• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.149-171
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• 2020

The period at university is to make decision about getting an actual job. As our society develops rapidly and highly, jobs are diversified, subdivided, and specialized, and students' job preparation period is also getting longer and longer. This study analyzed the log data of college students to see how the various activities that college students experience inside and outside of school might have influences on employment. For this experiment, students' various activities were systematically classified, recorded as an activity data and were divided into six core competencies (Job reinforcement competency, Leadership & teamwork competency, Globalization competency, Organizational commitment competency, Job exploration competency, and Autonomous implementation competency). The effect of the six competency levels on the employment status (employed group, unemployed group) was analyzed. As a result of the analysis, it was confirmed that the difference in level between the employed group and the unemployed group was significant for all of the six competencies, so it was possible to infer that the activities at the school are significant for employment. Next, in order to analyze the impact of the six competencies on the qualitative performance of employment, we had ANOVA analysis after dividing the each competency level into 2 groups (low and high group), and creating 6 groups by the range of first annual salary. Students with high levels of globalization capability, job search capability, and autonomous implementation capability were also found to belong to a higher annual salary group. The theoretical contributions of this study are as follows. First, it connects the competencies that can be extracted from the school experience with the competencies in the Human Resource Management field and adds job search competencies and autonomous implementation competencies which are required for university students to have their own successful career & life. Second, we have conducted this analysis with the competency data measured form actual activity and result data collected from the interview and research. Third, it analyzed not only quantitative performance (employment rate) but also qualitative performance (annual salary level). The practical use of this study is as follows. First, it can be a guide when establishing career development plans for college students. It is necessary to prepare for a job that can express one's strengths based on an analysis of the world of work and job, rather than having a no-strategy, unbalanced, or accumulating excessive specifications competition. Second, the person in charge of experience design for college students, at an organizations such as schools, businesses, local governments, and governments, can refer to the six competencies suggested in this study to for the user-useful experiences design that may motivate more participation. By doing so, one event may bring mutual benefits for both event designers and students. Third, in the era of digital transformation, the government's policy manager who envisions the balanced development of the country can make a policy in the direction of achieving the curiosity and energy of college students together with the balanced development of the country. A lot of manpower is required to start up novel platform services that have not existed before or to digitize existing analog products, services and corporate culture. The activities of current digital-generation-college-students are not only catalysts in all industries, but also for very benefit and necessary for college students by themselves for their own successful career development.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• The Korean Society of Law and Medicine
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• v.20 no.3
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• pp.47-79
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• 2019

The Korean Society of Law and Medicine has faithfully played the role of professional academic organizations last 20 years in terms of academic activities, accumulated achievements, diversity, professionalism, and influence on academic circles. The Korean Society of Law and Medicine and the Journal of Medical Law serve as a platform for academic information and exchange of opinions on medical law. Medical law began in the midst of increasing conflicts and disputes caused by medical malpractice and the enactment and legal coercion of medical care as pressure on medical workers. It tried to find a way to coexist with each other through the encounter and convergence of medicine and law. Medical criminal law extends from traditional crimes in the realm of life and body protection to bioethics violations caused by the development of biomedical technology, corruption and economic crime in the medical field. Medical law has evolved into a comprehensive legal area dealing with legal issues raised in medical treatment, healthcare, bioethics, and life sciences technology. On the legal side, medical law is not independent legal areas. It is overlapping with traditional law areas such as civil law, administrative law, criminal law, social law, civil and criminal procedure law. However, it is now established as a convergence study in medicine, bioethics, life science, as well as in various fields of law. It has become an area where collaboration is needed with the field of law, medicine, ethics, sociology and economics. Medical criminal law has undergone a dynamic development over the last two decades. The development of medicine and medical technology provides new and innovative methods of diagnosis and treatment. The achievements and risks of revolutionary developments in biotechnology, genetic engineering and medicine coexist. While there is a dazzling achievement that mankind has hoped for: combating disease and improving health, it also creates unwanted side effects and risks to humans. There is a need to reconsider ethical and legal principles. The discovery and development of patient identity and autonomy has changed the medical doctor-patient relationship. Furthermore, it was complicated by the triangle relationship of patients, medical doctors and insurance. Legal matters are also complicated. This is why the necessity of legislation is emerging. Criminal punishment provisions are also required. The Medical Law and Biomedical Law are systematically and coherently deformed as mosaic-based legislation that takes place whenever there are social issues, citizens' needs, and medical organizations' interests, rather than sufficient enactment and revision procedures. It needs a complete overhaul, and this is possible through interdisciplinary collaboration which is the strength of The Korean Society of Law and Medicine.

