• School Mathematics
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• v.4 no.3
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• pp.513-537
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• 2002

The aim of this study is to investigate the purpose of the performance assess-ment, to develop and to apply it's task. By reviewing previous study, I conclude that the performance assessment is sug-gested to evaluate mathematical thinking and attitude in the purpose of school mathematics. To develop the task to fit the purpose of the performance assessment, I refer to the middle and high level in Van den Heuvel's classification the task "Find the number in the star" and about the assess-ment of school mathematics. Then I apply the performance assessment task developed according to this level, analyse the responses of children to "Let's make the problem" and suggest it's assessment rubric and anchor papers for each level for illustrating the process of developing a rubric. Finally, considerations to improve the performance assessment are discussed.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.89-100
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• 2022

With the advancement in the Internet of things Technology (IoT) cloud computing, billions of physical devices have been interconnected for sharing and collecting data in different applications. Despite many advancements, some latency - specific application in the real world is not feasible due to existing constraints of IoT devices and distance between cloud and IoT devices. In order to address issues of latency sensitive applications, fog computing has been developed that involves the availability of computing and storage resources at the edge of the network near the IoT devices. However, fog computing suffers from many limitations such as heterogeneity, storage capabilities, processing capability, memory limitations etc. Therefore, it requires an adequate task scheduling method for utilizing computing resources optimally at the fog layer. This work presents a comprehensive review of different task scheduling methods in fog computing. It analyses different task scheduling methods developed for a fog computing environment in multiple dimensions and compares them to highlight the advantages and disadvantages of methods. Finally, it presents promising research directions for fellow researchers in the fog computing environment.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1417-1424
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• 2018

In order to improve the performance of image classifications using Convolutional Neural Networks (CNN), applying a category hierarchy to the classification can be a useful idea. However, the visual separation of object categories is very different according to the upper and lower category levels and highly uneven in image classifications. Therefore, it is doubtable whether the use of category hierarchies for classification is effective in CNN. In this paper, we have clarified whether the image classification using category hierarchies improves classification performance, and found at which level of hierarchy classification is more effective. For experiments we divided the image classification task according to the upper and lower category levels and assigned image data to each CNN model. We identified and compared the results of three classification models and analyzed them. Through the experiments, we could confirm that classification effectiveness was not improved by reduction of number of categories in a classification model. And we found that only with the re-training method in the last network layer, the performance of lower category classification was not improved although that of higher category classification was improved.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.449-452
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• 2023

The rapid development of the Internet has led to a significant increase in multimedia content in social networks. How to better analyze and improve video classification models has become an important task. Deep learning models have typical "black box" characteristics. The model requires explainable analysis. This article uses two classification models: ConvLSTM and VGG16+LSTM models. And combined with the explainable method of LRP, generate visualized explainable results. Finally, based on the experimental results, the accuracy of the classification model is: ConvLSTM: 75.94%, VGG16+LSTM: 92.50%. We conducted explainable analysis on the VGG16+LSTM model combined with the LRP method. We found VGG16+LSTM classification model tends to use the frames biased towards the latter half of the video and the last frame as the basis for classification.

• Journal of the East Asian Society of Dietary Life
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• v.5 no.1
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• pp.29-39
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• 1995

The purpose of this study was to evaluate job performance of dietitians. The survey also examined differences in job performance of dietitians by institutional classicification, types of task, levels of education and job experience. Data was collected form national samples. Results are summarized as follows. 1. By institutional classification, dietitians working in industry showed lowest score(3.0465${\pm}$.4151), which those working in hospital showed highest score(3.2883${\pm}$.4124) in job performance. 2. By types of task, the score of job performance is in order of hygience management(3.3933${\pm}$.4236), business management(3.3183${\pm}$.5435) and education management(2.3132${\pm}$.7551). 3. By educational level, dietitians who graduated universities scored higher than who graduated junior colleges in general. Specifically, the former had high scores in business management(3.4796${\pm}$.4692) and hygiene management, while the latter had high scores in hygiene management(3.3615${\pm}$.440) and business management, as in order. 4. By job experience, job performance increases after-3 year-experience and peaks in over-10 year-experience. 5. For reasons of negligence in specified taskes, 109 of respondents(22.7%) answered "don't know how to perform" and 108 of them(22.5%) answered "lack of human resources." Also, the lower in job experience the more answered "don't know how to perform" as a reason of negligence a their task(34.5% of below-2 year-experience and 24.2% of junior colleges answered to this reason).

