Reviewerly/exHarmony huggingface dataset:
WAYS OF FORMING INFORMATION AND DIGITAL COMPETENCE OF EDUCATIONAL PROCESS PARTICIPANTS

In the article the meaning a competence approach to learning in modern education and relation of key and object competencies are analyzed. The attention is concentrated on the importance of forming information and digital competence for all educational process participants, which contributes to improving the level of performance of their professional and social activities. It is established that an effective and dynamic informational and educational environment is necessary for the implementation of different forms of study in a modern educational institution. Based on this, a problem of providing educational and scientific space with the necessary resources is actual and important. The possibilities of organizing informational and educational environment of an educational institution based on cloud-oriented technologies are considered. The examples of their application in different directions of educational institution activity are given. One way of solving problem of forming the informational and educational space is proposed, that is developing digital education resources and their implementation in educational process. The experience of the Faculty of Mathematics and Computer Science of Vasyl Stefanyk Precarpathian National University is described in the example of implementation of G Suite for Education that is a suite of tools designed to empower educators and students. G Suite for Education provides wide opportunities for innovation in organizing the educational process, in general, and in creating digital educational resources, in particular. It is noted that the involvement in the process of development and testing of digital educational resources during the study gives students an opportunity to feel themselves as active players of educational activity and to be responsible for the result of this activity. The digital educational resources developed by teachers and students during the study (implementation of project, credit, exam, course and master's papers), as well as during scientific and pedagogical researches, which have been approbated and implemented in the educational process, are considered. The prospects of further experimental work are determined.
Nonparametric background modelling and segmentation to detect micro air vehicles using RGB-D sensor

A novel approach to detect micro air vehicles in GPS-denied environments using an external RGB-D sensor is presented. The nonparametric background subtraction technique incorporating several innovative mechanisms allows the detection of high-speed moving micro air vehicles by combining colour and depth information. The proposed method stores several colour and depth images as models and then compares each pixel from a frame with the stored models to classify the pixel as background or foreground. To adapt to scene changes, once a pixel is classified as background, the system updates the model by finding and substituting the closest pixel to the camera with the current pixel. The background model update presented uses different criteria from existing methods. Additionally, a blind update model is added to adapt to background sudden changes. The proposed architecture is compared with existing techniques using two different micro air vehicles and publicly available datasets. Results showing some improvements over existing methods are discussed.
A Novel Graph-modiﬁcation Technique for User Privacy-preserving on Social Networks

The growing popularity of social networks and the increasing need for publishing related data mean that protection of privacy becomes an important and challenging problem in social networks. This paper describes the (k,l k,l k,l)-anonymity model used for social network graph anonymization. The method is based on edge addition and is utility-aware, i.e. it is designed to generate a graph that is similar to the original one. Diﬀerent strategies are evaluated to this end and the results are compared based on common utility metrics. The outputs conﬁrm that the na¨ıve idea of adding some random or even minimum number of possible edges does not always produce useful anonymized social network graphs, thus creating some interesting alternatives for graph anonymization techniques.
A Map-based Job Recommender Model

Location is one of the most important factors to consider when looking for offering a new job.Currently, there exist many job recommender systems to help match the right candidate with the right job.A review of the existing recommender systems, included within this article, reveals that there is an absence of appropriate mapping support offering for job recommendation.This article aims to propose a general mapbased job recommender model, which is implemented and applied within a system for job seekers in Saudi Arabia.The system adapts content-based technique to recommend jobs using the cosine similarity and will help Saudi job seekers finding their desired job in an efficient way using interactive maps.This ultimately will contribute to Saudi Arabia moving forward to the digital transformation which is one of the major objectives to fulfill the Saudi vision 2030.
GAMIFICATION AS THE CURRENT TREND OF NATIONAL EDUCATION

