Reviewerly/exHarmony huggingface dataset:
The Effects of Problem-based Learning in Students Reading Comprehension for Mastering the Content and Vocabulary Acquisition

English reading comprehension is the most crucial language abilities in colleges where English is learned as a second language. A vigorous and powerful reading ability is certainly crucial in primary studies for students to accomplish in advanced education. However, the method in which they are instructed through lecture and paper pencil’s method were less relevant and effective in 21st Century. Students become passive, demotivated, bored, and feeling shy by the conventional teaching and learning. The purpose of this study is to examine the effectiveness of problem-based learning (PBL) on student’s English reading comprehension for mastering the contents and vocabulary acquisition. The study used a quantitative data where using an experimental pre-posttest design and the independent t-test and one-way ANOVA has been performed to test the hypothesis of the studies. The result indicated that there were statistically significant differences between the experimental and control groups for student’s reading comprehension for mastering the content and vocabulary acquisition using PBL. The results showed that the students who received PBL learning approached achieved higher performance outcome than student who is not received the PBL approach
Conditions for Existence of Second-Order and Third-Order Filters for Discrete Systems with Additive Noises

The problem of constructing functional optimal observers (filters) for stochastic control systems with additive noises in discrete time are studied in this work. Under the assumption that there is no filter of the first order, necessary and sufficient conditions for the existence of filters of the second and third order are obtained in the canonical basis. Analytical expressions of the transfer function matrix from the input noise to the estimation error are presented. A numerical example is given to compare the performance of filters by the quadratic criterion in the steady state.
A Novel Strategy for Improving the Counter Propagation Artificial Neural Networks in Classification Tasks

Counter-Propagation-Artificial-Neural-Networks (C P-ANNs) have been applied in several domains due to their learning and classification abilities. Regardless of their strength, the CP-ANNs still have some limitations in pattern recognition tasks when they encounter ambiguities during the learning process, which leads to the inaccurate classification of the Kohonen-Self-Organizing-Map (K-SOM). This problem has an impact on the performance of the CP-ANNs. Therefore, this paper proposes a novel strategy to improve the CP-ANNs by the Gram-Schmidt algorithm (GSHM) as a pre-processing step of the original data without changing their architecture. Three datasets examples from various domains, such as correlation, crop, and fertilizer, were employed for experimental validation. To obtain the results, we relied on two simulations. The first simulation uses CP-ANNs, and the datasets are inputted into the network without any prior pre-processing. The second simulation uses MCP-ANNs, and the datasets are pre-processed through the GSHM block. Experiment results show that the proposed MCP-ANNs recognize all patterns with a classification accuracy of 100% versus 62.5% for CP-ANNs in the Correlation Dataset. Furthermore, the proposed MCP-ANNs reduce the execution time and training parameter values in all datasets versus CP-ANNs. Thus, the proposed approach based on the GSHM algorithm significantly improves the performance of the CP-ANNs.
Exploring the Three Phases of Motivation in The Learning of Japanese as A Foreign Language

Motivation is one of the prerequisites in Japanese learning.A student who excels, but does not have enough motivation, will definitely not achieve the results as expected.This study is conducted to explore the three phases of motivation in the learning of Japanese language as a foreign language using the Model of Second Language Motivations (Dörnyei & Ottó, 1998).A quantitative survey consisting of 4 sections with items on the demographic profile and 39 items via Google Form using 5-Likert scales were used as the instruments.Questionnaires were answered by 255 students from three clusters; namely the clusters of Science and Technology, Arts and Humanities, and Business and Administration in a public university in Malaysia.Findings revealed that learners have the highest motivation during the actional stage as curiosity-driven has intrinsically motivated them to learn.Learners' initial motivation has reversed when they reached the actional stage due to their stress about the tests or examinations.However, learners' motivation bounce back during the post-actional stage and they are confident they will perform an excellent job on their assessments or assignments.This study reveals that instructors have a role to play to keep students motivated throughout the process of Japanese language learning.Further research can be done on preactional, actional and post actional stages by looking at the influence of external motivation by associating it with cognitive, affective, situational or circumstantial factors that will support and complement this study.
A Survey on Blind De-Blurring of Digital Image

Nowadays, huge digital images are used and transferred via the Internet. It has been the primary source of information in several domains in recent years. Blur image is one of the most common difficult challenges in image processing, which is caused via object movement or a camera shake. De-blurring is the main process to restore the sharp original image, so many techniques have been proposed, and a large number of research papers have been published to remove blurring from the image. This paper presented a review for the recent papers related to de-blurring published in the recent years (2017-2020). This paper focused on discussing various strategies related to enhancing the software's for image de-blur. The aim of this research is to help researchers to understand the current algorithms and techniques in this field, and eventually may developing new and more efficient algorithms for enhancing blurred images.
Exploring beach placer minerals in the east coast of Tamil Nadu, India, using EO-1 Hyperion data

