• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1600-1619
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• 2023

Organizations and other institutions have recently started using cloud service providers to store and share information in light of the Internet of Things (IoT). The major issues with this storage are preventing unauthorized access and data theft from outside parties. The Block chain based Security Sharing scheme with Data Access Control (BSSDAC) was implemented to improve access control and secure data transaction operations. The goal of this research is to strengthen Data Access Control (DAC) and security in IoT applications. To improve the security of personal data, cypher text-Policy Attribute-Based Encryption (CP-ABE) can be developed. The Aquila Optimization Algorithm (AOA) generates keys in the CP-ABE. DAC based on a block chain can be created to maintain the owner's security. The block chain based CP-ABE was developed to maintain secures data storage to sharing. With block chain technology, the data owner is enhancing data security and access management. Finally, a block chain-based solution can be used to secure data and restrict who has access to it. Performance of the suggested method is evaluated after it has been implemented in MATLAB. To compare the proposed method with current practices, Rivest-Shamir-Adleman (RSA) and Elliptic Curve Cryptography (ECC) are both used.

• Journal of the Korea Convergence Society
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• v.8 no.2
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• pp.27-33
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• 2017

To efficiently provide cloud computing services to users over the Internet, IT resources must be configured in the data center based on virtualization and distributed computing technology. This paper focuses specifically on the problem that new training data can be added at any time in a wide range of fields, and new attributes can be added to training data at any time. In such a case, rule generated by the training data with the former attribute set can not be used. Moreover, the rule can not be combined with the new data set(with the newly added attributes). This paper proposes further development of the new inference engine that can handle the above case naturally. Rule generated from former data set can be combined with the new data set to form the refined rule.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.4
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• pp.181-188
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• 2010

For a proper use of the integrated real estate information, there must be a process on linking the information of buildings and land data. This study aims to enhance the location information of the buildings on the digital topographic map by assigning the coordinates on the building layout plan of the Building Registers which does not have a positional information based on the cadastral boundary of the cadastral map. Also, the land and building attribute information are managed in various official registers and systems which are overlapped each other. The overlapped information must be corrected based on legislation. Therefore this study introduces a comprehensive attribute information excluding any overlapped information. In other words, this study proposes a single advanced real estate information by integrating the attribute information and the separated real estate information(buildings and land).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.406-409
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• 2008

We consider the problem of exploiting a taxonomy of propositionalized attributes in order to learn 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 sets show that the proposed algorithm can generate a classifier that is sometimes comparably compact and accurate to those produced by standard Naive Bayes learners.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.9
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• pp.2289-2297
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• 2014

There were a lot of attempts in the army to use various domestic thematic maps, but attribute data types of military topographic analysis maps use the FACC of DIGEST, so there is a limit in employing domestic thematic maps with different types of attribute codes. Therefore, this study analyzed the FACC as a data attribute based on the MIL-PRF-89040 of the US Army. Then, VITD was created by changing the attribute codes of domestic thematic maps produced in Korea to fit the FACC. Lastly, it was applied to the analysis of cross-country movement for maneuver defined in FM 5-33 in order to verify if it is applicable in practice. As a result, it was found that the suggested method was helpful in securing the cross-country movement for maneuver. This means that this method can be used not only in producing military topographic analysis maps using domestic thematic maps but in constructing emergency transport routes roads to transport by-products of forest in future.

• 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.

• 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.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.801-803
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• 2003

In this paper, we present a method for generating and providing spatially encoded video data that can be effectively used by GIS applications. We collect the video data by a mobile mapping system called 4S-Van that is equipped by GPS, INS, CCD camera, and DVR system. The information about spatial object appearing in video, such as occupied region in each frame, attribute value, and geo-coordinate, are generated and encoded. We suggest methods that can generate such data for each frame in semi-automatic manner. We adopt standard MPEG-7 metadata format for representation of the spatially encoded video data to be generally used by GIS application. The spatial and attribute information encoded to each video frame can make visual browsing between map and video possible. The generated video data can be provided and applied to various GIS applications where location and visual data are both important.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1634-1652
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• 2022

Due to the increasing need for data sharing in the age of big data, how to achieve data access control and implement user permission revocation in the blockchain environment becomes an urgent problem. To solve the above problems, we propose a novel blockchain-based data sharing scheme (BDSS) with fine-grained access control and permission revocation in this paper, which regards the medical environment as the application scenario. In this scheme, we separate the public part and private part of the electronic medical record (EMR). Then, we use symmetric searchable encryption (SSE) technology to encrypt these two parts separately, and use attribute-based encryption (ABE) technology to encrypt symmetric keys which used in SSE technology separately. This guarantees better fine-grained access control and makes patients to share data at ease. In addition, we design a mechanism for EMR permission grant and revocation so that hospital can verify attribute set to determine whether to grant and revoke access permission through blockchain, so it is no longer necessary for ciphertext re-encryption and key update. Finally, security analysis, security proof and performance evaluation demonstrate that the proposed scheme is safe and effective in practical applications.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.176-181
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• 2011

A role of cost modeler is that of facilitating design process by the systematic application of cost factors so as to maintain sensible and economic relationships between cost, quantity, utility and appearance. These relationships help to achieve the client's requirements within an agreed budget. The purpose of this study is to develop a parametric cost estimating model for the early design stage by using the multi-dimensional system of OLAP (On-line Analytical Processing) based on the case of quantity data related to architectural design features. The parametric cost estimating models have been adopted to support decision making in the early design stage. These models typically use a similar instance or a pattern of historical case. In order to effectively use this type of data model, it is required to set data classification and prediction methods. One of the methods is to find the similar class in line with attribute selection measure in the multi-dimensional data model. Therefore, this research is to analyze the relevance attribute influenced by architectural design features with the subject of case-based quantity data used for the parametric cost estimating model. The relevance attributes can be analyzed by Analytical Characterization. It helps determine what attributes to be included in the OLAP multi-dimension.