The article deals with the main provisions and principles of gaming technology and concludes that this is one of the most important areas of development of educational technologies. The introduction of gaming elements into the learning process contributes to raising pupils’ cognitive activity, forming interest in knowledge, developing learning motivation and initiative. The key concept of the research, namely “Gamification”, is analyzed, and the following definition is given: integration of game elements and game thinking in activities other than games. Gamification in education - is the process of spreading the game to various areas of education, which allows you to consider the game and as a method of teaching and education, and as a form of educational work, and as a means of organizing a holistic educational process. The analysis of scientific sources provided the basis for the selection of the main components of the gameplay process of learning and game elements that form the mechanics of the process of gaming, namely the challenge, tasks, tests, cooperation, feedback, resource accumulation, rewards, and the state of victory. In the course of the study, it was revealed that gaming has not yet been widespread in educational practice in Ukraine. The main reasons include the weak technological equipment of universities and schools, the incompetence of the teaching staff in the information and communication sphere, «English-language», which represents the majority of Internet platforms, methodological and methodological shortcomings in the use of the principles of gaming. So, in order to train teachers for the skills of introduction of gamification in education, the course " Gamification in the educational process of the school" was developed, which is being developed within the framework of the project “Modernization of Pedagogical Higher Education Using Innovative Teaching Tools” EU Erasmus + KA2 program the development of the potential of higher education (No. 586098-EPP-1-2017-1-UAEPPKA2- CBHE-JP). The article deals with the conceptual approaches to the course and outlines the main objective of the course - to prepare students - future teachers for the practical use of technologies of gaming in the educational process of a comprehensive school. Indicator of achievement of goals and tasks is formed in the form of the final results of training and competences.
Brain MR Image Segmentation in Small Dataset with Adversarial Defense and Task Reorganization

Medical image segmentation is challenging especially in dealing with small dataset of 3D MR images. Encoding the variation of brain anatomical structures from individual subjects cannot be easily achieved, which is further challenged by only a limited number of well labeled subjects for training. In this study, we aim to address the issue of brain MR image segmentation in small dataset. First, concerning the limited number of training images, we adopt adversarial defense to augment the training data and therefore increase the robustness of the network. Second, inspired by the prior knowledge of neural anatomies, we reorganize the segmentation tasks of different regions into several groups in a hierarchical way. Third, the task reorganization extends to the semantic level, as we incorporate an additional object-level classification task to contribute high-order visual features toward the pixel-level segmentation task. In experiments we validate our method by segmenting gray matter, white matter, and several major regions on a challenge dataset. The proposed method with only seven subjects for training can achieve 84.46% of Dice score in the onsite test set.
Perancangan Logika Fuzzy Untuk Sistem Pengendali Kelembaban Tanah dan Suhu Tanaman

Soil moisture and temperature are parameters needed by plants in terms of plant growth. In determining soil moisture and temperature according to plants, a control system is needed. This control system uses the Raspberry Pi as an input data processor to output. This control system is designed using fuzzy logic as a decision-making method to maintain soil moisture and temperature that is good for plants. Fuzzy logic is one of the decision support systems that is suitable to be applied to the control system. Fuzzy logic consists of fuzzyfication, formation of fuzzy rules and defuzification. This fuzzy logic uses two inputs and two outputs. The two inputs are soil moisture level and temperature degree. The desired output is the time needed to maintain soil moisture and temperature to keep it in accordance with the needs of the plant
Universal Adversarial Perturbation for Text Classification

Given a state-of-the-art deep neural network text classifier, we show the existence of a universal and very small perturbation vector (in the embedding space) that causes natural text to be misclassified with high probability. Unlike images on which a single fixed-size adversarial perturbation can be found, text is of variable length, so we define the "universality" as "token-agnostic", where a single perturbation is applied to each token, resulting in different perturbations of flexible sizes at the sequence level. We propose an algorithm to compute universal adversarial perturbations, and show that the state-of-the-art deep neural networks are highly vulnerable to them, even though they keep the neighborhood of tokens mostly preserved. We also show how to use these adversarial perturbations to generate adversarial text samples. The surprising existence of universal "token-agnostic" adversarial perturbations may reveal important properties of a text classifier.
Two Concepts of Indeterminacy in Music

In an interview recorded in 1976, Karlheinz Stockhausen looked back on his early influences and noted the profound effect that quantum mechanics had exerted upon him. “Quantum physics … was something we discussed every day,” he even suggested.1 It is not entirely clear which period he had in mind or who else he was referring to when he made this claim. Perhaps he was thinking of his time in Werner Meyer-Eppler’s classes in phonetics, communication science, and information theory at Bonn University, which he began attending in 1953–54.2 It seems likely, however, that the revolution in physics was in the air in new music circles in Europe before then. Meyer-Eppler, a scientist adept in multiple fields, was a regular presence at the International Summer Courses for New Music in Darmstadt in the early 1950s,3 and it is probable that he discussed quantum mechanics in a lecture delivered at this annual gathering as early as 1950, prior to Stockhausen’s first visit the following year. Meyer-Eppler’s unpublished lecture notes for this event refer to the “Gabor Matrix,”4 a theoretical construct designed for use in characterizing the phenomenology of auditory experience in terms of aggregates of “acoustical quanta.”5
AXIOLOGICAL ASPECT OF STUDENT PROFESSIONAL TRAINING: MATCHING DEMAND AND OFFERS OF LABOR MARKET

