• Title/Summary/Keyword: Multi-level Learning

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A STUDY OF SPATIAL ABILITY AND WINDOW PRESENTATION STYLES IN WEB-BASED INSTRUCTION (웹 기반 학습에 있어서 공간 지각력과 정보제공 창의 형태 간의 관계 분석)

  • Im, Yeon-Wook
    • Journal of The Korean Association of Information Education
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    • v.9 no.4
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    • pp.649-659
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    • 2005
  • A window presentation style, either tiled window or single page design, determines the spatial arrangement of information in a modern computer-based instructional design. This study investigates the interaction between spatial ability and window presentation style in terms of student's achievement of cognitive knowledge through Web-based instruction. Seventy-one students from the Falk School in Pennsylvania were pre-tested to determine their level of spatial ability, then randomly divided into two treatment groups in order to study a Web-based instructional unit on flowering plants. The Web-based instructional package was organized with either tiled window presentation or single page presentation. A posttest measured participants'acquisition of the instructional content. Posttest and spatial ability test scores were analyzed using multi-variate linear regression for the full sample (n=71) and three sub-samples: (a) 4th and 5th grade students only, (b) female students only, and (c) 4th and 5th grade female students only. The goals of the data analysis included the examination of (i) the correlation between spatial ability and posttest scores; (ii) the correlation between window presentation style and posttest score; and (iii) the interaction between spatial ability (aptitude) and presentation style (treatment).The data from all four sample groups showed a significant relationship between spatial ability and achievement of cognitive knowledge at the 1% level of significance. The aptitude-treatment interaction between spatial ability and style of window presentation was not significant in the full sample, but was significant in the sub-samples either at the 10% or 5% level. In neither the full sample nor any sub-sample data did window presentation style have an impact on average posttest score. In all analyses, the higher the level of spatial ability, the higher the posttest score. The sub-samples revealed that students with low spatial ability performed better with the tiled window presentation, while those with high spatial ability did better with the single page presentation. Neither window presentation style was shown to better foster learning by children of all levels of spatial ability.

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Engineering basic competencies level and educatkonal needs analysis of engineering college graduates (공대 졸업생들의 공학기초능력 수준과 교육 요구 분석)

  • Hahm, Seung-Yeon
    • 대한공업교육학회지
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    • v.34 no.1
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    • pp.196-209
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    • 2009
  • The purpose of this study was to analyze engineering basic competencies about the time completed a course and educational needs of engineering college graduates. Survey method using questionnaire was the major research method of this study. A survey of 807 engineering college graduates was carried out. Questionnaire were made of the level of engineering basic competencies of engineering college graduates, its priorities of actual vocation and level of engineering basic competencies of major. Major results of the study, some recommendations for future researches were made as follows: The level of engineering basic competencies of engineering college graduates was 3.3 average(5 full marks). Engineering basic competencies that educational needs were high respectively, were an ability to communicate effectively, an ability to design a system, component, or process to meet desired needs, an ability to function on multi-disciplinary teams, an ability to understand global culture and cooperate internationally, an ability to design and conduct experiment as well as to analyze and interpret data, and an ability to engage in life-long learning.

사회복지사 개인간 갈등 형성요인에 관한 연구

  • Kim, Gyo-Jeong
    • 한국사회복지학회:학술대회논문집
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    • 2004.10a
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    • pp.223-250
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    • 2004
  • In this paper, the proposing a fundamental data for a systematic and effective organizational management by examining the extent of the levels of conflict perceiving among individuals of social welfare workers in the Busan community social welfare center, a social welfare organizations, and investigating the related influencing factors are the purpose of the study, The summary of this study is as follows. At first, it is dysfunctional when the level of conflict is either exceedingly high or low in the interrelational conflict aspect. However, the social welfare workers' working in the community social welfare center, the extent of individual conflict is relatively low as 2.37 out of 5 points. Secondly, the multi-regression analysis is operated for controlling the population sociological factors and measuring the effectiveness of the conflict factors to the level of conflict. The influencing factors affects the result in the order of greater extent are communication factor, leader's flexibility of leadership factor, spontaneity among personality, confidentiality. As a result, Not like the case that in the technological bureaucratic organizations such as the enterprises or industrial organizations, in the human service organizations including social welfare organizations, these technological bureaucratic paradigm does not applying directly since the material of organization is composed of humans who are given moral values. Therefore, this paradigm should be put as a presupposition of the conflict management strategy. And, the communication among colleagues, learning a reasonable problem solution method, or the chances of education or training for establishing a sound human relationship should be prepared in order to lower the level of the conflict among individuals in the community social welfare department. Furthermore, professional education programs are needed for not only supporting an effective supervision and consultation(consultation with superiors and colleagues), but also, working with confidentiality and pride as a professional.

