• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.618-623
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• 2011

This paper proposes a novel method to discriminate meaningful tables from decorative one using a composite kernel for handling structural information of tables. In this paper, structural information of a table is extracted with two types of parse trees: context tree and table tree. A context tree contains structural information around a table, while a table tree presents structural information within a table. A composite kernel is proposed to efficiently handle these two types of trees based on a parse tree kernel. The support vector machines with the proposed kernel dised kuish meaningful tables from the decorative ones with rich structural information.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.1
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• pp.19-24
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• 2010

The rise of music resources has led to a parallel rise in the need to manage thousands of songs on user devices. So users have a tendency to build playlist for manage songs. However the manual selection of songs for creating playlist is a troublesome work. This paper proposes an auto playlist generation system considering user context of use and preferences. This system has two separated systems; 1) the mood and emotion classification system and 2) the music recommendation system. Firstly, users need to choose just one seed song for reflecting their context of use. Then system recommends candidate song list before the current song ends in order to fill up user playlist. User also can remove unsatisfied songs from the recommended song list to adapt the user preference model on the system for the next song list. The generated playlists show well defined mood and emotion of music and provide songs that the preference of the current user is reflected.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.195-201
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• 2024

With the Covid-19(Corona Virus) spread all around the world, people are using this propaganda and the desperate need of the citizens to know the news about this mysterious virus by spreading fake news. Some Countries arrested people who spread fake news about this, and others made them pay a fine. And since Social Media has become a significant source of news, .there is a profound need to detect these fake news. The main aim of this research is to develop a web-based model using a combination of machine learning algorithms to detect fake news. The proposed model includes an advanced framework to identify tweets with fake news using Context Analysis; We assumed that Natural Language Processing(NLP) wouldn't be enough alone to make context analysis as Tweets are usually short and do not follow even the most straightforward syntactic rules, so we used Tweets Features as several retweets, several likes and tweet-length we also added statistical credibility analysis for Twitter users. The proposed algorithms are tested on four different benchmark datasets. And Finally, to get the best accuracy, we combined two of the best algorithms used SVM ( which is widely accepted as baseline classifier, especially with binary classification problems ) and Naive Base.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.4
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• pp.51-60
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• 2011

Authentication model in the wireless environment has many security vulnerabilities. However, there is no adapting standard method in this field. Therefore, we propose a fuzzy logic based authentication model to enhance the security level in the authentication environment. We use fuzzy logic based classification to construct our model, and also additionally utilize improved AHP and case-based reasoning for an appropriate decision making. We compute the context information by using the improved AHP method, use the proposed model to compute the security level for the input data, and securely apply the proposed model to the wireless environment which has diverse context information. We look forward to better security model including cloud computing by extending the proposed method in the future.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.719-734
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• 2020

This paper proposes a new measurement scheme for estimating the processing level according to risk when performing de-identification in the use of personal information by practitioners in the organization in line with the recently revised Data 3 Act. Our proposed methods considered the surrounding circumstances surrounding the data, not just the data, for risk measurement, and divided the data situation into three categories more systematically so that it can be applied in all areas in a general-purpose environment, the data utilization environment, and the data (self) so that it can be calculated quantitatively based on each context risk according to the presented classification. The proposed method is designed to calculate the risk of existing de-identifiable information in a quantitative manner so that personal information controller in general organizations can use it in practice, not just in the qualitative judgment of experts.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.755-762
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• 2022

This paper propose a clustered algorithm that possible more efficient COVID-19 disease learning prediction within clustering using context-aware attribute information. In typically, clustering of COVID-19 diseases provides to classify interrelationships within disease cluster information in the clustering process. The clustering data will be as a degrade factor if new or newly processing information during treated as contaminated factors in comparative interrelationships information. In this paper, we have shown the solving the problems and developed a clustering algorithm that can extracting disease correlation information in using K-means algorithm. According to their attributes from disease clusters using accumulated information and interrelationships clustering, the proposed algorithm analyzes the disease correlation clustering possible and centering points. The proposed algorithm showed improved adaptability to prediction accuracy of the classification management system in terms of learning as a group of multiple disease attribute information of COVID-19 through the applied simulation results.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1315-1315
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• 2024

