Reviewerly/exHarmony huggingface dataset:
Frequency and probability of occurrence of clearness index for the region of Maceió-Alagoas

Solar radiation is essential because its distribution around the planet establishes the variations of all the meteorological elements, determining the atmospheric and oceanic circulation patterns and configuring the terrestrial climate, besides being currently widely used for the production of alternative energy. Thus, the objective of this work is to analyze the solar radiation through frequency and probability of occurrence of clearness index in different seasonal periods for the region of Maceió, state of Alagoas. The RG was obtained from data from the INMET automatic stations for the city of Maceió, from January 2014 to December 2017. The Ro was calculated through the solar constant, vector radius of the Earth's orbit and zenith angle. KT was calculated by the ratio of RG to Ro. Histograms of frequencies and accumulatedfrequencies of KT were made. For the seasonal analysis of the solar radiation and KT conditions the data were separated in different seasonal periods. The hourly frequency of KT observed in the dry and rainy season had the highest occurrence in partially cloudy sky conditions, with an average of 53.62% of the total values for both seasons. The frequency of daily KT for the seasons had higher occurrence in partially cloudy sky conditions with an average of 79.74% of the data and the probability of annual occurrence was also higher for partially cloudy sky conditions with 57.21% for the values and 83.29% for daily values.
Solve Your Google Storage Problems in Only 516 Days!

With Google’s announcement of a storage cap in 2021, effective mid-2022, many schools had to scramble to decrease their footprint rapidly, often with a constituent base that was disengaged and not checking for announcements. At CalArts, where students and alumni are in the same domain and whose work is very media-intensive, we were at 8 times the coming storage cap, and facing a significant communications, policy, and technical challenge to become compliant in time. Come learn about our approach and outcomes in what has proven to be a dynamic and fluid project involving the entire CalArts IT team.
Making Education Support Media About Flood Hazard in Samarinda in the Form of Digital Comic

Samarinda is the capital of East Kalimantan that is currently growing rapidly. However, the city of Samarinda still has a complex flood problem. This flood phenomenon occurs not only when it is the rainy season but also when it rains for 1-2 hours. This condition interferes with the activities of the community and the government in Samarinda in carrying out daily activities because it can immerse settlements and housing, damage public facilities, and cause casualties. Despite the frequent occurrence and inconvenience of the community, Samarinda's level of public awareness in preventing floods is still minimal. This can be seen from the large number of communities that still often throw garbage carelessly into rivers, especially residents living near rivers. The garbage thrown into the river hinders the flow of water to the point where it can create a high puddle. It is seen that there are many communities, especially young people who are open-minded about environmental issues. Therefore, it is necessary to design a flood prevention educational medium in Samarinda city that can start from oneself or a simple thing with the main target is Samarinda people itself, especially the youth with qualitative methods.
Machine Learning techniques for identifying Cyberbullying on digital networks

With the proliferation of virtual entertainment platforms around the world, particularly among young people, digital taunting and enmity have become real and annoying problems that networks must address. Threats can use these levels to attack and weaken others in their networks. To combat digital tormenting, various strategies and tactics have been used or proposed, including early detection and alarms that both detect and protect victims from such attacks. Machine Learning techniques with Artificial Intelligence Framework are being widely used to identify specific linguistic patterns that danger uses to hunt for their victims. The Feeling Analysis (FA) of virtual entertainment content is one of the expanding fields of research in AI. FA makes it possible to gradually identify online harassment and continuously recognize cyberbullying. This study recommends a SA model to identify cyberbullying messages in Facebook web-based entertainment. SVM and MAXENT classifier, controlled AI arrangement tools, are employed in this model. When a higher n-grams language model is applied to such texts in correlation with analogous prior research, the findings of the investigations carried out using this model showed encouraging results. Similar patterns in the results showed that these classifiers preferred execution measures over other classifiers on such remarks.
Transformers, Tables and Frame Semantics

Transformer-based language models are able to capture linguistic patterns at scale by encoding both syntactic and semantic dimensions of natural language representations with the aim of achieving language understanding. While Transformers have been adapted for generating table embeddings, less research effort has been dedicated to investigating the extent to which these models can encode table semantics. To address this limitation, we propose a method to transfer knowledge from pre-trained natural language models to encode schema-level relationships and analyze the resulting model with respect to two schema-related tasks in different data scenarios.
A BERT-enhanced Graph Neural Network for Knowledge Base Population

