Reviewerly/exHarmony huggingface dataset:
Improving Students’ Writing Ability in Descriptive Text through Writer Workshop Strategy at the Seventh Grade of SMP Negeri 2 Lahewa in 2021/2022

Writing is the process of using symbols like letter of the alphabet, punctuation and spaces to communicate thoughts and ideas in a readable form. To write clearly it is essential to understand the basic system of a language. In learning writing, most of students could not achieve the 70 MCC. There were problem faced by the students in studying writing especially in descriptive text at the seventh grade of SMP Negeri 2 Lahewa in 2021/2022 namely: the students were lack of vocabularies, the students were unable to describe the object and the students were unable to identify topic, main idea, content from descriptive text. Thus, to overcome these problems, the researchers conducted a research by using Writer Workshop Strategy at the seventh grade of SMP Negeri 2 Lahewa in 2021/2022. So, to achieve the purpose, the researchers used Classroom Action Research (CAR). This research has been conducted in two cycles, which each cycle consisted of two meetings, and each meeting consisted of four steps, they were planning, action, observation, and reflection. The result of the data in Cycle I showed there were 2 students (8%); 5 students (20%), 3 students (12%), 10 students (41%) and 4 students (16%). While in Cycle II there were 15 students (62%) got score among 86-100, 5 students (20%) got score among 71-85, and 4 students (16%) got score among 56-70. Based on the result, it is concluded that Writer Workshop Strategy improved the students’ writing ability in descriptive text at the seventh grade of SMP Negeri 2 Lahewa in 2021/2022.
Genetic Algorithm Application on the Risk Assessment for Accounting Resource Sharing Management

A risk assessment model of accounting resource sharing management based on genetic algorithm is proposed. By selecting the risk assessment indicators of accounting resource sharing management, the weight of risk assessment indicators is determined by the Delphi method, and the quantitative model of risk assessment of accounting resource sharing management is constructed. On this foundation, a genetic algorithm is utilised to compute the best solution of parameters, design risk degree assessment criteria to evaluate risk degree, and implement risk evaluation of accounting resource sharing management using a genetic algorithm. The experimental results show that compared with the traditional accounting resource sharing management risk evaluation model, the constructed accounting resource sharing management risk evaluation model greatly reduces the evaluation time and can accurately evaluate the accounting resource sharing management risk, which fully shows that the model has better evaluation performance.
Automatic Curriculum Generation Based on Expert Trajectory

The application of curriculum learning in reinforcement learning is expected to improve the learning efficiency and performance of autonomous agents by affecting their behavior acquisition. However, there are many challenges in generating the curriculum necessary for curriculum learning, as it is a prerequisite for having expert knowledge and deep prior understanding. In addition, curriculum generation methods are often specialized to the target task and lack versatility. In this study, new method is anticipated to solve the above problem by automatically generating a curriculum using clustering based on expert trajectories (state history). In the experiments, we compared the learning efficiency of normal agents and agents using the proposed method, and confirmed that the proposed method improved the performance in all aspects.
A Random and Scalable Blockchain Consensus Mechanism

The central piece of blockchain technologies is the consensus algorithm. The consensus is reached via consensus algorithms in the distributed network of the blockchain. The consensus becomes stronger if nearly all the nodes in the blockchain network take part in building the blockchain. Even if some nodes misbehave or malfunction, the consensus should not break down then. Since blockchain networks are distributed, even if some regions of the network or some nodes of the network leave, the blockchain network should not malfunction and it should be consistent. Therefore, the consistency of the blockchain transactions should be assured by all the nodes or nearly all the nodes, or most of the nodes. In other words, the consensus should be the issue of all the nodes. In this work, a novel consensus algorithm is presented to diffuse the mission of building the blockchain to all the nodes. In other words, the consensus should be the issue of all the nodes. In this work, a novel consensus algorithm is presented to diffuse the mission of building the blockchain to all the nodes. The algorithm increases the randomness of nodes and enforces the blockchain network to be more decentralized. Randomness is realized by employing the power of cryptography, especially by using public keys as a characteristic for miners, which are also called signers. Moreover, since the algorithm is realized with a few operations, it contributes to the scalability of the blockchain. Furthermore, digital signatures improve the security level and consistency of the blockchain.
Transformer Tracking with Cyclic Shifting Window Attention