• Microbiology and Biotechnology Letters
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• v.46 no.3
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• pp.253-260
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• 2018

Beneficial microorganisms are widely used in the forestry, livestock, and, in particular, agricultural sectors to control soilborne diseases and promote plant growth. However, the industrial utilization of these microorganisms is very limited, mainly due to uncertainty concerning their ability to colonize and persist in soil. In this study, the survival of beneficial microorganisms in field soil microcosms was investigated for 13 days using quantitative PCR with B. subtilis group-specific primers. Bacterial community dynamics of the treated soils were analyzed using 16S ribosomal RNA (rRNA) gene amplicon sequencing on the Illumina MiSeq platform. The average 16S rRNA gene copy number per g dry soil of Bacillus spp. was $4.37{\times}10^6$ after treatment, which was 1,000 times higher than that of the control. The gene copy number was generally maintained for a week and was reduced thereafter, but remained 100 times higher than that of the control. Bacterial community analysis indicated that Acidobacteria ($26.3{\pm}0.9%$), Proteobacteria ($24.2{\pm}0.5%$), Chloroflexi ($11.1{\pm}0.4%$), and Actinobacteria ($9.7{\pm}2.5%$) were abundant phyla in both treated and non-treated soils. In the treated soils, the relative abundance of Actinobacteria was lower, whereas those of Bacteroidetes and Firmicutes were higher compared to the control. Differences in total relative abundances of operational taxonomic units belonging to several genera were observed between the treated and non-treated soils, suggesting that inoculation of soil with the Bacillus strains influenced the relative abundances of certain groups of bacteria and, therefore, the dynamics of resident bacterial communities. These changes in resident soil bacterial communities in response to inoculation of soil with beneficial Bacillus spp. provide important information for the use of beneficial microorganisms in soil for sustainable agriculture.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.537-545
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• 2018

In this study, transcriptome analysis was conducted to collect various information from Fagopyrum esculentum and Fagopyrum tataricum during the early germination stage. Total RNA was extracted from the seeds and at 12, 24, and 36 hrs after germination of Jeju native Fagopyrum esculentum and Fagopyrum tataricum and sequenced using the Illumina Hiseq 2000 platform. Raw data analysis was conducted using the Dynamic Trim and Lengths ORT programs in the SolexaQA package, and assembly and annotation were performed. Based on RNA-seq raw data, we obtained 16.5 Gb and 16.2 Gb of transcriptome data corresponding to about 84.2% and 81.5% of raw data, respectively. De novo assembly and annotation revealed 43,494 representative transcripts corresponding to 47.5Mb. Among them, 23,165 sequences were shown to have similar sequences with annotation DB. Moreover, Gene Ontology (GO) analysis of buckwheat representative transcripts confirmed that the gene is involved in metabolic processes (49.49%) of biological processes, as well as cell function (46.12%) in metabolic process, and catalytic activity (80.43%) in molecular function In the case of gibberellin receptor GID1C, which is related to germination of seeds, the expression levels increased with time after germination in both F. esculentum and F. tataricum. The expression levels of gibberellin 20-oxidase 1 were increased within 12 hrs of gemination in F. esculentum but continuously until 36 hrs in F. tataricum. This buckwheat transcriptome profile analysis of the early germination stage will help to identify the mechanism causing functional and morphological differences between species.

• Journal of Nutrition and Health
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• v.53 no.5
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• pp.547-561
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• 2020

Purpose: This study was conducted to segment the delivery food market and to develop customized products and services. Methods: This study analyzed 636 responses collected from customers who ordered delivery food. Statistical analyses were conducted using the SPSS program (ver. 25.0) for frequency analysis, χ²-test, one-way analysis of variance, factor analysis, and cluster analysis. Results: Four factors were extracted by exploratory factor analysis (safety-orientation, convenience-orientation, taste-orientation, and economy-orientation) to explain the consumers' food-related lifestyles. The results of cluster analysis indicated that the 'low-interest group', 'convenience and economy-oriented group', and 'gourmet and economy-oriented group' should be regarded as the target segments. Characteristic analysis of each cluster showed that lowinterest group had higher rates of married (67.1%) and living with family (85.4%) than other clusters. The convenience and the economy-oriented group had higher rates of living alone (28.9%) than others. The gourmet and the economy-oriented group had a higher percentage of unmarried (62.0%) than the others. In addition, the average age of convenience and economy-oriented group (32.3 years) and gourmet and economy-oriented group (32.5 years) were significantly lower than the safety seeker (40.0 years) (p < 0.001). Difference analysis of the consumption practice according to the cluster, revealed significant differences in the order frequency (p < 0.001), main day to order (p < 0.05), source of information about delivery food (p < 0.001), order method (p < 0.001), and co-consumer (p < 0.01). In addition, the convenience and the economy-oriented group had significantly higher overall satisfaction than the others (p < 0.001). Conclusion: These findings suggest that customer segmentation based on a food-related lifestyle can be used to build a successful marketing strategy. Therefore, restaurant managers and delivery platform operators should consider developing products and services according to the segmentation to maximize customer satisfaction.

