Reviewerly/exHarmony huggingface dataset:
A Comparative Study Between Extrovert and Introvert Student’s Achievement in English Conversation Course

This research aimed to find out whether there is a significant difference in achievement between extrovert and introvert students in speaking ability in the English Conversation subject for 2nd-semester students of the University of Bengkulu. This research was causal-comparative research. The subject of this research was students at the 2nd semester English Department Study Program of University Bengkulu who has taken the English conversation subject in the academic year 2019/2020. The researcher used a Questionnaire and achievement result as the instrument. The result of this research shows that there were no significant differences between introvert and extrovert personalitiesin English Conversation Class achievement. It was concluded that the student's personality does not affect the score achieved in the speaking class.
Organizing Virtual Training - Experience Gained from Covid-19 Pandemic

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THE PREDICTIVE ANALYSIS AND MACHINE LEARNING PLATFORMS

In the last decades technology has evolved a lot, which at the beginning of the 20th century seemed a fiction today is a reality. Cordless phones, the internet, computers, spacecraft, robots are just some of the discoveries made over the past decade that have had a major influence on the world we live in. These discoveries have generally been made to facilitate human activities, such as: real-time communication at very large distances free of charge or with substantial costs, finding / obtaining easily the information you need from absolutely any field, obtaining fast results and with considerable precision. In this article I will present the concept of Artificial Intelligence, what it represents, what are its fields and technologies, where it is used and what facilities it offers us. It is a dynamic field driven by substantial investments in research and rapid growth of knowledge performance. All the efforts made by scientists or independent researchers go towards the desire to truly create an Artificial Intelligence.
An Approach to Growth Delimitation of Straight Line Segment Classifiers Based on a Minimum Bounding Box

Several supervised machine learning algorithms focused on binary classification for solving daily problems can be found in the literature. The straight-line segment classifier stands out for its low complexity and competitiveness, compared to well-knownconventional classifiers. This binary classifier is based on distances between points and two labeled sets of straight-line segments. Its training phase consists of finding the placement of labeled straight-line segment extremities (and consequently, their lengths) which gives the minimum mean square error. However, during the training phase, the straight-line segment lengths can grow significantly, giving a negative impact on the classification rate. Therefore, this paper proposes an approach for adjusting the placements of labeled straight-line segment extremities to build reliable classifiers in a constrained search space (tuned by a scale factor parameter) in order to restrict their lengths. Ten artificial and eight datasets from the UCI Machine Learning Repository were used to prove that our approach shows promising results, compared to other classifiers. We conclude that this classifier can be used in industry for decision-making problems, due to the straightforward interpretation and classification rates.
Sistem Pakar Mendiagnosa Penyakit Gigi dan Mulut Menggunakan Metode Naive Bayes

Technological advances that are growing rapidly are very beneficial for humans in completing their work. The high cost of consulting and checking teeth or mouth makes residents reluctant to go to the doctor. Expert systems are also widely used by humans in the medical field, especially in this research. In order to facilitate the consultation, a system is needed that can predict the dental and oral diseases that he is suffering from without having to go to the doctor to consult the disease. An expert system is a system for human-to-computer knowledge, so that computers can solve problems such as those that are usually tried by experts. Experts defined here are people who have special abilities who can solve problems that ordinary people cannot solve. Research on dental and oral disease expert systems for the manufacture of this expert system used the Naive Bayes method. The decision to diagnose dental and oral disease is carried out through a consultation process between the system and the user, if the answers are entered according to applicable regulations, then the system will share the results of the diagnosis of dental and oral diseases that he suffers from. We recommend that the procedures for collecting data in conducting this research are: interviews and literature review. In making this expert system with the Naive Bayes method, it is hoped that it can help residents to identify diseases, or provide solutions to problems with their teeth and mouth without having to go to the doctor first.
Game testing based on artificial intelligence emotional cognition

Artificial intelligence is developing rapidly, and AI technology is playing an important role in various IT fields. Compared with the traditional game testing work, adding more automated and intelligent systems to the testing will greatly reduce the human and material investment. It is the entry point of this paper to try to apply new artificial intelligence techniques to find new models to help game testing work. Through the analysis and comparison of existing algorithms, theories and models, the feasibility of some current mainstream AI models and algorithms in game testing sessions is inferred and identified, in an attempt to find the model basis and algorithmic foundation for the new approach. Game testing is different from ordinary software testing. In addition to the testing of product functions, more attention should be paid to the entertainment and user experience of the game. The current conventional experience testing uses in-game test play and user surveys. In emotional cognition, artificial intelligence excludes human factors, and through big data and its own learning and computing capabilities, it can obtain more objective test results, while also saving the investment of human and material costs. This paper analyzes three aspects of AI in software testing, game testing and the current state of technology of emotion cognition, and explores the feasibility of game testing based on AI emotion cognition.
Importancia de las tic en los procesos de enseñanza y aprendizaje

