• Journal of The Korean Association For Science Education
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• v.33 no.4
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• pp.863-879
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• 2013

The purpose of this study was to analyze task commitment types of science learning in high school students' biology classification. Thirty students were selected as the representatives of five task commitment types according to task commitment type inventory scores. They participated in think-aloud biology classification task. To analyze the procedural characteristics of task commitment, a coding scheme and think-aloud task were developed. Characteristics of respective task commitment types were identified from the result of the think-aloud protocol coding analysis. They are TGC(task goal commitment) type, LGC(low goal commitment) type, CC(conditional commitment) type, SC(suspended commitment) type, and DC(delayed commitment) type. Findings gained from this study are expected to serve as the foundation of task commitment enhancement strategies and as the information on the characteristics of each task commitment type. Also, future studies are required to investigate the commitment-related properties not only in biology classification but also in other science learning situations.

• Journal of Science Education
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• v.37 no.1
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• pp.170-185
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• 2013

The purpose of this study is to investigate a cognitive model of task commitment on biology classification inquiry. To achieve this goal, first, this study analyzed several literatures on task commitment in biology inquiry, and invented the tentative model of the task commitment. To investigate a tentative model invented, 2 main tasks were developed. These tasks were administered to 8 high-school students, first grade. Raw protocols were collected by thinking aloud method and a retrospective interview method. Collected protocols were converted to segmented protocols and coded by analyzing frame based invented model. The codes were analyzed. As a result, some problems were discovered, tentative model were revised. New analyzing frame based on Improved model were composed, and raw protocols were re-analyzed. Finally, a cognitive model of task commitment on biology classification inquiry was investigated. The investigated cognitive model of task commitment on biology classification inquiry was constructed 3 steps, 'Task commitment Induction', 'Task commitment Reinforcement', 'Task commitment Maintenance'. And each steps were consisted of several sub-factor. And commitment component were changed in each steps. Through this results, base information for strategy that improvement task commitment on biology classification inquiry is provided. Furthermore, the cognitive model of task commitment on biology classification inquiry will assist on evaluation and feedback by stage on task commitment.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.10a
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• pp.361-362
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• 2009

• Journal of Korean Society of Archives and Records Management
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• v.18 no.1
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• pp.155-175
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• 2018

The purpose of this study is to establish a classification system for the records of the Association for the Bereaved Families of the Hampyeong Massacre Victims. The content of the records is accordingly implemented through a functional source principle, and a classification table is presented in such a way that it reflects the characteristics by type and by production period so that the records can be used effectively. DIRKS, a methodology for the development of the functional classification system, is used to conduct a functional analysis of Hampyeong massacre victims' families to derive a task classification table that leads to task function-work activity-handling actions. The category is determined by taking into consideration the type and nature of the time of the production of the records of the Hampyeong massacre victims' families. The records are mapped according to the function classification system, which corresponds to the task classification table, and the multicategory system that drafts the type and period, which is used to classify the functions. The medical institution introduces a system for classifying records into task subjects, task activities, handling actions, types, and period.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.232-234
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• 2002

EEG-based brain computer interface (BCI) provides a new communication channel between human brain and computer. The classification of EEG data is an important task in EEG-based BCI. In this paper we present methods which jointly employ principal component analysis (PCA) and linear dynamical system (LDS) modeling for the task of EEG classification. Experimental study for the classification of EEG data during imagination of a left or right hand movement confirms the validity of our proposed methods.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.671-674
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• 2004

We propose accurate classification method of EEG signals during mental tasks. In the experimental task, the tasks of subjects show 3 major measurements; there are mathematical tasks, color decision tasks, and Chinese phrase tasks. The classifier implemented for this work is a feed-forward neural network that trained with the error back-propagation algorithm. The new BCI system is proposed by using neural network. In this system, tr e architecture of the neural network is composed of three layers with a feed-forward network, which implements the error back propagation-learning algorithm. By applying this algorithm to 4 subjects, we achieved $95{\%}$ classification rates. The results for BCI mathematical task experiments show performance better than those of the Chinese phrase tasks. The selection time of each task depends on the mental task of subjects. We expect that the proposed detection method can be a basic technology for brain-computer interface by combining with left/right hand movement or yes/no discrimination methods.

