Reviewerly/exHarmony huggingface dataset:
Forecasting: Analyze Online and Offline Learning Mode with Machine Learning Algorithms

Since the pandemic occurred, in March 2020, learning activities have changed from an offline to an online learning mode. This is the first time, such a huge change has occurred, simultaneously in the entire hemisphere. This learning mode opens a new discourse regarding the impact on the learning mode and educational evaluation results. The author aims to compare the results of the educational evaluation of the online learning mode during the pandemic with offline learning mode, so that differences will be known, as well as can be used to predict student learning outcomes, in order to obtain an overview of the effectiveness and efficiency of a learning mode. Data collection is carried out as an initial step in data processing, based on the final results of student learning, in certain courses taken every semester starting in 2017-2022. The data consists of 6 indicators, namely CI1-CI4, grades, and letter grades. The result of this study is the prediction of a more effective learning mode used, as decision support carried out by the forecasting method, comparing the Naïve Bayes and Decision Tree algorithm in getting the best accuracy value, by analyzing the learning mode offline to online.
Human Signature Verification System Using CNN with Tensor flow

One of the most popular verification biometrics is the signature. On checks, forms, letters, applications, minutes, and other documents, handwritten signatures are required. A person's handwritten signature must be individually identified because each individual's signature is unique by nature. Signature verification is a popular technique for confirming anyone's identity while they are not present. Human verification can be inaccurate and occasionally unsure. The use of Convolutional Neural Networks (CNN) for Writer-Dependent models in signature verification is examined in this research. In order to create forged signatures, random distortions were created in real photos using an auto encoder and then fed to the classifier during training. In addition to demonstrating various test outcomes for varying the number of training sets of images, the study describes all image pre-processing procedures that were applied to the image. In the Persian dataset, the system's average test accuracy is 83% after 22 real photos were used to train it. When the model was trained on nine real photos, accuracy dropped by 9.4%. Key Word: Offline Signature Verification, WD (Writer Dependent), CNN (Convolutional Neural Network), FAR (False Acceptance Ratio), FRR (False Rejection Ratio), Auto encoder
Assessment of the Implementation of Mother Tongue Based-Multilingual Education in the Province of Benguet

The Mother Tongue Based- Multilingual Education (MTB-MLE) Program has been in operation for over ten years. While the program has had positive effects, perceived negative impacts have also been noted. The study focused on predicting the implementation of MTB-MLE in the Province of Benguet. It aimed to determine the positive and the negative effects of the program and the challenges currently encountered by teachers providing base data on the areas of the program needing improvement. The qualitative approach to research has been employed engaging the elementary school teacher participants in interviews. The responses eventually went through the three coding levels. Results revealed that the use of the mother tongue as medium of instruction helps the learners understand the lesson easily, encourages active participation in class, and assists learners better in reading. Contrastingly, the perceived negative effects include hindering the learning and use of two languages, impeding the grasp of the concepts in the next grade level, and reduction of learners’ confidence in using the second/target language. Meanwhile, the challenges teachers encounter cover lack of teaching materials and heterogeneity of their and the learners’ first language (L1). With the program’s implementation through time, while implementers wish to invest on the program strengths, challenges still post limits and pressures. It is therefore suggested that attention is paid to addressing the weaknesses striking a balance with the strengths by providing teacher trainings towards addressing the linguistic considerations and developing instructional materials that suit the teaching-learning needs.
Miscarriage Australia: the use of a human centered design approach to design and develop a website for those affected by miscarriage

Background Past research has shown that Australians affected by miscarriage want a website specific to both miscarriage and their local region that is accessible, comprehensive, evidence-based and informed by experts. The aim of this study was to design, develop and evaluate the Miscarriage Australia website using human centered design. Methods A four stage human centered design approach was used to develop the Miscarriage Australia website which aimed to: (1) Understand the issue and why users need a website; (2) Define users’ specific needs; (3) Design solutions to meet those needs; and (4) Evaluate the design by testing with end users. Across the four stages, various types of data and data analysis were developed and utilized including interviews, desktop research, development of personas and tone of voice, followed by usability testing. Process and content were guided by designers, developers and an expert advisory committee of key stakeholders. Results Analysis and synthesis of user research across Stages 1 and Stage 2 found 11 key themes pertaining to user’s miscarriage experiences and support needs. Using the themes, common experiences, goals, motivations and behaviors of users were identified and similar user types grouped and used to inform the development of two personas. Using the personas and user research findings, design elements (Stage 3) including the “tone of voice guidelines” were developed recommending the Miscarriage Australia website be calm, empathetic, hopeful and authoritative. The tone of voice guidelines guided branding and over 100 pages of content was informed by the research team and reviewed by a 13-member Expert Clinical Advisory Committee over two rounds to ensure it was evidence based and reflected best practice. Using a contextual inquiry approach, usability testing was undertaken with 8 end users to test a low fidelity mockup and high-fidelity prototype of the website. Overall, end users reported the website was highly acceptable in terms of the design, content, layout, language and terminology, describing it in line with the intended tone of voice. Users reported the website was easy to use and navigate and provided useful and appropriate content and resources. Minor areas for improvement included slight changes to specific images, improved links for navigating sections, and a title change to one section heading. Conclusion The Miscarriage Australia website was successfully implemented and commended by users as meeting their needs. As a result of using human centered design, the Miscarriage Australia website provides an ideal template or blueprint on how to develop a successful and useful digital resource for users, particularly around sensitive women’s health issues.
An Active Cyber Insurance Policy Against Cybersecurity Risks Using Fuzzy Q-Learning

