Earticle

In the wake of the COVID-19 pandemic, unsubstantiated information concerning vaccines and the coronavirus has proliferated on various social media platforms. Consequently, we have considered viable actions to mitigate the impact of such unverified content, enabling individuals to use social media platforms more effectively and minimize any ensuing confusion. Recent measures in this area have included YouTube’s practice of labeling vaccine or corona videos as authoritative when emanating from reputable organizations and Twitter’s practice of flagging vaccine-related content as potentially misleading or taken out of context. This study seeks to explore how such contrasting labeling practices influence users’ believability and engagement differentially, while also examining the moderating impact of regulatory focus. The results indicate that authoritative labeling positively influenced users’ believability and engagement, whereas misleading labeling adversely affected users’ believability and engagement. Additionally, our findings revealed that authoritative labeling has a stronger impact on promotion-focused individuals, while misleading labeling has a more pronounced effect on prevention- focused individuals. Our findings offer insights into how social media platforms can design and present information to their users, taking into account their regulatory focus.

Chomsky(2024) proposes Box System(BS) while eliminating the segregation of A/A’- positions and A- and A’-movement. He applies this system to several constructions in the paper. However, he does not address how passive interrogative constructions are derived within the BS framework. The purpose of the present study is to reveal how passive interrogative constructions are formed within the BS framework, centering around a wh-object in single transitive constructions and a wh-indirect object in double-object constructions(DOC). To this end, this study (i) examines the motivation for eliminating the segregation of A/A’-positions and A- and A’-movement, (ii) demonstrates that Chomsky’s(2024) BS is more suitable than Kitahara and Seely’s (2025) version of BS, (iii) shows in detail how wh-objects are externalized within the Chomsky’s(2024) BS framework, (iv) demonstrates that passivized wh-subjects undergo boxing at the inner phase of v*P during the course of derivation, and (v) addresses why a wh-direct object in DOC cannot be passivized.

이 연구는 정체성 단서를 부각하는 언론의 이름짓기가 유발하는 효과를 탐색했다. 첨예한 사건과 이슈를 일컫는 방식이 특정한 정치적 효과를 창출할 수 있음에 주목하 고, 이 연구는 특정 정체성을 활성화할 수 있는 ‘이름짓기(labeling)’가 사람들의 내집 단-외집단 인식 및 감정, 그리고 이슈 태도에 미치는 효과를 분석했다. 이를 위해 젠더 정체성을 부각하는 이름짓기를 내포한 기사 처치물(‘이대녀 정책’ 혹은 ‘이대남 정책’) 을 작성하여, 20대 성인 남녀를 대상으로 온라인 실험 연구를 진행했다(N = 300). 연 구 결과, 예측과는 달리 젠더 내집단(동성 집단) 단서와 외집단(이성 집단) 단서에 노 출된 집단 모두 통제집단에 비해 젠더 내집단과 자신의 유사성을 낮게 인식하는 경향 을 나타냈다. 내집단 정체성 단서에 노출된 사람들은 아무런 단서에도 노출되지 않은 사람들에 비해 자신이 속한 젠더 집단에 대해 더 부정적 감정을 표출하기도 했다. 이 름짓기의 점화효과는 남성 집단에서만 미약하게 나타났는데, 해당 정책의 이름을 통 해 외집단 단서를 접한 남성 집단에서 젠더 내집단 정체감이 이슈 태도에 미치는 효과 가 다소 높아졌다. 이 연구는 정체성 단서를 활용해 사건을 호명하는 언론 행위에 대 한 학술적 분석으로서 의의를 가진다.

This study explores the political effects of news labeling, which highlights identity cues. Specifically, this study examines the effects of news labeling containing specific identity cues on individuals’ in-group vs. out-group perceptions, feelings, and issue attitudes. To this end, an online experiment study was conducted on a total of 300 men and women in their 20s by manipulating news articles that report on the ‘HPV vaccine policy’ as a way to highlight gender identities. According to the results, those groups exposed to gender in-group and out-group cues, compared to the control group with no gender cues, perceived a lesser degree of similarity between themselves and their own gender group. Individuals exposed to gender in-group cues expressed more negative feelings towards their own gender group. The priming effects of gender identity cues were observed only in the male group; the effects of gender identification on issue attitudes were slightly higher among the male participants who were exposed to out-group gender cues compared to no-cue control group. This research holds significance as a scholarly analysis of news media’s practices that name issues and events using various identity cues.

