• The Journal of Information Systems
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• v.31 no.2
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• pp.89-110
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• 2022

Purpose To verify the factors that encourage consumers' favorable reaction toward virtual influencer, we proposed consumers' perception of subjective and objective similarity. The purpose of this study is to comprehensively investigate the impacts of consumers' perception toward virtual influencer on purchase intention through psychological distance and parasocial relationship. Design/methodology/approach This study was designed to examine the structural relationships among consumers' perceived external similarity, internal similarity, controllability, animacy toward virtual influencer, psychological distance, parasocial relationship and purchase intention. Findings The results are as follows. First, perceived external similarity, internal similarity, perceived controllability, and animacy had a positive impact on psychological distance. Second, psychological distance had a positive impact on parasocial relationship, whereas it had no significant impact on purchase intention. However, we found that the relation between psychological distance and purchase intention was mediated by parasocial relationship. Lastly, parasocial relationship had a positive impact on purchase intention. Based on these results, this study can propose the way to generate revenue to companies that consider advertising campaign using virtual influencer.

• Microbiology and Biotechnology Letters
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• v.4 no.3
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• pp.123-130
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• 1976

• Journal of the Korea Society of Computer and Information
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• v.15 no.8
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• pp.117-126
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• 2010

Software complexity is used to evaluate a target system's maintainability. The existing complexity metrics on web applications are count-based, so it is hard to incorporate the understandability of developers or maintainers. To make up for this shortcomings, entropy-theory can be applied to define complexity, however, it is assumed that information quantity of each paper is identical. In this paper, structural complexity of a web application is defined based on information theory and similarity. In detail, the proposed complexity is defined using entropy as the previous approach, but the information quantity of individual pages is defined using similarity. That is, a page which are similar with many pages has smaller information quantity than a page which are dissimilar to others. Furthermore, various similarity measures can be used for various views, which results in many-sided complexity measures. Finally, several complexity properties are applied to verify the proposed metric and case studies shows the applicability of the metric.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.109-118
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• 2024

With the increasing number of social media users worldwide, cases of social media being abused to perpetrate various crimes are increasing. Specifically, drug distribution through social media is emerging as a serious social problem. Using social media channels, the curiosity of teenagers regarding drugs is stimulated through clever marketing. Further, social media easily facilitates drug purchases due to the high accessibility of drug sellers and consumers. Among various social media platforms, we focused on Instagram, which is the most used social media platform by young adults aged 19 to 24 years in South Korea. We collected four types of information, including profile photos, introductions, posts in the form of images, and posts in the form of texts on Instagram; then, we analyzed the similarity among each type of collected information. The profile photos and posts in the form of image were analyzed for similarity based on the SSIM(Structural Simplicity Index Measure), while introductions and posts in the form of text were analyzed for similarity using Jaccard and Cosine similarity techniques. Through the similarity analysis, the similarity among various accounts for each collected information type was measured, and accounts with similarity above the significance level were determined as the same drug sales account. By performing logistic regression analysis on the aforementioned information types, we confirmed that except posts in image form, profile photos, introductions, and posts in the text form were valid information for tracking the same drug sales account.

• Journal of KIISE:Software and Applications
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• v.36 no.4
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• pp.329-334
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• 2009

The ontology alignment has two kinds of major problems. First, the features used for ontology alignment are usually defined by experts, but it is highly possible for some critical features to be excluded from the feature set. Second, the semantic and the structural similarities are usually computed independently, and then they are combined in an ad-hoc way where the weights are determined heuristically. This paper proposes the modified parse tree kernel (MPTK) for ontology alignment. In order to compute the similarity between entities in the ontologies, a tree is adopted as a representation of an ontology. After transforming an ontology into a set of trees, their similarity is computed using MPTK without explicit enumeration of features. In computing the similarity between trees, the approximate string matching is adopted to naturally reflect not only the structural information but also the semantic information. According to a series of experiments with a standard data set, the kernel method outperforms other structural similarities such as GMO. In addition, the proposed method shows the state-of-the-art performance in the ontology alignment.

• The Journal of Korean Association of Computer Education
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• v.20 no.5
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• pp.79-88
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• 2017

To upgrade the subjective question grading, we need the syntactic-semantic analysis to analyze syntatic-semantic relation between words in answering. However, since the syntactic-semantic tree has structural and semantic relation between words, we can not apply the method calculating the similarity between vectors. This paper suggests the comparing system with syntactic-semantic tree which has structural and semantic relation between words. In this thesis, we suggest similarity calculation principles for comparing the trees and verify the principles through experiments. This system will help the subjective question grading by comparing the trees and be utilized in distinguishing similar documents.

• Journal of KIISE:Databases
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• v.30 no.3
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• pp.237-251
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• 2003

In this paper, we propose a video retrieval system to search logical structure and semantic contents of video data efficiently. The proposed system employs a layered modelling method that orBanifes video data in raw data layer, content layer and key frame layer. The layered modelling of the proposed system represents logical structures and semantic contents of video data in content layer. Also, the proposed system supports various types of searches such as text search, visual feature based similarity search, spatio-temporal relationship based similarity search and semantic contents search.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.208-217
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• 2021

3D-CNN is one of the deep learning techniques for learning time series data. However, these three-dimensional learning can generate many parameters, requiring high performance or having a significant impact on learning speed. We will use these 3D-CNNs to learn hand gesture and find the parameters that showed the highest accuracy, and then analyze how the accuracy of 3D-CNN varies through input data changes without any structural changes in 3D-CNN. First, choose the interval of the input data. This adjusts the ratio of the stop interval to the gesture interval. Secondly, the corresponding interframe mean value is obtained by measuring and normalizing the similarity of images through interclass 2D cross correlation analysis. This experiment demonstrates that changes in input data affect learning accuracy without structural changes in 3D-CNN. In this paper, we proposed two methods for changing input data. Experimental results show that input data can affect the accuracy of the model.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.10a
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• pp.543-543
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• 2010

The objective of this research is estimating the location of interested sound source by using the similarity between a beamformer output in time domain and the candidate signal. The waveform of beamformer output at the location of sound source is similar with the waveform emitted by that source. To estimate the location of sound source by using this feature, we define quantified similarity between candidate signal and beamformer output. The candidate signal describes the signal which is generated by interested source. In this paper, similarity is defined by four methods. The two methods use time vector comparison, and the other two methods use time-frequency map or linear prediction coefficients. To figure out the results and performance of localization by using similarities, we demonstrate two conditions. The one is when two pure tone sources exist and the other condition is when several bird sounds exist. As a consequence, inner product with two time-vectors and structural similarity with spectrograms can estimate the locations of interest sound source.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.6
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• pp.735-744
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• 2019

One of the key factors for recognition performance in the automatic target recognition for synthetic aperture radar imagery(SAR-ATR) system is reliability of the SAR target database. To achieve optimal performance, the database should be constructed using the images obtained under the same operating condition as the SAR sensor. However, it is impractical to have the extensive set of real-world SAR images, and thus those from the electro magnetic prediction tool with 3-D CAD models are suggested as an alternative where their reliability can be always questionable. In this paper, a method for similarity assessment between target SAR images is presented inspired by the fact that a target SAR image is mainly characterized by the features of scattering centers. The method is demonstrated using a variety of examples and quantitatively measures the similarity related to reliability. Its assessment performance is further compared with that of the existing metric, structural similarity(SSIM).