Reviewerly/exHarmony huggingface dataset:
THE STUDENTS' ERROR IN WRITING SHORT NARRATIVE USING STORYBIRD APPLICATION

Writing is an important and difficult skill in every level of education. In writing, students make errors in grammar, especially in arranging sentences. In this research, the researcher wanted to know the types of errors the students made and the frequency of errors found in narrative text made by the third semester students of English Department 2017. The subject of the study was the third semester students of English Department 2017 at STKIP PGRI Pasuruan. The researcher focused on Surface Strategy Taxonomy. It was divided into four categories: Omission, Addition, Misformation, and Misordering. As the result, the total number of errors was 379 items. The highest number of errors was misformation with 185 items (48.81%), omission 100 items (26.39%), addition 74 items (17.41%), and the least error occurred on misordering with 20 items (5.28%). It can be concluded that the students’ grammar understanding in narrative text was adequate. It was expected that the Writing and Grammar lecturer could give more attention to the students who have difficulties in grammar. In the other hand, the students were expected to be able to improve the mastery of English lesson especially in grammar. Keywords : error analysis, narrative text, storybird application, surface strategy taxonomy.
The Development of Key Success Processes for EPC Companies through AHP Technique

Purpose By developing the key success processes of the projects of domestic EPC companies participating in overseas power generation and petrochemical projects and presenting them to academics and industry, it will contribute to the study of project management and improve the international competitiveness by enhancing the project execution capability of domestic EPC companies. Methods The research method is to compare the proportion of each process to 49 processes functions in 10 knowledge areas and 5 process groups presented by PMBOK 6th edition by a group of experts with more than 20 years of experience in the representative plant EPC companies in Korea. After collecting expert opinion analysis data, we used the Analytic Hierarchy Process (AHP) technique and tiered and prioritized for each stage of project execution. Results The existing qualitative method is quantitatively analyzed to determine priorities for key success factors of project management and methodology accordingly. Conclusion By prioritizing the detailed elements of the EPC project management process in the overseas power generation and petrochemical sectors, we present strategic directions for the execution of overseas projects by domestic EPC companies.
k-Means clustering-based evolutionary algorithm for solving optimisation problems

Environmental adaptation method (EAM) is a newly developed optimisation algorithm for complex problems. Although EAM and its variants converge very fast in lower-dimensional problems, the performance of these algorithms falls drastically in higher-dimensional problems. This paper introduces a novel approach to improve the performance of the algorithm in higher-dimensional problems. In order to explore the whole search space, the problem search space is divided into multiple mutually exclusive clusters, and then parallel exploitation and exploration are achieved that produces better results. The solutions of independent clusters try to adopt a more suitable structure using the direction received from the local/global best and local/global worst solutions. The performance of the suggested algorithm is compared with other existing algorithms using the benchmark function of the COmparing Continuous Optimisers (COCO) framework. The experimental results have demonstrated that the proposed algorithm performs well in many ways.
Reassessing the locus of normalization in machine-assisted collation.

In this essay we explore the process of textual normalization in the context of machine-assisted collation, which is a common operation in digital textual scholarship. The Gothenburg modular architecture for computer-assisted collation situates normalization as the second stage within a five-stage computational pipeline, where it contributes to improving the eventual alignment. In this essay we argue that normalization, in fact, contributes not only to the alignment, but also to the interpretation of the texts. Furthermore, it occurs at several moments between transcription and rendering, and should not be regarded as happening all at once and only in one location.
말하기 교육에 대한 중학생의 인식

This study investigated the perception of middle school students regarding the importance of speaking education in school and the difficulties related to it. We conducted descriptive statistical analysis, correlation analysis, and analysis of free responses. Through this method, educational implications were derived by examining the speaking class experience of the students and evaluating their needs for the speaking class. Students recognized listening and speaking as the most important subjects, and perceived persuasion and explanation as more difficult than conversation. While recognizing the importance of private discourse, they seemed to not recognize public discourse. According to the results of the correlation analysis between the speaking class experience at school and the perception of speaking, speaking class at school actually contributed to improving the speaking ability of the students. It was speculated that they would be contributing little to the recognition that it could be developed through education. However, speaking class has not completely dispelled the perception that speaking ability is acquired naturally, and that it may not contribute to encouraging the students to make an effort to improve their speaking ability. Students’ demands for speaking classes could be categorized in three ways: classes with high suitability for real life, classes through actual performance, and classes on specific types of discourse.
A Study on Cyber Attacks and Vulnerabilities in Mobile Payment Applications

