Viewpoint Of Learning Research Articles

FUN PIANO LESSON FOR YOUNG BEGINNERS STUDENTS IN SURABAYA INDONESIA

Learning piano is not an easy thing, moreover for young beginner learners. The teacher of course not only giving materials to students but also considering the approach given to the young beginner students. The teacher is also needed to be creative, and always developing the ability in techniques and approach to children. This study tried to discuss how to make piano lessons fun. This research method uses grounded research with a focus on theoretical studies on piano learning through the views of piano experts, including Dalcroze, Suzuki, Schulwerk, and Kodaly. After that, the data is analyzed using content analysis. The results of the study showed that by exploring the viewpoint of Dalcroze, Suzuki, Schulwerk, and Kodaly piano learning, art learning must be set pleasantly by the teacher. The role of the teacher and student must also have a good relationship because, in pleasant learning, an effective correlation between learning materials, teachers, and students is needed.DOI: doi.org/10.24071/ijiet.2019.030204

Model-Based 3D Pose Estimation of a Single RGB Image Using a Deep Viewpoint Classification Neural Network

This paper presents a model-based approach for 3D pose estimation of a single RGB image to keep the 3D scene model up-to-date using a low-cost camera. A prelearned image model of the target scene is first reconstructed using a training RGB-D video. Next, the model is analyzed using the proposed multiple principal analysis to label the viewpoint class of each training RGB image and construct a training dataset for training a deep learning viewpoint classification neural network (DVCNN). For all training images in a viewpoint class, the DVCNN estimates their membership probabilities and defines the template of the class as the one of the highest probability. To achieve the goal of scene reconstruction in a 3D space using a camera, using the information of templates, a pose estimation algorithm follows to estimate the pose parameters and depth map of a single RGB image captured by navigating the camera to a specific viewpoint. Obviously, the pose estimation algorithm is the key to success for updating the status of the 3D scene. To compare with conventional pose estimation algorithms which use sparse features for pose estimation, our approach enhances the quality of reconstructing the 3D scene point cloud using the template-to-frame registration. Finally, we verify the ability of the established reconstruction system on publicly available benchmark datasets and compare it with the state-of-the-art pose estimation algorithms. The results indicate that our approach outperforms the compared methods in terms of the accuracy of pose estimation.

Symmetrical Viewpoint Representations in Face-Selective Regions Convey an Advantage in the Perception and Recognition of Faces.

Learning new identities is crucial for effective social interaction. A critical aspect of this process is the integration of different images from the same face into a view-invariant representation that can be used for recognition. The representation of symmetrical viewpoints has been proposed to be a key computational step in achieving view-invariance. The aim of this study was to determine whether the representation of symmetrical viewpoints in face-selective regions is directly linked to the perception and recognition of face identity. In Experiment 1, we measured fMRI responses while male and female human participants viewed images of real faces from different viewpoints (-90, -45, 0, 45, and 90° from full-face view). Within the face regions, patterns of neural response to symmetrical views (-45 and 45° or -90 and 90°) were more similar than responses to nonsymmetrical views in the fusiform face area and superior temporal sulcus, but not in the occipital face area. In Experiment 2, participants made perceptual similarity judgements to pairs of face images. Images with symmetrical viewpoints were reported as being more similar than nonsymmetric views. In Experiment 3, we asked whether symmetrical views also convey an advantage when learning new faces. We found that recognition was best when participants were tested with novel face images that were symmetrical to the learning viewpoint. Critically, the pattern of perceptual similarity and recognition across different viewpoints predicted the pattern of neural response in face-selective regions. Together, our results provide support for the functional value of symmetry as an intermediate step in generating view-invariant representations.SIGNIFICANCE STATEMENT The recognition of identity from faces is crucial for successful social interactions. A critical step in this process is the integration of different views into a unified, view-invariant representation. The representation of symmetrical views (e.g., left profile and right profile) has been proposed as an important intermediate step in computing view-invariant representations. We found view symmetric representations were specific to some face-selective regions, but not others. We also show that these neural representations influence the perception of faces. Symmetric views were perceived to be more similar and were recognized more accurately than nonsymmetric views. Moreover, the perception and recognition of faces at different viewpoints predicted patterns of response in those face regions with view symmetric representations.