This research refers to the importance of ICT in teaching and learning processes, which can be defined as the methods that allow the participation and empowerment of learners, generating new training experiences in which more flexible and flexible scenarios are created. Inclusive, that help them create and strengthen their skills in a participatory way. The study is based on a heuristic method and documentary analysis, this being a fundamental part within the experience developed and from which systematic and scientific investigative elements are extracted. The structure implemented for the documentary review was based on the search for information from scientific journals from articles that were closely related to the subject as well as on the extraction of pertinent and favorable information, in which the use of files was implemented. Bibliographies that allow organizing the information provided by the selected author. Based on the research carried out, it is considered that the integration of ICT in the teaching process contributes to educational quality; since this means breaking with all traditional methods and practices of education. They in turn enhance the way in which so many teachers and students access knowledge. In this sense, information and communication technologies strengthen cognitive abilities in students.
The Internet of Things Role in the Architecture of Digital Enterprises

Companies are changing their approach, culture, activities, and data frameworks to expand digitization endeavors or move closer to advanced initiatives. Advanced change deeply disturbs existing organizations and economies. In this day and age, many new business openings have arisen that improve the capacity of the Internet and related computing innovations: Internet of Things (IoT), Service Computing, Cloud Computing, Artificial Intelligence, Big Data with Analytics, Mobile Systems, Collaboration Networks, and Cyber. Realistic systems. Digitization favors the advancement of IT conditions with many small and somewhat circulating structures, such as the IoT, microservices, or other fine granular components. The design of fine granular structures greatly affects the engineering of advanced departments and elements. The shift from a closed world articulating viewpoint to an open world more adaptable of live programming and framework structures sets the context for adaptive programming and development, which is fundamental to advanced change. In this paper, we present some perspectives on computerized pledge engineering and choices to support respect for object-based programming frameworks and administrations and intelligent advanced managements. This paper describes a new data-shaping-based approach to integrating partial IoT objects, which are unified as a semi-automated cluster in an end-to-end Enterprise Architecture Management (EAM) environment.
Eye Blink Detection – A Survey

We live in world where nothing is impossible. The technology has come handy not only for the physically abled, but also for the disabled. Several products are being launched to help the section of the society which comprises of the physically challenged population. Blink detection based methods are being explored for the paralysed to help them. A survey in these methods is discussed in this paper.
Preservasi arsip digital sebagai upaya penyelamatan informasi di era cloud computing

The rapid development of information technology today has contributed to the birth of archives into new media called digital archives. Digital archives are archives that are stored in digital format to be freely accessible for a long period of time. Archival institutions have an important role in maintaining, storing and preserving archives that have important value in them. To maintain the integrity of the information and ensure that the value of the information contained in the archive can be maintained and used for a long time, the preservation of digital archives needs to be done regularly. The method used in writing this article is the literature study method. This article aims to provide knowledge to readers, namely the wider community and related institutions regarding the necessity of preserving and preserving archives in digital format, namely as an effort and also an alternative in saving important information contained in these archives. Digital preservation, the driving factor must be digital archive preservation as well as some understanding related to digital archive preservation.
Identity Testing Under Label Mismatch.

Testing whether the observed data conforms to a purported model (probability distribution) is a basic and fundamental statistical task, and one that is by now well understood. However, the standard formulation, identity testing, fails to capture many settings of interest; in this work, we focus on one such natural setting, identity testing under promise of permutation. In this setting, the unknown distribution is assumed to be equal to the purported one, up to a relabeling (permutation) of the model: however, due to a systematic error in the reporting of the data, this relabeling may not be the identity. The goal is then to test identity under this assumption: equivalently, whether this systematic labeling error led to a data distribution statistically far from the reference model.
Integrating usability into programming language design (keynote)

Programming language design research has traditionally focused primarily on theoretical properties and performance considerations. But programming languages are interfaces that programmers use to write programs. Recent work has begun to explore how to integrate interdisciplinary methods, including qualitative and quantitative user studies, into the language design process. I’ll describe how we applied such methods in two different designs, Obsidian and Glacier, some of the insights we gained, and discuss the larger impact these emerging methods can have on the field.
Penerapan Model Scramble Dalam Pembelajaran Membaca Bahasa Mandarin Pada Siswa Kelas IV SD Frater Thamrin Makassar

