Reviewerly/exHarmony huggingface dataset:
A Conflict Management Game in Project-based Learning

In project-based learning environments, students often face team frustration and conflicts, necessitating the development of conflict management skills. This study examines how integrating a game-based learning approach impacts students' perspectives on managing conflicts in game development project courses. Drawing on theoretical frameworks related to project-based learning, conflict management, and serious games in conflict resolution education, a case design is employed in higher education that incorporates the game "Cards of Conflict". Document analysis of student reflection notes is utilised to assess the outcomes. The results demonstrate heightened engagement among participants during the game session, leading to awareness and understanding of conflict managing principles. This encompasses the what, the why´s, and the how’s of conflict management, as well as the diverse perspectives of peer students.
Kuxtal: Student Motivation Through Megagames in Higher Education Design Students

Megagames are an engaging combination of different game resources. They involve role-play, social connection, simulations, rules, economics, politics, decision making among others that are merged into an immersive narrative, all of which are essential applications of experiential learning. They can last from one day up to one month, they are known to be collaborative and can be played by several players at the same time. In education, collaboration between individuals is an integral competence that has to be nurtured among the academic community. During the game dynamics, multiple experiences, events, and actions are activated that enhance curiosity and emotions among students which, in turn, impact their motivation in the learning process. Wicked problems are undetermined problems that are resistant to change, possess a social and cultural context, several stakeholders are involved and overall are complex to solve. In this paper we explore 1) the use of megagames as a component to motivate student-learning of Higher Education (HE) students of the fourth semester of a degree in Design at the Tecnologico de Monterrey institution, located in the northwestern region of Mexico in the State of Sonora. We also 2) analyzed if research and action was triggered among students through the megagame, Kuxtal. The megagame, aims to promote student-learning through understanding wicked problems: that all living beings face in regards to water: its scarcity, its pollution and its mismanagement. In this qualitative study, we used Jones (2021) user guide for assessing components of the MUSIC model of motivation to understand empowerment, usefulness, success, interest, and caring. With the incorporation of megagames in the classroom, educators were able to promote student motivation and students engaged in research methodology, all while adopting small world-changing actions in their everyday lives. This paper aims to promote the advantages of using megagames as an educational resource in Higher Education creative fields such as Design, Architecture and Communication.
Teaching Material and Method toward English Students at Vocational High School

This research aimed to analyze the English learning method toward students at SMK Hafsawaty Zainul Genggong Probolinggo and found out which material are relevant to their needs. This research used a qualitative research involving twenty students and English teachers at SMK Hafsawaty Zainul Hasan Genggong. Data obtained by using interviews, questionnaires, documentation, then analyzed using qualitative data analysis method. The interview was purposed for an English teacher and the questionnaire was filled by twenty students. The result of this research found that the students could be easy to understand the material that deliver by the teacher. It proved that the teaching method applied by the teacher was appropriate for the students need. From the teaching material, the school had to develop the education system by considering the teaching material needs for the vocational students. Then, when viewed from the needs, the material has not focused on the needs of students majoring in Islamic banking. However, English for specific purposes have to consider the material need for the students.
Machine Learning Techniques for Escaped Defect Analysis in Software Testing

Software testing is crucial to ensure the quality of a software under development. Once a potential bug is identified, a Bug Report (BR) is opened with information to describe and reproduce the found issue. Usually in big companies, hundreds of BRs are opened weekly by different testing teams, which have to be inspected and fixed adequately. This paper is focused on the use of Machine Learning (ML) techniques to automate the Escaped Defect Analysis (EDA), which is an important (but expensive) task to improve the effectiveness of the testing teams. In our work, Escaped Defects (EDs) are bugs or issues that should have been opened by a specific team, but which was accidentally found by another team. The occurrence of EDs is risky, as it is usually related to failures in the testing activities. EDA is usually performed manually by software engineers, who read each BR's textual content to judge whether it is an ED or not. This is challenging and time-consuming. In our solution, the BR's content is preprocessed by textual operations and then a feature representation is adopted by a ML classifier to return the probability of EDA labels. Experiments were performed in a dataset of 3767 BRs provided by the Motorola Mobility Comércio de Produtos Eletrônicos Ltda. Different ML algorithms were adopted to build classifiers, obtaining high AUC values (usually higher than 0.8), in a cross-validation experiment. This result indicates a good trade-off between the number of EDs correctly identified and the number of BRs that have to be actually inspected in the EDA process. This paper presents a ML based approach to classify escaped defects described in bug reports. EDs are bugs missed by the QA team in charge and happened to be uncovered by a different team. To automate the identification of EDs (a costly and error-prone task), a dataset of a partner company is leveraged, text processing operators are adopted for feature engineering and 6 classical ML algorithms are applied. The results show satisfactory accuracy and AUC and the experiments indicate a good trade-off between the number of EDs correctly identified and the number of BRs that have to be inspected in the EDA.
Mathematical Modeling and Analysis of Patterns in Structured Collections of Big Data