Strength of Character for the Fusion Age “Grit”: Research Trend Analysis : Focusing on Domestic, Master's and Doctoral Dissertations

a concept conceived in 2007 by Duckworth and others in the United States, is based on positive psychology that focuses on growth and development through individual strengths. Recently, Grit, which means patience and enthusiasm for long-term goals, has emerged as a key factor of personality strength. In Korea, Joo-hwan Kim (2013) was the first to conceptualize and study the subject of Grit. However, there have been no overview studies that systematically summarize the overall trends and flow in the research of Grit so far. There have been 147 research papers on Grit published so far in Korea. The purpose of this study was to conduct trend analysis on the subject of Grit by analyzing forty-three (43) master’s and doctoral dissertations, thus presenting the direction of future research on Grit through careful analysis. In the studies conducted, it was found that Grit is a very significant variable linked to self-efficacy. It is also a subjective belief that can help an individual achieve his/her educational goals, and go through failure resynchronization. In addition, Grit is very significant as a practical core indicator of how fusion talent is fostered for the fourth industrial revolution. Therefore, there is a need for more in-depth research from the viewpoints of workplace learning, experiential learning, or informal learning, as well as research into Grit characteristics.

Automatic optic disc detection using low-rank representation based semi-supervised extreme learning machine

Optic disc detection plays an important role in developing automatic screening systems for diabetic retinopathy. Several supervised learning-based approaches have been proposed for optic disc detection. However, these approaches demand that the input training examples are completely labelled. Essentially, in medical image analysis, it is difficult to prepare several training samples which were given reliable class labels due to the fact that manually labelling data is very expensive. Moreover, retinal images such as complex vessels structures in the optic disc constituting nonlinear relationships in high-dimensional observation space, which cannot work well by traditional linear classifiers. In this study, a novel approach named low-rank representation based semi-supervised extreme learning machine (LRR-SSELM) is proposed for automated optic disc detection. Our model has the following advantages. First, it detects the optic disc from the viewpoint of semi-supervised learning and overcomes the problem there are small portion of labelled samples. Second, a nonlinear classifier is introduced into our model to fully explore the nonlinear data. Third, the local and global structures of original data can be greatly persevered by low-rank representation (LRR). The performance of the proposed method is validated on three publicly available databases, DIARETDB0, DIARETDB1 and Messidor. The experimental results indicate the advantages and effectiveness of the proposed approach.

Robust unsupervised feature selection by nonnegative sparse subspace learning

Sparse subspace learning has been demonstrated to be effective in data mining and machine learning. In this paper, we cast the unsupervised feature selection scenario as a matrix factorization problem from the viewpoint of sparse subspace learning. By minimizing the reconstruction residual, the learned feature weight matrix with the l2,1-norm and the non-negative constraints not only removes the irrelevant features, but also captures the underlying low dimensional structure of the data points. Meanwhile in order to enhance the model's robustness, l1-norm error function is used to resistant to outliers and sparse noise. An efficient iterative algorithm is introduced to optimize this non-convex and non-smooth objective function and the proof of its convergence is given. Although, there is a subtraction item in our multiplicative update rule, we validate its non-negativity. The superiority of our model is demonstrated by comparative experiments on various original datasets with and without malicious pollution.

KNN-based ensemble selection for imbalance learning

Classification of imbalance datasets is one of the crucial issues in the field of machine learning. Since the distribution of imbalance dataset is extremely skew, the traditional classifications often come up with a disappointed performance. Different with the traditional methods, this paper reconsiders class imbalance problem from the viewpoint of ensemble learning. However, many ensembles tend to build base classifiers not perfectly; some bad performed classifiers which are helpless to improve the generalised ability of ensemble would be produced. To solve this problem, a novel method named ensemble algorithm named NNES (k-nearest neighbour based ensemble section) was proposed in this paper. To evaluate the local properties of an unlabeled instance, NNES tends to pay more attention to minority and improve its performance on imbalance datasets. Experimental results show that NNES can improve the classification performance of the imbalance datasets effectively. Moreover, this improvement would be strengthened when the sampling techniques were introduced in.

