Reviewerly/exHarmony huggingface dataset:
Antecedents of Perceived Business Performance Among Private Business Organizations: A Partial Least Square Approach

This study aims to assess the direct relationships between corporate image, employee engagement, organizational culture, employee loyalty, and business performance among private business organizations. This study is vital to be carried out because many private organizations face difficulty to maintain their business performance in the coming years. The research model of this study consists of three independent variables: corporate image, employee engagement, organizational culture, employee loyalty as a mediator, and business performance as a dependent variable. Primary data were utilized in this study and a survey questionnaire which was adopted and adapted from previous studies was used for data collection. 329 clean data were used in the data analysis by utilizing the structural equation modeling technique. Initially, the convergent validity was evaluated on the measurement model by assessing the construct reliability and validity. Then, the discriminant validity was assessed and confirmed through cross-loading and Hetrotrait-Monotrait (HTMT) ratios. Subsequently, the structural model was assessed and the hypotheses testing reveals that corporate image, employee engagement, and organizational culture, have a positive and significant influence on employee loyalty and business performance was strongly affected by employee loyalty.
Implementation and Efficient Analysis of Preprocessing Techniques in Deep Learning for Image Classification

Deep learning models have recently been preferred to perform certain image-processing tasks. Recently, with the increasing radiation, heat, and poor lighting conditions, the raw image samples may contain noisy and ambiguous information. To process these images, the deep learning model requires a large number of data samples to learn the missing information from other clear data samples. This necessitates training the neural network with a huge dataset. The researchers are now attempting to filter and improve such noisy images via preprocessing in order to provide valid and accurate feature information to the neural network layers. However, certain research studies claim that some useful information may be lost when the image is not preprocessed with an appropriate filter or enhancement technique. The MSA (meta-synthesis and analysis) approach is utilized in this work to present the impact of the image processing applications done with and without preprocessing steps. Also, this work summarizes the existing deep learning-based image processing models utilizing or not preprocessing steps in their implementation. Finally, this research provides various research insights on the requirement and non-requirement of preprocessing steps in a deep learning-based implementation. This work has also found that 85% of the existing techniques involve a preprocessing step while developing a deep learning model.
Construction and application of knowledge graph for grid dispatch fault handling based on pre-trained model

With the construction of new power systems, the power grid has become extremely large, with an increasing proportion of new energy and AC/DC hybrid connections. The dynamic characteristics and fault patterns of the power grid are complex; additionally, power grid control is difficult, operation risks are high, and the task of fault handling is arduous. Traditional power-grid fault handling relies primarily on human experience. The difference in and lack of knowledge reserve of control personnel restrict the accuracy and timeliness of fault handling. Therefore, this mode of operation is no longer suitable for the requirements of new systems. Based on the multi-source heterogeneous data of power grid dispatch, this paper proposes a joint entity–relationship extraction method for power-grid dispatch fault processing based on a pre-trained model, constructs a knowledge graph of power-grid dispatch fault processing and designs, and develops a fault-processing auxiliary decision-making system based on the knowledge graph. It was applied to study a provincial dispatch control center, and it effectively improved the accident processing ability and intelligent level of accident management and control of the power grid.
PENGGUNAAN APLIKASI QUIZIZZ SEBAGAI MEDIA UNTUK MELAKSANAKAN EVALUASI PEMBELAJARAN

To stop the spread of the Covid-19 virus, the learning system in Indonesia is also adapting by providing options for the implementation of online, offline, or blended learning processes. The use of online learning media is an alternative to the implementation of learning that reduces the risk of direct interaction between students. One of the online learning media that can be used during the learning process in general and the evaluation of learning in particular is the use of the quiziz application. This study aims to describe the quizizz media as an evaluation application for classroom learning. Quiziz is an educational game application that is narrative and flexible in nature that can be used as a means of delivering material as well as an interesting and fun learning evaluation medium. This research is a descriptive qualitative research that uses observation data collection methods and library research or library research, namely reading, studying, and reviewing books and writings related to the discussion. Based on the research, the results that can be achieved in the Quizizz application as a learning medium are: (1) The increased enthusiasm of students during the learning process increases because quiziz is one of the online learning media that contains games (interactive quizzes); (2) The increasing ability of students to understand the questions independently can be seen from the results of the pre-test, post-test, practice questions, and material strengthening; (3) Students participate actively during the learning process; (4) The use of the quiziz application can train students' time management accuracy in working on questions; (5) The use of the quiziz application can train students' calmness to work on questions or quizzes. Thus it can be concluded that the Quizizz application media is very effectively used in the learning evaluation process.&#x0D;
Detection of Acrimonious Attacks using Deep Learning Techniques and Edge Computing Devices

