Reviewerly/exHarmony huggingface dataset:
Investigating Learning Methods and Environment Representation in the Construction of Player Agents: Application on FIFA Game

The objective behind this study is to investigate Machine Learning (ML) techniques combined with methods from Computer Vision (CV) for state representation by images, to produce agents capable of solving problems, in real time, in environments with complex properties. Such difficulties require agents to be highly efficient in their learning (and, consequently, decision-making) and environmental perception processes, without which they will not be successful. The digital game FIFA - soccer simulator - is used as a case study because it represents a realistic and challenging environment. The ML techniques are investigated in the context of the Deep Learning (DL) approach provided by Convolutional Neural Networks (CNNs), being: imitation learning, used here with the purpose of endowing the agent with the ability to solve problems in a way closer to human; by deep reinforcement, in which the agent is trained in an attempt to autonomously abstract an optimal decision-making policy. Regarding the environmental perception, the following state representations approaches are investigated in this study: raw images - with and without color information - and through Object Detection Techniques (ODT). In order to further improve the performance of the agents produced, genetic algorithm techniques are explored to automatically define a CNN architecture to be used as the player agents decision-making module. In addition to corroborating the excellent results that DL combined with CV has been producing in the context of ML (particularly in games), the present work shows the great potential of the application of ODT in the process of enhancing the environmental perception, which counts as a relevant counterpart to the fact that ODT demands computational procedures with a higher cost in relation to the representations based on raw images.
ASSESSING THE INCONSISTENCY IN ONLINE NEWS

The information on the web can be inconsistent across different web pages.News articles are examples of information on the web that are inconsistent and this paper proposes an approach that enables the visual analysis of inconsistencies in online news.It presents an approach which will enable the visual identification of inconsistencies associated to a news headline of interest.It uses a visual assessment approach that relies on two techniques, namely Fault Tolerance and Co-occurrence techniques.The Fault Tolerance technique is used in extracting related news headlines on the internet while the Co-occurrence technique is used for grouping and scaling related news headlines on the web.Also the bar-chart is used to plot charts that summaries the inconsistencies from which news readers can visually assess related news headlines of particular context.
DEVELOPMENT OF A FOCUSED WEB PAGE CRAWLER BASED ON GENRE AND CONTENT

Focused crawlers are generally used to crawl pages that satisfy some particular property and that are relevant to a specific topic of interest and are important for a wide variety of applications.For particular situations, a focused crawling approach was proposed and developed where the topic of interest can be expressed by terms that describe the genre and content of the desired web pages.In order to improve the efficiency and effectiveness of such an original genre-aware approach to focused crawling, the following improvements have been proposed, developed and validated: relevant page location policy based on Link Context, semi-automatic seed page determination, automatic similarity threshold definition and automatic refinement of genre and content term sets.In this context, this work proposes to develop a complete and functional version of a crawler, called Yucca, following the original genre-aware approach to focused crawling and the improvements already developed and validated, so that it can be used by different users in a simple and robust way.To validate Yucca, experiments were performed involving the crawling of web pages referring to three distinct and current topics of interest.In general, Yucca presented itself as an effective focused crawler, since the levels of precision achieved by the crawling processes carried out were quite satisfactory, reaching more than 80% on average when considering 10 pages returned as relevant by the crawler.
Selection of the Best Biometric Systems Taking into Account the Characteristics

An analysis of very well-known biometric systems is made. The choice of the best biometric system according to the set of characteristics known by some methods of multicriteria optimization and the method of analysis of hierarchies is carried out. The findings show that the best type of biometric system is a technology based on the analysis of the iris.
Creative thinking: a holistic study in education

The objective was to review various scientific articles to identify the relevant concepts, characteristics, and theories that underpin the construction of creative thinking and analyze the proposals that contribute to its improvement. For this, databases such as Ebsco, Scopus, Eric were reviewed, from which 50 relevant articles were selected. The methodology used is descriptive-analytical, which allowed us to reach the following results: there are a diversity of definitions, characteristics, and strategies that promote the improvement of creative thinking, and its development in students is crucial for good growth in society. In conclusion, the educational system must promote the knowledge and development of creative thinking in the teaching-learning process since this skill is required to access a job.
Comparative analysis of b-learning and e-learning in ICT competencies for teaching in higher education

