Reviewerly/exHarmony huggingface dataset:
ANALISIS KETERAMPILAN DASAR MENJELASKAN OLEH GURU DALAM MENGAJARKAN MEMBACA PERMULAAN SISWA TUNAGRAHITA DI SDLB KASIH IBU PEKANBARU

The role of a teacher is so important in the learning process especially learning to read the beginning. Therefore, the role of a teacher greatly determines the success of students in learning to read the beginning, it is important for a teacher to have good teaching skills. One of these skills is explaining skills because in the learning process many demands the teacher can explain. The purpose of this study was to describe the teacher's explaining skills in teaching reading the beginnings of mentally retarded students at SDLB Kasih Ibu Pekanbaru. The research method used is qualitative research, this study tells of a symptom, event, or event that occurred now. Data obtained after the study can be seen that the teacher in applying explaining skills in teaching the beginning reading of mentally retarded students at SDLB Kasih Ibu Pekanbaru has been implemented quite well because the teacher has been able to carry out the components in explaining skills to achieve optimal learning.
Artifact removal in radiological ultrasound images using selective and adaptive median filter

Among the existing modalities, ultrasound (US) imaging is mostly utilized to screening abnormalities such as effective used in radiology. Computer-aided diagnosis (CAD) has become a second opinion US reader to improve radiologist's diagnoses by applying appropriate techniques of image processing. The existences of manually induced artifacts, such as caliper marking, rendering and anatomical labeling are negatively affected the results of segmentation in CAD. Moreover improper suppression methods will increase the emergency of undesired artifacts which degrading the quality and clinical applicability of US images. This paper presents the utility of selective and adaptive median filter for manually induced artifacts removal with preserving image features. The experimental results indicate that the proposed method is appropriate to remove artifacts in radiological US images, both in vision and quantitative measurements.
Calculation of the deficiency of some statistical estimators constructed from samples with random sizes

The purpose of this paper is to use deficiency to compare estimators constructed from samples with random size with those constructed from samples with non-random size. The deficiency can be a characteristic of a possible loss of accuracy of statistical i
MULTICULTURAL PROFESSIONAL COMPETENCE OF A TEACHER

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 5999-6005ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.1467Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
Do remote boundary conditions affect photodetection?

We study the photocurrent statistics in a homodyne setup with a mirror in the vacuum port. Conventional photodetection theory predicts a modulation of the photocurrent noise due to the mirror. We show that the mirror changes the quantum efficiency of the detector and affects both the mean and noise of the photocurrent. We discuss detector design parameters that will enhance this effect.
Educational Network Projects as Form of E-Learning

In the transition to digital technologies, the pedagogical community needs a fundamentally different methodological basis of education, it is necessary to radically update the goals, content, forms, methods and means of education, taking into account the flow of information and communication processes in which modern youth grows. The article notes a number of positive and negative aspects of e-learning, present's different opinions of scientists on the use of digital technologies in the educational process. The practice of introduction of e-learning has shown that this system of training raises a number of new problems arising from the insufficient volume of joint creative work of students with a teacher. Electronic network technologies provide great opportunities in the organization of joint activities of teachers and students, individualization of the educational process, the transformation of learning into self-learning and self-education, allow the organization of interactive classes and collective teaching, as well as various forms of control. The basis of one of these technologies is the method of projects. The article describes in detail the features of the use of the method of projects in the educational process. Design technology involves reliance on the principles, norms and rules of design, the totality of which allows you to organize the activities of the student from the idea to its practical implementation. One of the activities in the use of Internet technologies is an educational network project. The article describes the principles of using educational network projects. A distinctive feature of the educational space in the information society – is the requirement of training in cooperation and cooperation. These requirements can be implemented through educational network projects. Applying information technologies in the educational process, special attention should be paid to Web 2.0 services and cloud technologies. Network technologies open up fundamentally new opportunities for accelerated individual development of each student.
An EKF-Based Fixed-Point Iterative Filter for Nonlinear Systems

In this paper, a fixed-point iterative filter developed from the classical extended Kalman filter (EKF) was proposed for general nonlinear systems. As a nonlinear filter developed from EKF, the state estimate was obtained by applying the Kalman filter to the linearized system by discarding the higher-order Taylor series items of the original nonlinear system. In order to reduce the influence of the discarded higher-order Taylor series items and improve the filtering accuracy of the obtained state estimate of the steady-state EKF, a fixed-point function was solved though a nested iterative method, which resulted in a fixed-point iterative filter. The convergence of the fixed-point function is also discussed, which provided the existing conditions of the fixed-point iterative filter. Then, Steffensen’s iterative method is presented to accelerate the solution of the fixed-point function. The final simulation is provided to illustrate the feasibility and the effectiveness of the proposed nonlinear filtering method.
A Proposed Approach for Arabic Semantic Annotation