양화사 작용역의 중의성은 지배결속이론 시절부터 여러 학자들의 다양한 접근방식을 통해 꾸준히 연구되고 있지만, 작용역 중의성을 야기한다고 간주되는 특정 통사적 현상에 주로 초점을 맞추고 있을 뿐 양화사의 역(逆)작용의미를 인간 언어 본연의 관점에서 바라보고 어떤 기제를 통해 일어나는 현상인지에 관한 심도 깊은 설명이 없는 게 현실이다. 본 연구에서는 양화사의 작용역의 중의성을 최소주의의 가장 새로운 방식인 Chomsky(2013, 2014)의 표찰 알고리즘(Labeling Algorithm) 접근법에서의 자질 공유(feature sharing)와 Aoun and Li(1993)의 부가에 대한 제안을 적극 수용하여 최소주의 접근법으로 재분석하고자 한다. Chomsky(1995, 2000)와 Tang(2001)의 주장을 받아들여 양화사의 역 의미작용을 유발하는 양화자질의 존재를 가정한다. 양화자질의 해석성에 대해서는 양화자질을 비해석성 자질로 보는 Tang(2001)의 주장과 달리, 해석성 자질로 보는 Chomsky(1995, 2000)의 주장을 적극 수용한다. 그러나, 일치관계를 통한 자질 점검이 아니라 역 의미작용이 일어나는 지점인 ʋP의 지정어 외곽자리로 양화자질[QU]이 부가되고 경동사 ʋ에 중의성을 야기시키는 양화자질[QU]이 있다고 가정한다. 이렇게 함으로써 이 통사체의 표찰은 자질공유를 통해 <QU, QU>로 결정된다고 제안하고자 한다. 이런 과정을 통해<QU, QU>로의 표찰의 결정에 영향을 끼치는 양화자질은 의미접합부에서 중의성을 가지는 역 의미작용의 해석을 하게 하며, 이 때 표찰을 정하는 것은 의미접합부(Conceptual-Intentional Interface; CI Interface)에 관련 해석 정보를 전달하여 올바른 해석이 이뤄지도록 하기 위함이다.

This paper aims to provide a way to account for scope ambiguity in English through the most recent version of the Minimalist Program including (the very new system,) Labeling Algorithm. To do genuine explanation of the quantifier scope, I make a proposal that we need to assume Quantifier feature[QU] triggered by NP-adjunction at the outer SPEC of ʋ. The QU feature of the copied object quantifier appears to the [SPEC, ʋP] by NP adjunction or late merge based on Sideward Movement by Nunes(2001), and it brings about feature sharing with [QU] feature of ʋ. By labeling through feature sharing, they safely transfer relevant interpretable information to the CI interface to get the meaning of wide scope. To extend our idea, we adopted the suggestion of Tang(2001), Nunes(2001), and Chomsky(2000, 2013). The main goal of this paper is to extend their ideas to the scope interpretation at the CI interface, and propose that the inverse scope results from labeling in the narrow syntax and the [QU] feature activates the syntactic object to have a wide scope.

패턴인식 등에서 널리 이용되는 이진연결요소 라벨링은 오래전부터 연구되어온 영상처리분야의 기본적인 처리방법이다. 연결요소 라벨링에 대한 현재의 연구는 이중스캔을 이용한 방법이 주류를 이루고 있는데 최근 일차스캔시 인근 화소 여러 개를 한 번에 블록단위로 처리하는 것이 가장 성능이 뛰어난 것으로 보고되고 있다. 본 논문에서도 블록단위의 라벨링 방법을 이용하였지만 기존의 방법들보다 더 성능이 개선된 방법을 제시하고 있다. 제안된 방법은 블록단위의 라벨과 새로운 화소기반의 스캔마스크를 사용했는데 실험결과 현재까지 발표된 가장 빠른 라벨링 방법보다도 더 우수한 성능을 보이는 것으로 나타났다.

