• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.153-164
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• 2021

The increasing importance of such fields as embedded systems, pervasive computing, and hybrid systems control is increasing attention to the time-dependent aspects of system modeling. In this paper, we focus on modeling conceptual time. Conceptual time is time represented in conceptual modeling, where the notion of time does not always play a major role. Time modeling in computing is far from exhibiting a unified and comprehensive framework, and is often handled in an ad hoc manner. This paper contributes to the establishment of a broader understanding of time in conceptual modeling based on a software and system engineering model denoted thinging machine (TM). TM modeling is founded on a one-category ontology called a thimac (thing/machine) that is used to elaborate the design and analysis of ontological presumptions. The issue under study is a sample of abstract modeling domains as exemplified by time. The goal is to provide better understanding of the TM model by supplementing it with a conceptualization of time aspects. The results reveal new characteristics of time and related notions such as space, events, and system behavior.

• Korean Journal of Cognitive Science
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• v.11 no.3_4
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• pp.57-72
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• 2000

A Korean standardized set of pictures has been called for as more and more studies utilized picture stimuli among memory and representation research. This article presents a Korean standardized set of pictures for studies probing the cognitive mechanisms that underlie picture and word processing or studies that simply utilize pictures stimuli. This norm provides 213 pictures, data on several variables such as name agreement, appropriateness of pictures. and familiarity. Previous data on such variables as frequency. category. and frequency within a category have been integrated 1 into this norm. Limitation, usage. and application of this set are discussed in terms of 1 implicit and explicit memory, and those variables mentioned previously.

• Journal of Information Technology Applications and Management
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• v.28 no.1
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• pp.13-31
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• 2021

The purpose of this study is to find the factors that influence big data characteristics on decision satisfaction and utilization behavior, analyze the extent of their influence, and derive differences from existing studies. To summarize the results of this study, First, the study found that among the three categories that classify the characteristics of big data, qualitative attributes such as representation, purpose, interpretability, and innovation in the value innovation category greatly enhance decision confidence and decision effectiveness of decision makers who make decisions using big data. Second, the study found that, among the three categories that classify the characteristics of big data, the individuality properties belonging to the social impact category improve decision confidence and decision effectiveness of decision makers who use big data to make decisions. However, collectivity and bias characteristics have been shown to increase decision confidence, but not the effectiveness of decision making. Third, the study found that among the three categories that classify the characteristics of big data, the attributes of inclusiveness, realism, etc. in the integrity category greatly improve decision confidence and decision effectiveness of decision makers who make decisions using big data. Fourth, it was analyzed that using big data in organizational decision making has a positive impact on the behavior of big data users when the decision-making confidence and finally, decision-making effect of decision-makers increases.

• Journal of Korean Elementary Science Education
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• v.37 no.2
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• pp.219-231
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• 2018

This study surveyed the elementary school teachers' use of visual representations and their perceptions of the functions of visual representations in the teaching of electricity unit. A total of 110 elementary teachers who have experiences in teaching electricity unit responded to online survey. The result showed firstly that most of the teachers use visual representations in their teaching and it is mostly limited to those presented in textbooks or images that they can get easily from internet search. Secondly, elementary teachers thought that they have high ability in using visual representations and low ability in understanding students' visual presentation ability. Thirdly, visual representations are more often preferred to be used as teacher-centered ways than student-centered ways for motivating students and conceptual understanding. However, in case of scientific inquiry, both teacher-centered and student-centered ways were equally preferred. Lastly, the teachers' perceptions of the functions of visual representations were categorized into 'teaching-instrumental function', 'learning-instrumental function', 'communicative-instrumental function' and 8 subcategories were found. The most frequent function was the 'information delivery function' in the 'teaching-instrumental function' category. Implications for teacher education and further studies were discussed.

• School Mathematics
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• v.4 no.4
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• pp.643-655
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• 2002

In this paper we emphasize the introduction of ‘incommensurability’ on the teaching and learning the irrational number because we think of the origin of number as ‘ratio’. According to Greek classification of continuity as a ‘never ending’ divisibility, discrete number and continuous magnitude belong to another classes. That is, those components were dealt with respectively in category of arithmetic and that of geometry. But the comparison between magnitudes in terms of their ratios took the opportunity to relate ratios of magnitudes with numerical ratios. And at last Stevin coped with discrete and continuous quantity at the same time, using his instrumental decimal notation. We pay attention to the fact that Stevin constructed his number conception in reflecting the practice of measurement : He substituted ‘subdivision of units’ for ‘divisibility of quantities’. Number was the result of such a reflective abstraction. In other words, number was invented by regulation of measurement. Therefore, we suggest decimal representation from the point of measurement, considering the foregoing historical development of number. From the perspective that the conception of real number originated from measurement of ‘continuum’ and infinite decimals played a significant role in the ‘representation’ of measurement, decimal expression of real number should be introduced through contexts of measurement instead of being introduced as a result of algorithm.

