• Journal of Convergence for Information Technology
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• v.12 no.5
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• pp.139-149
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• 2022

The purpose of this study is to develop and analyze the effects of an educational program that can cultivate artificial intelligence(AI) convergence education competency for future education and enhance students' understanding of pre-service teachers. For this end, an AI convergence education program using Machine Learning for Kids and Scratch 3 was developed for 15 weeks under the theme of classifying the state of matter. The developed program were treated by K University pre-service teachers who participated voluntarily. As a result, pre-service teachers were able to metaphorically understand the learning process of students through understanding of machine learning training process. In addition, the pre-post t-test result of AI teaching efficacy showed a statistically significant improvement with t=-7.137 (p<.000). Therefore, it is suggested that the AI convergence education program developed in this study can help to increase the understanding of the pre-service teacher's students in an indirect way other than practice teaching, and can contribute to foster AI education competency.

• Journal of Technology Innovation
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• v.30 no.1
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• pp.1-20
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• 2022

Digital transformation refers to the economic and social effects of digitisation and digitalisation. Although digital transformation acts as a useful tool for economic/social development and enhancing the convenience of life, it can have negative effects (misuse of personal information, ethical problems, deepening social gaps, etc.). The government is actively establishing policies to promote digital transformation to secure competitiveness and technological hegemony, however, understanding of digital transformation-related risk issues and implementing policies to prevent them are relatively slow. Thus, this study systematically identifies risk issues of the future society that can be caused by digital transformation based on quantitative analysis of media articles big data through the Embedded Topic Modeling method. Specifically, first, detailed issues of negative effects of digital transformation in major countries were identified. Then detailed issues of negative effects of artificial intelligence in major countries and Korea were identified. Further, by synthesizing the results, future direction of the government's digital transformation policies for responding the negative effects was proposed. The policy implications are as follows. First, since the negative effects of digital transformation does not only affect technological fields but also affect the overall society, such as national security, social issues, and fairness issues. Therefore, the government should not only promote the positive functions of digital transformation, but also prepare policies to counter the negative functions of digital transformation. Second, the detailed issues of future social risks of digital transformation appear differently depending on contexts, so the government should establish a policy to respond to the negative effects of digital transformation in consideration of the national and social context. Third, the government should set a major direction for responding negative effects of digital transformation to minimize confusion among stakeholders, and prepare effective policy measures.

• The Mathematical Education
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• v.63 no.2
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• pp.165-186
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• 2024

Despite the growing attention on artificial intelligence-based automated scoring technology as a support method for the introduction of descriptive items in school environments and large-scale assessments, there is a noticeable lack of foundational research in mathematics compared to other subjects. This study developed an automated scoring model for two descriptive items in first-year middle school mathematics using the Random Forest algorithm, evaluated its performance, and explored ways to enhance this performance. The accuracy of the final models for the two items was found to be between 0.95 to 1.00 and 0.73 to 0.89, respectively, which is relatively high compared to automated scoring models in other subjects. We discovered that the strategic selection of the number of evaluation categories, taking into account the amount of data, is crucial for the effective development and performance of automated scoring models. Additionally, text preprocessing by mathematics education experts proved effective in improving both the performance and interpretability of the automated scoring model. Selecting a vectorization method that matches the characteristics of the items and data was identified as one way to enhance model performance. Furthermore, we confirmed that oversampling is a useful method to supplement performance in situations where practical limitations hinder balanced data collection. To enhance educational utility, further research is needed on how to utilize feature importance derived from the Random Forest-based automated scoring model to generate useful information for teaching and learning, such as feedback. This study is significant as foundational research in the field of mathematics descriptive automatic scoring, and there is a need for various subsequent studies through close collaboration between AI experts and math education experts.

