• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.125-139
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• 2013

Big data refers to the data that cannot be processes with conventional contemporary data technologies. As smart devices and social network services produces vast amount of data, big data attracts much attention from researchers. There are strong demands form governments and industries for bib data as it can create new values by drawing business insights from data. Since various new technologies to process big data introduced, academic communities also show much interest to the big data domain. A notable advance related to the big data technology has been in various fields. Big data technology makes it possible to access, collect, and save individual's personal data. These technologies enable the analysis of huge amounts of data with lower cost and less time, which is impossible to achieve with traditional methods. It even detects personal information that people do not want to open. Therefore, people using information technology such as the Internet or online services have some level of privacy concerns, and such feelings can hinder continued use of information systems. For example, SNS offers various benefits, but users are sometimes highly exposed to privacy intrusions because they write too much personal information on it. Even though users post their personal information on the Internet by themselves, the data sometimes is not under control of the users. Once the private data is posed on the Internet, it can be transferred to anywhere by a few clicks, and can be abused to create fake identity. In this way, privacy intrusion happens. This study aims to investigate how perceived personal information overload in SNS affects user's risk perception and information privacy concerns. Also, it examines the relationship between the concerns and user resistance behavior. A survey approach and structural equation modeling method are employed for data collection and analysis. This study contributes meaningful insights for academic researchers and policy makers who are planning to develop guidelines for privacy protection. The study shows that information overload on the social network services can bring the significant increase of users' perceived level of privacy risks. In turn, the perceived privacy risks leads to the increased level of privacy concerns. IF privacy concerns increase, it can affect users to from a negative or resistant attitude toward system use. The resistance attitude may lead users to discontinue the use of social network services. Furthermore, information overload is mediated by perceived risks to affect privacy concerns rather than has direct influence on perceived risk. It implies that resistance to the system use can be diminished by reducing perceived risks of users. Given that users' resistant behavior become salient when they have high privacy concerns, the measures to alleviate users' privacy concerns should be conceived. This study makes academic contribution of integrating traditional information overload theory and user resistance theory to investigate perceived privacy concerns in current IS contexts. There is little big data research which examined the technology with empirical and behavioral approach, as the research topic has just emerged. It also makes practical contributions. Information overload connects to the increased level of perceived privacy risks, and discontinued use of the information system. To keep users from departing the system, organizations should develop a system in which private data is controlled and managed with ease. This study suggests that actions to lower the level of perceived risks and privacy concerns should be taken for information systems continuance.

• Science of Emotion and Sensibility
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• v.21 no.4
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• pp.25-36
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• 2018

Nowadays, it is hard to imagine one's life without smart phones or the internet. Furthermore, not only do people form groups offline, but also online. Based on the cognitive dissonance theory, there have been many studies about how an offline group's initiation affects attitudes toward the group. However, there has not been a study about how an online group's initiation can affect attitudes toward the group. Therefore, this study aims to find out how cognitive dissonance aroused by initiation affects the attitudes toward the online community, which represents groups that are formed online. In addition, this study examined how perceived similarity affects changes in attitude aroused by cognitive dissonance. Participants were assigned to a group in three ways as follows: without a registration process, with a simple registration process, and/or with a complex registration process. Perceived similarity was calculated by the difference between the current body mass index (BMI) and the target BMI of the participant. Attitudes toward the online group were measured by perceived source credibility, perceived information quality, satisfaction, information usefulness, and continuance intention. Contrary to the cognitive dissonance theory, the results showed that when applied to offline social groups, there were conflicting results. There were cases where there was no difference in the evaluation between initiation conditions. However, other cases showed that groups with the most complex registration process were found to have the worst evaluation. People were more favorable toward the group when the perceived similarity was larger. Interestingly, people who had higher perceived similarity had more positive attitudes toward the groups that had been assigned with a registration process compared to the group formed without a registration process. Conversely, people with lower perceived similarity had more positive attitudes toward the group when there was no initiation process. Online communities may use the results of this study to design more suitable registration processes for their communities.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.43-61
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• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

• Science of Emotion and Sensibility
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• v.23 no.4
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• pp.105-116
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• 2020

