Reviewerly/exHarmony huggingface dataset:
Educational value chain with industry 4.0 technologies

The world is fast changing with immersive technology applications. In the next few years we will be seeing many new applications with Artificial Intelligence from big names, such as Google, Facebook, Amazon, Microsoft, Oracle and most recently Oracle with their cloud services. Fantasy has lured the educational environment to adopt augmented and virtual reality applications into the teaching and learning ecosystems. Today's technology has enabled us to work more effectively with learners in everyday, everyday settings and at school and helps us cultivate a collaborative learning environment, enabling us to develop and maintain the technology that the public wants, and that which can work on a community level. Springing up in the evolution, the pandemic has affected the quick implementation of digital technologies to be used for communication, sharing of data across the digital space, reaching unimaginably across all the nooks and corners of the world. In doing so, it has also allowed for the establishment of a world where the need for a single, decentralized 'platform' can be harnessed to promote social and economic growth and productivity. The content or data applications enhance the delivery capacity and understanding capability appropriately, to the specific process that is unique to the domain. The digital technologies available for our connected cities will accelerate the adoption and development of this emerging digital economy. There are more software and intelligence networked applications such as Simulations, and Big data which are systemic integrated through cloud computing, with appropriate cyber security, and comfortably assured with the 5G mobile telephony networks, propositioning accessibility, prompt delivery at and low cost and latency in connection. Thus, this industry 4.0 ages prompts to promote visualization, personalization, gamification and problem solving that are the essence in the changing pedagogy for a 24x7 erudite. The future is going to mean more growth and innovation. The past is no more over, but creates a vision which has in store something positive in the future, and hope of ever succeeding. This review paper covers the post-COVID changes involving a radical change in education across the world that impacted by Industry 4.0.
Transition from Online to Offline Learing during the Covid-19 Pandemic in SMAN 4 Kediri

During pandemic covid 19, students in different schools will face different problems in adapting the transition from online to offline learning. However, each school or class has different characteristics and obstacles. Therefore, this study would like to observe one of senior high schools in Kediri, East Java to find out the obstacles that could be found by the teachers and students based on teachers’ perception and classroom observation. The researcher used a qualitative descriptive approach in this study. The results show that the main problem is the students’ motivation in learning English. Besides, students’ condition at home and the use of gadgets during online learning also influence the students’ learning process even until the offline class is implemented again. The researcher also provides the recommendation strategy from some other studies to solve this kind of problem based on the observation and teacher’s interview. Considering, students’ intrinsic and extrinsic motivation in learning English are recommended. Gamification can be a good method to increase students’ motivation during the learning process.
PENGARUH METODE PEMBELAJARAN REWARD DAN PUNISHMENT TERHADAP KEAKTIFAN BELAJAR SISWA SD KELAS V

The purpose of this study was to determine the effect of the application of reward and punishment learning methods on student learning activity in fifth grade mathematics at SD Negeri Tempaling. This research is a tye of quantitative research with quasi experimental methods. The instrument used was a questionnaire in the form of an observation sheet. The population inthis study werw fifth grade student at Tempaling Elementary School eith a total of 25 students. The data analysis technique used is normality test and regression linierity test as a prerequisite analysis test, descriptive data analysis, regression linierity test,significance test, sample linear regression test and the coefficient of determnination (D). The results showed that there was a positive and significant influence between the application of the reward and punishment learning method (X1) on student learning activity (Y) in class V at Tempaling Elementary School
Student perspectives of hybrid delivery in a transnational education context during Covid-19

The Covid-19 pandemic created the need for (often rapid) transitions to online delivery in many higher education settings. As the world transitioned to living with the pandemic, the need to adequately support students with health concerns about returning to campus, either for themselves or those with whom they were living, plus a range of other access issues, created the need to shift to a hybrid delivery mode in many places. This study examines the student experiences of hybrid delivery in a TNE branch campus in the United Arab Emirates and their suggestions for how hybrid delivery could be improved for future delivery. The study employed open-ended survey questions to ask about the most effective, least effective and improvement areas from students who were enrolled in hybrid courses. The responses were analysed, and after examining the data provided in the survey, some measure of success in implementing a hybrid delivery model has been identified. However, a number of areas for concern were also raised in the responses. Additionally, students also identified solutions to some of the problems to improve the quality. Issues with technology, engagement, support and the benefit of remaining online in hybrid mode were some of the least and most effective issues raised by the students.
Mobile Robot Navigation

