• Title/Summary/Keyword: Big Data Structure

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Privacy measurement method using a graph structure on online social networks

  • Li, XueFeng;Zhao, Chensu;Tian, Keke
    • ETRI Journal
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    • v.43 no.5
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    • pp.812-824
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    • 2021
  • Recently, with an increase in Internet usage, users of online social networks (OSNs) have increased. Consequently, privacy leakage has become more serious. However, few studies have investigated the difference between privacy and actual behaviors. In particular, users' desire to change their privacy status is not supported by their privacy literacy. Presenting an accurate measurement of users' privacy status can cultivate the privacy literacy of users. However, the highly interactive nature of interpersonal communication on OSNs has promoted privacy to be viewed as a communal issue. As a large number of redundant users on social networks are unrelated to the user's privacy, existing algorithms are no longer applicable. To solve this problem, we propose a structural similarity measurement method suitable for the characteristics of social networks. The proposed method excludes redundant users and combines the attribute information to measure the privacy status of users. Using this approach, users can intuitively recognize their privacy status on OSNs. Experiments using real data show that our method can effectively and accurately help users improve their privacy disclosures.

Comparative Experimental Study on the Evaluation of the Unit-water Content of Mortar According to the Structure of the Deep Learning Model (딥러닝 모델 구조에 따른 모르타르의 단위수량 평가에 대한 비교 실험 연구)

  • Cho, Yang-Je;Yu, Seung-Hwan;Yang, Hyun-Min;Yoon, Jong-Wan;Park, Tae-Joon;Lee, Han-Seung
    • Proceedings of the Korean Institute of Building Construction Conference
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    • 2021.11a
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    • pp.8-9
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    • 2021
  • The unit-water content of concrete is one of the important factors in determining the quality of concrete and is directly related to the durability of the construction structure, and the current method of measuring the unit-water content of concrete is applied by the Air Meta Act and the Electrostatic Capacity Act. However, there are complex and time-consuming problems with measurement methods. Therefore, high frequency moisture sensor was used for quick and high measurement, and unit-water content of mortar was evaluated through machine running and deep running based on measurement big data. The multi-input deep learning model is as accurate as 24.25% higher than the OLS linear regression model, which shows that deep learning can more effectively identify the nonlinear relationship between high-frequency moisture sensor data and unit quantity than linear regression.

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CNN-LSTM Coupled Model for Prediction of Waterworks Operation Data

  • Cao, Kerang;Kim, Hangyung;Hwang, Chulhyun;Jung, Hoekyung
    • Journal of Information Processing Systems
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    • v.14 no.6
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    • pp.1508-1520
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    • 2018
  • In this paper, we propose an improved model to provide users with a better long-term prediction of waterworks operation data. The existing prediction models have been studied in various types of models such as multiple linear regression model while considering time, days and seasonal characteristics. But the existing model shows the rate of prediction for demand fluctuation and long-term prediction is insufficient. Particularly in the deep running model, the long-short-term memory (LSTM) model has been applied to predict data of water purification plant because its time series prediction is highly reliable. However, it is necessary to reflect the correlation among various related factors, and a supplementary model is needed to improve the long-term predictability. In this paper, convolutional neural network (CNN) model is introduced to select various input variables that have a necessary correlation and to improve long term prediction rate, thus increasing the prediction rate through the LSTM predictive value and the combined structure. In addition, a multiple linear regression model is applied to compile the predicted data of CNN and LSTM, which then confirms the data as the final predicted outcome.

Research of organized data extraction method for digital investigation in relational database system (데이터베이스 시스템에서 디지털 포렌식 조사를 위한 체계적인 데이터 추출 기법 연구)

  • Lee, Dong-Chan;Lee, Sang-Jin
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.22 no.3
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    • pp.565-573
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    • 2012
  • To investigate the business corruption, the obtainments of the business data such as personnel, manufacture, accounting and distribution etc., is absolutely necessary. Futhermore, the investigator should have the systematic extraction solution from the business data of the enterprise database, because most company manage each business data through the distributed database system, In the general business environment, the database exists in the system with upper layer application and big size file server. Besides, original resource data which input by user are distributed and stored in one or more table following the normalized rule. The earlier researches of the database structure analysis mainly handled the table relation for database's optimization and visualization. But, in the point of the digital forensic, the data, itself analysis is more important than the table relation. This paper suggests the extraction technique from the table relation which already defined in the database. Moreover, by the systematic analysis process based on the domain knowledge, analyzes the original business data structure stored in the database and proposes the solution to extract table which is related incident.

Provenance and Validation from the Humanities to Automatic Acquisition of Semantic Knowledge and Machine Reading for News and Historical Sources Indexing/Summary

  • NANETTI, Andrea;LIN, Chin-Yew;CHEONG, Siew Ann
    • Asian review of World Histories
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    • v.4 no.1
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    • pp.125-132
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    • 2016
  • This paper, as a conlcusion to this special issue, presents the future work that is being carried out at NTU Singapore in collaboration with Microsoft Research and Microsoft Azure for Research. For our research team the real frontier research in world histories starts when we want to use computers to structure historical information, model historical narratives, simulate theoretical large scale hypotheses, and incent world historians to use virtual assistants and/or engage them in teamwork using social media and/or seduce them with immersive spaces to provide new learning and sharing environments, in which new things can emerge and happen: "You do not know which will be the next idea. Just repeating the same things is not enough" (Carlo Rubbia, 1984 Nobel Price in Physics, at Nanyang Technological University on January 19, 2016).