The end-to-end mobile purchase process depends on the decisions and actions of many stakeholders, including consumers, mobile application developers, mobile payment service providers, merchants, financial institutions like banks and credit card companies, and their respective data centers. This paper presents a detailed look at mobile payments as a sequence of transactions to better understand what is required to authenticate, authorize, verify and process them, and where security vulnerabilities lie. This analysis was accomplished by conducting in-depth research on three popular use cases – Apple Pay, Google Pay, and Samsung Pay – analyzing their respective potentials for being compromised, and suggesting opportunities where higher levels of security can be attained. While many mechanisms exist that can contribute to safeguarding mobile transactions, this analysis shows many ways known vulnerabilities and attacks still can be leveraged to exploit users’ data within popular mobile payment solutions. Approaches for improving the security of mobile payment transactions are included as way ahead recommendations.
Assessment of Online Weather Platform Data for PV Output Power Forecasting

Forecasting is an essential element in supporting the integration of large-scale Grid-Connected Photovoltaic System (GCPV). However, meteorological data vital for accurate forecasting may not be available for a specific location of interest. This paper investigates the use of data obtained from online weather platforms in forecasting photovoltaic (PV) output power when site data is missing or unavailable. Three online platforms were compared and one is chosen as the basis of this paper. The analysis is benchmarked using real ground-based measurement installed at the Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka (UTeM). For forecasting, the Linear Regression (LR) method is used because the performance is easier to interpret. Comparison has been made between the real, calculated, and forecasted PV output power. Root Mean Squared Error and coefficient of determination are the statistical metrics used to evaluate the forecasting performance. The results show that the chosen online weather platform has the capability to complement missing or unavailable local weather parameters. On top of that, forecasting using a simple machine learning algorithm provides marginal improvement compared to calculated values with RMSE of 5.95% and 7.62% respectively.
Constraining the Intermodel Spread in Cloud and Water Vapor Feedback

Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Geophysical Research Letters. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v1]Constraining the Intermodel Spread in Cloud and Water Vapor FeedbackAuthorsHaozheHeiDRyan JKramerBrianSodenSee all authors Haozhe HeiDCorresponding Author• Submitting AuthorUniversity of MiamiiDhttps://orcid.org/0000-0002-8168-4373view email addressThe email was not providedcopy email addressRyan J KramerNASA/USRAview email addressThe email was not providedcopy email addressBrian SodenUniv. Miami, Miamiview email addressThe email was not providedcopy email address
Royale Heroes: A Unique RTS Game Using Deep Reinforcement Learning-based Autonomous Movement

A game, a set of rules or policies by one or more people to carry out an action that will generate excitement for those who do it, can be developed using one of two types of playing methods: turn-based and real-time. In real-time strategic (RTS) games with many characters like Warcraft and Chess, it is fun to play. However, fun decreases since the players have to focus on many points in the playing area. Hence, autonomous movement (AM), an assistant to move the character, can be one solution. An AM can be implemented using deep reinforcement learning (DRL), a new method that can continue to learn without erasing previously learned memory that makes AM better since it can adapt to the playing style. This paper describes the Royale Heroes, a unique RTS game that implements a DRL-based AM. First, a base model is created using a rule-based model based on conventional artificial intelligence (AI). Next, a DRL model is trained to imitate the behavior of previously created AI. Evaluation of six matches shows that the DRL provides high accuracy of up to 98% to copy a behavior. Hence, it can be used as an AM to help players make choices in playing the game. When the player plays this game, the model will continue learning to imitate the player behavior.
A Novel Approach Based on Associative Rule Mining Technique for Multi-label Classification (ARM-MLC)

In this paper, we have implemented an efficient and novel technique for multi-label class prediction using associative rule mining. Many of the research works for the classification have been carried out on single-label datasets, but it is not useful for all real-world application accounting to multi-label datasets like scene classification, text categorization, etc. Hence, we propose an algorithm for performing multi-label classification and solve the problems which come across in the domain pertaining to single-label classification. Our novel technique (ARM-MLC) will aim in enhancing the accuracy of any decision-making processes. Here, in multi-label classification, based on our work, we aim to predict the multiple characters of the instances.
EDUCATION OF THE PHYSICAL QUALITY OF STUDENTS IN PEDAGOGICAL UNIVERSITIES BY MEANS OF THE CLUSTER METHOD WITH THE HELP OF THE BADMINTON GAME

