Reviewerly/exHarmony huggingface dataset:
Korelasi Nilai Indeks Prestasi Kumulatif dengan Jumlah Hafalan Juz 30 Mahasiswa Program Studi Pendidikan Agama Islam STAIN Majene

Abstract The purpose of the research that will be studied is to describe the close correlation between the Grade Point Average and the total memorization of juz 30 students of the Islamic Religious Education Study Program (PAI) STAIN Majene. The method used in this study to collect data is by means of direct interviews with students who will be used as research samples. Analysis of the data used to describe the close correlation between the two variables is using Pearson correlation analysis. The results obtained in this study that the close correlation between the grade point average and the number of memorizations juz 30 of students of PAI STAIN Majene obtained a positive correlation. So, the higher the GPA value, the higher the memorization of Juz 30 students while the correlation coefficient value was 0.187, which means the relationship between the two the variable is very weak, it means that the academic achievement value does not have much contribution with the large number of juz memorization obtained by the 2017 PAI study program students. Keywords: Description of correlation, Grade Point Average
A Study on the Supply Chain Optimization Method using SNA-based product tracking manufacturing data - Focused on the Plating Surface Treatment in the Manufacturing Industry -

This study aims to comprehensively understand the complex dynamics within the supply chain by visualizing the local supply chain of Company K, an essential company in the metal surface treatment field in the region, through social network analysis and evaluating the structure, relationships, and interdependence between companies. Do this. Connectivity, betweenness, proximity, and eigenvector centrality indicators were used to identify the dominant influence of key hubs (S3HY, S3SY) and hidden pivotal nodes (S1SY) within the supply chain. This analysis is expected to be a theoretical and empirical basis for strategic decision-making and optimal supply chain strategy establishment.
Foreign bank presence and inclusive growth in Africa: the moderating role of financial development

Purpose The purpose of this study is to assess the extent to which the nexus between foreign bank presence (FBP) and inclusive growth is being impacted by the financial development. Design/methodology/approach The study used a two-stage system generalized method of moment (GMM), using 28 African countries from the period 2000 to 2018. Findings The study found a positive effect of FBP on inclusive growth. While financial development magnifies the positive effect of FBP, inclusive growth nexus, it has a direct effect on inclusive growth. Practical implications For Africa to ascertain the positive effect of FBP on inclusive growth, financial system must be developed to reduce the cream-skim behavior of foreign banks. Originality/value This paper assess the extent to which developing economy's developed financial system form synergies with FBP to further enhance the inclusiveness of growth.
Forged Video Detection Using Deep Learning: A SLR

In today’s digital landscape, video and image data have emerged as pivotal and widely adopted means of communication. They serve not only as a ubiquitous mode of conveying information but also as indispensable evidential and substantiating elements across diverse domains, encompassing law enforcement, forensic investigations, media, and numerous others. This study employs a systematic literature review (SLR) methodology to meticulously investigate the existing body of knowledge. An exhaustive review and analysis of precisely 90 primary research studies were conducted, unveiling a range of research methodologies instrumental in detecting forged videos. The study’s findings shed light on several research methodologies integral to the detection of forged videos, including deep neural networks, convolutional neural networks, Deepfake analysis, watermarking networks, and clustering, amongst others. This array of techniques highlights the field and emphasizes the need to combat the evolving challenges posed by forged video content. The study shows that videos are susceptible to an array of manipulations, with key issues including frame insertion, deletion, and duplication due to their dynamic nature. The main limitations of the domain are copy-move forgery, object-based forgery, and frame-based forgery. This study serves as a comprehensive repository of the latest advancements and techniques, structured, and summarized to benefit researchers and practitioners in the field. It elucidates the complex challenges inherent to video forensics.
Teamwork’s Experiences through Project-Based Webinar in English for Public Speaking

