• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.94-100
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• 2010

Data Mining is mainly used for pattern extracting and information discovery from collected data. However previous methods is difficult to reflect changing patterns with time. In this paper, we introduce Streaming Decision Tree(SDT) analyzing data with continuity, large scale, and changed patterns. SDT defines continuity data as blocks and extracts rules using a Decision Tree's learning method. The extracted rules are combined considering time of occurrence, frequency, and contradiction. In experiment, we applied time series data and confirmed resonable result.

• Journal of Korea Society of Digital Industry and Information Management
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• v.12 no.4
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• pp.59-69
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• 2016

We proposed a specialized model of performance measurement to measure the training performance of the trainees in cyber practical training. Cyber security professionals are cultivating their expertise, skills, and competencies through cyber practical training in specialized education and training institutions. The our proposed process of trainee evaluation is consisted of an evaluation component discovery, evaluation item selection, evaluation index catalog, ratings and criteria decision, and calculation formula. The trainee evaluation is consisted of a formative evaluation during the training and an overall evaluation after finished training. Formative evaluation includes progress evaluation and participation evaluation, and overall evaluation includes practice evaluation and learning evaluation. The evaluation is weighted according to the importance of evaluation type. Because it is evaluated actual skills and abilities, competencies are assigned a high weight, while knowledge and attitudes are assigned a low weight. If cyber security trainees are evaluated by the proposed evaluation model, cyber security professionals can be cultivated by each skill and knowledge level and can be deployed by importance of security task.

• Journal of Communications and Networks
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• v.15 no.3
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• pp.301-311
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• 2013

This paper proposes a policy-based quality of service (QoS) management framework for adaptive routing decisions. We present an approach considering interior gateway protocol (IGP) for path discovery mechanisms and QoS-aware policies for configuring the network elements. The integration of the aforementioned modules into this policy-based network management (PBNM) system is demonstrated by conducting experiments in a real environment, the hellenic public administration network SYZEFXIS. These experiments combine different traffic conditioning mechanisms through event detectors, consider IP service level agreement mechanisms that interoperate with the PBNM system and analyze the enforcement of IGP and QoS policies. Finally, validation and measurement tools are used to prove the efficiency of this framework. It is shown that this architecture offers significantly increased performance and learning capabilities, while the PBNM system achieves adaptive QoS routing through automated configuration considering the avoidance of suboptimal routing issues or under-performance conditions of the network entities.

• Human Ecology Research
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• v.56 no.3
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• pp.301-315
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• 2018

This study explores the critical reflection process experienced by home economics preservice teachers during practicum. Data were collected in a critical analysis of class, practicum review, and journals written by sixteen preservice teachers. Text material were composed of 188 transcripts on A4 paper and 36 page of mini-notes. The collected data were analyzed by a thematic coding method in qualitative research and proceeded in the order of three steps of transference, coding, and subject discovery. The emerging themes were: 1) Observing class 2) Practicing class 3) Growth of class practice 4) Reflecting class. First, the observing class was an exploration process through the viewing of daily classes that involved the process of recognizing the classroom situation and various classroom contexts. Second, the practicing class was to strengthen the consideration of the class to form a relationship that could lead to learning in educational situations. Third, the growth of class practice was intended to recognize the orientation of the subject matter with pedagogical content knowledge. Four, the reflecting class was the process of experiencing practice with a continuous understanding of the class, class reflection, and changing the perspective from the current status. There is a part where critical reflection is difficult to be promoted deeply during 4 weeks; however, there was a possibility of a reflection practice that could promote achievement through the experience of a practicing class.

• Korea Information Processing Society Review
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• v.11 no.6
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• pp.56-75
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• 2004

In this article we address some system characteristics and challenging issues in developing Context-aware Middleware for Ubiquitous Computing. The functionalities of a Context-aware Middleware includes gathering context data from hardware/software sensors, reasoning and inferring high-level context data, and disseminating/delivering appropriate context data to interested applications/services. The Middleware should facilitate the query, aggregation, and discovery for the contexts, as well as facilities to specify their privacy policy. Following a formal context model using ontology would enable syntactic and semantic interoperability, and knowledge sharing between different domains. Moddleware should also provide different kinds of context classification mechanical as pluggable modules, including rules written in different types of logic (first order logic, description logic, temporal/spatial logic, fuzzy logic, etc.) as well as machine-learning mechanical (supervised and unsupervised classifiers). Different mechanisms have different power, expressiveness and decidability properties, and system developers can choose the appropriate mechanism that best meets the reasoning requirements of each context. And finally, to promote the context-trigger actions in application level, it is important to provide a uniform and platform-independent interface for applications to express their need for different context data without knowing how that data is acquired. The action could involve adapting to the new environment, notifying the user, communicating with another device to exchange information, or performing any other task.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.481-486
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• 2021

Machine learning algorithms adopt criterion function as a key component to measure the quality of their model derived from data. Cluster analysis also uses this function to rate the clustering result. All the criterion functions have in general certain types of favoritism in producing high quality clusters. These clusters are then described by attributes and their values. Category utility and partition utility play an important role in cluster analysis. These are fully analyzed in this research particularly in terms of how they are related to the favoritism in the final results. In this research, several data sets are selected and analyzed to show how different results are induced from these criterion functions.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.339-345
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• 2022

