Reviewerly/exHarmony huggingface dataset:
GAMBARAN MOTIVASI BELAJAR PADA SISWA SMP PGRI 1 CIANJUR

This study aims to describe the learning motivation of junior high school students in Cianjur. Learning motivation has an important role for every student to have. This research method uses quantitative description. In this study, data was collected by filling out a questionnaire containing 26 statements carried out by 120 class VIII students. Based on the results of the study showed that the description of the learning motivation of the students of SMP PGRI 1 Cianjur was in the medium category. Which means that students are in the middle between the high and low categories, at this time students have enough ability and can be motivated to learn. For the results of the research presented in the form of a diagram depicting learning motivation, the results obtained are that 15% of students are low in the aspect of perseverance in learning, 73% of students are interested in learning and 12% of students are high in tenacity in learning. The results of this study are to determine the description of learning motivation that must be considered in helping to increase student motivation to learn. Keywords: : Learning Motivation, Students
PROBLEMS AND OPPORTUNITIES OF ARTIFICIAL INTELLIGENCE

This article reviews Artificial Intelligence (AI)’s challenges and opportunities and discusses where AI might be headed. In the first part of the article, it was tried to reveal the differences between Symbolic AI and Deep Learning approaches, then long promises but short deliveries of AI were mentioned. When we review the problems of AI in general terms, it is a problem that the media has high expectations about AI and keeps the problems and restrictions it creates low. Today, while AI is stuck with issues such as deepfake applications and carbon footprints that create moral and climatologic problems; on the other hand, it is struggling with problems such as deep learning models requiring huge amounts of data. Another problem with deep learning is that deep learning models are a black-box and not open to improvements because it is not known where mistakes were made. Among the new paths ahead of AI are Hierarchical Temporal Memory (HTM) models and hybrid models that generally try to bridge the gap between Symbolic AI and Connectionist AI. If we consider that the most important leaps in AI have been made with the features of the brain that AI can imitate, then the developed HTM models may also be a new opportunity for AI.
Fall Prevention through Cortico-Muscular Activity Monitoring: A Simulation Approach of MRCPs

This paper presents a first step towards designing a fall prevention system based on the monitoring of cortico-muscular activity, in the benefit of elderly people with neurodegenerative diseases.In this work, we simulate an EEG bio-signal resembling in terms of characteristics, the premotor potentials that precede voluntary movements of the lower limbs, namely an MRCP (Movement Related Cortical Potential).
Towards Compromise User Experience Design in Ambient Intelligent Environment

Building, testing and evaluating UX for applications for Agricultural Ambient Intelligence Environments can be a difficult and time-consuming job. It can be an even longer and more challenging process due to their complexity and area of scope for complex intelligent systems. Many studies address the issue of UX design and evaluation of website user interface, mobiles, tangible equipment, wearable equipment and other, but it is necessary to look for UX deficiencies in all possible functions, every possible task. Depending on the structure of expert teams, experts’ opinions can vary broadly vary or may even contradict. This paper presents possibilities of use the Best-Compromise-Mean (BeCoMe) method for evaluation UX design. BeCoMe was not used for UX evaluation yet. Verification of whether the BeCoMe method is suitable for UX evaluation is carried out on a tablet using two prototypes of control panels of an intelligent environment.
STUDI IMPLEMENTASI KEBIJAKAN STANDAR PELAYANAN MINIMAL PENDIDIKAN (Studi Pada SDN Di Wilayah Kelurahan Lamekongga Kecamatan Wundulako Kabupaten Kolaka)

Autonomy and decentralization policy of education of course very suitable with democratic principles, but which we have to concern is whether local government can provide educational services with the adequate quality. It is important to note because of the inproportionale ability condition between each local governments. Referring to this matter, The Minister of National Education have made decision number 053 year 2001 concerning guidance compilation of Minimum service standard for Elementary educational and Middle school management. Execution of this standard in the regional sub-district Lamekongga have been well done. There are at least four factors affecting namely communications, resource, disposition, and structure of bureaucracy.
Investigating Performance of Composite Quantile Regression with and without Penalization

