• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.12
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• pp.2357-2364
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• 2008

In this paper, we consider the problem of exploiting a taxonomy of propositionalized attributes in order to generate compact and robust classifiers. We introduce Propositionalized Attribute Taxonomy guided Naive Bayes Learner (PAT-NBL), an inductive learning algorithm that exploits a taxonomy of propositionalized attributes as prior knowledge to generate compact and accurate classifiers. PAT-NBL uses top-down and bottom-up search to find a locally optimal cut that corresponds to the instance space from propositionalized attribute taxonomy and data. Our experimental results on University of California-Irvine (UCI) repository data set, show that the proposed algorithm can generate a classifier that is sometimes comparably compact and accurate to those produced by standard Naive Bayes learners.

• Convergence Security Journal
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• v.19 no.1
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• pp.49-60
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• 2019

Medical record is part of the personal information that values the dignity and value of an individual, and can lead to serious social prejudice and disadvantage to an individual when it is breached illegally. In addition, the medical record has been highly threatened because its value is relatively high, and external threats are continuing. In this paper, we propose a medical record sharing framework that guarantees patient's privacy based on blockchain using ciphertext policy-based attribute based proxy re-encryption scheme. The proposed framework first uses the blockchain technology to ensure the integrity and transparency of medical records, and uses the stealth address to build the unlinkability between physician and patient. Besides, the ciphertext policy attribute-based proxy re-encryption scheme is used to enable fine-grained access control, and it is possible to share information in emergency situations without patient's agreement.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.5
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• pp.49-57
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• 2008

User authentication and key agreement are very important components to provide secure home network service. Although the TTA adopted the EEAP-PW protocol as a user authentication and key transmission standard, it has some problems including not to provide forward secrecy. This paper first provides an analysis of the problems in EEAP-PW and then proposes a new attribute-based authenticated key agreement protocol, denoted by EEAP-AK. to solve the problems. The proposed protocol supports the different level of security by diversifying network accessibility for the user attribute after the user attribute-based authentication and key agreement protocol steps. It efficiently solves the security problems in the EEAP-PW and we could support more secure home network service than the EEAP-AK.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.27-38
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• 2023

In this paper, we propose a conversational AI agent based on continual learning that can continuously learn and grow with new data over time. A continual learning-based conversational AI agent consists of three main components: Task manager, User attribute extraction, and Auto-growing knowledge graph. When a task manager finds new data during a conversation with a user, it creates a new task with previously learned knowledge. The user attribute extraction model extracts the user's characteristics from the new task, and the auto-growing knowledge graph continuously learns the new external knowledge. Unlike the existing conversational AI agents that learned based on a limited dataset, our proposed method enables conversations based on continuous user attribute learning and knowledge learning. A conversational AI agent with continual learning technology can respond personally as conversations with users accumulate. And it can respond to new knowledge continuously. This paper validate the possibility of our proposed method through experiments on performance changes in dialogue generation models over time.

• Journal of Korea Multimedia Society
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• v.9 no.9
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• pp.1231-1246
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• 2006

HTML is a format for rendering Web documents visually and uses tables to present a relational information. Since HTML has limits in terms of information processing and management by a computer, it is important to transform HTML tables into XML documents, which is able to represent logical structure information. As a prerequisite for extracting information from the Web, this paper presents an efficient method for extracting logical structures from HTML tables and transforming them into XML documents. The proposed method consists of two phases: Area segmentation and structure analysis. The area segmentation step removes noisy areas and extracts attribute and value areas through visual and semantic coherency checkup. The hierarchical structure between attribute and value areas are analyzed and transformed into XML representations using a proposed table model. Experimental results with 1,180 HTML tables show that the proposed method performs better than the conventional method, resulting in an average precision of 86.7%.