The article discusses the relevance of the study of the formation of the readiness of future teachers for innovative activities using the cluster method. The characteristics of the basic concepts health, general physical development, development of sports and health-improving clusters are given from the standpoint of different authors. The components of students' readiness for innovative activity are considered, the types of software that are used in the university in preparing students are described.
A Comparative Study of Korean bound noun ‘geos’ and Japanese bound noun ‘koto, mono, no’

The purpose of this study is as follows: 1) identifying the type of Japanese expression that corresponds to Korean bound noun ‘geos’, 2) analyzing responsive expressions by form, function, and meaning for finding out discourse functions, 3) identifying the order of words in the setences of bound noun. The Korean bound noun ‘geos’ corresponds to the Japanese bound noun ‘koto, mono, no’ and is used in various meanings and functions within the sentence. In other words, there are various functions of discourse. The semantic functions of bound noun sometimes make subjective logical reasoning when describing situations, objects, or people, and when explaining or emphasizing them. The word order in Korean is [previous element – bound noun ‘geos’ - trailing element] and the Japanese word order is [previous element – bound noun ‘koto, mono, no’ – trailing element]. The preceding elements are verbs, adjectives, directives, and investigations. Learning about the usage and function of Korean bound noun ‘-geos’ is considered to be of great use to Japanese learning Korean.
The More, the Merrier? A Study on In-Car IR-based Head Pose Estimation.

Deep learning methods have proven useful for head pose estimation, but the effect of their depth, type and input resolution based on infrared (IR) images still need to be explored. In this paper, we present a study on in-car head pose estimation on the IR images of the AutoPOSE dataset, where we extract 64 x 64 and 128 x 128 pixel cropped head images. We propose the novel networks Head Orientation Network (HON) and ResNetHG and compare them with state-of-the-art methods like the HPN model from DriveAHead on different input resolutions. In addition, we evaluate multiple depths within our HON and ResNetHG networks and their effect on the accuracy. Our experiments show that higher resolution images lead to lower estimation errors. Furthermore, we show that deep learning methods with fewer layers perform better on head orientation regression based on IR images. Our HON and ResNetHG18 architectures outperform the state-of-the-art on IR images on four different metrics, where we achieve a reduction of the residual error of up to 74%.
IoT-Based Recycle Rebate System – Securing Website and Database

Web database is a database application that is designed to be managed and accessed through the Internet and has gained the trust of clients and users over the globe. But without having a proper security implementation towards the web database, users and clients may lose trust. To mitigate them, we approach the problem by following the process of security assessment.  In this paper, we introduce security assessment method through Vulnerability Assessment and Penetration Testing Process and Risk Matrix results to find the vulnerabilities and threats on the website and database of the Recycle Rebate System. The implementation of mitigation method used are based on the results scanning of the VAPT process and the Risk Matrix Table. All the results of the VAPT scanning were documented into a Risk Assessment Report which calculate the risk level of each existing results found and can be easily viewed and understand in the table form named Risk Matrix.
A Machine Learning Approach for Estimating Missing Data in Nonstationary Environments

The assumption with most learning techniques and algorithms developed this far is that data is complete and will continuously be available or that data conforms to a stationary distribution. Real world applications are often streaming and their measurements are often sampled after an extended period of time thereby giving rise to the formation of a time series. Measurement devices assigned to measure nonstationary quantities are subject to failure. When a failure occurs, the process of approximating missing values becomes difficulty. The process of approximating missing values in such dynamic environments is further exacerbated by the chaotic and unpredictable nature of the evolving data. Typical examples include stock market, network intrusion detection systems and seismic waves. To estimate missing values with traditional statistical methods can lead to bias when applied to environments that evolve with time. This paper introduces an ensemble of regressors approach to approximate missing data in online nonstationary data. The approach learns new concepts incrementally and the current learnt concept is then used to approximate the missing values.
PEMANFAATAN TEKNOLOGI GOOGLE MAPS API DALAM PEMANFAATAN POTENSI DESA BANDUNGBARU BERBASIS ANDROID

Bandungbaru Village is a village located in Pringsewu District and a village that has competitive potential and can be superior in Pringsewu Regency. Bandungbaru is the entrance to Pringsewu Regency from Central Lampung Regency. In the era of regional autonomy, the village is a very important part in determining the success of development and being in direct contact with the community, especially in public services. The village has a very decisive role because the success of implementing various government programs in Pringsewu Regency at all levels is very dependent on the preparation of planning based on data and information and the use of technology is very important in village progress, the use of the system is expected to be a good step for the progress of a village using smartphones and android applications Communication is one of the important things in describing something, such as as an intermediary for presenting something to the public. The use of the google maps application will make it easier for village officials and non-residents in Bandung Baru village to easily find locations and potentials in Bandungbaru Village.
Finite Time Decentralized Cubature Information Filtering with Directed Graph

