Reviewerly/exHarmony huggingface dataset:
The Effect of Rainfall on Rice and Corn Crop Production in Sleman District

Rainwater that falls to the surface of the earth is very important for the growth of agricultural crops such as rice and corn. Therefore, this study aims to know rice and corn production in 2018 and 2020, determine rainfall in 2018 and 2020 and analyze the effect of rainfall on rice and corn production in 2018 and 2020 in Sleman District. The data used by this research is data from Sleman District in 2018 and 2020 Figures. The data were analyzed in a qualitative descriptive manner. The results of the study found that rice production in 2018 amounted to 1,574.3 tons, rose to 16,135 tons in 2020, while corn production in 2018 was 452.7 tons, rose to 5,763 tons in 2020. The total rainfall in Sleman District in 2018 was 2,526 mm with the maximum rainfall occurring in December at 178 mm, while the total rainfall in 2020 was 3,272 mm with maximum rainfall occurring in December with rainfall of 184 mm slightly lower than in 2018. The study also found that rainfall in 2018 and 2020 directly affected rice and jangun production.
Knowledge sharing, perceived risk and environmental information on energy saving behaviors of hotel guests

Energy efficiency is one of the critical aspects, because the hotel sector has significant energy consumption and has an impact on environmental information. Energy efficiency helps reduce operational costs. By reducing excessive energy consumption, hotels can set a good example of responsible and sustainable business practices. It is important to understand the factors that affect energy efficiency. Some of the relevant factors are knowledge sharing, risk perception, and environmental information. This study aims to analyze the effect of knowledge sharing, risk perception, and environmental information factors on energy efficiency in the hospitality industry. The research method used in this study is a quantitative method with a survey approach. The number of samples used in this study were 176 hotel managers in Indonesia. Questionnaires were distributed to respondents using a Likert scale of 1 to 7. Data were analyzed using the Partial Least Square (PLS) method using SmartPLS software. The research results conclude that knowledge sharing, perceived risk, and environmental information have a significant influence on energy efficiency in the hospitality industry. Well-informed consumers tend to be more aware of the impact of their decisions and actions on environmental information, so they are more likely to choose hotels that adopt sustainable and energy efficient practices. In addition, the perception of risk also affects energy efficiency in the hospitality industry. Consumers who perceive risks associated with low sustainability or negative environmental information impacts are less likely to choose the hotel. Environmental information factors also play an important role in energy efficiency. Hotels that have an environment that supports and facilitates sustainable practices have the potential to achieve higher energy efficiency.
Factors influencing the acceptance of using telemedicine: A study of Jordanian public healthcare organizations

During Covid-19, organizations, particularly hospitals, encountered difficulties in providing services. Telemedicine has shown to be an alternative in service provision during these times. Based on this, acceptance of telemedicine in Jordanian public hospitals has become a very important issue to increase the attention of the health care organization toward it. A conceptual model was constructed based on previous literature. The model includes government policy, the capacity of external suppliers, and the capacity of the project team, top management support, as independent variables where their influence on the acceptance of telemedicine in Jordanian public hospitals as a dependent variable is examined. Respondents were chosen using a Purposive sampling technique. Questionnaires were delivered to 320 respondents using Google Forms. SEM was used for statistical analysis. The findings revealed that all the proposed factors including government policy, external supplier capacity, project team capacity, and top management support have a significant influence toward accepting telemedicine. The results of this study may aid Jordanian public hospitals in making the best use of the proposed factors to increase the acceptance of telemedicine in Jordanian public hospitals.
The effects of big data analytics and workplace pressures on productivity

This study set out to find out on how big data analytics, workload, work wages, and organizational structure pressures affect employee job performance and overall business performance in the Jordan banking sector in the context of that country's banking sector. 292 samples from the research study were collected, analyzed, and debated to test the study's hypotheses. Employee and company success were found to be affected by workload, compensation, and organizational structure. Workload was reflected by job completion rate, average daily issues, and work completion time, while big data was reflected by external and internal, unique application, indexing, and source correctness. Work compensation, including employee compensation, employee experience and skill, and employee incentives and rewards, is the third primary factor. Communities of practice, group work, and physical infrastructure make up the fourth and last important factor, the organization's structure. The study finds that when dealing with large amounts of data, reducing the workload can improve employee performance, increasing employee motivation through higher wages and bonuses can improve performance, and having an organizational structure that promotes teamwork and work teams can improve employees' abilities to solve problems and improve work performance, which in turn affects overall productivity. This study contributes fresh information to an emerging field that requires more investigation to fully understand the interplay between big data, workload, work wages, and organizational structure pressures. The topic of this study, the Jordan banking sector, is both innovative and highly relevant because it may help financial institutions enhance their operations and provide superior customer service.
Analyzing the effects of data mining techniques on management decision making and information exchange in the industrial sector: the role of cooperation as a moderating factor in Saudi Arabia