Recent advancements in remote sensing facilitate the mapping of mineral occurrences but lack quantitative estimation of mineral abundances. In the present study, EO-1 Hyperion data are used for mapping and estimating beach placer minerals in the Cuddalore coastal stretch of Tamil Nadu, India, using spectral angle mapper (SAM) algorithm, continuum removed band depth analysis, and random forest (RF) regression. Strategic beach placer minerals are exposed in the image due to their specific spectral reflection/absorption characteristics; these unique settings are used to explore their spatial distribution from associated features. EO-1 Hyperion images are analyzed for (i) atmospheric correction; (ii) selection and transformation of optimum bands; (iii) fixing the input pixels from selected bands; (iv) generating n-D angle—for the preparation of end-members with unique spectra of mineral classes; (v) comparative analysis between true end-member and reference spectra of minerals using SAM, spectral feature fitting, and binary encoding algorithms; and (vi) image classification using SAM. The result of SAM shows the occurrence of placer mineral deposits such as zircon with an overall accuracy of 81.06% and Kappa coefficient of 0.71. The zircon shows strong absorption in spectral geometric parameters of EO-1 Hyperion data, such as band depth and band area at the spectral range of 1075 to 1150 nm. The positive correlation between geometric parameters of mineral spectra in its absorption characteristics and the mineral concentration of in situ samplings is used for developing an RF prediction model for estimating the placer minerals from beach sand deposits. Significantly, the lower RMSE values estimated at 0.082 and 0.156 for reference spectra (in situ) and image spectra respectively proved the ability of EO-1 Hyperion data for quantifying mineral resources.
Distance Teaching and Learning of Science during the COVID-19 Pandemic in Greece: Views of K-6 Students

In this paper the findings from research conducted with primary education students (K-6) are presented, explicitly focusing on students’ views concerning how Science was taught through distance learning due to the COVID-19 pandemic during the school year 2020-21. The sample of 378 students was not satisfied by the distance teaching and learning of Science except for the increased use of supporting audiovisual resources. Some of the results that occurred due to this transition were technical connection problems, a lack of communication between classmates and their teacher, a lack of concentration by students, their limited personal participation and the absence of face-to-face experimentation. Consequently, the limited understanding of scientific concepts shaped the students’ preference for learning Science in real classroom conditions.
FACTORS FOR ACHIEVING LEARNING OUTCOMES: OVERVIEW OF ASTANA IT UNIVERSITY’S EXPERIENCE

The article gives an overview of Astana IT University’s (AITU) experience in performing the teaching conditions for achieving the learning outcomes. The introduction of a competency-based approach to the formation and assessment of learning outcomes has led to a new system of training and assessment tools. Stakeholdership as a modern mechanism, the problem of focusing on employers’ needs, the stages of the education programme (EP) development and the criteria for assessing the learning outcomes achievement are discussed in details. The quality of education is determined by the quality of the results of the educational process, where the educational achievements of students and the qualifications of graduates become the main components of education quality. The purpose of the study is aimed to summarize the practice of using assessment tools as the key factors and conditions for establishing learning outcomes. The research methodology used is quantitative and qualitative data analysis as well as analysis of class observation in AITU done within the research on learning outcomes achievement. The choice and design of teaching technology are primarily determined by the type of students’ competencies, characteristics of the planned learning outcomes for each level of competence (knowledge, skills, and experience). Constant improvement of EP content and educational technologies as a key factor of education services quality is a vital demand.
Word representation using refined contexts

In this paper, inspired from the idea that the contextual distances and positions may have a substantial impact on distinguishing the relationships between words, a novel word association method with two weighting schemes for refining contexts, named as Weighted Point-wise Mutual Information with Contextual Distances and Positions (PMIDP), is proposed to eliminate the noisy and redundant information hidden in an imbalanced corpus. One weighting scheme, called PMIdist, revises the Point-wise Mutual Information (PMI) method by scaling the co-occurrence counts according to the distance between a word and the context. The second weighting scheme is a ratio that can measure the contextual position variation within the window of a given word. Then, the refined word association in PMIDP is defined as the multiplication of the two proposed weighting schemes, which essentially aims to flexibly adjust the word association when solving target-oriented similarity tasks. The proposed word association method has been applied on two widely known models, i.e., the positive PMI matrix with truncated Singular Vector Decomposition (PPMI-SVD) model and the Global Vectors (GloVe) model. Experimental results demonstrate that the PMIDP method can significantly improve the performances of the two models on both semantic and relational similarity tasks and show advantages when compared with other state-of-the-art models.
SUSY in the light of the new “MUON G-2” Result

