Reviewerly/exHarmony huggingface dataset:
Breaching the Education Boundaries: COVID, Classrooms and Shifting Technologies

Globally, the COVID-19 pandemic has had an impact on education and schooling, including in the Eastern Caribbean, forcing the shift to the online environment. Ironically, for many years, the notion of integrating technology into the teaching and learning transaction was resisted by many educators in the region. Yet, it is this resource that provided an avenue for reasonable forms of education and schooling to take place. Indeed, the pandemic opened possibilities in education that might otherwise have gone unnoticed and underused. These possibilities align with the Global Cultural Flows Theory (Appadurai 2006), and one dimension in particular, technoscapes, which speak to the rapid movement of technology. Technology can circumnavigate physical boundaries that could be hindrances under other circumstances. Despite the initial reluctance to adopt the technology in the face of the pandemic, viewing it through the lens of technoscapes sheds light on the way in which said technology can breach the boundaries of national education to support the continuance of schooling in the Eastern Caribbean. This chapter explores these issues in this context. It takes into consideration the influence of the technology on different groups in the community, focusing on principles such as equity and inclusion. It examines how different groups are affected using technology for education and schooling. It also interrogates what Ministries of Education and other education stakeholders have been doing, and the impact of their actions, whether positive or negative. It provides some practical recommendations.
The Effect of Flashcard Learning Media in Developing Cognitive Children Aged 5-6 Years in Al-Mauizhah Kindergarten, Silau Jawa Village, Bandar Pasir Mandoge District, Academic Year 2022/2023

This study aims to determine the effect of using flashcard learning media in developing the cognitive development of children aged 5-6 years in Al-Mauizhah Kindergarten, Silau Jawa Village, Bandar Pasir Mandoge District, Academic Year 2022/2023. This type of research is a quantitative research (quasi experiment) with the form of One Group Pretest-Posttest Design. The population of this study were all class B children at Al-Mauizhah Kindergarten, Silau Jawa Village, Bandar Pasir Mandoge District, 2022/2023 Academic Year, with a total of 30 children. The sampling technique is by looking at the list of the lowest scores from the teacher's data, namely 10 children. The data collection instrument in this study used observation and documentation sheets. Data analysis technique using non-parametric statistical Wilcoxon test. The results showed that the flashcard media from the calculation of the Wilcoxon Rank-K Test, the Z value was close to -2.825b and the p value (Asymp.Sig 2-tailed) was close to .005. It can be concluded that the "Hypothesis is accepted" because the number .005 is smaller than 0.05. There are differences in the results between the results of the use of flashcard media in the pretest and posttest, so it can be concluded that the use of flashcard media has an effect. about the Cognitive Development of Children Aged 5-6 Years in Al-Mauizhah Kindergarten, Bandar Pasir Mandoge Village, Academic Year 2022–2023.
Youth Empowerment in Prevention of Drug Abuse in Muara Badak Ulu Village, Muara Badak District, Kutai Kartanegara Regency, East Kalimantan Province

This drug problem is very troubling among teenagers. Where more and more teenagers are involved in drug abuse. Adolescence is a very appropriate target to be used as a strong foundation in preventing the increasingly widespread drug abuse. The aim of this service is to empower teenagers in preventing drug abuse in Muara Badak Ulu Village, Muara Badak District, Kutai Kartanegara Regency. The method used is through lectures and discussions. The results of community service activities for 10 teenagers in Muara Badak Ulu Village, Muara Badak District, are that it is hoped that teenagers can fortify themselves with faith and positive activities so that they can avoid drug abuse and become a successful and happy generation in the future.
The Effect of the Educational Game "Edu-Games Bobby Bola" on Cognitive Ability (Using Number Symbols to Count) Children Aged 5-6 Years at Pembina Tanjung Morawa State Kindergarten 2022/2023 Academic Year

This research was conducted with the aim of knowing the effect of the educational game "Edu-Games bobby bola" on cognitive abilities (using number symbols to count) of children aged 5-6 years at Pembina Tanjung Morawa Kindergarten. The study population consisted of all children aged 5-6 years, totaling 32 students. The research sample consisted of 16 students who were taken using simple random sampling. Data was collected using the test method, namely the pretest and posttest, and documentation, namely the results of students' cognitive abilities, so on the data were analyzed using the Wilcoxon marked ranking test. The results showed that the educational game "Edu-Games bobby bola" had a significant effect on the cognitive abilities (using number symbols to count) of children aged 5-6 years at Pembina Tanjung Morawa Kindergarten. Based on the findings of this research, it is suggested for teachers to continue working to increase productivity towards new skills and knowledge, especially in implementing the educational game "Edu-Games Bobby Bola".
Design of Scalability Enabled Low Cost Automatic Speaker Recognition System Using Light Weight Multistage Classifier

