Reviewerly/exHarmony huggingface dataset:
Sistem Informasi Pengelolaan Data Berbasis Website Pada DPP LSM KPK di Pekanbaru

Non-Governmental Organization (NGO) is one of the non-profit institutions in Indonesia, one of which is in the city of Pekanbaru, namely the Central Leadership Council of the Corruption Eradication Community Non-Governmental Organization (CLC CEC NGO). The data management information system at the CLC CEC NGO still uses a simple system, which consists of financial data management, meeting decision data management, correspondence data management, data management findings from both the CLC CEC NGO and from each Regional Leadership Council, Region Leadership , and Branch Leadership Council, agenda data management, and information data management. Data management is still simple, there are often problems encountered such as loss of documents, the preparation of documents that are not organized so that it is difficult to find them and requires a lot of time, also repeated data recording. The problems faced can be solved by building a website-based data management information system. The purpose of making this data management information system is to provide convenience in data management at the CLC CEC NGO. The method that the author uses in making this data management system is to use the waterfall method, while making this website, the author uses the CodeIgniter framework and bootstrap. For website testing, the author uses Black Box Testing. With this data management website, it is hoped that it will provide convenience in data management for the CLC CEC NGO
Bitcoin Price Prediction using Deep Learning

Bitcoin has recently become a household name due to its incredible growth, unpredictable volatility, and interesting applications. Bitcoin is currently a thriving open-source community and payment network, which is currently used by approximately 10 million people. As the value of Bitcoin in US Dollar fluctuates every day, it would be very interesting for investors to forecast the Bitcoin value but at the same time making it difficult to predict. This work focuses on predicting Bitcoin prices using a Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU) algorithm. The goal is to ascertain with what accuracy the direction of Bitcoin price in USD can be predicted.
Upaya Membangun Karakter Peserta Didik Melalui Literasi Digital Dalam Tantangan Pendidikan Abad 21

Living conditions in the 21st century are full of challenges and competition. This has an impact, among others, on the level of great mental stress in addition to the availability of opportunities for those who have life competence, and have multiliteracy that strengthens the physical, mental, and intellectual capacities of students. Therefore digital literacy needs to be developed in today's world of education to build a better national character and be more prepared to face the 21st century learning era. The purpose of compiling this article is to find out how the method of building student character as elementary school level students through digital literacy (Digitalage Literacy) in education to face 21st century education. have an understanding of these digital literal dimensions and can develop materials and procedures for digital literacy education in school and outside of school, in order to face the era of 21st century education.
Improving Mathematical Skills in Geometry Using Visual Python

In this chapter, we show students how to use computational modeling to better comprehend and apply mathematical ideas, particularly when dealing with 3D objects like vectors and geometrical shapes. Without any prior coding expertise, students can utilize the Visual Python (VPython) integrated development learning environment to create various 3D objects such as arrows, spheres, and boxes. Building 3D arrows and manipulating by adding or subtracting two or more vectors can help students improve their spatial thinking in a three-dimensional system. The aim of this study is to utilize computational modeling to help students improve their spatial thinking in geometry so that they may better understand and apply mathematical concepts and solve problems. Using VPython, students can adjust the parameters of different vectors, their position, and also attribute different colors based on their preferences. In addition, using mathematical formulas, they will be able to find the solution directly based on some pre-given information. As a result, using VPython provides the educational benefit of empowering students to do more physical modeling and enabling them to better depict processes, both of which help students comprehend concepts better.
Multi-feature fusion gaze estimation based on attention mechanism

The variable head pose and low-quality eye images in natural scenes can lead to low accuracy of gaze estimation. In this paper, we propose a multi-feature fusion gaze estimation model based on the attention mechanism. First, face and eye feature extractors based on the group convolution channel and spatial attention mechanism (GCCSAM) are designed to use channel and spatial information to adaptively select and enhance important features in face images and two eye images, and suppress information irrelevant to gaze estimation. Then we design two feature fusion networks to fuse the features of face, two eyes and pupil center position, thus avoiding the effects of two-eye asymmetry and inaccurate head pose estimation on gaze estimation. The average angular error of the proposed method is 4.1° on MPIIGaze and 5.2° on EyeDiap. Compared with the current mainstream methods, our method effectively improves the accuracy and robustness of gaze estimation in natural scenes.
PECULIARITIES OF “TEACHER–STUDENT” INTERACTION IN THE CONTEXT OF ONLINE EDUCATION

