• Journal of Information Technology Services
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• v.19 no.6
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• pp.15-29
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• 2020

Facing the era of intelligence, the demands for changes of e-Government system appears to be rising upon the development of AI, VR, IoT and other new technologies. However, it also comes with the increasing possibility of mobile digital divide among regions, ethnicities and social classes. As the e-Government Service is only available for specific groups, the national government should proactively build an information environment to promote the convenient access and use of e-Government Service comprehensively by all the people. In the light of the sociodemographic characteristics, this study tests whether the difference is statistically significant involving the mobile digital divide between different groups with the analysis of variance, while determining the relationship between the mobile digital divide and the usage intention of e-Government Service through multiple regression analysis. The main findings of this study are as follows. First, there are differences and statistical significance between groups of different ethnicities and regions on accessibility with electronic products and networks. Second, there are obvious differences and statistical significance between groups of different ages, educational levels and ethnicities on the competency and usability of electronic products and networks. Third, the accessibility to electronic products and networks has no effect on the usage intention of e-Government Service, while the competency and usability of electronic products and networks have a strong positive impact on the usage intention of e-Government Service. The author hopes that the research findings of this paper can provide reference suggestions and opinions for bridging the digital divide among regions in China and promoting the popularization of the e-Government Service.

• Journal of Information Technology Services
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• v.20 no.1
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• pp.131-143
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• 2021

Commercial transactions, one of the pillars of the capitalist economy, are occurring countless times every day, especially small and medium-sized businesses. However, small and medium-sized enterprises are bound to be the legal underdogs in contracts for commercial transactions and do not receive legal support for contracts for fair and legitimate commercial transactions. When subcontracting contracts are concluded among small and medium-sized enterprises, 58.2% of them do not apply standard contracts and sign contracts that have not undergone legal review. In order to support small and medium-sized enterprises' fair and legitimate contracts, small and medium-sized enterprises can be protected from legal threats if they can reduce the risk of signing contracts by analyzing various risks in the contract and analyzing and informing them of toxic clauses and omitted contracts in advance. We propose a risk prediction model for the machine reading-based legal contract to minimize legal damage to small and medium-sized business owners in the legal blind spots. We have established our own set of legal questions and answers based on the legal data disclosed for the purpose of building a model specialized in legal contracts. Quantitative verification was carried out through indicators such as EM and F1 Score by applying pine tuning and hostile learning to pre-learned machine reading models. The highest F1 score was 87.93, with an EM value of 72.41.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.57-62
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• 2021

As the parallel computing capability for artificial intelligence improves, the field of artificial intelligence technology is expanding in various industries. In particular, artificial intelligence is being introduced to process data generated from IoT sensors that have enoumous data. However, the limitation exists when applying the AI techniques on IoT network because IoT has time series data, where the importance of data changes over time. In this paper, we propose time-weighted and user-state based artificial intelligence processing techniques to effectively process IoT sensor data. This technique aims to effectively classify IoT sensor data through a data pre-processing process that personalizes time series data and places a weight on the time series data before artificial intelligence learning and use status of personal data. Based on the research, it is possible to propose a method of applying artificial intelligence learning in various fields.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.25-29
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• 2021

The strip binary is a binary from which debug symbol information has been deleted, and therefore it is difficult to analyze the binary through techniques such as reverse engineering. Traditional binary analysis tools rely on debug symbolic information to analyze binaries, making it difficult to detect or analyze malicious code with features of these strip binaries. In order to solve this problem, the need for a technology capable of effectively extracting the information of the strip binary has emerged. In this paper, focusing on the fact that the byte code of the binary file is generated very differently depending on compiler version, optimazer level, etc. For effective compiler version extraction, the entire byte code is read and imaged as the target of the stripped binaries and this is applied to the convolution neural network. Finally, we achieve an accuracy of 93.5%, and we provide an opportunity to analyze stripped binary more effectively than before.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.103-110
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• 2022

Since satellite images generally include clouds in the atmosphere, it is essential to detect or mask clouds before satellite image processing. Clouds were detected using physical characteristics of clouds in previous research. Cloud detection methods using deep learning techniques such as CNN or the modified U-Net in image segmentation field have been studied recently. Since image segmentation is the process of assigning a label to every pixel in an image, precise pixel-based dataset is required for cloud detection. Obtaining accurate training datasets is more important than a network configuration in image segmentation for cloud detection. Existing deep learning techniques used different training datasets. And test datasets were extracted from intra-dataset which were acquired by same sensor and procedure as training dataset. Different datasets make it difficult to determine which network shows a better overall performance. To verify the effectiveness of the cloud detection network such as Cloud-Net, two types of networks were trained using the cloud dataset from KOMPSAT-3 images provided by the AIHUB site and the L8-Cloud dataset from Landsat8 images which was publicly opened by a Cloud-Net author. Test data from intra-dataset of KOMPSAT-3 cloud dataset were used for validating the network. The simulation results show that the network trained with KOMPSAT-3 cloud dataset shows good performance on the network trained with L8-Cloud dataset. Because Landsat8 and KOMPSAT-3 satellite images have different GSDs, making it difficult to achieve good results from cross-sensor validation. The network could be superior for intra-dataset, but it could be inferior for cross-sensor data. It is necessary to study techniques that show good results in cross-senor validation dataset in the future.

