• Journal of Korean Elementary Science Education
• /
• v.40 no.3
• /
• pp.311-325
• /
• 2021

Modern society demands a new science teacher image. Compared to other school ages, elementary school students are the time when the teacher's influence plays a large role and is the time when they first encounter science subjects. The role of science teachers is very important as the starting point for the initial image of science learning and attitudes toward science by elementary science teachers. Therefore, it is very important to correctly establish an image of an elementary science teacher. The purpose of this study is to analyze the images of science teachers that elementary school students prefer and avoid according to their class situation. To this end, 534 elementary school students were divided into five classes: class type, class material presentation method, subject instruction method, subject content explanation method, and class atmosphere, and the image of science teacher who prefers and avoids is described in an open format. Concepts presented by elementary school students were analyzed using Semantic network analysis. The conclusions of this study are as follows. First, the image of a science teacher preferred or avoided by elementary school students was determined according to how the science teacher did the class. Second, elementary school students prefer activity-oriented classes such as experimental classes, and there is a need for classes to be conducted in this manner. Lastly, small changes and efforts of teachers in teaching methods are needed so that changes to science classes preferred by elementary school students can be achieved.

• Journal of Broadcast Engineering
• /
• v.23 no.6
• /
• pp.855-865
• /
• 2018

Sound event detection is one of the research areas to model human auditory cognitive characteristics by recognizing events in an environment with multiple acoustic events and determining the onset and offset time for each event. DCASE, a research group on acoustic scene classification and sound event detection, is proceeding challenges to encourage participation of researchers and to activate sound event detection research. However, the size of the dataset provided by the DCASE Challenge is relatively small compared to ImageNet, which is a representative dataset for visual object recognition, and there are not many open sources for the acoustic dataset. In this study, the sound events that can occur in indoor and outdoor are collected on a larger scale and annotated for dataset construction. Furthermore, to improve the performance of the sound event detection task, we developed a dual CNN structured sound event detection system by adding a supplementary neural network to a convolutional neural network to determine the presence of sound events. Finally, we conducted a comparative experiment with both baseline systems of the DCASE 2016 and 2017.

• KIPS Transactions on Computer and Communication Systems
• /
• v.9 no.12
• /
• pp.291-306
• /
• 2020

Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

• The korean journal of orthodontics
• /
• v.52 no.1
• /
• pp.3-19
• /
• 2022

Objective: The purpose of this study was to investigate the accuracy of one-step automated orthodontic diagnosis of skeletodental discrepancies using a convolutional neural network (CNN) and lateral cephalogram images with different qualities from nationwide multi-hospitals. Methods: Among 2,174 lateral cephalograms, 1,993 cephalograms from two hospitals were used for training and internal test sets and 181 cephalograms from eight other hospitals were used for an external test set. They were divided into three classification groups according to anteroposterior skeletal discrepancies (Class I, II, and III), vertical skeletal discrepancies (normodivergent, hypodivergent, and hyperdivergent patterns), and vertical dental discrepancies (normal overbite, deep bite, and open bite) as a gold standard. Pre-trained DenseNet-169 was used as a CNN classifier model. Diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis, t-stochastic neighbor embedding (t-SNE), and gradient-weighted class activation mapping (Grad-CAM). Results: In the ROC analysis, the mean area under the curve and the mean accuracy of all classifications were high with both internal and external test sets (all, > 0.89 and > 0.80). In the t-SNE analysis, our model succeeded in creating good separation between three classification groups. Grad-CAM figures showed differences in the location and size of the focus areas between three classification groups in each diagnosis. Conclusions: Since the accuracy of our model was validated with both internal and external test sets, it shows the possible usefulness of a one-step automated orthodontic diagnosis tool using a CNN model. However, it still needs technical improvement in terms of classifying vertical dental discrepancies.

