Reviewerly/exHarmony huggingface dataset:
The Performance Machine Learning Powel-Beale for Predicting Rubber Plant Production in Sumatera

This study aims to predict rubber plants in Sumatra; rubber plants have a relatively high economic value; rubber sap must be cultivated because it is a product of the rubber plant, which is the raw material for the rubber industry, so in large quantities. Therefore, rubber sap, the selling value will increase so that it can increase farmers' income. Rubber production in Sumatra experiences ups and downs; therefore, this study aims to predict rubber plants using the Powell-Beale algorithm method, one of the Artificial Neural Network methods often used for data prediction, implemented using Matlab software. That supports it. This study does not discuss the prediction results. Still, it discusses the ability of the Powell-Beale algorithm to make predictions based on datasets of rubber plant production in recent years obtained from the Central Statistics Agency. Based on this data, a network architecture model will be formed and determined, including 6-10-1, 6-15-1, 6-30-1, 6-45-1 and 6-50-1. The best architecture is 6-15-1, with the lowest Performance/MSE test score of 0.00791984.
The Utilization Of The Conjugate Gradient Algorithm For Predicting School Year Expectations By Province

Expected Length of School (HLS) is the length of school (in years) that is expected to be felt by children at a certain age in the future. It is assumed that the probability that the child will remain in school at the following ages is the same as the probability of the population attending school per total population for the current age. Length of School is also a benchmark for evaluating government programs in improving Human Resources that excel in the competition of technological advances. The purpose of this study is to apply the Conjugate Gradient Algorithm with the Best Performance for Predicting School Life Expectancy in Indonesia. Research data on the Expectation of Schooling in Indonesia consists of 10 Provinces obtained from the Central Statistics Agency from 2016 to 2021. This study uses 5 architectural models, namely 2-10-1, 2-15-1, 2-20-1, 2-25-1 and 2-30-1. Of the five architectural models used, the best architectural model is 2-3-1 with an MSE of 0.000000002 in two seconds. Based on this best architectural model, it will be used to predict the Expectation of Old Schools in Indonesia for the next five years, namely from 2022 to 2026.
The Application of the Fletcher-Reeves Algorithm to Predict Spinach Vegetable Production in Sumatra

&#x0D; &#x0D; &#x0D; &#x0D; Determination of spinach plant predictions is one of the most critical decision-making processes. In predicting spinach plants in each period, it depends on each period, both the previous and subsequent periods. The production of spinach plants that change every period causes uncertainty in predicting. The method used to indicate the data is the Fletcher-Reeves algorithm, it is an appropriate development technique compared to the backpropagation strategy because this strategy can speed up the preparation time to arrive at the minimum convergence value. This paper does not discuss the prediction results. Still, it discusses the ability of the Fletcher-Reeves algorithm to make predictions based on the spinach production dataset obtained from the Central Statistics Agency. The purpose of this research is to see the accuracy and performance measurement of the algorithm in the search for the best results to solve the prediction of spinach plants in Sumatra. The research data used are spinach vegetable production data in North Sumatra. Based on this data, a network architecture model will be formed and determined, including 2-20-1, 2-30-1, 2-35-1, 2-45-1, and 2-50-1. After training and testing, these five models show that the best architectural model is 2-20-1 with an MSE value of 0.00608399, the lowest among the other four models. So the model can be used to predict spinach plants in Sumatra.A well-prepared abstract enables the reader to identify the basic content of a document quickly and accurately, to determine its relevance to their interests, and thus to decide whether to read the document in its entirety.&#x0D; &#x0D; &#x0D; &#x0D;
The Implementation Of The Fletcher-Reeves Algorithm In Predicting The Growth Of Forest Plant Cultures

Forest protection and development are essential because forests are the world's lungs. In addition, the HTI organization (modern manor backwoods) began to hide again. However, due to the great interest in wood to be used as raw material for material and property production lines, large organizations started to develop hamlet wood which was then marketed abroad, such as pressed wood, rattan, sawn timber, and done jobs for individuals in the area around the hamlet. By making a prediction, knowledge about the growth of forest plants can be known so that they can anticipate or minimize the risks that may arise. They can assist in determining policies and making decisions. This study aims to predict the growth of forest plants in the following year using an Artificial Neural Network Algorithm. The information used in this study is from the Central Bureau of Statistics from 2011 to 2022. The method of implementing this research uses the Fletcher-Reeves Algorithm, one of the Artificial Neural Network methods using 5 models, including 7-10-1, 7-15- 1, 7-20-1, 7-25-1, and 7-30-1. Of the five models, the structural model is 7-20-1 with an MSE value of 0.00037397. It can be said that this model can be used because it produces a fast combination and a short period of time.
Reflection as a Factor in the Success of Learners’ Innovative Activity