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The Impact of Human Resource Innovativeness, Learning Orientation, and Their Interaction on Innovation Effect and Business Performance : Comparison of Small and Medium-Sized vs. Large-Sized Companies (인적자원의 혁신성, 학습지향성, 이들의 상호작용이 혁신효과 및 사업성과에 미치는 영향 : 중소기업과 대기업의 비교연구)

  • Yoh, Eunah
    • Korean small business review
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    • v.31 no.2
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    • pp.19-37
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    • 2009
  • The purpose of this research is to explore differences between small and medium-sized companies and large-sized companies in the impact of human resource innovativeness(HRI), learning orientation(LO), and HRI-LO interaction on innovation effect and business performance. Although learning orientation has long been considered as a key factor influencing good performance of a business, little research was devoted to exploring the effect of HRI-LO interaction on innovation effect and business performance. In this study, it is investigated whether there is a synergy effect between innovative human workforce and learning orientation corporate culture, in addition to each by itself, to generate good business performance as well as a success of new innovations in the market. Research hypotheses were as follows, including H1) human resource innovativeness(HRI), learning orientation(LO), and interactions of HRI and LO(HRI-LO interaction) positively affect innovation effect, H2) there is a difference of the effect of HRI, LO, and HRI-LO interaction on innovation effect between large-sized and small-sized companies, H3) HRI, LO, HRI-LO interaction, innovation effect positively affect business performance, and H4) there is a difference of the effect of HRI, LO, HRI-LO interaction, and innovation effect on business performance between large-sized and small-sized companies. Data were obtained from 479 practitioners through a web survey since the web survey is an efficient method to collect a national data at a variety of fields. A single respondent from a company was allowed to participate in the study after checking whether they have more than 5-year work experiences in the company. To check whether a common source bias is existed in the sample, additional data from a convenient sample of 97 companies were gathered through the traditional survey method, and were used to confirm correlations between research variables of the original sample and the additional sample. Data were divided into two groups according to company size, such as 352 small and medium-sized companies with less than 300 employees and 127 large-sized companies with 300 or more employees. Data were analyzed through t-test and regression analyses. HRI which is the innovativeness of human resources in the company was measured with 9 items assessing the innovativenss of practitioners in staff, manager, and executive-level positions. LO is the company's effort to encourage employees' development, sharing, and utilizing of knowledge through consistent learning. LO was measured by 18 items assessing commitment to learning, vision sharing, and open-mindedness. Innovation effect which assesses a success of new products/services in the market, was measured with 3 items. Business performance was measured by respondents' evaluations on profitability, sales increase, market share, and general business performance, compared to other companies in the same field. All items were measured by using 6-point Likert scales. Means of multiple items measuring a construct were used as variables based on acceptable reliability and validity. To reduce multi-collinearity problems generated on the regression analysis of interaction terms, centered data were used for HRI, LO, and Innovation effect on regression analyses. In group comparison, large-sized companies were superior on annual sales, annual net profit, the number of new products/services in the last 3 years, the number of new processes advanced in the last 3 years, and the number of R&D personnel, compared to small and medium-sized companies. Also, large-sized companies indicated a higher level of HRI, LO, HRI-LO interaction, innovation effect and business performance than did small and medium-sized companies. The results indicate that large-sized companies tend to have more innovative human resources and invest more on learning orientation than did small-sized companies, therefore, large-sized companies tend to have more success of a new product/service in the market, generating better business performance. In order to test research hypotheses, a series of multiple-regression analysis was conducted. In the regression analysis examining the impact on innovation effect, important results were generated as : 1) HRI, LO, and HRI-LO affected innovation effect, and 2) company size indicated a moderating effect. Based on the result, the impact of HRI on innovation effect would be greater in small and medium-sized companies than in large-sized companies whereas the impact of LO on innovation effect would be greater in large-sized companies than in small and medium-sized companies. In other words, innovative workforce would be more important in making new products/services that would be successful in the market for small and medium-sized companies than for large-sized companies. Otherwise, learning orientation culture would be more effective in making successful products/services for large-sized companies than for small and medium-sized companies. Based on these results, research hypotheses 1 and 2 were supported. In the analysis of a regression examining the impact on business performance, important results were generated as : 1) innovation effect, LO, and HRI-LO affected business performance, 2) HRI by itself did not have a direct effect on business performance regardless of company size, and 3) company size indicated a moderating effect. Specifically, an effect of the HRI-LO interaction on business performance was stronger in large-sized companies than in small and medium-sized companies. It means that the synergy effect of innovative human resources and learning orientation culture tends to be stronger as company is larger. Referring to these result, research hypothesis 3 was partially supported whereas hypothesis 4 was supported. Based on research results, implications for companies were generated. Regardless of company size, companies need to develop the learning orientation corporate culture as well as human resources' innovativeness together in order to achieve successful development of innovative products and services as well as to improve sales and profits. However, the effectiveness of the HRI-LO interaction would be varied by company size. Specifically, the synergy effect of HRI-LO was stronger to make a success of new products/services in small and medium-sized companies than in large-sized companies. However, the synergy effect of HRI-LO was more effective to increase business performance of large-sized companies than that of small and medium-sized companies. In the case of small and medium-sized companies, business performance was achieved more through the success of new products/services than much directly affected by HRI-LO. The most meaningful result of this study is that the effect of HRI-LO interaction on innovation effect and business performance was confirmed. It was often ignored in the previous research. Also, it was found that the innovativeness of human workforce would not directly influence in generating good business performance, however, innovative human resources would indirectly affect making good business performance by contributing to achieving the development of new products/services that would be successful in the market. These findings would provide valuable managerial implications specifically in regard to the development of corporate culture and education program of small and medium-sized as well as large-sized companies in a variety of fields.

