Reviewerly/exHarmony huggingface dataset:
IMPROVING STUDENTS’ MOTIVATION IN STUDYING ENGLISH USING AUDIO-LINGUAL METHOD

This research is based on the students’ motivation in studying English. It intends to prove that students can improve their motivation and it is seen in their achievement in their study. The purpose of this research is to prove that this method is effective in order to improve students’ motivation to learn English. This research is conducted at SMP Negeri 11 Bitung. The subject of this research is a seventh-grade student of SMP Negeri 11 Bitung consisted of 24 students. The design of this research is a pre-experimental design with one group pre-test and post-test design. The data are collected through multiple choice as an instrument and with questionnaires and interviews to support the data. In analyzing the data writers use percentage formula. The result of this research shows that the score of the post-test is higher (72.09%) than the score of the pre-test (44.16%). Based on the fact of the result of this research, it can be concluded that the Audio-lingual method is effective to be applied in class to improve students’ motivation in studying English. Keywords: Teaching; Motivation; Audio-lingual method.
THE CORRELATION BETWEEN STUDENTS’ PERSONALITY AND STUDENTS’ SPEAKING SKILL

The objective of the study is to find out whether any correlation between students’ personality and students’ speaking skill. The research design was correlational research. This research has two variables, the students’ personality was treated as an independent variable (X), and students’ speaking skill was treated as dependent variable (Y). The sampling technique was probability sampling technique with cluster sampling. The sample in this research was forty students of VIII A Class. The instruments to check the students’ personality was Myers Briggs Type Indicator (MBTI) Personality Test while the instrument to measure the students’ speaking skill was a speaking test in oral presentation of a short and simple recount text related to students’ personal experiences. After testing the validity and reliability, the researcher distributed the test. The researcher analyzed the correlation by using Point-Biserial Correlation. The result showed that the rcount was 51200. The ttable with sample 38 (N-2) at the 5% significance level were 2.024. It means that rcount is higher than ttable and Ho is rejected and Ha is accepted. The research found that there was significant correlation between Students’ Personality and Students’ Speaking Skills at second grade students of SMP Negeri 50 Batam.
Модально-интенциональное понимание рефлексии

In modern conditions of increasing complexity and uncertainty, reflection as a necessary condition for self-organization and self-development, is given increased attention in psychological research. Within the framework of the non-classical paradigm, the activity components of reflection are investigated: methods, types, forms, means, mechanisms, processes, stages, properties that are revealed in the existence of reflection in thinking, consciousness, personality, perception and activity. We turned to the definition of the independent existence of reflection, therefore, its ontology. We have identified the modalities of reflection,developed on the basis of the principle of triangulation a triangular network of modalities of reflection, permeated with the intentionality of reflection and regulated on the basis of dynamic balance with the help of reflection itself, its modalities. Such a systemic view of the integrity of reflection corresponds to the principles of the post-nonclassical paradigm and has an applied meaning – modal – intentional analysis of reflection allows to single out not only the level of development of modalities and orientations, but also to purposefully influence development to increase the stability of the personality in situations of problematicity and uncertainty.
Features of lecturing Philosophy to students of natural science and technical specialties

Highlighted and analyzed are features of lecturing Philosophy to students of natural science and technical specialties. The problem of low motivation of students in this category to study philosophical disciplines (Philosophy for Bachelors, methodology of scientific knowledge for undergraduates, History and Philosophy of Science for graduate students) is considered. Some reasons for this phenomenon are noted. Proposals were formulated to increase the interest of science students and technicians in teaching Philosophy. A method developed and tested in pedagogical practice of the authors to demonstrate the productivity of philosophical ideas in scientific and technical research on specific historical material is proposed. This method of teaching solves three main tasks, i.e. to show the audience, how certain philosophical ideas of specific scientists contributed to their scientific creativity, to show, what abstract objects were modeled in philosophical thinking long before such objects (such properties and relationships) came into the sphere of scientific thinking and practical development, and to show, what fundamental cognitive problems were posed by philosophers of the past to subsequent generations and what significance for science was the reflection on these problems. The described method and results obtained can be used by teachers of philosophical disciplines in training of Bachelors, undergraduates and postgraduates of natural science and technical specialties.
Playground for Early Automotive Service Architecture Design and Evaluation.