The purpose of the article: The Relevance of the study is determined by modern trends in the development of the labor market, based on demand and supply of goods - a graduate of the University. At the same time, student’s professional training in the educational process of the University is traditionally carried out based on the basic values of the specialist's individual, due to the need for them, and the demand and supply of the labor market are determined by the demand for a specialist as an intellectual resource of production.&#x0D; Materials and methods: The methodology of this study is based on the innovative content of professional training of a University student, focused on the needs of a specialist in professional self-affirmation as a personal and social value corresponding to the demand and supply of the labor market, presented in the concept of cultural competency-based approach to training, in the theory of changes in the labor market, in projects of future changes in the labor market, where demand and supply for the training of a modern competent specialist are used as driving forces.&#x0D; Results of the research: the algorithm of designing cognitive, motivational, behavioral, emotional components of the values of the student’s professional training is justified and their compliance with the demand and supply of the labor market is proved. Knowledge about the value aspects of a University student’s professional training, about the features of their compliance with the labor market’s demand and supply, will help to avoid, localize or eliminate problems in the real educational process.&#x0D; Applications: This research can be used for universities, teachers, and students.&#x0D; Novelty/Originality: In this research, the model of an axiological aspect of student professional training: matching demand and offers of labor market is presented in a comprehensive and complete manner.
TRAINING OF PEDAGOGICAL EDUCATION MASTERS: PRACTICE-ORIENTED MODEL

Purpose: The article deals with the problems of training, attraction, and retention of teachers of the required qualification in educational institutions. Methodology: This problem is analyzed at the regional level in the context of reforms in the higher education system, the transition to a two-level model in accordance with the provisions of the Bologna process. Result: Based on the analysis of open data in the work it is stated that the shortage of teachers in the region (Samara region) is significant and has a negative impact on the educational process of educational institutions; it cannot be compensated exclusively by bachelors of pedagogical education, issued by pedagogical universities. Applications: This research can be used for universities, teachers, and students. Novelty/Originality: In this research, the model of training of pedagogical education masters: practice-oriented model is presented in a comprehensive and complete manner.
Multimodal Brain Tumor Segmentation Using Encoder-Decoder with Hierarchical Separable Convolution

To address automatic segmentation of brain tumor from multi-modal MRI volumes, a light-weight encoder-decoder network is presented. Exploring effective way to trade off the range of spatial contexts and computational efficiency is crucial to address challenges of 3D segmentation. To this end, we introduce hierarchical separable convolution (HSC), an integration of view- and group-wise separable convolution, which can simultaneously encode multi-scale context in 3D and reduce memory overhead without sacrificing accuracy. Specifically, typical 3D convolution is replaced with complementary 2D convolutions at multiple scales and thus multiple fields-of-view, which results in a light-weight but stronger model. Moreover, thanks to the decomposed convolutions, we ensemble 3D segmentations with different focal views to further improve segmentation accuracy. Experiments on the BRATS 2017 benchmark showed that our method achieved state-of-the-art performance in Dice, i.e., 0.901, 0.809 and 0.762 for the whole tumor, tumor core and enhancing tumor core, respectively.
Ctrl-Z: Recovering from Instability in Reinforcement Learning

When learning behavior, training data is often generated by the learner itself; this can result in unstable training dynamics, and this problem has particularly important applications in safety-sensitive real-world control tasks such as robotics. In this work, we propose a principled and model-agnostic approach to mitigate the issue of unstable learning dynamics by maintaining a history of a reinforcement learning agent over the course of training, and reverting to the parameters of a previous agent whenever performance significantly decreases. We develop techniques for evaluating this performance through statistical hypothesis testing of continued improvement, and evaluate them on a standard suite of challenging benchmark tasks involving continuous control of simulated robots. We show improvements over state-of-the-art reinforcement learning algorithms in performance and robustness to hyperparameters, outperforming DDPG in 5 out of 6 evaluation environments and showing no decrease in performance with TD3, which is known to be relatively stable. In this way, our approach takes an important step towards increasing data efficiency and stability in training for real-world robotic applications.
Prototyping a game engine architecture as a multi-agent system