Design, Development and Testing of the Modular Unmanned Surface Vehicle Platform for Marine Waste Detection

  • Vasilj, Josip;Stancic, Ivo;Grujic, Tamara;Music, Josip
    • Journal of Multimedia Information System
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    • v.4 no.4
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    • pp.195-204
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    • 2017
  • Mobile robots are used for years as a valuable research and educational tool in form of available open-platform designs and Do-It-Yourself kits. Rapid development and costs reduction of Unmanned Air Vehicles (UAV) and ground based mobile robots in recent years allowed researchers to utilize them as an affordable research platform. Despite of recent developments in the area of ground and airborne robotics, only few examples of Unmanned Surface Vehicle (USV) platforms targeted for research purposes can be found. Aim of this paper is to present the development of open-design USV drone with integrated multi-level control hardware architecture. Proposed catamaran - type water surface drone enables direct control over wireless radio link, separate development of algorithms for optimal propulsion control, navigation and communication with the ground-based control station. Whole design is highly modular, where each component can be replaced or modified according to desired task, payload or environmental conditions. Developed USV is planned to be utilized as a part of the system for detection and identification of marine and lake waste. Cameras mounted to the USV would record sea or lake surfaces, and recorded video sequences and images would be processed by state-of-the-art computer vision and machine learning algorithms in order to identify and classify marine and lake waste.

Recognition of License Plates Using a Hybrid Statistical Feature Model and Neural Networks (하이브리드 통계적 특징 모델과 신경망을 이용한 자동차 번호판 인식)

  • Lew, Sheen;Jeong, Byeong-Jun;Kang, Hyun-Chul
    • Journal of KIISE:Software and Applications
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    • v.36 no.12
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    • pp.1016-1023
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    • 2009
  • A license plate recognition system consists of image processing in which characters and features are extracted, and pattern recognition in which extracted characters are classified. Feature extraction plays an important role in not only the level of data reduction but also performance of recognition. Thus, in this paper, we focused on the recognition of numeral characters especially on the feature extraction of numeral characters which has much effect in the result of plate recognition. We suggest a hybrid statistical feature model which assures the best dispersion of input data by reassignment of clustering property of input data. And we verify the effectiveness of suggested model using multi-layer perceptron and learning vector quantization neural networks. The results show that the proposed feature extraction method preserves the information of a license plate well and also is robust and effective for even noisy and external environment.

A Computational Intelligence Based Online Data Imputation Method: An Application For Banking

  • Nishanth, Kancherla Jonah;Ravi, Vadlamani
    • Journal of Information Processing Systems
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    • v.9 no.4
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    • pp.633-650
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    • 2013
  • All the imputation techniques proposed so far in literature for data imputation are offline techniques as they require a number of iterations to learn the characteristics of data during training and they also consume a lot of computational time. Hence, these techniques are not suitable for applications that require the imputation to be performed on demand and near real-time. The paper proposes a computational intelligence based architecture for online data imputation and extended versions of an existing offline data imputation method as well. The proposed online imputation technique has 2 stages. In stage 1, Evolving Clustering Method (ECM) is used to replace the missing values with cluster centers, as part of the local learning strategy. Stage 2 refines the resultant approximate values using a General Regression Neural Network (GRNN) as part of the global approximation strategy. We also propose extended versions of an existing offline imputation technique. The offline imputation techniques employ K-Means or K-Medoids and Multi Layer Perceptron (MLP)or GRNN in Stage-1and Stage-2respectively. Several experiments were conducted on 8benchmark datasets and 4 bank related datasets to assess the effectiveness of the proposed online and offline imputation techniques. In terms of Mean Absolute Percentage Error (MAPE), the results indicate that the difference between the proposed best offline imputation method viz., K-Medoids+GRNN and the proposed online imputation method viz., ECM+GRNN is statistically insignificant at a 1% level of significance. Consequently, the proposed online technique, being less expensive and faster, can be employed for imputation instead of the existing and proposed offline imputation techniques. This is the significant outcome of the study. Furthermore, GRNN in stage-2 uniformly reduced MAPE values in both offline and online imputation methods on all datasets.

