Reviewerly/exHarmony huggingface dataset:
INDONESIAN EFL TEACHERS TEACHING READING TO ENGLISH YOUNG LEARNERS IN INDONESIA AND THAILAND

Among myriad research on teaching reading on English Young Learners, little has been discussed the practice within two different socio-culture settings. Indeed, the success stories of teaching reading to young learners with different social and cultural circumstances will be able to offer insight and new perspectives of teaching reading to EYL for researchers, mainly English teachers. This study aimed at narrating and exploring the process of teaching reading within two different countries, Indonesia and Thailand. This study invited two Indonesians EFL teachers who teach English to young learners in Indonesia and Thailand. Underpinning the narrative research design, this study narrated the experience of the participants from interview process triangulated by their narrative framework and teaching reading materials. From the obtained data, the findings were presented using four major themes: teaching experience, the students, teaching reading, and teaching reading materials. The result revealed that the participants had some similarities and distinction aspects. Teaching technique that the participants used could be said as the similar aspect of the participants. Meanwhile, teaching materials could be said as the integral factors underlaying the differences. Furthermore, organizational system and community around participants played significant role in affecting the differences and similarities.
Existence of the Mathematical Prediction Equation and its Convergence Condition and its Simplified Derivation

Abstract The mathematical prediction equation (MPE) will give the mathematical definition of prediction / forecast to various physical motion (wave) fields. MPE of space-time infinite order accuracy in the Taylor series was proposed in this study (MPE is also an infinite series), and its sufficient and necessary convergence condition was reported, which whold take place of the famous CFL (Courant-Friedrichs-levy) calculation stability condition in textbooks of modern numerical weather forecast. Meanwhile, simplified derivation or inference was given for MPE (it is hoped that the mathematical circle will perfectly prove MPE). The mathematical definition of prediction was provided during derivation, including mathematical relationships between the Euler constant equation and Newton displacement, velocity and acceleration. Furthermore, by combining MPE and its convergence condition with the cubic spline function, closed prediction equations of ideal gas pressure, temperature, and flow fields with space-time second-order accuracy were established. And the success of modern numerical predictions, such as weather forecast, implies the existence of MPE. (It is also hoped that the physics circle will investigate the application of MPE in quantum fields, to discuss the mathematical definition and difference between Newton displacement and Einstein displacement).
Data Serialization with S-Expressions

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The OCaml Platform

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Concurrent Programming with Async

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Promoting Social Media Dissemination of Digital Images Through CBR-Based Tag Recommendation

Multimedia content has become an essential tool to share knowledge, sell products or disseminate messages.Some social networks use multimedia content to promote information and create social communities.In order to increase the impact of the digital content, those images or videos are labeled with different words, denominated tags.In this paper, we propose a recommender system which analyzes multimedia content and suggests tags to maximize its influence in the social community.It implements a Case-Based Reasoning architecture (CBR), which allows to learn from previous tagged content.The system has been evaluated through cross fold validation with a training and validation sets carefully constructed and extracted from Instagram.The results demonstrate that the system can suggest good options to label our image and maximize the influence of the multimedia content.
PERCEPTION, MOTIVATION, AND STUDENT LEARNING OUTCOMES AFTER USING THE BOT TELEGRAM INTERFACE APPLICATION

Learning in the millennial century really requires soft skills and ICT support skills by utilizing the sophisticated technological sophistication available. The use of various learning models and media, both in the learning process and in the evaluation, gives a new nuance to students (students). Students are always waiting for new things that are presented by the lecturer. A good educator is always missed by his students, because he is always innovating in the learning process. This indirectly provides new experiences in online learning, and students can interact in the form of chatting. The purpose of this study was to obtain information about students 'motivation to study biology education courses, as well as to find out students' improvement and understanding of concepts and subjects. This study uses survey research methods with a quantitative approach. Data collection was conducted through a survey of Biology education study program students in semester 2 for Animal Diversity courses. Data analysis was calculated based on the number of checklists filled out by respondents, then presented to determine the motivation of each student and look for the value of the gain and the difference between the initial and final knowledge of learning. Each respondent scores are summed with other respondents and averaged so that the average score of respondents' perceptions is obtained overall, then tabulated. The results showed that the perception of students using telegram chatbot with a very happy category by 58%, and very motivational by 67%. While the level of student knowledge becomes more increased with the gain value obtained by 0.78 and is categorized high.
Explore on Online English Listening Evaluation System Using the Genetic Algorithm