This research explores the influence of data mining methods on the managerial decision-making in Saudi Arabia's industrial sector, emphasizing the moderating function of cooperation. A total of 500 questionnaires were distributed to information technology managers, with 265 responses selected for data analysis. Smart PLS 4 software was used for the data analysis, and statistical measures were used to analyze the correlations between variables. The findings show that data mining approaches have a substantial positive relationship with improving decision-making and information exchange within external and internal contexts. The study also demonstrates that cooperation plays an important moderating role in these interactions, emphasizing the significance of building a cooperative atmosphere to improve the influence of data mining methods on decision-making and information sharing. The study's conclusions have practical relevance for organizations in the industrial sector. Organizations may improve their decision-making processes and information sharing by adopting data mining tools and boosting collaboration, enhancing performance and competitiveness.
Ethical enactivism for smart and inclusive STEAM learning design

Current global challenges of the 21st century promote STEAM (science, technology, engineering, arts and mathematics) education and digitalization as a means for humans to be the central actors in the construction of a sustainable society that favors a sense of worth and global wellbeing. In this scenario, new educational technology tools and immersive learning affordances (possibilities), offer unprecedented potential for the design of smart and dynamic learning systems and contexts that can enhance learning processes across varied audiences and educational settings. However, current STEAM education practice lacks attention to equipping all citizens with the necessary skills to use digital technologies in an ethical, critical and creative way. This gap calls for attention in design processes, principles and practices that are attentive to ethical considerations and values-based approaches. On the other hand, in its formulation STEAM as an educational approach is framed in four fundamental pillars: creativity, inclusion, citizenship and emerging technologies, which also put attention on the inclusion of disadvantaged and underrepresented social groups during STEAM education design. Following an apparent need to explore ethical and inclusive design in STEAM education, and inspired in the 4E cognition framework, ethical enactivism and embodied and ecosomaesthetics experience design, here we propose a theoretical framework grounded on systems thinking for the design of smart and dynamic STEAM learning systems and settings. The framework is aimed at STEAM educational psychologists, educational technologists, learning designers and educational practitioners who wish to address the global challenges of 21st century education by means of creative, innovative and inclusive education design.
Fuzzy implications based on quasi-copula and fuzzy negations

In this particular paper the connection of fuzzy implications to the basic concepts of probability theory such as copula, quasi-copula and semi-copula is being studied.This study showed that fuzzy implications produced through copula, quasicopula or semi-copula, apart from having as a common characteristic the Lipschitz condition with constant 1, this characteristic is also the cornerstone for grouping fuzzy implications according to the original generator which is no other than a copula, quasi-copula or semi-copula.
The impact of creative thinking of marketing ideas on the design structure of the modern Jorda-nian advertisement: Evidence from the Jordanian telecommunications companies

The study aimed to investigate the impact of creative thinking on marketing ideas and their influence on the structure of modern Jordanian advertisements for telecommunications companies. The methodology involved developing a survey instrument with four independent variables and one dependent variable. The independent variables were Creative Marketing (CM), Marketing Strategy (MS), Online Marketing (OM), and Social Media Marketing (SMM). The dependent variable was the Structure of the Modern Jordanian Advertisement (SMJA). The study population is customers of Jordanian telecommunications companies who reside in Jordan. A sample size of 270 respondents was selected through a convenience sampling method. The data were analyzed using statistical software, such as SPSS and AMOS 27 version, to test the research hypotheses. The results indicate that all four paths are statistically significant. The first hypothesis, which relates to creative marketing, has the highest estimated result of 0.62, while the second, third and the fourth hypotheses have estimated results of 0.152, 0.133, and 0.111, respectively. Overall, the results support the hypothesis that all four marketing domains have a significant impact on SMJA.
Viewpoint-agnostic Taekwondo Action Recognition using a Synthesized Two-dimensional Skeletons