The mobile robot navigation is a crucial capacity of the mobile manipulator to localize its own positions in the reference frame and then plan a collision-free path toward some goals. Core features of the mobile robot navigation should be the localization and mapping, environmental modeling, and path planning. Here, the localization and mapping helps robots to explore and understand their working environments. In another role, the environmental modeling is implemented to check traversable directions during the navigation. Namely, the robot model, robot states, and the environmental information are updated frequently to help the mobile robot to avoid the environmental constraint violation and the obstacle collision. Finally, the path planning expresses two problems, including global planner (for the path generation) and path tracking (to track the generated path). In this chapter, these core features of the robot navigation are presented.
Manipulator Manipulation

To manipulate the targeted objects during the execution of the designed tasks, the manipulator on the mobile robot plays a crucial role. In the chapter Robot System Analysis, transformation coordinates as well as kinematic and dynamic relationships among joints and links of the manipulator are theoretically presented. However, to describe a robot in a software system, it is necessary to use a standardized file format. After that, trajectory planning and control should be conducted also. This chapter briefly presents the manipulator manipulation work of the proposed robot system, regarding the formulation of the manipulator modeling, trajectory planning, and control. These works are conducted by using formats of the Robot Operating System 2.
A spectrum of surveillance: Charting functions of epistemic inequality across EdTech platforms in the post-COVID-19 era

Covid-19 and the public health policies emerging in response have laid bare a multiplicity of issues related to educational access and knowledge equity on a global scale. Among these, the quick shift to online and hybrid education models led teachers to adapt a plethora of digital platforms to deliver content and sponsor interactions). Such platforms range from institutionally sanctioned (and subscribed) Learning Management Systems (LMSs) to software provided by organizations beyond the institution and can pose a threat to student data and privacy. Data surveillance in educational contexts is not a new issue, nor is it only a strictly digital problem. However, the current milieu of constant and continuing public health crises has led to more frequent, uncritical, and hurried adoption of learning technologies. This article challenges professionals in higher education specifically to take a more critical look at the various EdTech platforms they are, have, and will adopt in the post-COVID-19 era, and the spectrum of surveillance such platforms enact. Through a review of common entities such as LMSs, Google Workspace for Education, and Zoom video conferencing software, this article demonstrates how these technologies place both teachers and students in a relationship to data and learning characterised by “epistemic inequality” or “unequal access to learning imposed by private commercial mechanisms''. By taking a closer look at the problematic surveillance functioning across EdTech, this article makes a case for Commons-based Peer Production communities as equitable, open educational alternatives that have resisted market-based neoliberalism and surveillance capitalism.
Cluster Analysis of the Teacher – Applicants to be Enlisted in the Registry of Qualified Applicants for Public School Teachers: Basis for Policy Formu-lation

The Recruitment, Selection and Placement Core Group of the PRIME-HRM of the Department of Education Division of Butuan City conducted this research mainly to address the issues on the assessment of the Teacher applicants particularly in the scoring system which may eventually be hired in the Department of Education. To identify gaps in assessing teacher competencies, the researchers used the K-means cluster analysis to generate knowledge that could be the basis for policy formulation. An applicant can be in the Registry of Qualified Applicants if he/she passes the criteria in the selection relative to their qualifications and competencies. The researchers found two issues that need to be addressed. First, it is the experience and the English Proficiency Test (EPT) results. Experience is necessary because teaching is a skill which requires expertise, but we do not prioritize it over other criteria. Another gap is the English Proficiency Test result. English is an essential factor in the teaching profession since it is our second medium of speech and teachers are facilitators in the learning process of pupils and students. Thus, there is a need to revisit the EPT tool and propose modification if deemed necessary; English skills enhancement is duly recommended from Senior High School to Higher Education to develop mainly its basic communication proficiency; newly hired teachers will undergo training to enrich English aptitude; enhance the policy on recruitment process.
Implementation of Educational Technology in the Development Area in Christian Religious Education in the Digital Age

Along with effort transformation in field education , technology education have very important role. In article this outlined about application technology education area development. For get inside data article this, done study descriptive qualitative with use method observation and interview to three lecturers Christian religious education and some students in the faculty psychology, faculty agriculture and faculty technique. From fifth area technology education, by special article this discuss application technology education area development in Christian religious education in the digital age concerns aspect technology print, audio visual technology, technology based computer and multimedia technology.
A Rhetorical Analysis of the Agile Manifesto on its 20th Anniversary

