• Journal of The Korean Association of Information Education
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• v.23 no.2
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• pp.159-167
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• 2019

Recently, a learning management system incorporating ICT technology into learning has helped students improve self-directed learning skills. Self-directed learning using LMS promotes and stimulates learners' participation in learning, focusing on the advantages of efficient use of learning resources and the spread of communication. In this study, we study the impact of self-directed learning using the learning management system on elementary school students' motivation and academic performance. We expect learners will be able to achieve effective academic achievement by learning problems that fit their level through the algorithms of the proposed learning management system. For this study, a total of 16 classes were conducted for eight weeks using the proposed learning management system for 21 elementary school students. Research has shown significant improvement in the learning orientation and interest areas of the learners who participated in the experiment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.9
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• pp.3274-3297
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• 2021

Permission delegation is an important research issue in access control. It allows a user to delegate some of his permissions to others to reduce his workload, or enables others to complete some tasks on his behalf when he is unavailable to do so. As an ideal solution for controlling read access on outsourced data objects on the cloud, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has attracted much attention. Some existing CP-ABE schemes handle the read permission delegation through the delegation of the user's private key to others. Still, these schemes lack the further consideration of granularity and traceability of the permission delegation. To this end, this article proposes a flexible and fine-grained CP-ABE key delegation approach that supports white-box traceability. In this approach, the key delegator first examines the relations between the data objects, read permission thereof that he intends to delegate, and the attributes associated with the access policies of these data objects. Then he chooses a minimal attribute set from his attributes according to the principle of least privilege. He constructs the delegation key with the minimal attribute set. Thus, we can achieve the shortest delegation key and minimize the time of key delegation under the premise of guaranteeing the delegator's access control requirement. The Key Generation Center (KGC) then embeds the delegatee's identity into the key to trace the route of the delegation key. Our approach prevents the delegatee from combining his existing key with the new delegation key to access unauthorized data objects. Theoretical analysis and test results show that our approach helps the KGC transfer some of its burdensome key generation tasks to regular users (delegators) to accommodate more users.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3858-3874
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• 2021

As an essential part of the urban transportation system, precise perception of the traffic flow parameters at the traffic signal intersection ensures traffic safety and fully improves the intersection's capacity. Traditional detection methods of road traffic flow parameter can be divided into the micro and the macro. The microscopic detection methods include geomagnetic induction coil technology, aerial detection technology based on the unmanned aerial vehicles (UAV) and camera video detection technology based on the fixed scene. The macroscopic detection methods include floating car data analysis technology. All the above methods have their advantages and disadvantages. Recently, indoor location methods based on wireless signals have attracted wide attention due to their applicability and low cost. This paper extends the wireless signal indoor location method to the outdoor intersection scene for traffic flow parameter estimation. In this paper, the detection scene is constructed at the intersection based on the received signal strength indication (RSSI) ranging technology extracted from the wireless signal. We extracted the RSSI data from the wireless signals sent to the road side unit (RSU) by the vehicle nodes, calibrated the RSSI ranging model, and finally obtained the traffic flow parameters of the intersection entrance road. We measured the average speed of traffic flow through multiple simulation experiments, the trajectory of traffic flow, and the spatiotemporal map at a single intersection inlet. Finally, we obtained the queue length of the inlet lane at the intersection. The simulation results of the experiment show that the RSSI ranging positioning method based on wireless signals can accurately estimate the traffic flow parameters at the intersection, which also provides a foundation for accurately estimating the traffic flow state in the future era of the Internet of Vehicles.

• Journal of Internet Computing and Services
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• v.22 no.5
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• pp.79-86
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• 2021

As the usefulness of big data analysis has been drawing attention, many studies in the business research area begin to use big data to predict firm performance. Previous studies mainly rely on data outside of the firm through news articles and social media platforms. The voices within the firm in the form of employee satisfaction or evaluation of the strength and weakness of the firm can potentially affect firm value. However, there is insufficient evidence that online employee reviews are valid to predict firm value because the data is relatively difficult to obtain. To fill this gap, from 2014 to 2019, we employed 97,216 reviews collected by JobPlanet, an online firm review website in Korea, and developed a machine learning-based predictive model. Among the proposed models, the LSTM-based model showed the highest accuracy at 73.2%, and the MAE showed the lowest error at 0.359. We expect that this study can be a useful case in the field of firm value prediction on domestic companies.

