Reviewerly/exHarmony huggingface dataset:
DEVELOPMENT OF WEBAPPS-BASED E-LEARNING AT SMK MIFTAHUL ANWAR MENES

Limitations when teaching and learning in the classroom are sometimes many things that hinder teachers from all learning materials to students. This is a case topic for teachers who want to discuss in detail. This difficulty was complained by teachers and students at Miftahul Anwar Menes Vocational High School, sometimes material that needed explanation in class actually had to be explained in a significant amount of time. The method used was Research and Development (R and D) using the example of waterfall development, research and analysis of the required needs. To develop this online learning implementation design product, it uses several stages: 1) Needs Analysis 2) Design 3) Implementation 4) System testing. teachers students) then the final output is declared feasible using the output of the teacher's score the Category 53 Percentage Criteria table is in the interval X more than 42 while the students' Category Criteria Percentage table is 40.8. user ratings are in the interval X 39 which is in the "Very Eligible" category.
EVALUATION OF SAP IMPLEMENTATION ACCEPTANCE WITH THEORY OF PLANNED BEHAVIOR AT PT KERETA API INDONESIA (PERSERO)

PT Kereta Api Indonesia (Persero) is a State-Owned Enterprise (BUMN) that has the leading business process to provide, manage, and regulate rail services in Indonesia. SAP is one software that can support the company's business processes. SAP implementation has been carried out since 2012 until now. In implementing a system, success or failure can be assessed on non-technical aspects; namely, the user can accept and operate SAP properly. Thus, this study will analyze the success of SAP implementation, namely how, the attitude of users in accepting the use of SAP at PT Kereta Api Indonesia (Persero) using the theory of planned behavior models. The variables in this study are attitudes, perceived behavioral control, behavioral intentions, subjective norms, and behavior. In this study, behavioral intention and behavior explain how individuals operate SAP. Meanwhile, attitudes, subjective norms, and perceived behavioral control affect the individual's intention to operate SAP, and behavior is influenced by behavioral intentions. Data collection was carried out by distributing questionnaires to a number of respondents, as many as 20 SAP users at PT Kereta Api Indonesia (Persero). Data processing uses smartPLS 3.3.9 with a partial least square (PLS) approach. Hypothesis testing was carried out with a significance level of 0.05, and the results obtained were that attitude (attitude) had a positive and significant effect on behavioral intentions (intentions), behavioral intentions had a positive and significant influence on behavior, subjective norms, and behavioral control had a negative effect on behavioral intentions in using SAP.
European Petrophysics Consortium's Contributions to IODP

The European Petrophysics Consortium (EPC) is part of the ECORD Science Operator (ESO). EPC comprises the University of Leicester and Ge&amp;#769;osciences Montpellier and provides petrophysics staff scientists and petrophysicists, as well as expertise in downhole logging and core petrophysics programmes. The EPC has dedicated equipment for core logging and discrete measurements and is involved in data calibration, quality control, evaluation and interpretation of these data. The EPC is also involved in post-expedition activities, the preparation of upcoming expeditions, capability development, and training for IODP MSP expeditions and other key activities, including education and training.Over the past pandemic years, EPC has been active within expeditions and the community. EPC recognizes the importance of scientific drilling to palaeoclimate studies amongst other key topics, which is also reflected in our new science and operations roadmap: i) hired new staff members with a paleoclimate background; ii) developed a system for knowledge exchange between petrophysics and climate scientists, for instance via the ECORD summer schools; iii) renewed focus to include the development of measurement protocols and data analysis techniques to better serve the IODP community.EPC also has a website (http://www.le.ac.uk/epc) and will host the next ECORD Summer School Downhole Logging for IODP Science in person in Leicester in summer 2023.&amp;#160;*European Petrophysics Consortium Team Members:Sarah Davies, Simon Draper, Tim van Peer, Andrew McIntyre, Marisa Rydzy (University of Leicester).Philippe Pezard, Johanna Lofi, Erwan Le Ber, Laurent Brun (University of Montpellier).
PASSION: a workflow for the estimation of rooftop photovoltaic potential from satellite imagery.