We present BGKBP, a deep-learning algorithm based on BERT, and a graph neural network for knowledge base population (KBP). Our experiments showed that a straightforward application of BERT and GNN on a large knowledge base (e.g., Wikidata) improves KBP quality and outperforms the previous state-of-the-art methods. We developed four techniques to improve the BGKBP’s KBP capability: (1) serialization, (2) fine-tuning, (3) node regression, and (4) text augmentation. BGKBP achieved the best F1 scores of 0.723 and 0.495 on entity linking and new entity detection, respectively. We further showed that using text augmentation (BGKBP-TA) achieved the best F1 score of 0.547 on relation linking, which is more difficult than entity linking because of the various representations of some of the relations.
Implementasi Literasi Membaca dalam Pembelajaran di Kelas 5B Pasca Covid-19 di SD Negeri 141 Pekanbaru

Reading literacy is an ability in which a person can understand, use, and reflect on texts through direct involvement to gain experience in order to achieve certain goals. The purpose of this study was to determine the implementation of reading literacy in learning in class 5B post-covid-19 at SDN 141 Pekanbaru, the supporting factors for reading literacy in learning in class 5B post-covid-19 at SDN 141 Pekanbaru, and the inhibiting factors for reading literacy in learning at class 5B post-covid-19 at SDN 141 Pekanbaru. The method used in this research is a qualitative method with a descriptive approach. Collecting data using observation techniques, interviews, and observations. Based on the results of the research, the implementation of reading literacy in learning in class 5B post-covid-19 at SDN 141 Pekanbaru has been well implemented. Starting from pre-reading activities, reading activities, and post-reading. The three activities are carried out by the teacher in stages according to the procedure. Teachers also include three stages of reading literacy in preparing lesson plans. In addition, schools also play a role in fostering students' reading literacy through platforms such as libraries and reading corners. This shows that students' reading literacy is facilitated in schools because teachers try to revive reading literacy and schools provide support to provide a platform so that students' reading literacy can increase.
Analisis Motivasi Belajar Peserta Didik Kelas X Mata Pelajaran Ekonomi di MA Muhammadiyah Nangahure

This study aims to find out how students' learning motivation in economics subjects at MA Muhammadiyah Nangahure. This is motivated by the low learning motivation of students in economics subjects which can be seen from the symptoms: There are still students who do not complete assignments given by the teacher, students who do not pay attention to the teacher when explaining subject matter, and students who do not dare to express opinions . This type of research is descriptive qualitative and the subjects are students and teachers. The object of this study was the learning motivation of the students and the informants consisted of 3 students as the main informants and 1 economics teacher as an additional informant. Data collection techniques in this study were observation, interviews, and documentation. The data analysis technique is descriptive statistical analysis of the presentation. Based on the results of the study it can be concluded that the learning motivation of students in economics subjects at MA Muhammadiyah Nangahure is already relatively high and overall the average learning motivation of students in economics subjects at MA Muhammadiyah Nangahure is at a high level of motivation but still when viewed from each -Each student himself is still not motivated in learning economics.
The Ability to Write Personal Letters on Eleventh Grade at SMKN 3 Pinrang

This study titled The Ability To Write Personal Letters On Eleventh Grade At SMKN 3 Pinrang. This study aims to determine the level of writing ability personal letter of class XI SMKN 3 Pinrang. The method used is descriptive analysis method with qualitative approach. Data of this study is to test write personal letters. (1) The results of this study found the ability to write the place and date of the letter student class XI SMKN 3 Pinrang (2) The ability of a salutation (3)The ability to write a personal letter student class XI SMKN 3 Pinrang (4) the ability to write greeting cover (5) The ability to write signature. Of the five aspects that I researched it can be concluded that the student class XI SMKN 3 Pinrang have the high ability with a score of 88.5.&#x0D;
Undergraduate research in software engineering. An experience and evaluation report &amp;nbsp;

The purpose of this paper is to present an undergraduate research experience process model and the evaluation of seven years of its application in an undergraduate research program in software engineering. Undergraduate students who participated in research projects between 2015 and 2022 were surveyed to find out a) their motivations for participating in research projects in software engineering, b) the skills they consider they have acquired or improved by participating in those projects, and c) their perception of benefits and utility for their future work and professional activities. Results reveal that participation in real research projects in software engineering is highly valued by undergraduate students, who perceive benefits in the development of research and soft skills, and for their future professional activity. In addition, these undergraduate research projects and the process followed show that it is feasible to make original contributions to the body of knowledge of software engineering.
RANCANG BANGUN APLIKASI BELAJAR ARAB UNTUK ANDROID MENGGUNAKAN JETPACK COMPOSE DAN KOTLIN