• Journal of the Korea Society of Computer and Information
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• v.26 no.9
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• pp.97-104
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• 2021

Since 1993, Successfully localized naval combat System has made steady development on various domestic and foreign ships. On the other hand, Engineering Control System(ECS) is dependent on foreign companies. Therefore, there is a lot of interest and research in the localization of ECS in the navy defense industry. As one of various studies, a preliminary study of domestic ECS software that can be commonly applied to naval ships is in progress. This paper propose Ecs Obts Scalable Platform(EOSPA) as the standard architecture of ECS On-Board Training System(OBTS) software by applying object-oriented programming and standardization. And this introduces EOSPA's structure, function, and features of each component. Furthermore, high reusability and maintainability are expected in the development of ECS OBTS software applying EOSPA in various naval ships.

• Journal of Platform Technology
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• v.9 no.1
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• pp.46-57
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• 2021

Recently, research has been actively conducted to utilize blockchain technology in various fields. In particular, blockchain-based smart contracts are applied to various automation systems that require reliability as they have the characteristics of recording data in a distributed ledger environment to verify the integrity and validity of data. However, blockchain does not provide data access control and information security because data is shared among network participants. In this paper, we propose a user dynamic access control mechanism utilizing smart contracts in blockchain environments. The proposed mechanism identifies the user's contextual information when accessing data, allocating the user's role and dynamically controlling the data access range. This can increase the security of the system and the efficiency of data management by granting data access dynamically at the time of user authentication, rather than providing the same services in roles assigned to each user group of the network system. The proposed mechanism is expected to provide flexible authentication capabilities through dynamic data access control by users to enhance the security of data stored within blockchain networks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.8
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• pp.1064-1070
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• 2020

In Korea, video-based news broadcasters are primarily classified into terrestrial broadcasters, general programming cable broadcasters and YouTube broadcasters. Recently, news broadcasters get subjective while targeting the desired specific audience. This violates normative expectations of impartiality and neutrality on journalism from its audience. This phenomenon may have a negative impact on audience perceptions of issues. This study examined whether broadcast news reporting conveys emotions and if so, how news broadcasters differ according to emotion type. Emotion types were classified into neutrality, happiness, sadness and anger using a convolutional neural network which is a class of deep neural networks. Results showed that news anchors or reporters tend to express their emotions during TV broadcasts regardless of broadcast systems. This study provides the first quantative investigation of emotions in broadcasting news. In addition, this study is the first deep learning-based approach to emotion analysis of broadcasting news.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.1
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• pp.35-44
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• 2021

Recent advances in the 4th Industrial Revolution have accelerated the change of the working environment, such that the paradigm of education has been shifted in accordance with career education including the free semester system and the high school credit system. While the purpose of those systems is students' self-motivated career exploration, educational limitations for teachers and students exist due to the rapid change of the information on education. Also, education technology research to tackle these limitations is relatively insufficient. To this end, this study first defines three requirements that education technologies for the career education system should consider. Then, through data-driven artificial intelligence technology, this study proposes a data system and an artificial intelligence recommendation model that incorporates the topics for career exploration, courses, and majors in one scheme. Finally, this study demonstrates that the set-based artificial intelligence model shows satisfactory performances on recommending career education contents such as courses and majors, and further confirms that the actual application of this system in the educational field is acceptable.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1710-1719
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• 2019

This study is intended to understand defense technologies that require the convergence of various technologies and the integration of systems, and to propose valuation methods that reflect the characteristics of each field. The measure of technology level, integration (system integration technology) between technologies, preparation of manufacturing, and maturity scale of the weapons system to be valued are measured according to the items of measurement factors to verify system performance, and to present a framework for estimating the quantitative values of core technologies using system maturity. Considering the characteristics of each technology field, the research suggests a proper valuation method. In evaluating the value of defense technologies, A proposal is made to evaluate the value of defense technology by competent technical experts in each field, using SRL, which can be evaluated according to the evaluation criteria reflecting the technical characteristics of each field, and to evaluate the completion of the entire system in quantitative terms.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.83-91
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• 2021

Health is an outset of psychological, social, financial, and physical state. Several macroeconomic factors are entangled with health and mortality. Infant mortality and life expectancy are two keyguard on demographic research context on last few decades. On the other hand, foreign inflows play an unprecedent role for raising economic circulation and providing more opportunities to build a better society. The study aims to investigate the relationship between foreign direct investment (FDI), economic growth, and Bangladesh's health. This study employs time-series data from 1980 to 2018. Results show, with Auto-regressive Distribute Lag (ARDL) model, that there is significant cointegration among variables. Foreign investment and economic output relate significantly and positively to health. On the contrary, education is quasi-linked with a different sign-on different model. For model validation, pitfalls of time-series multicollinearity, heteroscedasiticy, and autocorrelation are not present. Also, CUSUM and CUSUMSQ tests are validating the model as stable and fit for future prediction. Medical assessment and education need more attention from the government as well as the private sector. FDI can play a catalyst role for improving the health sector, raising opportunity in educating and creating a better lifestyle. In order to optimize foreign investment, the government should implement necessary reforms and policies.