Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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2009.05a
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pp.521-524
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2009
Everyone can easily acquire learning materials on web environment that rapidly develops. Because the importance of English education has been emphasized day by day, many English education systems are introduced. However, previous most English education systems support only single user mode, and cannot deal with a newly coined word such as 'WIKIPEDIA'. In order to lead a user's learning ability with interest and enjoyment, this paper propose an online English word game system implementing a 'scrabble' board game. The proposed English word game system has the following characteristics. First, the proposed system supports both single user mode and multi user mode with a virtual user based on artificial intelligence. Second, the proposed system can recognize newly coined words such as 'WIKIPEDIA' by using NEVER Open API dictionary. Third, the proposed system offers familiar user interface so that a user can play the game without any manual. Therefore, it is expected that the proposed system can help users to learn English words with interest and enjoyment.
Professional identity formation (PIF) is an essential concept in professional education. Many scholars have explored conceptual frameworks of PIF and conducted empirical studies to advance an understanding of the construct in medical education. Despite its importance, it is unclear what educational approaches and assessment practices are actually implemented in medical education settings. Therefore, we conducted a literature review of empirical studies reporting educational practices for medical learners' PIF. We searched the Web of Science database using keywords and chose 37 papers for analysis based on inclusion and exclusion criteria. Thematic analysis was conducted. Most empirical papers (92%) were from North America and Western Europe and used qualitative research methods, including mixed methods (99%). The papers reported the use of reflection activities and elective courses for specific purposes, such as art as an educational activity. Patient and healthcare experiences were also found to be a central theme in medical learners' PIF. Through an iterative analysis of the key themes that emerged from the PIF studies, we derived the following key concepts and implications: (1) the importance of creating informal and incidental learning environments, (2) ordinary yet authentic patient experiences, (3) a climate of psychosocial safety in a learning environment embracing individual learners' background and emotional development, and (4) the reconceptualization of PIF education and assessment. In conclusion, research on PIF should be diversified to include various cultural and social contexts. Theoretical frameworks should also be diversified and developed beyond Kegan's developmental framework to accommodate the nonlinear and dynamic nature of PIF.
Background: The occurrence of environmental disease is known to be associated with chronic exposure to toxic chemicals, including waterborne contaminants, air/indoor pollutants, asbestos, ingredients in humidifier disinfectants, etc. Objectives: In this study, we reviewed toxicological studies related to environmental disease as defined by the Environmental Health Act in Korea and toxic chemicals. We also suggested a direction for future toxicological research necessary for the prevention and management of environmental disease. Methods: Trends in previous studies related to environmental disease were investigated through PubMed and Web of Science. A detailed review was provided on toxicological studies related to the humidifier disinfectants. We identified adverse outcome pathways (AOPs) that can be linked to the induction of environmental diseases, and proposed a chemical screening system that uses AOP, chemical toxicity big data, and deep learning models to select chemicals that induce environmental disease. Results: Research on chemical toxicity is increasing every year, but there is a limitation to revealing a clear causal relationship between exposure to chemicals and the occurrence of environmental disease. It is necessary to develop various exposure- and effect-biomarkers related to disease occurrence and to conduct toxicokinetic studies. A novel chemical screening system that uses AOP and chemical toxicity big data could be useful for selecting chemicals that cause environmental diseases. Conclusions: From a toxicological point of view, developing AOP related to environmental diseases and a deep learning-based chemical screening system will contribute to the prevention of environmental diseases in advance.
International Journal of Computer Science & Network Security
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v.22
no.6
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pp.390-399
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2022
Cyber security and resilience are phrases that describe safeguards of ICTs (information and communication technologies) from cyber-attacks or mitigations of cyber event impacts. The sole purpose of Risk models are detections, analyses, and handling by considering all relevant perceptions of risks. The current research effort has resulted in the development of a new paradigm for safeguarding services offered online which can be utilized by both service providers and users. customers. However, rather of relying on detailed studies, this approach emphasizes task selection and execution that leads to successful risk treatment outcomes. Modelling intelligent CSGs (Cyber Security Games) using MLTs (machine learning techniques) was the focus of this research. By limiting mission risk, CSGs maximize ability of systems to operate unhindered in cyber environments. The suggested framework's main components are the Threat and Risk models. These models are tailored to meet the special characteristics of online services as well as the cyberspace environment. A risk management procedure is included in the framework. Risk scores are computed by combining probabilities of successful attacks with findings of impact models that predict cyber catastrophe consequences. To assess successful attacks, models emulating defense against threats can be used in topologies. CSGs consider widespread interconnectivity of cyber systems which forces defending all multi-step attack paths. In contrast, attackers just need one of the paths to succeed. CSGs are game-theoretic methods for identifying defense measures and reducing risks for systems and probe for maximum cyber risks using game formulations (MiniMax). To detect the impacts, the attacker player creates an attack tree for each state of the game using a modified Extreme Gradient Boosting Decision Tree (that sees numerous compromises ahead). Based on the findings, the proposed model has a high level of security for the web sources used in the experiment.