Binary connected-components labeling, which is widely used in the field of the pattern recognition, has been researched for a long time as one of the basic image processing techniques. Two-scan algorithm has been mainly used in the researches of the connected-components labeling. Recently, for the first scan in the two-scan algorithm, block-based labeling approaches have been used and reported as the fastest methods. In this paper, a new efficient scan mask for connected-components labeling with a block-based labeling approach is proposed. Labeling with the new pixel-based scan mask is more efficient than any other existing method. The results of the experiments show that the proposed method is faster than the existing fastest method.

최근 코로나바이러스 이후, 금리인상에 따른 부동산 거래시장 침체와 프롭테크 업체 성 장으로 개업공인중개사의 폐업이 증가하고 매출이 감소하는 등 부동산 중개업 시장이 경 제적으로 어렵지만, 의무위반에 따른 과태료 부과기준과 금액은 다른 전문자격사와 비교 하면 불합리한 현실이다. 이에, 본 연구는 중개대상물의 표시・광고의무의 과태료 부과 기준과 체계 등을 합리적으로 개선하기 위해 다른 자격사의 과태료 부과기준과 부과 금액 을 비교하여 정부와 협회로 구분한 개선방안을 제시하였다. 정부는 다른 전문자격사와의 형평성을 고려하여 시행령과 고시의 과태료 부과체계를 재검토해야 한다. 특히, 과태료 최고한도 금액을 위반행위 경중에 따른 기준으로 설정하 고 위반행위의 횟수와 단계적인 반납의무 지연 등은 위반한 기간 또는 횟수에 따라 단계 적으로 부과하여야 한다. 또한, 중개사무소 개설등록시 의무가입과 개설등록의 권한을 협회에 부여하여 자율적 규제를 하도록 독려해야 한다. 협회는 자율적 규제를 위하여 회원에 대한 고도의 윤리규정을 제정하여 준수하고, 위반 하면 제재를 가하며 중개대상물의 표시・광고에 대해서도 자체적인 규정을 마련해야 한 다. 또한, 정보공개시스템을 갖추어 매물 등록과 현황이 아닌 국민의 알권리에 부응하는 시장분석 및 수요예측 등 정보를 제공하여 개업공인중개사만이 아닌 국민을 위한 공공성 격의 기관으로 도약할 것을 제안한다.

After the recent coronavirus, the real estate brokerage market is economically difficult, with the real estate transaction market stagnating due to interest rate hikes and the growth of proptech companies increasing the closure of licensed real estate agents and decreasing sales, but the standards and amount of fines for violating obligations are unreasonable compared to other qualified professionals. Accordingly, this study proposed an improvement plan divided into the government and association by comparing the fines imposed by other qualified companies and the amount imposed to reasonably improve the standards and systems for imposing fines for display and advertising obligations of brokerage objects. The government should review the system for imposing fines under the enforcement decree and notification in consideration of equity with other professional qualifiers. In particular, the maximum amount of fines shall be set as a standard for the severity of the violation, and the number of violations and delays in the obligation to return them in stages shall be imposed according to the period or number of violations. In addition, when registering the opening of a brokerage office, the association should be given the authority to register for mandatory registration and opening to encourage autonomous regulation. For voluntary regulation, the association shall establish and comply with high-level ethical regulations for members, impose sanctions for violations, and prepare its own regulations for the display and advertisement of brokerage objects. It is also proposed to establish an information disclosure system to leap to a public institution not only for licensed real estate agents but also for the public by providing information such as market analysis and demand forecasting in response to the public's right to know, not the registration and status of sales.

