• Title/Summary/Keyword: Similarity evaluation

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Comparison procedure in evaluation analysis of source code comparison on Embedded system (정보기기 소스코드 유사성 분석에서 목적물 검증)

  • Nam, SangYep;Kim, Do-Hyeun;Lee, Kyu-Tae
    • Journal of Software Assessment and Valuation
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    • v.17 no.2
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    • pp.31-38
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    • 2021
  • In order to analyze the similarity of the source code object material, the source code on both sides must be able to be compiled and executed. In particular, in the case of hardware-integrated software, it is necessary to check whether the hardware interface matches. However, currently, the source code is provided in an incomplete state which is not original of source code used in developing steps. The complainant confirms that the executing characteristics are similar to their own in the expression and function of the output, and request an evaluation. When a source code compilation error occurs during the evaluation process, the experts draw a flowchart of the source code and applies the method of tracing the code flow for each function as indirect method. However, this method is indirect and the subjective judgment is applied, so there is concern about the contention of objectivity in the similarity evaluation result. In this paper, the problems of unverified source code similarity analysis and improvement directions are dealt with, through the analysis cases of source code disputes applied to embedded systems.

Hand Gesture Recognition Using Shape Similarity Based On Feature Points Of Contour (윤곽선 특징점 기반 형태 유사도를 이용한 손동작 인식)

  • Yi, Hong-Ryoul;Choi, Chang;Kim, Pan-Koo
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2008.05a
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    • pp.585-588
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    • 2008
  • This paper proposes hand gesture recognition using shape similarity method. For this, we require two steps which are aquisition of Hand area and similarity evaluation. First step is extracting hand area using YCbCr color spare. Then eliminate noise through filter and analyzing histogram. For doing this, we ran measure similarity of hand gesture by applying TSR after getting contour. Finally, we utilize shape similarity for recognizing of hand gesture.

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Study on Enumerating the Degree of Similarity in Pairs of the Stnadardized Scores and Lower and Upper tail Probabilites using the Folded Normal Distribution (동형고사에서 표준점수차의 확률분포를 이용한 상사성의 측정과 평가치 산정에 관한 연구)

  • 홍석강
    • The Mathematical Education
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    • v.39 no.2
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    • pp.167-177
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    • 2000
  • In this thesis we concerned with the degree of similarity in pairs of scores having a common mean and variance could express similarity in terms of the absolute difference between the standardized scores. We particulary discussed the distribution of absolute differences between pairs of T scores among many standardized scores and demonstrated the procedures for calculating the lower limit of Med(│d│) values i. e. the maximum possible similarity with medians and correlation coefficients of the equivalent form tests by using the folded normal distribution, although other researchers expressed the degree of similarity using only the standard normal distribution. We also described many cases how to use those techniques and to apply effectively them in real evaluation fields.

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Clustering Validity of Social Network Subgroup Using Attribute Similarity (속성유사도에 따른 사회연결망 서브그룹의 군집유효성)

  • Yoon, Han-Seong
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.17 no.1
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    • pp.75-84
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    • 2021
  • For analyzing big data, the social network is increasingly being utilized through relational data, which means the connection characteristics between entities such as people and objects. When the relational data does not exist directly, a social network can be configured by calculating relational data such as attribute similarity from attribute data of entities and using it as links. In this paper, the composition method of the social network using the attribute similarity between entities as a connection relationship, and the clustering method using subgroups for the configured social network are suggested, and the clustering effectiveness of the clustering results is evaluated. The analysis results can vary depending on the type and characteristics of the data to be analyzed, the type of attribute similarity selected, and the criterion value. In addition, the clustering effectiveness may not be consistent depending on the its evaluation method. Therefore, selections and experiments are necessary for better analysis results. Since the analysis results may be different depending on the type and characteristics of the analysis target, options for clustering, etc., there is a limitation. In addition, for performance evaluation of clustering, a study is needed to compare the method of this paper with the conventional method such as k-means.

AI-Based Project Similarity Evaluation Model Using Project Scope Statements

  • Ko, Taewoo;Jeong, H. David;Lee, JeeHee
    • International conference on construction engineering and project management
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    • 2022.06a
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    • pp.284-291
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    • 2022
  • Historical data from comparable projects can serve as benchmarking data for an ongoing project's planning during the project scoping phase. As project owners typically store substantial amounts of data generated throughout project life cycles in digitized databases, they can capture appropriate data to support various project planning activities by accessing digital databases. One of the most important work tasks in this process is identifying one or more past projects comparable to a new project. The uniqueness and complexity of construction projects along with unorganized data, impede the reliable identification of comparable past projects. A project scope document provides the preliminary overview of a project in terms of the extent of the project and project requirements. However, narratives and free-formatted descriptions of project scopes are a significant and time-consuming barrier if a human needs to review them and determine similar projects. This study proposes an Artificial Intelligence-driven model for analyzing project scope descriptions and evaluating project similarity using natural language processing (NLP) techniques. The proposed algorithm can intelligently a) extract major work activities from unstructured descriptions held in a database and b) quantify similarities by considering the semantic features of texts representing work activities. The proposed model enhances historical comparable project identification by systematically analyzing project scopes.