We proposed a novel approach for an automatic attack alerting system that is helpful for citizens. Our proposed model uses the YOLO object detection model, which can detect violent objects and the pose estimation model, which can estimate dangerous actions. Then, the output of these two models is entered into the LSTM model as input at the same time and the LSTM model is used to classify the type of attacks. The whole system is deployed into edge devices. Finally, based on the output from the LSTM model. If the threat is detected, the edge device can notify an automatic alert to the victim’s family members or friends to minimize harm. The performance proves that the proposed model can detect the objects and accurately classify the attack types.
A Model to Determine Optimal Numbers of Monograph Copies for Preservation in Shared Print Collections

In this study we developed a model and a spreadsheet tool for calculating, based on user input informed by available data, the probability of at least one usable copy of a monograph title surviving at various time horizons in shared print collections. The calculation incorporates four risk factors, which were assigned values based on research in the literature and our own studies. We applied the model to sample selected time horizons and risk tolerances, which suggests a minimum number of copies of a title needed for retention.
IrekiaLFes: a New Open Benchmark and Baseline Systems for Spanish Automatic Text Simplification

Automatic Text simplification (ATS) seeks to reduce the complexity of a text for a general public or a target audience. In the last years, deep learning methods have become the most used systems in ATS research, but these systems need large and good quality datasets to be evaluated. Moreover, these data are available on a large scale only for English and in some cases with restrictive licenses. In this paper, we present IrekiaLF_es, an open-license benchmark for Spanish text simplification. It consists of a document-level corpus and a sentence-level test set that has been manually aligned. We also conduct a neurolinguistically-based evaluation of the corpus in order to reveal its suitability for text simplification. This evaluation follows the Lexicon-Unification-Linearity (LeULi) model of neurolinguistic complexity assessment. Finally, we present a set of experiments and baselines of ATS systems in a zero-shot scenario.
Multi-Type Vehicle Detection Algorithm Based On Improved YOLOv5

With the aim of addressing the existing challenges related to vehicle target detection across various environments, this study introduces an enhanced YOLOv5-based algorithm for vehicle detection. By incorporating a small target detection layer, the algorithm effectively addresses the issues of missed detections and false positives in the experimental settings, thus enhancing the model’s robustness in diverse environments. In this research, BiFPN is employed as the feature fusion network instead of PANet, and an adaptive weighted fusion strategy along with jump connections is adopted to tackle the challenge of integrating multi-scale information. Regarding image detection accuracy, the experimental outcomes demonstrate that the proposed improved YOLOv5 algorithm exhibits promising performance on vehicle datasets, achieving a detection accuracy of 94%. This notable enhancement in performance compared to the original algorithm confirms the feasibility of real-time vehicle detection using the algorithm proposed in this study.
Students’ Writing Ability and Their Attitude: Examining the Effect of Online PjBL

This study examines the effect of online PjBL on students' writing abilities and their attitudes toward online PjBL. A mixed method was employed in this research. 72 students majoring in English at a university in Indonesia took part as participants with informed consent. Two groups—control and experimental—were formed. The experimental group utilized online PjBL, whereas the control group used traditional teaching. Tests, questionnaires, and interview questions were employed to collect data. The collected data were analyzed both quantitatively and qualitatively. The findings of the research showed that the mean score of the experimental group was higher than that of the control group. Furthermore, an independent sample t-test revealed that the sig. value was 0.023 = 0.05. It indicated that there is a significant effect of online PjBL on students’ writing ability. Furthermore, the students showed positive attitudes toward the implementation of online PjBL. It is indicated by the students’ responses to the questionnaire, where thirty out of thirty-six students indicated that they strongly agree that the online PjBL makes them more comfortable writing an argumentative essay. Seven out of ten students interviewed said peer feedback activities improve writing the most in online PjBL. Recommendations for further researchers were also provided.
A Continuous-Variable Quantum Secure Direct Communication Protocol with Squeezed States

