Reviewerly/exHarmony huggingface dataset:
Second Order Runge-Kutta Method in Solving Renewable Natural Resources Model Case Study: Fishes and Water Resources Management

This research presents a study of a dynamical system which models natural resources. Using fishes and water resources as the case study. In this work a model equation was used to determine the density of the resources. We also introduced second stage Runge Kutta method, which was used to obtain the result for harvesting terms from 10% to 50% producing the population of life in the pond after each harvesting.&#x0D; In this research work, the solution of a dynamic system that can model natural resources using the second stage Runge-Kutta method was reported. To actualize this result, a model, a first order ordinary differential equation and the famous Runge-Kutta second stage method is used. Harvesting terms from 10% to 50% is used for the iteration to demonstrate the validity of the result. The result of this study was applied to the population of fish in a pond, which is a renewable natural resource, with great success.&#x0D; In line with the objectives stated at the beginning of the work, the results of this research has shown that by applying the second order Runge-Kutta method, the solution of a dynamical system can be obtained and applied to model natural resources with great success.
Aplikasi Keamanan Data Berbasis Web Menggunakan Algoritma AES 128 Untuk Enkripsi Dan Dekripsi Data

Folio Sheeter Division is a division of PT. Indah Kiat Pulp And Paper, which is located in Siak Regency, which is one of the leading paper mills in Riau Province. The Folio Sheeter Division requires an application that can process production data. The application can only be accessed internally, the production process is a company secret because in production there are several mix processes or mix formulations of quality and production quality so that the materials and data are not published. The process is digital text that is encrypted by user to user. Every production result, the supervisor will inform the assistant about the combination of the mix of production materials that will be processed to carry out production according to what consumers want. From the author's observations at the research site that the information shared by supervisors can be read by all employees or staff, this information can be seen clearly by users or staff who are not interested so that the production and process data can be known by other staff who are not interested, so it is possible that the data could be given to irresponsible parties. The types of data or files that exist are in the form of data records and supporting document files such as doc, xls and pdf. Production data will be encrypted with cryptographic techniques so that production data cannot be seen by others but the user himself or the user who is permitted in the form of decryption who can access it. Notification of information to staff or assistants is still fairly manual, because the provision of information in paper form and easily read by others. For this reason, the authors are interested in using the AES 128 algorithm to encrypt and decrypt data so that the process and production results in the form of data can be maintained.
Using machine learning in learner corpus research: error annotation and (extra-)linguistic metadata as proxies for approaching the interlanguage

Abstract This article investigates the potential of using error-annotated L2 utterances and their associated language profile to more accurately assess the language proficiency of an L2 learner. The main goal of this paper is to demonstrate the use of machine learning methods for uncovering features of the learner's interlanguage and their proficiency level, as well as to answer three questions: a) is it feasible to use error-annotated data along with linguistic profile metadata to trace patterns related to aspects of learners’ interlanguage? b) How important may error annotation be in estimating the learner’s proficiency level? c) Which of the (extra-)linguistic profile metadata stand out in contributing to the correct classification of the proficiency level? GLCII, the largest online freely available L2-Greek corpus, is utilized as an example corpus to illustrate the approach proposed in this paper with which for any L2, central questions can potentially be addressed by the relevant machine learning method so that, in a second stage, useful conclusion can be drawn. As a final point, we urge the implementation of ML techniques as a way of expediting research in a variety of related areas in applied linguistics.
The Elderly"s Physical Activity Experience in the COVID-19 Pandemic Situation

The study explored the experience and meaning of physical activity in the elderly in the pandemic situation of the new coronavirus infection. The study was conducted for 16 weeks by applying the case study method. Data were collected through in-depth interviews and observations with nine participants aged 65 or older. Older people, who usually exercised more than three times a week, felt fear as the pandemic of the new coronavirus infection began and they could not go out without seeing people. Due to the ongoing pandemic of the new coronavirus infection, I was unable to participate in physical activities. The elderly, who are no longer physically active, have decreased physical function and memory, increasing concerns and fears about their health. The elderly tried to recover their health by themselves because they could not see people, could not go out, and could not go out of the house for a long time. They included taking a walk with their dog, creating a physical activity routine at home, and walking without taking public transportation. The pandemic of the new coronavirus infection has isolated the elderly from society, and it has been recognized that it is important to provide a variety of physical activity programs that the elderly can do on their own. The quality of life will improve if the elderly live healthy lives. The government should be able to develop and provide various programs for the elderly to actively participate in physical activities. The importance of physical activity programs and the development of programs will have to be sustained in follow-up studies so that the elderly can continue physical activity due to the resurgent infectious disease.
Safe Control of Autonomous Cloud Entities in Distributed Systems