The analysis of the psychological grounds for learners’ innovative activity would provide avenues for development of innovation capabilities in the younger generation. In the article, reflection is considered a mechanism that impacts the efficacy of action. In this regard, the purpose of the study was to analyze how reflective practice is manifested in learners who are engaged in the university innovative activity but demonstrate its varying degrees. As a working hypothesis, it was assumed that students with varying degrees of innovative activity differ in the level of general reflection of their own activity and its individual components: information-based activity, motivation, benchmarking, decision-making, and performance. The study showed that students experience the greatest difficulties when analyzing the conditions for organizing activities, assessing their own resources and possible risks. Also, significant differences were found in the degree of intensity of such a component as the information-based activity among students with varying degrees of innovative engagement.
Digital Competencies in Collaborative Learning of Students in a Public University in Lima

Recently, traditional education is experiencing a digital transformation shift, caused by the Covid-19 pandemic. This process generates in students the need to strengthen digital competences through the practice of collaborative learning. The objective of the research was to determine the impact of digital competences on the collaborative learning of students at a public university in Lima. The study was a basic, causal correlational, non-experimental, cross-sectional design. The population was 1,150 undergraduate students from the School of Education of a public university in Lima and the sample consisted of 289 students. The descriptive results showed that digital competences are at a middle level (44.3%) while cooperative learning is at a regular level (36.3%). Finally, it is concluded that digital competences have a significant influence on collaborative learning, with Nagelkerke’s R2 value (0.704) demonstrating that the proposed model explains this influence.
Regular SE(3) Group Convolutions for Volumetric Medical Image Analysis

Regular group convolutional neural networks (G-CNNs) have been shown to increase model performance and improve equivariance to different geometrical symmetries. This work addresses the problem of SE(3), i.e., roto-translation equivariance, on volumetric data. Volumetric image data is prevalent in many medical settings. Motivated by the recent work on separable group convolutions, we devise a SE(3) group convolution kernel separated into a continuous SO(3) (rotation) kernel and a spatial kernel. We approximate equivariance to the continuous setting by sampling uniform SO(3) grids. Our continuous SO(3) kernel is parameterized via RBF interpolation on similarly uniform grids. We demonstrate the advantages of our approach in volumetric medical image analysis. Our SE(3) equivariant models consistently outperform CNNs and regular discrete G-CNNs on challenging medical classification tasks and show significantly improved generalization capabilities. Our approach achieves up to a 16.5% gain in accuracy over regular CNNs.
Detecting Domain Shift in Multiple Instance Learning for Digital Pathology Using Fréchet Domain Distance

Multiple-instance learning (MIL) is an attractive approach for digital pathology applications as it reduces the costs related to data collection and labelling. However, it is not clear how sensitive MIL is to clinically realistic domain shifts, i.e., differences in data distribution that could negatively affect performance, and if already existing metrics for detecting domain shifts work well with these algorithms. We trained an attention-based MIL algorithm to classify whether a whole-slide image of a lymph node contains breast tumour metastases. The algorithm was evaluated on data from a hospital in a different country and various subsets of this data that correspond to different levels of domain shift. Our contributions include showing that MIL for digital pathology is affected by clinically realistic differences in data, evaluating which features from a MIL model are most suitable for detecting changes in performance, and proposing an unsupervised metric named Fréchet Domain Distance (FDD) for quantification of domain shifts. Shift measure performance was evaluated through the mean Pearson correlation to change in classification performance, where FDD achieved 0.70 on 10-fold cross-validation models. The baselines included Deep ensemble, Difference of Confidence, and Representation shift which resulted in 0.45, −0.29, and 0.56 mean Pearson correlation, respectively. FDD could be a valuable tool for care providers and vendors who need to verify if a MIL system is likely to perform reliably when implemented at a new site, without requiring any additional annotations from pathologists.
IMPLEMENTASI FAILOVER VPN KANTOR PUSAT DAN CABANG MENGGUNAKAN TEKNOLOGI SDWAN DENGAN STRATEGI BEST QUALITY