This paper addresses the issue of creating and applying mathematical models and methods for finding generalized solutions when working with structured collections of “big data”. We reviewed the modern methodologies used to solve problems of this class. The mathematical model presented describes an ordered set of all subsets formed from a finite ordered base set of arbitrary size and data type. We explored a set of functional dependencies of five discrete input variables to work with this mathematical model. Some of these functional dependencies are derived for specific solutions with specified boundary conditions. The paper also presents examples of how the derived functional dependencies are applied in the implementation of mathematical methods using this model. This required us to conduct a comparative assessment of the search time for a solution with and without the use of these mathematical methods. Comparative graphs are demonstrated to show the rate of increase in the number of operations depending on the size of the original finite base set with and without the use of these mathematical methods. As a result of this, logical conclusions are drawn regarding the impact of mathematical methods for working with structured collections on minimizing time and computational resources.
Analisis Kepuasan Pengguna Terhadap Penerapan Sistem Informasi Terpadu Layanan Prodi (SIPLO) Menggunakan End User Computing Satisfaction (EUCS)

SIPLO is information system created and developed in order to manage academic activity scheduling data so that it can make it easier for users in intranet and internet campus academic activities. This study aims to determine the level of satisfaction of the Sistem Informasi Terpadu Program Studi (SIPLO) using the End User Computing Satisfaction (EUCS) model which includes 5 variables, namely content, accuracy, format, easy of use and timeliness. The results of the study showed that the satisfaction level of SIPLO users was 24% very satisfied, 52% satisfied, 21% quite satisfied, 2% dissatisfied and 1% very dissatisfied. The results of the hypothesis test showed that 5 variables had an effect on satisfaction and the hypothesis was accepted while the other 2 variables had no effect on satisfaction and the hypothesis was rejected.
Research on Intelligent Server Room Integrated Operation and Maintenance Management System

As the center for storing and transmitting data, the server room makes the safety of the server room particularly important. To this end, this paper proposes an intelligent server room integrated operation and maintenance management system, which makes comprehensive use of modern science and technology and means represented by Internet of Things, 3D visualization technology, deep learning, image processing, multimodality, and situational awareness through various sensors and other monitoring means, and through comprehensive sensing, comprehensive integration, comprehensive mining, comprehensive analysis, comprehensive sharing and comprehensive Through comprehensive sensing, comprehensive integration, comprehensive mining, comprehensive analysis, comprehensive sharing and comprehensive coordination of data resources of various subsystems in the server room, the overall security performance of the server room can be evaluated, and various equipment in the server room can be monitored, maintained and managed in a uniform manner. The final realization of modern unattended intelligent server room, improve the management level and security of the server room.
Pedagogical Views of V. Zenkivskyi

The article deals with the pedagogical interpretation of the leading meanings of education in V. Zenkivskyi's pedagogy. The purpose of the article is to analyze and characterize the pedagogical views of V. Zenkivskyi. The research methods used are the following decisive ones: analysis and generalization of scientific and methodical sources for the theoretical substantiation of the research problem, modelling, obtained data synthesis. Results. In the pedagogy of V. Zenkivskyi, special attention is paid to the interpretation of education and to setting the purpose and objectives of education in the light of the divine predestination, as well as the influence of the idea of the divine predestination in the development of the pupil’s personality. The providential nature of education in the pedagogy of V. Zenkivskyi subordinated both learning and all areas of child development. Thanks to the providentialist understanding of education, V. Zenkivskyi overcomes pedagogical relativism, which consists in a mistaken understanding of the «freedom» of choice presented to a student who cannot use it. The author of the article also states that orientation to providential principles in education is a necessary condition for accepting the V. Zenkivskyi’s educational concept, which cannot be separated into components and used in line with other pedagogical currents, it cannot be analyzed «biased» from the point of view of other philosophical and religious attitudes. Conclusions. The benefit of this study is rtghe author’s conclusion that the pedagogical views of V. Zenkivskyi are conductively oriented and based on the spirituality and creative potential of the individual, which helps to reproduce and philosophically understand the integrity of the pedagogical existence, as well as to comprehend the diversity of pedagogical interpretations of the history of world and national culture. Thus, the concept of education built by V. Zenkivskyi in the light of divine providence is of unconditional scientific and practical interest as one of the highly value sources of fruitful ideas, valuable methodological technologies, the creative understanding of theoretical and practical potential of which can contribute to the further development of pedagogical science.
The future of the university: four models of development