Background: Online teaching and learning extend the duration of using gadgets such as mobile phones and tablets. A prolonged usage of these gadgets in a static position can lead to musculoskeletal disorders (MSD). Therefore, this study aims to identify the risk factors related to musculoskeletal symptoms while using gadgets during work from home due to the COVID-19 pandemic. Method: A cross-sectional survey with online-based questionnaires was collected from the University of Indonesia, consisting of lecturers, students, and managerial staff. The minimum number of respondents was 1,080 and was defined by stratified random sampling. Furthermore, the dependent variable was musculoskeletal symptoms, while the independent were age, gender, job position, duration, activity when using gadgets, and how to hold them. Result: Most of the respondents had mobile phones but only 16% had tablets. Furthermore, about 56.7% have used a mobile phone for more than 10 years, while about 89.7% have used a tablet for less than 10 years. A multivariate analysis found factors that were significantly associated with MSD symptoms while using a mobile phone, such as age, gender, web browsing activity, work, or college activities. These activities include doing assignments and holding the phone with two hands with two thumbs actively operating. The factors that were significantly associated with MSD symptoms when using tablets were gender, academic position, social media activity, and placing the tablet on a table with two actively working index fingers. Conclusion: Therefore, from the results of this study it is necessary to have WFH and e-learning policies to reduce MSD symptoms and enhance productivity at work.
The local history research started from the 1960's postmodernism neted in the local history as the subject of diversity instead of National history. The local is being magnified as a new research theme among history scholar. In these circumstances, the local history research shows sign of activity and the movement that used in various ways at education has become more active. Compared to the contents business of local history, development of education contents what serviced for student is insufficient. Therefore, this research suggests development plan of local history education contents using archives which efficient tool of history education. Students can grow the ability of historical inquiry, thinking, insight through archives-assisted learning. Also, self-learning is possible instead of a lecture by teacher. This research shows a development of archival contents for local history education though literature research, abroad case analysis, focus group interview with history teachers. Concepts of the local history, local history education, education contents are examined at literature research. Local history education of the State Archives of the United States of America, the United Kingdom, Australia web site is analyzed. These state archives have been providing the web based service of archival contents for local history education for a long time. With these theoretical background, carry out a focus group interview with middle school history teachers. It draw conclusion that 14 category and 35 subcategory and these are reflected in the development of archival contents for local history education.
Journal of the Korea Society of Computer and Information
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v.25
no.12
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pp.63-71
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2020
In this paper, we propose a fire detection technology using YOLOv3 and EfficientDet, the most reliable artificial intelligence detection algorithm recently, an alert service that simultaneously transmits four kinds of notifications: text, web, app and e-mail, and an AWS system that links fire detection and notification service. There are two types of our highly accurate fire detection algorithms; the fire detection model based on YOLOv3, which operates locally, used more than 2000 fire data and learned through data augmentation, and the EfficientDet, which operates in the cloud, has conducted transfer learning on the pretrained model. Four types of notification services were established using AWS service and FCM service; in the case of the web, app, and mail, notifications were received immediately after notification transmission, and in the case of the text messaging system through the base station, the delay time was fast enough within one second. We proved the accuracy of our fire detection technology through fire detection experiments using the fire video, and we also measured the time of fire detection and notification service to check detecting time and notification time. Our AI fire detection and notification service system in this paper is expected to be more accurate and faster than past fire detection systems, which will greatly help secure golden time in the event of fire accidents.
