Reviewerly/exHarmony huggingface dataset:
Sosialisasi Pembuatan Biopori Untuk Menanggulangi Banjir Dan Genangan Di Desa Kutamukti

Floods are one of the disasters that still often occur in various regions in Indonesia, floods occur because of high rainfall that is not accompanied by green open spaces and clean river flows from garbage, therefore the plan for the biopore program is intended to minimize the occurrence of puddles. and flooding because water from the soil surface flows directly into the biopori media so that the water does not stay long on the soil surface, Biopori aims to improve the quality of groundwater and nutrients in the soil because the biopori media is filled with organic materials such as vegetable waste and leaves. fall / dry so that it is expected to be able to minimize air pollution from burning garbage because it is not uncommon for people who still burn garbage and waste in their yards, therefore the author makes a program with socialization and training methods so that people want to implement a biopore system around their yards to minimize flood control and puddles as well as organic waste in the biopore media can be used as compost. Overall, the community service activities that have been carried out are going well. Participation, cooperation, and assistance from the parties involved, both internally and externally, were carried out well. The results of the implementation of this community service activity are compiled with and adjusted to the stages of implementing the activities that have been carried out.
Utilization of E-Module as Blended Learning Source at C-Package Equivalence Education Program PKBM Abdi Pertiwi Serang

The use of e-module as source of learning is a process on making e-module to be more beneficial. This research aims to describe the utilization of e-module as learning source in blended learning on C-package equivalence program at PKBM Abdi Pertiwi Serang. The approach used in this research is quali
THE PERCEPTION OF STUDENTS AND TEACHERS TOWARDS THE USE OF CODE SWITCHING IN ENGLISH CLASSROOM IN MBOONI WEST SUB COUNTY, KENYA

The study looks into the perception of students and teachers towards the use of code switching in English classroom in Mbooni West Sub County. The study applied Communication Accommodation Theory developed by Giles and Coupland (1991). The study adopted a descriptive Survey Research design. The area of study was 15 selected schools in Mbooni west Sub County. The methods of collecting data were: an observation schedule, teachers and students’ questionnaires. The target population for this study was the teachers and students in 41 public schools in Mbooni West Sub County. The sample size of the study was 300 students and 60 teachers of English from 15 schools that were sampled through purposive and simple random sampling methods. Both qualitative and quantitative methods were applied in the analysis of the data. The findings of the study also show that while students had positive perceptions towards Code Switching, teachers had both positive and negative perceptions. They also show that Code Switching has both positive and negative impact on the performance of English. Therefore, the study recommends that, teachers need to use Code Switching wisely, and also maximize the exposure to English language in the classrooms. The Stakeholders in education should be made aware on the presence of Code Switching to determine whether it should be incorporated into the curriculum designed. It is hoped that the findings of this study inform teachers and students on how to navigate through Code Switching as a communicative strategy and as an important technique in learning of the English Language.
FACTORS AFFECTING DISCIPLINE AND ACADEMIC PERFORMANCE OF GRADE I PUPILS IN BOBULON ELEMENTARY SCHOOL, SAN FELIPE, ZAMBALES

The study aimed to determine the factors affecting discipline and academic performance of the 49 Grade I pupils in Bobulon Elementary School, San Felipe, Zambales geared towards developing discipline. The researcher made use of descriptive correlation method. Majority of the respondents were males belonging to age six (6) years old with one (1) to two (2) siblings and within the P4,999 and below bracket of monthly family income. Most of them perceived that school factor and teacher factor always affect discipline, home factor oftentimes affect discipline, environmental factor sometimes affect discipline, and technology factor seldom affect discipline. The academic performance of the respondents for first quarter was satisfactory and very satisfactory for second quarter. Hence, there are factors that affect discipline and academic performance of the pupils that have led them to instill discipline to improve their academic performance. The study recommends that parents must find jobs or income generating activities for additional budget of the family. Parents must be aware that the environment of the pupils, regardless of their gender will be contributory factors in the formation and establishment of their discipline. Parents must attend community assemblies to air their concerns to assure that their children will have a good environmental foundation. The proposed action plan in discipline must be implemented to improve the academic performance of the pupils
CONTRIBUTION OF INSPIRATIONAL MOTIVATION ON TEACHERS JOB SATISFACTION IN PUBLIC SECONDARY SCHOOLS IN KAGERA REGION, TANZANIA