Semantic annotation refers to the process of annotating documents using the ontology in order to data becomes meaningful. Most of the techniques and methods of the field of semantic annotation and retrieval are used for dealing broadly in the English language. This paper aims to enhance the process of information retrieval for Arabic language that depends on the ontology in the process of document annotation. To achieve this aim, it is determined and processed the problems of the Arabic language through the proposed approach. This paper depends on semantic annotation based on ontology and Resource Description Framework (RDF). The results achieved high precision and high recall for the semantic annotation based on the proposed approach.
Efficient and Robust Homography Estimation Using Compressed Convolutional Neural Network

Homography estimation is one of the important ways to calculate the transformation between images. For most embedded terminal devices, an efficient and robust homography estimation algorithm is extremely necessary. In this paper, we design an innovative compressed convolutional neural network to estimate homographies which work very well. The model size of the network is less than 10 MB, which is small enough to be used on mobile devices. In addition, to improve the estimated accuracy in challenging environment, we present a novel loss function to train our network. Finally, we compare our algorithm with traditional methods and other learning-based methods. Experiments on our compressed network demonstrate that the innovative network achieves better accuracy compared to other learning-based algorithms, and is more robust to illumination changes compared to traditional algorithms.
Analysis of Mobile-based Vocabulary Learning Activities on University Students' Vocabulary Acquisition and Online Learning Behaviors

The purpose of this study was to explore how the mobile-based vocabulary learning positively affected learners’ lexical competence, language learning...
The Hexlite Solver

$$\textsc {hexlite}$$ is a lightweight solver for the $$\textsc {hex}$$ formalism which integrates Answer Set Programming (ASP) with external computations. The main goal of $$\textsc {hexlite}$$ is efficiency and simplicity, both in implementation as well as in installation of the system. We define the Pragmatic $$\textsc {hex}$$ Fragment which permits to partition external computations into two kinds: those that can be evaluated during the program instantiation phase, and those that need to be evaluated during the answer set search phase. $$\textsc {hexlite}$$ is written in $$\textsc {python}$$ and suitable for evaluating this fragment with external computations that are realized in $$\textsc {python}$$ . Most performance-critical tasks are delegated to the $$\textsc {python}$$ module of $$\textsc {clingo}$$ . We demonstrate that the Pragmatic $$\textsc {hex}$$ Fragment is sufficient for many use cases and that it permits $$\textsc {hexlite}$$ to have superior performance compared to the $$\textsc {dlvhex}$$ system in relevant application scenarios.
Experimental observations of boundary conditions of continuous-time quantum walks

The continuous-time quantum walk (CTQW) is the quantum analogue of the continuous-time classical walk and is widely used in universal quantum computations. Here, taking the advantages of the waveguide arrays, we implement large-scale CTQWs on chips. We couple the single-photon source into the middle port of the waveguide arrays and measure the emergent photon number distributions by utilizing the fiber coupling platform. Subsequently, we simulate the photon number distributions of the waveguide arrays by considering the boundary conditions. The boundary conditions are quite necessary in solving the problems of quantum mazes.
Multi-angle-view monocular camera using a polarization image sensor

A wide-angle view can be conventionally acquired with a monocular camera by means of a fish-eye lens. However, the acquired image using the fish-eye lens is generally distorted with a view angle of less than 180 deg. Thus, a multi-angle-view monocular camera using a polarization image sensor to capture distortion-free images of multi-angle views with wide viewing angles is proposed here where the polarization image sensor has four different directional polarizing filters on every four pixels. Using optical elements such as a beam splitter and a polarizing filter, three independent views can be superpositionally captured by the polarization sensor. The three view images can be reconstructed from the superpositioned polarization images using a reconstruction method based on a matrix manipulation. A prototype multi-angle-view monocular camera is fabricated and shows that the three views can be reconstructed using the reconstruction method.
Enhancing Opaquenamespace.org: Refinement of Local Name Authority Files and Workflows

This article discusses Opaquenamspace.org, the controlled vocabulary manager that assists with the local, regional, and national linked open data authority work for Oregon Digital, the shared digital collections repository of Oregon State University Libraries and Press and University of Oregon Libraries. This article will share information about the creation and development of the system, the workflows and processes for uniform resource identifiers creation, management, and dissemination, as well as address mistakes that were made as early adopters for other institutions to consider before creating and managing their own local name authority files.
Research on cloud computing-based power security management software