In recent years, Cloud Computing and Big Data have been considered the most attractive areas that are revolutionizing the IT world. Cloud Computing paradigm has recently appeared that allows running proprietary or difficult portable applications outside their original software environment on one or more virtual hardware platforms. Therefore, we are to developing such techniques which make it possible to secure communication between the communicating Cloud entities. These techniques must take into account several factors due to the data transmitted in this type of environment is proprietary and of significant size. Conventional data security techniques are not suitable for today's cloud usage. Hence, the main research of this thesis is to define an adaptable architecture with the aim to propose a scalable system that supports cloud services. We will define feasible security solutions dedicated to the Cloud computing context in order to robustly protect data stored in the Cloud. We are more precisely looking for working on NoSQL databases. We also intend to propose a secure solution based on the blockchain that has powerful features like decentralization, autonomy, security, reliability, and transparency.
Creating English Environment at School Through English Club Extracurricular

English proficiency has become something that should be mastered in this modern era. The limitations of educational institutions make the students lack mastery of the English language itself. Some schools solve the problem by making English extracurricular a “container” for students to express their interest and potential in English. This study aims to find out how the existence of an extracurricular English club can create a comfortable English environment for students. This study uses qualitative research with descriptive methods conducted through interviews and observations over 10 students as the respondence. The techniques for analyzing the data in this study used the Miles and Huberman model, including data reduction, data display, and drawing the conclusions. The results of the study found that the English club was a good means for students to practice English, as well as preparing students to be more mature and ready to compete
PENINGKATAN HASIL BELAJAR EKONOMI DENGAN PEMBELAJARAN CONTEXTUAL TEACHING AND LEARNING BERBASIS MEDIA POWER POINT

The purpose of this study was to determine the application of the Contextual Teaching and Learning learning model based on Power Point media in economic learning to improve student learning outcomes. In this study the problems that arise in learning economics is the low learning outcomes achieved by students. The research subjects were students of class XI IPA 3 MAN 2 Klaten consisting of 35 students. The research method used is classroom action research in 2 cycles. The data collection technique uses a test while the data analysis technique is descriptive quantitative. The results showed that in the pre-cycle phase the level of students' mastery in economics learning was 17%. In the first cycle the level of completeness reached 37.14%, while in the second cycle it reached 100%. The results of this study indicate that the application of the Contextual Teaching and Learning learning model based on Power Point media in economic learning is to improve student learning outcomes in class XI IPA 3 MAN 2 Klaten. ABSTRAKTujuan dari penelitian ini adalah untuk mengetahui penerapan model pembelajaran Contextual Teaching and Learning berbasis media Power Point pada pembelajaran ekonomi untuk mmeningkatkan hasil belajar siswa. Dalam penelitian ini permasalahan yang timbul dalam pembelajaran ekonomi adalah rendahnya hasil belajar yang dicapai oleh siswa. Subyek penelitian adalah siswa kelas XI IPA 3 MAN 2 Klaten yang terdiri dari 35 siswa. Metode penelitian yang digunakan adalah penelitian tindakan kelas dalam 2 siklus. Teknik pengumpulan data menggunakan tes sedangkan teknik Analisa datanya adalah kuantitatif deskriptif. Hasil penelitian menunjukkan bahwa pada fase prasiklus tingkat ketuntasan siswa dalam pembelajaran ekonpmi adalah 17%. Pada siklus I tingkat ketuntasan mencapai 37,14%, sedangkan pada siklus II telah mencapai 100%. Dari hasil penelitian ini menunjukkan bahwa penerapan model pembelajaran Contextual Teaching and Learning berbasis media Power Point pada pembelajaran ekonomi untuk mmeningkatkan hasil belajar siswa kelas XI IPA 3 MAN 2 Klaten.
The The Effectiveness of Learning Pronunciation Through English Content by English With Lucy on YouTube

Pronunciation is fundamental and includes what anyone who wants to communicate or is in the process of communicating learning English should understand. One way to practice pronunciation is to use YouTube. YouTube is a good audiovisual combination for pronunciation practice because it not only shows recordings or videos, but also provides sound. One of the content creators who explained the pronunciation material was a YouTuber running the English With Lucy channel. This study uses a qualitative method. The study specifically targeted high school students who studied remotely online to practice their pronunciation skills. In collecting the data, this study used several tools: testing and observation. This results of data analysis is in form statements responden. Learning English pronunciation requires hands-on work with the right model, such as a native speaker. So, using YouTube training videos with native speakers as a resource, you can learn English pronunciation very easily.
Hybrid Siamese-attention Robust Tracker for SOT