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A Study on Influence of Stroke Element Properties to find Hangul Typeface Similarity (한글 글꼴 유사성 판단을 위한 획 요소 속성의 영향력 분석)

  • Park, Dong-Yeon;Jeon, Ja-Yeon;Lim, Seo-Young;Lim, Soon-Bum
    • Journal of Korea Multimedia Society
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    • v.23 no.12
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    • pp.1552-1564
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    • 2020
  • As various styles of fonts were used, there were problems such as output errors due to uninstalled fonts and difficulty in font recognition. To solve these problems, research on font recognition and recommendation were actively conducted. However, Hangul font research remains at the basic level. Therefore, in order to automate the comparison on Hangul font similarity in the future, we analyze the influence of each stroke element property. First, we select seven representative properties based on Hangul stroke shape elements. Second, we design a calculation model to compare similarity between fonts. Third, we analyze the effect of each stroke element through the cosine similarity between the user's evaluation and the results of the model. As a result, there was no significant difference in the individual effect of each representative property. Also, the more accurate similarity comparison was possible when many representative properties were used.

A New Similarity Measure based on Separation of Common Ratings for Collaborative Filtering

  • Lee, Soojung
    • Journal of the Korea Society of Computer and Information
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    • v.26 no.11
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    • pp.149-156
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    • 2021
  • Among various implementation techniques of recommender systems, collaborative filtering selects nearest neighbors with high similarity based on past rating history, recommends products preferred by them, and has been successfully utilized by many commercial sites. Accurate estimation of similarity is an important factor that determines performance of the system. Various similarity measures have been developed, which are mostly based on integrating traditional similarity measures and several indices already developed. This study suggests a similarity measure of a novel approach. It separates the common rating area between two users by the magnitude of ratings, estimates similarity for each subarea, and integrates them with weights. This enables identifying similar subareas and reflecting it onto a final similarity value. Performance evaluation using two open datasets is conducted, resulting in that the proposed outperforms the previous one in terms of prediction accuracy, rank accuracy, and mean average precision especially with the dense dataset. The proposed similarity measure is expected to be utilized in various commercial systems for recommending products more suited to user preference.

IDENTIFYING EMOTIONAL ELEMENTS OF APARTMENT NOISE (공동주택 소음에 대한 감성 평가)

  • 민윤기;은희준;조문재;손진훈
    • Proceedings of the Korean Society for Emotion and Sensibility Conference
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    • 1999.03a
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    • pp.39-44
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    • 1999
  • The purpose of this study was to extract emotional dimensions from Korean adjectives relating to apartment noise. Noise-related 296 Korean adjectives were extracted from a dictionary and three evaluators selected 96 adjectives from those by removing very similar ones in meaning. Two types of 96 7-point scales were conducted to college students for evaluation, whether each adjective describes apartment noise appropriately. From this evaluation, 28 adjectives having above 4.5 points were selected. Again, 8 different types of 7-point scales on 378 adjective pairs(28 x 27/2) were administrated to separate college students to evaluate the degree of similarity between 28 adjectives. Based upon this evaluation, 14 adjectives were finally selected and scores on similarity sere analyzed through two different statistical analyses (Multi-dimensional scale and Cluster analysis). The results showed that three dimensions (displeasure, sensitivity and perceived loudness) exist in peoples' emotional response state to apartment noise. The previous studies have treated annoyance and sensitivity as separate measures to noise. However, this study showed that these two factors were on the same emotional dimension labeled as 'sensitivity' In addition, new dimension, labeled as 'displeasure', was found.

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A Berkovich Indentation Technique Based on 3D FEA solutions for Material Property Evaluation (3차원 유한요소해에 기초한 Berkovich 압입 물성평가법)

  • Kim, Min-Soo;Hyun, Hong-Chul;Lee, Kyoung-Yoon;Lee, Hyung-Yil
    • Proceedings of the KSME Conference
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    • 2008.11a
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    • pp.1-6
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    • 2008
  • Due to the self-similarity of Berkovich and conical indenters, different materials may show the same loaddepth curve for single indentation. In this study, we first compare the load-depth characteristics of conical and Berkovich indenters via finite element method. We also analyze the variation of load-depth curves with angle of Berkovich indenter, indentation parameters, and material properties. With numerical regressions of obtained data, we then propose dual-Berkovich indentation formulae for material property evaluation. The proposed approach provides the values of elastic modulus, yield strength and strain-hardening exponent and corresponding stress-strain curve with an average error of less than 3%. The method is valid for any elastic indenters made of tungsten carbide and diamond for instance.

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The Evaluation Measure of Text Clustering for the Variable Number of Clusters (가변적 클러스터 개수에 대한 문서군집화 평가방법)

  • Jo, Tae-Ho
    • Proceedings of the Korean Information Science Society Conference
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    • 2006.10b
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    • pp.233-237
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    • 2006
  • This study proposes an innovative measure for evaluating the performance of text clustering. In using K-means algorithm and Kohonen Networks for text clustering, the number clusters is fixed initially by configuring it as their parameter, while in using single pass algorithm for text clustering, the number of clusters is not predictable. Using labeled documents, the result of text clustering using K-means algorithm or Kohonen Network is able to be evaluated by setting the number of clusters as the number of the given target categories, mapping each cluster to a target category, and using the evaluation measures of text. But in using single pass algorithm, if the number of clusters is different from the number of target categories, such measures are useless for evaluating the result of text clustering. This study proposes an evaluation measure of text clustering based on intra-cluster similarity and inter-cluster similarity, what is called CI (Clustering Index) in this article.

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