Testing is a good teaching tool. The online testing system can realize automatic aggregation, scoring, statistics, and analysis of test questions, which saves a lot of time and money for the organizers and participants, and is an effective means to promote the modernization of education in China. The purpose of implementing English intelligence is to allow teachers and students to make better use of their strengths in the English learning process and to improve English teaching based on the results of quantitative analysis of intelligence, which will play a positive role in promoting quality language education in secondary schools, thus truly reducing the burden on teachers and students. The genetic algorithm is an intelligent algorithm based on natural selection and genetic variation in the world of bionic organisms. It has the remarkable advantages of simplicity and generality, robustness, parallel processing, efficiency, and practicality, and has been widely used in automation and other fields with good results. The method can effectively overcome the traditional problems of slow grouping speed, low success rate, and poor grouping quality. The system is scientific, reasonable, and practical and can meet the needs of users to the maximum extent.
Crosswalks among stewardship maturity assessment approaches promoting trustworthy FAIR data and repositories

Various maturity assessment approaches have been developed to help research data repositories effectively manage their holdings at both the organizational and dataset levels. Repositories can use these approaches as self-assessment tools-potentially leading to formal certification-to benchmark the maturity of their data holdings, highlight gaps in their practices, and improve their sustainability. Understanding the differences among these assessment approaches can provide beneficial information on stewardship best practices for supporting FAIR data managed by Trustworthy Data Repositories. However, it is a daunting task due to diversity in the perspectives of the approaches and the potential for subjective interpretation of individual criteria. In this article, we outline the commonalities and distinctions of three established assessment approaches: i) CoreTrustSeal Trustworthy Data Repositories Requirements, ii) Data Stewardship Maturity Matrix, and iii) FAIR Guiding Principles. Strong correlations are found in data discovery, accessibility, interoperability, and usability due to overlapping requirements in digital object management. The study also reveals that the complexity of the approaches can lead to a large variety of inferred crosswalks among them.
The Effect of Interactive Video Use on Early Childhood Cognitive Ability

This study aims to determine the effect of using interactive video on the cognitive abilities of early childhood in Bukittinggi City. The research method used is the Mixed Method with the type of research Sequential Explanatory Design, with the first testing phase using quantitative methods and then deepening with qualitative methods. Data collection techniques for quantitative data using questionnaires and qualitative data with observations and interviews. Quantitative testing was carried out with Pearson's product moment while qualitative data testing was carried out by data triangulation. The results of the coefficient test indicate that between the independent variable and the dependent variable there is a strong and positive and significant relationship between the use of interactive videos on early childhood math skills. The results of qualitative tests from observations and interviews conducted showed that the use of interactive videos was very effective in influencing early childhood math skills. Based on the results of the research above, it can be concluded that there is a positive and significant effect between the use of interactive videos on the math skills of early childhood in Bukittinggi City.
Expressive Writing Diary Book To Improve Self-Adjustment

The purpose of this study was to create an expressive writing diary as a way to improve the adjustment of seventh grade students at SMP A. Wahid Hasyim Jombang. The research method used is a type of development research with the Borg &amp; Gall model which has 10 steps, namely: 1) research and information gathering, (2) planning, (3) product format development, (4) initial trial, (5) revision product, (6) field trial, (7) product revision, (8) field test, (9) final product revision, and (10) dissemination and implementation. However, with limited time, the researcher only reached stage 5. The results of the material expert test were 88.23%, the media expert test was 92.8%, and the expert test for prospective users was 78.33% so that the overall criteria were very good. , does not need to be revised and good, does not need to be revised. The results of the prototype of the expressive writing diary in the form of a diary with A5 size. The expressive writing diary book media to improve the adjustment of seventh grade students in junior high school has met the product acceptability criteria based on usability, feasibility, accuracy and propriety.
Lexical Cohesion in Sunset Magazine's Articles