Photovoltaic (PV) and wind are currently the highest growing renewable energies according to the annual World Energy Outlook 2022. However, in the case of solar PV in Europe, this growth is mainly driven by utility-scale installations. Distributed residential generation has many benefits, such as relieving the electrical grid and an increase of self-sufficiency. One challenge of this topic is to accurately estimate the rooftop PV potential of different regions, in order to best allocate economic resources and regulate accordingly. Multiple approaches have been proposed in the past, such as infering from proxy variables like population density, automatically analyzing residential 3D point clouds or automatically analyzing satellite images. The latter has gained popularity in the recent years given the increased availability of satellite imagery and the improvement of Computer Vision methods. However, in research, the analysis of satellite imagery is impeded by the lack of transparency, reproducibility, and standardization of methods. Studies are heterogeneous, target different types of potential with redundant efforts, and are mostly not open source or using private datasets for training. This makes it challenging for users of various backgrounds to find and use the existing approaches.For these reasons, this paper proposes a conceptual framework that describes and categorizes the tasks that need to be considered when estimating PV potential, thus creating a clear framework along which the contents of this research report can be classified. Addidionally the open source workflow PASSION is introduced, which integrates the assessment of geographical, technical and economic potentials of regions under consideration along with the calculation of surface areas, orientations and slopes of individual rooftop sections. It also includes the detection of obstacles and existing PV installations. It is based on a novel two-look approach, in which three independent models are deployed in parallel for the identification of rooftops, sections and superstructures. The three models show a mean Intersection Over Union (IoU) between classes of 0.847, 0.753 and 0.462 respectively, and more importantly show consistent results in non-selected real life samples.
AUTOMATING ON-THE-JOB-TRAINING: GEEKAPP

On-the-Job Training (OJT) is essential for upskilling employees and enhancing productivity, but it can be timeconsuming and resource-intensive.Automation can streamline the on-the-job training process by reusing the knowledge of skilled workers, improving the effectiveness and efficiency of training programs.By automating the knowledge transfer process, organizations can ensure that employees are trained more quickly and thoroughly, resulting in better performance and higher job satisfaction.This paper explores the benefits of automation in OJT and a sample case study for reusing skilled worker knowledge to optimize the training process.
Deep Learning and Multiclass Machine Learning Classifier Approach for Predicting Primary Tumors

Deep Learning (DL) and Machine Learning (ML) have the great prospect to play a significant role in the medical field in disease prediction. The tumor or cancer is one of the major health issues that each nation is currently dealing with, and it is the topic of this essay. The prediction of unidentified primary tumors in the dataset is delineated in this paper. Given that it provides significantly higher accuracy than binary classifiers, different multiclass classifier such as K-Nearest Neighbor (KNN), CatBoost Classifier, Random Forest Classifier, Gradient Boosting Classifier, Light Gradient Boosting Machine, Ada Boost Classifier, Decision Tree Classifier, SVM - Linear Kernel, Naive Bayes and Deep neural networks (DNN1, DNN2, and DNN3) are used to categorize multiclass datasets available in the UCI machine learning repository. Among the stated machine learning classifiers, the k-Nearest Neighbor (KNN) had the highest classification accuracy of 92.92%. The three layer deep neural network (DNN2), among deep learning techniques, had produced the best accuracy of 97.66% using the chosen features as input. The gathered results from this work showed that deep neural networks outperformed machine learning techniques.
School Operational Assistance Fund Management, Human Resources Competence and Governance, is it Still a Problem?

Submission of financial reports on time is often a problem, one of which is the case with the Department of Education, Youth and Sports. This quantitative study aims to obtain information on the moderating effect of organizational commitment on the competence of human resources managing School Operational Assistance (BOS) funds and governance in accountability for the use of their budget. Drawing a sample of 351 from all managers of BOS funds in public elementary and junior high schools. Data collection uses a questionnaire, with a quota sampling technique, data processing uses SmartPLS. The results of this study inform that human resource competence and governance affect the quality of financial reports, but organizational commitment is not able to strengthen its influence. Procurement of appropriate training, optimization of existing systems, verification of Activity Plans and School Budgets, reconciliation of budget implementation. For further research, it is better to collect data directly to the educational unit, so that all respondents' statements are in accordance with the facts.
Guess about the equations in the form of gravity

By analogy with Maxwell's equations in electromagnetic form, we can find a group of equations in gravitational form that looks very interesting.
FACE MASK DETECTION UNDER LOW LIGHT CONDITION USING CONVOLUTIONAL NEURAL NETWORK (CNN)

The COVID-19 pandemic has been around for 3 years, and the virus is still spreading until now and using mask is an alternative for people to not get infected, but some people tend to let go of the mask for inconvenience reasons, especially under low light conditions which is difficult for humans to identify. Thus, this paper proposed and implemented a face mask detection model which can accurately detect a person that using a mask or not in such a condition as low light by using Convolutional Neural Network (CNN) architecture with OpenCV, TensorFlow and Keras. To achieve this, the first step is to transform the data by using Python Imaging Library (PIL) to create a low light image, then we process the data by using Contrast Limited Adaptive Histogram Equalization and with Gamma Correction. The second step is to augment the data by using TensorFlow ImageDataGenerator and define the CNN model. The final step is to create the face mask prediction by using Haar Cascade Algorithm to detect the face mask. The results of this research shows that CNN model can be trained with a recreational low light images to detect face mask under low light conditions. The result of the model produced an accuracy of 98%.
Characteristics of microseismicity in the Kiskatinaw area, northeastern British Columbia, Canada