The Convergent Influence of MultiCultural Acceptability, Empathy and Global Citizenship in Nursing Students (간호대학생의 다문화수용성, 공감능력이 세계시민의식에 미치는 융합적 영향)

  • Ko, Jin-Hee;Kang, Myung-Ju;Kim, Hye-Jin
    • Journal of Convergence for Information Technology
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    • v.9 no.9
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    • pp.108-116
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    • 2019
  • This study is a descriptive survey study intended to analyze the relationship between multicultural acceptability, empathy, and global citizenship of nursing students and identify the factors influencing global citizenship. The data was collected targeting 187 nursing students at universities located in the cities of B, U, and S. The collected data was analyzed using t-test, ANOVA, Pearson correlation coefficients, and stepwise multiple regression analysis. The results were as follows: global citizenship of nursing students was positively correlated with multicultural acceptability and empathy. Factors influencing the level of global citizenship were empathy, multicultural acceptability, which together explained 40.0% of the total variance in global citizenship. Consequently, this study raises the need for the development and application of steady practical programs and various learning methods for the enhancement of global citizenship, which is required in the fields of nursing education where global competence is considered to be important.

Improvement of Attack Traffic Classification Performance of Intrusion Detection Model Using the Characteristics of Softmax Function (소프트맥스 함수 특성을 활용한 침입탐지 모델의 공격 트래픽 분류성능 향상 방안)

  • Kim, Young-won;Lee, Soo-jin
    • Convergence Security Journal
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    • v.20 no.4
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    • pp.81-90
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    • 2020
  • In the real world, new types of attacks or variants are constantly emerging, but attack traffic classification models developed through artificial neural networks and supervised learning do not properly detect new types of attacks that have not been trained. Most of the previous studies overlooked this problem and focused only on improving the structure of their artificial neural networks. As a result, a number of new attacks were frequently classified as normal traffic, and attack traffic classification performance was severly degraded. On the other hand, the softmax function, which outputs the probability that each class is correctly classified in the multi-class classification as a result, also has a significant impact on the classification performance because it fails to calculate the softmax score properly for a new type of attack traffic that has not been trained. In this paper, based on this characteristic of softmax function, we propose an efficient method to improve the classification performance against new types of attacks by classifying traffic with a probability below a certain level as attacks, and demonstrate the efficiency of our approach through experiments.

Segmentation of Mammography Breast Images using Automatic Segmen Adversarial Network with Unet Neural Networks

  • Suriya Priyadharsini.M;J.G.R Sathiaseelan
    • International Journal of Computer Science & Network Security
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    • v.23 no.12
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    • pp.151-160
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    • 2023
  • Breast cancer is the most dangerous and deadly form of cancer. Initial detection of breast cancer can significantly improve treatment effectiveness. The second most common cancer among Indian women in rural areas. Early detection of symptoms and signs is the most important technique to effectively treat breast cancer, as it enhances the odds of receiving an earlier, more specialist care. As a result, it has the possible to significantly improve survival odds by delaying or entirely eliminating cancer. Mammography is a high-resolution radiography technique that is an important factor in avoiding and diagnosing cancer at an early stage. Automatic segmentation of the breast part using Mammography pictures can help reduce the area available for cancer search while also saving time and effort compared to manual segmentation. Autoencoder-like convolutional and deconvolutional neural networks (CN-DCNN) were utilised in previous studies to automatically segment the breast area in Mammography pictures. We present Automatic SegmenAN, a unique end-to-end adversarial neural network for the job of medical image segmentation, in this paper. Because image segmentation necessitates extensive, pixel-level labelling, a standard GAN's discriminator's single scalar real/fake output may be inefficient in providing steady and appropriate gradient feedback to the networks. Instead of utilising a fully convolutional neural network as the segmentor, we suggested a new adversarial critic network with a multi-scale L1 loss function to force the critic and segmentor to learn both global and local attributes that collect long- and short-range spatial relations among pixels. We demonstrate that an Automatic SegmenAN perspective is more up to date and reliable for segmentation tasks than the state-of-the-art U-net segmentation technique.