This study aimed to investigate the Contribution of Inspirational Motivation on Teachers’ Job Satisfaction in Public Secondary Schools in Kagera Region, Tanzania. The study was guided by transformational leadership theory by Burns 1978 and was improved by one research question. A convergent research design under mixed research methods was employed. The target population was 201 public secondary schools, 201 heads of schools, 201 assistant heads of schools and 3631 teachers. Stratified random sampling and purposive sampling techniques were used to determine a sample of the study that consisted 216 of respondents. Questionnaire and interview guides used for data collection. Instruments validation was ensured by research experts and Cronbach Alpha was used to ensure the reliability of quantitative. Quantitative data were analyzed using descriptive statistics with frequencies, percentages and mean scores. Qualitative data were analyzed using thematic analysis. The findings revealed that heads of schools applied inspirational motivation which enabled teachers to satisfy in a moderate level. The study concludes that the heads of schools had skills on inspirational motivation which moderately improved teachers’ job satisfaction. The study recommended that heads of schools should be equipped with more knowledge and skills of inspirational motivation to encourage its contribution on teachers’ job satisfaction.
Study on Flight Attitude Control of Four-rotor UAV

Four-rotor UAV is a type of small aircraft. Because of its flexibility, stability, good maneuverability and other characteristics, it can be widely used in various fields. Attitude control of four-rotor UAV is a core technology in the field of UAVs, which is worth studying. In this paper, previous research progress in the field of attitude control of four-rotor UAV is reviewed. The main control methods can be divided into linear control, nonlinear control and intelligent control. In this paper, PID control, sliding mode control, backward step control, intelligent control, neural network control and fuzzy control are introduced in detail, and the characteristics, advantages, disadvantages and research status of these different attitude control technologies are summarized and analyzed. Relevant references show that the focus of researches in resent five years is to combine a variety of control technologies, improve the traditional control technology to obtain better control effects, and use MATLAB/Simulink simulation to draw conclusions. Then, the research on attitude stability control technology of four-rotor UAV in response to adverse weather environment is analyzed, and some future research directions are proposed, including but not limited to establishing a reasonable environmental disturbance model, improving UAV dynamics model in wind field environment, developing fault-tolerant control technology and reducing the delay of system response.
EMPOWERING CONTENT CREATION WITH GENERATIVE AI IN METAVERSE

Metaverse platforms have garnered substantial attention as collaborative virtual environments serving diverse purposes. This paper introduces an extension to the VoRtex Metaverse Platform for Gamified Collaborative Learning, incorporating a generative artificial intelligence (AI) tool to streamline content creation within virtual worlds. Leveraging advanced AI technologies such as GPT-4, GET3D, and stable diffusion, our objective is to tackle the chicken-and-egg problem by facilitating the development of captivating virtual environments for early adopters. The integration of the generative AI tool into the VoRtex platform empowers users to effortlessly generate diverse and immersive content, eliminating the need for extensive technical expertise. The AI tool aids in the creation of virtual objects, characters, scenarios, and interactive elements, thereby enhancing the overall learning experience. This paper highlights the potential impact of AI-powered content creation on platform development and user engagement. Our research findings underscore that the integration of AI-powered content creation tools represents a significant stride toward enriching educational experiences within metaverse environments. Moreover, the implications of AI-assisted content creation in fostering collaborative learning are notable, offering insights into the potential challenges and future directions for integrating generative AI tools into metaverse platforms.
CONVOLUTIONAL NEURAL NETWORKS FOR CAMERA SELF-CALIBRATION WITH VARYING INTRINSIC PARAMETERS

Stereo camera self-calibration is a complex problem that has been tackled by computer vision and pattern recognition communities, and significant progress has been made. In the same context, this work proposes a new and robust fully automated approach of camera self-calibration with varying intrinsic parameters from two images of an unknown 3D object. This method is based on deep learning, such as convolutional neural networks (CNN), to reduce mathematical complexities and estimation time to a minimum. These networks use a special architecture that is particularly well adapted to estimate the intrinsic parameters of the camera through back propagation and gradient descent. First, our learning-based method introduces the self-calibration equations formulated into a non-linear cost function. The new optimization of this cost function makes it possible to estimate the intrinsic parameters of the cameras. The strong point of the proposed architecture is that it merges the high effectiveness of the CNN algorithm with the four pillars of our non-linear cost function formulation (two images, four matches, any camera and unknown 3D scene) to form the fast and robust proposed network. The experimental results on synthetic and real data prove the performance of the proposed technique in terms of simplicity, precision, and convergence. Keywords: Self-Calibration, Varying Intrinsic Parameters, Convolutional Neural Network, Matching DOI： https://doi.org/10.35741/issn.0258-2724.58.5.18
BEYOND WORDS: HARNESSING THE POWER OF NONVERBAL COMMUNICATION IN LANGUAGE TEACHING