Outcome-based education (OBE) is a tried-and-true teaching technique based on a set of predetermined goals. Program Educational Objectives (PEOs), Program Outcomes (POs), and Course Outcomes (COs) are the components of OBE. At the end of each year, the Program Outcomes are evaluated, and faculty members can submit many recommended measures which dependent on the relationship between the program outcomes and its courses outcomes to improve the quality of program and hence the overall educational program. When a vast number of courses are considered, bad actions may be proposed, resulting in unwanted and incorrect decisions. In this paper, a recommender system, using collaborative filtering and association rules algorithms, is proposed for predicting the best relationship between the program outcomes and its courses in order to improve the attributes of the graduates. First, a parallel algorithm is used for Collaborative Filtering on Data Model, which is designed to increase the efficiency of processing big data. Then, a parallel similar learning outcomes discovery method based on matrix correlation is proposed by mining association rules. As a case study, the proposed recommender system is applied to the Computer Information Systems program, College of Computer Sciences and Information Technology, Al-Baha University, Saudi Arabia for helping Program Quality Administration improving the quality of program outcomes. The obtained results revealed that the suggested recommender system provides more actions for boosting Graduate Attributes quality.

• Journal of The Korean Association For Science Education
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• v.24 no.2
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• pp.357-374
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• 2004

This paper described the types and characteristics of educational programs in major natural history museums of the world. Data were collected from the websites, annual reports, and written materials of the Natural History Museum at London, Australian Museum at Sydney, Field Museum of Natural History at Chicago, Smithsonian Museum at Washington D.C, Royal Tyrrell Museum at Canada and American Museum of Natural History at New York. As the result of this study, we presented ten types of educational programs, which were moveable museums, workshops, lecture/courses, festival/events, discovery activities, scientific exploration/research projects, field trips, youth curators/internship, loan materials and camp/tours. We also described the examples equivalent to each program type. The characteristics of educational programs provided by museums as informal learning settings were analyzed in terms of their themes, participation levels, connection with exhibition, relation to curriculum, and learning activity levels. Information in this paper will assist science teachers, museum educators and curators: (1) to design and implement various types and contents of educational programs; (2) to use characteristics of educational programs to assess and develop them; (3) to make important contributions to science education that involves the introduction of various scientific aspects and collections to the public, and the use of programs for science learning and teaching coherent to existing curricula.

• Communications of Mathematical Education
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• v.29 no.3
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• pp.465-489
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• 2015

The purpose of this study is to suggest implications for the development and revision of future teaching materials for mathematically gifted students by using network text analysis of secondary mathematics materials. Subjects of the analysis were learning goals of 110 teaching materials in a gifted education center in science attached to a university from 2002 to 2014. In analysing the frequency of the texts that appeared in the learning goals, key words were selected. A co-occurrence matrix of the key words was established, and a basic information of network, centrality, centralization, component, and k-core were deducted. For the analysis, KrKwic, KrTitle, and NetMiner4.0 programs were used, respectively. The results of this study were as follows. First, there was a pivot of the network formed with core hubs including 'diversity', 'understanding' 'concept' 'method', 'application', 'connection' 'problem solving', 'basic', 'real life', and 'thinking ability' in the whole network from 2002 to 2014. In addition, knowledge aspects were well reflected in teaching materials based on the centralization analysis. Second, network text analysis based on the three periods of the Mater Plan for the promotion of gifted education was conducted. As a result, a network was built up with 'understanding', and there were strong ties among 'question', 'answer', and 'problem solving' regardless of the periods. On the contrary, the centrality analysis showed that 'communication', 'discovery', and 'proof' only appeared in the first, second, and third period of Master Plan, respectively. Therefore, the results of this study suggest that affective aspects and activities with high cognitive process should be accompanied, and learning goals' mannerism and ahistoricism be prevented in developing and revising teaching materials.

• Analytical Science and Technology
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• v.24 no.6
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• pp.533-543
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• 2011

The hERG (human ether-a-go-go related gene) ion channel is a main factor for cardiac repolarization, and the blockade of this channel could induce arrhythmia and sudden death. Therefore, potential hERG ion channel inhibitors are now a primary concern in the drug discovery process, and lots of efforts are focused on the minimizing the cardiotoxic side effect. In this study, $IC_{50}$ data of 202 organic compounds in HEK (human embryonic kidney) cell from literatures were used to develop predictive 2D-QSAR model. Multiple linear regression (MLR), Support Vector Machine (SVM), and artificial neural network (ANN) were utilized to predict inhibition concentration of hERG ion channel as machine learning methods. Population based-forward selection method with cross-validation procedure was combined with each learning method and used to select best subset descriptors for each learning algorithm. The best model was ANN model based on 14 descriptors ($R^2_{CV}$=0.617, RMSECV=0.762, MAECV=0.583) and the MLR model could describe the structural characteristics of inhibitors and interaction with hERG receptors. The validation of QSAR models was evaluated through the 5-fold cross-validation and Y-scrambling test.