Reviewerly/exHarmony huggingface dataset:
ICA&amp;D – The platform to collate meteorological datasets and provide climate services.

Historical climate data is fundamental for understanding local climate trends and extremes, and evaluating the impact of climate change on agriculture, food security and water resources. The data can be used for implementing adaptation measures for protecting lives and adapting socio-economic sectors to the changing climate conditions. While global climate trends can already be assessed with available data, regional data disparities result in blind-spots for climate change assessments and services&amp;#8212;particularly for areas which are the most vulnerable. Coordination between countries is vital, since weather and climate phenomena often span country borders. To contribute to filling this gap, International Climate Assessment &amp;amp; Dataset (ICA&amp;amp;D), with support from the EU-funded ClimSA Programme, is being extended to the climate-vulnerable areas of the Caribbean, the Pacific and in 5 sub-regions in Africa. ICA&amp;amp;D will be managed in each ClimSA sub-region by a WMO Regional Climate Centre (RCC)/proposed RCC. ICA&amp;amp;D stores for each sub-region validated daily precipitation and temperature series collected from the National Meteorological and Hydrological Services (NMHSs) in that region. It provides an accessible, web-based mean to collect, prepare, quality control, and analyze the meteorological data. The ICA&amp;amp;D workflow was developed over 25 years ago by the Meteorological Service of the Netherlands (KNMI) as the backbone of the European WMO data-node of the RCC and implemented in Southeast Asia by meteorological service of Indonesia (BMKG). WMO and KNMI are presently collaborating &amp;#160;to expand ICA&amp;amp;D to two pilot regions, the Caribbean (caribbean.icad-wmo.org) and West-Africa (west-africa.icad-wmo.org). The collaboration has led to setting up new websites which are designed to be user friendly and highly cyber secure. The websites, one for each region, consist of information platforms, including a geographical map with the locations of the stations&amp;#8217; datasets, a feature to quickly plot timeseries from derived data and an e-learning module on the website&amp;#8217;s functions. The data sharing policy ensures that access to raw observational data from each NMHS remain restricted while allowing derived indices to be accessed by the public. With support from ClimSA, WMO and KNMI are facilitating the discussion on sharing meteorological observations.
Estimation of atmospheric parameters and solar irradiance based on a sky imaging system

The presentation refers to DeepSky, a project aiming to develop an innovative and flexible on-site measurement system to fully address the needs of end-users of the meteorological, atmospheric and solar communities. DeepSky is based on the analysis of information retrieved by two head camera (an all-sky imager (ASI) and a thermal camera), radiative transfer in solar / thermal spectral bands and methods of computational optics and deep learning. The proposed system is able to reliably assess a range of geophysical variables (solar radiation, cloud cover percentage and type, aerosol optical properties, etc.). For this scope, a bouquet of methodologies has been developed. Aerosol Optical Depth (AOD) is retrieved by the ratio of red to blue (RBR) color intensities, which are relevant to radiances at wavelengths 440 nm and 675 nm. A cloud type identification methodology is presented by exploiting the sky condition information: the proposed methodology uses a k-Nearest-Neighbor algorithm, considering as inputs specific information derived from the ASI such as color intensity, the cloud coverage, the saturated area around the Sun, the raindrop appearances and solar zenith angle. A methodology to retrieve precipitable water under cloud-free conditions using images from a thermal-infrared camera is also presented, by examining the relationship between PW and zenith-sky temperature. Finally, this work is focused on modeling the global and diffuse horizontal irradiances (GHI and DHI) using deep learning techniques and ASI-derived information. The preliminary estimations underestimate GHI and DHI observations with systematic biases of &amp;#8211;1.8 W m&amp;#8211;2 and &amp;#8211;0.5 W m&amp;#8211;2, while the dispersion errors are 82.7 W m&amp;#8211;2 and 39.8 W m&amp;#8211;2, respectively. The correlation coefficient is high, approaching 0.95 and 0.85 for GHI and DHI. Overall, a new on-site monitoring system is presented covering multiple needs in the areas of atmospheric physics and solar energy and providing parameters for which separate instruments would be needed.
FORMATIVE SCIENCE ASSESSMENT IN AN E-EDUCATION ENVIRONMENT