The pandemic of the new coronavirus infection and the subse- quent distance learning have become a problem that requires an immediate solution from the pedagogical community. The current situation set some chal- lenges for “teacher – student” interaction that were not sufficiently obvious in the traditional form of education. The loss of students’ motivation to learn, as well as decrease in interest in events taking place in the outside world, came to the fore. Many students found themselves isolated from family and friends, so relationships with classmates and teachers appeared of importance. In addi- tion, students with special educational needs (SEN) suffer in these conditions greatly. The author of the article examines the reasons for the “loss of meaning”, and also suggests ways to increase motivation when teaching a foreign lan- guage online, including the use of game teaching methods, unusual approaches to the organization of extracurricular forms of work, etc. Moreover, the article emphasizes the importance of ensuring psychological comfort for students.
Enhanced Service Level Management Process as a Result of Best Practices Integration

This work has been supported by the Agencia Estatal de Investigacion of Spain under project PID2019-108713RBC51/ AEI/10.13039/501100011033.
Traditional Machine Learning or Deep Learning Methods for Embedded Computer Vision Study on Biometric Application

Today, Convolutional Neural Networks (CNN) provide one of the best performances in state of the art image recognition, while requiring high computational power and being very time consuming. So it raises the following questions: can deep learning achieve better performances than machine learning over different computer vision aspect, such as development time, processing time, raw performances or resource consumption? Considering strong constraints on this listing, can we still consider Machine Learning for a specific computer vision application? Hence, the purpose of this paper is to provide an insight into these questions by comparing methods from both Deep Learning and traditional Machine Learning, applied on a real-time person authentication application, using 2D faces under a binary classification problem. These methods are embedded in a restricted power computational unit, forming a smart camera composing a low-cost security system. This application needs the biometric model to be minimized so it can be stored on a remote personal media (10 KB).
Radio Waveforms Classification via Deep Q Learning Network

Radio waveforms classification plays a foundation role in cognitive radio, which promises a broad prospect in spectrum monitoring and management. In this paper, a radio waveforms classification via deep Q learning is proposed, in which a deep reinforcement learning agent is trained to classify signal modulation type. Differ from the widely applied deep learning strategy, the proposed method has strong self-learning decision-making ability, which can find the optimal strategy by trial and error. The simulation results show that it can realize classification of radio signal modulation type with high accuracy.
MINAT BELAJAR SISWA MELALUI PENGELOLAAN KELAS : The students' interest in learning through classroom management

The method of this research was descriptive method. The population of this research consisted of Senior High School Students in 2014/2015 academic year. It used random sampling technique where the writer took one class of the second-year students which consisted of 33 students. The data were collected through questionnaires. The data analyzed using Likert Scale. The result of data analysis showed that the second-year students at SMA Negeri 1 Anggeraja shows their high interest in learning English through classroom management. The use of classroom management to learn English gave many benefits to the students, such as; (1) they felt relax, more serious and active in the class, they had a high initiative and strong enthusiastic in learning, (2) create friendly and cooperate atmosphere for language learning, (3) attract the attention of the students and willing to learn, (4) and create a strong motivation to learn. It was proven by the result of the mean score of the students’ questionnaire namely 87 which mean that the majority of the students agreed with the positive statements and disagreed with the negative ones about the use of classroom management in learn English.
Utilization of Alkin-WP-Based Digital Library Evaluation Software as Evaluation Tool of Digital Library Effectiveness