• Knowledge Management Research
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• v.21 no.3
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• pp.249-269
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• 2020

Recently, as the online market has matured, it is facing many problems to prevent the growth. The most common problem is the homogenization of online products, which fails to increase the number of customers any more. Moreover, although the portion of the online market has increased significantly, it now becomes essential to expand offline for further development. In response, many online firms have recently sought to expand their businesses and marketing channels by securing offline spaces that can complement the limitations of online platforms, on top of their existing advantages of online channels. Based on their competitive advantage in terms of analyzing large volumes of customer data utilizing information technologies (e.g., big data and artificial intelligence), they are reinforcing their offline influence as well through this online for offline (O4O) business model. On the other hand, most of the existing research has primarily focused on online to offline (O2O) business model, and there is still a lack of research on O4O business models, which have been actively attempted in various industrial fields in recent years. Since a few of O4O-related studies have been conducted only in an experience marketing setting following a case study method, it is critical to conduct an empirical study on O4O selection attributes and their impact on customer satisfaction and loyalty. Therefore, focusing on China's representative O4O business model, 'Fresh Hema,' this study attempts to identify some key selection attributes specialized for O4O services from the customers' viewpoint and examine the impact of these attributes on customer satisfaction and loyalty. The results of the structural equation modeling (SEM) with 300 O4O (Fresh Hema) experienced customers, reveal that, out of seven O4O selection attributes, four (mobile app quality, mobile payment, product quality, and store facilities) have an impact on customer satisfaction, which also leads to customer loyalty (reuse intention, recommendation intention, and brand attachment). This study would help managers in an O4O area well adapt to rapidly changing customer needs and provide them with some guidelines for enhancing both customer satisfaction and loyalty by allocating more resources to more significant selection attributes, rather than less significant ones.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1361-1371
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• 2021

Among smart city services, the crime and disaster prevention sector accounted for the highest 24% in 2018. The most important platform for providing real-time situation information is CCTV (Closed-Circuit Television). Therefore, it is essential to create the actual CCTV surveillance coverage to maximize the usability of CCTV. However, the amount of CCTV installed in Korea exceeds one million units, including those operated by the local government, and manual identification of CCTV coverage is a time-consuming and inefficient process. This study proposed a method to efficiently construct CCTV's actual surveillance coverage and reduce the time required for the decision-maker to manage the situation. For this purpose, first, the exterior orientation parameters and focal lengths of the pre-installed CCTV cameras, which are difficult to access, were calculated using the point cloud data of the MMS (Mobile Mapping System), and the FOV (Field of View) was calculated accordingly. Second, using the FOV result calculated in the first step, CCTV's actual surveillance coverage area was constructed with 1 m, 2 m, 3 m, 5 m, and 10 m grid interval considering the occluded regions caused by the buildings. As a result of applying our approach to 5 CCTV images located in Uljin-gun, Gyeongsnagbuk-do the average re-projection error was about 9.31 pixels. The coordinate difference between calculated CCTV and location obtained from MMS was about 1.688 m on average. When the grid length was 3 m, the surveillance coverage calculated through our research matched the actual surveillance obtained from visual inspection with a minimum of 70.21% to a maximum of 93.82%.

• Journal of Nutrition and Health
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• v.52 no.5
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• pp.488-500
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• 2019

Purpose: This study examined the effects of a mobile app-based program to reduce the dietary sugar intake according to the stages of the behavioral change in dietary sugar reduction in female college students. Methods: The program used in this study can monitor the dietary sugar intake after recording the dietary intake and provide education message for the reduction of dietary sugar intake. In an eight-week pre-post intervention study, 68 female college students were instructed to record all the food they consumed daily and received weekly education information. At pre-post intervention, the subjects were asked to answer the questionnaire about sugar-related nutrition knowledge, sugar-intake behavior, and sugar-intake frequency. For statistical analysis, ANOVA and a paired t-test were used for comparative analysis according Precontemplation (PC), Contemplation Preparation (C P), and A M (Action Maintenance) stage. Results: Significant differences were observed in the frequency of snacking, experience of nutrition education, and preference for sweetness according to the stages of behavior change in dietary sugar reduction. After finishing an intervention, the sugar-related nutrition knowledge score was increased significantly in the stages of Precontemplation (PC) and Contemplation Preparation (C P). The score of the sugar intake behavior increased significantly in all stages. The intake frequency of chocolate, muffins or cakes, and drinking yogurt decreased significantly in the PC stage and the intake frequency of biscuits, carbonated beverages, and fruit juice decreased significantly in the C P stage. Subjects in the PC and C P stages had an undesirable propensity in nutrition knowledge, sugar-intake behavior, and sugar-intake frequency compared to the A M stage, but this intervention improved significantly their nutrition knowledge, sugar-intake behavior, and sugar-intake frequency. Conclusion: This program can be an effective educational tool in the stages of PC and C P, and is expected to further increase the usability and sustainability of mobile application if supplemented appropriately to a health platform program.