The performance of composite quantile regression with and without penalization was investigated and results compared to Lasso Quantile Regression (LQR), Lasso Regression (LR) and Ridge Regression (RR). The mean square error, Akiake information criteria and mean absolute percentage error were used as the comparative criteria. The comparison was illustrated using real dataset and simulated data sets of sample sizes n=30,100,300,500 and 1000. Five consecutive quantiles; 0.19, 0.39, 0.59, 0.79 and 0.99 were used for the quantile regression methods while the lasso regression and ridge regression were based on the mean effect. Another set of quantiles; 0.25, 0.5, 0.75 and 0.95 were also accessed based on only CQR and CQR_AL. The results shows that the composite quantile regression without penalization (CQR) and composite quantile regression with penalization (CQR_AL) achieved same results with the lowest variance on estimated effects and was best fitted model for very large data sets (n=100, 300, 500 &amp; 1000). For the real life data with sample size of 318, the CQR_AL showed the least MSE=421.7653 and AIC=1930.136, but its prediction accuracy was low at 19.03%. Considering sample size 30, the LQR at the 0.59th quantile had the lowest variance with MSE of 1.3940 and was best fitted with AIC of 340.20, also it was noticed that COR_AL achieved the next lowest value for MSE and AIC. These results led to the conclusion that CQR_AL and CQR can be used alternatively with large data sets. But when the sample size is small the LQR is most suited, but if a combined quantile effect is sort the CQR_AL should be opted for.
THE ROLE OF THE MANUSCRIPT COLLECTOR IN BANDA ACEH

The goal of this research is to determine what function manuscript collectors play in Banda Aceh and what manuscript collections are available to them. This study used a qualitative research method in which the data was collected by observation, interviews, and documentation procedures, as well as a literature review and previous references. According to the findings of the study, manuscript collectors in Banda Aceh play a variety of activities, including caring for and preserving manuscripts, teaching, using social media, and motivating the younger generation. Tarmizi Abdul Hamid and Masykur Syafruddin are two manuscript collectors discussed by the authors. The collections of these two individuals are very distinct. The collections of these two collectors are quite distinct. Masykur has roughly 495 manuscript collections that have been inventoried with varied themes, whereas Tarmizi has 598 collections. The lack of funding and human resources for manuscript maintenance are problems that collectors encounter in general.
PATTERNS OF GRAMMATICAL ERRORS IN STUDENTS’ THESIS

Grammar is badly needed by all thesis writers in order that they can produce good outputs as expected by the English Education Department. Therefore, the study aims to reveal the types of grammatical errors and to identify the most types of grammatical errors in students’ thesis. In this research, the researcher employed a descriptive qualitative method. In this study, the researchers only focused on analyzing the grammatical errors in English students’ thesis at the University of Mataram using a surface strategy taxonomy. The researchers used a purposive sampling method to gather the data. The data were derived from students’ theses submitted between 2018 and 2020. There were a total of 9 theses studied for the purpose. The data were classified into 4 determined categories suggested by the theory of Surface Strategy Taxonomy, i.e. (1) Omission, (2) Addition, (3) Misformation and (4) Misordering. The result of the study shows that the theses under investigation contained the four categories of errors. There were a total number of 275 errors found in students’ theses. The most common types of errors were in Misformation (49.81%) and the least one was Misordering (0.72%). Implication of the present study is provided succinctly in the concluding part of this article.
Towards Data Storage Security in Cloud Computing Using Hybridized Advanced Encryption Standard &amp; Authentication Scheme