본 연구에서는 중요정보고시 개선방안을 도출하기 위해 국내 및 국외 관련제도 고찰, 소비자피해구제 현황을 분석하고, 이러한 내용분석에 근거하여 이 제도에 대한 소비자의 인지도, 활용도 및 요구도 조사를 실시하였다. 그 결과 다음과 같은 결론을 도출하였다. 첫째, 기존 업종⋅분야 중 활용도나 요구도가 낮은 것들은 수정 혹은 삭제할 필요가 있다. 둘째, 중요정보고시의 집행 실효성을 위해 과태료 등 법집행을 강화할 필요가 있다. 셋째, 중요정보고시와 개별법과의 체계적인 조화가 필요하다. 또한 소비자조사에 포함시키지 못하였으나 해당 업종과 관련된 각종 소비자문제가 대두되고 있는 몇 개 업종에 대한 도입가능성 논의 및 고시체계 정비의 필요성에 대하여 제언하고, 이를 토대로 최종적인 개선안을 제안하였다. 먼저 장의업을 상조서비스업으로 통합․추가하고 단기해외연수프로그램알선업, 콘도회원권 판매․분양업, 자동차용품업(네비게이션), 중고차판매업의 총 5개 업종 추가 및 화물운송서비스업 중 이사화물에 대한 중요정보항목의 추가가 필요하다. 이와 함께 현행 중요정보고시제도의 분야․업종으로 단순이분되어 사용자편의에 부합되지 않는 분류체계에서 탈피, 의미없는 분야는 세부업종항목으로 포함시키되 업종분류는 한국표준산업분류체계(KISC)를 따라 고시 활용에 있어 검색의 편이를 도모하게 변화시키는 것이 바람직하다.

The purpose of this study is to help to make improvements in 『Designation System of Critical Information to be Included in Labeling and Advertising』(hereinafter, 『Designation System of Critical Information』). Based on the results from (a) an analysis on Korean and other international legislative system, (b) a content analysis on consumer remedies in Korea, (c) a survey of consumers’ cognition of 『Designation System of Critical Information』, the efficiency of this system and consumers’ needs for this, our study came to the following conclusions: First, there were some fileds (categories) or industries needed to be deleted or revised in current 『Designation System of Critical Information』. Second, law enforcement should be intensified for the efficiency of 『Designation System of Critical Information』 considering the results from our survey. Third, 『Designation System of Critical Information』』needs to be balanced and systematically harmonized with other related laws. In addition, discourses on the possibilities of introducing several new categories of industries to the current 『Designation System of Critical Information』 and on the reorganization of this system were followed after the analysis of the main survey. At the last part of this study, we suggest the final version of 『Designation System of Critical Information』. First of all, some sub-categories and critical information in industries need to be added: Funeral services, agents of short-term training program (esp. English-learning) overseas, sales of Condos’ membership, automobile supplies (car-navigations), used car sales and critical information on moving services as sub-categories of freight services. In addition to that, reorganization of current system or new way of assortment has to be considered. It is simply divided into 2 parts: “Fields”, broader meaning than just one “industry” therefore several industries are included in one field, and “industries.” However, this naive classification does not reflect user-centric viewpoint at all. For the ease of use in browsing and for the coordination with legislative system in Korea, (a) several fields which showed very low rate of use in survey should be combined or integrated into relative sub-categories of industries, and (b) sub- categories of industries require to be changed to follow the classification of KISC for the accordance with other legislation.

본 논문은 도로 환경에 효율적인 새로운 차선 검출 방법을 제안한다. 기존 차선 검출 방 법은 환경적인 변화 속에서 낮은 신뢰성의 문제가 있다. 이러한 문제를 보완하기 위해 그레 이 레벨 분할을 이용하여 차선 후보가 되는 영역을 강조한다. 그리고 허프 변환을 이용하여 차선에 가까운 영역의 직선 성분을 추출하고 추출 된 좌표를 기반으로 각 직선마다 ROI 생 성한다. 그리고 생성 된 ROI 이미지를 논리연산으로 통합한다. 그리고 ROI 이미지에 이중 큐를 이용한 객체 분할로 차선을 결정한다. 제안하는 방법은 기존과 다르게 환경적인 변화에 도 차선 검출이 가능하였으며, 하늘, 산 등 배경에 해당하는 영역을 효율적으로 제거하는 장 점과 높은 신뢰성을 얻을 수 있다.

In this paper, we propose a new real-time lane detection method that is efficient for road environment. Existing methods have a problem of low reliability under environmental changes. In order to overcome this problem, we emphasize the lane candidate area by using gray level division. And Extracts a straight line component near the lane by using the Hough transform, and generates an ROI for each straight line based on the extracted coordinates. And integrates the generated ROI images. Then, the lane is determined by dividing the object using the dual queue in the ROI image. The proposed method is able to detect lanes even in the environmental change unlike the conventional method. And It is possible to obtain an advantage that the area corresponding to the background such as sky, mountain, etc. is efficiently removed and high reliability is obtained.