The purpose of this study is to progress the limitations and disadvantages of existing safety clothing by applying high technology to current safety clothing that is produced and distributed only with fluorescent fabrics and retroreflective materials. Therefore, the industrial suspender-type safety belt and engineering technology are introduced, designed, and fabricated to help save a life in an emergency. First, the suspender-type safety belt to be developed is designed to emit light by LED attached to the film, and the body of the belt-wearer is recognized from a distance through retroreflection from the flashing LED. It aims to support people's safety by preventing accidents during roadside work, rescue activities, and sports activities at night. Second, with the development of advanced devices when the user is in an unconscious state due to distress or falls into an unconscious state due to distress or accident, the tilt sensor of the control unit attached to the belt automatically detects the angle of the human body and generates light and sound. It is intended to further enhance the utilization by mounting a sensing and signaling device that generates a distress signal and shaping it in the form of a belt attached to a vest that can be easily detached from the outside of the garment. When the wearer falls due to an accident, the tilt sensor of this belt detects the angle change and then the controller generates a high-frequency sound and repeated LED blinking signals at the same time. In the case of conventional safety vests, it is almost impossible to detect that the person is wearing a vest when there is no ambient light, but in case of the safety belts in this study, the sound and light signals of the safety belt enable us to find the wearer within 100 meters even when there is no ambient light.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.2
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• pp.1-7
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• 2022

The purpose of this study is to discuss the impact of unmanned aerial vehicle service and how to activate it. The discussion on the impact of the introduction of the unmanned aerial vehicle service was examined in terms of economic, environmental, and social acceptance, and a plan to revitalize the industry was presented. In terms of economic impact, if transportation services are increased using unmanned aerial vehicles in the future, road-based transportation cargo may decrease and road movement speed may increase due to reduced road congestion. This can have a positive effect on the increase in the value of land or real estate assets, and it also provides an impact on smart city design. In terms of environmental impact, unmanned aerial vehicles (UAVs) generally move through electricity, so they emit less exhaust gas compared to other existing devices, such as vehicles and railroads, and thus have less environmental impact. However, noise can have a negative impact on the habitat in the presence of wild animals along their migration routes. In terms of social acceptability of unmanned aerial vehicles (UAV) technology, areas that are declining due to the emergence of new services may appear, and at the same time, organizations that create profits may appear, causing conflicts between industries. Therefore, it is essential to form a social consensus on the acceptance of emerging industries. The government should come up with various countermeasures to minimize the negative impact that reflects the characteristics of the unmanned aerial vehicle use service. Just as various systems such as road signs were introduced so that vehicles can be operated on the ground to secure air routes in the mid- to long-term for revitalization of unmanned-based industries, development and establishment of services that should be introduced and applied prior to constructing air routes I need this. In addition, the design and implementation of information collection and operation plans for unmanned air traffic management in Korea and a plan to secure a control system for each region should also be made. This study can contribute to providing ideas for mid- to long-term research on new areas with the development of the unmanned aerial vehicle industry.

• Journal of Korea Entertainment Industry Association
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• v.14 no.4
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• pp.105-120
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• 2020

