• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.3
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• pp.405-415
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• 2020

With the increase in smartphone user, mobile forensics has become an essential element in modern digital forensic investigation. Mobile messenger data is very important data in mobile forensics because it can acquire information such as user's life pattern and mental state. In order to analyze messenger data, a decryption technique of an encrypted messenger data is required. Since most messengers provide a message deleting function, a technique for recovering deleted messages is required. WeChat Messenger, a messenger used by about 1 billion people around the world, uses IMEI (International Mobile Equipment Identity) information to encrypt data and provides message deletion function. In this paper, we propose a data decryption method in the absence of IMEI information and propose a method for recovering deleted messages using FTS (Full Text Search) database created for full-text search function of SQLite database.

• Journal of Digital Convergence
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• v.17 no.9
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• pp.407-420
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• 2019

The purpose of this study is to explore the possibility of measuring advertising effectiveness through the ad evaluation text. The 384 university students were asked to evaluate the positive and negative evaluation ads of high and low involvement products by self-report method and to write ad evaluation text online. The written ad evaluation text were analyzed by KLIWC and to examine the difference between the comment of positive and negative advertising. The results of the analysis are as follows. First, there were differences between positive and negative ads in 17 psychosocial variables. Second, there were differences between positive and negative ads in 9 linguistic variables. Third, there was a significant correlation between KLIWC variables(Positive & negative emotions, inhibition, conviction, physical condition & function and sleep/dreams) and advertising effect variables. This study suggests that the advertising evaluation comment reflects the consumer's psychological reaction to advertising and the possibility of measuring the advertising effectiveness using advertising evaluation text.

• Journal of Digital Convergence
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• v.17 no.4
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• pp.211-217
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• 2019

As the widespread use of mobile health intervention among Korean patients with chronic disease, it is needed to identify research trends in mobile health intervention on chronic care using text mining technique. This secondary data analysis was conducted to investigate characteristics and main research topics in intervention studies from 2005 to 2018 with a total of 20 peer reviewed articles. Microsoft Excel and Text Analyzer were used for data analysis. Mobile health interventions were mainly applied to hypertension, diabetes, stroke, and coronary artery disease. The most common type of intervention was to develop mobile application. Lately, 'feasibility', 'mobile health', and 'outcome measure' were frequently presented. Future larger studies are needed to identify the relationships among key terms and the effectiveness of mobile health intervention using social network analysis.

• ETRI Journal
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• v.9 no.4
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• pp.26-35
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• 1987

Switching system can be regarded as the key node of Integrated Services Digital Networks(ISDN) because it plays the Important role in realization of ISDN. As ISDN is evolved from the telephony Integrated Digital Network(IDN), the switching system for ISDN will be evolved from the function and structure of telephony digital switching system. At near future, ISDN switching system is expected to implement the integrated access and transparent switching functions for both voice and data. As final goals of switching system, advanced switching methods for information having various traffic characteristics such as voice, data, text and image should be considered. This paper considered. This paper will show some of the fundamental design challenges and architectural trends in ISDN switching system.

• Journal of information and communication convergence engineering
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• v.15 no.3
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• pp.170-174
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• 2017

Nowadays, people share and discuss scientific papers on social media such as the Web 2.0, big data, online forums, blogs, Twitter, Facebook and scholar community, etc. In addition to a variety of metrics such as numbers of citation, download, recommendation, etc., paper review text is also one of the effective resources for the study of scientific impact. The social media tools improve the research process: recording a series online scholarly behaviors. This paper aims to research the huge amount of paper reviews which have generated in the social media platforms to explore the implicit information about research papers. We implemented and shown the result of text mining on review texts using R language. And we found that Zika virus was the research hotspot and association research methods were widely used in 2016. We also mined the news review about one paper and derived the public opinion.

• The Journal of Information Systems
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• v.23 no.3
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• pp.127-142
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• 2014

Plagiarism occurs when the content is copied without permission or citation, and the problem of plagiarism has rapidly increased because of the digital era of resources available on the World Wide Web. An important task in plagiarism detection is measuring and determining similar text portions between a given pair of documents. One of the main difficulties of this task is that not all similar text fragments are examples of plagiarism, since thematic coincidences also tend to produce portions of similar text. In order to handle this problem, this paper proposed association analysis in data mining to detect plagiarism. This method is able to detect common actions performed by plagiarists such as word deletion, insertion and transposition, allowing to obtain plausible portions of plagiarized text. Experimental results employing an unsupervised document classification strategy showed that the proposed method outperformed traditionally used approaches.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.5
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• pp.51-56
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• 2014

In the digital world of the internet, steganography is introduced to hide the existence of the secret communication by concealing a secret message inside another unsuspicious cover medium. The third parties are unaware that a stego medium is being communicated. There exists a large variety of steganography methods based on texts. In this paper, analyzed the advantages and significant disadvantages of each existing text steganography method and how new approach could be proposed as a solution. The objective of this paper is to propose a method for hiding the secret messages in safer manner from external attacks by encryption rearrangement key.

• Journal of information and communication convergence engineering
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• v.16 no.2
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• pp.106-113
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• 2018

Although deep learning methods of convolutional neural networks (CNNs) and long-/short-term memory (LSTM) are widely used for text categorization, they still have certain shortcomings. CNNs require that the text retain some order, that the pooling lengths be identical, and that collateral analysis is impossible; In case of LSTM, it requires the unidirectional operation and the inputs/outputs are very complex. Against these problems, we thus improved these traditional deep learning methods in the following ways: We created collateral CNNs accepting disorder and variable-length pooling, and we removed the input/output gates when creating bidirectional LSTMs. We have used four benchmark datasets for topic and sentiment classification using the new methods that we propose. The best results were obtained by combining LTSM regional embeddings with data convolution. Our method is better than all previous methods (including deep learning methods) in terms of topic and sentiment classification.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.2C
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• pp.167-172
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• 2008

Since a 2-dimensional (2D) bar code can retrieve data and information quickly, it is widely used and recognized as a useful tool for many industrial applications. However, the information capacity of the 2D bar code is still limited. Recently the analog-digital code (AD code), which has the largest storage capacity yet contained in a code, has been developed, thereby expanding the bar code's application range because it overcomes the limitation of data capacity. In this paper, we present the AD code and implement an effective embedded system which can transform text information into voice using the 2D AD code and Text To Speech (TTS). This voice information can also be transmitted to blind people as well as the old by capturing the AD code on paper or in books.

• Journal of Digital Convergence
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• v.17 no.4
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• pp.49-55
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• 2019

This study is on the trend of real estate market using text mining and big data. The data were collected through internet news posted on Naver from August 2016 to August 2017. As a result of TF-IDF analysis, the frequency was high in the order of housing, sale, household, real estate market, and region. Many words related to policies such as loan, government, countermeasures, and regulations were extracted, and the region - related words appeared the most frequently in Seoul. The combination of the words related to the region showed that the frequencies of 'Seoul - Gangnam', 'Seoul - Metropolitan area', 'Gangnam - reconstruction' and 'Seoul - reconstruction' appeared frequently. It can be seen that the people's interest and expectation about the reconstruction of Gangnam area is high.