Several problems are faced in the English for Public Speaking course. Lecturer wants to implement project-based webinar which held by students’ teamwork. This research aims to identify the implementation of project-based webinar from teamwork’s experiences to organize webinar in English for Public Speaking course. The researcher conducted qualitative method with basic interpretative approach through observation. This research conducted at D-4 English for Tourism Industry, Politeknik Negeri Malang. The subjects were 18 students of the third semester students. Observation was managed along the process of the Project Based Learning implementation in the English for Public Speaking. The result of this study about teamwork’s experiences are being a chief committee, secretary, treasurer, event division, public relation division, also publication and documentation division. The process of the Project Based Learning implementation by creating webinar gives many advantages for the students. It makes the students become more creative and train to be critical due to the problem which is given is based on real problem faced in that time.
Frustration, Confusion, Surprise, Confidence, And Self-Doubt: Cyber Operators’ Affects During A Realistic Experiment

In this paper we examine data from a complex two-day experiment with professional cyber red teamers to determine what creates, or contributes, to affective states that may further disrupt attackers. Participants rated their frustration, surprise, confidence, self-doubt, and confusion at the end of each day, and these responses were scored on a Likert scale and using Thematic analysis. We found several elements strongly contributed to the frequency and severity of these affective states, supporting our initial hypothesis, and further that (1) experiencing a Lack of Progress impacted four of the five states with moderate severity, and (2) interacting with elements in the environment that Deviated from what was expected was associated strongly with surprise. Implications for cyber defense and limitations of the method are discussed.
Legal Maintenance of Innovative Activities of Scientific and Educational Organizations of Higher Education

In the article the main regulatory legal acts in the sphere of conducting innovative activities of scientific and educational organizations of higher education at various levels (federal, regional, municipal and local) as well as other sources of advisory nature are identified and analyzed. The basic concepts and categories used in the current legislation in the given field are researched. As a result, taking into account the recommendations of World Intellectual Property Organization, some proposals to improve the present innovation legislation of the Russian Federation as well as the main directions of work of scientific and educational organizations on the adoption of local regulations ensuring their conduct of innovation activities are defined.
Analysis of Approximating and Detailing Wavelet Decomposition Coefficients in the Risk Potential Predictive Algorithm

The paper addresses the challenges related to the information task of an APCS used in nuclear power plants. Specifically, it discusses the development of predictive models capable of calculating technical and economic indicators, utilizing essential parameters of the power unit. The models are built by conducting a thorough analysis of wavelet decomposition coefficients, ensuring accurate approximation and detailed analysis. Additionally, the paper presents an improved algorithm for evaluating the risk potential associated with the technological process.
A Correlation-XGBoost Based Distributed Photovoltaic Output Prediction Method Considering Regional Meteorological Factor

With the development of distributed resources and new types of loads in the distribution network, distributed photovoltaics (PV) have been vigorously developed. Due to the strong correlation between the power output of photovoltaics and meteorological factors such as irradiance, wind speed, temperature, and humidity, it has volatility and newly built distributed photovoltaics lack accurate historical meteorological and power generation data, making it difficult to predict. Therefore, this paper proposes a distributed photovoltaic power output prediction method based on regional meteorological characteristic correlation-XGBoost. Firstly, the physical entity of the distributed photovoltaic system is combined with the digital driving model to achieve data synchronization and real-time updates, then, using the gray correlation analysis method to select the meteorological factors that have a greater impact on the power output of distributed photovoltaics, the selected meteorological correlation data set is trained using the XGBoost algorithm and combined with the weather forecast to predict the power output of distributed photovoltaics. Finally, the training model of the distributed photovoltaics with historical data is used to predict the newly built distributed photovoltaics without historical data. An example is used to analyze and predict the distributed photovoltaic data in a certain area, which verifies the effectiveness and rationality of the proposed method and can obtain more accurate prediction results than traditional prediction methods.
An Effective Optimization Method for Fuzzy $k$-Means With Entropy Regularization