AbstractProtracted learning processes and dedicated computational power are the quality determinants in automatic speaker recognition (ASR). Conventionally, speakers are separately modelled using their acoustic features and surrounding environment features. Analytical speaker modelling via the Gaussian mixture model (GMM) and the Universal Background Model (UBM) is predominant over invariant speech condition models. A high number of speakers, fluctuated surroundings, and limited capacity processors are persisting performance barriers in both analytical and automatic ASR models. This paper argues on the ASR scalability approach which ensures fine model performance under a large number of speakers with varying biometrics. It also aimed to bridge performance-hindering barriers via using Light Weight Multistage classification. Consequently, 100% recognition accuracy could be achieved over three different databases of 12, 19, and 118 speakers.KEYWORDS: ASRGMMMS-Li-FFNNMFCCscalabilityUBM Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsHusham IbadiHusham Ibadi received a bachelor’s degree in electrical engineering from Kufa University in 2010 and received an ME Degree in electronics and telecommunications fom Pune University. He is currently pursuing PhD in electronics engineering at Terna College of Engineering, Navi Mumbai, India. His research interests are automatic speech processing and classifier optimization, automatic object detection, and deep learning. Corresponding author. Email: ibadihh@gmail.comS. S. MandeSudhakar Mande is currently working as a professor and dean administration at Don Bosco Institute of Technology, Kurla(W), Mumbai. He has obtained an MTech and PhD in microelectronics and VLSI Design from IIT Bombay. He has worked with Taiwan Semiconductor Manufacturing Company (TSMC), world’s largest chip manufacturing company. While working with TSMC, he created a technique to forecast VLSI chip yield in the presence of process variability. His area of research interest includes nanoscale semiconductor devices, circuits and systems, low-power VLSI design, FPGA-based system design, and hardware accelerators for AI and ML. Email: ssmande1@gmail.comImdad RizviImdad Rizvi received the BE and ME degrees in electronics engineering from the University of Mumbai, Mumbai, India, in 2000 and 2004, respectively and PhD from the Indian Institute of Technology (IIT) Bombay, Mumbai, India, in 2012. He is currently a visiting professor in the electrical engineering eivision, Higher Colleges of Technology, Sharjah Campus, UAE. His areas of research are supervised image classification, object-based image analysis, and multi-resolution algorithms. Email: drimdadrizvi@gmail.com
Education for collective intelligence

Collective Intelligence (CI) is important for groups that seek to address shared problems. CI in human groups can be mediated by educational technologies. The current paper presents a framework to support design thinking in relation to CI educational technologies. Our framework is grounded in an organismic-contextualist developmental perspective that orients enquiry to the design of increasingly complex and integrated CI systems that support coordinated group problem solving behaviour. We focus on pedagogies and infrastructure and we argue that project-based learning provides a sound basis for CI education, allowing for different forms of CI behaviour to be integrated, including swarm behaviour, stigmergy, and collaborative behaviour. We highlight CI technologies already being used in educational environments while also pointing to opportunities and needs for further creative designs to support the development of CI capabilities across the lifespan. We argue that CI education grounded in dialogue and the application of CI methods across a range of project-based learning challenges can provide a common bridge for diverse transitions into public and private sector jobs and a shared learning experience that supports cooperative public-private partnerships, which can further reinforce advanced human capabilities in system design.
Deep Reinforcement Learning with Heuristic Corrections for UGV Navigation

Abstract Mapless navigation for mobile Unmanned Ground Vehicles (UGVs) using Deep Reinforcement Learning (DRL) has attracted significantly rising attention in robotic and related research communities. Collision avoidance from dynamic obstacles in unstructured environments, such as pedestrians and other vehicles, is one of the key challenges for mapless navigation. This paper proposes a DRL algorithm based on heuristic correction learning for autonomous navigation of a UGV in mapless configuration. We use a 24-dimensional lidar sensor, and merge the target position information and the speed information of the UGV as the input of the reinforcement learning agent. The actions of the UGV are produced as the output of the agent. Our proposed algorithm has been trained and evaluated in both static and dynamic environments. The experimental result shows that our proposed algorithm can reach the target in less time with shorter distances under the premise of ensuring safety than other algorithms. Especially, the success rate of our proposed algorithm is 2.05 times higher than the second effective algorithm and the trajectory efficiency is improved by $$24\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>24</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> in the dynamic environment. Finally, our proposed algorithm is deployed on a real robot in the real-world environment to validate and evaluate the algorithm performance. Experimental results show that our proposed algorithm can be directly applied to real robots robustly.
Digitalization of Management Information Systems at Intuition Bookstore Makassar City