Single object tracking (SOT) finds the object correspondence in a spatio-temporal domain, and incorporates various applications in Computer Vision. The Siamese trackers have recently improved the accuracy of visual tracking, however, with limited accuracy that would be enhanced using an attention-based model. In this paper, an integrated attention-based Siamese (Att-siamMask) network is presented for SOT to improve the Siamese discrimination ability at both semantic and textural feature processing levels. In the proposed approach, the correlation step is performed to enhance the matching results of the sub-Siamese network. Experimentally, results show the robustness of the Att-siamMask model that outperforms the competing Siamese-based trackers by an average improvement of 1.4%, 19.5% and 27% in terms of accuracy, expected average overlap and robustness metrics, respectively, using the two standard video sequences VOT2016 and VOT2018.
Mengenali Isu Akuntabilitas Manajemen Dana Bantuan Operasional Sekolah (BOS) di Provinsi Papua

The study aims to identify issues of accountability for school operational assistance (BOS) in Papua Province. This study using a qualitative approach, namely content analysis. Content analysis is used to analyse the audited Regional Government Financial Reports (LKPD) and the Audit Results Report (LHP) as well as literature review of government regulations regarding BOS Funds. Based on the data analysis in 2019, the majority of audit findings in 22 of 29 regencies in Papua including the provincial government were related to mismanagement of the BOS fund. Planning issues such as the schools did not prepare their budget work plans. Thus, the school budget work plans not budgeted in the goverment Local Education Agency. As the result, The BOS Fund were not reported in the regional revenue and expenditure budget (APBD). The organizing issues such as there were no management team decision letters for the management team, no proper recording of assets, bookkeeping erors, no records of bank interests and administration fees as well as taxes charged on the bank accounts. The reporting issues that there were no expenditures otorisation letters and less orderly administrations for the disbursement requests. Therefore, the reporting of the BOS Fund in the LKPD were not properly disclosed. The schools’ accountability report was also a problem because schools fail to submit the BOS Fund report in time. This study shed light to accountability issues of BOS Fund mangement. Thus, the governments need to design practical assistance such as budget and financial management training for these schools management teams
Research on a Shallow Water Mooring with a Tower-shaped Buoy for a Floating Offshore Wind Turbine

A technical challenge in deploying a floating offshore wind turbine in shallow water is dissolution of the poor performance of a catenary mooring system due to the decreased absorbable potential energy. It is common practice to improve mooring performance by using buoys and sinkers to enhance the potential energy absorbed by the mooring system. In this research, a tower-shaped buoy (tower-buoy) is proposed which is a buoy and a sinker integrated structure. In this design, a relatively large buoy and the sinker are connected by a column, and a chain subject to large tension by connecting the buoy and the sinker is removed. A mooring configuration is also proposed to maximize the effect of the tower-buoy.
Ground Low-speed Moving Target Tracking Method for FMCW Radar Based on Inter-frame Plots-Centroid Algorithm

In this paper, the FMCW radar is installed above the prevention and control area to detect and track the ground low-speed moving targets in the area. Aiming at the problems of low ranging accuracy and data association failure in low-speed target tracking method based on intra-frame plots-centroid algorithm, an effective low-speed target tracking method based on inter-frame plots-centroid algorithm is proposed for FMCW radar. The algorithm adopts multi-frame merging processing, which improves the ranging accuracy and solves the problem of false alarms caused by multi-path effects. After practical engineering application, it is proved that this method has a great improvement in tracking results compared with the original method.
Study on Tolerance Range of Phase Discrimination Error Based on Interferometer Direction Finding

For the direction finding algorithm of multi baseline interferometer, the problem of when the ambiguity resolution error will occur when the phase discrimination error is increasing is studied. Through simulation, it is compared whether the tolerance range of phase discrimination error can be greatly improved compared with the traditional direction finding system of interferometer with a group of long and short baselines.
Towards Understanding the Effects of Evolving the MCTS UCT Selection Policy