Issues of fairness and consistency in Taekwondo poomsae evaluation have emerged owing to the lack of an objective evaluation method. This study proposes a three-dimensional (3D) convolutional neural network (CNN)-based action recognition model for the objective evaluation of Taekwondo poomsae. The model exhibits robust recognition performance regardless of variation in perspective by reducing the discrepancies between training and test images. The model uses 3D skeletons of the poomsae unit action collected using a full-body motion-capture suit to generate synthesized two-dimensional (2D) skeletons from the desired perspective. This approach aids in obtaining 2D skeletons from diverse perspectives as part of the training dataset and ensures consistent recognition performance regardless of the viewpoint. The model was trained using 2D skeletons projected from diverse viewpoints, and its performance was evaluated using various test datasets, including projected 2D skeletons and RGB images captured from various viewpoints. Comparison of the performance of the proposed model with that of previously reported action recognition models demonstrated the superiority of the model, underscoring its effectiveness in recognizing and classifying Taekwondo poomsae actions.
Deep Reinforcement Learning-Based Approach for Video Streaming DASH

Dynamic adaptive video streaming over HTTP (DASH) plays a crucial role in video transmission across networks. Traditional adaptive bitrate (ABR) algorithms adjust the quality of video segments based on network conditions and buffer occupancy. However, these algorithms rely on fixed rules within a complex environment, making it challenging to achieve optimal decisions considering the overall context. In this paper, we propose a novel Deep Reinforcement Learning-based approach for streaming DASH, focusing on maintaining consistent perceived video quality throughout the streaming session to enhance user experience. Our approach optimizes the Quality of Experience (QoE) by dynamically controlling the quality distance factor between consecutive video segments. We evaluate this approach through a simulation model that encompasses diverse wireless network environments and various video sequences. Additionally, we compare our proposed approach with state-of-the-art methods. The experimental results demonstrate significant improvements in QoE, ensuring users enjoy stable, high-quality video streaming sessions.
A Survey of Machine Learning Modelling for Agricultural Soil Properties Analysis and Fertility Status Predictions

The problem of low soil fertility and limited research in agricultural data driven tools, may lead to low crop productivity which makes it imperative to research in applications of high throughput computational algorithms such as of machine learning (ML) for effective soil analysis and fertility status prediction in order to assist in optimal soil fertility management decision-making activities. However, difficulties in the choice of the key soil properties parameters for use in reliable soil nutrients analysis and fertility prediction. Also, individual ML algorithms setbacks and modelling expert implementation procedures subjectivity, may lead to exploitation of worst fertility level targets and soil fertility status targets classification models performance reported variations. This paper surveys state-of-affair in ML for agricultural soil nutrients analysis and fertility status prediction. Prominent soil properties and widely used classical modelling algorithms and procedures are identified. Empirically exploited fertility status target classes are scrutinized, and reported soil fertility prediction model performances are depicted. The three pass method, with mixed method of qualitative content analysis and qualitative simple descriptive statistics were used in this survey. Observably, the frequently used soil nutrients and chemical properties were organic carbon, phosphorus, potassium, and potential Hydrogen, followed by iron, manganese, copper and zinc. Predominant algorithms included Random Forest, and Naïve Bayes, followed by Support Vector Machine. Model performances varied, with highest accuracy 98.93% and 98.15% achieved by ensemble methods, and the least being 60%. Interdisciplinary ML related researchers may consider using ensemble methods to develop high performance soil fertility status prediction models.
Transitional Challenges And Factors Affecting English-Speaking Learners In Learning The Filipino Language

Learning a new language can present numerous difficulties, especially for individuals who are native English speakers and are embarking on the journey of acquiring the Filipino language. This study aimed to determine the transitional challenges faced by English-speaking learners during the process of acquiring proficiency in Filipino, as well as identify the factors that affect their language-learning journey. By understanding these challenges and factors, educators and language instructors can design effective strategies and interventions to facilitate a smoother transition for English-speaking learners into learning the Filipino language. This study used qualitative design as a research method, data were collected from 8 participants through interviews. Exploratory research was carried out to extract narratives and experiences that are relevant to English-speaking learners in learning the Filipino language. The results showed that English-speaking learners often struggle with understanding and speaking the language, particularly with pronunciation, grammar structures, and vocabulary. Additionally, learners may lose interest in the language, making language acquisition more difficult. The factors that facilitate the language transition process were interest and exposure to the target language. The findings of this research will contribute to the existing literature on second language acquisition, specifically focusing on the challenges and factors affecting English-speaking learners in learning the Filipino language. The results will provide valuable insights for language educators and curriculum developers to develop effective teaching methodologies, language materials, and support systems to facilitate a more successful transition for English-speaking learners.
Alternative Pseudo-Labeling for Semi-Supervised Automatic Speech Recognition