One source of learning in universities is a digital library. In the era of industry 4.0, most universities have implemented digital libraries in supporting the learning process. However, the reality shows that digital library management is still ineffective. Therefore, the implementation of digital libraries needs to be evaluated for determining the digital library effectiveness used as learning resources in supporting the learning process in universities. Many evaluation tools are used to evaluate the effectiveness of digital libraries but have not provided accurate recommendation results to support decision-making. This research presents an innovation in the form of an evaluation tool that can be used to evaluate the digital library effectiveness in universities. That evaluation tool is called the Alkin-WP-based digital library evaluation software. This software is a desktop platform that contains aspects of measuring the digital library effectiveness by referring to the components of the Alkin evaluation model and the WP (Weighted Product) method. This research aimed to show the effectiveness level of the utilization of Alkin-WP-based digital library evaluation software. This research method was R &amp; D (Research &amp; Development) which refers to the ten development stages of the Borg and Gall model. In this research, development was focused only on a few stages, included: usage trials, final product revision, dissemination, and implementation. The subjects involved in assessing the implementation/utilization of the Alkin-WP-based digital library evaluation software were 35 people, in the usage trials were six people, in product revision were three people, and at the stage of dissemination were 15 people. The tools used to collect data were questionnaires and interview guidelines. The data analysis technique used was descriptive quantitative. The effectiveness level of utilizing the Alkin-WP-based digital library evaluation software was 88.34%. It showed that the evaluation software had effective. The impact of this research results on the scientific field of educational evaluation is being able to show the existence of a new evaluation tool based on educational evaluation and artificial intelligence. That evaluation tool can easier for library heads to make policies for revamping digital library services based on accurate recommendations. Doi: 10.28991/esj-2021-01308 Full Text: PDF
Behind the Rocketing Bitcoin: What Nourishes Trust?

Bitcoin has no fundamental value and, therefore, it cannot be valued rationally. Behind its price we basically find trust or confidence. Until now, trust has been associated with the technology underlying bitcoin, blockchain. Trust arises from the idea of distributed data. But confidence may be reinforced, or weakened, by information. People need information to socialize, to make decisions and formulate opinions. This paper aims to test whether information (external and internal) can intensify the power of trust as the key element describing the bitcoin evolution. General news is a source of external information that may affect both prices and demand. But price variations may also result from demand if such demand is an internal information source associated with utility and confidence. This paper analyses what type of information is related with trust and how it affected bitcoin returns and trading volume in a particularly interesting period of time: from December 2013 to December 2017. 2017 is considered the first breakout of bitcoin trading, and our purpose is to focus on this first significant and striking change in bitcoin market´s dynamic, so that we can infer which are the main drivers of this market. On the one hand, our results indicate that, contrary to what happens in other financial markets, good news significantly influences prices and volume and, on the other hand, some specific macroeconomic news inducing lack of confidence in traditional markets can drive investors´ demand in some countries (such as China or Japan) towards the bitcoin market.
PENGEMBANGAN MULTIMEDIA KOMIK UNTUK MENINGKATKAN SIKAP DAN PRESTASI BELAJAR SISWA (Studi Pada Mata Pelajaran Geografi Kelas X IPS Di SMA Negeri Bengkulu Utara)

The purpose of this research is to produce a comic multimedia product that is feasible as a geography learning medium that improves student learning attitudes, describes an increase in student learning achievement using multimedia comics and describes student achievement using multimedia comics compared to the learning media used so far. The research method used is the research and development method (Research and Development -R &amp; D-) according to Brog and Gall. The research subjects were students of SMAN 11 Bengkulu Utara, SMAN 2 Bengkulu Utara, SMAN 4 Bengkulu Utara, and students SMAN 13 Bengkulu Utara. Data collection techniques in this study were expert assessment sheets, questionnaires and tests. The data analysis technique used in this research was the average score (mean), the percentage score, the Ngain Score and the t-test. This study shows that multimedia comics are suitable to be used as a learning medium in geography learning and the use of comic multimedia can improve student attitudes and learning achievement
On Eventual Applicability of Plans in Dynamic Environments with Cyclic Phenomena

Planning and acting in dynamic environments deals with non-deterministic events that might change the state of the environment without consent of the agent. In the worst case, some events might cause the agent to become ``trapped'' in a dead-end state, which in practice might mean damage or destruction of the agent. Presence of non-deterministic events often considerably increases the number of alternatives that might occur in a single step and hence traditional non-deterministic planning techniques might not scale. In this paper, we address a class of problems where non-deterministic events represent ``cyclic phenomena''. If they interfere with the agent, they might be dangerous for it (e.g. ships cruising through the area of AUV operations). We present techniques that initially analyse the problem whether it falls within this class by considering the notion of event reversibility and if so, these techniques generate a plan such that encountered unsafe states, in which the ``cyclic phenomena'' might interfere with the agent, can be eventually crossed without any risk of ``falling'' into a dead-end state. Our approach is evaluated in the AUV and Perestroika domains.
Optimized Ga-Bp Neural Network Based Video Streaming In Wireless Technology For Online Teaching