Insurance is an extremely diversified from one clear-cut policy to another depending on a rising demand. Internet based businesses are a booming industry and falls under categories of small, medium and enterprise. It is no exception that cybersecurity risks are exponentially presents as a continues fears in this business environment. To ensure this insecurity is handled effectively, cyber insurance policies introduced by insurance companies. However, the nature of cyber risks must be addressed in much more robust and complex algorithms. Autonomic computing with the combination of Fuzzy and Q-Learning is introduced to ensure active policies are ready to handle the uncertainty and together with the ability to learn and mitigate the unrest situation.
Developed Module for the Course Teaching Mathematics in the Primary Grades: Its Development, Validation, and Effectiveness

The insufficiency of learning materials can have far-reaching negative consequences that can hinder the quality of education and limit students' opportunities for success. Moreover, the insufficiency of learning materials particularly for courses under the new curriculum is a pressing issue among higher education institutions including, among others, the Ilocos Sur Polytechnic State College – Cervantes Campus. Descriptive Developmental Research endeavored to scientifically develop a module for the course Teaching Mathematics in the Primary Grades grounded on the learner-centered frameworks, validated, and effective. Recency, validity, and relevance were the criteria for choosing the documents/literature for the development of the module`s framework. Thirteen (13) experts in the field validated the study. Twenty-one (21) students served as respondents during the testing. As to the developed framework, the developed module is self-contained, self-Instructional, user Friendly, adaptive, and consistent. Its contents are primarily based on CMO No. 74, s. 2017. It is of quality and effective. This means that the course module has helped improve the BEED students` performance. With this, the paper recommends the utilization of said module, after subjecting it to system-wide evaluation.
ALGORITHMS FOR CONSTRUCTION, CLASSIFICATION AND ENUMERATION OF CLOSED KNIGHT’S PATHS

Two algorithms for constructing all closed knight’s paths of lengths up to 16 are presented. An approach for classification (up to equivalence) of all such paths is considered. Two closed knight’s paths are called equivalent if one can be obtained from the other by applying one or more of the equivalences: translation, rotation, symmetry, or when the corresponding polygons (whose vertices are the cells visited by the knight), are geometrically congruent. By applying the construction algorithms and classification approach, we enumerate both nonequivalent and non-self-intersecting knight’s paths and show the obtained results. Some pedagogical aspects related to the problems under consideration and the teaching of subjects such as “Programming”, “Algorithms and Data Structures”, “Graph Algorithms” and “Competitive Programming” are also discussed.
GAME-BASED PROGRAMMING TRAINING FOR BEGINNERS IN THE PYGAME ZERO ENVIRONMENT - SAMPLE ASSIGNMENTS WITH PYTHON TURTLE GRAPHICS

In recent years, an increasing digitalization has been observed in all spheres of life on a global scale. The demand for specialists in the field of ICT requires their preparation to start at school with the study of programming and IT. Good practices and increasing interest in game-based learning are observed at all levels of secondary education. Different environments, programming languages and learning approaches is used in the Computer Modeling and Informatics disciplines. The focus of the article are sample tasks and methodical implementation for nonprofiled training in programming for high school students by creating games. The terminology related to game-based learning and the role of tasks in mastering learning content are presented. The main focus is on creating fun games implemented in Python programming language using turtle graphics in Mu programming environment. The sample tasks are supported by methodical instructions. Guidelines are also given for their use in training. In the conclusion, the possibilities for training in programming in profiled and non-profiled high schools are discussed.
LAYANAN BIMBINGAN KELOMPOK MENGGUNAKAN TEKNIK DISKUSI UNTUK SISWA KECANDUAN GAME ONLINE DI SMA AL AMANAH CIWIDEY