Quantum secure direct communication (QSDC) is a recently developed practical solution, which transmits secret messages between legitimate parties, without setting up a cryptographic key. The QSDC protocols are mainly classified into entanglement-based and single photon-based approaches. Their security has been analyzed using Wyner's wiretap channel theory, and their secrecy capacity has been calculated [1]. Hitherto, QSDC protocols have been implemented on optical fiber and free-space channels, all in discrete-variable (DV) encoding that employs single-photon detectors [1]–[2]. However, the implementation of continuous-variable (CV) QSDC, in which the keys are encoded into the quadratures of quantized electromagnetic fields (e.g., coherent states) and detected via homodyne techniques, is preferred due to: its lower costs, excellent integrability with existing optical communication systems, easy implementation from state preparation to measurement, and fast experimental realization. Furthermore, it has been proved that CV-QSDC protocols using squeezed states show higher tolerance in purely lossy channels and enhanced robustness against highly noisy ones, as well [3]. Nevertheless, none of the CV-QSDC protocols employing either coherent or squeezed quantum states have been implemented yet.
The Impact of Problem-Based Learning on Collaborative Problem-Solving and Communication Skill Development

This study aimed to investigate the impact of problem-based learning on students' collaborative problem-solving and communication skills. Conducted from September 2022 to December 2022, the research involved university-level students enrolled in a general English course. Participants were divided into an experimental group, receiving problem-based learning instruction, and a control group, receiving traditional instructional methods. The study measured collaborative problem-solving and communication skills and compared the differences between the two groups. Results indicated significant differences in collaborative problem-solving ability (M = 19.67) and communication skills (M = 86.88) for the experimental group. These findings emphasize the value of implementing problem-based learning in educational settings to enhance students' collaborative problem-solving and communication skills, potentially leading to improved academic achievement and social competence.
Changes in Extreme Rainfall in New Capital of Indonesia (IKN) Based on 20 Years of GPM-IMERG Data

The Indonesian government has decided to relocate the nation’s capital city from Jakarta on Java to the Nusantara Capital (IKN) on Kalimantan for political, socioeconomic, and infrastructure reasons, among others, including the threat of flood, earthquake, and land subsidence in Jakarta. However, IKN remains susceptible to hydro-meteorological disasters due to its equatorial location and high yearly rainfall amounts. In this article, we investigated changes in extreme rainfall in the IKN area over the last 20 years (2001- 2020) based on Integrated Multi-Satellite Retrievals for GPM (IMERG) version 6 data. Overall, extreme rain in the IKN area, especially in the Sepaku and Semboja sub-districts, has slightly declined in the last 2 decades, as shown by negative and positive Sen’s slopes of precipitation amount-based indices and the consecutive dry days (CDD), respectively. A decrease was also observed in RX1 day (annual maximum 1-day precipitation) and SDII (annual total rainfall divided by the number of wet days in the year) indices. This indicates that the IKN area is getting drier and could impact the availability of clean water in the future by increasing the intensity and duration of the drought. While the total amount of precipitation has decreased over the past 2 decades, there has been an increase in the intensity of more extreme precipitation events. This is evident from the rise in precipitation frequency-based indices (annual count of days with rainfall exceeding certain thresholds), such as R20mm and R50mm. The RX5day index (maximum 5-day precipitation) has also shown an increase. These changes increase the risk of flooding, so it is crucial to implement appropriate measures to mitigate the risk of floods in the IKN area. HIGHLIGHTS Extreme rain in the IKN area, especially in Sepaku and Semboja sub-districts, has slightly declined in the last 20 years. Increases were observed in the consecutive dry days (CDD) and some precipitation frequency-based indices. It is important to take appropriate measures to reduce the risk of floods and drought in the IKN areas. GRAPHICAL ABSTRACT
K Value Effect on Accuracy Using the K-NN for Heart Failure Dataset