Context: We consider the structure of service-oriented architectures in vehicular software. Aim: We aim at evaluating the structure and grouping of service architectures. Method: We propose and discuss architectural metrics tailored towards automotive service-oriented architectures. We apply the metrics on an adaptive cruise control case example extracted from the AUTOSAR standard. Results: The application of the proposed metrics to two different service groupings for ACC points clearly to the same service grouping that we consider, after a thorough analysis, to be better with respect to coupling and cohesion attributes. Conclusion: We demonstrate the usefulness of proposed service group metrics in early design phases of the development process and validate the metrics on the case example of an adaptive cruise control function.
On Synthesizing Network Traces – Case Studies in Network Steganalysis and Packet Analysis

There are many situations where synthetic data finds applicability. This paper discusses the use of synthetic data in general and describes the process of synthesizing network traces. It then discusses two case studies where synthesized network traces find use: in network steganalysis where a re-embedding technique uncovers the amount of hidden data in network packets, and in a computer network course teaching network packet analysis.
Deep Reinforcement Learning methods for StarCraft II Learning Environment

Reinforcement Learning (RL) is a Machine Learning framework in which an agent learns to solve a task by trial-and-error interaction with the surrounding environment.The recent adoption of artificial neural networks in this field pushed forward the boundaries of the tasks that Reinforcement Learning algorithms are able to solve, but also introduced great challenges in terms of algorithmic stability and sample efficiency.Game environments are often used as proxies for real environments to test new algorithms, since they provide tasks that are typically challenging for humans and let the RL agents make experience much faster and at a cheaper price than if they were to make it in the real world. In this thesis state-of-the-art Deep Reinforcement Learning methods are presented and applied to solve four mini-games of the StarCraft II learning environment.StarCraft II is a real-time strategy game with large action and state space, which requires learning complex long-term strategies in order to be solved; StarCraft mini-games are auxiliary tasks of increasing difficulty that test an agent's ability to learn different dynamics of the game.A first algorithm, the Advantage Actor-Critic (A2C), is studied in depth in the CartPole environment and in a simple setting of the StarCraft environment, then is trained on four out of seven StarCraft mini-games.A second algorithm, the Importance Weighted Actor-Learner Architecture (IMPALA), is introduced and trained on the same mini-games, resulting approximately 16 times faster than the A2C and achieving far better scores on the two hardest mini-games, lower score on one and equal score on the easiest one.Both agents were trained for 5 runs for each mini-game, making use of 20 CPU cores and a GPU and up to 72 hours of computation time.The best scores from the 5 runs of IMPALA are compared with the results obtained by the DeepMind team author of the paper StarCraft II: A New Challenge for Reinforcement Learning, which report the best scores out of 100 runs that used approximately two orders of magnitude more of training steps than our runs.Our IMPALA agent surpasses the performance of the DeepMind agent in two out of the four mini-games considered and obtains slightly lower scores on the other two.
Digital library vs electronic library (Digital vs Electronic)

The etymology of the terms «digital» and «electronic» and their interpretation in world-known English language dictionaries and professional terminological dictionaries as applied to the library and information sector are analyzed; relevant conclusions by prominent Russian and foreign scholars are cited. The analysis findings on how the adjectives «digital» and «electronic» are used to characterize the libraries in the national professional publications of the recent years are presented. The conclusion is made that both adjectives may be used as synonyms for solving periodical and practical problems of library and information activities.
Using NLP Techniques for Log Analysis to Recommend Activities For Troubleshooting Processes

Continuous Integration is the practice of building and testing software every time a code change is merged into its entire codebase. At the merge, the source code is compiled, dependencies are reso ...
Generative Model based Data Augmentation for Special Person Classification.

Big data leads to a great success of deep learning in computer vision. Unfortunately, big datasets are often not balanced in all dimensions and rare cases are often underrepresented. On-board data collection by a moving vehicle can capture thousands of normal pedestrians and vehicles, but what about special persons like police officers, road workers, and school guards? Not only that those types of classes are hard to get, they are crucial to be recognized and classified as such for the task of automated driving. Future self-driving cars need to interact with their environment and need to also understand and follow the signals and instructions of those special persons. In this paper, we show how to classify special person types using Convolutional Neural Networks. The big data imbalance is handled by data augmentation using Generative Models, showing a clear advantage over classical data augmentation. The classification performance of special persons can be significantly improved using our Generative Model based Data Augmentation.
Quantum algorithmic differentiation

In this work we present an algorithm to perform algorithmic differentiation in the context of quantum computing. We present two versions of the algorithm, one which is fully quantum and one which employees a classical step (hybrid approach). Since the implementation of elementary functions is already possible on quantum computers, the scheme that we propose can be easily applied. Moreover, since some steps (such as the CNOT operator) can (or will be) faster on a quantum computer than on a classical one, our procedure may ultimately emonstrate that quantum algorithmic differentiation has an advantage relative to its classical counterpart.
Investigación en educación matemática, en Ecuador y la región: caso Universidad de Cuenca