Monte Carlo Tree Search (MCTS) is a sampling best-first method to search for optimal decisions. The success of MCTS depends heavily on how the MCTS statistical tree is built and the selection policy plays a fundamental role in this. A particular selection policy that works particularly well, widely adopted in MCTS, is the Upper Confidence Bounds for Trees, referred to as UCT. Other more sophisticated bounds have been proposed by the community with the goal to improve MCTS performance on particular problems. Thus, it is evident that while the MCTS UCT behaves generally well, some variants might behave better. As a result of this, multiple works have been proposed to evolve a selection policy to be used in MCTS. Although all these works are inspiring, none of them have carried out an in-depth analysis shedding light under what circumstances an evolved alternative of MCTS UCT might be beneficial in MCTS due to focusing on a single type of problem. In sharp contrast to this, in this work we use five functions of different nature, going from a unimodal function, covering multimodal functions to deceptive functions. We demonstrate how the evolution of the MCTS UCT might be beneficial in multimodal and deceptive scenarios, whereas the MCTS UCT is robust in unimodal scenarios and competitive in the rest of the scenarios used in this study.
On the Drivers of Technical Analysis Profits in Cryptocurrency Markets: A Distributed Lag Approach

The cryptocurrency literature on technical analysis has largely ignored drivers of technical analysis return adjusted by transaction costs (i.e., adjusted returns). To that end, we propose a Heterogeneous Autoregressive Distributed Lag Model of Returns (HARDL-R) to examine the impact from EPU, VIX, and SP500 returns to adjusted returns. We provide evidence that these three drivers matter during bubble periods compared to non-bubble periods. When not differentiating bubble periods, we find that VIX is the only driver influencing the dynamics of adjusted returns from 2016 to 2021. These findings remain relatively stable after controlling for the volume of transactions.
A Particle Swarm Optimization Decomposition Strategy for Large Scale Global Optimization

Countless large-scale global optimization (LSGO) problems occur in an ever-growing number of professions. Cooperative co-evolution (CC) has been shown to assist in discovering encouraging solutions to such complicated issues effectively. CC does this by breaking down a massive problem into distinct smaller sub-problems, which, when solved, are combined to form a solution to the original problem. How a problem is broken down is referred to as decomposition. CCs performance on LSGO problems is highly dependent on the decomposition used. Numerous LSGO decomposition methods have been introduced to address this issue; however, finding a favourable decomposition is challenging, hinting there is still room for improvement and further exploration. This paper presents a new particle swarm optimization decomposition (PSOD) strategy for tackling LSGO problems. PSOD, in addition to its parameters, is explored, showing they provide statistically significant importance in their selection for a few of the CEC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">,</sup> 2013 benchmark functions while being arbitrary for others. Further empirical studies compare PSOD's performance to other leading decomposition algorithms, resulting in PSOD performing best on the fully-separable Ackley function, being interchangeable for a few others, and performing competitively with the rest. PSOD attempts to combine Particle Swarm Optimization (PSO) and Cooperative Particle Swarm Optimization (CPSO) to evolve a decomposition while simultaneously optimizing an objective function for improved performance.
A Multi-scale Contextual Attention Mechanism For Convolutional Neural Networks

In recent years, attention mechanism has been widely studied in the field of computer vision, which can effectively improve the performance of visual tasks. In the past, many classical attention models have studied the modeling of nonlinear relationships in the spatial or channel dimensions of feature maps, ignoring the use of contextual relationships to capture the information interaction of the three dimensions to obtain a global attention feature map. In this paper, we investigate an effective multi-scale contextual attention mechanism, which can obtain feature information of different receptive fields through the combination of multi-branch conventional convolution and dilated convolution, which can increase the image receptive field, and combine global features and detailed features to effectively use contextual information. In addition, since the input tensors interact with each other on the three dimensions of the feature map and was adjusted by an adaptive parameter, this also makes the three-dimensional attention weights we obtain more differentiated. Our MCA model is simple and can be flexibly embedded in a variety of classical backbone networks, and experimental evaluation of the proposed attention mechanism on common datasets for image classification and object detection also proves the effectiveness of our attention meachine.
Embedding-Based Asynchronous Entity Classification Algorithm Framework for the Defect Knowledge Graph of Distribution Network Equipment

The defect record of distribution network equipment can form a fault report and provide data support for related users. Knowledge graph can be used to realize the knowledge interconnection of distribution network equipment defect records. Entity classification is a very important sub-task in the complete task of knowledge graph, which is beneficial to the perfection of skeleton structure in knowledge graph. Therefore, it is of great significance to study the entity classification technology of distribution network equipment defects. At present, the entity classification technology of knowledge graphs can be divided into two types: asynchronous-based entity classification method and synchronous-based entity classification method. This paper proposed a new embedding-based asynchronous entity classification algorithm framework. Compared with entity classification based on the synchronous training method, the performance of the proposed method was verified.
Chain Reaction

Blockchain technology could enable payment for goods and services with your car – which has implications for tolling, traffic management and even the current global dominance of data companies.