Permanent technological training favors teaching and its importance in learning modalities. Therefore, it is proposed to analyze the academic performance of permanent training in ICT skills for teachers and compare the performance of digital learning modalities. The research was quantitative, descriptive, cross-sectional in a higher education institution with continuous training processes. Two editions of courses were considered in which online TIC tools were taught for teachers. The activity records made it possible to analyze the academic performance of permanent training in TIC skills for teachers and compare the learning modalities' performance through them. In this sense, the results of the teaching-learning process in TIC skills reflect that the evaluations of the first b-learning modality course with 44 participants present a 98% approval rate compared to the second e-learning modality course with 37 participants with an 86% pass rate. The results determine that the management of b-learning promotes a better academic performance of the participants and that the continuous evaluation processes benefit the development of creditable products.
Mirror neurons in e-learning in high school students

Mirror neurons are recognized for their relationship with empathy and that they contribute to virtual education. The objective of the article was to analyze the influence of mirror neurons on virtual learning in upper elementary students of the educational unit "José María Santana Salazar" of the Manta centon. The methodology used was quantitative-qualitative, descriptive, primary, and secondary sources of information were collected, through the deductive and analytical methods. The survey technique applied to an intentional sample of teachers, a student observation card, was used. An interview was applied with an expert in clinical psychology. The results indicated in 59% that mirror neurons promote learning through imitation and in 34% that it improves social relationships through empathy. It is concluded that teachers cannot take advantage of them because they have limited resources to apply them, making it very difficult to motivate and strengthen students' empathy in the virtual context.
Creative teaching in virtual environments for the development of emotional competencies

The present research conducted at the upper basic level of the "José María Santana Salazar" Educational Unit of the Manta canton, had the objective of analyzing the influence of creative teaching in virtual environments, to develop emotional competencies in students. A qualitative, exploratory approach was used to collect the points of view of teachers and experts on the subject through the interview instrument, applied to a purposive sample. The results showed that teachers include creative strategies through motivational messages, positive attitudes, empathy, curious data, visual dynamics, among others, to create favorable environments for teaching and learning. It is concluded that virtual environments have not allowed the satisfactory development of emotional competencies in students, presenting inconveniences in the adaptation of the current study modality. For this reason, teachers have had to be trained in an accelerated way to cope with the demands of virtual education, applying creative and innovative strategies.
An Efficient Feature Engineering Method for Fraud Detection in E-commerce

With the speedy expansion of e-commerce, credit cards have also become rising vogue, and that makes online transactions sleek and suitable. In conjunction with rising in online transactions, credit card fraud also increasing, which contributes to losses incurred yearly. As a result, many deep and machine learning methods are produced to fix such as problems like Logistic Regression (LR), Support Vector Machine (SVM), Naïve Bayes (NB), and other algorithms, but the current models are still not accurate. Moreover, sometimes the used datasets still need further preprocessing, since that has been approved the important role of feature engineering in performance optimization. In this paper, effective feature engineering and feature selection methods have been produced for preprocessing the raw dataset, which was transformed with Exploratory Data Analysis (EDA). Then LightGBM, XGboost, and Random forest classifiers are used for fraud detection. Experiments show that the LightGBM and XGboost models achieved the best accuracy with 100% after applying further preprocessing on the dataset. Index Terms— Fraud detection, Feature engineering, Machine Learning, LightGBM, Random forest, XGboost.
A Framework to Run C/C++ Application on Web-based OS

With the development of IoT (Internet of things) and its related technologies, Web-based operating system (OS) becomes popular. However, current mobile applications are mainly developed for Android and iOS, which cannot be directly deployed on Web-based OS because of the platform heterogeneity. Therefore, Web-based OS suffers from a poor application ecosystem. The research found that many mobile applications are developed in a mode, in which the core functionalities are written in C/C++ and encapsulated into an SDK, while the presentation layer is platform-dependent. To enrich the application ecosystem of Web-based OS, we propose a framework to transplant existing C/C++ applications to Web-based OS. Aiming to implement the framework, we firstly construct Web APIs to build interaction between web applications and web engine; secondly, we deliver the event information from web applications to the SDK; finally, we implement the graphic rendering interface required by the SDK and display drawn images. To draw and display graphics, we propose Skia-based method and Canvas-based method: The Skia-based method draws graphics by Skia and displays them through HTML &#x003C;img&#x003E; element. The Canvas-based method uses the CanvasRenderingContext2D interface to draw graphics and display them directly through the HTML &#x003C;canvas&#x003E; element. To validate the effectiveness of the proposed framework, we conduct experiments on a commercial IM (input method) application by transplanting it to a Web-based OS (Firefox OS). Experimental results show that the IM application could run smoothly on the Web-based OS, and compared with the Skia-based method, the Canvas-based method significantly reduces (i) interaction time between the application and the user by 84.71&#x0025;, (ii) CPU usage time by 74.35&#x0025; and (iii) memory overhead, revealing that it is more suitable for the framework.
The Ability of the 4th Semester Students of English Education Study Program of Muhammadiyah University Palu in Writing Paragraph