Cloud computing is at the heart of modern day computing and thus the rise in malicious attacks are increasing every day towards it. In recent times hackers have been engaging in different string concept on login fields and phishing in order to intrude cloud accounts. This research is proposed to carry out effective security mechanisms on loopholes in cloud data storage. We intend to engage the use of the Advanced Encryption Standard (AES), security Algorithms like Secure Hash Algorithm 1 &amp; 3 (SHA1, SHA 3) and Message-Digest Algorithm (MDS) and the Standard Authentication Scheme (AS) as sure guide against phishing which when implemented can provide various login stages and codes like OTP (One Time Password) to mitigate attacks. With the application of these methods towards securing cloud computing data there will be less intrusion in accounts and loss of files. Keywords: Brute-force, Cracking, Hacking, Advanced Encryption Standard, Authentication Scheme, AES, ES, Security
Stylistic approaches to pop culture

The rationale for this collection of papers and the conference from which they emerged is that pop cultural texts are a part of pop culture, that the primary tool or lead discipline in the analysis of such texts should be linguistics, and that therefore the linguistic (or stylistic) analysis of relevant artifacts is necessary. The place of pop cultural texts among the forms of cultural studies is guaranteed in advance.
Development of a very high resolution solar radiation cadaster for estimating solar energy potential across the entire federal state of Salzburg, Austria

&amp;lt;p&amp;gt;The effect of local shadows cast by e.g. infrastructure or vegetation is of paramount importance for accurately estimating the local solar energy potential. Given the high spatial and temporal resolution that is required to provide accurate estimates of solar radiation components at very fine scales, deriving a solar cadaster across large areas is a computationally demanding undertaking. Here we present the development of a solar cadaster for the Austrian federal state of Salzburg, covering an area of more than 7.150 km&amp;amp;#178; at a spatial resolution of 0.5 meters and a temporal resolution of 1 hour.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The dataset is based on the radiation model STRAHLGRID, which calculates near-surface direct and diffuse solar radiation on horizontal, real and arbitrarily inclined surfaces in the spectral range 0.3-3 &amp;amp;#956;m, their component sum (global radiation), and sunshine duration. Results are provided at a spatial resolution of 100 m and a temporal resolution of 15 minutes across the national territory of Austria in near real-time. The model takes atmospheric turbidity, cloudiness, terrain shading, multiple / terrain reflections and ground albedo feedbacks into account. It integrates temporal changes of atmospheric turbidity by including precipitable water (water vapor transmittance) and visibility fields (aerosol transmittance) as obtained from the nowcasting model INCA as well as a cloud raster based on measured sunshine fraction and satellite data. The underlying digital elevation model (DEM) used as input in STRAHLGRID and INCA has a spatial resolution of 100 m.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The downscaling procedure used for providing a consistent solar radiation cadaster is based on combining the 100 m radiation data with a very high resolution digital surface model (VHR-DSM) obtained from airborne laser scanning. First, a test reference year is computed by aggregating the 15 min data to one hour and averaging across all years between 2006 and 2021 on an hourly basis. Second, diffuse and direct radiation on the horizontal surface are upsampled using bilinear interpolation. Third, radiation datasets are modified based on the VHR-DSM. Diffuse radiation is corrected using an updated version of the sky view factor based on the original DEM and the VHR-DSM. Direct radiation is corrected using shade maps from the VHR-DSM derived from the horizon angle and the solar position, computed using azimuth angle steps of 1 degree and time steps of 4 minutes. In addition to applying the shadow mask, direct radiation on the real surface is obtained using a correction term for the tilted surface based on slope, aspect and solar position. Finally, global radiation is computed as the sum of diffuse and direct radiation, which is of high relevance for spatial energy planning and solar energy applications at diverse scales.&amp;lt;/p&amp;gt;
Pareto optical flow solutions for ensemble, satellite-based forecasts of irradiance and PV&amp;#160;