• Phonetics and Speech Sciences
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• v.4 no.2
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• pp.95-104
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• 2012

Thyroidectomy patients may have vocal paralysis or paresis, resulting in a breathy voice. The aim of this study was to investigate the aerodynamic and acoustic characteristics of a breathy voice in thyroidectomy patients. Thirty-five subjects who have vocal paralysis after thyroidectomy participated in this study. According to perceptual judgements by three speech pathologists and one phonetic scholar, subjects were divided into two groups: breathy voice group (n = 21) and non-breathy voice group (n = 14). Aerodynamic analysis was conducted by three tasks (Voicing Efficiency, Maximum Sustained Phonation, Vital Capacity) and acoustic analysis was measured during Maximum Sustained Phonation task. The breathy voice group had significantly higher subglottal pressure and more pathological voice characteristics than the non breathy voice group. Showing 94.1% classification accuracy in result logistic regression of aerodynamic analysis, the predictor parameters for breathiness were maximum sound pressure level, sound pressure level range, phonation time of Maximum Sustained Phonation task and Pitch range, peak air pressure, and mean peak air pressure of Voicing Efficiency task. Classification accuracy of acoustic logistic regression was 88.6%, and five frequency perturbation parameters were shown as predictors. Vocal paralysis creates air turbulence at the glottis. It fluctuates frequency-related parameters and increases aspiration in high frequency areas. These changes determine perceptual breathiness.

• Journal of the Ergonomics Society of Korea
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• v.31 no.4
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• pp.493-498
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• 2012

Objective: The purpose of this study is to suggest the hierarchical structure with three layers of input task, input interaction, and input device. Background: Understanding the input interaction is very helpful to design an interface design. Method: We made a model of three layered input structure based on empirical approach and applied to a gesture interaction in TV. Result: We categorized the input tasks into six elementary tasks which are select, position, orient, text, and quantify. The five interactions described in this paper could accomplish the full range of input interaction, although the criteria for classification were not consistent. We analyzed the Microsoft kinect with this structure. Conclusion: The input interactions of command, 4 way, cursor, touch, and intelligence are basic interaction structure to understanding input system. Application: It is expected the model can be used to design a new input interaction and user interface.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.207-212
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• 2021

Clustering Unlabeled Spatial-datasets to convert them to Labeled Spatial-datasets is a challenging task specially for geographical information systems. In this research study we investigated the NYC Taxi Limousine Commission dataset and discover that all of the spatial-temporal trajectory are unlabeled Spatial-datasets, which is in this case it is not suitable for any data mining tasks, such as classification and regression. Therefore, it is necessary to convert unlabeled Spatial-datasets into labeled Spatial-datasets. In this research study we are going to use the Clustering Technique to do this task for all the Trajectory datasets. A key difficulty for applying machine learning classification algorithms for many applications is that they require a lot of labeled datasets. Labeling a Big-data in many cases is a costly process. In this paper, we show the effectiveness of utilizing a Clustering Technique for labeling spatial data that leads to a high-accuracy classifier.

• Journal of English Language & Literature
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• v.57 no.6
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• pp.911-938
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• 2011

The present study examined the effect of two variables of task complexity, reasoning demand and time pressure, each from the resourcedirecting and resource-dispersing dimension in Robinson's (2001) framework of task classification. Reasoning demand was operationalized as the two types of texts to read and summarize, expository and argumentative. Time pressure was operationalized as the two modes of performance, oral and written. Six university students summarized the two types of text orally and twenty four students from the same school summarized them in the written form. Results from t test and ANCOVA showed that in the oral mode, reasoning demand tends to heighten the complexity of the language used in the summary in competition with accuracy but such an effect disappeared in the written mode. It was interpreted that the degree of time pressure is not the only difference between the oral and written modes but that the two modes may be fundamentally different cognitive tasks, and that Robinson's (2001) and Skehan's (1998) models were differentially supported by the oral mode of tasks but not by the written mode of the tasks.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.260-263
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• 2003

EEC is an electrical signal, which occurs during information processing in the brain. These EEG signals has been used clinically, but nowadays we are mainly studying Brain-Computer Interface(BCI) such as interfacing with a computer through the EEG controlling the machine through the EEG The ultimate purpose of BCI study is specifying the EEG at various mental states so as to control the computer and machine. A BCI has to perform two tasks, the parameter estimation task, which attemps to describe the properties of the EEG signal and the classification task, which separates the different EEC patterns based on the estimated parameters. First, we have to do parameter estimation of EEG to embody BCI system. It is important to improve performance of classifier, But, It is not easy to do parameter estimation by reason of EEG is sensitivity and undergo various influences. Therefore, this research should do parameter estimation and classification of the EEG to use various analysis algorithm.