• Journal of Multimedia Information System
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• v.3 no.3
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• pp.53-61
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• 2016

This research proposes the index optimization as a classification task and application of the graph based KNN. We need the index optimization as an important task for maximizing the information retrieval performance. And we try to solve the problems in encoding words into numerical vectors, such as huge dimensionality and sparse distribution, by encoding them into graphs as the alternative representations to numerical vectors. In this research, the index optimization is viewed as a classification task, the similarity measure between graphs is defined, and the KNN is modified into the graph based version based on the similarity measure, and it is applied to the index optimization task. As the benefits from this research, by modifying the KNN so, we expect the improvement of classification performance, more graphical representations of words which is inherent in graphs, the ability to trace more easily results from classifying words. In this research, we will validate empirically the proposed version in optimizing index on the two text collections: NewsPage.com and 20NewsGroups.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.45-55
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• 2022

Recently, large-scale language models (LPLM) have been shown state-of-the-art performances in various tasks of natural language processing including intent classification. However, fine-tuning LPLM requires much computational cost for training and inference which is not appropriate for dialog system. In this paper, we propose compressed intent classification model for multi-agent in low-resource like CPU. Our method consists of two stages. First, we trained sentence encoder from LPLM then compressed it through knowledge distillation. Second, we trained agent-specific adapter for intent classification. The results of three intent classification datasets show that our method achieved 98% of the accuracy of LPLM with only 21% size of it.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.108-117
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• 2024

The selection and recommendation of a suitable job applicant from the pool of thousands of applications are often daunting jobs for an employer. The recommendation and selection process significantly increases the workload of the concerned department of an employer. Thus, Resume Classification System using the Natural Language Processing (NLP) and Machine Learning (ML) techniques could automate this tedious process and ease the job of an employer. Moreover, the automation of this process can significantly expedite and transparent the applicants' selection process with mere human involvement. Nevertheless, various Machine Learning approaches have been proposed to develop Resume Classification Systems. However, this study presents an automated NLP and ML-based system that classifies the Resumes according to job categories with performance guarantees. This study employs various ML algorithms and NLP techniques to measure the accuracy of Resume Classification Systems and proposes a solution with better accuracy and reliability in different settings. To demonstrate the significance of NLP & ML techniques for processing & classification of Resumes, the extracted features were tested on nine machine learning models Support Vector Machine - SVM (Linear, SGD, SVC & NuSVC), Naïve Bayes (Bernoulli, Multinomial & Gaussian), K-Nearest Neighbor (KNN) and Logistic Regression (LR). The Term-Frequency Inverse Document (TF-IDF) feature representation scheme proven suitable for Resume Classification Task. The developed models were evaluated using F-Score_M, Recall_M, Precission_M, and overall Accuracy. The experimental results indicate that using the One-Vs-Rest-Classification strategy for this multi-class Resume Classification task, the SVM class of Machine Learning algorithms performed better on the study dataset with over 96% overall accuracy. The promising results suggest that NLP & ML techniques employed in this study could be used for the Resume Classification task.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.830-841
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• 2023

Tokenization is the process of segmenting the input text into smaller units of text, and it is a preprocessing task that is mainly performed to improve the efficiency of the machine learning process. Various tokenization methods have been proposed for application in the field of natural language processing, but studies have primarily focused on efficiently segmenting text. Few studies have been conducted on the Korean language to explore what tokenization methods are suitable for document classification task. In this paper, an exploratory study was performed to find the most suitable tokenization method to improve the performance of a representative spatio-temporal document classifier in Korean. For the experiment, a convolutional neural network model was used, and for the final performance comparison, tasks were selected for document classification where performance largely depends on the tokenization method. As a tokenization method for comparative experiments, commonly used Jamo, Character, and Word units were adopted. As a result of the experiment, it was confirmed that the tokenization of word units showed excellent performance in the case of representative spatio-temporal document classification task where the semantic embedding ability of the token itself is important.