When labeled data is insufficient, semi-supervised learning with the pseudo-labeling technique can significantly improve the performance of automatic speech recognition. However, pseudo-labels are often noisy, containing numerous incorrect tokens. Taking noisy labels as ground-truth in the loss function results in suboptimal performance. Previous works attempted to mitigate this issue by either filtering out the nosiest pseudo-labels or improving the overall quality of pseudo-labels. While these methods are effective to some extent, it is unrealistic to entirely eliminate incorrect tokens in pseudo-labels. In this work, we propose a novel framework named alternative pseudo-labeling to tackle the issue of noisy pseudo-labels from the perspective of the training objective. The framework comprises several components. Firstly, a generalized CTC loss function is introduced to handle noisy pseudo-labels by accepting alternative tokens in the positions of incorrect tokens. Applying this loss function in pseudo-labeling requires detecting incorrect tokens in the predicted pseudo-labels. In this work, we adopt a confidence-based error detection method that identifies the incorrect tokens by comparing their confidence scores with a given threshold, thus necessitating the confidence score to be discriminative. Hence, the second proposed technique is the contrastive CTC loss function that widens the confidence gap between the correctly and incorrectly predicted tokens, thereby improving the error detection ability. Additionally, obtaining satisfactory performance with confidence-based error detection typically requires extensive threshold tuning. Instead, we propose an automatic thresholding method that uses labeled data as a proxy for determining the threshold, thus saving the pain of manual tuning. Experiments demonstrate that alternative pseudo-labeling outperforms existing pseudo-labeling approaches on datasets in various domains and languages.
Federated Transfer Learning Strategy: A Novel Cross-Device Fault Diagnosis Method Based on Repaired Data

Federated learning has attracted much attention in fault diagnosis since it can effectively protect data privacy. However, efficient fault diagnosis performance relies on the uninterrupted training of model parameters with massive amounts of perfect data. To solve the problems of model training difficulty and parameter negative transfer caused by data corruption, a novel cross-device fault diagnosis method based on repaired data is proposed. Specifically, the local model training link in each source client performs random forest regression fitting on the fault samples with missing fragments, and then the repaired data is used for network training. To avoid inpainting fragments to produce the wrong characteristics of faulty samples, joint domain discrepancy loss is introduced to correct the phenomenon of parameter bias during local model training. Considering the randomness of the overall performance change brought about by the local model update, an adaptive update is proposed for each round of global model download and local model update. Finally, the experimental verification was carried out in various industrial scenarios established by three sets of bearing data sets, and the effectiveness of the proposed method in terms of fault diagnosis performance and data privacy protection was verified by comparison with various currently popular federated transfer learning methods.
Posture Classification with a Bed-Monitoring System Using Radio Frequency Identification

Aging of the population and the declining birthrate in Japan have produced severe human resource shortages in the medical and long-term care industries. Reportedly, falls account for more than 50% of all accidents in nursing homes. Recently, various bed-release sensors have become commercially available. In fact, clip sensors, mat sensors, and infrared sensors are used widely in hospitals and nursing care facilities. We propose a simple and inexpensive monitoring system for elderly people as a technology capable of detecting bed activity, aimed particularly at preventing accidents involving falls. Based on findings obtained using that system, we aim at realizing a simple and inexpensive bed-monitoring system that improves quality of life. For this study, we developed a bed-monitoring system for detecting bed activity. It can predict bed release using RFID, which can achieve contactless measurements. The proposed bed-monitoring system incorporates an RFID antenna and tags, with a method for classifying postures based on the RFID communication status. Experimentation confirmed that three postures can be classified with two tags, seven postures with four tags, and nine postures with six tags. The detection rates were 90% for two tags, 75% for four tags, and more than 50% for six tags.
Improved Generative Adversarial Network for Super-Resolution Reconstruction of Coal Photomicrographs

Analyzing the photomicrographs of coal and conducting maceral analysis are essential steps in understanding the coal’s characteristics, quality, and potential uses. However, due to limitations of equipment and technology, the obtained coal photomicrographs may have low resolution, failing to show clear details. In this study, we introduce a novel Generative Adversarial Network (GAN) to restore high-definition coal photomicrographs. Compared to traditional image restoration methods, the lightweight GAN-based network generates more explicit and realistic results. In particular, we employ the Wide Residual Block to eliminate the influence of artifacts and improve non-linear fitting ability. Moreover, we adopt a multi-scale attention block embedded in the generator network to capture long-range feature correlations across multiple scales. Experimental results on 468 photomicrographs demonstrate that the proposed method achieves a peak signal-to-noise ratio of 31.12 dB and a structural similarity index of 0.906, significantly higher than state-of-the-art super-resolution reconstruction approaches.