This study explores the process of the actual content production and distribution, by creating a YouTube channel to promote the popular music contents produced by the researcher, which thus reflects the reality where the production of video contents rapidly increases. A YouTube channel titled "Alida Music", of which the focus was to promote indie musicians, was created on February 2019. The contents of 10 indie musicians were produced in one-take live format. The information of the indie musicians was displayed in the form of a screen business card, with their e-mail address and SNS account at the top. Therefore, this promotional design was named "Card Live". Promotional video contents marked with the QR code in the lower right on the screen were produced, along with the promotional phrase "Communicate directly with the artist through the QR code", which allows viewers to watch other contents of the indie musician when they scan the QR code. This research conducted a study on how to improve and promote "Card Live" contents of "Alida Music", which were produced through this process. A group interview targeting five indie musicians, among whom one participant deemed significant was selected to conduct a one-to-one in-depth interview. As a result of the study, the following three conclusions were drawn. First, YouTube was found to be the medium with the greatest influence and highest efficiency at the lowest cost. Second, the evaluation of the participants on "Card Live" were divided into the three categories: need for one-take live, the design elements of "Card Live", and scanning issues of the QR code. Third, there is a need for promotional methods that can effectively utilize the media aspects of YouTube: the channel management issues such as raising public awareness as well as the number of subscribers of "Alida Music" should be resolved and measures to effectively use various media including other SNS should be developed. In terms of its content, it is imperative to recruit diverse performers to make various contents, as well as to come up with ways to link "Card Live" contents with offline. Based on these results, "Card Live" contents should be further revised and complemented in order to provide interesting contents to consumers, which will further develop "Alida Music" as a platform where various musicians and companies meet, thereby inducing contracts with popular music agencies and generating advertising revenues. However, since this study was carried out only with the limited number of participants, future studies should include more participants to bring forth a variety of promotional plans and improvement measures. Also, in the era of consuming contents through smart devices, the fact that some features of "Card Live" were available only on PC, did not fully reflect the characteristics of the times. In the future research, various contents that smartphone users can access and view freely without PC should be produced.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.191-207
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• 2021

Up to this day, mobile communications have evolved rapidly over the decades, mainly focusing on speed-up to meet the growing data demands of 2G to 5G. And with the start of the 5G era, efforts are being made to provide such various services to customers, as IoT, V2X, robots, artificial intelligence, augmented virtual reality, and smart cities, which are expected to change the environment of our lives and industries as a whole. In a bid to provide those services, on top of high speed data, reduced latency and reliability are critical for real-time services. Thus, 5G has paved the way for service delivery through maximum speed of 20Gbps, a delay of 1ms, and a connecting device of 106/㎢ In particular, in intelligent traffic control systems and services using various vehicle-based Vehicle to X (V2X), such as traffic control, in addition to high-speed data speed, reduction of delay and reliability for real-time services are very important. 5G communication uses high frequencies of 3.5Ghz and 28Ghz. These high-frequency waves can go with high-speed thanks to their straightness while their short wavelength and small diffraction angle limit their reach to distance and prevent them from penetrating walls, causing restrictions on their use indoors. Therefore, under existing networks it's difficult to overcome these constraints. The underlying centralized SDN also has a limited capability in offering delay-sensitive services because communication with many nodes creates overload in its processing. Basically, SDN, which means a structure that separates signals from the control plane from packets in the data plane, requires control of the delay-related tree structure available in the event of an emergency during autonomous driving. In these scenarios, the network architecture that handles in-vehicle information is a major variable of delay. Since SDNs in general centralized structures are difficult to meet the desired delay level, studies on the optimal size of SDNs for information processing should be conducted. Thus, SDNs need to be separated on a certain scale and construct a new type of network, which can efficiently respond to dynamically changing traffic and provide high-quality, flexible services. Moreover, the structure of these networks is closely related to ultra-low latency, high confidence, and hyper-connectivity and should be based on a new form of split SDN rather than an existing centralized SDN structure, even in the case of the worst condition. And in these SDN structural networks, where automobiles pass through small 5G cells very quickly, the information change cycle, round trip delay (RTD), and the data processing time of SDN are highly correlated with the delay. Of these, RDT is not a significant factor because it has sufficient speed and less than 1 ms of delay, but the information change cycle and data processing time of SDN are factors that greatly affect the delay. Especially, in an emergency of self-driving environment linked to an ITS(Intelligent Traffic System) that requires low latency and high reliability, information should be transmitted and processed very quickly. That is a case in point where delay plays a very sensitive role. In this paper, we study the SDN architecture in emergencies during autonomous driving and conduct analysis through simulation of the correlation with the cell layer in which the vehicle should request relevant information according to the information flow. For simulation: As the Data Rate of 5G is high enough, we can assume the information for neighbor vehicle support to the car without errors. Furthermore, we assumed 5G small cells within 50 ~ 250 m in cell radius, and the maximum speed of the vehicle was considered as a 30km ~ 200 km/hour in order to examine the network architecture to minimize the delay.