Producing quality in writing is not easy to do. The selection of vocabulary in writing affects the quality of writing. There are still difficulties in writing, especially for students. The students are still lack of the understanding of lexical cohesion. Regarding the problems identified, this research aims to analyze the employment of lexical cohesion in magazine articles. The results of this research show how article writers, which are the experts in writing published works for a large audience, make their writing cohesive. This research uses a qualitative descriptive method to explain the lexical cohesion occurring in the magazine articles. After identifying and analyzing the data, it is found that all lexical cohesion types have occurred 307 times in the magazine articles: repetition, synonymy, antonymy, hyponymy, meronymy, and collocation. Synonymy is the most frequently used lexical cohesion type in the magazine articles, followed by collocation. The use of synonymy and collocation creates the cohesiveness of the text. The magazine articles become rich in vocabulary, not monotonous, and understandable to the readers.
The Compiler Frontend: Parsing and Type Checking

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Assured data deletion in cloud computing: security analysis and requirements

With the rapid development of cloud storage, more data owners store their data on the remote cloud to reduce the heavy local storage overhead. Cloud storage provides clients with a storage space that they may outsource and use on a pay-as-you-go basis. Due to data ownership separation and management, local data owners lose control over their data. Hence, all the operations over the outsourced data such as data transfer, update, and deletion, will be executed by the remote cloud server. As a result of that, various security challenges appear in terms of data privacy and integrity. In addition to data deletion that becomes an important security challenge, once a cloud user intends to delete his data, it must be sure that data is deleted from all cloud storage sources and prevent the cloud server from reserving the data maliciously for economic interests. In this paper, we present and discuss several types of research that use different technologies to solve assured deletion problems and verification the deletion result. The paper also presents a thorough analysis of the surveyed protocols in terms of fine-grained, security, performance, and requirements of remote cloud storage design.
Predictive Control of the Mobile Robot under the Deep Long-Short Term Memory Neural Network Model

At present, there is a phenomenon of network data packet loss in the trajectory tracking control system, which will degrade or even destabilize the system’s performance. Therefore, this work first explains the theory of the deep long-short term memory (LSTM) neural network model, the kinematic model of mobile robots, and the trajectory tracking error model. The reasons for data packet loss in the control system are analyzed. Second, a prediction model based on the LSTM network is designed according to the theory mentioned above. Finally, the training effect of the LSTM model and the robot trajectory tracking effect based on the model are tested by setting up simulation experiments. The research results are as follows: (1) The pose test error of the mobile robot will eventually tend to zero through the simulation curve generated by the pose parameters (x, y, θ) of the mobile robot. (2) The trajectory tracking error of the deep LSTM neural network prediction and compensation method with the packet loss rate of 5% is less than that with the packet loss rate of 10%. (3) The linear velocity <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>υ</mi> </math> of the mobile robot based on the prediction model of the LSTM network varies greatly but is always in the interval (−2, 2). Its angular velocity <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>ω</mi> </math> initially fluctuates greatly but gradually tends to zero after about 13 s. (4) When the prediction model tracks the trajectory of the robot, the horizontal position x, the vertical position y, and the angle <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>θ</mi> </math> coincide with the reference trajectory. The exploration is conducted to provide a reference for the research on data packet loss in the networked mobile robot trajectory tracking system.
Knowledge graph-based method for solutions detection and evaluation in an online problem-solving community

&lt;span lang="EN-US"&gt;Online communities are a real medium for human experiences sharing. They contain rich knowledge of lived situations and experiences that can be used to support decision-making process and problem-solving. This work presents an approach for extracting, representing, and evaluating components of problem-solving knowledge shared in online communities. Few studies have tackled the issue of knowledge extraction and its usefulness evaluation in online communities. In this study, we propose a new approach to detect and evaluate best solutions to problems discussed by members of online communities. Our approach is based on knowledge graph technology and graphs theory enabling the representation of knowledge shared by the community and facilitating its reuse. Our process of problem-solving knowledge extraction in online communities (PSKEOC) consists of three phases: problems and solutions detection and classification, knowledge graph constitution and finally best solutions evaluation. The experimental results are compared to the World Health Organization (WHO) model chapter about Infant and young child feeding and show that our approach succeed to extract and reveal important problem-solving knowledge contained in online community’s conversations. Our proposed approach leads to the construction of an experiential knowledge graph as a representation of the constructed knowledge base in the community studied in this paper.&lt;/span&gt;
Threat modeling in application security planning citizen service complaints