Head office and branch VPN networks are used to connect the head office with branches spread across geographically separated areas. However, network security and availability can be affected by various factors, such as internet connection failures, excessive bandwidth usage, and other disturbances. To overcome this, researchers provide a solution for implementing Failover VPN at the head office and branches using SDWAN technology. With SDWAN, companies can choose a connection path that is more reliable and has a higher speed, thereby optimizing bandwidth usage and improving network availability. In this study, researchers tried to simulate Headquarters and Branch Office VPN Failover Using SDWAN Technology with the Best Quality Strategy at PNETLab. This study uses 2 connections, namely internet connection and MPLS. MPLS is Primary and the internet as Backup. The test is carried out using three schemes, scheme 1 when in normal conditions, scheme 2 when the packet loss on the MPLS link increases then traffic is taken over by the internet connection, and scheme 3 when the packet loss on the MPLS link returns to normal.
Research on Energy Saving Scene of 5G Base Stations Based on SOM + K-Means Two-Stage Clustering Algorithm

This paper proposes a SOM + Kmeans two-stage clustering algorithm to adaptively cluster the daily load curve of 5G base stations and use silhouette coefficients to select the best clustering results, in order to maximize the energy-saving space of the base station and solve the problem of intelligent discovery of green base stations in the whole network. The daily tidal phenomenon and energy-saving periods are further analyzed to identify the energy-saving scene, and then a differentiated energy-saving strategy is periodically adopted for the base station to maximize network utilization and achieve the effect of smart energy-saving. Experimental analysis proves the efficiency and effectiveness of the algorithm. It is estimated that the energy- saving can reach about 15%, which can be applied to the intelligent management of 5G base station energy consumption to improve the energy efficiency of 5G networks.
Sales Record Application for Boarding House

The rapid penetration of information technology causes people to take advantage of technology in various ways to facilitate tasks such as accessing, disseminating information and even assisting in daily business work including managing boarding houses. This situation further encourages people to continue to carry out various kinds of experiments and research to develop ways that bring more benefits to make it easier to do business (Yusmaida et al., 2020). Website-based applications that can be used in all fields such as business or social have been widely used and have proven to be beneficial to society (Rachmadi et al., 2016). Websites can be an effective tool in the boarding house rental business to provide information to boarding house owners, tenants and managers to manage everything related to boarding houses in one integrated web-based information system (Shinta Siti Sundari, 2015). A web-based information system can be used to help owners deal with various administrative and financial problems for a boarding house business. The system can help prospective tenants monitor rooms that are filled, damaged, or unfilled, can help tenants pay rent, and can help owners with financial reporting (Mursid &amp; Arman, 2021). The manual process of managing a boarding house can be complicated, time-consuming, and there is a risk of errors in data input. Especially if the number of rooms managed is tens to hundreds of rooms. Therefore, an efficient and easy-to-use solution is needed to manage boarding houses and using a website can be the ideal solution. Based on the background that has been described previously, the problems that arise is how to design a accounting information system application for boarding house. The limitation of this application is focus on revenue cycle. Keywords: Sales Record, Application, Boarding House
On-Line Authenticity Verification of a Biometric Signature Using Dynamic Time Warping Method and Neural Networks

To ensure proper authentication, e.g. in banking systems, multimodal verification are becoming more prevalent. In this paper an on-line signature based on dynamic time warping (DTW) coupled with neural networks has been proposed. The goal of this research was to test if combining neural networks with DTW improves the effectiveness of verification of a handwritten signature, compared to the classifier based on fixed thresholds. The DTW algorithm was used as a feature extraction method and a similarity measure. Two neural network architectures were tested: multilayer perceptron (MLP) and one with convolutional neural network (CNN). A dataset containing model, verification and forged signatures gathered from a research group using a biometric pen has been created. The research has proved that the DTW coupled with neural networks perform significantly better than the baseline method - DTW model based on constant thresholds. The results are presented and discussed in this paper.
Organization of competence-based training in the teaching of the Nature subject in general education schools

