• Journal of Advanced Navigation Technology
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• v.28 no.1
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• pp.142-148
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• 2024

It is difficult to apply authentication methods of existing wired or wireless networks to Internet of Things (IoT) devices due to their poor environment, low capacity, and low-performance processor. In particular, there are many problems in applying methods such as blockchain to the IoT environment. In this paper, edge computing is used to serve as a server that authenticates disposable templates among biometric information in an IoT environment. In this environment, we propose a lightweight and strong authentication procedure using the IoT-edge computing (IoT-EC) system based on elliptic curve cryptographic (ECC) and evaluate its safety.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.145-165
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• 2023

Recommendation system plays a significant role on relieving difficulties of selecting information among rapidly increasing amount of information caused by the development of the Internet and on efficiently displaying information that fits individual personal interest. In particular, without the help of recommendation system, E-commerce and OTT companies cannot overcome the long-tail phenomenon, a phenomenon in which only popular products are consumed, as the number of products and contents are rapidly increasing. Therefore, the research on recommendation systems is being actively conducted to overcome the phenomenon and to provide information or contents that are aligned with users' individual interests, in order to induce customers to consume various products or contents. Usually, collaborative filtering which utilizes users' historical behavioral data shows better performance than contents-based filtering which utilizes users' preferred contents. However, collaborative filtering can suffer from cold-start problem which occurs when there is lack of users' historical behavioral data. In this paper, hybrid music recommendation system, which can solve cold-start problem, is proposed based on the playlist data of Melon music streaming service that is given by Kakao Arena for music playlist continuation competition. The goal of this research is to use music tracks, that are included in the playlists, and metadata of music tracks and playlists in order to predict other music tracks when the half or whole of the tracks are masked. Therefore, two different recommendation procedures were conducted depending on the two different situations. When music tracks are included in the playlist, LightFM is used in order to utilize the music track list of the playlists and metadata of each music tracks. Then, the result of Item2Vec model, which uses vector embeddings of music tracks, tags and titles for recommendation, is combined with the result of LightFM model to create final recommendation list. When there are no music tracks available in the playlists but only playlists' tags and titles are available, recommendation was made by finding similar playlists based on playlists vectors which was made by the aggregation of FastText pre-trained embedding vectors of tags and titles of each playlists. As a result, not only cold-start problem can be resolved, but also achieved better performance than ALS, BPR and Item2Vec by using the metadata of both music tracks and playlists. In addition, it was found that the LightFM model, which uses only artist information as an item feature, shows the best performance compared to other LightFM models which use other item features of music tracks.

• The Journal of the Korea Contents Association
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• v.7 no.2
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• pp.191-201
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• 2007

Through contemporary researches on policing, individual employees in law enforcement agencies has gained more and more attention from researchers and police organization managers. Unfortunately an important but largely ignored area of current research on individual police officers concerns the value orientations obtaining among Korean police officers. And during last five decades or so, no research has been done on this issue. Studying individual value orientations is important because a substantial body of research indicates that particular patterns of value orientation predict world views and hence can in turn predict behavior at the workplace and behavioral predispositions on salient social issues. Therefore in this research, the authors intended to answer these issues. (1) What are the characteristics of value orientations among Korean police officers. (2) Is there any differences between Korean and American police officers on the value orientations among them.

• The Korean Journal of Food And Nutrition
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• v.13 no.4
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• pp.372-378
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• 2000

After making survey of edibility of dog meat on 963 male adults and 539 female adults, totalled 1,502 persons, the results were primarily divided into ages and sexes. dealing wish statistics by Statistical Analysis System. As a result, dog meat food most favored is Bosintang(dog meat soup), followed by Jeongol(dog meat stew), Suyuk(boiled dog meat), Muchim(boils dog meat added by spice and mixed). The frequency of having dog meat is two or three times a year. The age of having firstly had dog meat is most at the age of 21∼30 in male, and in female, 11∼20. Among dog meat cookery of Chosun dynasty known by respondents, Gaejang(dog meat soup) is most, and Musulzu(wine made from dog meat), Ott-bosintang(dog meat soup boiled with lacker tree), Pyeonyuk(boiled and sliced dog meat) follow respectively. It shows that the largest number of respondents answered what was improved after having dog meat was to \`become healthy, \`followed by \`become energetic\`. It reveals that dog meat cuisine desired to be newly developed was roasted dog meat, on which respondents answered most, followed by Tangsuyuk (fried dog meat served with syrup) and impromptu Bosintang.

• Journal of Popular Narrative
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• v.26 no.2
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• pp.39-85
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• 2020

This paper examines the aspects of disaster narrative, which makes the most of the concept of 'masterplot' as a narrative simulation to solve problems. By analyzing and comparing the remnants of 'masterplots' operating in the disaster narratives of Korea, the United States, and Japan, the differences between each country and social community problem recognition and resolution will be discussed. Disaster narrative is the most suitable genre for applying the 'masterplot' toward community problem solving in today's global risk society, and the problem-solving method has cognitive differences for each community. First, in the case of American disaster narratives, civilian experts' response to natural disasters tracks the changes of heroes in today's 'Marvel Comic Universe' (MCU). Compared to the past, the close relationship between heroism and nationalism has been reduced, but the state remains functional even if it is bolstered by the heroes' voluntary cooperation and reflection ability. On the other hand, in Korea's disaster narratives, the disappearance of the country and paralysis of the function are foregrounded. In order to fill the void, a new family narrative occurs, consisting of a righteous army or people abandoned by the state. Korea's disaster narratives are sensitive to changes after the disaster, and the nation's recovery never returns to normal after the disaster. Finally, Japan's disaster narratives are defensive and neurotic. A fully state-led bureaucratic system depicts an obsessive nationalism that seeks to control all disasters, or even counteracts anti-heroic individuals who reject voluntary sacrifices and even abandon disaster conditions This paper was able to diagnose the impact and value of a 'masterplot' today by comparing a series of 'masterplots' and their variations and uses. In a time when the understanding and utilization of 'masterplots' are becoming more and more important in today's world where Over-the top(OTT) services are being provided worldwide, this paper attempt could be a fragmentary model for the distribution and sharing of global stories.

• Journal of Internet Computing and Services
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• v.22 no.4
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• pp.123-134
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• 2021

This paper suggests that the Internet is not free through analysis of U.S. Internet history and lawsuits related to the Charter merger in 2016. Generally speaking, the players in internet connectivity market agree to Non-Disclosure Agreement, when connecting their facilities and networks each other. So, I adopted the case study & analysis as research methodologies due to limitation of collecting the transaction data between them. The former finds that Internet access has never been free in U.S Internet history. As we know, some including Content Providers(CPs) argue that the Internet is a free network and there are many cases to use the internet for free, so they came to conclusion that ISPs have no right to charge the users like CPs. This study refutes these arguments in two ways. One is that using the internet has never been free. From ARPANET, known as the beginning of the U.S. Internet, to the commercialization of backbone, no Internet has been considered or implemented for free since the early Internet network was devised. Also, the U.S government was paying subsidies or institutions were paying fees to secure network operations for the NSFNET backbone. the other is that "free peering" refers to barter transactions between ISPs, not to free access to counterpart internet networks. Second, this study analyze the FCC' executive order of conditioned merger approval and the court's related ruling and verify that using the internet is not free. According to the analysis, this study finds that it's real situation to make paid settlements between ISP-CPs (including OTTs) in the US Internet market at the moment. This study concludes that the Internet has never been free in terms of its technical characteristics, network structure, network operation, and system. Also it proposes how to improve the domestic settlement system between ISPs-CPs in terms of policy and regulation.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.95-112
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• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.