This research dives into the far-reaching and essential results of incorporating nonverbal communication strategies into language teaching. This study employs a case study technique to investigate the complex relationship between nonverbal signals and students’ degree of involvement. Thematic analysis reveals how much of an effect gesture, facial expressions, voice modulation, and proxemics have on linguistic development. These signals not only help students learn more but also create an engaging and all-encompassing environment. To quantify the complex connections between nonverbal signals and student involvement, correlation analysis is a useful technique. The research revealed both significant and moderate correlations between the two variables, providing insight into the relationship between nonverbal signals and students’ attention. The implications for teaching stress the importance of teachers’ using these hints to create lively and interesting classroom settings. This study fills a substantial gap in the literature on the incorporation of nonverbal communication strategies into language education. The findings provide teachers with new tools to improve language classes by drawing on the power of nonverbal clues. Keywords: Nonverbal Communication, Language Teaching, Student Engagement DOI： https://doi.org/10.35741/issn.0258-2724.58.5.15
ROLE OF TECHNOLOGY IN REPORTING STUDENT LEARNING PROGRESS

The aim of this research was to examine the benefits and challenges related to the integration of technology in the reporting of students’ academic progress in the Saudi Arabian educational setting. The research employed a mixed-methods approach. The research involved a cohort of 300 educators from diverse educational institutions situated within the geographical boundaries of Saudi Arabia. The research used a mixed-methods methodology, incorporating a survey instrument to collect quantitative data, which was subsequently subjected to analysis using descriptive statistics. Furthermore, semi-structured interviews were conducted to gather qualitative data, which were subsequently analyzed thematically. As per the findings of the study, a noteworthy percentage of the respondents maintained the perspective that technology had expedited the procedure of documenting student advancement and had augmented correspondence with guardians. The qualitative data highlight the benefits of technology in terms of ease of use, flexibility, and capacity to promote student engagement. However, some individuals have expressed concerns regarding the potential for technology to worsen the digital divide. In brief, this study provides noteworthy insights into the benefits and limitations of using technology for tracking student progress in Saudi Arabia. This highlights the imperative need to prudently assess the prospective impacts of technology on student learning and parental involvement. Keywords: Technology, Students’ Report, Learning Progress DOI： https://doi.org/10.35741/issn.0258-2724.58.5.42
Variance Reduced Domain Randomization for Reinforcement Learning With Policy Gradient

By introducing randomness on the environments, domain randomization (DR) imposes diversity to the policy training of deep reinforcement learning, and thus improves its capability of generalization. The randomization of environments, however, introduces another source of variability for the estimate of policy gradients, in addition to the already high variance incurred by trajectory sampling. Therefore, with standard state-dependent baselines, the policy gradient methods may still suffer high variance, causing a low sample efficiency during the training of DR. In this paper, we theoretically derive a bias-free and state/environment-dependent optimal baseline for DR, and analytically show its ability to achieve further variance reduction over the standard constant and state-dependent baselines for DR. Based on our theory, we further propose a variance reduced domain randomization (VRDR) approach for policy gradient methods, to strike a tradeoff between the variance reduction and computational complexity for the practical implementation. By dividing the entire space of environments into some subspaces and then estimating the state/subspace-dependent baseline, VRDR enjoys a theoretical guarantee of variance reduction and faster convergence than the state-dependent baselines. Empirical evaluations on six robot control tasks with randomized dynamics demonstrate that VRDR not only accelerates the convergence of policy training, but can consistently achieve a better eventual policy with improved training stability.
Bootstrap-based Layer-wise Refining for Causal Structure Learning

Learning causal structures from observational data is critical for causal discovery and many machine learning tasks. Traditional constraint-based methods first adopt conditional independence (CI) tests to learn a global skeleton layer by layer and then orient the undirected edges to obtain a causal structure. However, the reliability of these statistical tests largely depends on the quality of data samples. In real-life scenarios, the presence of data noise or limited samples often makes many CI tests unreliable at each layer in the skeleton learning phase, leading to an inaccurate skeleton. As the number of layers increases, the inaccurate skeleton will continue to impair the skeleton construction of subsequent layers. Furthermore, an unreliable skeleton hampers the skeleton orientation procedure, resulting in an unsatisfactory causal structure. In this paper, we propose a Bootstrap-based Layer-wise Refining (BLR) algorithm for causal structure learning, which includes two new procedures to solve the above problems. First, BLR utilizes a novel layer-wise skeleton refining procedure to construct the global skeleton layer by layer based on the bootstrap sampling. Second, BLR employs a collective skeleton orientation procedure that incorporates scoring techniques to collectively orient the global skeleton. The experimental results show that BLR outperforms the state-ofthe-art methods on the benchmark Bayesian Network datasets.
Improving Multi-Person Pose Tracking with A Confidence Network