The main aim of the article is to study the organization of the Nature subject in the competence-based teaching at schools. In recent years, the active participation of our Republic in various research programs on the assessment of student achievements has led to positive changes in textbook policy. In such programs (such as TIMSS and PISA), the reading, mathematical, and scientific literacy skills of students are studied and evaluated with specially designed tools. It should be noted that according to the results of the latest TIMSS study, our students were able to demonstrate high mathematical literacy skills. Unfortunately, their scientific literacy scores were lower than the average research score. However, the formation of scientific literacy in the natural sciences is extremely important in training competent young people for the future. In the countries whose students show high results in the TIMSS and PISA research programs (Singapore, China, South Korea, Finland, etc.), the formation of scientific literacy competence in the teaching of Natural Sciences is the main goal of national education. In the article, the way to solve the problem related to the formation of scientific literacy competence in our students at the general secondary education level with the teaching of the integrative "Nature" subject was studied; the importance and necessity of studying this subject were justified. The results of Azerbaijani students in the field of Natural Sciences in comparison with international studies are not satisfactory, the reason for this is that students do not have enough knowledge of scientific concepts and laws in Natural Sciences, and they have difficulty answering real-life situational tasks. Application importance: this theory can be implemented in general education schools.
CauSim: A Causal Learning Framework for Fine-Grained Image Similarity

Learning image similarity is useful in many computer vision applications. In fine-grained visual classification (FGVC), learning image similarity is more challenging due to the subtle inter-class differences. This paper proposes CauSim: a framework for deep learning image similarity based on causality. CauSim applies counterfactual reasoning on Convolution Neural Networks (CNNs) to identify significant filters responding to important regions, then it measures the similarity distance based on the counterfactual information learned with respect to each filter. We have verified the effectiveness of the method on the ImageNet dataset, in addition to four fine-grained datasets. Moreover, comprehensive experiments conducted on fine-grained datasets showed that CauSim can enhance the accuracy of existing FGVC architectures. The results can be reproduced using the code available in the GitHub repository https://github.com/HichemDebbi/CauSim .
The role of periodical scientific publications in developing professional habits of library specialists in Azerbaijan and their role in Pedagogy

The main aim of the article is to explore the role of periodical scientific publications and their role in pedagogy. Successful state policy in the field of building an information-aware society in the Republic of Azerbaijan, adopted state programs and normative-legal documents make it necessary to solve the issues of reconstruction and development of scientific activity on the basis of modern information and communication technologies (ICT) within the framework of reforms in science. The application of ICT in scientific activities and issues of scientific management is implemented within the framework of electronic science as one of the priority directions of reforms in the field of science in the Republic. The construction and development of scientific activity in accordance with modern requirements, the improvement of scientific management, the formation of a national scientific and informational space, the creation of close ties between scientific organizations, collectives and scientists, the improvement of the efficiency of scientific management and research in all areas of science in the modern world due to the wide use of opportunities for ICT development at the level of standards and integration into the world scientific space are relevant issues nowadays. After the restoration of the state independence of the Republic of Azerbaijan, as a result of the gradual integration of academic librarians into the international scientific environment, new quality requirements appeared. The informational needs of specialists began to be met in three directions. Firstly, scientific-theoretical and experimental problems, published and presented in important scientific journal titled "Respublika" (Republic). Azerbaijani scientists participated more comprehensively and productively on this platform. Scientific innovations, innovative projects and analytical works of scientists were reflected on this platform. Additinally, the merits of professor A. A. Khalafov in compiling educational programs in the areas of library work, bibliography and bookkeeping, writing educational literature, presenting scientific articles in the scientific press, and, at the same time, in training specialists who meet new requirements were great. In the same year, Professor H.Ismailov, who for a long time was the dean of the Faculty of Libraries and Informatics of Baku State University, along with the training of professional specialists, founded the scientific and practical journal "Library and Bibliography" in 1997 and organized its activities, and for a long time he was the only Azerbaijan specialized scientific journal, distinguished by high professionalism. In 2010, Professor A. A. Khalafov founded the scientific and applied journal "Librarianship and Information", soon the first issue of the journal was published. The article is based on the method of comparative analysis. Application importance: the material can be used in lecturers and seminars in Higher Education Institutions.
A landscape view of news recommendation systems based on MIND dataset

Nowadays, it’s a very important way for researchers and all people to find their desired meaning instead of searching for a specific topic. Recommendation systems are strategies to solve the problems of search, finding or reducing the time of interest content for users under complex information environments. R.S. can show us the related results that are close to what we desire. In this paper, we list rates of the most used techniques applied over MIND dataset and show the results and comparisons among these techniques, we have proposed our analysis on that dataset, which has been collected from Microsoft in 2019, and we proposed new techniques and explain the views of researchers in previous studies and the techniques in R.S. that depend on.
Using and referencing re3data

&lt;em&gt;The German version of this article has been published on the DINI AG-FIS and AG-E-Pub blog.&lt;/em&gt; As a global directory, re3data describes research data repositories from all scientific disciplines.