Since the early 2000s, lots of induced earthquakes have occurred due to fluid-injection during the development of unconventional resources at the Kiskatinaw Seismic Monitoring and Mitigation Area (KSMMA) located in northeastern British Columbia, Canada. The spatial-temporal distribution of microearthquakes induced by fluid-injection are important to understand the characteristics of crack and movement of fluid. Also, to mitigate earthquake disasters, it is essential to continuously monitor microearthquakes in fluid-injection areas. We used the seismic data recorded at the EON-ROSE seismic network, which is a dense seismic network consisting of 16 broadband seismic stations, and GSC-BCOGC seismic network to analyze the characteristics of microseismicity of the KSMMA in 2020. We detected the seismic signal (P- and S-wave) using the automatic seismic phase detection method, which is based on the short-term-average to long-term-average ratio (STA/LTA) and kurtosis. And then, we associated the seismic phase arrival data to combine to earthquakes from the automatic seismic phase association method using the temporal distribution of the detected signals and the spatial distribution of the seismic stations used. The hypocenter parameters of associated earthquakes were determined with the HYPOINVERSE location algorithm and the existing 1-D velocity model of KSMMA. The epicenter distributions of the detected earthquakes are concentrated in the area known as active fluid-injection, and the focal depths are also distributed at about 2 km. We analyzed the seismicity by dividing it with three periods based on COVID-19 lockdown and confirmed the low-seismicity of the lockdown period, which is consistent with the result of the independent study performed at the region.
Research on Optimization of AR-HUD Visual Interaction Design

This study aims to explore AR-HUD(Augmented Reality-Head up Display) visual interaction cognitive load’s prediction algorithm model and obtain the best adaptation mode of AR-HUD interface visual Interaction Design. Through immersive driving simulation experiments, a driver assistance test system was established to analyze drivers’ eye movement behavior and visual resource allocation characteristics. The driver’s attention will be less focused on the driving task and correspondingly less on elements of the driving environment, negatively affecting the recovery of cognitive resources. The focus of this study is to establish a visual cognitive load index by combining the visual intensity model and the user’s subjective cognitive load evaluation of the interface. The AR-HUD visual Interaction Design coding and visual cognitive load index are used as the input and output layers to establish a visual cognitive load prediction neural network model. The neural network model was introduced into the genetic algorithm’s fitness function. The genetic algorithm was used to obtain the optimal AR-HUD Visual Interaction Design solution in the finite solution space. Then the optimal AR-HUD visual Interaction Design was obtained. The CH Scale scale was used to assess the validation of the algorithm’s soundness.
Human Regular Activities Recognition Using Convolutional Neural Network

Capturing commonly occurring behaviors is a tough issue in computer vision. A few of them are recreation, touring, leisure pursuits, and religious practice. A comprehensive effort has already been dedicated to this aspect to deal with this issue. In this work, we recreated a dataset with five categories, including household activities, farming, exercise, sports, and occupation, to identify human daily actions. This collection has 4328 colored images in total, among them 630 are set aside for testing, and 3698 for training. Deep learning and standard image-based strategies are being explored to address the issues. In this paper, we have designed a deep learning paradigm to classify the regular activities of human beings. To characterize people's daily chores, we use the CNN model, one of the greatest tools for visual identification. We also have chosen two already-trained VGG16 and ResNet50 models. When we compare our model with the existing techniques, the investigation's findings demonstrate that the suggested network has a better recognition accuracy of 91%. Additionally, we have observed that accuracy varies throughout different epochs, and after 25 epochs we got better stable results from our model. The reader may find this article instructive in grasping CNN models for various recognizing applications.
Factors of Ethereum Profitability as a Platform for Creating Decentrilized Applications

By now, cryptocurrencies have almost become a part of the modern financial asset space, but the cryptocurrency market itself is not homogeneous, and individual cryptocurrencies can differ significantly in their properties and functions. For example, the cryptocurrency Ether is second in capitalization after Bitcoin, but the Ethereum and Bitcoin blockchains differ significantly in their properties and functions. In particular, Ethereum is the most popular digital platform for creating decentralized applications (dApps). The purpose of this work is to try to answer the question "Does the market take into account the features of the Ethereum blockchain in the price dynamics of the Ether cryptocurrency?" This question is also directly related to the search for potential fundamental factors that can explain the price dynamics of Ether. The main econometric method used in the study is generalized autoregressive conditional heteroskedasticity (GARCH) models. Having evaluated about 15 thousand different specifications of GARCH models, where various Ethereum blockchain usage metrics were used as explanatory variables, we obtained the results that Ethereum network usage metrics do not significantly correlate with Ether cryptocurrency returns. Moreover, these metrics are also unable to explain the relative strengthening/weakening of Ether relative to Bitcoin. Thus, we conclude that despite the presence of a number of special functional properties of the Ethereum blockchain, the price dynamics of the Ether cryptocurrency does not reflect them.
PENGUATAN MINAT BACA DAN KOSA KATA BAHASA INGGRIS MELALUI STORY TELLING PADA ANAK DITAMAN BACA INOVATOR ROBERT FOULTON KOTA SORONG