The mobile-based service complaint application is one way to implement good governance today. Public facilitated to make complaints without going through a complicated process. Security aspects must be considered to protect user privacy. The security design must be considered so that no one is harmed by the application's users damaged in the application's use. This study used threat modeling during the planning stage of developing a citizen service complaint application to obtain information about vulnerabilities. The researcher uses the threat modeling process that the open web application security project (OWASP) organization has formulated as a framework. The researchers took steps to describe application information, determine and rank threats, countermeasures, and mitigation. In the final stage, the spoofing, tampering, repudiation, information disclosure, denial of service and elevation of privilege (STRIDE) threat modeling methodology is used to analyze and assess mitigation actions against threats in the application. The researcher gets a defense strategy to reduce the danger based on the threat analysis results. Threat modeling in the early phase software development life cycle process is constructive in ensuring that software is developed with adequate security based on threat mitigation from the beginning.
Asynchronous learning: evaluation of virtual classroom metrics according to the perception of university students

The process and means that the university offers its students must be efficient and of quality, even more so considering the influence of the quality of educational services on student satisfaction. The objective of this article is to analyze the perception of asynchronous learning according to the evaluation of the virtual classroom metrics, carried out by business administration students, the results will allow to continue improving the teaching-learning process in the virtual context of education higher. The methodology of this study is qualitative at the descriptive level, the validation of the data by Cronbach's Alpha, gave a reliability value of 0.985. The results show us that 73.8% of the students perceive that the virtual classroom improved communication and helped the exchange of information between students and 71.4% indicated that the use of the virtual classroom made them more efficient and secure asynchronous learning activities. According to these results, there is a higher percentage of students who consider that the use of the virtual classroom positively influences their asynchronous learning, therefore, it is proposed to continue improving the skills in the use and appropriation of Technologies of information and communication technology (ICT) in virtual students.
Improved Glowworm Swarm Optimization Algorithm Based on a Sigmoid Function for the Absolute Value Equation

Solving the absolute value equation (AVE) is a nondifferentiable NP-hard and continuous optimization problem with a wide range of applications. Because its solutions have different forms, it is challenging to design the most efficient algorithm that can solve different AVEs without using overcomplicated technical improvement and problem-dependent objectives. Hence, this paper proposed an improved glowworm swarm optimization (GSO) algorithm with an adaptive step size strategy based on the sigmoid function (SIGGSO) that solves the AVEs. Seven test AVEs, including multisolution and high-dimensional AVEs, are selected for testing and compared with seven metaheuristic algorithms. The experimental results show that the proposed SIGGSO algorithm has higher solution accuracy and stability when seeking multiple solution of AVEs compared to the basic GSO. Moreover, it obtains competitive advantages on multisolution and high-dimensional AVEs compared with other metaheuristic algorithms and provides an effective method for engineering and scientific calculations.
Data Management and Service Mode of Library Based on Data Mining Algorithm

Data management for large-scale data library services with mining procedures improves the availability and readiness of heterogeneous sources. The heterogeneous data sources are assimilated as a single entity through mining procedures to meet the data demands. This article introduces connectivity-persistent data mining method (CDMM) to improve the data handling precision with boosting availability. The proposed method relies on federated learning for identifying the service demands, thereby providing data mining. The learning paradigm accumulates information on shared data library existence over various services. Based on the availability, further mining demands are forwarded to the data management system. If the existence verified by the federated learning is adaptable, then sharing-enabled mining is endorsed for the connected users. The data management then augments several heterogeneous shared libraries to meet the mining requirements. This process is reversible based on the service mode and existence. Therefore, the proposed method improves data availability with less mining and access time and fewer failures.
Graphical-based password for user authentication in internet of things

Internet of Things has become a significant and evolving technology that cannot be avoidable in most of the sectors. However, the Internet of Things security became a concern due to the huge amount of the sensitive data that transferring through IoT resources. Secure the users' authentication process of the IoT is the first line of defense to protect the users' data from violation. Typically, the alphanumeric-based password is the popular method to authenticate the users of the IoT. But it is a vulnerable mechanism that can be violated easily. For that, this research aims to develop a graphical-based password scheme to support the traditional text password in the IoT technology. The proposed scheme is a hybrid (Two-factor) approach, based on two types of Knowledge-based Authentication method (alphanumeric-based password and graphical-based password) naming as IoT-GP. IoT-GP aims to improve the users' authentication security considering the usability enhancement. The results obtain from the conducted field study indicated that IoT-GP significantly improved the security and the usability. The results of the password entropy and password space indicated that IoT-GP obtained a high rate comparing to another schemes, which reflected on the IoT-GP ability to resist the guessing and brute force attacks.