Indonesia has the world's largest Muslim population, so learning Arabic is quite important and is widely practised throughout the country. However, learning Arabic is often done with less efficient and effective methods due to the problem of language differences that are quite far from the Indonesian Language. These differences can be seen in sound or phonetic systems, letters, grammar, and vocabulary management. This research was conducted with the aim of designing and building a system in the form of a simple language game with self-dictionary to overcome the problems faced by Indonesians who have or will learn Arabic. This Arabic learning app will be designed to be interactive and fun for Arabic learners in Indonesia with the default app instruction language also using The Indonesia Language. The design method will use the Agile method. The result of designing and building this Arabic Learning app is successful because all of the feature test conducted has been passed.
Task Scheduling Optimization in Cloud Computing by Jaya Algorithm

Cloud computing provides resources to its consumers as a service. The cloud computing paradigm offers dynamic services by providing virtualized resources via the internet for enabling applications, and these services are provided by large-scale data centers known as clouds. Cloud computing is entirely reliant on the internet to provide its services to consumers. Cloud computing offers several advantages, including the fact that users only pay for what they use weekly, monthly, or yearly, that anybody with an internet connection may use the cloud, and that there is no need to purchase resources, hardware, or software on their own. This paper proposes an efficient task scheduling algorithm based on the Jaya algorithm for the cloud computing environment. We evaluate the performance of our method by applying it to three instances. The recommended technique produced the optimal solution in makespan, speedup, efficiency, and throughput, according to the findings.
COMPARATIVE STUDY BETWEEN A NOVEL DETERMINISTIC TEST FOR MERSENNE PRIMES AND THE WELL-KNOWN PRIMALITY TESTS

In this article, a new deterministic primality test for Mersenne primes is presented. It also includes a comparative study between well-known primality tests in order to identify the best test. Moreover, new modiﬁcations are suggested in order to eliminate pseudoprimes. The study covers random primes such as Mersenne primes and Proth primes. Finally, these tests are arranged from the best to the worst according to strength, speed, and effectiveness based on the results obtained through programs prepared and operated by Mathematica, and the results are presented through tables and graphs.
A Comparative Study on Association Rule Mining Algorithms on the Hospital Infection Control Dataset

Administrative procedures in various organizations produce numerous crucial records and data. These records and data are also used in other processes like customer relationship management and accounting operations.It is incredibly challenging to use and extract valuable and meaningful information from these data and records because they are frequently enormous and continuously growing in size and complexity.Data mining is the act of sorting through large data sets to find patterns and relationships that might aid in the data analysis process of resolving business issues. Using data mining techniques, enterprises can forecast future trends and make better business decisions.The Apriori algorithm has been introduced to calculate the association rules between objects; the primary goal of this algorithm is to establish an association rule between various things. The association rule describes how two or more objects are related.We have employed the Apriori property and Apriori Mlxtend algorithms in this study and we applied them on the hospital database; and, by using python coding, the results showed that the performance of Apriori Mlxtend was faster, and it was 0.38622, and the Apriori property algorithm was 0.090909. That means the Apriori Mlxtend was better than the Apriori property algorithm.
A Systematic Literature Review of Sustainable Supply Chain Management using Block Chain Technology in Fashion Industries and Proposing ABC Framework

Abstract: Indian Fashion industries using Blockchain in sustainable supply chain practices are still developing. The literature base is also significantly less in BT(Blockchain) using SSCM (Sustainable supply chain Management) fashion industries. To understand the literature clearly, the PRISMA technique is used to identify, screen, and include the related research concepts in the area of BT using SSCM fashion industries. The analysis of PRISMA gives the result of proposing the ABC framework shows significant factors involved in the A (adoption of Blockchain), B (the barriers during the implementation), and C (critical success factors) to be maintained to create a competitive advantage. The adoption of blockchain in SSCM has to be studied and involved through 1. Force Field Theory, and 2. When implementing the SSCM using BT, specific barriers have a concept of TOE framework, which includes (Technology, Organisation, and Environment) related to organizational performance. The performance factors should be identified to implement the factors in such a way as to get the maximum benefits out of it. The performance indicator factors are organizational, environmental, economic, information, and customer performance. After the implementation, the organization should maintain the factors effectively with the help of critical success factors such as top management support, financial constraints, supplier performance, customer acceptance, and Government regulations. This is the first paper that discusses the adoption, barriers, and challenges in implementing SSCM using BT in the fashion industries. Industry practitioners, academicians, and research scholars can use this paper as a guide to know the essential factors suggested by various eminent authors in BT and SSCM.
ODIN AD: A Framework Supporting the Life-Cycle of Time Series Anomaly Detection Applications