• Journal of KIBIM
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• v.11 no.3
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• pp.67-74
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• 2021

An IFC file is dependent on the IFC schema. Because of this relationship, most IFC-using software reads and interprets the IFC File by employing an early binding method, which uses a standard IFC schema. In the case of most open sources, early binding methods using standard IFC schema have a problem that they cannot express extra information of IFC File out of extended IFC schema. Also, in the case of previous studies, they suggested schema extension, such as adding attribute information to the schema, rather than the interpretation of IFC File. This study research on method of extracting shape and attribute information was conducted by analyzing the IFC File produced through the Port schema, which is an extended IFC schema. Three objects were created using the reference relationship between the Port schema definition and the IFC entity, and, at the end, the three objects were combined into one object. It has been confirmed that the shape and property data were express properly while delivering the combined object to the viewer. The process is possible because of the method of matching IFC schema and IFC File, which is dependent on IFC schema but not early binding method. However, this method has some drawbacks, such that contemporaneously generated many objects consume many memory spaces. Future research to investigate that issue further is needed.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.133-142
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• 2021

Travel pattern recognition is widely used in many aspects such as user trajectory query, user behavior prediction, interest recommendation based on user location, user privacy protection and municipal transportation planning. Because the current recognition accuracy cannot meet the application requirements, the study of travel pattern recognition is the focus of trajectory data research. With the popularization of GPS navigation technology and intelligent mobile devices, a large amount of user mobile data information can be obtained from it, and many meaningful researches can be carried out based on this information. In the current travel pattern research method, the feature extraction of trajectory is limited to the basic attributes of trajectory (speed, angle, acceleration, etc.). In this paper, permutation entropy was used as an eigenvalue of trajectory to participate in the research of trajectory classification, and also used as an attribute to measure the complexity of time series. Velocity permutation entropy and angle permutation entropy were used as characteristics of trajectory to participate in the classification of travel patterns, and the accuracy of attribute classification based on permutation entropy used in this paper reached 81.47%.

• Korean Journal of Construction Engineering and Management
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• v.11 no.4
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• pp.22-31
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• 2010

Because the estimated cost at early stage has great influence on decisions of project owner, the importance of early cost estimation is increasing. However, it depends on experience and knowledge of the estimator mainly due to shortage of information. Those tendency developed into case-based reasoning(CBR) method which solves new problems by adapting previous solution to similar past problems. The performance of CBR model is affected by attribute weight, so that its accurate determination is necessary. Previous research utilizes mathematical method or subjective judgement of estimator. In order to improve the problem of previous research, this suggests CBR schematic cost estimation method using genetic algorithm to determine attribute weight. The cost model employs nearest neighbor retrieval for selecting past case. And it estimates the cost of new cases based on cost information of extracted cases. As the result of validation for 17 testing cases, 3.57% of error rate is calculated. This rate is superior to accuracy rate proposed by AACE and the method to determine attribute weight using multiple regression analysis and feature counting. The CBR cost estimation method improve the accuracy by introducing genetic algorithm for attribute weight. Moreover, this makes user understand the problem-solving process easier than other artificial intelligence method, and find solution within short time through case retrieval algorithm.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.6
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• pp.47-54
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• 2014

Based on the indiscernible relation of rough set, the inevitability of superposition and inconsistency of data makes the reduction of attributes very important in information system. Rough set has difficulty in the difference of attribute reduction between consistent and inconsistent information system. In this paper, we propose the new uncertainty measure and attribute reduction algorithm by Bayesian posterior probability for correlation analysis between condition and decision attributes. We compare the proposed method and the conditional information entropy to address the uncertainty of inconsistent information system. As the result, our method has more accuracy than conditional information entropy in dealing with uncertainty via mutual information of condition and decision attributes of information system.

• The KIPS Transactions:PartB
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• v.13B no.2 s.105
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• pp.133-138
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• 2006

Learning with Attribute Value Taxonomies (AVT) has shown that it is possible to construct accurate, compact and robust classifiers from a partially missing dataset (dataset that contains attribute values specified with different level of precision). Yet, in many cases AVTs are generated from experts or people with specialized knowledge in their domain. Unfortunately these user-provided AVTs can be time-consuming to construct and misguided during the AVT building process. Moreover experts are occasionally unavailable to provide an AVT for a particular domain. Against these backgrounds, this paper introduces an AVT generating method called GA-AVT-Learner, which finds a near optimal AVT with a given training dataset using a genetic algorithm. This paper conducted experiments generating AVTs through GA-AVT-Learner with a variety of real world datasets. We compared these AVTs with other types of AVTs such as HAC-AVTs and user-provided AVTs. Through the experiments we have proved that GA-AVT-Learner provides AVTs that yield more accurate and compact classifiers and improve performance in learning missing data.