The purpose of this research is to find out the ability of the 4th semester students of English department in writing descriptive paragraph. The subject of the research is the 4th semester students in academic year of 2017 consisting of seven students. This research used qualitative descriptive method.Written test and interview as techniques of data collection, and technique of data analysis were used percentage that was described qualitatively. Research finding shows that the mean score of the seven students is 77. Three of the seven students got 81, 82, 89.They belonged to excellent and good categories, and four students got 71, 72,74 and 66, they belonged to enough and poor categories. The result of interview indicated the seven students have different difficulties or problems; most the students faced problems on content and language uses.
TECHNOLOGIES OF TEACHING TO IDENTIFY FAKES IN THE “INFOMEDIA LITERACY” COURSE

Today, in the conditions of increased information pressure, the need for critical perception of media messages has become acute. Media literacy skills should become the basic competencies of every person. In this context, the significance of the course “Information Media Literacy” is growing as its aim is to develop in students ability to thoughtfully perceive media materials and critically interpret them, including identification of fakes and manipulations. The Russian-Ukrainian war provoked a quantitative increase in the deliberate spread of misinformation, which includes fakes. Their spread is extremely dangerous. False messages can be used to manipulate peopleʼs minds and cause interpersonal and global conflicts. The purpose of the publication is to substantiate the need and provide technologies for the development of skills to identify fake messages while studying the course “Information Media Literacy”. To achieve this goal, the following methods have been used: descriptive (classification, document analysis), media linguistic (rhetorical, cognitive, discursive analysis of media texts), content monitoring, content analysis.&#x0D; The research findings revealed that awareness of the need to include media literacy in the educational process dates back to the 60s of the twentieth century, and its main objectives were formulated by UNESCO in 2007. In 2010, the Concept of Implementing Media Education in the educational process was adopted in Ukraine. The discipline “Information Media Literacy” became the basis for its implementation in TNPU, which involves the development of studentsʼ relevant media and cultural competencies. The formation of critical immunity of the media audience is based on the algorithm of identification of fake information. The study of fake markers was the subject of practical educational technologies tested in the educational process of TNPU. The proposed tasks are designed to develop skills for recognizing signs of fakeness in the information space. Each task provides methodical tools: the purpose, program competencies and learning outcomes, the order of performing and options for justification of answers. In particular, the tasks involved developing skills to work with sources of information; ask questions, justify oneʼs own point of view; evaluate and interpret events; identify fakes, propaganda and manipulation tools in texts and headlines. Tasks are aimed at achieving the following learning outcomes by students: the ability to demonstrate the appropriate level of critical thinking and understanding of the level of information impact on the individual; ability to interpret current phenomena (fake news, information attacks, manipulation, etc.) of the information space for a wide audience, to increase the level of its media literacy.
Safe Social Distance Monitoring and Face Mask Detection for Controlling COVID-19 Spread

Coronavirus is an infectious virus that is mainly transmitted through droplets generated when an infected person coughs or sneezes. Getting in contact with these droplets may cause infection. To date, the virus has caused mayhem across the globe, and in its wake has left destruction, both economic and social. Along with deaths, the pandemic has hindered the GDP growth of many countries, with several countries witnessing record contraction in their GDPs and many people losing their jobs. This has left governments across the globe with no other choice but to gradually lift the lockdown while taking precautions and adhering to the guidelines provided by the WHO. These guidelines include maintaining social distancing, i.e. there should be a distance of at least 1 meter between two persons and wearing face masks as the infected individual might spread the droplets containing the virus in the air while coughing or sneezing. If people adhere to these guidelines, the spread of the virus can be curbed. This chapter proposes deep learning-based methods to monitor social distancing and to detect whether people in the live feed are wearing masks or not. It is an automated inspection model which will help in reducing the manpower for controlling and monitoring public gatherings. To detect humans and in order to measure the distance between them, Yolo v3 is used as it can detect the objects in real time with high average precision, and to detect the facemask, MobileNet v2 is used, which is a CNN architecture with low complexity cost and small model size of the network.
Construction of Credit Risk Assessment Index System of SMEs based on Blockchain-driven Supply Chain Finance