This study aims to obtain an overview of group guidance using discussion techniques forstudents who are indicated to be addicted to online games at SMA AL Amanah Ciwidey, toobtainresultsfromtheimplementationofgroupguidanceservicesusingdiscussiontechniques for students who are addicted to online games at SMA AL Amanah Ciwidey,Student responses after being given groupguidance services using discussion techniquesfor students who are addicted to online games at SMA AL Amanah Ciwidey and theconstraintsofBKteacherswhenprovidinggroupguidanceservicesusingdiscussiontechniques for students who are addicted to online games at SMA AL Amanah Ciwidey.Thisresearchisadescriptivestudywithaqualitativeapproachwhereobservations,interviewsanddocumentationareusedfordatacollection.Keywords: GroupGuidance,DiscussionTechniques,OnlineGameAddiction
Sky Detection in Outdoor Spaces

Sky detection involves detecting the pixels in an image or video that corresponds to the sky. Horizontal and background information as well vision-based autonomous ground robot navigation is obtained from the sky region in an image. For sky detection, various existing methods are being used. These include techniques of computer vision, probability models, and a number of different machine learning algorithms. Various parameters affect the accuracy of a sky detection algorithm. One major factor is haze or smog in the sky. Because of haze, dust, smoke, and other dry particulates conceal the clarity of the sky. Some other factors include time of the day, weather, and season. Hence, by taking these factors into consideration, this paper aims at building a model that detects the sky in the image that was taken. The model is developed using existing algorithms of machine learning.
Detection of Moving Object Using Modified Fuzzy C-Means Clustering from the Complex and Non-stationary Background Scenes

Detecting moving things in a video sequence is tough, and reliable moving object identification in video frames for computer vision application is a difficult challenge to solve. In many vision applications, object detection is a crucial step in automating video analysis. The effectiveness of object detectors and trackers has substantially increased due to the rapid growth of deep learning (DL) networks and GPU processing capability. This paper proposed a novel technique in moving object detection using clustering and feature fusion techniques. Here the video dataset has been processed and segmented for clustering process using modified fuzzy C-means (MFCM) clustering, and then the clustered video frames have been processed for feature fusion. So feature fusion is carried out using Maximum Likelihood Optimisation Pattern Matching (MLOPM)-based convolutional neural network (CNN) fusion technique. The experimental results show segmented video frames and parametric examination for the video frames in recall, precision, accuracy, F-1 score and RMSE.
The usefulness of online learning on quality of education during COVID-19 pandemic: evidence from Jordanian universities' students

The research aimed to illustrate the usefulness of online learning on quality of education during COVID-19 pandemic collecting the evidence from Jordanian universities' students. The research design of the study was based on quantitative data which was gathered from students belonging to Jordan universities. In addition, a close-ended survey questionnaire was used for collecting data, and the sample size on which relevant tests were conducted was 316. The results were analysed using structural equation modelling (SEM) which included confirmatory factor analysis (CFA) and assessment of the proposed path. The tests were conducted on SmartPLS. The results showed that online system of education in Jordanian universities during COVID-19 was dependent on various factors such as: institutional support, technology infrastructure, online teaching expertise, students' readiness to move online, and development of course presenting.
Resource Recovery Vehicle Picking Up Resource Recovery Bin Robot Arm Structure Design

In recent years, domestic waste is increasing day by day, which puts forward higher requirements for waste recycling capacity. In order to make the garbage collection process more efficient, the structure of the garbage collection manipulator is optimized. Firstly, the simulation software UG nx12.0 is used for three-dimensional modeling, and then the virtual simulation software ADAMS is used for kinematic simulation analysis. Finally, the finite element static analysis and modal analysis are carried out for the whole manipulator. The results show that the optimized manipulator has no resonance in the recovery process and operates stably.
Parallel progressive-based inductive subspace and fuzzy-based firefly algorithm for high ensemble data clustering

Recently, the framework is proposed with techniques such as random subspace and constraint propagation for handling high dimensional data ensemble clustering. Huge dataset clustering is difficult for conventional sequential clustering methods since it requires higher computation time. Distributed parallel processing and methods are consequently useful towards attaining results and scalability constraints of clustering huge datasets. So, in this work, the parallel progressive-based inductive subspace ensemble clustering (PPISEC) algorithm is introduced with the concept of MapReduce (MR) to perform high dimensional data clustering. Depending on micro-clusters and correspondence relative, the clustering method is designed which is easily parallelised via MR and completed in moderately with a small number of MR rounds. However, in the PPISEC algorithm, the centroid values are selected with the help of an improved support vector machine (ISVM) classifier. Thus, the incremental ensemble member chosen (IEMC) progression is performed with fuzzy-based firefly algorithm (FFA), and the normalised cut algorithm is established to accomplish high dimensional data clustering. The outcome shows that, the proposed PPISEC framework, which performs well on three benchmark samples by utilising high dimensionality and enhanced the results than the conventional clustering ensemble approaches.
A new method for human activity identification using convolutional neural networks