The article presents theoretical aspects of formative assessment in the process of learning science in secondary school in the context of different educational environments. Functions of formative assessment related to planning, (self) management of the learning process are described, including its possibilities for improving the learning process as a self-regulating process. A survey was conducted with science teachers about their views and practical solutions for assessing student achievement in distance learning in an electronic environment. On the basis of the obtained results, analyzes and summaries of encountered difficulties, ways of evaluation and self-evaluation in learning natural sciences at a distance in an electronic environment have been derived.
Difficulty Level Identification of Indonesian and Mathematics Multiple Choice Questions using Machine Learning Approach

Examination question design is an important factor that could improve education, which could help teachers to analyze student understandings. Designing question should consider difficulty level, which commonly classified into three types: easy, medium, difficult. Predicting the difficulty level of questions is very important to help teachers form questions and know the level of student ability. In this study, we tackle question difficulty level identification as a classification problem. We use a dataset of Indonesian and mathematic question from elementary and junior or school exercise questions set and employ several machine learning methods on classification. We use Random Forest, Logistic Regression, SVM, Gaussian, and Dense NN on the experiment, with embeddings, lexical, and syntactic feature. The evaluation result shows that the best method on identifying question difficult level on Indonesian subject is Random Forest with 83% accuracy, while on mathematic subject the best method is Random Forest with 83% accuracy. Result analysis shows that embedding feature affect the model accuracy.
Decision Support System for Selecting Student Thesis Study Fields

A thesis is a requirement for students to obtain an undergraduate degree. These requirements apply to the Department of Information Systems at the University of Tanjungpura. The first step in completing a thesis is to determine a title associated with the field of study, which is provided by the Department of Information Systems at the University of Tanjungpura. In the Information Systems Department, the process of determining the field of thesis study is done manually, leading to confusion and difficulty in selecting the appropriate field for students' theses. A decision support system has been developed to facilitate students in determining the field of study for their theses. The system utilizes the profile-matching method with the aim of providing mathematical and accurate recommendations for the thesis's field of study. The system testing employs the black box testing method.
Intelligent transmission line fault diagnosis using the Apriori associated rule algorithm under cloud computing environment

&lt;p&gt;Electric power production data has the characteristics of massive data scale, high update frequency and fast growth rate. It is significant to process and analyse electric power production data to diagnose a fault. High levels of informationalisation and intellectualization can be achieved in the actual details of developing a Power Plant Fault Diagnosis Management System. Furthermore, cloud computing technology and association rule mining as the core technology based on analysis of domestic and foreign research. In this paper, the optimised Apriori association rule algorithm is used as technical support to realise the function of interlocking fault diagnosis in the intelligent fault diagnosis system module. Hadoop distributed architecture is used to design and implement the power private cloud computing cluster. The functions of private cloud computing clusters for power extensive data management and analysis are realised through MapReduce computing framework and Hbase database. The leakage fault cases verify the algorithm’s applicability and complete the correlation diagnosis of water wall leakage fault. Through analysing the functional requirements of the system in the project, using MySQL database and Enhancer platform, the intelligent fault diagnosis management system of cloud computing power plant is designed and developed, which realises the functions of system modules such as system authority management, electronic equipment account, technical supervision, expert database, data centre. The result shows that the proposed method improves the security problem of the system, the message-digest algorithm (MD5) is used to encrypt the user password, and a strict role authorisation system is designed to realise the access and manage the system’s security.&lt;/p&gt;
An Analysis of Student Counseling Volunteers’ Experiences on Activities in Their First Year

The purpose of this study was to deeply analyze student counseling volunteers’ experiences in their first year. For the purpose of the study, thirteen participants who had experienced in the first year, 2021 as a student counseling volunteers from the Daegu Office of Education were selected. The participants were interviewed based on questions about their counseling experiences in their first year. Interview data were collected and analyzed according to the qualitative content analysis procedure. The study resulted in four domains including ‘rewards of volunteer activities’, ‘a changed self through volunteer activities’, ‘perceived difficulties through volunteer activities’, and ‘required support for better volunteer activities’, and 34 categories. The participants felt rewarded by experiencing the clients’ positive changes and they also mentioned their responsibilities as a counselor and the need to improve professional competencies. Physical and financial support was considered to be directly related to psychological compensation and to affect future volunteer activities. The study also proposed the implications for counseling activities of student counseling volunteers and the limitations of the study and suggestion for future study.
Analysis of Factors and Effects that Affect the Management of Smart Supply Chains from Pharmaceutical Companies: Focus on Utilizing Big Data