The development of a market economy, the COVID-19 pandemic, globalization, digitalization, the development of the online sphere, the crisis of values and many other factors contribute to changing the role of universities in the modern world. While a number of researchers believe that after a protracted pandemic, the axis of world higher education will return to its former traditional course, others are confident that universities will soon disappear altogether, being thrown overboard by massive online courses. The main purpose of this study is to analyze and present the main scenarios for the development of universities in the future, based on the research of leading philosophers, as well as to highlight the general prospects for the development of higher education. The main methods of this foresight research are content analysis of research literature, descriptive method of future scenarios, as well as their historical and philosophical analysis. The study is of a review and theoretical nature. The results and discussions present four main "ideas" of the university, the goals and social legitimacy of which are now accepted in society as true and indisputable. This study will complement the ideas about the future of higher education, the transformation of universities under the influence of globalization, market capitalism and other factors. Its results will be useful to teachers of higher educational institutions, philosophers of education and anyone interested in the problems of higher education. Key words: philosophy of education, neoliberalism, classical university, MOOC courses, online courses, transformation of education, crisis of values.
MSDU: Multi-step Delayed Communication for Efficient Distributed Deep Learning

Distributed deep learning has emerged as the principal training paradigm in recent years. However, significant communication overhead often leads to a severe degradation of performance in data parallelism, which limits the scalability of distributed training. To address this problem, this paper introduce a novel MSDU (Multi-Step Delayed Update) method. MSDU mitigates the negative impact of communication overhead on training efficiency by introducing a delay in the parameter aggregation process. Which allowing for overlap between computation and communication. As a result, MSDU can improve the performance of distributed deep learning, particularly in scenarios with limited communication bandwidth, such as PCIe-based communication. Experimental results demonstrate that employing the MSDU method in data parallelism can significantly reduce training time by up to 45.7%, with only a limited loss of model accuracy.
Perceived Organizational Support as an Antecedent of Knowledge Sharing Behaviors: Achieving Sustainability of Change Management Efforts

Knowledge is the only way organizations gain sustainable competitive advantage in such a hyper-competitive era. Given its importance, the study examines the antecedents to tacit and explicit knowledge and the mediating role of psychological ownership. We have collected a sample of 157 using online Google forms and physically visiting the banks in certain cases. The study used Smart PLS for statistical analysis. Structural equation modeling was performed on Smart PLS for data analysis as the constructs are reflective. The study found that perceived organizational-support positively influences both dimensions of knowledge-sharing behavior – tacit and explicit knowledge. Psychological ownership positively “mediates perceived organizational support and explicit knowledge sharing.” However, we did not find support for the mediating role of psychological ownership between perceived organizational-support and tacit knowledge sharing. The study recommends the HR department must create awareness of the support it provides to employees. HR professionals must build an all-inclusive workplace culture that embodies organizational support. Furthermore, customized incentives such as extrinsic and intrinsic benefits can encourage employees to share knowledge.
Image Enhancement of Degraded Sand-dust Images Based on Channel Compensation and Brightness Partitioning

This paper proposes a new method for enhancing sand-dust images by correcting color deviation and improving contrast, which is aimed at addressing the visual interference caused by sand-blind conditions for pilots. Firstly, to compensate for the color attenuation of the blue channel and better correct the yellowish appearance of sand-dust images, a channel compensation module based on the standard deviation of the yellow histogram is proposed. Secondly, a color normalization module that maintains the yellow mean value is designed to correct the color imbalance issue by making the red, blue, and green histograms similar. Finally, a partition-based enhancement module based on the brightness feature is constructed to decompose the brightness space V into a highlight region B and a dark region D, which are enhanced by limiting contrast adaptive histogram equalization and nonlinear transformation, respectively. The experimental results demonstrate that the proposed algorithm achieves good enhancement performance on sand-dust images with different degrees of color deviation, and outperforms SDIE in terms of NIQE, NBIQA, BRISQUE, and NPQI metrics, achieving performance improvements of 14.22%, 19.87%, 13.40%, and 8.96%, respectively, with more effective significance detection performance.
Pig Face Recognition Based on Multi Supervising Forms