DEA-AR/AHP Model Design for Efficiency Evaluation of Metropolitan Rapid Transit (지하철 효율성 평가를 위한 DEA-AR/AHP 모형 설계)

  • Sim, Gwang-Sic;Kim, Jae-Yun
    • Journal of the Korean Operations Research and Management Science Society
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    • v.34 no.3
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    • pp.105-124
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    • 2009
  • Data Envelopment Analysis (DEA) is a methodology of computing the relative efficiency of each decision making unit (DMU) by comparing it with other DMUs having similar input and output structure. In this paper, we compare the efficiency of Korean rail transit corporations using DEA. To do this, we design a DEA-AR/AHP model, and evaluate efficiency by comparing the subway operating agencies of six big cities. The analysis reveals that Seoul Metro and Seoul city railroad construction turn out to be the most efficient groups. The result of this research can provide helpful information for effective management in a domestic subway operating agency.

Comparison with Water Quality of main Rivers in the world, based on OECD reports

  • Kambe, Junko;Aoyama, Tomoo;Nagashima, Umpei
    • 제어로봇시스템학회:학술대회논문집
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    • 2005.06a
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    • pp.935-940
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    • 2005
  • We are faced with water pollutions on a population explosion. Considering the importance, we research European rivers based on OECD reports. Observations in the reports have defects that make evaluation of environmental situations be difficult. By using interpolations in the compensation quantitative structure-activity relation ships (CQSAR), we complement the defects in the water quality of rivers through big cities. Thus, we get complete data set for dissolved oxygen, biochemical oxygen demand, and total phosphorus. Using the data set, we examine re-naturalization of the Rhein and the Donau in Germany. We investigate the effect of dams between Slovakia and Hungary, by using reconstructions of neural networks in CQSAR. The reconstructions have functions to extract a principal relation. On the investigation, we examine assertions of conservation groups. As the result, we confirm the re-naturalization is effective, and find a negative effect of the dam construction on changes of dissolved oxygens in the Hungary Donau. We investigate the Seine and the Thames, too.

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Development of Database System for Management of Quality Control for Earthworks (토공노반 다짐품질관리를 위한 DB개발소개)

  • Choi, Chan-Yong;Bae, Jae-Hoon;Lee, Jin-Wook
    • Proceedings of the KSR Conference
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    • 2008.06a
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    • pp.1848-1852
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    • 2008
  • Maintenance of quality of the compaction is accomplished with the plate load test and density management in roadbed. With the different structure differently the quality of roadbed evaluates indirectly as density and bearing capacity. Control of quality of the compaction very importance but the maintenance of quality is managed still so far with the paper. Recognizes the importance of maintenance of quality from the present paper consequently, and develops a data base one maintenance of quality system with hereafter is expected with the fact that the big help will become in DATA BASE system roadbed plan which is continuous and maintenance of quality.

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Influence of Hot Pressing on the Pore Structure of Nafion Electrolyte Membrane Investigated by 1H NMR

  • Jeonga, Soon-Yong;Han, Oc-Hee
    • Bulletin of the Korean Chemical Society
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    • v.30 no.7
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    • pp.1559-1562
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    • 2009
  • The influence of hot pressing on the pore structures of Nafion membranes was investigated by observing the Nafion before and after hot pressing with $^1H$ nuclear magnetic resonance (NMR) spectroscopy. The freezing point depression and chemical shift data of water in the Nafion indicated the presence of two different pore size ranges in Nafion. Hot pressing mainly reduced the sizes and number of the big pores. The reduction of water uptake and proton conductivity after hot pressing was explained by this variation of pore size and number. We demonstrated the potential application of chemical shift data and NMR cryoporometry experiments to measure the relative pore sizes, on a nano scale, and numbers.

Development of a displacement measurement system for architectural structures using artificial intelligence techniques (인공지능 기법을 활용한 건축 구조물 변위측정시스템 개발)

  • Kang, Ye-Jin;Kim, Dae-Geon;Woo, Jong-Yeol;Lee, Dong-Oun
    • Proceedings of the Korean Institute of Building Construction Conference
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    • 2022.04a
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    • pp.135-136
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    • 2022
  • As a recent technology, it is possible to partially grasp the occurrence of displacement of the entire building through artificial intelligence technology for big data through scanning. However, scanning and data processing take a lot of time, so there is a limit to constant monitoring, so constant monitoring technology of building behavior that combines wireless remote sensors and 3D shape scanning is required. Therefore, in this study, artificial intelligence program coding technology is linked. In addition, a technology capable of real-time wireless remote measurement of structure displacement will be developed through technology development in response to safety management that combines existing building technologies such as sensors. Through this, it is possible to establish an integrated management system for safety inspection and diagnosis.

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