Along with the development of the business world, computers have an important role in facilitating the completion of a job and helping to develop a maximum service system to support company performance, one of which is by building an information system. The process of managing book data can run smoothly and produce precise and accurate information, a digitization system is needed that can process the book management system as a whole in order to minimize errors that often arise. The purpose of this research is to design a desktop-based INTUISI bookstore management information system. The design method uses the UML method with the data analysis process using the PIECES method. The result is that the management digitization process at the INTUISI bookstore becomes easier and more efficient in reporting the management that occurs, this can be seen from the results of the User Acceptance Test (UAT).
Identifying Anomalous Behavior in Aircraft Landing Trajectory Using a Bayesian Autoencoder

Anomalous behavior during the aircraft landing phase can significantly increase the probability of adverse events. Automated anomaly detection during the landing phase can help aviation safety-related organizations to efficiently detect anomalous behavior and consider mitigation strategies. This paper develops a Bayesian autoencoder neural network model to identify anomalous behavior in landing trajectories by reconstructing the flight data because the reconstruction error is larger for anomalous flights. Different loss functions, such as Huber loss, mean squared error loss, and least trimmed squares are investigated to construct the Bayesian autoencoder model; and their performances are compared using different measures: the mean of the reconstruction error, the standard deviation of the reconstruction error, and both the mean and standard deviation of the reconstruction error. Different loss function-based models show differences in performance, depending on which measure is used for anomaly detection; among all the options considered, one of the Huber loss options appears to give the best performance, as indicated by the F1 score. Furthermore, the mean and standard deviation of the reconstruction error for a single flight are used to identify the time of occurrence and the flight parameters related to anomalous behavior.
The Influence of E-Learning-Based Learning Methods in Midwifery Courses on Midwifery Undergraduate Students' Learning Motivation

This study aims to determine the effect of e-learning based learning methods in midwifery care courses on student learning motivation. This research is clearly analytic research with case control research method. Accidental sampling method, sampling technique slovin formula, univariate data analysis and bivariate analysis with Odds Ratio Analysis. The results of the study can be concluded that the results of statistical tests obtained OR = 0.070 (CI = 0.014-0.0314) which shows that E-learning has an effect of 0.070 times on student learning motivation compared to those who do not do E-learning. Because the OR value &lt; 1 and the range of confidence intervals does not include number one, the hypothesis is accepted which means that E-learning based learning in midwifery care courses has an effect on student learning motivation.
Informational matraquage and infobesity: Discourse realities of the modern media space

The research aims to highlight the universal linguosemiotic features of the modern media-digital cluster. The scientific novelty of the paper consists in a comprehensive identification of the features of the discourse models for discursive manipulation in the texts of modern mass media in line with the study of the linguosemiotic mechanisms of the functioning of matraquage discourse, both in the space of a complete utterance and at the level of the universal pool of world linguoculture. The scientific novelty of the research also consists in introducing into active scientific use the terms “raw information”, “matraquage discourse” and “infobesity” in order to clarify the conceptual apparatus and to achieve a clearer semantic structuring of the terminological system of information theory and communication theory. One of the main epistemological aspects of the paper is the development of the problems of macrodiscursive interpretation of the media space as a semiotic whole and value, i.e. the issues of searching for the foundations of subjective or universal identity, which can be addressed, in one way or another, by referring to language. An important point of the research is the synergy between the conceptual and epistemic dimensions of knowledge, in other words, between the linguistic and conceptual analysis of media discourse in line with the interpretative-semiological approach. The achieved results showed the predominance of universal features in media discourse and also made it possible to detect information about the elements of the worldview and cognition of the collective actor of matraquage discourse at the level of media discourse functioning. The obtained data made it possible to identify a number of socio-cultural features in the linguistic thinking of the consumer of media information that are required to overcome crisis-related and stressful situations associated with infobesity on the path of existential and psychological stabilisation of cognition and communication behavioural models.
Mathematical modeling for further improving task scheduling on Big Data systems

Abstract In the big data era which we have entered, the development of smart scheduler has become a necessity. A Distributed Stream Processing System (DSPS) has the role of assigning processing tasks to the available resources (dynamically or not) and route streaming data between them. Smart and efficient task scheduling can reduce latencies and eliminate network congestions. The most commonly used scheduler is the default Storm scheduler, which has proven to have certain disadvantages, like the inability to handle system changes in a dynamic environment. In such cases, rescheduling is necessary. This paper is an extension of a previous work on dynamic task scheduling. In such a scenario, some type of rescheduling is necessary to have the system working in the most efficient way. In this paper, we extend our previous works Souravlas and Anastasiadou (Appl Sci 10(14):4796, 2020); Souravlas et al. (Appl Sci 11(1):61, 2021) and present a mathematical model that offers better balance and produces fewer communication steps. The scheduler is based on the idea of generating larger sets of communication steps among the system nodes, which we call superclasses. Our experiments have shown that this scheme achieves better balancing and reduces the overall latency.
Does the First Amendment Protect Free Speech or Threats and Scams?