Anomaly detection (AD) in numerical temporal data series is a prominent task in many domains, including the analysis of industrial equipment operation, the processing of IoT data streams, and the monitoring of appliance energy consumption. The life-cycle of an AD application with a Machine Learning (ML) approach requires data collection and preparation, algorithm design and selection, training, and evaluation. All these activities contain repetitive tasks which could be supported by tools. This paper describes ODIN AD, a framework assisting the life-cycle of AD applications in the phases of data preparation, prediction performance evaluation, and error diagnosis.
Learning-Based Model for Auto-Form Filling

In the age of computer technology, most humans now depend upon online applications for all daily life purposes, from online shopping to government business. Often such users need to provide their information to websites through browser-based forms to interact further (Ponmurugesh et al. in Int Res J Eng Technol (IRJET) 06(01) (2019) [1]). Generally, users have to fill these online forms manually by typing similar information repeatedly. It becomes very monotonous for the human to perform the same actions again and again for a task that could be handled by the machine itself. Recently, many new techniques have emerged that store user data and automatically fill it when required. In this paper revolves around an improvised and better technique for automatically filling web browser-based forms for data which the user has entered previously, wherever required exactly or to similar meaning fields. The main idea here is to collect provided information and feed it to a database for future reference. The system makes association rules to judge what fields may be similar to others and connects their data; this enables other users’ applications to understand these similarities in fields without even having experienced it. To make this application more feasible, safe and to generalize its use, it is best to develop it as an extension in a browser (like Google Chrome) and drive the functions locally.
Att-TCN: An Advertising Attribution Method Based on Temporal Convolutional Network with Attention Mechanism

Advertising attribution is an effective method to measure the effect of various advertisements in the era of Internet advertising. It enables scientific tracking back and refined operation of advertising by quantitatively evaluating the contribution of different advertising channels to reach the target. It is challenging to predict conversion results based on users' journey data accurately, reflect the impact of ads on user conversions, and appropriately assign attribution values to advertising channels. To achieve the above objectives, we propose an advertising attribution method based on a temporal convolutional network with an attention mechanism (Att-TCN). By mining the hidden information in time and attribution channels using the attention mechanism, our proposed method can improve the accuracy of conversion prediction and thus more rationally assign attribution values across channels. Experimental validation on a real dataset obtained from an operational commercial advertising monitoring company shows that our proposed attribution method has better accuracy than other state-of-the-art attribution methods and gives an open-ended approach to attribution value assignment.
Digital Twin Driven Mixed Reality Assembly Inspection Method

Aiming at the low recognition rate of assembly features caused by angle, light and other factors in mixed reality assembly, this paper proposes a digital twin driven intelligent assembly detection method, which enhances the original data set from image enhancement, geometric enhancement and digital twin space virtual data enhancement, effectively increasing the diversity of samples. At the same time, it solves the problem of large tasks of Sample labeling. In this paper, seven features of three auto parts are selected for experiments, The experimental results show that this method can effectively expand the data set and improve the feature recognition rate.
Pre-training Fine-tuning data Enhancement method based on active learning

With the development of Internet technology, the number of Internet users increases rapidly, and the amount of data generated on the Internet is very large every day. At the same time, with the development of storage technology and query technology, it is very easy to collect massive data, but the information value contained in these data is uneven, and most of them are unmarked. However, traditional supervised learning has a great demand for labeled samples. Faced with a large number of unlabeled samples, there is a problem of the lack of effective automatic labeling methods, and manual labeling costs are high. If the strategy of simple random sampling is used for annotation, it may lead to the selection of noisy information and waste of resources, and low-quality training data could also have an influence on the prediction accuracy of the model. Meanwhile, the training effect of traditional deep learning methods is very limited for small sample labeled training sets.This paper takes the text emotion analysis task in natural language processing as the background, selects IMDB film review data as the training set and test set, starts with the design of active learning algorithm based on clustering analysis, combined with the appropriate pre-training fine-tuning model, constructs a data enhancement method based on active learning. In the experiment, it is found that when the labeled training set is reduced by 90%, the prediction accuracy of the pre-training model is reduced by no more than 2%, which verifies the effectiveness of the data enhancement method combining active learning with the pre-training model.