Transformer architecture has been showing its great strength in visual object tracking, for its effective attention mechanism. Existing transformer-based approaches adopt the pixel-to-pixel attention strategy on flattened image features and unavoidably ignore the integrity of objects. In this paper, we propose a new transformer architecture with multi-scale cyclic shifting window attention for visual object tracking, elevating the attention from pixel to window level. The cross-window multi-scale attention has the advantage of aggregating attention at different scales and generates the best fine-scale match for the target object. Furthermore, the cyclic shifting strategy brings greater accuracy by expanding the window samples with positional information, and at the same time saves huge amounts of computational power by removing redundant calculations. Extensive experiments demonstrate the superior performance of our method, which also sets the new state-of-the-art records on five challenging datasets, along with the VOT2020, UAV123, LaSOT, TrackingNet, and GOT-10k benchmarks.
Investigation into the Reliability and Encryption Performance of Wireless Signal Transmission by Network Coding Combined with Blockchain in Natural Disaster Scenarios

To enhance the reliability and encryption of unmanned aerial vehicle (UAV) wireless signals in natural disaster scenarios, firstly, the present work analyzes the current wireless signal transmission mode of UAV. In addition, it discusses the efficient network coding of wireless signal transmission model and blockchain with strong encryption ability. Secondly, the signal transmission model of network coding is explored. Finally, the network coding wireless signal transmission model is combined with the blockchain wireless signal transmission model to realize efficient and encrypted data transmission. The research results show that the network coding wireless signal transmission model combined with blockchain technology realizes the maximum difference of network coding wireless signal transmission rate of 1.5 Mb/s. The model also encrypts the data in the simulation scenario of natural disasters through blockchain technology. After encryption, the data protection effect of the transmission model is significantly improved and meets the requirements of standard data transmission. Meanwhile, the wireless signal transmission model can reduce the resource occupation rate in the process of data transmission and improve the success rate and universality of data transmission in natural disaster scenarios. The probability of data being successfully protected in disaster is more than 90%. The results provide technical support for the stable transmission of wireless signals in natural disaster scenarios and provide a reference for improving the reliability and encryption of wireless signal transmission.
INFORMALITY AND TOTAL FACTOR PRODUCTIVITY IN KENYA

Purpose: The Kenyan economy is largely informal. The objective of the study was to establish the effect of size of the informal sector on total factor productivity in the country.&#x0D; Methodology: The study was based on the dualist theory of the economy. Data covering the period 1974 to 2016 was sourced from government publications (Economic Surveys and Statistical Abstracts), the Global Financial Development Database and the World Development Indicators. A growth accounting exercise was conducted using the Cobb-Douglas production function based on the Solow growth model. This enabled the decomposition of output growth to the contributions of labour and capital with a residual, commonly referred to as the total factor productivity which was the dependent variable in the study. The study was non-experimental and utilized a longitudinal research design using macro-level data thus limiting the possibility of data manipulation. Various theoretically and empirically recognized determinants on TFP were included as control variables. The analysis was conducted using ordinary least squares.&#x0D; Findings: The findings show that the size of the informal sector has a negative and statistically significant effect on total factor productivity in Kenya. Given the large informal sector, the study concluded that there is need to increase productivity of the sector in the country for improved economic performance.&#x0D; Unique contribution to theory, practice and policy: From the study findings, the informal sector has a negative and statistically significant effect on total factor productivity in Kenya. The study recommends the development and implementation of policies to enhance productivity in the sector. These include market and technological development, improved infrastructure and access to credit facilities.
PENINGKATAN MOTIVASI BELAJAR MATEMATIKA SISWA DALAM PEMBELAJARAN DARING MELALUI PERAN ORANG TUA DAN KOMPETENSI GURU

This study aims to determine how high the influence of the role of parents and teacher competence on students' learning motivation in online learning at SMP N 1 Ranah Pesisir. This type of research is quantitative with an explanatory research approach. There were 105 students of SMP N 1 Ranah Pesisir who were selected as samples according to the cluster random sampling technique. Data were collected using a questionnaire with a 5-point Likert measurement scale. The results of the study using multiple linear regression analysis showed that the results of the minor analysis were partially accepted. H1 was accepted proving that there was a significant positive effect of the parent's role on students' learning motivation, and H2 was accepted, meaning that there was a significant positive effect of teacher competence on student learning motivation, while the results of the major analysis were H3 is accepted, which means that there is a significant positive effect of the role of parents and teacher competence on student learning motivation. The contribution of the variables of the role of parents and teacher competence to students' learning motivation in online learning reached 65.3 percent, the remaining 34.7 percent were other factors outside of the research.
PACMHCI V6, ETRA, May 2022 Editorial

We are delighted to present a first issue of the Proceedings of the ACM on Human-Computer Interaction to focus on contributions from the Eye Tracking Research and Applications (ETRA) community.
Quantifying excess heavy metal concentrations in drainage basins using conservative mixing models

