• Journal of Digital Convergence
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• v.18 no.9
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• pp.379-389
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• 2020

This is a qualitative study about Google 'Nest Hub Max' that displays proactive intelligent personal assistant. Following the step of grounded theory, an in-depth interview for 6 users who had used this device for a month was taken. 186 concepts were discovered, categorized as 11 top-categories and 24 sub-categories. Paradigm diagram, considering axis-coding, was made and it have been narrowed down to 'Usage patterns' of proactive IPA, considering selective coding aspects. 'Usage patterns' were divided to passive and active user. Thus, neither passive user nor active user was satisfied about device and proactive IPA. This study is meaningful that it constructed basic data about the user experience of proactive IPA on this device. It will support the device or service that consists proactive IPA in the future.

• Journal of the Semiconductor & Display Technology
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• v.20 no.2
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• pp.121-125
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• 2021

Cloud service has the characteristic that it must be always available and that it must be able to respond immediately to user requests. This study suggests a method for constructing a proactive and autonomous quality and performance management system to meet these characteristics of cloud services. To this end, we identify quantitative measurement factors for cloud service quality and performance management, define a structure for applying a time series framework to cloud service application quality and performance management for proactive management, and then use big data and artificial intelligence for autonomous management. The flow of data processing and the configuration and flow of big data and artificial intelligence platforms were defined to combine intelligent technologies. In addition, the effectiveness was confirmed by applying it to the cloud service quality and performance management system through a case study. Using the methodology presented in this study, it is possible to improve the service management system that has been managed artificially and retrospectively through various convergence. However, since it requires the collection, processing, and processing of various types of data, it also has limitations in that data standardization must be prioritized in each technology and industry.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.2
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• pp.15-20
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• 2023

This study aims to enhance the accuracy of fine dust predictions by analyzing various factors within the local environment, in addition to atmospheric conditions. In the atmospheric environment, meteorological and air pollution data were utilized, and additional factors contributing to fine dust generation within the region, such as traffic volume and electricity transaction data, were sequentially incorporated for analysis. XGBoost, Random Forest, and ANN (Artificial Neural Network) were employed for the analysis. As variables were added, all algorithms demonstrated improved performance. Particularly noteworthy was the Artificial Neural Network, which, when using atmospheric conditions as a variable, resulted in an MAE of 6.25. Upon the addition of traffic volume, the MAE decreased to 5.49, and further inclusion of power transaction data led to a notable improvement, resulting in an MAE of 4.61. This research provides valuable insights for proactive measures against air pollution by predicting future fine dust levels.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.207-213
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• 2021

Networks in Mobile ad hoc contain distribution and do not have a predefined structure which practically means that network modes can play the role of being clients or servers. The routing protocols used in mobile Ad-hoc networks (MANETs) are characterized by limited bandwidth, mobility, limited power supply, and routing protocols. Hybrid routing protocols solve the delay problem of reactive routing protocols and the routing overhead of proactive routing protocols. The Ant Colony Optimization (ACO) algorithm is used to solve other real-life problems such as the travelling salesman problem, capacity planning, and the vehicle routing challenge. Bio-inspired methods have probed lethal in helping to solve the problem domains in these networks. Hybrid routing protocols combine the distance vector routing protocol (DVRP) and the link-state routing protocol (LSRP) to solve the routing problem.

• Journal of Information Technology Services
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• v.17 no.2
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• pp.191-205
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• 2018

With the advancement of artificial intelligence (AI) techniques, many consumer products have adopted AI features for providing proactive and personalized services to customers. One of the most prominent products featuring AI techniques is a smart speaker. The fundamental of smart speaker is a portable wireless Internet connecting speaker which already have existed in a consumer market. By applying AI techniques, smart speakers can recognize human voices and communicate with them. In addition, they can control other connecting devices and provide offline services. The goal of this study is to identify the impact of AI techniques for customer rating to the products. We compared customer reviews of other portable speakers without AI features and those of a smart speaker. Amazon echo is used for a smart speaker and JBL Flip 4 Bluetooth Speaker and Ultimate Ears BOOM 2 Panther Limited Edition are used for the comparison. These products are in the same price range ($50~100) and selected as featured products in Amazon.com. All reviews for the products were collected and common words for all products and unique words of the smart speaker were identified. Information gain values were calculated to identify the influences of words to be rated as positive or negative. Positive and negative words in all the products or in Amazon echo were identified, too. Topic modeling was applied to the customer reviews on Amazon echo and the importance of each topic were measured by summating information gain values of each topic. This study provides a way of identifying customer responses on the AI feature and measuring the importance of the feature among diverse features of the products.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.2
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• pp.15-21
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• 2024

The purpose of this study is to propose a framework for the introduction and evaluation of artificial intelligence (AI) services not only in general applications but also in public policies. To achieve this, the study explores AI service management and governance toolkits, providing insights into how to introduce AI services in public policies. Firstly, it offers guidelines on the direction of AI service development and what aspects to avoid. Secondly, in the development phase, it recommends using the AI governance toolkit to review content through checklists at each stage of design, development, and deployment. Thirdly, when operating AI services, it emphasizes the importance of adhering to principles related to 1) planning and design, 2) the lifecycle, 3) model construction and validation, 4) deployment and monitoring, and 5) accountability. The governance perspective of AI services is crucial for mitigating risks associated with service provision, and research in risk management aspects should be conducted. While embracing the advantages of AI, proactive measures should be taken to address limitations and risks. Efforts should be made to efficiently formulate policies using AI technology to create high value and provide meaningful societal impacts.