In recent years, the building industry has seen a fundamental transition due to Digitalization Transformation (DX), with the aim of improving efficiency, productivity, sustainability, and cost-effectiveness. In particular, literature has significantly emphasized Smart Buildings (SBs), which are expected to grow in the global marketplace in the coming years. The most noticeable benefits include energy efficiency, increased occupant comfort and productivity, and a reduction in the building's impact on the environment. Most importantly, the shift to SBs has resulted in major changes to how traditional business practices are carried out. The Facility Asset Management (FAM) domain is one key area undergoing considerable changes to meet the needs of managing functional SBs. Despite this shifting landscape, the changes and prospective extensions to the business areas of FAM in the context of SBs remain largely unexplored. Thus, to address this limitation, this paper aims to investigate the potential changes (i.e., either the addition of a new function or the expansion of an existing function) of the FAM domain from the context of SBs. To achieve this objective, • First, based on a generic model of FAM proposed by Jin et al. (2024), a three-level hierarchical classification of FAM business functions for a conventional building is proposed. • Second, the concept of SBs is thoroughly discussed, including its drivers, features, enablers, and improvement areas. • Finally, a new FAM business function for SB is proposed, aligning with the distinct characteristics of SBs. As there are no established functional taxonomies of FAM, the comprehensive breakdown of FAM business functions presented in this study can be used as a standardized functional breakdown of the FAM domain. Moreover, it can also be used to facilitate robust and integrated information management practices throughout the whole lifecycle of SB facilities.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.1
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• pp.105-113
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• 2011

Since existing thematic maps have been made with medium- to low-resolution satellite images, they have several shortcomings including low positional accuracy and low precision of presented thematic information. Digital aerial photo image taken recently can express panchromatic and color bands as well as NIR (Near Infrared) bands which can be used in interpreting forest areas. High resolution images are also available, so it would be possible to conduct precision land cover classification. In this context, this paper implemented object-based land cover classification by using digital aerial photos with 0.12m GSD (Ground Sample Distance) resolution and IKONOS satellite images with 1m GSD resolution, both of which were taken on the same area, and also executed qualitative analysis with ortho images and existing land cover maps to check the possibility of object-based land cover classification using digital aerial photos and to present usability of digital aerial photos. Also, the accuracy of such classification was analyzed by generating TTA(Training and Test Area) masks and also analyzed their accuracy through comparison of classified areas using screen digitizing. The result showed that it was possible to make a land cover map with digital aerial photos, which allows more detailed classification compared to satellite images.

• Journal of The Korean Association For Science Education
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• v.34 no.2
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• pp.55-62
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• 2014

This study is to reclassify the classifications or definitions of scientific literacy in scientific literacy researches since 1960s and grasp the classification trends of scientific literacy definitions. Sixteen articles have been selected among the articles that have been introduced in the two articles. Classification criteria are as follows: 1) "be learned," "competence," or "be able to function in society" as meanings of "literate," 2) "terms" or "description" as the ways of representing scientific literacy, 3) "singular structure," "hierarchical structure," or "parallel structure" as the inner structure of scientific literacy definitions. The results of this study are as follows: First, hierarchical structures in scientific literacy have almost always accompanied "terms" representing scientific literacy and also accepted the hierarchy between "be learned" and "competence," but not the definition of scientific literacy as functioning in society. All parallel structures in scientific literacy have accompanied the definition as functioning in society. And singular structure almost always appears in researches based on the views of scientific literacy in relatively recent times. Second, researches who have used "terms" as ways of representing scientific literacy have increased. Based on the results in this study, the meanings of scientific literacy have been emphasized in view of the ability of playing a role in a social context as well as learning and competence these days. To meet this movement in scientific literacy actively, science education community should get out of traditional teaching and learning scientific concepts and give emphasis on application in various context and social role of science learners.

• Korean Journal of Cognitive Science
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• v.35 no.1
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• pp.23-48
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• 2024

Building human-aligned artificial intelligence (AI) for social support remains challenging despite the advancement of Large Language Models. We present a novel method, the Chain of Empathy (CoE) prompting, that utilizes insights from psychotherapy to induce LLMs to reason about human emotional states. This method is inspired by various psychotherapy approaches-Cognitive-Behavioral Therapy (CBT), Dialectical Behavior Therapy (DBT), Person-Centered Therapy (PCT), and Reality Therapy (RT)-each leading to different patterns of interpreting clients' mental states. LLMs without CoE reasoning generated predominantly exploratory responses. However, when LLMs used CoE reasoning, we found a more comprehensive range of empathic responses aligned with each psychotherapy model's different reasoning patterns. For empathic expression classification, the CBT-based CoE resulted in the most balanced classification of empathic expression labels and the text generation of empathic responses. However, regarding emotion reasoning, other approaches like DBT and PCT showed higher performance in emotion reaction classification. We further conducted qualitative analysis and alignment scoring of each prompt-generated output. The findings underscore the importance of understanding the emotional context and how it affects human-AI communication. Our research contributes to understanding how psychotherapy models can be incorporated into LLMs, facilitating the development of context-aware, safe, and empathically responsive AI.