OBJECTIVE: To maintain the stable operation of cloud computing-based power safety management software. METHODS: Through the analysis of cloud computing software, the paper proposes relevant improvement strategies based on the shortcomings of these software. RESULTS AND ANALYSIS: Through the TAO arc hitecture for security services, safe communication is achieved to ensure stable operation. CONCLUSION: The security service structure designed in this paper has made two application modes possible, including the user-independent general security agent mode and the interface method-level fine security control mode, and the infrastructure improves the reliability of software operation and is worthy of promotion.
A cryptographic encryption technique byte – Spiral rotation encryption algorithm

Security is one of the most important fundamental issues for the communication of data in the wireless world. There are various types of encryption algorithms using different techniques like AES, DES, Triple DES, RSA, Blowfish etc. Moreover, the main aim of the encryption is to increase the level of security and minimize the time and complexity. In our research paper, we have provided a cryptographic encryption technique that encrypts the messages by using a new technique called Spiral Rotation Encryption Algorithm. The process of the algorithm is to change the data into a parallel sequence of matrices containing converted and shuffled numeric, by the process of spiral rotation about the diagonal entries so that the process of the encryption time is reduced and encryption steps are minimized.
Robotic Arm Representation Using Image-Based Feedback for Deep Reinforcement Learning

This paper presents a technique that uses feedback images for implementing a robotic task using Deep Reinforcement Learning (DRL). The agent (controller) type is Actor-Critic that uses Temporal-Difference (TD) to gain knowledge about the environment (plant). The representation of the robot is implemented in the V-Rep simulator.
Comparison of Feature Descriptors for Visible and Thermal Face Recognition

Face recognition, as one of the biometric methods, in controlled environments reaches recognition accuracy of more than 99%. Regardless of that, variations in lighting conditions, pose, facial expressions and occlusions result in performance degradation. Including thermal infrared facial images in face recognition systems can provide a solution to these problems, especially for limitations caused by illumination. Both thermal and visible face recognition systems can use the same, common image processing feature descriptors. This paper compares the two most common feature descriptors, HOG and LBP, and examines their performance both in visible and thermal face recognition applications in order to find more effective feature descriptor for each image sensor.
SAR for Kids: Spatial Augmented Reality as Tool for Art Education

This article, after describing some possible developments of spatial augmented reality (SAR) in urban areas since the first experiments by Bruno Munari, will present two SAR experiences into public spaces for cultural heritage education of school-age children. These experiences were aimed, on the one hand, to verify the potential of live drawing on large dimensions and on the other to represent one’s own personal graphic interpretation of an urban façade on the basis of suggestions deriving from a specific event: the passage of the Giro d’Italia.
What do teachers do?

Education is probably the most powerful influence on the development of our society. The education that young people receive through schooling goes beyond knowledge about a body of academic subjects: it is built upon and shapes the values, rights and responsibilities that make our society distinctive. Nobody entering the teaching profession does so as a blank canvas; everyone has experienced education and this shapes their sense of what teachers are and do. Everyone has an opinion of what teachers do. Often these views are formed by personal experiences of school, and they are often idealised by the passage of time. The media, television, cinemas and literature also provide people with many and varied representations of teachers. Teaching is a deeply personal activity. Pupils respond strongly to individual teachers. Teaching is a responsible activity. This includes the values and priorities that shape the education that pupils receive. Teaching is a purposeful activity, both in the short-term and the long-term.
No-reference image quality assessment based on deep convolutional neural networks

A no-reference image quality assessment technique can measure the visual distortion in an image without any reference image data. Image distortions can be caused through the acquisition, compression or transmission of digital images. From the several types of image distortions, JPEG and JPEG2000 compression distortions, addition of white noise, Gaussian blur and fast fading are the most common ones. A typical real-world image may have multiple types of distortion. Our aim is to determine the different types of distortion that are present in an image and find the total distortion levels using a novel architecture using multiple Deep Convolutional Neural Networks (MDNN). The proposed model will classify different types of distortion that are present in an image thereby achieving both these objectives. Initially, local contrast normalization (LCN) is performed on images which are fed into the deep neural network for training. The images are then processed by a convolution-based distortion classifier which estimates the probability of each distortion type. Next, the distortion quality is predicted for each class. These probabilities are fused using the weighted average-pooling algorithm to get a single regressor output. We also experimented on the different parameters of the neural network, including optimizers (Adam, Adadelta, SGD, Rmsprop) and activation functions (RELU, SoftMax, Sigmoid, and Linear). The LIVE II database is used for the training, since it has five of the major distortion types. Cross-dataset validation is done on the CSIQ and TID2008 database. The results were evaluated using different correlation coefficients (SORCC, PLCC) and we achieved a linear correlation with the differential mean opinion scores (DMOS) for each of these coefficients in the tests conducted.
Global Trifocal Adjustment

