• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.103-114
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• 2021

A conceptual model can be used to manage complexity in both the design and implementation phases of the system development life cycle. Such a model requires a firm grasp of the abstract principles on which a system is based, as well as an understanding of the high-level nature of the representation of entities and processes. In this context, models can have distinct architectural characteristics. This paper discusses model multiplicity (e.g., unified modeling language [UML]), model singularity (e.g., object-process methodology [OPM], thinging machine [TM]), and a heterogeneous model that involves multiplicity and singularity. The basic idea of model multiplicity is that it is not possible to present all views in a single representation, so a number of models are used, with each model representing a different view. The model singularity approach uses only a single unified model that assimilates its subsystems into one system. This paper is concerned with current approaches, especially in software engineering texts, where multimodal UML is introduced as the general-purpose modeling language (i.e., UML is modeling). In such a situation, we suggest raising the issue of multiplicity versus singularity in modeling. This would foster a basic appreciation of the UML advantages and difficulties that may be faced during modeling, especially in the educational setting. Furthermore, we advocate the claim that a multiplicity of views does not necessitate a multiplicity of models. The model singularity approach can represent multiple views (static, behavior) without resorting to a collection of multiple models with various notations. We present an example of such a model where the static representation is developed first. Then, the dynamic view and behavioral representations are built by incorporating a decomposition strategy interleaved with the notion of time.

• Journal of The Korean Association For Science Education
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• v.40 no.4
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• pp.375-383
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• 2020

In this study, we investigated the effects of the multiple representation-based learning strategies using augmented reality in terms of students' conceptual understanding, achievement, and enjoyment of science lessons. 136 8^th-grade students in a coed middle school were randomly assigned to the treatment and the control group. The students learned the concept of the particulate nature of matter related to the properties of substances for four class periods. The multiple representation-based learning strategies designed to facilitate the connecting and integrating representations provided from augmented reality were developed and administered to the students of the treatment group. Results of two-way ANCOVA revealed that the scores of a conceptions test and enjoyment of science lessons test of the treatment group were significantly higher than those of the control group, regardless of their prior science achievement. In a conceptions test, there was a significant difference in the concept of preservation of particles. However, the difference was not statistically significant in the concept of distribution and motion of particles. In terms of an achievement test, there was a significant interaction effect by their prior science achievement. The scores of low-level students were significantly improved, but the effects were not significant to high-level students. On the bases of the results, educational implications for effective teaching and learning using augmented reality are discussed.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.

• The Mathematical Education
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• v.56 no.3
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• pp.281-300
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• 2017

This study analyzed the difficulties of middle school students in computer simulation of the law of large numbers through eye movement analysis. Some students did not attend to the simulation results and could not make meaningful inferences. It is observed that students keep the existing concept even though they observe the simulation results which are inconsistent with the misconceptions they have. Since probabilistic intuition influence student's thinking very strongly, it is necessary to design a task that allows students to clearly recognize the difference between their erroneous intuitions and simulation results. In addition, we could confirm through eye movements analysis that students could not make meaningful observations and inferences if too much reasoning was needed even though the simulation included a rich context. It is necessary to use visual representations such as graphs to provide immediate feedback to students, to encourage students to attend to the results in a certain intentional way to discover the underlying mathematical structure rather than simply presenting experimental data. Some students focused their attention on the visually salient feature of the experimental results and have made incorrect conclusion. The simulation should be designed so that the patterns of the experimental results that the student must discover are not visually distorted and allow the students to perform a sufficient number of simulations. Based on the results of this study, we suggested that cumulative relative frequency graph showing multiple results at the same time, and the term 'generally tends to get closer' should be used in learning of the law of large numbers. In addition, it was confirmed that eye-tracking method is a useful tool for analyzing interaction in technology-based probabilistic learning.

• Journal of Korea Game Society
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• v.15 no.2
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• pp.63-72
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• 2015

Avatars are a visual representation of users in a virtual world such as an MMORPG. These avatars are often seen as representations or idealizations of the user's actual self. However, this view does not account for the prevalence of two behaviors: multiple avatars and 'gender swapping'. An exercise and questionnaire were created to study avatar customization practices across players outside the context of any particular game to understand better user motivations in creating their virtual selves. A preliminary trial of the exercise showed little correlation between age or gender and gender swapping. While those of non-traditional sexuality were more likely to gender swap, half of traditional sexuality also swapped. Finally, the personality trait, Openness to Experience, showed promising correlation with gender swapping.