Most of the vital legal precedents in US law for the First Amendment were written when free speech meant talking to a crowd of people, publishing a newspaper, and, after 1927, operating a radio station. Congress has passed no laws regulating the Internet since 1996, before social media, text messaging, and the growth of global economics. Free speech now means protections sometimes for fake news, scams, hate speech, and invasions of privacy. In conclusion, are some suggestions for updating the First Amendment.
DEVELOPMENT OF ANDROID-BASED INTERACTIVE LEARNING MEDIA IN BASIC GRAPHIC DESIGN SUBJECTS AT VOCATIONAL SCHOOLS

The problem in this study is that students do not understand the material as a result of which many students score low, lack of interest in learning and student motivation in the teaching and learning process, there is no android-based interactive media. The purpose of this study was to design interactive android-based learning media in the class X Basic Graphic Design subject at SMKN 1 X Koto Above using the smart app creator application, as well as knowing the quality of the media in designing Android-based interactive learning media in the Basic Graphic Design class. X at SMKN 1 X Koto Above. The method used in this study is MDLC (Multimedia Development Life Cycle) which consists of 6 stages namely Concept, Design, Material Collecting, Assembly, Testing, Distribution. By using alpha and beta testing. Alpha testing is carried out using the Black Box method, while beta testing includes material validity, media validity and user feasibility testing. The results of the Black Box test properly, beta test results on material validation 0.95% with the valid category, 0.94% media test results with the valid category, user feasibility test 88.8%, with very good category.
Randomized Block Adaptive Linear System Solvers

Randomized linear solvers randomly compress and solve a linear system with compelling theoretical convergence rates and computational complexities. However, such solvers suffer a substantial disconnect between their theoretical rates and actual efficiency in practice. Fortunately, these solvers are quite flexible and can be adapted to specific problems and computing environments to ensure high efficiency in practice, even at the cost of lower effectiveness (i.e., having a slower theoretical rate of convergence). While highly efficient adapted solvers can be readily designed by application experts, will such solvers still converge and at what rate? To answer this, we distill three general criteria for randomized adaptive solvers, which, as we show, will guarantee a worst-case exponential rate of convergence of the solver applied to consistent and inconsistent linear systems irrespective of whether such systems are overdetermined, underdetermined, or rank deficient. As a result, we enable application experts to design randomized adaptive solvers that achieve efficiency and can be verified for effectiveness using our theory. We demonstrate our theory on 26 solvers, nine of which are novel or novel block extensions of existing methods to the best of our knowledge.
Tensor Codes and Their Invariants

In 1991, Roth introduced a natural generalization of rank-metric codes, namely, tensor codes. The latter are defined to be subspaces of -tensors, where the ambient space is endowed with the tensor rank as a distance function. In this work, we describe the general class of tensor codes and we study their invariants corresponding to different families of anticodes. In our context, an anticode is a perfect space that has some additional properties. A perfect space is one that is spanned by tensors of rank 1. Our use of the anticode concept is motivated by an interest in capturing structural properties of tensor codes. In particular, we indentify four different classes of tensor anticodes and show how these gives different information on the codes they describe. We also define the binomial moments and the weight distribution of a code with respect to a family of anticodes and establish a bijection between these invariants. We use the binomial moments to define the concept of a binomial moment determined code, which is an extremal code in relation to an inequality arising from them. Finally, we give MacWilliams identities for binomial moments.
Meningkatkan Prestasi Belajar melalui Model Pembelajaran Cooperative Learning Strategies (CLS)

Abstract The selection of the right learning model greatly affects the success in determining the high and low results achieved by students in carrying out learning. However, to be able to have the right learning model, careful observation is needed by looking for the causes behind the difficulty of students learning the given material. The emphasis is on the importance of activities and interactions during the learning process to achieve what is the purpose of teaching. Thus the success of students in the learning process will achieve maximum results. Improve student learning outcomes with the Cooperative Learning Strategy learning model in the process of implementing Biology subject learning in class XII Science 2 at SMA Negeri 2 Cikarang Utara. This model is considered appropriate to be applied to students because it has the principle of responsibility between individuals, interdependence in positive terms, communicative among others, students, doing face-to-face to interact, accustomed to evaluating in carrying out a better process. Achievement in carrying out the learning process using this model gets significant results in improving student learning outcomes. The basis for this achievement is accompanied by data during the model implementation process KEYWORDS : Cooperative Learning Strategies, Learning Outcomes, Biology