• Economic and Environmental Geology
• /
• v.55 no.3
• /
• pp.309-316
• /
• 2022

In order to minimize the human and time consumption required for rock classification, research on rock classification using artificial intelligence (AI) has recently developed. In this study, basic volcanic rocks were subdivided by using polarizing microscope thin section images. A convolutional neural network (CNN) model based on Tensorflow and Keras libraries was self-producted for rock classification. A total of 720 images of olivine basalt, basaltic andesite, olivine tholeiite, trachytic olivine basalt reference specimens were mounted with open nicol, cross nicol, and adding gypsum plates, and trained at the training : test = 7 : 3 ratio. As a result of machine learning, the classification accuracy was over 80-90%. When we confirmed the classification accuracy of each AI model, it is expected that the rock classification method of this model will not be much different from the rock classification process of a geologist. Furthermore, if not only this model but also models that subdivide more diverse rock types are produced and integrated, the AI model that satisfies both the speed of data classification and the accessibility of non-experts can be developed, thereby providing a new framework for basic petrology research.

• The Plant Pathology Journal
• /
• v.38 no.5
• /
• pp.541-549
• /
• 2022

Potato late blight caused by Phytophthora infestans is a destructive disease in Korea. To elucidate the genomic variation of the mitochondrial (mt) genome, we assembled its complete mt genome and compared its sequence among different haplotypes. The mt genome sequences of four Korean P. infestans isolates were revealed by Illumina HiSeq. The size of the circular mt genome of the four major genotypes, KR_1_A1, KR_2_A2, SIB-1, and US-11, was 39,872, 39,836, 39,872, and 39,840 bp, respectively. All genotypes contained the same 61 genes in the same order, comprising two RNA-encoding genes, 16 ribosomal genes, 25 transfer RNA, 17 genes encoding electron transport and ATP synthesis, 11 open reading frames of unknown function, and one protein import-related gene, tatC. The coding region comprised 91% of the genome, and GC content was 22.3%. The haplotypes were further analyzed based on sequence polymorphism at two hypervariable regions (HVRi), carrying a 2 kb insertion/deletion sequence, and HVRii, carrying 36 bp variable number tandem repeats (VNTRs). All four genotypes carried the 2 kb insertion/deletion sequence in HVRi, whereas HVRii had two VNTRs in KR_1_A1 and SIB-1 but three VNTRs in US-11 and KR_2_A2. Minimal spanning network and phylogenetic analysis based on 5,814 bp of mtDNA sequences from five loci, KR_1_A1 and SIB-1 were classified as IIa-6 haplotype, and isolates KR_1_A2 and US-11 as haplotypes IIa-5 and IIb-2, respectively. mtDNA sequences of KR_1_A1 and SIB-1 shared 100% sequence identity, and both were 99.9% similar to those of KR_2_A2 and US-11.

• International Journal of Computer Science & Network Security
• /
• v.22 no.7
• /
• pp.57-64
• /
• 2022

This scientific article is devoted to the study of the legal significance of such a category of legal status of the purchaser of another's thing, as its good faith. The essence of this phenomenon has been studied, it has been established that the criterion of good faith attaches significant importance to the claims of the participants of these relations for the acquisition or preservation of private property rights. The paper emphasizes that, in addition to the importance of good conscience at the time of possession of another's thing, which gives legal certainty the possibility of registration of the title and is part of the actual composition for the acquisition of property or the right of ancient possession, bona fides also characterizes the behavior of the occupier. In this case, good conscience only has some legal consequences when it is opposed to subjective law. Under such conditions, it acquires direct legal significance, including as a condition for the acquisition and protection of rights. Good faith possession of another's property is an internal indicator of the subject's awareness of a certain property status. This sense, the article assesses this status from the standpoint of the scientific concept of the visibility of law. According to this theory, prescription is also considered as a consequence of the appearance of law, however, because it arises and lasts against the will of the parties and despite their awareness of this fact. Therefore, bona fide continuous and open possession of property as one's own, during the acquisition period, was most significantly associated with the appearance of property. Therefore, the concept of good faith, in the sense of personal perception of real values, is closely related to the principle of protection of the appearance of law, as it is aimed at understanding it by third parties. The paper notes certain differences in the application of the theory of the appearance of the right in the acquisition of property by a bona fide purchaser from an unauthorized alienator and the acquisitive prescription. It is emphasized that such a mechanism must be used in presuming the attitude to the thing as its own, by the holder of movable property. But there should be exceptions to the rule, in particular, if the owner has grounds for vindication of the thing.