Fuzzy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -Means with Entropy Regularization method (ERFKM) is an extension to Fuzzy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -Means (FKM) by introducing a maximum entropy term to FKM, whose purpose is trading off fuzziness and compactness. However, ERFKM often converges to a poor local minimum, which affects its performance. In this paper, we propose an effective optimization method to solve this problem, called IRW-ERFKM. First a new equivalent problem for ERFKM is proposed; then we solve it through Iteratively Re-Weighted (IRW) method. Since IRW-ERFKM optimizes the problem with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k\times 1$</tex-math></inline-formula> instead of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$d\times k$</tex-math></inline-formula> intermediate variables, the space complexity of IRW-ERFKM is greatly reduced. Extensive experiments on clustering performance and objective function value show IRW-ERFKM can get a better local minimum than ERFKM with fewer iterations. Through time complexity analysis, it verifies IRW-ERFKM and ERFKM have the same linear time complexity. Moreover, IRW-ERFKM has advantages on evaluation metrics compared with other methods. What's more, there are two interesting findings. One is when we use IRW method to solve the equivalent problem of ERFKM with one factor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf{U}$</tex-math></inline-formula> , it is equivalent to ERFKM. The other is when the inner loop of IRW-ERFKM is executed only once, IRW-ERFKM and ERFKM are equivalent in this case.
On the acceptability of the not so dummy auxiliary ‘do’ in Dutch

Abstract The auxiliary doen ‘do’ in standard Dutch is usually described as ‘dummy’ because it supposedly adds nothing substantial to the meaning of the sentence. We argue, however, that the auxiliary does have a function in the sentence, as a marker of either habitual or intentional aspect. In an online production experiment, we investigated the acceptability of the allegedly dummy auxiliary doen ‘do’. Results show that the degree of acceptability of the auxiliary doen ‘do’ is very low, even lower than the widely disapproved use of hun ‘them’ as a subject in Dutch. However, because a significant difference was found in the acceptability between the habitual and the intentional reading, we conclude that the auxiliary doen ‘do’ in Dutch cannot be dummy, i.e. semantically empty.
Improved triplet loss for domain adaptation

Abstract A technique known as domain adaptation is utilised to address classification challenges in an unlabelled target domain by leveraging labelled source domains. Previous domain adaptation approaches have predominantly focussed on global domain adaptation, neglecting class‐level information and resulting in suboptimal transfer performance. In recent years, a considerable number of researchers have explored class‐level domain adaptation, aiming to precisely align the distribution of diverse domains. Nevertheless, existing research on class‐level alignment tends to align domain features either on or in proximity to classification boundaries, which introduces ambiguous samples that can impact classification accuracy. In this study, the authors propose a novel strategy called class guided constraints (CGC) to tackle this issue. Specifically, CGC is employed to preserve the compactness within classes and separability between classes of domain features prior to class‐level alignment. Furthermore, the authors incorporate CGC in conjunction with similarity guided constraint. Comprehensive evaluations conducted on four public datasets demonstrate that our approach outperforms numerous state‐of‐the‐art domain adaptation methods significantly and achieves greater improvements compared to the baseline approach.
Completely Positive Map for Noisy Driven Quantum Systems Derived by Keldysh Expansion

Accurate modeling of decoherence errors in quantum processors is crucial for analyzing and improving gate fidelities. To increase the accuracy beyond that of the Lindblad dynamical map, several generalizations have been proposed, and the exploration of simpler and more systematic frameworks is still ongoing. In this paper, we introduce a decoherence model based on the Keldysh formalism. This formalism allows us to include non-periodic drives and correlated quantum noise in our model. In addition to its wide range of applications, our method is also numerically simple, and yields a CPTP map. These features allow us to integrate the Keldysh map with quantum-optimal-control techniques. We demonstrate that this strategy generates pulses that mitigate correlated quantum noise in qubit state-transfer and gate operations.
Improving chemistry tabulation with partition of unity networks.

ANNs which preserve notions of convergence while performing high-dimensional approximation, discovering a mesh-free partition of space which may be used to perform optimal polynomial approximation. In this work, we assess their performance with respect to accuracy and model complexity in reconstructing unstructured flamelet data representative of nonadiabatic pool fire models. Our results show that POUnets can provide the desirable accuracy of classical spline-based interpolants with the low memory footprint of traditional ANNs while converging faster to significantly lower errors than ANNs. For example, we observe POUnets obtaining target accuracies in two dimensions with 40 to 50 times less memory and roughly double the compression in three dimensions. We also address the practical matter of efficiently training accurate POUnets by studying convergence over key hyperparameters, the impact of partition/basis formulation, and the sensitivity to initialization.
Collaborative Learning in Higher Education in the Fourth Industrial Revolution: A Systematic Literature Review and Future Research