With the current surge in information, big data is no longer a distant concept, but is closely related to and close to everyone. With the rapid development of mobile communication and the Internet, big data has gradually influenced and penetrated various fields, resulting in new service and business models. In this study, we focused on the problems in the medical supply chain through a big data background and empirically examined how technology, organization, and environment factors affect the utilization and business effect of big data-based supply chain management systems in pharmaceutical companies. Our results revealed that all three independent variables (organization, technology, and environment) positively affect both the supply chain management of pharmaceutical companies and, furthermore, the overall business effect.. Therefore, the overall business value of a company can likely be realized through dynamic adoption of big data in terms of organizational, technical, and environmental aspects, and that it can respond quickly through various surveys and systematic analysis in the rapidly changing market.
Privacy and Security in Cloud-Based Computing: Challenges and Solutions

Cloud computing is now one of the most rapidly growing sectors in the information technology industry. The term "cloud computing" refers to various devices, tools, programs, and services that can be accessed via the internet or internet-based networks. There are numerous risks that can jeopardise the privacy and security of the internet environment. Thus, these issues, as well as potential solutions, are discussed in this paper. Consumer hesitance to use cloud computing services is still primarily driven by concerns pertaining to the security of cloud computing, notably the security and privacy of data in the cloud. To be more precise, these concerns include loss of user data, data leaks, and privacy exposure. Cloud computing provides companies and government organisations with a suitable working environment and various other benefits. Thus, there are significant issues with data security and privacy. Third parties are in charge of managing and storing the user's information. Most cloud computing users want their data to be protected against unauthorised access or misuse. Both individuals and businesses who use the cloud are impacted by the lack of data protection, which leads to a number of challenges and problems, including data theft. Concerns about privacy increase as "cloud computing" expands and changes quickly. Privacy and security worries are a hindrance to the adoption of cloud computing. In line with this, cloud security issues and potential solutions will be discussed in this work. For researchers and security experts. This article will provide researchers and security experts with important information on privacy concerns, as well as potential solutions.
A Comparative Analysis of Teachers Workload Reduction Policy in South Korea and China -Focusing on Seoul and Beijing

[Purpose] The purpose of this study is to analyze the teacher workload reduction policies in Seoul and Beijing, the capital cities of Korea and China, respectively, from a comparative perspective.&#x0D; [Method] The analysis criteria for this study are the purpose of the teacher workload reduction policy and the three main measures (reduction of official documents, school human resource management, and work restructuring). A comparative methodology was used to identify the similarities and differences between the policies of the two cities by collecting and analyzing data such as policy documents, newspaper articles, and cases in addition to references such as articles and books.&#x0D; [Results] Although there are some differences in the contents and aspects of the policies in Seoul and Beijing, due to their different resources and policy priorities, they share a common goal of reducing the administrative workload of teachers in order to make their work easier. Regulations to reduce official documents, reallocation of human resources, and designation of dedicated administrative departments were attempted as a means of reducing teacher workload. However, it was found that both cities were unable to overcome practices of administrative dominance that are deeply rooted in bureaucratic traditions.&#x0D; [Conclusion] It was found that both cities are promoting administrative work reduction policies to create an environment where teachers can focus on teaching, although the specific measures are different. Nevertheless, teachers in both cities do not feel that their workloads have been reduced, and policy implications for improvement have been drawn and presented.
INVESTIGATING COMMUNICATIVE COMPETENCE AS A VITAL OF PEDAGOGICAL CATEGORY

The trends occurring in the sphere of contemporary education make the study of the issue of the development of communicative skills relevant. The unfettered development of a person, creative initiative, and student independence are the three areas where socioeconomic changes are influencing education. The subjectivity, individuality, and psychological readiness of secondary school pupils for intensive communication activity in the context of business communication, as well as the lack of sufficient scientific elaboration of this issue in pedagogy, determine the relevance of studying the process of formation of communicative competence. It is vital to perform educational research in this regard to identify efficient techniques for developing communicative skills.
COMPARATIVE ANALYSIS OF MENTAL MODELS AND ARTIFICIAL INTELLIGENCE