본 논문은 이미지에서 효과적인 문자검출을 위해 색상단순화 및 윤곽선에서의 패턴 분석을 통한 문자 검출방법을 제안한다. 윤곽선 기반방법을 사용하는 문자검출 알고리즘은 단순한 배경의 이미지에서는 우수한 성능을 보이지만, 복잡한 배경의 이미지에서는 성능이 떨어지는 단점이 있다. 따라서 제안하는 방법은 복잡한 배경에서의 비문자영역을 최소화하기 위해 이미지 단순화 및 패턴분석을 통한 문자 검출 알고리즘을 제안한다. 먼저 이미지에서의 문자영역 부분을 검출하기 위하여 전처리 과정으로 K-means 군집화를 사용하여 이미지의 색상을 단순화하고, 색상 단순화 과정에서의 물체의 경계의 흐릿해짐을 개선하기 위해 고주파통과필터를 통해 물체의 경계를 강화한다. 그 후 모폴로지 기법의 팽창과 침식의 차이를 이용하여 물체의 윤곽선을 검출하고, 획득한 영역의 윤곽선 부분의 정보(높이, 너비 면적)를 구한 후 패턴분석을 통해 조건을 줌으로써 문자 후보영역을 판별하여 문자가 아닌 불필요한 영역(그림, 배경)을 제거한다. 최종 결과로 라벨링을 통해 불필요한 영역이 제거된 결과를 보여준다.

In this paper, we propose a text extraction method by pattern analysis on contour for effective text detection in image. Text extraction algorithms using edge based methods show good performance in images with simple backgrounds, The images of complex background has a poor performance shortcomings. The proposed method simplifies the color of the image by using K-means clustering in the preprocessing process to detect the character region in the image. Enhance the boundaries of the object through the High pass filter to improve the inaccuracy of the boundary of the object in the color simplification process. Then, by using the difference between the expansion and erosion of the morphology technique, the edges of the object is detected, and the character candidate region is discriminated by analyzing the pattern of the contour portion of the acquired region to remove the unnecessary region (picture, background). As a final result, we have shown that the characters included in the candidate character region are extracted by removing unnecessary regions.

본 논문에서는 초점/비초점 영상에서 깊이 지도를 효율적으로 추출하기 위하여 그래프 컷(Graph Cut)과 이산 웨이블릿 변환(Discrete Wavelet Transform)을 이용한 깊이 지도 생성 기법을 제안한다. 제안하는 방법은 우선 해당 영상을 영역 별로 처리하기 위해 그래프 컷 방법으로 각 픽셀 간의 유사도를 이용하여 분할한다. 그 다음 분할 영역 을 레이블링 하여 원 영상의 분할 영역 정보를 생성한다. 그리고 이산 웨이블릿 변환을 이용하여 원 영상 내의 주파 수 정보를 나타내는 LL, LH, HH, HL 부대역(Subband)을 생성한다. 마지막으로 4개의 부대역 중 영상의 초점/비초점 영역을 분석할 단서가 되는 HH, HL 대역을 이용하여 주파수 지도를 생성한 뒤 분할 영역에 따라 깊이 정보를 계산 함으로써 깊이 지도를 추출한다. 제안하는 방법은 초점 정보인 블러(Blur)의 양에 따라 동적인 깊이의 할당이 가능하 여 효율적인 깊이 지도의 생성이 가능하였다. 실험으로 PSNR(Peak Signal to Noise Ratio) 방법을 통해 제안하는 방 법의 성능을 평가하였다.

In this paper, we propose an efficient detph map creation method using Graph Cut and Discrete Wavelet Transform. First, we have segmented the original image by using Graph Cut to process with its each areas. After that, the information which describes segmented areas of original image have been created by proposed labeling method for segmented areas. And then, we have created four subbands which contain the original image’s frequency information. Finally, the depth map have been created by frequency map which made with HH, HL subbands and depth information calculation along the each segmented areas. The proposed method can perform efficient depth map creation process because of dynamic allocation using depth information. We also have tested the proposed method using PSNR(Peak Signal to Noise Ratio) method to evaluate ours.