This article highlights the importance of research in mathematics education in the region and the possible trends it has for the practice and teaching of experimental sciences; It is approached from different theoretical perspectives and making a brief state of the art on the main contributions of research in mathematics education in the region, highlights the main events carried out among them the IV COBISEMAT as a milestone of presentation of results and proposals. It is expected that the reflections in this document correspond to an innovation front for research in mathematics education and the continuous improvement of teaching practice, as well as the emergence of new frontiers for the continuous professional development that is key in teaching in Experimental sciences.Reflects on the mathematical competences achieved by our students both in Ecuador and in the region, which has convened several academic research events in mathematical education, in which the main problems related to this topic have been addressed, presenting proposals to overcome difficulties and giving greater importance to continuous training.In turn, three aspects of the investigations are mentioned in the bibliographic review: aspects related to the method that was applied for teaching, development of specific material and software applications in teaching-learning of Mathematics. Each of these aspects has been gaining importance from the publication of articles, to specialized books and training and professional development courses for teachers in the respective area.In the different graphs included in the results obtained in this research, the high participation of both local and international institutions can be observed, where the great contribution of the National University of Education and the University of Cuenca stands out, without ignoring the work researchers from foreign universities who make important contributions to the teaching and learning process in the area of ​​mathematics.On the other hand, the valuable participation of teachers and students is mentioned, in whom the concern for the continuous improvement in the process of teaching mathematics can be noted, at the levels of Basic General Education (EGB), Baccalaureate and University in topics such as: Algebra, Calculus, Geometry, among others, taking into account the research trends that were of concrete material and methodological proposals.
Expanding the Application of EFQM model for Institutional Accreditation of Educational Organizations

Relevance of this article topic consists in the necessity to find some alternative to the existing practice of using of the 2015 Standards and Guidelines for Quality Assurance in the EHEA (ESG-15) for accreditation of educational organizations in Kazakhstan.&#x0D; This study purpose is to develop the ways of expanding the European Foundation for Quality Management (EFQM) use for institutional accreditation of universities. Methods: comparativ analysis, formalization method. Results: 1) the genesis, functions and levels of educational organizations’ accreditation in Kazakhstan are described; 2) existence of the link between EFQM and ESG-15 is proved, however, EFQM is more adapted to institutional accreditation of higher education institutions; and 3) it is shown if formalized data are used to prescribe the scores when filling in the RADAR matrix, then filling in the RADAR matrix will be easier, and subjectivity will decrease, and accuracy will increase.
THE APPLICATION OF INQUIRY BASED LEARNING (IBL) STRATEGY IN TEACHING ENGLISH AT ISLAMIC JUNIOR HIGH SCHOOL

This research aims for described: (1) described the teacher using of IBL in teaching English at MTSN 4 Banjarmasin(2) described the students perceptions about the used of IBL strategy in learning English (3) To describe of the use IBL strategy in MTSN 4 Banjarmasin. The subjects of this research are the teachers and students for eighth grade class A and Class C.This type of research is descriptive qualitative research. Collection the data in this study are interviews and documentation. The collected data were analyzed using qualitative descriptive analysis method.The results show that (1) the teacher using IBL strategy in teaching English to the existing Lesson Plan and according with the steps IBL strategy. (2) the students' perceptions of used IBL strategy researchers found that most of the students expressed their liking to use the IBL strategy in learning English bacause the IBL strategy emphasizes students to think critically and logically to find and find their own answers to a problem being asked and a few student’s stated that applicating the IBL strategy was difficult because they had not been able to use this strategy in learning beacuse at first all the information conveyed by the teacher now has to find and find it on their own so it's a little difficult to understand and adjust. (3) IBL is successful because there is an increase in student performance in learning until it reaches very good criteria, indicating an increase in students' intellectual skills and scientific processes. This is likely due to learning using inquiry. By using inquiry each learning sequence has
Augmented Spanish-Persian Neural Machine Translation

Neural Machine Translation (NMT) performs training of a neural network employing an encoder-decoder architecture. However, the quality of the neural-based translations predominantly depends on the availability of a large amount of bilingual training dataset. In this paper, we explore the performance of translations predicted by attention-based NMT systems for Spanish to Persian low-resource language pairs. We analyze the errors of NMT systems that occur in the Persian language and provide an in-depth comparison of the performance of the system based on variations in sentence length and size of the training dataset. We evaluate our translation results using BLEU and human evaluation measures based on the adequacy, fluency, and overall rating.
The Study of Projective Methods in Psychology