The recently published result from the Fermilab "MUON G-2" experiment has confirmed the persistent 3 -4 σ discrepancy between the experimental result from BNL for the anomalous magnetic moment of the muon, (g-2) µ , and its Standard Model (SM) prediction.The combination of the two measurements yields a deviation of 4.2 σ from the SM value.Here, we review the parameter space of the electroweak (EW) sector of the Minimal Supersymmetric Standard Model (MSSM), that can accommodate the anomaly while being in full agreement with other experimental data, particularly the direct searches for EW particles at the LHC and dark matter (DM) relic density and direct detection constraints.We find that the combined constraints set an upper limit of ∼ 600 GeV for the LSP and NLSP masses establishing clear targets for the future collider searches.
Prognosis on Stratification of Breast Cancer using Data Mining Models

Breast cancer classification can be useful for discovering the genetic behavior of tumors and envision the outcome of some diseases. Through this paper we are predicting the noxious behavior of a tumor. The prediction models used are Random Forest, Naïve Bayes, IBK (Instance Based Learner), SMO (Sequential minimal optimization), and Multi Class Classifier. This prediction model which can potentially be used as a biomarker of breast cancer is based on physical attributes of a breast mass and which is gathered from digitized image of Fine Needle Aspirate (FNA). These can be helpful in prediction and reduction of invasive tumors
Modern learning models through teacher and student dynamic interaction in HEI towards COVID-19 pandemic condition

The aims of the study is an empirical assessing the dynamic interaction of a teacher and a student at the HEI during a pandemic as an important component of the distance learning model, identifying methods and ways to ensure the integration of this component in the distance learning program for further use in the practice of HEIs. The methodology is based on the method of case analysis of the University of Oxford in the organization of educational activities during the pandemic following the Business Continuity Planning (BCP) framework. The results show that planning of crises should be used and, accordingly, scenarios for organizing educational activities should be developed for the effectiveness of the digital learning model and the provision of a dynamic online environment. It has been revealed that the University of Oxford’s Business Continuity Planning (BCP) framework provides insight into the current crisis. The next factor in the effectiveness of training at HEIs during a pandemic is the planning of training courses based on templates developed by the leadership of universities. Templates of hybrid, virtual, personal courses provide unified teaching and learning processes, greatly facilitating the interaction of students and teachers.
Expression Recognition Using Improved AlexNet Network in Robot Intelligent Interactive System

Aiming at the insufficient feature extraction in the expression feature extraction stage of traditional convolutional neural network and the misclassification of mislabeled samples, an expression recognition and robot intelligent interaction method using deep learning is proposed. First, in image preprocessing, the dimension of the color image is reduced by image gray adjustment to reduce the amount of calculation, the shadow interference is eliminated by the average method, and the image is enhanced by histogram equalization. Second, multichannel convolution is used to replace the single convolution size in the second convolution layer in AlexNet, the Global Average Pooling layer is introduced to replace the fully connected layer, and Batch Normalization is introduced to improve the feature extraction ability of the model and avoid gradient explosion. Finally, the Focal Loss is improved by setting the probability threshold to avoid the impact of mislabeling samples on the classification performance of the model. The experimental results show that the recognition accuracy of the model on FER2013 data set is 98.36%. The effectiveness of the algorithm is verified on the intelligent interactive system of service robot based on expression recognition. Compared with other expression recognition methods, the proposed method can extract more expression features and recognize facial expression more accurately.
Evrişimsel Sinir Ağı Temelli Yüz Tanıma Yöntemleri ile Robot Resim Oluşturma Uygulaması

Create a Sketch Application with Convolutional Neural Network Based Face Recognition Methods
Enhanced Data Security in Cloud Computing: Survey

-The next era in the advent of the internet is internet based computing also known as cloud computing. It has been the emphasis in recent years, but security concerns are one among the most important impediments to the rise of cloud computing.. It essentially sends user data and application programs to massive data centers, i.e. a distant cloud, where consumers have little control and data management may be insecure. However, numerous security issues raised by the cloud's unique nature must be addressed and fully acknowledged.. Amongst the most critical concerns which must be tackled is the security issue. Issues with data security created by user data and software in the provider's jurisdiction. Cloud users search for cloud resources with secure data management. When sharing their personal data over public clouds, some cloud users may prefer to manage their data with more privacy. This document focuses on improving privacy-related data security in the cloud and related works that have been done to enhance data security in cloud computing. This research review work analyzes and discusses several of the security and privacy improvements evaluated in related existing systems.
VADER Sentiment Analysis without and with English Punctuation Marks