Body fitness monitoring applications are using mobile sensors to identify human activities. Human activity identification is a challenging task because of the wide availability of human activities. This paper proposes a novel technique that extracts the discriminative dimensions for human activity identification. Particularly, a novel technique with convolutional neural networks (CNN) is used for catching dependency. A deep convolutional neural network (DCNN) consists of two different types of layers, convolutional and pooling are used. The depth of each filter increases from left to right in the network. Three activities like walking, running, remaining still are collected from smart mobile sensors. The axis like x, y, and z information was transferred with column vector magnitude information and utilised for studying or training CNN. Experimental results show that CNN-based method achieves 93.67% accuracy than the baseline random forest approach's 89.20%.
Novel task assignment policies using enhanced hyper-heuristic approach in cloud

Cloud computing plays a vital role in all fields of today's business. The processor sharing server farm is one of the most used server farms in the cloud environment. The key challenge for the mentioned server farms is to provide an optimal scheduling policy to process the computational jobs in the cloud. Many scheduling policies were introduced and deployed by the existing approaches to build an optimal cloud environment. The existing approaches of the heuristic algorithms such as meta-heuristic and hyper-heuristic approaches were the most frequently used scheduling algorithms for the past years. These approaches work well only in the limited types of tasks and resources in a processor sharing server farms in the cloud environment. In the proposed system, novel task assignment policies have been proposed by enhancing the hyper-heuristic approach for the type low task and high resource in the cloud environment. The results of the proposed approach are compared with the existing approaches and the performance evaluation of the proposed approach is also done. As a result, the proposed enhanced hyper-heuristic approach performs well for processor sharing server farms in the cloud environment.
Hybrid dense matching features for cloud-based face recognition

Cloud computing is a computing service done not on a local device but an internet connection to a data centre infrastructure. The cloud computing system also provides a scalability solution where cloud computing can increase the resources needed when doing larger data processing. In recent years, face bio-metric plays an important role in biometric authentication, where security is the primary concern. The two major difficulties to be addressed in face recognition are illumination and pose variation. This work proposes a cloud computing-based face recognition technique by consolidating four-patch LBP and the local landmark features to the acquired feature vector. After that, the SVM is utilised to recognise the individual. The feature set used for training and testing is also made available in the cloud. At some stage in the training phase, the kernel parameters of SVM are optimised with GWO to improve the recognition performance. This method yields a maximum recognition accuracy of 99.00% with LFW and 98.0% accuracy with YTF.
Azure Storage Ecosystem: Overview and Development with Azure Blob Storage

Whenever I run an AZ-204 (Azure Developer Fundamentals) training session, I always start with Azure Storage. Storage is not necessarily sexy, and it’s not the first thing you often think about when getting ready to study for an Azure exam. However, storage is the essential backbone to pretty much anything you do in Azure.
Sizing and Deploying a VMware Cloud on AWS SDDC

In this chapter, I will talk about the key elements needed to plan, size, and deploy your first software-defined datacenter (SDDC) on VMware Cloud on AWS from application discovery to actual design considering high availability, scalability, and security.
Operating VMware Cloud on AWS

This final chapter provides the basic understanding on how to deploy and manage all the underlying components that make the VMware Cloud on AWS SDDC including compute, storage, and networks.
Securing Workloads on VMWare Cloud on AWS

This chapter provides the basic understanding on how VMware NSX is integrated into VMware Cloud on AWS to deliver the virtual networking features that customers need to run their workloads on the cloud.
Azure Container Ecosystem: Azure Container Registry, Azure Container Instances, and Azure Container Apps

If you have been working in Microsoft technologies for some time, you may have little exposure to containers. In some cases, it’s possible that you’ve never been exposed to containers at all. Additionally, you might be in a scenario where you feel that you will never need containers. If this describes you, this chapter can help you realize the power and potential of containerized applications.
Implement Caching for Solutions

Up to this point, every request made on your website is going directly to your Azure App Service instance. This includes all of the dynamic content such as calls to authenticate the user. This also includes the delivery of all of the content that is static, such as the images, Cascading Style Sheets (.css) files, and JavaScript (.js) files.
Introduction to VMware Cloud on AWS

Today, companies are faced with challenges that increase the risks and complexity of and add an additional layer of costs to the migration into the public cloud. VMware Cloud on AWS enables customers to accelerate their cloud migration in the simplest, fastest, and lowest-risk way with compelling TCO. It not only allows customers to extend the capacity to the cloud but also helps prepare for application modernization by easily leveraging cloud-native services from AWS.