Malicious synchrophasor detection based on highly imbalanced historical operational data

By maliciously manipulating the synchrophasors produced by phasor measurement units in power systems, cyber attackers can mislead the control center into taking wrong actions. From the viewpoint of machine learning, normal and malicious synchrophasors may exhibit different spatial distribution characteristics when mapped into a latent space. Hence, a malicious synchrophasor detector can be acquired by training a classification model with instances derived from historical operational synchrophasor data. However, malicious synchrophasors occur infrequently in practice. It is likely to incur a great deal of effort and may even introduce inevitable experience errors when extracting and labeling a sufficient number of malicious synchrophasors from historical operational data for training. For most existing detectors, if they are directly trained with highly imbalanced datasets, their performances may severely deteriorate. In this paper, a novel type of malicious synchrophasor detector is developed based on a combinatorial use of data rebalancing, Bagging-based ensemble learning, and the widely recognized eXtreme Gradient Boosting (XGBoost) classifier. Experiments show that although fewer malicious instances are provided, the proposed detector is still capable of detecting malicious synchrophasors.

A Study on Effective Instructional Design for IoT Security Education Focusing on Experiential Learning

This paper discusses about an effective instructional design for fostering IoT security engineers from the viewpoint of experiential learning. The authors examined the learning effectiveness comparing instructional design of an experiential and those of non-experiential learning. The learning effectiveness was evaluated by several metrics; pre-/post-test scores, delayed-test scores and learning motivation scores. The evaluation result was that there was no significant difference about pre-/post-test scores and learning motivation scores between the two group. However, there was significant difference about delayed-test scores between them. The result indicates that the learners in the both groups had higher learning motivation and they actually obtained knowledge about IoT security in each lecture, nevertheless, almost learners in the non-experiential learning group couldn’t keep to retain the knowledge they obtained several weeks ago. The fact becomes one of the evidences that instructional design including experiential learning brings superior learning effectiveness for IoT security education.

Feature Learning Viewpoint of Adaboost and a New Algorithm

The AdaBoost algorithm has the superiority of resisting overfitting. Understanding the mysteries of this phenomena is a very fascinating fundamental theoretical problem. Many studies are devoted to explaining it from statistical view and margin theory. In this paper, we illustrate it from feature learning viewpoint, and propose the AdaBoost+SVM algorithm, which can explain the resistant to overfitting of AdaBoost directly and easily to understand. Firstly, we adopt the AdaBoost algorithm to learn the base classifiers. Then, instead of directly weighted combination the base classifiers, we regard them as features and input them to SVM classifier. With this, the new coefficient and bias can be obtained, which can be used to construct the final classifier. We explain the rationality of this and illustrate the theorem that when the dimension of these features increases, the performance of SVM would not be worse, which can explain the resistant to overfitting of AdaBoost.

DO HETEROGENEOUS EXPECTATIONS CONSTITUTE A CHALLENGE FOR POLICY INTERACTION?

Yes, indeed; at least for macroeconomic policy interaction. We examine a Neo-Classical economy and provide the conditions for policy arrangements to successfully stabilize the economy when agents have either rational or adaptive expectations. For a contemporaneous-data monetary policy rule, the monetarist solution is unique and stationary under a passive fiscal/active monetary policy regime if monetary policy appropriately incorporates expectational heterogeneity. In contrast, the active fiscal/passive monetary policy regime's fiscalist solution is prone to explosiveness due to empirically plausible expectational heterogeneity. Nevertheless, this can be a well-defined, rather orthodox equilibrium. For operational monetary policy rules, only the results for the fiscalist solution prevail. Moreover, our results are plausible from an adaptive learning viewpoint.