The Agile Software Development (ASD) method is guided by the Agile manifesto which consists of an Agile philosophy and a set of 12 principles. However, despite the indisputable impact of Agile philosophy and principles on software development around the world, the phenomenon of its popularity has not received requisite attention by researchers. In this article we use the rhetorical analysis by focus group of eight industry experts and academics to understand the appeal and attractiveness of ASD to the software development community. We discover that the time was ripe for its introduction with many paradigms and established approaches getting challenged due to rapid pace of technological changes and rise in business uncertainties. The Agile manifesto and its principles tapped into the mood of the moment and perhaps unwittingly through an amalgamation of popular theories of the time from management and manufacturing created a movement generally welcomed by the software developers, but which intrigued many among the software engineering community who were traditionally strong adherents of Taylorism and its principles.
What more can Software Development Learn from Agile Manufacturing? Some Pointers on the 20th Anniversary of the Agile Manifesto

The concept of agility originated in manufacturing and was later adopted by the software development discipline. In this article we argue that in the process some important aspects of the agility theory have been either ignored or misinterpreted. A historical review of the evolving paradigms and practices in software development and manufacturing on the 20th anniversary of the Agile Manifesto (2001) suggests that if the ideas and principles underlying agility are faithfully implemented it would lead to significant improvement in the software development process.
CMOS-enabled Activation Functions for Low-power Hardware Neural Networks

&lt;p&gt;A hardware neuron that can generate a broad class of activation functions is introduced. The requirements on the explicit form of activation functions within neural networks is not stringent. It is shown that the use of standard transistors can readily provide a variety of nonlinear transfer functions that can serve as activation functions. The similarities and differences of the hardware generated activation functions to commonly used mathematical activation functions are presented. A hardware neural network (HNN) based upon the neuron design is evaluated for pattern recognition. The accuracy and robustness of the HNN for the application is found to be comparable to its software counterpart even with moderate performance degradation due to perturbations to the inputs and component variability. This CMOS compatible neuron allows for HNN designs that are readily manufacturable leading to a practical, low-power platform for including HNNs into applications such as the Internet of Things (IoT) and systems-on-a- chip (SoC).&lt;/p&gt;
Retraction of: P094 Sleep quality assessment among Chronic Obstructive Pulmonary Disease patients in a tertiary teaching hospital

[This retracts the article DOI: 10.1093/sleepadvances/zpac029.164.].
Research on the Curriculum System of Electronic Information Engineering for the Certification of Engineering Education

The curriculum system is very important in the professional training program, and it is also an important inspection link in the professional certification of engineering education. For electronic information majors, some common problems will be encountered when setting up and optimizing the curriculum system. This paper takes the electronic information engineering major of Beijing University of Posts and Telecommunications as an example to introduce the cases in the curriculum system.
AgriBlockchain: Agriculture Supply Chain Using Blockchain

This paper proposes a smart architecture for secure agriculture supply chain management based on blockchain technology. The architecture of multiple entities involved in the agriculture supply chain, as well as their roles and interactions with other entities, is discussed in this paper. Smart contracts are used between the farmer and the customer, the farmer and the delivery partner, customer and delivery partner as well as the farmer and the quality assurance authority. The remix IDE is used to create the decentralized system, which is based on solidity programming smart contracts. During the execution of the proposed model, the size of the blockchain transaction submission message and the message interaction between nodes is compared. In this paper, we have also performed security analysis of the proposed model to check its robustness against cyber attacks such as impersonation, man-in-the-middle and non-repudiation.
Federated Learning Assisted Deep Q-Learning for Joint Task Offloading and Fronthaul Segment Routing in Open RAN

Offloading computation-intensive tasks to edge clouds has become an efficient way to support resource constraint edge devices. However, task offloading delay is an issue largely due to the networks with limited capacities between edge clouds and edge devices. In this paper, we consider task offloading in Open Radio Access Network (O-RAN), which is a new 5G RAN architecture allowing Open Central Unit (O-CU) to be co-located with Open Distributed Unit (DU) at the edge cloud for low-latency services. O-RAN relies on fronthaul network to connect O-RAN Radio Units (O-RUs) and edge clouds that host O-DUs. Consequently, tasks are offloaded onto the edge clouds via wireless and fronthaul networks, which requires routing. Since edge clouds do not have the same available computation resources and tasks’ computation deadlines are different, we need a task distribution approach to multiple edge clouds. Prior work has never addressed this joint problem of task offloading, fronthaul routing, and edge computing. To this end, using segment routing, O-RAN intelligent controllers, and multiple edge clouds, we formulate an optimization problem to minimize offloading, fronthaul routing, and computation delays in O-RAN. To determine the solution of this NP-hard problem, we use Deep Q-Learning assisted by federated learning with a reward function that reduces the Cost of Delay (CoD). The simulation results show that our solution maximizes the reward in minimizing CoD.
ANALISE DO PERFIL DEGLUTITÓRIO DE PACIENTES EXTUBADOS PÓS COVID-19 EM HOSPITAL PARTICULAR DE MINAS GERAIS