&amp;lt;div&amp;gt;&amp;amp;#160;&amp;lt;/div&amp;gt;&amp;lt;div&amp;gt; &amp;lt;p&amp;gt;&amp;lt;span&amp;gt;The sustainable electric grid of the future will rely on comprehensive measurements and forecasts of its millions of components. For PV, such high-resolution forecasts will benefit from the near real-time data and detail provided by satellite observations. Nowcasting or forecasting into the future from detailed satellite images based on e.g. root mean squared error optimization, (e.g. machine learning), however inevitably promotes smoothing and removes detail from the forecast, calling into question the definition of forecast quality. Probabilistic forecasting in the form of ensemble solutions offers an answer, allowing for the detail from satellite images without the expectation of deterministic pixel point accuracy. (Ensemble numerical weather predictions exist on high-resolution grids, but also present smoothed predictions of clouds).&amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;&amp;amp;#160;&amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt; &amp;lt;/div&amp;gt;&amp;lt;div&amp;gt; &amp;lt;p&amp;gt;&amp;lt;span&amp;gt;This study creates ensemble forecasts using a new version of the optical-flow-based nowcasting system presented in past sessions that solves for the global cloud motion using Taylor-approximated streamlines. The optimized flow field is physically constrained through a combination of mass&amp;amp;#160;and angular momentum conservation. The errors for the motion of different structures in the image are discerned as secondary objectives to the overall optimization. The optical flow algorithm uses ant-colony, multi-objective optimization, following many solutions before arriving at a &amp;lt;/span&amp;gt;&amp;lt;em&amp;gt;&amp;lt;span&amp;gt;Pareto&amp;lt;/span&amp;gt;&amp;lt;/em&amp;gt;&amp;lt;span&amp;gt; optimum.&amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;&amp;amp;#160;&amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt; &amp;lt;/div&amp;gt;&amp;lt;div&amp;gt; &amp;lt;p&amp;gt;&amp;lt;span&amp;gt;The experiments test the viability of using other cloud motion solutions in&amp;amp;#160;the Pareto front to generate ensemble forecasts of the cloud cover and subsequently of irradiance maps and regional PV power. Where regions are fully cloudy or clear,&amp;amp;#160;the ensemble solutions should be uniform, while moving regions of variable cloudiness aim for&amp;amp;#160;realistic ensemble distributions. The reliability and potential of such forecasts are evaluated and compared to numerical ensemble weather predictions using continual ranked probability scores (CRPS).&amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;&amp;amp;#160;&amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt; &amp;lt;/div&amp;gt;
The Influence of Contextual Data on the Results of the Input Assessment of the Subject Content for the Computer Science Course of Secondary General Education

The article analyzes the problem of the influence of contextual data on the results of the input assessment of the subject content for the computer science course of secondary general education for 1st-year students of the Faculty of Physics and Mathematics of Voronezh State Pedagogical University in 2019–2021. The purpose of the article is to identify and analyze the influence of independent factors on the results of the input assessment of the subject content for the computer science course of secondary general education for 1st year students in 2019–2021. Methodology. The input assessment of the level of formation of the subject content of the computer science course on the basis of the Unified State Exam for the academic subject “Informatics” was carried out in the 1st semester of first-year students (september-october) in 2019–2021. A total of 174 1st year students took part in it. To conduct the study, the following contextual data were used: the biological sex of students; the location of the educational organization (general education or professional) that the 1st year students completed before entering the university; participation and results of the USE students in 2019–2021 in the academic subject “Computer Science”; preparation of students for the Unified State Exam in 2019-2021 for admission to university; features of the family in which students were brought up (full and incomplete, the presence of higher education from parents in the family, the presence of work from parents in the family). The study of the results of the input evaluation, comparison and analysis of the results obtained during the study was carried out using the Kruskal-Wallis H test, the Mann-Whitney U test, and correlation analysis. In conclusion, the conclusion is made about the different influence of contextual data on the results of the input assessment of the subject content for the computer science course of secondary general education for 1st-year students of the Faculty of Physics and Mathematics in 2019–2021.
A Study of the Relationship between Self-Regulated Learning Strategies and Academic Achievement of Secondary School Learners