Sentiment analysis is the foremost task in Natural Language Processing to understand the user’s attitude (positive, neutral, or negative) by capturing their thoughts, opinions, and feeling about a particular product. This helps companies to fulfill customer satisfaction and make better future decisions about the product. Various techniques have been used in the literature forsentiment analysis, such as polarity scores, classifications, and automated sentiment analysis. In this paper, Valence Aware Dictionary and sEntiment Reasoner (VADER) sentiment analysis tool has been employed on a Twitter dataset (downloaded from https://www.kaggle.com). The study aims to measure the performance of VADER sentiment while concatenating fourteen English language punctuations marks, including Exclamation (!), Comma (,), Full Stop (.), Question Mark (?), Round Brackets (), Curly Brackets {}, Square Brackets [], Colon (:), Apostrophe (‘), Dash (-), Hyphen (--), Semi-Colon (;), Slash (/), Quotation Mark (“ ”) and to observe whether the polarity (positive, neutral and negative) of a sentence changes or remains the same. After the analysis, the study found that Exclamation (!) maximizes the average positive polarity and average negative polarity and lowers the average neutralpolarity. The Hyphen (--) and Comma (,) increase the average positive and neutral polarity and decrease the aver-age negative polarity. For Round Brackets (), Curly Brackets {}, Square Brackets [], Colon (:), Apostrophe (‘), Dash (-), Semi-Colon (;), Slash (/) and Full Stop (.) the average positive and average neutral polarity decreases and average negative polarity increases.
Ensemble Boosted Tree based Mammogram image classification using Texture features and extracted smart features of Deep Neural Network

&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; This work proposes a technique of breast cancer detection from mammogram images. It is a multistage process which classifies the mammogram images into benign or malignant category. During preprocessing, images of Mammographic Image Analysis Society (MIAS) database are passed through a couple of filters for noise removal, thresholding and cropping techniques to extract the region of interest, followed by augmentation process on database to enhance its size. Features from Deep Convolution Neural Network (DCNN) are merged with texture features to form final feature vector. Using transfer learning, deep features are extracted from a modified DCNN, whose training is performed on 69% of randomly selected images of database from both categories. Features of Grey Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP) are merged to form texture features. Mean and variance of four parameters (contrast, correlation, homogeneity and entropy) of GLCM are computed in four angular directions, at ten distances. Ensemble Boosted Tree classifier using five-fold cross-validation mode, achieved an accuracy, sensitivity, specificity of 98.8%, 100% and 92.55% respectively on this feature vector.&#x0D; &#x0D; &#x0D; &#x0D;
Penilaian Kinerja Bangunan Hijau dengan EDGE Building App pada Perancangan Klinik yang Menerapkan Strategi Passive Design dari Climate Consultant

Carbon emissions contributed by the construction sector currently have a major impact on environmental climate change. In the construction sector, the solutions offered in mitigating the impacts of climate change include developing sustainable building designs in terms of energy efficiency. The design approach with building performance simulation could ensure the application of environmentally friendly principles in the design process. The research methodology utilized a building performance simulation method using the Climate Consultant application in presenting a summary of climate data on the site and generating recommendations for passive design strategies. After the application of these recommendations to the design of the clinic, then an evaluation of the performance of the building was carried out using the EDGE Building App. From building performance simulations carried out in study case examples using the Climate Consultant application, recommendations were obtained in the form of 20 passive design strategies in which ten strategies could be applied. The design results obtained are proven to have an arguably good energy efficiency when evaluated with the EDGE Building App. This evaluation also provided recommendations for several reduction and saving steps from three aspects of building elements, namely: energy consumption (28.53%), water use consumption (20.77%), and types of building materials (84.02%).
Android-Based Mobile Learning Resource for Chemistry Students in Comprehending the Concept of Redox Reactions

This study aimed to develop an Android learning media on the concept of redox as an effective, efficient, practical, real-time learning media and in accordance with the facilities owned by students and evaluate the quality and usability of the app. The approach used in this research was a quantitative approach in the form of development research, while the model used was the Hannafin and Peck (1988) model. The data of this study were collected from the process of development, expert evaluations (expert reviews), and small group evaluation. 20 students of the chemistry education study program at a university in Indonesia volunteered to use and evaluate the app, Data was collected by using questionnaires. The results indicated that the quality of the application obtained from expert review was 94.7%. Furthermore, the small group evaluation shows that the usability of RedoksPro version 1.1 scored 86.7. The Android-based learning media of redox concept (RedoksPro application version 1.1) could be utilized as a mobile learning resource in learning the specific concept of reduction and oxydation reactions.