This paper focuses on the distributed state estimation problem with finite communication time and directed graph for the nonlinear discrete system. A finite time decentralized cubature information filtering algorithm is proposed, of which the goal is to estimate the interested states in a fully distributed manner. The proposed filtering algorithm is applicable for the directed graph and only demands finite communication time to obtain satisfactory performance. Besides, the proposed filtering algorithm possesses guaranteed stability in the mean square sense. Finally, the effectiveness of the proposed algorithm is validated through the numerical simulation.
A Personalized Recommendation Algorithm with Time Factors for Technical Patent Matching

How to accurately locate the information required by researchers in the massive patent resources is an urgent problem to be solved in the patent database. Traditional collaborative filtering algorithms, mainly based on the users' rating scores, ignore their interests or direction changes and the active user's effect on recommended precision. To solve these problems, this paper reconstructs the recommendation weight with the consideration of time factor and item similarity, and proposed a practical method for calculating the similarity of item. This method can obtain a more reliable item similarity and better-recommended results by weakening the influence of active users on similarity calculation. The experimental results show that the proposed method obviously improves the recommendation performance. Compared with several traditional recommendation algorithms and deep learning algorithms in five datasets, the precision and recall rate improve by 5.2% and 9.5% on average, respectively.
Sources and Illustrations of Nonclassical Light

With the dawning of the prospect of realizing quantum information processing in the regime of continuous variable, the need for generating a bright light with a robust quantum feature is growing with a faster rate than ever before.
청소년 소설에 나타난 크로노토프와 문학교육적 함의

In this study, the chronotopes and the educational implications for literature in the adolescent novel In Kilimanjaro, Goodbye are discussed. In the text, the speaker Seongmin met various characters in various spaces and times and looked at the process of a true self. This process was largely represented by two chronotopes. In other words, the chronotopes of the crisis of ‘leaving’ and ‘departure’ form a chronotope of ‘meeting’ and ‘gift’ as it moves toward the threshold of life, forming a relationship with the chronotope of ‘Kilimanjaro’, the lead-in to self-growth. In this paper, the meaning of In Kilimanjaro, Goodbye was viewed as a construct that can encompass the present and the future, and has the power of continuity, as the meaning of “Goodbye” composes the plot and is examined through several chronotopes. Finally, it is possible to find the meaning that adolescent learners can examine in the process of self-exploration.
Level of satisfaction of online classes by students of Physical Education of Chile in times of pandemic

Introduction: In Chile the first case of COVID-19 was diagnosed on March 3, 2020 and on the eighteenth of the same month the president decreed state of catastrophe, so that classes at school and university level went from the presence to the virtuality The present study aims to know the level of satisfaction of the virtual classes of the students of Physical Education Methodology: Quantitative, non experimental, transversal The sample consisted of 542 students of Physical Education from different study houses in Chile The Satisfaction scale online classes was adapted and validated Results: there are significant differences comparing theoretical and practical subjects, with theoretical chairs being better valued Differences were also found by sex, where males have a more negative perception about virtual classes and when comparing by course, freshmen have a more positive perception about virtual classes in relation to higher courses Conclusions: there is a resistance on the part of students to virtual classes in Physical Education, because, although significant differences were found between the theoretical and practical subjects, values were always around 3 on a scale of 1 to 5 Future research with other variables such as physical activity, stress levels and strategies for the teaching of Virtual Physical Education are necessary © Copyright: Federacion Espanola deAsociaciones de Docentes de Educacion Fisica (FEADEF)
Laporan Tugas Akhir Penerapan Monitoring Inventori Susu Sapi Murni Berbasis WEB Di KUB. Tirta Sari Kresna Gemilang Malang

Tirtasari Kresna Gemilang Malang is a cooperative that coordinates breeders in Pujon Malang with raw materials for Pure Cow Milk. It begins with data collection using manual methods where errors often occur in recording or loss of proof of depositing pure cow's milk from breeders. Therefore, in this study the authors help create a web-based inventory system to facilitate the data collection of milk in and out of pure cow's milk that is deposited by breeders and also to overcome errors that often occur in KUB, the results of this inventory system can make the system work effective and efficient. in modern administrationKeyword: Tirtasari Kresna gemilang malang,KUB