Self-regulated learning (SRL) is acknowledged as a significant indicator of student academic achievement (Xiao, Yao, & Wang (2019).This research focuses on the value of the use of selfregulated learning strategies for the learning outcome i.e. academic achievement of X grade learners.In this study, the relationship between the two research variables, the use of SRL strategies and academic achievement of students, was examined.An ex-post facto research design was used for study.The study was carried out with the sample of 230 X grade students randomly selected from Faisalabad District Punjab Province, Pakistan.The instrument namely Academic Self-Regulated Learning Scale (A-SRL-S) was adapted according the local norms.The results indicate that majority of the secondary school learners make use of Academic Self Regulated Learning Strategies (ASRLS) in their academic activities.The study also found moderate level relationship between ASRLS and academic achievement of secondary school learners.Finally, students of both genders were found to be similar in their academic achievement and in the use of ASRLS for their studies.Similarly, the respondents from rural and urban localities were found to be almost similar in their use of ASRLS during their studies whereas they differ significantly in their academic achievement on the basis of their localities.
Cloud Shadows in Satellite-Based Solar irradiance Estimation: Improved Correction using EUMETSAT&amp;#8217;s Cloud Top Height Data

&amp;lt;p&amp;gt;The estimation of solar surface irradiance at high spatio-temporal resolution from geo-stationary satellite images is a well-established technique, for example by using the Heliosat method. The method has widely reduced the need for expensive ground measurements, especially in remote regions. However, the location of cloud shadows at the ground is difficult to determine and thus a significant source of errors when either the distance from the sub-satellite point or the cloud top height (CTH) increases. Although several methods have been proposed in the literature to reduce these errors, it is still an issue. We present a novel approach to correct the cloud shadow location based on the satellite-cloud-sun geometry using the CTH maps from the EUMETSAT data archive. It uses satellite viewing angles and solar position angles to determine the correct cloud shadow location for each cloudy pixel. The method is tested on cloud index (CI) maps for the months of July, August and September 2018 derived by applying the Heliosat method on the 0.6 um visible channel images from Meteosat-8 located at 41.5&amp;amp;#176;E. Convective clouds with large CTHs are frequently observed over the Indian subcontinent in these three months due to the Indian summer monsoon. The global horizontal solar irradiance (GHI) obtained from the corrected CI image is validated at two BSRN stations. The normalized root mean square error (nRMSE) is reduced from 23.2% to 20.9% for the Gurgaon station and from 15.4% to 13.9% at Tiruvallur. In general, correcting the cloud shadow location on CI map improved the accuracy of the estimated GHI. Nonetheless, the method is sensitive to the accuracy of the CTH dataset and individual cases were found for which the correction reduced the accuracy.&amp;lt;/p&amp;gt;
Sistem Prototype Klasifikasi Risiko Kehamilan Dengan Algoritma k-Nearest Neighbor (k-NN)

The increasing maternal mortality rate (MMR) in Indonesia in the last two decades has become a serious concern for the government. Moreover, Jember Regency is one of the areas with the highest MMR in East Java. Where in 2018 the AKI of Jember Regency was ranked 10th with an AKI of 114/100,000 KH and was the 5th highest rank in 2019 with 133.4/100,000 KH. The process of recording pregnancy data that is still done manually can also affect the AKI process because it can slow down the decision-making process for pregnant women who are at risk. In this study, the focus is on creating a recording system for pregnant women according to cohort data and equipped with features to support pregnancy risk classification according to the KSPR standard. So that it is expected to provide an early decision on the risk of pregnancy to related parties. The results of the system trial show that the k-NN system developed is able to help the computational process faster by complementing the classification results with an accuracy rate of up to 80%.
A Design-to-Test Technique for Inclusion Coefficients in AI-Driven Systems

AI drives the trending explosion in incorporated technologies like social interactive software systems. Such AI-Driven Systems (AIDS) in social technologies like Facebook, Google, Twitter, etc are unaided by human control and are rarely tested for articles of inclusion like identity, race, gender, privacy, ethics, etc. As a result, this paper proposed an architecture that designs AIDS to measure using Key Performance Index (KPI) metrics in testing for the coefficients of inclusion. As a research agenda, the conceptual architecture presented is designed in three layers; presentation layer, business logic layer and the data layer. These layers use in-built resources and/or API services (internal or external) for defining inclusion properties, article assortment and decision pattern for classifying articles or objects of inclusion.