Mental models are a representation of the external world. They are a hypothetical example of relevant elements to the knowing subject, that are configured by analogy to the world`s being. The formation of warranted beliefs can be a result of the very same reliability of this type of representation. The article argues for this position and defends the thesis that the world is epistemically insecure. This means that there is no objective reason in and of itself for certain elements to be preferred rather than other. This is a serious problem for AI researchers, who find difficulty in recreating the visual orientation ability of humans. By this analogy, mental models have high value in orienting us in the epistemic landscape. This article argues that mental models have higher epistemic value compared to AI.
The Importance of Library Websites in Accessing Electronic Resources Suresh Kumar

A library website is a platform that offers access to various -resources, including electronic resources. A user friendly library website can boost user experience and empower easy access to electronic resources. To effectively utilise electronic resources on a library website, users should be conversant with the site's interface, understand how to conduct searches, and have access to technical support. Electronic resources through library websites offer many benefits, such as convenience, 24/7 access, and cost-effectiveness, but limitations, such as restricted access, should also be considered. In summary, a user-friendly library website is vital for accessing electronic resources efficiently, and while electronic resources have many profits, their limitations should also be acknowledged.
Deep Learning Approaches for High-resolution Solar Irradiance and Solar Power Nowcasting

PV / solar power production is fostered to become one of the most powerful renewable energy sources in Central Europe to tackle our growing energy demands. While the just recently emerging PV power observations often record highly resolved real-time data for a relatively short time-horizon, solar irradiance offers long time-series using automatized weather station networks. Being closely linked, irradiance can act as a relevant input and estimate for PV forecasting. Particularly, long time series may be exploited for generating synthetic training datasets. However, both irradiance and PV can be challenging in forecasts using machine learning methods as they embrace a high degree of diurnal and seasonal variation. Deep learning offers us new opportunities to generate highly resolved weather forecasts by learning relations in complex datasets. In this study, we investigate the suitability of several deep learning techniques for irradiance and PV nowcasts. Our main models investigated includes a sequence-to-sequence LSTM (long-short-term-memory; a type of artificial neural network) model using a climatological background model or NWP (numeric weather forecasting model) for post-processing, a Graph ANN (artificial neural network) model, and an analogs based deep learning method. Relevant input features include 3D-fields from NWP models (e.g.: AROME), satellite data and products (e.g.: &amp;#160;CAMS), radiation time series from remote sensing, and observation time-series. Results for selected topologically diverse locations obtained by the developed method yield, in general, high forecast-skills, where we elaborate on interesting cases studies from a meteorological point of view. Different combinations of inputs and processing-steps are considered. We compare obtained forecast results to forecasts produced by traditional methods.
On Virtual Enhancement of Learning Creativity Regarding Computer Aided Instructional Processes (Neural Networks Approach)

A growing community represents the field of learning sciences internationally.Many experts now recognize that conventional ways of conceiving knowledge, educational systems and technology-mediated learning are facing increasing challenges in this time of rapid technological and social changes.Since beginning of last decade, Artificial Neural Networks (ANN S ) models have been adopted to investigate systematically critical and challenging educational phenomena.Specifically, this piece of research deals with conceptual analysis and evaluation of quantified learning creativity as an interesting educational phenomenon.In other words, quantitative assessment of learning creativity phenomenon is a rather critical and challenging issue.More precisely, the presented piece of research adopted systematic quantified Investigation of learning creativity phenomenon as an interesting and challenging educational issue.It introduces a novel interdisciplinary approach integrating educational sciences with brain functional modeling across computer simulation of (ANNs), that supported by practical educational field (case study) results.In more details, by adopting realistic ANN s simulation and modeling to quantify learning creativity.Optimal selectivity for values of gain factor, learning rate parameters and number of neurons contributing to learning process are relevant in improving observed quantified learning creativity phenomenon.Interestingly, obtained presented results herein seemed to be valuable and promising for future more elaborate and systematic research in learning creativity as well as other educational phenomena.