A Study of BSC Evaluation of a Local Government on the Final Achievement

본 연구는 부서의 BSC평가가 부서의 최종성과에 미치는 영향을 살펴봄으로써 지자체의 성과달성에 BSC의 기여를 평가하고자 한다. 이는 성과평가의 중요도가 증가되는 시점에서 공직자들이 최종성과를 높이기 위하여 자발적으로 BSC요인과 기타요인을 향상하도록 동기부여를 제공한다는 측면에서 의의가 있다. 평가방법의 다양화를 위하여 BSC평가를 4개 관점인 재무관점, 고객관점, 내부관점, 학습관점으로 분류하고, 전체부서를 공통지표와 부서의 특성을 반영한 고유지표로 분류하여 연구를 실시하였다. 추가로 BSC이외의 기타달성이 최종성과에 미치는 영향도 분석하였다. 본 연구의 분석대상기간은 2014년부터 2017년까지로 4년간 지자체 39개 부서의 성과결과를 대상으로 다중회귀분석을 실시하였다. 분석결과로는 부서의 BSC달성이 높으면 부서의 최종성과도 높은 것으로 나타나 BSC의 성과평가시스템으로써의 역할은 유용한 것으로 나타났다. 4개 관점 결과는 내부관점지표는 유의적으로 정(+)의 관련성을 나타냈으며 학습관점, 고객관점, 재무관점은 연도별로 혼재된 결과를 나타냈다. 민원부서와 비민원부서로 구분한 분석결과 민원부서는 고객지표가 매우 유의적으로 나타나고 기타부서는 재무관점지표가 매우 유의적으로 나타나 부서의 업무특성이 잘 반영되었으며 특히, 비민원 부서에서는 고객관점지표와 학습관점지표가 전혀 유의적이지 않게 나타나 부서의 업무특성을 고려한 지표개선의 필요성이 지적되었다. 고유지표와 공통지표는 모두 유의적으로 나타났으며 공통지표보다 고유지표 계수가 더욱 크게 나타나 부서의 특성을 반영한 변수가 더 영향이 있음을 알 수 있었다. 이는 향후 성과달성을 높이기 위해 고유지표의 비중을 늘리는 방안을 제시할 수 있으며 한편으로 공통지표에 주력할 수 있는 제도적 뒷받침이 필요한 것도 제기된다. 추가적으로 기타달성의 기여도도 유의적인 역할을 하는 것으로 나타나 다른 평가와 통합한 BSC로 나아가는 지표개선이 바람직함을 나타냈다.This study aims to evaluate contribution of BSC, as means of evaluating results of a local government, by examining the effects of BSC achievement on the final results of a department. In that it provides a motive so that public officials may voluntarily increase the level of BSC and others to raise the final result at the time importance of performance evaluation. For diversification of evaluation methods, this study was conducted by classifying the evaluation of BSC into 4 viewpoints, that is, a financial, a customer, an internal and a learning viewpoint, and by reflecting joint indices and features of a department in the total departments and by classifying them into inherent indices. In addition to that, the influence of other achievements that BSC on the final results. The analysis period of this study was for 4 years from 2014 to 2017 and, on the basis of results evaluation of a local government, a multiple regression analysis was conducted concerning 156 materials. As a result of an actual proof analysis, it was found that the higher BSC achievement of a department, the higher the final results of a department, accordingly, it turned out the role of a department as results evaluation system was useful. As to 4 viewpoints, the internal viewpoint index indicated significant positive(+) relation, and the other viewpoints indicated mix results during for years. As a result of analysis after dividing departments into civil affairs department and other department, the civil affairs department showed a very significant customer index, and other department revealed a very significant financial viewpoint index, so that business features of a department was well reflected, especially, in other department, a customer viewpoint and a learning viewpoint index was not significant at all, and thus the necessity for index improvement in consideration of business features of a department was proposed. Both of an inherent index and a joint index were significant, the coefficient of an inherent index was larger than that of an inherent index, so it could be known that the variable in which features of a department had more effect. This implies that a method of increasing proportion of an inherent index can be proposed for raising the future results, and in the meantime, a systematic support allowing concentrating on a joint index is needed. Besides that, it was found that contribution of other achievement plays a significant role, indicating an index toward BSC integrating other evaluation is desirable.