High concentrations of heavy metals and other pollutants in river sediments can have detrimental effects on the ecosystem and humans. The composition of river sediments throughout drainage basins therefore provides important information for environmental monitoring. An obvious first step for using river sediment compositions for monitoring is to quantify natural baseline concentrations. Once baselines have been quantified, is it straightforward to compare them to observations to identify excesses generated by, for example, anthropogenic inputs. In this study a new strategy for mapping element concentrations along rivers from discrete geochemical observations upstream is presented. We demonstrate our approach in a case study of the Clyde drainage basin in western Scotland, UK. First, continuous baselines are generated using simple forward models that conservatively mix source region concentrations along drainage networks. 1185 measurements of elemental concentrations from first-order streams are used to parameterise the source region. The calculated baselines are then compared to concentrations measured at 60 localities along the main channel of the Clyde river. For a range of major and trace elements (e.g., Mg, Sr, K, Mn), the downstream observations are in close agreement with baseline concentrations predicted by conservative mixing models. However, some heavy metal concentrations (Pb, Cu, Zn) tend to exceed predicted baseline concentrations. Therefore, the second part of our approach calculates element concentrations in source areas required to match the observed Pb, Cu and Zn concentrations measured along the river. An inverse approach is used to `unmix' the observed concentrations utilising, again, a conservative mixing model. Model resolution is determined by the spatial distribution of the data. Resultant calculated natural baselines and heavy metal concentrations along the river can easily be compared to estimate excesses. We tentatively suggest that anthropogenic input to sediment composition along the river is equivalent to annual fluxes of 9.7, 1.5 and 5.7 tonnes (10^6 g) of Pb, Cu and Zn, respectively.
MODELING OF THE DISPERSION OF THE RELEASE OF POLLUTANTS, TAKING INTO ACCOUNT THE EFFECT OF THEIR ADSORPTION ON THE CHANGE IN CONCENTRATION IN THE ATMOSPHERE

The categories of stability of the atmosphere, modeling of dispersion of emissions of pollutants, taking into account adsorption on changes in concentration in the atmosphere, are considered
It Isn't Sh!tposting, It's My CAT Posting

In this paper, we describe a novel architecture which can generate hilarious captions for a given input image. The architecture is split into two halves, i.e. image captioning and hilarious text conversion. The architecture starts with a pre-trained CNN model, VGG16 in this implementation, and applies attention LSTM on it to generate normal caption. These normal captions then are fed forward to our hilarious text conversion transformer which converts this text into something hilarious while maintaining the context of the input image. The architecture can also be split into two halves and only the seq2seq transformer can be used to generate hilarious caption by inputting a sentence.This paper aims to help everyday user to be more lazy and hilarious at the same time by generating captions using CATNet.
Secure Approach Using Visual Cryptography

An Image Encryption and Decryption Using AES (Advance Encryption Standard) Algorithm is proposed in this paper. Visual cryptography is the most popular technique attracted various researchers to improve the security level while utilizing the images and videos as encryption key, instead of text which is easy to crack. Visual cryptography computes complex multimedia information which may leads to computational overhead when simple processing techniques and architectures were utilized. So, various researchers had taken over this challenge and trying to resolve while introducing the various high speed techniques and architectures which would make visual cryptography process highly secured and energy efficient. This paper presents an algorithm in which the image is an input to AES Encryption to get the encrypted image and the encrypted image is the input to AES Decryption to get the original image. Keywords: Visual cryptography, security, energy efficient, high speed architecture, accurate prediction outcome, AES algorithm, image encryption, image decryption
System Design and Simulation for Square Dance Movement Monitoring Based on Machine Learning

Since the reform and opening, China's economy has grown rapidly, and people's living standards have improved significantly. As one of the most effective ways to implement national fitness, square dance has gradually become the main lifestyle of urban communities, an important part of China's sports construction, and an important indicator to reflect the fitness of the masses and the construction of a well-off society in an all-round way. On the other hand, with the rapid development of internet of things technology, many people can use intelligent bracelets based on machine learning technology to realize motion detection. This technology is also applicable in square dance, which is of great significance to exercise and protect health. This paper first reviews the research status of the internet of things communication protocol and cloud platform, then introduces and analyzes the MQTT communication protocol and Netty high-performance network framework, and studies the integration technology of the internet of things and machine learning. Then, according to the characteristics of the internet of things, a scheme to realize data preprocessing is proposed. The value to be completed is calculated based on the correlation of other attributes corresponding to the k-nearest neighbor model (KNN) and the regression model. Finally, the machine learning algorithm is used to train the results of the three models to obtain the final filling value. The whole scheme design allows the machine learning algorithm to obtain relatively high-quality data in the internal environment. This paper designs a sports monitoring data system for square dance by combining machine learning and internet of things technology, so as to promote national fitness.
Does Corruption Invade the Effectiveness of Foreign Aid on Income Inequality in Developing Economies?