• Korean Journal of Radiology
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• v.24 no.5
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• pp.454-464
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• 2023

Objective: We aimed to investigate current expectations and clinical adoption of artificial intelligence (AI) software among neuroradiologists in Korea. Materials and Methods: In April 2022, a 30-item online survey was conducted by neuroradiologists from the Korean Society of Neuroradiology (KSNR) to assess current user experiences, perceptions, attitudes, and future expectations regarding AI for neuro-applications. Respondents with experience in AI software were further investigated in terms of the number and type of software used, period of use, clinical usefulness, and future scope. Results were compared between respondents with and without experience with AI software through multivariable logistic regression and mediation analyses. Results: The survey was completed by 73 respondents, accounting for 21.9% (73/334) of the KSNR members; 72.6% (53/73) were familiar with AI and 58.9% (43/73) had used AI software, with approximately 86% (37/43) using 1-3 AI software programs and 51.2% (22/43) having up to one year of experience with AI software. Among AI software types, brain volumetry software was the most common (62.8% [27/43]). Although 52.1% (38/73) assumed that AI is currently useful in practice, 86.3% (63/73) expected it to be useful for clinical practice within 10 years. The main expected benefits were reducing the time spent on repetitive tasks (91.8% [67/73]) and improving reading accuracy and reducing errors (72.6% [53/73]). Those who experienced AI software were more familiar with AI (adjusted odds ratio, 7.1 [95% confidence interval, 1.81-27.81]; P = 0.005). More than half of the respondents with AI software experience (55.8% [24/43]) agreed that AI should be included in training curriculums, while almost all (95.3% [41/43]) believed that radiologists should coordinate to improve its performance. Conclusion: A majority of respondents experienced AI software and showed a proactive attitude toward adopting AI in clinical practice, suggesting that AI should be incorporated into training and active participation in AI development should be encouraged.

• Information Systems Review
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• v.22 no.3
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• pp.99-118
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• 2020

At present, our society is developing into the intelligent information society in the wave of the 4th industrial revolution, and this change will have the positive effect of innovating all industry fields. However, due to the duality of technology, there will be positive and negative effects. With intelligence, threats to cyber dysfunction such as hacking, terrorism, privacy infringement, and illegal content distribution will become more serious. Until now, the security system of the Internet has been a proactive security system, but in recent years, a proposal for a trust network, a preventive security system, has been introduced. Therefore, this study aims to analyze the possibility of resolving cyber dysfunction of intelligent information society about Bright Internet, one of trust network technologies. This study defines the cyber dysfunction of the intelligent information society and analyzes the perceptions of changes in the cyber dysfunction of the intelligent information society on the introduction of the five principles of the Bright Internet. The change of cyber dysfunction severity of the intelligent information society due to the introduction of the trust network is analyzed to reflect the technical and social demands. This work will guide the structure of the trust network and the direction of practical technological introduction and its influence.

• Journal of the HCI Society of Korea
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• v.14 no.1
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• pp.15-22
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• 2019

The purpose of this study is to investigate the users' expectation and expectation gap about the attributes of smart speaker as an intelligent agent, ie autonomy, sociality, responsiveness, activeness, time continuity, goal orientation. To this end, semi-structured interviews were conducted for smart speaker users and analyzed based on ground theory. Result has shown that people have huge expectation gap about the sociality and human-likeness of smart speakers, due to limitations in technology. The responsiveness of smart speakers was found to have positive expectation gap. For the memory of time-sequential information, there was an ambivalent expectation gap depending on the degree of information sensitivity and presentation method. We also found that there was a low expectation level for autonomous aspects of smart speakers. In addition, proactive aspects were preferred only when appropriate for the context. This study presents implications for designing a way to interact with smart speakers and managing expectations.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1087-1098
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• 2023

The rapid advancement of artificial intelligence (AI) technology has led to its proactive utilization across various fields. However, this widespread adoption of AI-based systems has raised concerns about the increasing threat of attacks on these systems. In particular, deep neural networks, commonly used in deep learning, have been found vulnerable to adversarial attacks that intentionally manipulate input data to induce model errors. In this study, we propose a method to protect image classification models from visually imperceptible One-Pixel attacks, where only a single pixel is altered in an image. The proposed defense technique utilizes an autoencoder model to remove potential threat elements from input images before forwarding them to the classification model. Experimental results, using the CIFAR-10 dataset, demonstrate that the autoencoder-based defense approach significantly improves the robustness of pretrained image classification models against One-Pixel attacks, with an average defense rate enhancement of 81.2%, all without the need for modifications to the existing models.