This study is classroom action research (CAR). The aim of this the process of planning conducted, and implementing the scramble model in reading Mandarin at fourth graders student of Frater Thamrin Elementary School of Makassar. The subjects of this study were 20 students of fourth graders at Frater Thamrin Elementary School. Data were collected through observation and Mandarin reading tests. And were with presentation analyzed. The results of this study showed of the students in the first cycle is 67.87% and in the second cycle is 90.87%. so it can be conculed that, the implementation of scramble model improves the reading ability the students of graders fourth student at Frater Thamrin Elementary School of Makassar. Keywords: Application, Scramble, Reading, Mandarin.
IMPROVING THE PEDAGOGICAL PROFILE OF DEVIANT BEHAVIOR OF MINORS THROUGH COLLECTIVE CREATIVE ACTIVITY

Behavior preventive maintenance c deviations and the prevention of offenses is one of priority problems of pedagogical collective of college. In article perfection of organizational-pedagogical activity on behavior preventive maintenance c is considered by deviations of trained college.
Of Artificial Intelligence and Patent Litigation

Just as AI enables computers to perform increasingly complex tasks, it, however, also raises a host of novel issues at its intersection with the law, including issues for litigants in patent infringement suits. The investment in AI technologies likely will lead to an increase in AI-related patent litigation, and the introduction of a nonhuman actor into a traditional patent infringement analysis means that litigants will face unique issues stemming from the dynamic nature of AI technology. From whom and where to sue, to investigating, pleading and (dis)proving infringement, patent owners and accused infringers alike will need to adopt new strategies to confront the challenges posed by the latest frontier in patent litigation. This essay explores these AI-related patent litigation issues in depth. To wit, Parts I–IV seek to raises issues concerning four basic questions pertinent to AI-related patent infringement cases: (I) who exactly is the infringer?, (II) where is the infringer located?, (III) how is infringement pled?, and (IV) how is infringement proven?
The Role of Psychological Training in The Formation of Students' Personality

This article presents the results of a study of the problem of organizing psychological training with students, as well as interpersonal relationships arising in the process of communication.
Towards a part-of-speech tagger for Sranan Tongo

his paper is the continuation of a work submitted to the International Conference Corpus Linguistics 2021 [1]. On that occasion, a rule-based stochastic hybrid part-of-speech tagger (POS) was introduced for Sranan Tongo, a Creole language from South America with around half a million speakers. Since Sranan Tongo does not have a written corpus and text annotation is an expensive and time-consuming task, it was proposed to take a first step in training a POS tagger using only 550 hand-annotated sentences with part of speech tags. In this new contribution, the development of the POS tagger for Sranan Tongo goes a step further with the addition of more training data. For this matter, the tagger was used to annotate 2,406 sentences. The tagging results were hand-corrected and employed to retrain the model. A comparison is shown between the performance of the POS tagger on three texts before and after the inclusion of the new training data.
What Drives Financial Sector Development in Africa? Insights from Machine Learning

This study uses machine learning techniques to identify the key drivers of financial development in Africa. To this end, four regularization techniques— the Standard lasso, Adaptive lasso, the minimum Schwarz Bayesian information criterion lasso, and the Elasticnet are trained based on a dataset containing 86 covariates of financial development for the period 1990 – 2019. The results show that variables such as cell phones, economic globalisation, institutional effectiveness, and literacy are crucial for financial sector development in Africa. Evidence from the Partialing-out lasso instrumental variable regression reveals that while inflation and agricultural sector employment suppress financial sector development, cell phones and institutional effectiveness are remarkable in spurring financial sector development in Africa. Policy recommendations are provided in line with the rise in globalisation, and technological progress in Africa.
Ontology-based n-ball Concept Embeddings Informing Few-shot Image Classification.