• Current Optics and Photonics
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• v.5 no.3
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• pp.270-277
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• 2021

The adaptive sparse representation (ASR) can effectively combine the structure information of a sample dictionary and the sparsity of coding coefficients. This algorithm can effectively consider the correlation between training samples and convert between sparse representation-based classifier (SRC) and collaborative representation classification (CRC) under different training samples. Unlike SRC and CRC which use fixed norm constraints, ASR can adaptively adjust the constraints based on the correlation between different training samples, seeking a balance between l₁ and l₂ norm, greatly strengthening the robustness and adaptability of the classification algorithm. The correlation coefficients (CC) can better identify the pixels with strong correlation. Therefore, this article proposes a hyperspectral image classification method called correlation coefficients and adaptive sparse representation (CCASR), based on ASR and CC. This method is divided into three steps. In the first step, we determine the pixel to be measured and calculate the CC value between the pixel to be tested and various training samples. Then we represent the pixel using ASR and calculate the reconstruction error corresponding to each category. Finally, the target pixels are classified according to the reconstruction error and the CC value. In this article, a new hyperspectral image classification method is proposed by fusing CC and ASR. The method in this paper is verified through two sets of experimental data. In the hyperspectral image (Indian Pines), the overall accuracy of CCASR has reached 0.9596. In the hyperspectral images taken by HIS-300, the classification results show that the classification accuracy of the proposed method achieves 0.9354, which is better than other commonly used methods.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.38
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• pp.43-50
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• 1996

Evolution Programs, a form of Genetic Algorithms transformed from chromosome representation, are applied to the Timetabling of University which is one of the NP-hard problems. At the step of algorithms application, each class is established to be a specific category in feasible solution space. At. the same time, the exiting gene used in chromosome expression of Evolution Programs is modified to satisfy constraints effectively by transformation of gene which has multi-information. The new crossover method for fester operation in the Recombination attempted.. Roulette wheel selection and tournament selection are prepared.

• Journal of Information Science Theory and Practice
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• v.6 no.2
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• pp.46-61
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• 2018

The mobile social networking application Instagram is a well-known platform for sharing photos and videos. Since it is folksonomy-oriented, it provides the possibility for image indexing and knowledge representation through the assignment of hashtags to posted content. The purpose of this study is to analyze how Instagram users tag their pictures regarding different kinds of picture and hashtag categories. For such a content analysis, a distinction is made between Food, Pets, Selfies, Friends, Activity, Art, Fashion, Quotes (captioned photos), Landscape, and Architecture image categories as well as Content-relatedness (ofness, aboutness, and iconology), Emotiveness, Isness, Performativeness, Fakeness, "Insta"-Tags, and Sentences as hashtag categories. Altogether, 14,649 hashtags of 1,000 Instagram images were intellectually analyzed (100 pictures for each image category). Research questions are stated as follows: RQ1: Are there any differences in relative frequencies of hashtags in the picture categories? On average the number of hashtags per picture is 15. Lowest average values received the categories Selfie (average 10.9 tags per picture) and Friends (average 11.7 tags per picture); for highest, the categories Pet (average 18.6 tags), Fashion (average 17.6 tags), and Landscape (average 16.8 tags). RQ2: Given a picture category, what is the distribution of hashtag categories; and given a hashtag category, what is the distribution of picture categories? 60.20% of all hashtags were classified into the category Content-relatedness. Categories Emotiveness (about 4.38%) and Sentences (0.99%) were less often frequent. RQ3: Is there any association between image categories and hashtag categories? A statistically significant association between hashtag categories and image categories on Instagram exists, as a chi-square test of independence shows. This study enables a first broad overview on the tagging behavior of Instagram users and is not limited to a specific hashtag or picture motive, like previous studies.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.411-414
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• 2021

One of the elements of technology that has become extremely critical within the field of education today is Deep learning. It has been especially used in the area of natural language processing, with some word-representation vectors playing a critical role. However, some of the low-resource languages, such as Swahili, which is spoken in East and Central Africa, do not fall into this category. Natural Language Processing is a field of artificial intelligence where systems and computational algorithms are built that can automatically understand, analyze, manipulate, and potentially generate human language. After coming to discover that some African languages fail to have a proper representation within language processing, even going so far as to describe them as lower resource languages because of inadequate data for NLP, we decided to study the Swahili language. As it stands currently, language modeling using neural networks requires adequate data to guarantee quality word representation, which is important for natural language processing (NLP) tasks. Most African languages have no data for such processing. The main aim of this project is to recognize and focus on the classification of words in English, Swahili, and Korean with a particular emphasis on the low-resource Swahili language. Finally, we are going to create our own dataset and reprocess the data using Python Script, formulate the syllabic alphabet, and finally develop an English, Swahili, and Korean word analogy dataset.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5530-5546
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• 2016

Automatic analysis and understanding of human activities is a challenging task in computer vision, especially for the surveillance scenarios which typically contains crowds, complex motions and occlusions. To address these issues, a Bag-of-words representation of videos is developed by leveraging information including crowd positions, motion directions and velocities. We infer the crowd activity in a motion field using Category Constrained Correlated Topic Model (CC-CTM) with latent topics. We represent each video by a mixture of learned motion patterns, and predict the associated activity by training a SVM classifier. The experiment dataset we constructed are from Crowd_PETS09 bench dataset and UCF_Crowds dataset, including 2000 documents. Experimental results demonstrate that accuracy reaches 90%, and the proposed approach outperforms the state-of-the-arts by a large margin.