Reviewerly/exHarmony huggingface dataset:
Machine Assisted Human Translation: The Dichotomy of Translatability and Untranslatability of Terminology

The accessibility of a bilingual lexicon for a domain of interest is viewed to benefit the translation and translators. Machine Translation via translation software offers time saving equivalences from one language to another. However, the governance of the assistant, in this case the translator, needs to choose the right word, terminology or repository that goes along with the jargon of the source text. The machine human assisted translation ought to balance the human translation attempts while enhancing the translation efficiency. The technologization of translation comes with pitfalls, and criticism for some of the translations produced through its use  highlights two main dichotomous notions in the translation field of study, namely translatability and untranslatability. This paper aims therefore to bring to the fore the limitations and failure of machine human assisted translation in providing equivalences of terminologies from different domains.
PERFORMANCE EVALUATION OF ALGORITHMS FOR BREAST CANCER PREDICTION

Breast cancer is a cause of a large number of deaths of women around the world. The prediction of medical disease can be efficiently done by machine learning techniques, for early prediction of breast cancer, this paper presents machine learning algorithms such as Logistic Regression, Random Forest, Sequential Minimal Optimization, and proposes a Deep learning algorithm. In this paper, we have proposed a deep learning algorithm that obtained optimal results using the hyper-parameter techniques. The dataset applied on experimental tool revealed that our proposed deep neural network (DNN) algorithm is 98% accurate for the prediction of breast cancer dataset, whereas the existing algorithm shows the accuracy up to 96%, 97% and 96% with respect to Logistic Regression (LR), Random Forest (RF) and Sequential Minimal Optimization (SMO), respectively. Therefore, we conclude that the proposed deep neural network (DNN) algorithm leads to the maximum accuracy up to 98% by using the hyper-parameter techniques.
PEMANFAATAN ALAT PERAGA SEBAGAI SUMBER BELAJAR DALAM PELAKSANAAN PEMBELAJARAN MATEMATIKA

This study aims to determine the use of teaching aids as a source of learning mathematics in supporting and increasing students' knowledge. This study uses a descriptive qualitative approach. The subject of this study was a teacher in the field of mathematics at the State Junior High School 2 Bima City. This research is focused on the use of teaching aids by teachers as a source of learning mathematics. To obtain valid data accuracy in the study, the researcher used the following instruments: Interview (Interview) via mobile phone with a class teacher who teaches mathematics subjects which aims to obtain data directly from the research site. The results of the study indicate that the use of teaching aids in schools has not been used optimally, so to further maximize the use of teaching aids, it is expected that the principal's role in monitoring teachers and students to use teaching aids is expected.
ONLINE STUDIES IN HIGHER EDUCATION DURING THE COVID-19 PANDEMIC: STUDENTS' PERSPECTIVE

Globally universities today almost universally employ online (distance, remote) teaching and learning due to COVID-19 coronavirus pandemic.Fully online studies were not universally offered by all higher education institutions before the pandemic both globally and in Lithuania.Therefore, it can be argued that before 2020, studies, which analysed distance teaching and learning were to a major degree intended to discuss experiences and attitudes of technology enthusiasts and visionaries.The aim of this paper is to discuss a swift and forced innovation in terms of universal expansion in online learning models and to reveal the perspective of students towards online studies, because the lessons learnt in 2020 may serve for enhancement of higher education didactics even after the quarantine is over.
Convolutional Neural Network: A Systemic Review and Its Application using Keras

Object detection and object captioning have become important tasks in various applications, especially in the domains of medical science, social networking, and surveillance. They have become part of an emerging field in this decade due to the abundant and diversified data available on the Internet. Convolutional neural network (CNN) is a major breakthrough in computer vision as it has special design architecture to detect complex features in data. It is a special type of neural network that has shown exemplary performance in image classification and segmentation competitions, too. Essentially, it is a deep learning algorithm that inputs an image, assigns weights and biases to various parts of the image, and is thereby able to perform image detection and classification. CNN reduces the image matrix with the use of filters and applies activation functions (ReLU, tanh, Softmax). Further, it applies a combination of convolutional layers along with pooling and strides to reduce image size and flatten the input for a fully connected network. CNN has the capability to capture spatial and temporal dependencies with the application of various filters. Abundant availability of data from various sources and improvement in hardware has accelerated research in CNN. It has been observed that the use of different optimization functions, the activation function, along with different architectural designs has generated a lot of scope in innovation and new ideas in CNN. Basically, we aim to introduce how CNNs work, an application using Python, and a systematic review of CNNs along with its latest research trends.
Detection and Prevention of SQL Injection Attacks at Runtime Using Decision Tree Classification