Human pose estimation and tracking are fundamental tasks for understanding human behaviors in videos. Existing top-down framework-based methods usually perform three-stage tasks: human detection, pose estimation and tracking. Although promising results have been achieved, these methods rely heavily on high-performance detectors and may fail to track persons who are occluded or miss-detected. To overcome these problems, in this paper, we develop a novel keypoint confidence network and a tracking pipeline to improve human detection and pose estimation in top-down approaches. Specifically, the keypoint confidence network is designed to determine whether each keypoint is occluded, and it is incorporated into the pose estimation module. In the tracking pipeline, we propose the Bboxrevision module to reduce missing detection and the ID-retrieve module to correct lost trajectories, improving the performance of the detection stage. Experimental results show that our approach is universal in human detection and pose estimation, achieving state-of-the-art performance on both PoseTrack 2017 and 2018 datasets.
Adopting Software Engineering Concepts in Scientific Research: Insights from Physicists and Mathematicians Turned Consultants

Physicists and mathematicians spend a fair amount of their research time developing software, as most modern calculations cannot be solved analytically and require numerical solutions. Despite this situation, researchers in these areas tend to place little importance on integrating formal software engineering concepts (SECs) into their projects. To investigate the potential benefits of SECs in scientific projects, a survey among 93 former physics and mathematics researchers who today work as consultants in the software engineering domain was conducted. In the survey, the participants reflected on the usefulness of various SECs for improving repeatability, reproducibility, and correctness of research results. The responses suggest that research in these fields could benefit from an increased usage of SECs, particularly Agile Development, Continuous Integration, and Containerization. The study presents a novelty in this field, as previous work has focused on surveying active researchers who typically lack experience in professional software engineering.
Mathematical Modeling and Optimal Stopping Theory-based Extra Layers for 30-Day Rate Risk Prediction of Readmission to Intensive Care Units

The importance of 30-day patients’ readmissions (PRs) to intensive unit care stems from the significant cost and mortality risk when the patient’s chosen class (i.e., readmitted or not to the hospital) is incorrect. The overall accuracy of the PRs classification obtained in the literature is still moderate, particularly for machine learning (ML) and deep artificial neural networks (ANNs), where the overall accuracy is around 65%, resulting in 35% of critical wrong decisions. The suggested technique for enhancing such an overall accuracy consists of three distinct phases. The first layer is an ML-assisted algorithm that uses support vector machines (SVMs) and ANNs techniques, while the second layer uses an arbitrary dataset’s distribution and mathematical modeling of the problem to determine the optimal class probabilities interval that has the highest percentage of misclassified elements. Next, all items’ categories in that interval are altered, with the exception of those deemed by the generalized secretary problem (GSP) algorithm (the third layer) to be most likely correct. We provide a new theorem that applies to every distribution type and yields the GSP’s optimum parameter. Starting with evidence that the Gilbrat and Generalized logistic distributions suit the class probabilities produced by ANN and SVM, respectively, we demonstrate that our technique improves the overall accuracy by 5% and 19%, reaching 85% and 87%, respectively. It is important to point out that the suggested additional two layers may be utilized for any binary classification problem; this is independent of the dataset distribution and the stages that came before.
Interview with Martynas Jusevičius

This week on Machine-Centric Science, I interviewed Martynas Jusevičius, currently at AtomGraph and based in Copenhagen, Denmark.
Who Validates the Validators?

Given a fip:Metadata-schema and a validator for it, such as a sh:Validator or a JSON Schema, how do you determine that the validator is…valid? That it speaks the desired fip:Knowledge-representation-language, that it knows all the terms in a desired fip:Structured-vocabulary and checks their usage against a desired fip:Semantic-model? In other words, that it adheres to a doap:Specification? I do not know.
Translating Identifiers

Good identifiers are opaque, so translation is by association – owl:sameAs, skos:exactMatch, or some other relationship. Translation doesn’t follow from reading a sign, but from retrieving a sense. If metadata is relationships between identifiers,&lt;sup&gt;1&lt;/sup&gt; then metadata is the medium of conceptual convergence.
Library Spending Plan and Its Strategies

Budgeting is an important component of collection development and management. librarians will more and more to be necessary to defend their budgets and seek outside funding for their materials. From a sum of money writing to contribution, all librarians need expertise in dealing with the monetary aspects of librarianship. The budget for the library is a tool for making library fantasies a reality. The resources allotted to each library programmer and the assistance your library will provide are determined by the budget. A correctly crafted budget will guarantee that the funds are used to achieve the service goal of your library. A budget is a economic expression of the library's plan and aim. Therefore, before a budget can be formulated, the plan for library services help must be developed and the goals and objectives established. System the library budget essentially an understanding of value centres, cash flow forecasts, economic management reporting, variance analysis and corrective measures to keep the budget on apath.