test got higher score in Test II in general. This result implies that indirect vocabulary learning strategy is effective for enhancing reading comprehension and ability to draw inference. In sum, vocabulary plays a pivotal role in reading comprehension for Korean EFL students and the best way to learn new words is to direct life experiences and to expose various experiences or to read a clear context for word learning and more complex ideas.