Heart failure is included in the category of cardiovascular disease. Heart disease is not easy to detect, and its detection needs to be done by experienced and skilled medical professionals. Most patients with heart failure require hospitalization. Common symptoms of heart disease, such as chest pain and high or low blood pressure, vary from person to person. This study aims to find the most optimal k value based on the accuracy obtained based on calculations by testing different k values, namely 1, 3, 5, 7, and 9. After getting the results of the accuracy of the five k values, compare which accuracy has the highest value, best for K-Nearest Neighbor (K-NN) models. The classification process uses the K-NN algorithm. This algorithm is quite easy to use because some parameters work using distance metrics and k values. Therefore, the value of k in the K-NN algorithm greatly affects the accuracy that will be produced. In the results of this study, the accuracy obtained was k = 7 and k = 9, which are the most optimal results because they have the highest accuracy compared to other k values, with an accuracy of 88%. The expected benefit of this research is that it can make a scientific contribution to research in the field of machine learning classification, especially in predicting heart failure
Based Learning Media Model Website for Making Mood Boards Fashion at Vocational Education Institute

The research was conducted to design a learning media model that can be used online. The method in this study uses a 4D development model, a stage that is carried out only to the development stage. The application used to design learning media is Dreamweaver and then the qualification test is carried out by media experts and material experts and the trial test is done on 35 trainees in training institutions and courses. The resulting learning media model has qualifications in terms of usability, functionality, and visual communications aspects by media experts in highly qualified categories, and the qualification results by material experts in relation to learning design, material content, language, and communications things are obtained in the category Highly qualified, and evaluations by students of the results based on usability aspects in most of these applications are acceptable to students with highly satisfactory answers on components P1 to P10 with highly qualifying categories.
Web-Based Offline Game Suit Design: A Model Overview

The rapid development of information and communication technology has made it easier for people to complete work. To keep up with the times in the game world, information technology students learn about the HTML (Hyper Text Mark Up Language), CSS (Cascading Style Sheet), and PHP (Hypertext Preprocessor) programming languages to create website-based games. Using qualitative research methods, IT students create website-based games with the HTML, CSS, and PHP programming languages that can be accessed via the XAMPP localhost. Students can play this Game Suit game with a computer offline. So that with this website-based game it will make it easier for people to access it and do not require storage such as downloading game applications.
Revisiting Adversarial Attacks on Graph Neural Networks for Graph Classification

Graph neural networks (GNNs) have achieved tremendous success in the task of graph classification and its diverse downstream real-world applications. Despite the huge success in learning graph representations, current GNN models have demonstrated their vulnerability to potentially existent adversarial examples on graph-structured data. Existing approaches are either limited to structure attacks or restricted to local informatio, urging for the design of a more general attack framework on graph classification, which faces significant challenges due to the complexity of generating <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">local-node-level</i> adversarial examples using the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">global-graph-level</i> information. To address this ”global-to-local” attack challenge, we present a novel and general framework <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CAMA</i> to generate adversarial examples via manipulating graph structure and node features. Specifically, we make use of Graph Class Activation Mapping and its variant to produce node-level importance corresponding to the graph classification task. Then through a heuristic design of algorithms, we can perform both feature and structure attacks under unnoticeable perturbation budgets with the help of both node-level and subgraph-level importance. Experiments towards attacking four state-of-the-art graph classification models on six real-world benchmarks verify the flexibility and effectiveness of our framework.
INFORMATION-LOGICAL MODEL OF EDUCATION OPTIMIZATION IN REMOTE MODE

The educational optimization process is widely researched in the theoretical aspect. Analyzing existing sources made it possible to highlight the research issues: the need to create an optimizing distance studying model, in which work with weaker students becomes possible both within the educational process and in individual or group independent work. The study aims to develop an information-logical model for optimizing distance studying. This model should provide for the learning process organization in such a way as to strengthen the weaknesses of students, reveal their potential and focus on the comprehensive development of knowledge and skills. The task formalization is carried out using the Hungarian algorithm and Boolean variables. The main work limitation is the operation with integers. Discreteness manifests itself already at the modelling stages in many problems, for example, when working with Boolean variables. An example with the most straightforward information model construction using the logical functions "true/false" with the transition to a chain of matrices is given. It demonstrates the studying optimization algorithm and presents an expanded information-logical model. The presented model was preliminary tested in one of the academic groups of the S. Seifullin Kazakh Agro Technical Research University. Students' knowledge inspections were carried out at the approbation beginning. Then a student group working on a student project was divided into subgroups according to the algorithm. The knowledge inspection showed an 11.3% improvement in results at the work's end. Further research on this topic may consist of expanding the presented model's capabilities and developing appropriate modules for knowledge control and algorithmization of related tasks.