In this paper we introduce a fast and robust structure-less alternative to full bundle adjustment. The method is based on optimizing algebraic errors for trilinear constraints from triplets of views. It is shown that the error generated by a triplet of views can be described by a fixed triangular $$27 \times 27$$ matrix regardless of the number of feature correspondences between the views. The method has been evaluated on various real and synthetic datasets and shows good convergence properties with a large convergence basin and solutions that are close to the optimal solution. The method has been compared to Global Epipolar Adjustment, GEA, which is based on the bilinear constraint. It will be shown that the method can handle the degenerate configurations of GEA.
Images research by content

This paper presents a new approach of Content Based Image Retrieval (CBIR), which combines the classification by SVM with the similarity measure using the GLCM, Gabor and Surf descriptors. The aim of this work has especially been concentrated on the level of improvement of these descriptors. So this improvement encourages us to propose a new approach of image research. The proposed approach is applied to synthetic and real data, and the results are compared to those obtained by other method. The compared results of those of algorithms of images search has shown the good performance of the proposed approach as well as the capacities of SVM as a classification tool of images.
A Hybrid Ensemble Method for Multiclass Classification and Outlier Detection

Multiclass problem has continued to be an active research area due to the challenges paused by the issue of imbalance datasets and lack of a unifying classification algorithms. Real world problems are of multiclass nature with skewed representations. The study focused on the challenges of multiclass classification. Multiclass datasets were adopted from UCI machine learning repository. The research developed a heterogeneous ensemble model for multiclass classification and outlier detection that combined several strategies and ensemble techniques. Preprocessing involved filtering global outliers and resampling datasets using synthetic minority oversampling technique (SMOTE) algorithm. Datasets binarization was done using OnevsOne decomposing technique. Heterogeneous ensemble model was constructed using adaboost, random subspace algorithms and random forest as the base classifier. The classifiers built were combined using average of probabilities voting rule and evaluated using 10 fold stratified cross validation. The model showed better performance in terms of outlier detection and classification prediction for multiclass problem. The model outperformed other commonly used classical algorithms. The study findings established proper preprocessing and decomposing multiclass results in an improved performance of minority outlier classes while safe guarding integrity of the majority classes.
Survey of data cleaning algorithms in wireless sensor networks

This paper aims to provide insight into attempts of solving the problems of data cleaning in big data wireless sensor networks that could be used in smart cities. We focus on data cleaning algorithms and case studies of some of the more specialized problems that occur in this field, like error models, benchmark datasets, impact of certain components in datasets and the proposed order of data cleaning.
Blockchain & Multi-Agent System: A New Promising Approach for Cloud Data Integrity Auditing with Deduplication

Recently, data storage represents one of the most important services in Cloud Computing. The cloud provider should ensure two major requirements which are data integrity and storage efficiency. Blockchain data structure and the efficient data deduplication represent possible solutions to address these exigencies. Several approaches have been proposed, some of them implement deduplication in Cloud server side, which involves a lot of computation to eliminate the redundant data and it becomes more and more complex. Therefore, this paper proposed an efficient, reliable and secure approach, in which the authors propose a Multi-Agent System in order to manipulate deduplication technique that permits to reduce data volumes thereby reduce storage overhead. On the other side, the loss of physical control over data introduces security challenges such as data loss, data tampering and data modification. To solve similar problems, the authors also propose Blockchain as a database for storing metadata of client files. This database serves as logging database that ensures data integrity auditing function.
Augmented Reality as a Higher Education Form for Students with Delimited Ability

Augmented Reality (AR) is one of technology that dramatically shifts the location and timing of learning and training, but for students with delimited ability to shift education and learning illimitable. This paper describes our research of AR, how it applies to learning and training for students with delimited ability, and the potential impact on the future of education. We developed apps in which is included virtual speaking head, speech synthesis, convert text to speech, and speech to text because for hearing impairments students and for students with delimited ability is the best way of learning. Delimited ability students are by this way included in normal fully valued life.