blockchain-driven supply chain finance (BC-driven SCF) can effectively integrate the credit resources of upstream and downstream enterprises and enhance the financing capacity of all enterprises in the chain, taking the core enterprise as the center and the blockchain technology as the platform, which plays an important role in the upgrading of the industrial chain. In order to accurately and objectively evaluate the SMEs’ credit risk based on BC-driven SCF, we need to establish a scientific and complete credit risk assessment index system, formulate strict design ideas and select appropriate design method. By analyzing the formation mechanism of blockchain-driven supply chain finance model, and using relevant theoretical and empirical methods, this paper puts forward a credit risk assessment index system suitable for the supply chain financial environment. Finally, 27 three-level indicators, 13 two-level indicators and 5 primary indicators were established in index system. This conclusion provides a reference for the application of blockchain in supply chain to reduce the credit risk. At the same time, the selected indicators are more comprehensive, which provides a strong basis for the subsequent construction of credit risk assessment model
Book review of three excellent books on R language for statistics

"Book review of three excellent books on R language for statistics." Journal of Statistical Computation and Simulation, 92(12), pp. 2642–2643
Analisis Keterampilan Smash

The purpose of this study was to determine the level of high school skills in UKM students of Muhammadiyah University of Surakarta. The survey method and data collection techniques for tests and measurements Nurhasan. The results of this study are based on the results of the Descriptive Analysis of Data frequency and are stated in the form of Percentages and the results of the Descriptive Analysis of Smash Open 1 sample are in the interval 17.16 or fall into the very good category with a score of 2.50%, 11 samples are in the interval 13.05-17.16 or good category with a score of 27.50%, 15 samples are in the interval 8.93-13.04 or moderate category with a score of 37.50%, 12 samples are in the interval 4.80-8.92 or in the bad category with a score of 30.00%, and 1 sample is included in the interval &lt; 4.80 or in the very bad category with a score of 3.00%. And for Smah Semi, 4 samples are in the interval 18.38 or in the very good category with a score of 10.00%, 9 samples are in the interval 13.70-18.38 or good category with a score of 22.50%, 10 samples were in the interval 9.10-13.69 or the moderate category with a score of 25.00%, 15 samples were in the interval 4.32-9.00 or the poor category with the acquisition of scores by 37.50%, and 2 samples fall into the interval &lt; 4.32 or very bad category with a score of 5.00%.
Hostile witness

Witness is someone who has relevant information about a crime. They have to make an oath or solemnly state the truth in court. Witnesses are an important constituent of the administration in justice. Witnesses perform a duty of assisting the court to discover the truth. Hostile witnesses are unfavourable witnesses who can cause unjust acquittal of the guilty. Witnesses turning hostile is a major issue in our country and it can be due to the reason which have been mentioned in this paper. The laws mentioned relating to witness security are not enough due to which they turn hostile. The paper presents the problems faced by the witnesses. The author suggest that it is high time to make laws which are more secure for the witnesses which also improves the judiciary system.
Generalised Zero-shot Learning for Entailment-based Text Classification with External Knowledge

Text classification techniques have been substantially important to many smart computing applications, e.g. topic extraction and event detection. However, classification is always challenging when only insufficient amount of labelled data for model training is available. To mitigate this issue, zero-shot learning (ZSL) has been introduced for models to recognise new classes that have not been observed during the training stage. We propose an entailment-based zero-shot text classification model, named as S-BERT-CAM, to better capture the relationship between the premise and hypothesis in the BERT embedding space. Two widely used textual datasets are utilised to conduct the experiments. We fine-tune our model using 50% of the labels for each dataset and evaluate it on the label space containing all labels (including both seen and unseen labels). The experimental results demonstrate that our model is more robust to the generalised ZSL and significantly improves the overall performance against baselines.
Exploring the Effect of Job Satisfaction on Commitment of School Teachers

An organization's one of the concern mainly rely on the performance of the employees.They explore the factors that aid to upgrade the efficiency of the employees in the firm.The Commitment and Job satisfaction of the employees are the factors that have a high contribution in increasing the performance of the employees.This research paper explores the association among school teachers' Job satisfaction and Commitment.For this study, the schools from Northern region of Tamil Nadu is chosen and the samples are taken from the teachers who teaches for grade 11 and grade 12 and the size is 150.The outline of the study indicates that a positive and significant association among Teachers ' Job Satisfaction and Commitment.