This article focuses on the possibilities, conveniences, and peculiarities of using projective methods in the study of personality in psychology.
Entropy Approach to Naphthide Systems

It is shown that along with other geoinformation methods, one can also use the value of the Chenon entropy to assess the maturity of oil fields. It was found that in the series of naphthides natural gas →gas condensate → associated gas → oil, entropy increases, while geoecological syntropy decreases. It is curious that for natural gas H = 0.41, which indicates a high degree of freedom of the gas phase, while for oil the entropy and syntropy are approximately equal, which indicates that when oil is formed in a trap mainly from the mantle of a high-energy liquid, the structural formation organization is in equilibrium.
TransINT: Embedding Implication Rules in Knowledge Graphs with Isomorphic Intersections of Linear Subspaces

Knowledge Graphs (KG), composed of entities and relations, provide a structured representation of knowledge. For easy access to statistical approaches on relational data, multiple methods to embed a KG into f(KG) $\in$ R^d have been introduced. We propose TransINT, a novel and interpretable KG embedding method that isomorphically preserves the implication ordering among relations in the embedding space. Given implication rules, TransINT maps set of entities (tied by a relation) to continuous sets of vectors that are inclusion-ordered isomorphically to relation implications. With a novel parameter sharing scheme, TransINT enables automatic training on missing but implied facts without rule grounding. On a benchmark dataset, we outperform the best existing state-of-the-art rule integration embedding methods with significant margins in link Prediction and triple Classification. The angles between the continuous sets embedded by TransINT provide an interpretable way to mine semantic relatedness and implication rules among relations.
Формування особистості дітей із затримкою психічного розвитку й готовність майбутніх дефектологів до практичної роботи

The conceptual foundations of general pedagogical and methodological training of defectologists for the formation of the personality of children time-lagged psychical development (TLPD) include: the need to educate teachers to treat themselves as a subject of the educational process by means of integrating professional disciplines, filling the students with humanistic meanings of educational practice and engaging them in research activities. The criteria, indicators, types, and psychological and pedagogical conditions for optimizing the didactic process for the formation of the defectologist are determined. Personal and professional competence are considered as components of this readiness.
Creating Reusable Components

So far, we’ve created a product list component for our Rocket Coffee example. While this component works, we want to start employing strategies that ensure reusability and resiliency to change.
Real Time Foreground Segmentation for Video Sequences with Dynamic Background

Modeling background and segmentation of foreground objects are techniques required for various video surveillance and related applications. We propose an intensity and colour based background modeling technique for pixel level foreground segmentation of video sequences with dynamic background. It models the background using initial few frames called training frames and calculates a threshold using statistical analysis on these frames. As the new frames appear in real time, the value of threshold is updated and the background model adapts to the changing background with each incoming frame. Quantitative results suggest that the proposed method can handle the sequences with highly dynamic background.
Pointer Network Based Deep Learning Algorithm for the Maximum Clique Problem

The maximum clique problem (MCP) is a famous NP-hard problem, which is difficult for the exact algorithm to solve when the dimension is large. In this paper, we applied the pointer network based method to solve this problem. First, we illustrated how to train the network with supervised learning strategy to obtain the solution to the maximum clique problem. We then further trained the pointer network with reinforcement learning strategy to obtain the vertices from the graph. For both strategies, backtracking algorithm is used to reselect the vertices. From the experimental results, we can see that both supervised learning and reinforcement learning work well. Promising results can be obtained up to 100 dimensions. This illustrates that the deep neural network based algorithms have great potentials for solving the maximum clique problem effectively and efficiently.
A Modular Architecture for Procedural Generation of Towns, Intersections and Scenarios for Testing Autonomous Vehicles.

Simulation-based testing is critical for ensuring safety of autonomous vehicles. Autonomous vehicles are enabled by deep learning techniques which require a large quantity of data. With simulation testing, we can create rare events for testing and training of autonomous vehicles. Procedural generation of roads and modeling of driving behaviors in an easily extendable architecture ensures that we are able to create rare scenarios at scale with minimal artistic burden. In this paper, we present CruzWay, a system that both supports and creates these scenarios. With CruzWay, we are able to procedurally generate town sized road networks or road intersections. CruzWay supports generation of road meshes as well as navigation meshes from SUMO road network files. CruzWay can generate cars as well as pedestrians run by behavior trees (BTs) in this environment. The self-contained, modular nature of BTs in combination with procedural roads allows us to create a large number of scenarios.