Nowadays, XML has been favored by many companies internally and externally as a means of sharing and distributing data. there are many researches and systems for modeling and storing XML documents by an object-oriented method as for the method of saving and managing web-based multimedia document more easily. The representative tool for the object-oriented modeling of XML documents is UML (Unified Modeling Language). UML at the beginning was used as the integrated methodology for software development, but now it is used more frequently as the modeling language of various objects. Currently, UML supports various diagrams for object-oriented analysis and design like class diagram and is widely used as a tool of creating various database schema and object-oriented codes from them. This paper proposes an Efficinet Query Modelling of XML-GL using the UML class diagram and OCL for searching XML document which its application scope is widely extended due to the increased use of WWW and its flexible and open nature. In order to accomplish this, we propose the modeling rules and algorithm that map XML-GL. which has the modeling function for XML document and DTD and the graphical query function about that. In order to describe precisely about the constraint of model component, it is defined by OCL (Object Constraint Language). By using proposed technique creates a query for the XML document of holding various properties of object-oriented model by modeling the XML-GL query from XML document, XML DTD, and XML query while using the class diagram of UML. By converting, saving and managing XML document visually into the object-oriented graphic data model, user can prepare the base that can express the search and query on XML document intuitively and visually. As compared to existing XML-based query languages, it has various object-oriented characteristics and uses the UML notation that is widely used as object modeling tool. Hence, user can construct graphical and intuitive queries on XML-based web document without learning a new query language. By using the same modeling tool, UML class diagram on XML document content, query syntax and semantics, it allows consistently performing all the processes such as searching and saving XML document from/to object-oriented database.
Jeong Hoon Lee;Ki Hwan Kim;Eun Hye Lee;Jong Seok Ahn;Jung Kyu Ryu;Young Mi Park;Gi Won Shin;Young Joong Kim;Hye Young Choi
Korean Journal of Radiology
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v.23
no.5
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pp.505-516
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2022
Objective: To evaluate whether artificial intelligence (AI) for detecting breast cancer on mammography can improve the performance and time efficiency of radiologists reading mammograms. Materials and Methods: A commercial deep learning-based software for mammography was validated using external data collected from 200 patients, 100 each with and without breast cancer (40 with benign lesions and 60 without lesions) from one hospital. Ten readers, including five breast specialist radiologists (BSRs) and five general radiologists (GRs), assessed all mammography images using a seven-point scale to rate the likelihood of malignancy in two sessions, with and without the aid of the AI-based software, and the reading time was automatically recorded using a web-based reporting system. Two reading sessions were conducted with a two-month washout period in between. Differences in the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and reading time between reading with and without AI were analyzed, accounting for data clustering by readers when indicated. Results: The AUROC of the AI alone, BSR (average across five readers), and GR (average across five readers) groups was 0.915 (95% confidence interval, 0.876-0.954), 0.813 (0.756-0.870), and 0.684 (0.616-0.752), respectively. With AI assistance, the AUROC significantly increased to 0.884 (0.840-0.928) and 0.833 (0.779-0.887) in the BSR and GR groups, respectively (p = 0.007 and p < 0.001, respectively). Sensitivity was improved by AI assistance in both groups (74.6% vs. 88.6% in BSR, p < 0.001; 52.1% vs. 79.4% in GR, p < 0.001), but the specificity did not differ significantly (66.6% vs. 66.4% in BSR, p = 0.238; 70.8% vs. 70.0% in GR, p = 0.689). The average reading time pooled across readers was significantly decreased by AI assistance for BSRs (82.73 vs. 73.04 seconds, p < 0.001) but increased in GRs (35.44 vs. 42.52 seconds, p < 0.001). Conclusion: AI-based software improved the performance of radiologists regardless of their experience and affected the reading time.
Journal of the Korea Academia-Industrial cooperation Society
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v.15
no.7
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pp.4573-4579
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2014
In the 21st century, university education is changing from an objective knowledge and information to critical thinking and problem-solving ability. Moreover, university education should change rapidly towards a learner-centered educational environment because it has an educational goal to have college students experience authentic tasks they will be in charge of after graduation, and improves self-directed learning ability and cooperative learning ability. PBL is a pedagogical strategy for posing significant, contextualized, real world situations, and providing resources, guidance, and instruction to learners as they develop content knowledge and problem-solving skills. In problem based learning, the students collaborate to study the issues of a problem as they strive to create viable solution. For these advantages of PBL, the application of PBL in school has been enlarged. On the other hand, the application of PBL in engineering education has not been enlarged. To improve these instruction methods, the development or applications of new instructional methods will be needed. This study examined the PBL instructional design of a creative engineering design subject, which aims to foster talent. The PBL model developed in this study consists of Analysis, Design, Development, Implementation, and Evaluation. A plan of creative engineering design subject was developed based on PBL, and focused on the process of PBL. To determine the effects of this model, studies applying this instructional design to many lecturers should be implemented.
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