The goal of this study is to determine the variables and difficulties that contribute to collaborative learning at tertiary institutions during the fourth industrial revolution and future research gaps. This study employs a systematic literature review to summarise and provide a comprehensive understanding of collaborative learning. The systematic literature review process used the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) technique between 2008 and 2022, supported by NVivo and Microsoft Excel. A systematic literature review, which is an appropriate method for strengthening research issues and gaps, was conducted with 61 papers. It should be noted that this manuscript only presents work from the Scopus database with journal criteria. This study has identified six factors related to collaborative learning in tertiary institutions in the fourth industrial revolution era including Task &amp; Context, Environment, Social, Technology, Teacher, and Learner. This study presents a comprehensive literature assessment of collaborative learning in tertiary institutions during the fourth industrial revolution in order to acquire a clear grasp of its importance and application in higher education. Furthermore, Several higher education models have adopted the online-based collaborative learning paradigm, according to the study's findings.
Classification of mammograms by breast density

The risk of getting breast cancer is directly affected by the type of breast tissue predominant in the individual. The aim is to investigatehistogram-based image attributes in order to separate mammographic images by degree of breast density using the clustering technique. 75 mammographic images from the MIAS database were used, 25 of them belonging to each of the three classes: fatty, fatty-glandular and dense. After the selection of attributes, it obtained a 96% success rate in classifying the mammograms within the three classes when the attribute’s mean gray levels and the highest peak intensity of the histogram were used simultaneously in the clustering technique
Development of Reconfigurable Low-Power Measuring IoT Device for Detecting Common Radioactive Elements for Earth and Space Applications

This research study aims to collect environmental samples from various locations nationwide to analyze the concentration of alpha, beta, and gamma radiation. Initially, a state-of-the-art radiation detector is employed to identify potential radioactive elements. Subsequently, the study focuses on developing a new set of radiation detectors using IoT elements like Raspberry Pi, Arduino, and ESP single-board computing elements, coupled with analog sensors. This aims to create a low-power detector suitable for building a national radiation monitoring network. The network will enable real-time, 24/7 monitoring of radiation activities nationwide. Additionally, the study explores using rad-hard reconfigurable platforms such as FPGA in conjunction with peripheral analog sensors like the BM-20 Geiger-Muller Tube. This combination facilitates the collection of target data on radiation elements, including alpha, beta, and gamma radiation prevalent in outer space. Moreover, the research intends to enhance the proposed detector’s security by incorporating lightweight cryptographic primitives. This involves implementing the Diffie-Hellman key exchange protocol between the local board and an Android app, ensuring secure communication of sensor readings. In summary, this research study involves sample collection, radiation detection using IoT elements, integration with reconfigurable platforms, and the inclusion of cryptographic measures to enhance the security and functionality of the radiation detector. (1) Study the scalability possibilities for extending the design to multiple nodes nationwide for having a low-power runtime radiation network in strategic locations. (2) Extend the possibilities of using such design in multiple space tiny devices such as CubeSat for short space.
FEATURES OF FORMING SOCIAL AND PEDAGOGICAL COMPETENCE OF A TEACHER

The article highlights the features of socio-pedagogical competence formation, its components, characteristics and conditions that develop the professional qualities of a teacher. The authors present the main stages of the formation of socio-pedagogical competence and reveal the key aspects of their content. The following components of the socio-pedagogical competence are of particular interest: epistemological, emotional-volitional, operational-activity and semantic components. They allow you to expand the areas of the interaction with students and build trusting relationships, both in the classroom and in the process of extracurricular work. At the same time, the consistent achievement by the teacher of the levels of socio-pedagogical competence formation described in the article provides an opportunity to use the individual psychological characteristics of students to improve the results of educational activities, as well as find their calling and develop a personal professional position. In addition, the article presents main steps in the formation of the socio-pedagogical competence of a teacher, including the development of basic knowledge in their professional field, the implementation of acquired skills and abilities, gaining practical experience and acquiring in-depth knowledge. It is argued that in the course of the educational process, provisions related to the vigorous activity of all its subjects must be fulfilled. This will make it possible to create conditions during classes conducive to the formation and development of a research position, the inclusion of each of its participants in interactive activities and active interaction. The article serves to deepen the theoretical understanding of the socio-pedagogical competence and its role in educational activities.