We propose a novel framework named ViOCE that integrates ontology-based background knowledge in the form of $n$-ball concept embeddings into a neural network based vision architecture. The approach consists of two components - converting symbolic knowledge of an ontology into continuous space by learning n-ball embeddings that capture properties of subsumption and disjointness, and guiding the training and inference of a vision model using the learnt embeddings. We evaluate ViOCE using the task of few-shot image classification, where it demonstrates superior performance on two standard benchmarks.
Bibliometric detection of research trends in academic mobility in 1990–2019

The phenomenon of academic mobility attracts the increased attention of researchers in recent years due to complicating scientific interactions, escalation of research migration flows and enhanced significance of mobility for scientific progress, dissemination of knowledge, and career development of researchers. The rapid increase in the number of papers on academic mobility is also caused by the emergence of new analytical tools including bibliometric ones. The report demonstrates current types of academic mobility and development of main subject areas detected via citation analysis in the last 30 years.
A Systematic Review of Research Approach in Data Management of Cloud Computing

In the twenty-first century, data management in cloud computing has become the most up-to-date go-to solution for the commercial sector because of the cost-effectiveness, scalability, litheness, and accessibility of data which is mostly based on grid computing. Cloud computing is a modern network computing system that can perform its activities over the internet with the help of its internally connected hardware, application software, and internet infrastructure. Data management includes effective storage, searching, retrieval, query processing, and optimization of data in a cloud network database. Using the capability of adopting cloud infrastructure, application area, and functionalities any business organization can be benefited for the movement and synchronization of customer data over the system effectively and efficiently. It is a system to organize data across the cloud environment, which is highly useful for data storage in terms of disaster revival, backup and data achievement. In this research paper, we are explaining a systematic review on various aspects of the research approach in the data management of cloud computing field with the help of Scopus and the Web of Science database. For the data analysis part, various experiments are performed considering various factors like authorship, publication time, publishing organization, citation number, source, and documents separately for the Web of Science and Scopus database in an international perspective. It is observed that there is a remarkable development of research in data management of cloud computing due to the increasing recognition of this field.
Музыкально-образовательные учреждения Иркутска в период Великой Отечественной войны (1941–1945 гг.)

This article is devoted to the peculiarities of the functioning of the system of musical and educational institutions in Irkutsk during the Great Patriotic War. The analysis of positive and negative trends in this area was created on the basis of archive materials and eyewitness testimonies. The novelty of this article lies in the fact that the events, that are considered in this publication, have not yet become the objects of detail consideration in scientific publications. Meanwhile, the consideration of these events and the introduction of previously unknown facts into scientific use complements the biographies of famous musical figures of Irkutsk (V.F. Sukhinenko, G.E. Lane, L.N. Sementsova, L.P. Kholodilova and so on), as well as those who arrived here in evacuation (L.S. Gurov, V.N. Pronin, A.V. Abramovich). The material of the article can be useful to musicians, historians and people, which have interest to the history of Siberian culture.
Measurement and application of spectrum curve for virtual digital studio simulation scene

Virtual studio is a new type of studio technology. People make movies in the virtual digital studio, and after the post-processing system calculates and synthesizes the background, it can produce the same effect as the traditional studio and the real scene. At present, the digital virtual studio basically adopts low spacing LED large screen, which displays real-time images. The image output of real-time engine is combined with camera tracking, and the reasonable lighting and actor position are matched to generate the final image and video completely in the lens.One of the reasons is that the color, CCT (Correlated Color Temperature), CRI (Color Render Index) and other characteristic parameters of some virtual backgrounds are not consistent with the real scene, that is, the spectrum curve of the image is not consistent with the real scene. This paper studies the spectrum curve of the simulation scene, through the actual measurement of the spectral curve data of different real scenes, and according to the real scene spectrum curve to design the algorithm of digital image and video, establish the relevant spectral characteristic spectrum model, do the corresponding digital image processing for the image and video of the virtual background, adjust the relevant CCT, chromaticity, illuminance, and so on Color rendering index and other parameters make the image displayed on the LED screen closer to the real scene and improve the production level of film and television.
An Optimized Random Forest Classification Method for Processing Imbalanced Data Sets of Alzheimer's Disease

In this work we propose an optimized random forest classification method to solve the problem of imbalanced samples in the Alzheimer's disease (AD). The improved algorithm is based on Synthetic Minority Oversampling Technique (SMOTE) and multi-dimensional Gaussian probability density hypothesis combined with Random Forest (RF). Through this technique, some reasonable pseudo samples can be established for minority classes to balance the number of samples of each type before the RF classifier is used to classify them. This method can effectively solve the problem of sample imbalance, improve the accuracy and recall rate of the minority classes to a certain extent, and avoid overfitting of the classifier. For the purpose of demonstrating the effectiveness of the algorithm proposed in this paper, Support Vector Machine (SVM) algorithm, RF algorithm and the algorithm proposed in this paper are compared. Experimental results have proved that the algorithm proposed in this paper has obvious improvement in accuracy and recall rate compared with the other two algorithms.