This paper proposes the labeling method for solving bin-picking problem in robot vision. It has the processing steps such as image thresholding, region labeling, and moment computation. To determine a target object from object, the modified labeling method is used to. The moment concept applied to determine the position and orientation of target object. Finally, some experiment result are illustrated and compared with the results of conventional shrinking algorithm and collision fronts algorithm. The proposed labeling method has reduced processing time.

Capsule endoscopy is one of the increasingly demanded diagnostic methods among patients in recent years because of its ability to observe small intestine difficulties. It is often conducted for 12 to 14 hours, but significant frames constitute only 10% of whole frames. Thus, it has been designed to automatically acquire significant frames through deep learning. For example, studies to track the position of the capsule (stomach, small intestine, etc.) or to extract lesion-related information (polyps, etc.) have been conducted. However, although grouping or labeling the training images according to similar features can improve the performance of a learning model, various attributes (such as degree of wrinkles, presence of valves, etc.) are not considered in conventional approaches. Therefore, we propose a class-labeling method that can be used to design a learning model by constructing a knowledge model focused on main lesions defined in standard terminologies for capsule endoscopy (minimal standard terminology, capsule endoscopy structured terminology). This method enables the designing of a systematic learning model by labeling detailed classes through differentiation of similar characteristics.

In this paper, we present an ontology-based approach to labeling influential topics of scientific articles. First, to look for influential topics from scientific article, topic modeling is performed, and then social network analysis is applied to the selected topic models. Abstracts of research papers related to data mining published over the 20 years from 1995 to 2015 are collected and analyzed in this research. Second, to interpret and to explain selected influential topics, the UniDM ontology is constructed from Wikipedia and serves as concept hierarchies of topic models. Our experimental results show that the subjects of data management and queries are identified in the most interrelated topic among other topics, which is followed by that of recommender systems and text mining. Also, the subjects of recommender systems and context-aware systems belong to the most influential topic, and the subject of k-nearest neighbor classifier belongs to the closest topic to other topics. The proposed framework provides a general model for interpreting topics in topic models, which plays an important role in overcoming ambiguous and arbitrary interpretation of topics in topic modeling.

Multiple AV engines are used to ensure more effective system protection against malicious files. These AV engines are capable of distinguishing between benign and malicious files, but even if a file of interest is proven to be malicious, it is still necessary to refer to a list of AV labels provided by each AV engine to determine what family name the malicious file belongs to. However, oftentimes, such AV labels lack a consistent naming scheme, and even family names differ from one AV engine to another. The present study presents Sumav, a fully automated labeling tool that assigns each file a family name based on AV labels. According to previous studies, such a task required prior knowledge or malicious file datasets that had already been labeled. In contrast, Sumav can assign family names with only the AV labels. This system also requires no maintenance and can provide high-quality labeling performance even if sudden changes have been made to the AV label system.

The Manchu language holds historical significance, but a complete dataset of Manchu script letters for training optical character recognition machine-learning models is currently unavailable. Therefore, this paper describes the process of creating a robust dataset of extracted Manchu script letters. Rather than performing automatic letter segmentation based on whitespace or the thickness of the central word stem, an image of the Manchu script was manually inspected, and one copy of the desired letter was selected as a region of interest. This selected region of interest was used as a template to match all other occurrences of the same letter within the Manchu script image. Although the dataset in this study contained only 4,000 images of five Manchu script letters, these letters were collected from twenty-eight writing styles. A full dataset of Manchu letters is expected to be obtained through this process. The collected dataset was normalized and trained using a simple convolutional neural network to verify its effectiveness.

최근 AV 벤더들의 악성코드 동향 보고서에 따르면 신종, 변종 악성코드의 출현 개수가 기하급수적으로 증가하고 있다. 이에 따라 분석 속도가 떨어지는 수동적 분석방법을 대체하고자 기계학습을 적용하는 악성코드 분석 연구가 활발히 연구되고 있다. 하지만 지도학습기반의 기계학습을 이용할 때 많은 연구에서 AV 벤더가 제공하는 신뢰성이 낮은 악성코드 패밀리명을 레이블로 사용하고 있다. 이와 같이 악성코드 레이블의 낮은 신뢰성 문제를 해결하기 위해 본 논문에서는 새로운 레이블링 기법인 "Unified Labeling"을 소개하고 나아가 Fine-grained 방식의 특징 분석을 통해 악성 행위 유사성을 검증한다. 본 연구의 검증을 위해 다양한 기반의 클러스터링 알고리즘을 이용하여 기존의 레이블링 기법과 비교하였다.