With the development of intelligent breeding, non-contact pig face recognition based on deep learning has been widely studied, enabling accurate and intensive management. Existing face recognition network training in limited categories cannot meet the requirements for breeding short-growth period edible livestock. Moreover, using a single supervising function is hard to distinguish pig faces that are more similar to humans. Therefore, this paper improves Inner origin loss, which can adaptively adjust repulsive circles, allowing for fast and efficient pig face feature extraction. In addition, we propose the TACO loss which can learn facial features to separate pig faces. The experiments conducted on our dataset indicate that in the 1200 pairs of pig face verification pairs composed of 15 pigs, the accuracy of open-set is 96.08%. This study provides a technical reference for pig face recognition research.
Porting and Optimization of Electric Power Consumers Anomaly Behaviour Monitoring on ARM Platform

With the development of smart grid, finer grid monitoring becomes possible. We propose a framework for abnormal behavior monitoring of power users, and carry out targeted deployment and optimization on the ARM platform. In order to align the anomaly detection performance of the model with that of humans, we designed a human-in-the-loop pipeline to achieve more accurate anomaly detection results with a small number of expert annotations. We validate the effectiveness of the proposed method on the electricity data of a region in China.
Insulator Detection Based on Improved Lightweight Rotation Detector

Insulator overheating defect detection is an important task in power inspection. The traditional horizontal detector is not precise enough for positioning inclined insulators. Although the general rotation detector can produce a more accurate rotation detection box, it is not suitable for edge device with limited computing power due to its large amount of model parameters. In this paper, we propose a lightweight Rotated RetinaNet (LR-RetinaNet) to address these issues. Firstly, we propose Brief PAFPN to achieve more efficient feature fusion and effectively reduce model parameters. Secondly, we design a lighter Brief Rotated Head to address the increased model computational complexity caused by the heavy head of RetinaNet. Finally, we conducted a series of experiments, which showed that our proposed LR-RetinaNet can effectively reduce model parameters while ensuring detection accuracy, making it more suitable for edge device.
Multi-scale Siamese Dual Decoding Network for Remote Sensing Tank Image Segmentation

To address the multi-scale problem and interference of differences between data in remote sensing image segmentation, a multi-scale Siamese dual decoding network is proposed. The twin network is used as the backbone network for parallel processing and weight sharing. The dilated stratification attention module is designed to extract effective multi-scale features. Different branches can obtain receptive fields of different sizes in order to learn more partition features and suppress background interference. The global attention module is designed to extract common characteristics between datasets and to focus on the characteristics of the datasets, thus extracting connections between datasets. The residual fusion module is also designed for adaptive learning of feature weights of different encoding layers. Experimental results demonstrate that the proposed method has better efficacy and robustness compared to classical networks, and has stronger recognition efficacy for different complexity scenarios, meeting the expectations of this study.
EVALUASI PROGRAM FLOOD MANAGEMENT IN SELECTED RIVER BASINS DI KABUPATEN PANDEGLANG

The problem of flooding and erosion is a natural phenomenon that greatly affects the community. Losses resulting from these events can reduce the quality of human life in various aspects of life. So there is a need for immediate management with various strategies. One of them is the Evaluation of the Flood Management in Selected River Basins Program in Pandeglang Regency. The purpose of this study was to evaluate the Farmland Management and Sustainable Agriculture Practices for Flood Management in Selected River Basins program Sector Project Pandeglang with the perspective of the CIPP evaluation model (Context, Input, Process, Product) and to recommend sustainability strategies. The research was conducted using a descriptive method with a qualitative approach. The results showed that the existence of the FMSRB program was able to answer various problems with critical lands and flood risk. Then the most effective continuation strategy that must be carried out is carried out by the Pandeglang Regency government with a Collaborative Governance strategy, namely collaboration between the Regency government and the private sector, universities/academics, and so on. The conclusion of the research is that the CIPP evaluation model can answer the main causes of flooding in the Pandeglang Regency.