The game engines are one of the essential and daily used applications on the game development field.These applications are designed to assist in the creation of this type of contents.Nevertheless, their usage and development are very complex.Moreover, there are not many research papers about the game engine architecture definition and specification.In this sense, this work presents a methodology to specify a game engine defined as a multi-agent system.In such a way, from a multi-agent approximation, a game engine architecture can be prototyped in a fast way.Also, this implementation can be exported to a general programming language for maximum performance, facilitating the definition and comprehension of the mechanisms of the game engine.
Hybrid Deduction–Refutation Systems

Hybrid deduction–refutation systems are deductive systems intended to derive both valid and non-valid, i.e., semantically refutable, formulae of a given logical system, by employing together separate derivability operators for each of these and combining ‘hybrid derivation rules’ that involve both deduction and refutation. The goal of this paper is to develop a basic theory and ‘meta-proof’ theory of hybrid deduction–refutation systems. I then illustrate the concept on a hybrid derivation system of natural deduction for classical propositional logic, for which I show soundness and completeness for both deductions and refutations.
Visual Detection of Generic Defects in Industrial Components using Generative Adversarial Networks

Detection of general or unspecified anomalies in industrial environments using camera images is a challenging task, and one which is the focus of much current research. Many studies are application focused, and demonstrate results on domain-specific datasets which are often not widely available, and hence it is difficult to directly compare performance of new techniques. We propose the use of Generative Adversarial Networks (GAN)s in performing generalised visual anomaly detection and the performance of various state-of-the-art GAN based methods is evaluated and compared to previous results. A simple method for generation of a dataset with automatically annotated anomalies in the form of Gaussian blobs in images, demonstrated on the well-known MNIST hand-written digits dataset, in order that unsupervised visual anomaly detection techniques can be compared without access to industry-specific datasets.
Indexing<i>k</i>-mers in linear space for quality value compression

Many bioinformatics tools heavily rely on k-mer dictionaries to describe the composition of sequences and allow for faster reference-free algorithms or look-ups. Unfortunately, naive k-mer dictionaries are very memory-inefficient, requiring very large amount of storage space to save each k-mer. This problem is generally worsened by the necessity of an index for fast queries. In this work, we discuss how to build an indexed linear reference containing a set of input k-mers and its application to the compression of quality scores in FASTQ files. Most of the entropies of sequencing data lie in the quality scores, and thus they are difficult to compress. Here, we present an application to improve the compressibility of quality values while preserving the information for SNP calling. We show how a dictionary of significant k-mers, obtained from SNP databases and multiple genomes, can be indexed in linear space and used to improve the compression of quality value. Availability: The software is freely available at https://github.com/yhhshb/yalff.
Trajectory Generation for Underactuated Multirotor Vehicles with Tilted Propellers via a Flatness-based Method

This paper considers a class of rotary-wing aerial robots with unaligned propellers. By studying the dynamics of these vehicles, we show that the position and heading angle remain flat outputs of the system (similar to conventional quadrotors). The implication is that they can be commanded to follow desired trajectory setpoints in 3D space. We propose a numerical strategy based on the collocation method to facilitate the trajectory generation. This enables convenient computation of the nominal robot's attitude and control inputs. The proposed methods are numerically verified for three multirotor robots with different dynamics. These include a tricopter with a tilting propeller, and a quadrotor and a hexacopter with unparalleled propellers.
Self-Supervised Sim-to-Real Adaptation for Visual Robotic Manipulation

Collecting and automatically obtaining reward signals from real robotic visual data for the purposes of training reinforcement learning algorithms can be quite challenging and time-consuming. Methods for utilizing unlabeled data can have a huge potential to further accelerate robotic learning. We consider here the problem of performing manipulation tasks from pixels. In such tasks, choosing an appropriate state representation is crucial for planning and control. This is even more relevant with real images where noise, occlusions and resolution affect the accuracy and reliability of state estimation. In this work, we learn a latent state representation implicitly with deep reinforcement learning in simulation, and then adapt it to the real domain using unlabeled real robot data. We propose to do so by optimizing sequence-based self supervised objectives. These exploit the temporal nature of robot experience, and can be common in both the simulated and real domains, without assuming any alignment of underlying states in simulated and unlabeled real images. We propose Contrastive Forward Dynamics loss, which combines dynamics model learning with time-contrastive techniques. The learned state representation that results from our methods can be used to robustly solve a manipulation task in simulation and to successfully transfer the learned skill on a real system. We demonstrate the effectiveness of our approaches by training a vision-based reinforcement learning agent for cube stacking. Agents trained with our method, using only 5 hours of unlabeled real robot data for adaptation, shows a clear improvement over domain randomization, and standard visual domain adaptation techniques for sim-to-real transfer.