According to a recent report by anti-virus vendors, the number of new and modified malware increased exponentially. Therefore, malware analysis research using machine learning has been actively researched in order to replace passive analysis method which has low analysis speed. However, when using supervised learning based machine learning, many studies use low-reliability malware family name provided by the antivirus vendor as the label. In order to solve the problem of low-reliability of malware label, this paper introduces a new labeling technique, "Unified Labeling", and further verifies the malicious behavior similarity through the feature analysis of the fine-grained method. To verify this study, various clustering algorithms were used and compared with existing labeling techniques.

Despite the rapid strides in content-based image retrieval, a notable disparity persists between the visual features of images and the semantic features discerned by humans. Hence, image retrieval based on the association of semantic similarities recognized by humans with visual similarities is a difficult task for most image-retrieval systems. Our study endeavors to bridge this gap by refining image semantics, aligning them more closely with human perception. Deep learning techniques are used to semantically classify images and retrieve those that are semantically similar to personalized images. Moreover, we introduce a keyword-based image retrieval, enabling automatic labeling of images in mobile environments. The proposed approach can improve the performance of a mobile device with limited resources and bandwidth by performing retrieval based on the visual features and keywords of the image on the mobile device.

This paper presents the method for the extraction of the lungs part from the other parts for the diagnostic of the lungs part. The proposed method is based on the calculation of the connected component and the centroid of the image. Connected Component labeling is used to label the each objects in the binarized image. After the labeling is done, centroid value is calculated for each object. The filing operation is applied which helps to extract the lungs part from the image retaining all the parts of the original lungs image. The whole process is explained in the following steps and experimental results shows it's significant.

Age-related changes in bone metabolism are well established by biochemical markers of bone matrix in serum and urine, but analysis of the residual bone matrix, which is still turning over, has not been investigated. In the present study, we measured in vivo rates of bone protein synthesis using a precursor-product method based on the exchange of ²H from ²H₂O into amino acids. Four percent ²H₂O was administered to mice in drinking water after intraperitonial (i.p) bolus injection of 99.9% ²H₂O. Mice were divided into the two groups: growing young mice were administered 4% ²H₂O for 12 weeks after an i.p bolus injection at 5 week of age, whereas weight stable adult mice started drinking 4% ²H₂O 8 weeks later than the growing group and continued 4% ²H₂O drinking for 8 weeks. Mass isotopomer abundance in alanine from bone protein was analyzed by gas chromatography/mass spectrometry. Body ²H₂O enrichments were in the range of 1.88-2.41% over the labeling period. The fractional synthesis rates (ks) of bone protein were 2.000±0.071%/d for growing mice and 0.243±0.014%/d for adult mice. These results demonstrate that the bone protein synthesis rate decreases with age and present direct evidence of age-related changes in bone protein synthesis.

Real-time emotion recognition technology is required for human-robot interaction. There is a method using the Somatic Nervous System (SNS). The PPG signal, which is easy to acquire data and can be used by dividing it into pulse units, is easy for real-time emotion recognition. However, each pulse label is the most important factor in segmenting and using the short-term 1-second to 3-second PPG signal. However, DEAP and MAHNOB-HCI are public bio-signal data, but DEAP Dataset only provides Self-Assessment Labeling for emotion-inducing videos of 60 seconds. The MAHNOB-HCI Database provides Annotation Labeling in which the observer sees the frontal image of the subject's face and measures emotion in real-time. Annotation labeling is effective to split the labels of PPG signals by pulse units. As a result of the experiment, Arousalbased positives 2415/neutral 3884/negative 4201, Valence-based positives 3570/neutral 2835/negative 4095.labels were drived. Annotation labeling allows us to check the emotions of participants in various distributions for 60 seconds. Analyzing the label setting and data preprocessing process contributes to improving short-term real-time emotion recognition.