Language is known as a communication tool that can be used by one person or more in spoken or written forms. Efforts to support the presence of language as a means of communication need to be proven with language skills such as: reading, listening, writing, and speaking. One of the problems that exist in the environment of the Robert Fulton Reading Park Km. 9.5 The city of Sorong is still lacking in terms of increasing children's interest in reading and mastering English vocabulary. Therefore, the Community Serving Movement Team (GRAMMAR) is here to provide encouragement and enthusiasm for children through the methods used to try to strengthen children's interest in reading and English vocabulary with the story telling method. This activity begins with the processes of initial observation, permits, implementation of activities, and reports and evaluation of those activities. The results of the activities carried out provide enthusiasm and encouragement for children in an effort to strengthen interest in reading through a commitment to reflection sessions, which are packaged in one unit of these community service activities. It is hoped that the efforts that have been implemented will continue to synergize in efforts to support the national education goal of educating the lives of the nation’s children
Applied Salt Technique to Secure Steganographic Algorithm

Digital multimedia assets, including photographs, movies, and audio files, have become a staple of contemporary life.Steganography is a method for undetectable information concealment in these files.One can communicate messages to another by modifying multimedia signals so that a human would be unable to tell the difference between the original signal and the altered one.The widespread use of digital data in practical applications has prompted the development of new and efficient methods for ensuring its security.Steganographic techniques can be used to, at least in part, achieve efficient secrecy.There have been suggested new and adaptable audio steganographic techniques.By using cryptography, readable language is converted to unintelligible data.In order to send and receive text, multimedia, or other important digital files safely, this paper discusses secure communication media.To have secure communication tools, the tools must lessen potential risks and weaknesses.Therefore, the primary factor to take into account for creating a solid communication system is transferred media.The objective of steganographic systems is to find a secure and reliable method to hide a significant amount of secret data.This research focuses on digital image audio steganography, which has become a popular method for data concealment.
WORKSHOP APLIKASI DASHBOARD INTERNAL SDM DENGAN MENGINTEGRASIKAN KNIME DAN APPSHEET DI KPP PMA SATU KALIBATA

This workshop is aimed at providing participants with the skills to create internal HR dashboard applications without the need for programming or coding skills. The workshop will cover the creation of internal HR dashboard applications using KNIME and Appsheet for android platforms, integrating data from various sources, and displaying data in an easily readable and understandable format. The methods used include direct learning, group discussions, and independent practice. Participants who can attend this workshop include HR staff, HR managers, or other staff who wish to learn how to create internal HR dashboard applications. Through this workshop, participants will benefit from being able to create internal HR dashboard applications without the need for programming or coding skills, managing HR data more effectively and efficiently, and increasing the effectiveness of HR management in the organization
A benchmarking procedure for quantum networks

We propose network benchmarking: a procedure to efficiently benchmark the quality of a quantum network link connecting quantum processors in a quantum network. This procedure is based on the standard randomized benchmarking protocol and provides an estimate for the fidelity of a quantum network link. We provide statistical analysis of the protocol as well as a simulated implementation inspired by NV-center systems using Netsquid, a special purpose simulator for noisy quantum networks.
A Logic Design-based Approach for Frequent Itemsets Mining Using LCO Algorithm

Frequent itemsets (FIs) mining is the main challenge in analyzing association rules since the complexity of association rule mining is determined mainly by identifying all frequent itemsets.There are many approaches to FIs mining; each approach includes many algorithms, but there is a major weakness in the logic-design-based FIs approach because there is a limited number of methods for logic circuit or expression optimization, each of which suffers from some drawbacks that are transferred to the mining process when they are utilized for this purpose.We propose LCOFI, a new algorithm for frequent itemset mining based on the LCO algorithm, in this paper.LCOA is a new algorithm for logic circuit optimization.LCOFI utilizes the suggested operations for a bipartite graph in LCOA.The proposed algorithm is simple and efficient and supports a large number of input items.It scans the transaction database only once to construct the bipartite graphs of frequent 1-itemsets and avoids the generation of candidate itemsets.It does not require complex data structures such as trees and hash tables to validate the frequency of FIs.When applied to a variety of datasets with varying characteristics, the proposed algorithm, LCOFI, outperformed the Apriori and Fp-growth algorithms in all the experiments.