• Structural Engineering and Mechanics
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• v.13 no.4
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• pp.369-385
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• 2002

Ductility capacity is comprehensively studied for steel moment-resisting frames. Local, story and global ductility are being considered. An appropriate measure of global ductility is suggested. A time domain nonlinear seismic response algorithm is used to evaluate several definitions of ductility. It is observed that for one-story structures, resembling a single degree of freedom (SDOF) system, all definitions of global ductility seem to give reasonable values. However, for complex structures it may give unreasonable values. It indicates that using SDOF systems to estimate the ductility capacity may be a very crude approximation. For multi degree of freedom (MDOF) systems some definitions may not be appropriate, even though they are used in the profession. Results also indicate that the structural global ductility of 4, commonly used for moment-resisting steel frames, cannot be justified based on this study. The ductility of MDOF structural systems and the corresponding equivalent SDOF systems is studied. The global ductility values are very different for the two representations. The ductility reduction factor $F_{\mu}$ is also estimated. For a given frame, the values of the $F_{\mu}$ parameter significantly vary from one earthquake to another, even though the maximum deformation in terms of the interstory displacement is roughly the same for all earthquakes. This is because the $F_{\mu}$ values depend on the amount of dissipated energy, which in turn depends on the plastic mechanism, formed in the frames as well as on the loading, unloading and reloading process at plastic hinges. Based on the results of this study, the Newmark and Hall procedure to relate the ductility reduction factor and the ductility parameter cannot be justified. The reason for this is that SDOF systems were used to model real frames in these studies. Higher mode effects were neglected and energy dissipation was not explicitly considered. In addition, it is not possible to observe the formation of a collapse mechanism in the equivalent SDOF systems. Therefore, the ductility parameter and the force reduction factor should be estimated by using the MDOF representation.

• The KIPS Transactions:PartD
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• v.8D no.5
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• pp.516-572
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• 2001

Geographic Information System(CIS) deal with data which can potentially be useful for a wide range of applications. The information needed by each application can be vary, specially in resolution, detail level, application view, and representation style, as defined in the modeling phase of the geographic database design. To be able to deal with such diverse needs, GIS must offer features that allow multiple representation for each geographic entity of phenomenon. This paper addresses on the problem of formal definition of the objects and their relationships on the geographical information systems. The geographical data is divided into two main classes : geo-objects and geo-fields, which describe discrete and continuous representations of spatial reality. I studied the attributes and the relationship roles over geo-object and nongeo-object. Therefore, this paper contributed on the efficient design of geographical class hierarchy schema by means of formalizing attribute-domains of classes.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.6
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• pp.1-17
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• 2001

Nowadays, spatiotemporal data models deal with objects which can be potentially useful for wide range applications in order to describe complex objects with spatial and/or temporal facilities. However, the information needed by each application usually varies, specially in the geographic information which depends on the kind of time oriented views, as defined in the modeling phase of the spatiotemporal geographic data design. To be able to deal with such diverse needs, geographic information systems must offer features that manipulate geometric, space-dependent(i.e, thematic), and spatial relationship positions with multiple time oriented views. This paper addresses problems of the formal definition of relationships among spatiotemporal objects and their properties on geographic information systems. The geographical data are divided in two main classes : geo-objects and geo-fields, which describe discrete and continuous representations of the spatial reality. I study semantics and syntax about the temporal changes of attributes and the relationship roles on geo-objects and non-geo-objects, This result will contribute on the design of object oriented spatiotemporal data model which is distinguishied from the recent geographic information system of the homogeneously anchored spatial objects

• Journal of Elementary Mathematics Education in Korea
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• v.23 no.1
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• pp.119-141
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• 2019

This study was carried out in order to draw some implications for teaching statistical graph in mathematics education with respect to practical statistics education for promoting students' statistical literacy. We analyze 133 graphs appearing in 99 elementary textbooks of other subjects (subjects except from mathematics) by subjects and types, and identify some cases of graphs addressed by other subjects. As a results, statistical graph was most addressed in social studies, and bar graphs, line graphs, tables, and circle graphs are most used in other subjects. Moreover, there are some issues related to contents-(1) the problem of curricular sequencing between mathematics and other subjects, (2) the level of addressing ratio graph, and (3) the use of wavy lines. In terms of forms, (1) the visual variation of graphical representations, (2) representation combining multiple graphs, and (2) graphs specialized for particular subjects are drawn as other issues. We suggest some implications to be considered when teaching the statistical graph in elementary mathematics classes.

• Communications of Mathematical Education
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• v.33 no.3
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• pp.377-394
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• 2019

The purpose of this study is to scrutinize the characteristics of a teacher's discursive competence on the basis of mathematical competencies. For this purpose, we observed all semester-long classes of a middle school teacher, who changed her own teaching methods for the last 20 years, collected video clips on them, and analyzed classroom discourse. Data analysis shows that in problem solving competency, she helped students focus on mathematically important components for problem understanding, and in reasoning competency, there was a discursive competence which articulated thinking processes for understanding the needs of mathematical justification. And in creativity and confluence competency, there was a discursive competence which developed class discussions by sharing peers' problem solving methods and encouraging students to apply alternative problem solving methods, whereas in communication competency, there was a discursive competency which explored mathematical relationships through the need for multiple mathematical representations and discussions about their differences. These results can provide concrete directions to developing curricula for future teacher education by suggesting ideas about how to combine practices with PCK needed for mathematics teaching.