• Information Systems Review
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• v.25 no.2
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• pp.145-162
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• 2023

Asset management is a complex and difficult field that requires insight into numerous variables and even human psychology. Thus, it has traditionally been the domain of professionals, and these services have been expensive to obtain. Changes are taking place in these markets, and the driving force is the digital revolution, so-called the fourth industrial revolution. Among them, the Robo-Advisor service using artificial intelligence technology is the highlight. The reason is that it is possible to popularize investment advisory services with convenient accessibility and low cost. This study aims to clarify what factors are critically important when selecting robo-advisors for service users and providers in Korea, and what perception differences exist in the selection factors between user and provider groups. The framework of the study was based on the marketing mix 4C model, and the design and analysis of the model used Delphi survey and AHP. Through the study design, 4 main criteria and 15 sub-criteria were derived, and the findings of the study are as follows. First, the importance of the four main criteria was in the order of customer needs > customer convenience > customer cost > customer communication for both groups. Second, looking at the 15 sub-criteria, it was found that investment purpose coverage, investment propensity coverage, fee level and accessibility factors were the most important. Third, when comparing between groups, the user group found that the fee level and accessibility factors were the most important, and the provider group recognized the investment purpose coverage and investment propensity coverage factors as important. This study derived useful implications in practice. First, when designing for the spread of the robo-advisor service, the basis for constructing a user-oriented system was prepared by considering the priority of importance according to the weight difference between the four main criteria and the 15 sub-criteria. In addition, the difference in priority of each sub-criteria shown in the group comparison and the cause of the sub-criteria with large weight differences were identified. In addition, it was suggested that it is very important to form a consensus to resolve the difference in perception of factors between those in charge of strategy and marketing and system development within the provider group. Academically, it is meaningful in that it is an early study that presented various perspectives and perspectives by deriving a number of robo-advisor selection factors. Through the findings of this study, it is expected that a successful user-oriented robo-advisor system can be built and spread in Korea to help users.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.117-126
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• 2020

A maritime object detection system is an intelligent assistance system to maritime autonomous surface ship(MASS). It detects automatically floating debris, which has a clash risk with objects in the surrounding water and used to be checked by a captain with a naked eye, at a similar level of accuracy to the human check method. It is used to detect objects around a ship. In the past, they were detected with information gathered from radars or sonar devices. With the development of artificial intelligence technology, intelligent CCTV installed in a ship are used to detect various types of floating debris on the course of sailing. If the speed of processing video data slows down due to the various requirements and complexity of MASS, however, there is no guarantee for safety as well as smooth service support. Trying to solve this issue, this study conducted research on the minimization of computation volumes for video data and the increased speed of data processing to detect maritime objects. Unlike previous studies that used the Hough transform algorithm to find the horizon and secure the areas of interest for the concerned objects, the present study proposed a new method of optimizing a binarization algorithm and finding areas whose locations were similar to actual objects in order to improve the speed. A maritime object detection system was materialized based on deep learning CNN to demonstrate the usefulness of the proposed method and assess the performance of the algorithm. The proposed algorithm performed at a speed that was 4 times faster than the old method while keeping the detection accuracy of the old method.

• Journal of Intelligence and Information Systems
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• v.11 no.1
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• pp.35-51
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• 2005

The wide spread of Internet and rapid development of e-commerce-related technology have brought sweeping changes on the traditional commercial transactions. Accordingly, many efforts to transform these transactions electronically under e-commerce environment have been carried out. As most transactions are usually made through negotiations, the function of automated negotiation is also required in the e-commerce environment. This paper aims to develop the method to generate and evaluate the multi-attribute negotiation proposals for automated negotiation systems. To this end the related articles are reviewed and the method dealing with e-negotiation strategy is suggested. In this method, the seller generates his or her own negotiation proposal and then evaluates the buyer's proposal based on SAW (Simple Additive Weighting Method), one of the MADM (Multi Attribute Decision Making) methods. To verify the suggested method, a case study is conducted in the order-based manufacturing environment.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.113-130
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• 2010

During the decades, considerable human interventions to comprehend the web information were increased continually. The successful expansion of the web services made it more complex and required more contributions of the users. Many researchers have tried to improve the comprehension ability of computers in supporting an intelligent web service. One reasonable approach is enriching the information with machine understandable semantics. They applied ontology design, intelligent reasoning and other logical representation schemes to design an infrastructure of the semantic web. For the features, the semantic web is considered as an intelligent access to understanding, transforming, storing, retrieving, and processing the information gathered from heterogeneous, distributed web resources. The goal of this study is firstly to explore the problems that restrict the applications of web services and the basic concepts, languages, and tools of the semantic web. Then we highlight some of the researches, solutions, and projects that have attempted to combine the semantic web and business support, and find out the pros and cons of the approaches. Through the study, we were able to know that the semantic web technology is trying to offer a new and higher level of web service to the online users. The services are overcoming the limitations of traditional web technologies/services. In traditional web services, too much human interventions were needed to seek and interpret the information. The semantic web service, however, is based on machine-understandable semantics and knowledge representation. Therefore, most of information processing activities will be executed by computers. The main elements required to develop a semantic web-based business support are business logics, ontologies, ontology languages, intelligent agents, applications, and etc. In using/managing the infrastructure of the semantic web services, software developers, service consumers, and service providers are the main representatives. Some researchers integrated those technologies, languages, tools, mechanisms, and applications into a semantic web services framework. Therefore, future directions of the semantic web-based business support should be start over from the infrastructure.