The use of web applications has become increasingly popular in our routine activities, such as reading the news, paying bills, and shopping on-line. As the availability of these services grows, we are witnessing an increase in the number and sophistication of attacks that target web applications. SQL injection attacks are a serious security threat to web applications in the cyberspace. SQL injection attacks allow attackers to gain unlimited access to a database that includes                 applications and potentially sensitive information. Although researchers and practitioners have proposed different methods to solve the SQL injection problem, current approaches either fail to solve the full scope of the problem or have limitations that prevent their use and adoption. This study is designed to provide a method for detecting and preventing SQL injection attacks at runtime, which can detect and continuously monitor the existing and new attacks. The proposed detection and          prevention method by runtime monitoring and implementation of the decision tree classification on the SQL injection database, blocks existing SQL injection attacks and also detects new attacks using the database supervisor. In the end, the proposed method is compared with other methods for detecting and preventing existing SQL injection attacks, the results showing that the proposed method is significantly successful in detecting and preventing SQL injection attacks. Compared to the two methods explored in this article, the presented method increases the accuracy by 12% for one method and 16% for the other.
THE EFFECT OF STORY MAPS STRATEGY ON READING COMPREHENSION NARRATIVE TEXT IN ENGLISH DEPARTMENT STUDENTS

The research was conducted in seventh semester students of Prose Course English Department of STKIP PGRI Bandar Lampung. The objective of this research is to know whether there is the influence of story maps strategy towards students’ reading comprehension in narrative text. The samples of this research was two classes. The first class was VII (A) as the experimental class and the second class was VII (B) as the control class. The experimental class consisted of 32 students and the control class consisted of 32 students. In this research, the researcher used experimental method. In collecting the data, the researcher used reading test with the form of multiple choice and consist of four options. Based on the data analysis, the value of t-test is 6,41 and t-table is 2,66. It means that t-test is higher than t-table (6,41>2,66) in significant level 0,05. So, the null hypothesis (Ho) is rejected and hypothesis alternative (HI) is accepted. Finally, the researcher concluded that there is a significant influence of teaching reading comprehension in narrative text through Story Maps Strategy in English department students of STKIP PGRI Bandar Lampung in academic year 2018/2019.
Sistem Pakar Diagnosa Vitiligo Menerapkan Metode Fuzzy Sugeno

Vitiligo is a disease that causes skin color to fade. Besides being able to attack any skin area on the body, this fading can also occur on the inside of the mouth, eyes, and hair. For the general public, to find out a diagnosis of vitiligo, you must consult an expert who is an expert in the diagnosis of this disease. However, the problem that often occurs is the inefficient use of time for experts to serve many patients for consultations so that it is difficult to handle patient problems in an efficient time. So that we need an application that can help, namely an expert system. An expert system is a computer program that contains knowledge from one or more human experts regarding a specific field. Expert systems can solve problems in accordance with the knowledge of an expert that is entered into the system. Expert systems in general are often used to carry out activities to diagnose a problem that only an expert can solve. Fuzzy Sugeno is a method that can be used to obtain the results of a diagnosis on an expert system. The stages of the Sugeno fuzzy method are the formation of fuzzy sets applying the implication function (rules). Rule composition, obtained from a data set of relationships between rules. Defuzzification, the input of defuzzification is a constant or linear equation.
Article 26—Application in Time

Abstract This chapter highlights the time of application of Directive 2019/790, a copyright directive in the Digital Single Market in Europe, which is included in Chapter 26. It emphasizes that Directive 2019/790 will apply in respect of all works and other subject matter that are protected by national law in the field of copyright. The directive will be implemented on or after 7 June 2021. The chapter points out that Directive 2019/790 will apply without prejudice to any acts concluded and rights acquired before 7 June 2021. It also cites the legislation on the negotiation mechanism, works of visual art in the public domain, and information about collective licensing with an extended effect.
МАССОВОЕ СОЗНАНИЕ И ЕГО МИФОЛОГИЗАЦИЯ В СОВРЕМЕННОМ МИРЕ

In the modern world, there is a gradual mythologization of the consciousness of the masses. The mythologization of mass consciousness occurs in all countries of the world. With the development of education in the world, there is a process of rejection of scientific knowledge and a gradual mythologization of scientific achievements. For example, during the global Covid-19 pandemic, a movement of anti-vaccinators who believe in chipping the population appeared in the post-Soviet countries. What we are trying to explain from the point of view of the mythologization of the consciousness of the masses. The extensive development of different types of media from traditional newspapers to bloggers leads to the availability of information in the modern world. Including the dissemination of false information that carries disinformation and undermines the credibility of the achievements of modern science. It is in this context that it is important to define the principle of the mythologization of mass consciousness. This work reveals the very concept of the mythologization of mass consciousness, and also tries to determine the causes. The principle of mythologizing consciousness was proper for antique societies, now the faith in myths and legends may be considered as a scientific case.
THE ROLE OF WORKS OF ORAL FOLK ART IN ENHANCING THE SPIRITUALITY OF YOUTH