• International Journal of Computer Science & Network Security
• /
• v.23 no.11
• /
• pp.142-148
• /
• 2023

The National Agency for Quality Assurance in Higher Education plays a crucial role in education in Ukraine, as an independent entity creates and ensures quality standards of higher education, which allow to properly implement the educational policy of the state, develop the economy and society as a whole.The purpose of the article: to reveal the crucial role of the National Agency for Quality Assurance in Higher Education to create quality management of higher education institutions, to show its mechanism as an independent entity that creates and ensures quality standards of higher education. and society as a whole. The mission of the National Agency for Quality Assurance in Higher Education is to become a catalyst for positive changes in higher education and the formation of a culture of its quality. The strategic goals of the National Agency are implemented in three main areas: the quality of educational services, recognition of the quality of scientific results, ensuring the systemic impact of the National Agency. The National Agency for Quality Assurance in Higher Education exercises various powers, which can be divided into: regulatory, analytical, accreditation, control, communication.The effectiveness of the work of the National Agency for Quality Assurance in Higher Education for 2020 has been proved. The results of a survey conducted by 183 higher education institutions of Ukraine conducted by the National Agency for Quality Assurance in Higher Education are shown. Emphasis was placed on the development of "Recommendations of the National Agency for Quality Assurance in Higher Education regarding the introduction of an internal quality assurance system." The international activity and international recognition of the National Agency for Quality Assurance in Higher Education are shown.

• Korean Journal of Radiology
• /
• v.25 no.7
• /
• pp.613-622
• /
• 2024

Objective: In Korea, radiology has been positioned towards the early adoption of artificial intelligence-based software as medical devices (AI-SaMDs); however, little is known about the current usage, implementation, and future needs of AI-SaMDs. We surveyed the current trends and expectations for AI-SaMDs among members of the Korean Society of Radiology (KSR). Materials and Methods: An anonymous and voluntary online survey was open to all KSR members between April 17 and May 15, 2023. The survey was focused on the experiences of using AI-SaMDs, patterns of usage, levels of satisfaction, and expectations regarding the use of AI-SaMDs, including the roles of the industry, government, and KSR regarding the clinical use of AI-SaMDs. Results: Among the 370 respondents (response rate: 7.7% [370/4792]; 340 board-certified radiologists; 210 from academic institutions), 60.3% (223/370) had experience using AI-SaMDs. The two most common use-case of AI-SaMDs among the respondents were lesion detection (82.1%, 183/223), lesion diagnosis/classification (55.2%, 123/223), with the target imaging modalities being plain radiography (62.3%, 139/223), CT (42.6%, 95/223), mammography (29.1%, 65/223), and MRI (28.7%, 64/223). Most users were satisfied with AI-SaMDs (67.6% [115/170, for improvement of patient management] to 85.1% [189/222, for performance]). Regarding the expansion of clinical applications, most respondents expressed a preference for AI-SaMDs to assist in detection/diagnosis (77.0%, 285/370) and to perform automated measurement/quantification (63.5%, 235/370). Most respondents indicated that future development of AI-SaMDs should focus on improving practice efficiency (81.9%, 303/370) and quality (71.4%, 264/370). Overall, 91.9% of the respondents (340/370) agreed that there is a need for education or guidelines driven by the KSR regarding the use of AI-SaMDs. Conclusion: The penetration rate of AI-SaMDs in clinical practice and the corresponding satisfaction levels were high among members of the KSR. Most AI-SaMDs have been used for lesion detection, diagnosis, and classification. Most respondents requested KSR-driven education or guidelines on the use of AI-SaMDs.

• Journal of Digital Convergence
• /
• v.12 no.4
• /
• pp.43-49
• /
• 2014

Paradigm shift in government services means the evolution to the service with active participation based on information technology. Opening public information proceeds to an extent that private sector participation can be a basis and driving force, and extends to a stage that free and practical use is possible for private sector. Therefore, the government is preparing for legal and institutional foundation for various fields. The government needs to build open network from user-oriented point of view rather than provider-centric point of view, improve communication, and change the way of working due to the fact that flexible and rapid business process is required. It is time to prepare development plans for public the functions of platform-type government that public sector can participate in the role of government, create new value, and give rise to innovation in order to change the functionality of the government and meet the new needs of citizen. This study tries to analyze platform-type government and to study efficient role allocation for sharing resources including informant and system between the government and the private sector, focusing on innovation of public information sharing.