• The Journal of the Korea Contents Association
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• v.13 no.5
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• pp.48-57
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• 2013

Smartphones provide users with environment for communication and sharing information and at the same time play an important role of mobile technology and mobile art development. Smartphone technology-related researches are being accelerated especially with the advent of mobile Augmented Reality(AR) age, but the studies on user participation that is essential for AR content industry were insufficient. In that regard, the assistance from mobile art area that has already developed these characteristics is essential. Thus, this article is to classify mobile art that has not been studied a lot domestically into feature phone usage and smartphone usage and to analyze each example case with the three most used methods. The usage of feature phones which use the sound and images of mobile devices can be divided into three: installation and performing methods, single channel video art method and five senses communication method. On the other hand, the usage of smartphones that use sensors, cameras, GPS and AR can be divided into location-based AR, marker-based AR and markerless AR. Also, as a result of examining mobile AR content utilization technology by industries, combined methods are utilized; tourism and game-related industries use location-based AR, education and medicine-related industries use marker-based AR, and shopping-related industries use markerless AR. The development of AR content industry is expected to be accelerated with mobile art that makes use of combined technology method and constant communication method through active participation of users. The future development direction of mobile AR industry is predicted to have minimized HMD, integration of hologram technology and artificial intelligence and make the most of big data and social network so that we could overcome the technological limitation of AR.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.

• World Technopolis Review
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• v.4 no.1
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• pp.11-22
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• 2015

Globalization, increasing technological advancements and dynamic knowledge diffusion are moving our world closer together at a unique scale and pace. At the same time, our rapidly changing society is confronted with major challenges ranging from demographic to economic ones; challenges that necessitate highly innovative solutions, forcing us to reconsider the way that we actually innovate and create shared value. As such the linear, centralized innovation models of the past need to be replaced with new approaches; approaches that are based upon an open and collaborative, global network perspective where all innovation actors strategically network and collaborate, openly distribute their ideas and co-innovate/co-create in a global context utilizing our society's full innovation potential (Innovation 4.0 - Open Innovation 2.0). These emerging innovation paradigms create "an opportunity for a new entrepreneurial renaissance which can drive a Cambrian like explosion of sustainable wealth creation" (Curley 2013). Thus, in order to materialize this entrepreneurial renaissance, it is critical not only to value but also to actively employ this new innovation paradigms so as to derive community-driven shared value that stems from global innovation networks. This paper argues that there is a gap in existing business incubation model that needs to be filled, in that the innovation and entrepreneurship community cannot afford to ignore the emerging innovation paradigms and rely upon closed incubation models but has to adopt an "open incubation" (Ziouvelou 2013). The open incubation model is based on the principles of open innovation, crowdsourcing and co-creation of shared value and enables individual users and innovation stakeholders to strategically network, find collaborators and partners, co-create ideas and prototypes, share their ideas/prototypes and utilize the wisdom of the crowd to assess the value of these project ideas/prototypes, while at the same time find connections/partners, business and technical information, knowledge on start-up related topics, online tools, online content, open data and open educational material and most importantly access to capital and crowd-funding. By introducing a new incubation phase, namely the "interest phase", open incubation bridges the gap between entrepreneurial need and action and addresses the wantpreneurial needs during the innovation conception phase. In this context one such ecosystem that aligns fully with the open incubation model and theoretical approach, is the VOICE ecosystem. VOICE is an international, community-driven innovation and entrepreneurship ecosystem based on open innovation, crowdsourcing and co-creation principles that has no physical location as opposed to traditional business incubators. VOICE aims to tap into the collective intelligence of the crowd and turn their entrepreneurial interest or need into a collaborative project that will result into a prototype and to a successful "crowd-venture".