Foreign aid is critical for developing economies to meet their developmental objectives. Income disparity is a major problem that can be addressed with foreign resources; however unscrupulous behaviors such as misappropriation of funds for personal gain might diminish the effectiveness of foreign aid. This paper analyses the influence of foreign aid (ODA) on income inequality for a panel of 62 developing nations, including regional and income categories by using the Generalized Method of Moments (GMM) on model, with two specifications. The results have shown a negative impact of foreign aid on income inequality in developing economies overall and in regional and income groups of economies except Sub-Saharan Africa (SSA) particularly in the first specification where corruption has not been included in the model. In the second specification where corruption has been included in the model, the corruption has shown boosting impact on income inequality in developing economies as well as in all the regional and income groups of the economies. However, the effect of ODA on income inequality has become insignificant in developing economies but positive in all groups except SSA. It explains that corruption leads to foreign aid ineffectiveness in developing economies. In the control variables, FDI has a diminishing effect on income inequality in the panel of developing economies as well regional and income groups of the economies. It is proposed that to reap the benefits of foreign aid in developing economies, corruption is necessary to be eliminated.
Penerapan Model Pembelajaran Berbasis Teks pada Mata Pelajaran Bahasa Inggris di SMPN 2 Kumai

This study aims to determine student learning outcomes in English subjects by applying text-based learning, increasing activity, and student learning outcomes. The results showed that the learning process using the Text-Based Learning model was BKOF (Building Knowledge of Field), MOT (Modelling of Text), and JCOT (Join Construction of Text). The implementation of the research from cycle 1 to cycle 2 has improved. This study shows that student learning outcomes have increased after the action. The percentage of students who have not completed continues to decline from cycle 1 to cycle 2 (from 26.47% to 8.82%). The percentage of students who have completed has increased from cycle 1 to cycle 2 (from 73.53% to 91.18%). The indicator of the success of this CAR is that this CAR is said to be successful if the percentage of students whose learning outcomes are assessed as complete reaches a minimum of 85%. The table shows that the percentage of students whose grades have been completed has reached 91.18%, so this CAR has been successful.
POSSIBILITIES OF A COLLEGIAL SUPPORT GROUP FOR IMPROVING THE PROFESSIONAL CONPETENCES OF TEACHERS DURING REMOTE WORK

In educational institutions, collegial support groups as the form of pedagogical work have long been known and have been incorporated into the system of the organization of methodological work. These are mainly administratively formed long-term activity groups. This study focuses on the less studied so far freely formed short-term activity groups in an educational establishment. The analysis of the activities of the above-mentioned groups as a whole highlight their capabilities in remote working conditions. The study is based on literature analysis and pooling and analysis of structured interview data. The structured interview was designed to find out whether and how teachers come together in groups during remote work, to solve problems that have arisen during the work and what results this work has given. The study concluded that the remote activities of the collegial support group are successful, provided that the educational institution already has a sufficiently long tradition of organizing these groups.
DIGITAL EDUCATIONAL ENVIRONMENT PRINCIPAL PRINCIPLES OF CREATION

As in any process, the digitization of the education system requires a thorough theoretical and methodological justification. Among the methodological foundations of creating a digital learning environment, principles also take precedence. Consequently, the principles determine the organizational and methodological correctness of the activity, the rational definition of directions, the relationship between the goal and the result. The article discusses the description of the priority principles of creating a digital learning environment.
ABOUT SOLVING LOGICAL PROBLEMS IN THE PROLOGUE

The solution of one logical problem in the SWIpl package is given
Pengembangan Website Pembelajaran Materi Aset Tetap Berwujud Dengan Memanfaatkan Google Sites

Distance learning activities are considered less effective due to the limited ability to manage and utilize existing technology. Learning tends to use Power Point Slides which makes it difficult for students to understand the material of tangible fixed assets so that learning media for tangible fixed assets is needed that is effective and easy to understand. This study aims to produce a tangible fixed asset learning website and determine the feasibility of a tangible fixed asset material learning website and determine student responses to a tangible fixed asset material learning website. The test subjects were students of Accounting Education 2018 State University of Surabaya. The development model in this research is 4D (Define, Design, Development, Disseminate). Data collection techniques are using assessment questionnaires and questionnaires. The data analysis technique used descriptive analysis technique. The results of the research are in the form of a learning website with a development model carried out to the Development stage. The feasibility of the material learning website for fixed assets in the form of material aspects is in the very feasible category, the media aspect is in the very feasible category and the results of the student response questionnaire are in the very good category. So it can be concluded that the tangible fixed asset learning website is very suitable for use in secondary financial accounting learning 1.