• Journal of Convergence for Information Technology
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• v.12 no.1
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• pp.15-21
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• 2022

In the content-based video retrieval applications, the information on the movement of an object can be used as important in classifying the content. In particular, analyzing and classifying human movement can be used for various purposes as well as retrieval. In this paper, a method to improve the performance of the shape variation descriptor and shape sequence to describe and classify movement using shape information that changes according to the movement of an object is proposed. By selecting a shape descriptor to more efficiently describe the shape information of an object and comparing the distance function used to measure the similarity, the description and retrieval efficiency of movement information can be increased. Through experiments, it was shown that the proposed method can describe movement information more efficiently and increase the retrieval efficiency compared to the previous method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.726-741
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• 2022

The purpose of this study is to provide instructive theoretical models for art (music) education institutions especially when unpredictable risks, such as pandemics, occur again. Based on the customer behavior theory of the business-to-business-to-customer (B2B2C) platform, and in combination with the technology acceptance model (TAM) and expectation confirmation model (ECM), this study proposes an online education model from the perspective of art education. The framework is based on the three decision-making processes of the customer, and includes the product owner, content owner, and customer area. This paper highlights the factors that influence customers in making decisions when art education institutions are product owners. Regression analysis was introduced to study the factors influencing the expectation confirmation, and the overall fitting testing and six hypotheses testing of 385 effective samples were performed using the structural equation modeling (SEM). The results show that the course-design and after-service positively influenced the expectation confirmation, and the domain image positively influenced the continuance behavior. Negative emotions skipped the mediator (expectation confirmation) and directly exerted a significant negative impact on customers' willingness to continue system usage (continuance behavior). In addition, expectation confirmation positively influenced continuance behavior. The paths of detailed items comprising course-design, after-service, and negative emotion were also analyzed and discussed. In this path analysis, ordinary art learners did not believe that AI partners can play a very good auxiliary role. The findings contribute to the scope of information systems acting as an art education platform academically, and provide effective and theoretical support for the actual operation of art education institutions.

• Journal of Internet of Things and Convergence
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• v.8 no.2
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• pp.79-84
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• 2022

This paper presents an enhancement method of document restoration capability which is robust for security, loss, and contamination, It is based on two methods, that is, encryption and DnCNN(DeNoise Convolution Neural Network). In order to implement this encryption method, a mathematical model is applied as a spatial frequency transfer function used in optics of 2D image information. Then a method is proposed with optical interference patterns as encryption using spatial frequency transfer functions and using mathematical variables of spatial frequency transfer functions as ciphers. In addition, by applying the DnCNN method which is bsed on deep learning technique, the restoration capability is enhanced by removing noise. With an experimental evaluation, with 65% information loss, by applying Pre-Training DnCNN Deep Learning, the peak signal-to-noise ratio (PSNR) shows 11% or more superior in compared to that of the spatial frequency transfer function only. In addition, it is confirmed that the characteristic of CC(Correlation Coefficient) is enhanced by 16% or more.

• Journal of Platform Technology
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• v.9 no.4
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• pp.90-96
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• 2021

Most of the vehicles currently under development and in operation are equipped with various IoT sensors, but some of the factors that cause car accidents are relatively difficult to detect. One of the major risk factors among these factors is black ice. Black ice is one of the factors most likely to cause major accidents, as it can affect all vehicles passing through areas covered with black ice. Therefore, black ice detection technique is essential to prevent major accidents. For this purpose, some studies have been carried out in the past, but unrealistic factors have been reflected in some parts, so research to supplement this is needed. In this paper, we tried to detect black ice by analyzing color images using the CNN technique, and we succeeded in detecting black ice to a certain level. However, there were differences from previous studies, and the reason was analyzed.

• Journal of the Microelectronics and Packaging Society
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• v.29 no.1
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• pp.35-41
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• 2022

In the era of Fifth-Generation (5G), technology requirements such as Artificial Intelligence (AI), Cloud computing, automatic vehicles, and smart manufacturing are increasing. For high efficiency of electronic devices, research on high-intensity circuits and packaging for miniaturized electronic components is important. A solder paste which consists of small solder powders is one of common solder for high density packaging, whereas an electroplated solder has limitation of uniformity of bump composition. Researches are underway to improve wettability through the addition of nanoparticles into a solder paste or the surface finish of a substrate, and to suppress the formation of IMC growth at the metal pad interface. This paper describes the principles of improving the wettability of solder paste and suppressing interfacial IMC growth by addition of nanoparticles.