Learning maximum excluding ellipsoids from imbalanced data with theoretical guarantees

In this paper, we address the problem of learning from imbalanced data. We consider the scenario where the number of negative examples is much larger than the number of positive ones. We propose a theoretically-founded method which learns a set of local ellipsoids centered at the minority class examples while excluding the negative examples of the majority class. We address this task from a Mahalanobis-like metric learning point of view and we derive generalization guarantees on the learned metric using the uniform stability framework. Our experimental evaluation on classic benchmarks and on a proprietary dataset in bank fraud detection shows the effectiveness of our approach, particularly when the imbalancy is huge.

A consideration concerning the training of “Specialty for educational guidance in Social Studies” required for school education in next generation: From the viewpoint of sophisticated and diversified learning expected for Professional Schools for Teacher Education

2017年版の学習指導要領は、汎用性に富む資質・能力の育成をより明確にめざすものとなり、それを実現する授業改善が求められるようになった。この授業改善を社会科においても進めるためには、どのような学習指導の専門性が期待されるのか。本研究では、学校現場で社会科教育をリードする教員の育成を目的として、東京学芸大学に新設される教職大学院・社会科教育サブプログラムを事例に取り上げ、学習指導要領改訂の要点である「主体的・対話的で深い学び」と「カリキュラム・マネジメント」、さらにはそれらを具現化していく専門性を培う「理論と実践の往還」について、大学院生に求められる学びのありようを検討する。これは、教職大学院において社会科授業研究を深める過程で、授業改善を可能にする多様な専門性を、次世代教育に求められる「主体的・対話的で深い学び」と「カリキュラム・マネジメント」を軸に構造化する試みであり、養成・研修等の教師教育においてそれらの育成をより確かなものにする社会科内容構成学を構想する試みでもある。そこでは特に今後の社会科教育を見据えた新旧の専門性の再構成、「理論と実践の往還」の問い直しと教科内容構成学の構築に向けた取り組み等が主な課題となる。The government course of 2017 aims, more specifically, toward the cultivation of endowments and ability which are abundant of versatility. In addition, the class teaching improvement has been requested to achieve it. To advance the class teaching improvement in Social Studies, highly sophisticated professionality for educational guidance is expected. In this study, we consider Professional Schools, Subprogram of education of Social Studies that shall be newly-established in Tokyo Gakugei University in order to cultivate the teachers who can lead the education of Social Studies in the classrooms. In conjunction with “Subjective and Interactive profound learning” which is essential of the revision of the government course and “Curriculum Management”, and moreover we consider the way of learning about “The revolution between Theory and Practice” for the specialty that concretes them requested from graduate students. This is an experiment to structuralize the diverse specialties that enable class improvement in the process of making the research of class in Social Studies profound at Professional Schools for Teacher Education with the axis of “Subjective and Interactive profound learning” and “Curriculum Management” required from the education in next generation. Also it is an experiment to structuralize the Social Studies Subject Content Synthesiology that ensure the development of them in Teacher Education such as training and induction course. There the reconstruction of old and new specialties fixing especially Education in Social Studies in the future, the reconsideration of “Theory and Practice” and the approach to establish the School Subject Content Synthesiology shall be a main theme.

Testing and mental health

We are living in a time when a particular viewpoint of teaching and learning is fashionable. Followers of Twitter will be familiar with the ‘debates’ – this month's analysis of the latest news in the early years.

Sparse Graph Embedding Unsupervised Feature Selection

High dimensionality is quite commonly encountered in data mining problems, and hence dimensionality reduction becomes an important task in order to improve the efficiency of learning algorithms. As a widely used technique of dimensionality reduction, feature selection is about selecting a feature subset being guided by certain criterion. In this paper, three unsupervised feature selection algorithms are proposed and addressed from the viewpoint of sparse graph embedding learning. First, using the self-characterization of the given data, we view the data themselves as a dictionary, conduct sparse coding and propose the sparsity preserving feature selection (SPFS) algorithm. Second, considering the locality preservation of neighborhoods for the data, we study a special case of the SPFS problem, namely, neighborhood preserving feature selection problem, and come up with a suitable algorithm. Third, we incorporate sparse coding and feature selection into one unified framework, and propose a neighborhood embedding feature selection (NEFS) criterion. Drawing support from nonnegative matrix factorization, the corresponding algorithm for NEFS is presented and its convergence is proved. Finally, the three proposed algorithms are validated with the use of eight publicly available real-world datasets from machine learning repository. Extensive experimental results demonstrate the superiority of the proposed algorithms over four compared state-of-the-art unsupervised feature selection methods.