Online teaching and education programs are most needed and directly insist us to adapt during this pandemic situation and increasing rapidly. Faculty need to be competent in their role and possess the skills necessary to positively impact student outcomes. As a novel online teaching network expertise, streaming media expertise can realize flexible and efficient two-way communication between teachers and students, simulate virtual face-to-face teaching environment, and yield enough emotional quality for both sides in the corresponding time and space. In view of the poor communication quality and flexibility of current streaming media technology, this paper will build a selective streaming media online teaching architecture based on animation media service platform. The system innovatively uses DXSDK filtering technology to realize real-time audio acquisition and filtering processing and solves the communication quality problem of streaming media online teaching. Aiming at the flexibility of streaming media online teaching, the system adds online auxiliary teaching function, which can realize online text communication, file information management, and learning tracking interaction between teaching and learning. From this research, experimental results, the stability and flexibility of the system are greatly improved within a certain range of users, which has obvious practical value. It has high stability and application value.
Fruit traceability via mobile application

The traceability of agricultural products is essential to guarantee food security, monitor the production chain and is a requirement of Brazilian legislation. The optimization of this activity can be performed with the use of digital technologies. The present study verified the efficiency of a mobile app to track agricultural products and use it as a digital field book. The study was developed with 13 participants who used the mobile app to record agricultural activities: input pesticide sprays and fertilization; cultural management and registration of georeferenced harvest lots. The use of the app simplified the registration of tasks as digital field book and tracked agricultural products throughout the productive chain. An alert system for withholding period and maximum limit for pesticide sprays had been developed and enabled greater control, both are considered critical points in traceability system. QR-code of fruit lots were produced in each harvest and reports of costs, production, yield and fruit quality were generated by the app. Thus, mobile app was efficient to track agricultural production and use it as digital field book. In addition, managing of agricultural activities and production costs were produced and it has been shown that an innovative and low-cost technology that can be used by fruit producers.
DEVELOPING THE EMOTIONAL INTELLIGENCE OF STUDENTS IN THE PROCESS OF MUSICAL ACTIVITY

The article is devoted to the problem of forming emotional intelligence as the most important component of a person’s success in social and personal terms. The necessity of developing emotional intelligence in primary school age is substantiated. The experience of musical activity is considered as a means of emotional development. The potential of various kinds of musical activity (musical perception, choral singing, instrumental ensemble music playing, musical creativity, musical educational and cognitive activity) for the development of children's emotional intelligence is revealed. The main types of tasks enriching the system of musical and didactic means and contributing to the improvement of emotional intelligence are characterized.
Finding the best suited autoencoder for reducing model complexity

Basically, machine learning models use input data to produce results. Sometimes, the input data is too complicated for the models to learn useful patterns. Therefore, feature engineering is a crucial data preprocessing step for constructing a proper feature set to improve the performance of such models. One of the most efficient methods for automating feature engineering is the autoencoder, which transforms the data from its original space into a latent space. However certain factors, including the datasets, the machine learning models, and the number of dimensions of the latent space (denoted by k), should be carefully considered when using the autoencoder. In this study, we design a framework to compare two data preprocessing approaches: with and without autoencoder and to observe the impact of these factors on autoencoder. We then conduct experiments using autoencoders with classifiers on popular datasets. The empirical results provide a perspective regarding the best suited autoencoder for these factors.
Genetic algorithm approach to cluster analysis

In this paper, performance of Genetic Algorithm based clustering method has been compared with conventional clustering methods that are K-means and Ward’s clustering methods. The cluster quality has been compared using three cluster validity indices that are Calinski-Harabasz, Dunn and Average Silhouette Width. The results showed that genetic algorithm based clustering method performed better than other clustering methods under all the three cluster validity measures.