INTRODUCTION: The manifestations of the Covid-19 virus, begins in fever, cough, fatigue, upper respiratory tract infections, taste and smell modification. Worsening can happen with the emergence of comorbidities, requiring prolonged orotracheal intubation in ICUs, which may present risk factors for dysphagia and losses such as aspiration pneumonia, malnutrition and even death. OBJECTIVE: To verify which symptoms are related to swallowing in patients after orotracheal intubation by Covid-19. METHODS: Cross-sectional, descriptive and quantitative study, performed analysis of medical records of post-intubation patients due to COVID-19, admitted to the ICU of a hospital in Minas Gerais, according to the inclusion and exclusion criteria of the study. RESULTS: 23 participants met the research inclusion criteria, (n = 11) were male and (n = 12) were female, with a mean age of 56.13 years, standard deviation ±14.27 years, of these 60 .9% (n = 14) age group &lt; 60 years and 39.1% (n = 9) aged ≥ 60 years. All patients included (23) had a nasogastric (NG) or nasoenteric (NET) tube and 78.3% (n = 18) were using tracheostomy. CONCLUSION: The most reported symptoms are the reduction of saliva and dyspnea, oral cavity between open for long periods together with drug administration generated dryness of the oral and pharyngeal mucosa providing post-tube breathing difficulties. Symptoms of odynophagia and dysphonia are changes in laryngeal biomechanics related to prolonged intubation time. Therefore, numerous changes related to post-extubation swallowing by COVID 19 were reported. KEYWORDS: Dysphagia; Post Orotracheal Intubation; SARS-CoV-2.
System nonlinearity correction based on a multi-output support vector regression machine

In a fringe projection profilometry system, the phase error introduced by the projector's gamma distortion is the main source of errors. To overcome this problem, we present a phase compensation scheme for multi-dimensional harmonic coefficient prediction based on a multi-output support vector regression machine(M-SVR), The scheme first constructs a significant characteristic relationship between phase probability density function (PDF) and phase multi-harmonic coefficients, creates simulation data without a priori knowledge, constructs a data set with a certain sample size, and then trains the M-SVR model. The trained M-SVR model is used to capture the potential features of the experimental distorted phase and output the multi-dimensional harmonic parameters with nonlinear relationships, followed by error compensation of the distorted phase using an immobile point iteration algorithm for the purpose of correcting the system nonlinearity. We demonstrate the validity and stability of the model through simulation and experimental trials. Most importantly, the preprocessed M-SVR model also has the potential to participate in error correction of other measurement experiments with reasonable sample and hyperparameter settings, which greatly saves the time and cost of multiple experiments.
Animalia Detection Using Machine Learning

&lt;p&gt;Animal recognition is the need of the hour specially in a country like India. This paper provides the details of the safari system which is implemented with ease and in the best possible way. It involves the usage of efficient algorithms and technologies available to us. We have kept in mind the accuracy, cost and skill required for the implementation of the project in the course of conceptualization as well as formulation of code. We have used the YOLO V3 object detection algorithm to identify the animals. The Firebase stores the change in data as an object appears in front of the camera. This triggers the app and data are sent to the nearest found applications i.e., the safari van in use. &lt;/p&gt;
Coherent long-time integration and Bayesian detection with Bernoulli track-before-detect

&lt;p&gt;We consider the problem of detecting small and manoeuvring objects with staring array radars. Coherent processing and long-time integration are key to addressing the undesirably low signal-to-noise/background conditions in this scenario and are complicated by the object manoeuvres. We propose a Bayesian solution that builds upon a Bernoulli state space model equipped with the likelihood of the radar data cubes through the radar ambiguity function. Likelihood evaluation in this model corresponds to coherent long-time integration. The proposed processing scheme consists of Bernoulli filtering within expectation maximisation iterations that aims at approximately finding complex reflection coefficients. We demonstrate the efficacy of our approach in a simulation example.&lt;/p&gt;