Distributed cooperative learning for a group of uncertain systems via output feedback and neural networks

This paper studies the problem of adaptive neural network (NN) output-feedback control for a group of uncertain nonlinear multi-agent systems (MASs) from the viewpoint of cooperative learning. It is assumed that all MASs have identical unknown nonlinear dynamic models but carry out different periodic control tasks, i.e., each agent system has its own periodic reference trajectory. By establishing a network topology among systems, we propose a new consensus-based distributed cooperative learning (DCL) law for the unknown weights of radial basis function (RBF) neural networks appearing in output-feedback control laws. The main advantage of such a learning scheme is that all estimated weights converge to a small neighborhood of the optimal value over the union of all system estimated state orbits. Thus, the learned NN weights have better generalization ability than those obtained by traditional NN learning laws. Our control approach also guarantees the convergence of tracking errors and the stability of closed-loop system. Under the assumption that the network topology is undirected and connected, we give a strict proof by verifying the cooperative persisting excitation condition of RBF regression vectors. This condition is defined in our recent work and plays a key role in analyzing the convergence of adaptive parameters. Finally, two simulation examples are provided to verify the effectiveness and advantages of the control scheme proposed in this paper.

Design of e-Teaching Portfolio System Framework for e-Tutor Competency Management

In e-learning, tutors support learners through their understanding of e-learning environments as well as their knowledge of the subjects under their charge, and also play the role of preventing the dropout of learners. Therefore, measures to manage efficient competency of tutors are necessary. Whereas e-portfolios are made from the viewpoint of learner's learning, e-teaching portfolios are made from the viewpoint of the activities of teachers including tutors. Although there have been discussions about the roles and competency of tutors, thus far, the presentation of concrete items to be managed in the system is insufficient, and discussions about e-teaching portfolio systems are insufficient. In this study, attempts were made to define the items of tutor competency necessary for systematic management of tutors’ competency in e-teaching portfolio systems. In results, tutor information, tutors’ personal history, and tutor competency were presented as areas that constitute e-teaching portfolios. The competencies that must be possessed by tutors in e-learning were also presented from the viewpoints of knowledge (e.g., e-learning, teaching-learning, etc.), skills (e.g., ICT literacy, LMS, etc.), and attitudes (e.g., responsibility, positive attitudes, etc.). The results of the present study will provide implications when e-teaching portfolio systems are designed for tutor competency management in e-learning.

Improving the Robustness of Prediction Model by Transfer Learning for Interference Suppression of Electronic Nose

This paper gives a solution to solve the interference problem of electronic nose (e-nose), which is ill-posed due to the uncertainty and unpredictability of its instable behavior. Traditional methods for interference suppression are component correction frameworks, which are laborious or little efficient. With interference (especially background interference and sensor drift), the distribution of test data obtained in practical application is different from that of the previous training data. From the viewpoint of machine learning, a novel domain correction and adaptive extreme learning machines (DC-AELM) framework with transferring capability is proposed to solve the serious interference problem in e-nose. The framework consists of two parts: 1) DC, which makes the distributions of two domains close and 2) AELM, which realizes the knowledge transfer at the decision level and makes the robustness of the prediction model improved. This method is motivated by the idea of transfer learning, especially from the perspective of domain correction and decision-making, to realize the knowledge transfer for interference suppression. A background interference data set obtained by our designed e-nose and a public benchmark sensor drift data set are used to verify the effectiveness of the proposed DC-AELM method.

Least Squares Method from the View Point of Deep Learning

The least squares method is one of the most fundamental methods in Statistics to estimate correlations among various data. On the other hand, Deep Learning is the heart of Artificial Intelligence and it is a learning method based on the least squares. In this paper we reconsider the least squares method from the view point of Deep Learning and we carry out the computation thoroughly for the gradient descent sequence in a very simple setting. Depending on the values of the learning rate, an essential parameter of Deep Learning, the least squares methods of Statistics and Deep Learning reveal an interesting difference.