This article expresses the influence of folklore on the ordering of spiritual values, which reveals the identity of people, the disclosure of national character traits and the means of education.Although folklore does not have a deep scientific basis, it reflects humanity, knowledge, hard work, justice, respect for adults, honesty, hospitality and other moral qualities that are the basis of spiritual values. A nation that respects its own values and oral art will never be humiliated.
FACE RECOGNITION ATTENDANCE SYSTEM

Uniqueness or individuality of an individual face is the representation of one's identity.In this project face of an individual is used for the purpose of attendance making automatically.Attendance of the student is very important for every college, universities and school.Conventional methodology for taking attendance is by calling the name or roll number of the student and the attendance is recorded.Time consumption for this purpose is an important point of concern.Assume that the duration for one subject is around 60 minutes or 1 hour & to record attendance takes 5 to 10 minutes.For every tutor this is consumption of time.To stay away from these losses, an automatic process is used in this project which is based on image processing.In this project face detection and face recognition is used.Face detection is used to locate the position of face region and face recognition is used for marking the understudy's attendance.The database of all the students in the class is stored and when the face of the individual student matches with one of the faces stored in the database then the attendance is recorded.
INFECTED INFLATION AND SYMPTOMS WITHOUT THE IMPACT OF COVID 19 WITH AHP CALCULATION METHOD

Indonesia is one of the corona virus-positive countries (Covid19).ThefirstcasethatoccurredinthecountryhappenedtotworesidentsofDepok,WestJava.ThiswasannouncedbyPresidentJokoWidododirectlyatthePresidentialPalace.SinceitbrokeoutinDecember2019untilnow,anewtype of coronavirus has made people worry about coughing orsneezing. Imagine, the virus that causes Covid-19 is transmitted throughdroplets or droplets of particles from coughing, sneezing, or when talking.The discussion in this paper is to design a piece of information that willfacilitate the decision of the Analytical Hierarchy Process (AHP) to see thedevelopment of infected viruses and OTG (People without Symptoms).This study uses the Analytical Hierarchy Process (AHP) method is amethod of decision making by making pairwise comparisons between theselection criteria and also pairwise comparisons between available choices.So that the data displayed will be following the information will give themedical staff information according to the needs, interactive and moreefficient.
An automatic method for prostate segmentation on 3D MRI scans using local phylogenetic indexes and XGBoost

The detection, diagnosis, and treatment of prostate cancer depends on the correct determination of the prostate anatomy. In current practice, the prostate segmentation is performed manually by a radiologist, which is extremely time-consuming that demands experience and concentration. Therefore, this paper proposes an automatic method for prostate segmentation on 3D magnetic resonance imaging scans using a superpixel technique, phylogenetic indexes, and an optimized XGBoost algorithm. The proposed method has been evaluated on the Prostate 3T and PROMISE12 databases presenting a dice similarity coefﬁcient of 84.48% and a volumetric similarity of 95.91%, demonstrating the high-performance potential of the proposed method.
Книжная культура: диалектика «статичного» и «динамичного»

The integrity of book culture is examined from the viewpoint of dialectical unity of the dynamic and the static: what ensures reader interest and long life of printed books in the historical time and what makes the mobile superstructure adapting the book culture to the changing environment and expanding its life. The author emphasizes that the book culture subject-object and subject categories, i.e. the humans and books/documents, take up the more stable and static positions. The book culture stability is manifested through its autonomy and persistence within the changing historical and cultural landscape (homeostasis, autopoiesis, and self-referencing). The dynamics is manifested in the processes of organization, disorganization and self-reorganization. The author substantiates that the World Wide Web enforces the fundamentals of the printed books and printed word; the functionality of libraries is to change in the future as they will be functioning both offline and online. These factors will influence the book culture and give rise to its new forms and contents. The technological era challenges the human ecosystem. The deep processes have been changing the book culture. However, the inevitability of the process must not be overestimated. Being analyzed and interpreted, the process can be managed.
Asymptotic Normality and Rates of Convergence for Random Forests via Generalized U-statistics

Random forests are among the most popular off-the-shelf supervised learning algorithms. Despite their well-documented empirical success, however, until recently, few theoretical results were available to describe their performance and behavior. In this work we push beyond recent work on consistency and asymptotic normality by establishing rates of convergence for random forests and other supervised learning ensembles. We develop the notion of generalized U-statistics and show that within this framework, random forest predictions can remain asymptotically normal for larger subsample sizes and under weaker conditions than previously established. Moreover, we provide Berry-Esseen bounds in order to quantify the rate at which this convergence occurs, making explicit the roles of the subsample size and the number of trees in determining the distribution of random forest predictions. When these generalized estimators are reduced to their classical U-statistic form, the rates we establish are faster than any available in the existing literature. We also provide a consistency estimate of the variance of random forest and illustrate that quantifying the uncertainty of random forest is typically more expensive than obtaining the random forest itself.
LibGuides: BUS 301 - Business in Action: Newspapers

The guide to to assist researchers in finding relevant resources for their experiential learning projects.
The Issue of E.V.M.

Electronic Voting Machine is a combination of mechanical, electromechanical, or electronic equipment that is used to define ballots; to cast and count votes; to report or display election results. The different types of voting machines allow for various kinds of interaction, such as using a touch screen technology, using a dial wheel, touching a paper panel, or pressing a button on an LCD screen. The paper presents an overview of shift from traditional system of voting to electronic voting machine. It discusses about different types of voting machines, their potentials effects, and the issues related to EVMs. Electronic Voting Machine (EVM) possess all the characteristics of voting by ballot papers, while making polling a lot more expedient. Being fast and absolutely definitive, the EVM saves considerable time, money and manpower. And, of course, helps maintain total voting secrecy without the use of ballot papers. The study will aim to answer the question: Whether EVMs meet the legal requirements set out in the Information Technology Act, 2000. It will also deal with that, whether Election Commission of India (ECI) has become Partisan in its defense of EVMs. In spite of the machines simplicity and minimal software trusted computing base, they are prone to serious attacks that can alter results of elections and violate the secrecy of the ballot. Furthermore, case laws related to it and need for further development. The challenges faced are: Accessibility, Age and Technical Experience, biasness, accountability and verifiability, loss of transparency.
International experience in modeling and measuring universal competencies in higher education

The article raises the problem of assessing and forming universal competencies in higher education - complex constructs that are associated with a person's success in various professional and living areas. Based on the analysis of the experience that the world community has accumulated in the field of assessing universal competencies, we considered Evidence-Centered Design (a method of evidence-based argumentation) as a promising approach to assessing them. The problem of assessing universal competencies is directly related to the issues of their formation and implementation in the educational process. The example of critical thinking demonstrates the difficulties and challenges that the educational community faces along the way. The article concludes with a section on the opportunities offered to universities by assessing universal competencies.
Сучасні тренди використання інноваційних технологій в управлінні закладом дошкільної освіти

The abstract reveals the problem of using innovative technologies in present-day conditions of preschool education. Forms of organization of innovative activity are offered. The author reveals ways to increase the efficiency of preschool education management which should be implemented in the practice of innovations. Innovations in management allow to raise to a higher level of organization, planning and other management functions of the director, to develop the self-governing bodies of the preschool institution, to improve the organizational structure and more. All this creates favorable conditions for professional and creative growth of teachers, affects the quality of the educational process and increases the competitiveness of the preschool institution. Important in the introduction of innovative management technologies are management mechanisms that cover various aspects of management.
Implementasi Algoritme Decision Tree (C4.5) dengan Optimize Weights (PSO) untuk Memprediksi Kelulusan Mahasiswa Tepat Waktu

The implementation of quality education is a requirement for higher education institutions to produce quality students. Government regulations regarding the limitation of the maximum length of lectures that students can take are intended so that the institutions involved in the educational process have careful planning in the learning process. Good planning is expected to be able to accreditation value of higher education institutions and deliver student studies so that they can graduate on time. In order to achieve these results, aspects that are so influential on the optimal value of accreditation results are needed, one of which is that students graduate on time and play an importan role in determining the results of accreditation. Decision tree itself is a very simple algorithm and easy understanding, decision tree algorithm alonei is enough to produc good and optimal accuracy values. Therefore, the optimization method particle swarm optimization (PSO) with its advantages can increase the level of accuracy by removing irrelevant features. The results of the study using a student dataset of class 2016 - 2017 explain that the optimization ofparticle swarm optimization (PSO) can produce 92.36% accuracy and increase the accuracy of 01.05%, with the decision tre method C4.5 with an accuracy rate of 91.31%. Furthermore, the T-test testing process is carried out on the decision tree algorithm C4.5 and the decison tree algorithm C4.5 optimization of particle swarm optimization (PSO) with the final result alpha = 0.635 where the results are less significant, it is said to be significant if the test results are below alpha = 0.050.