Reviewerly/exHarmony huggingface dataset:
Formation of readyness of future teachers of initial classes to combination of methods of education in extracurricular work

Democratic society aims to recreate principles of the humane civilized communication in an educational process. International pedagogical acquisitions of progressive countries are built on principles of collaboration between teachers, students, parents and public that worries from Ukrainian modern school. We determine readiness as system formation of personality that characterizes her selective activity at preparation and including to activity is integrated. It is expedient to examine attitude the components of readiness toward activity, reasons, knowledge about an object and methods of activity, ability and methods them practical embodiment. In relation to readiness of teachers to the use of methods of education these components can be specified thus: realization personality and public meaningfulness of methods of education; positive motivation of perfection of knowledge and abilities is in industry of application of methods of education; knowledge is about essence of methods of education, their classification, criteria of choice of methods of education, comparative efficiency of separate methods of education and their combinations; possessing the algorithms of choice of separate methods of education and their combinations. The system over of training employments on those students meet with modelled is brought by situations that are analogical to the situations of their next professional activity. During development of training employments we leaned against position in relation to their organization, influence on personality and use in trade education and improvement of pedagogical mastery of teachers, modified existent exercises and interactive methods of studies. Key words: readiness of future teacher, methods of education, extracurricular work, teacher of initial classes, training.
Internet resources for learning Ukrainian in a learning process

The article focuses on the basic transitions of informational and communicational technologies into the educational process, and on the specific of the usage of electronic textbooks and the requirements for their creation. It is important to note that, in the course of computer technology, the computer-related technologies of educational process would allow the student to complete the homework at convenient time, study the material better due to the simultaneous combination of the visual, auditory and other forms of perception, to conduct the computerized self-control and so on. Also, analyzes the issues of teaching Ukrainian as a foreign language, discusses the problems that students face when learning a language. The advantages (formation of reading skills, use of materials of varying degrees of complexity, improvement of listening skills, monologue and dialogic speech, replenishment of vocabulary) and disadvantages (technical, psychological) use of Internet resources are outlined. An attempt was made to structure the Internet resources into groups. Key words: Internet, online resources, online forums, communicational, online platform, online courses, website, computer technology, electronic textbooks
THE UNIVERSITY’S RECEPTIVITY TO SOCIAL INNOVATIONS: THEORETICAL ASPECT

The relevance of this study. Social innovations issues still cover a very small part of the scientific work in the field of education. It should be stressed that such innovations are particularly important in the education system, as their development not only contributes to the improvement of the quality of teaching or the progress of educational institutions, but also makes a significant contribution to the creation of a sustainable future and to highlighting and solving social problems in society. The main problem. What is the conceptual basis of a university that enables a university to be receptive to social innovations? Object of research: University receptivity to social innovations. The aim of this study is to reveal which concepts lead a university to be the most receptive to social innovations. Results. Social innovations can not only meet the needs of an individual, but also drive social inclusion. Susceptibility to innovation is traditionally defined as the ability of entities to apply innovative ideas and solutions, to be able to create innovations and disseminate them in society. A modern university should be open, innovative, dynamic, responsive to society and its needs. Regardless of the concept chosen by a university, the third mission of higher education should not be forgotten – to use the university’s knowledge for a common goal, be socially responsible, and contribute to the well-being of society. Research methods. This analysis is prepared by using the methods of semi – systematic thematic analysis. Social innovations can not only meet the needs of an individual, but also drive social inclusion. In most cases, the benefits of social innovation reduce public spending by eliminating a specific social problem and creating some added value for the group of people targeted by social innovations. Susceptibility to innovation is traditionally defined as the ability of entities to apply innovative ideas and solutions, to be able to create innovations and disseminate them in society. Susceptibility to social innovations is determined by innovation potential and innovative activity. Management, which is determined by the choice of managerial staff, communication skills, applied methods and support of managers to apply social innovations, is particularly important in the structure of innovation potential. Internal and external factors/ components are also important in shaping receptivity to social innovations.
Project training in the professional training of the future flight operator managers

The article shows the essence and role of project-based learning in the process of professional training of higher education seekers. The classification of educational projects is given. The peculiarities of using project-based teaching methods as a basis for professional training of future flight disputchers are demonstrated by specific examples. Key words: project training, future flight disputchers, self-assisted work
Anomalous diffusion dynamics of learning in deep neural networks

Learning in deep neural networks (DNNs) is implemented through minimizing a highly non-convex loss function, typically by a stochastic gradient descent (SGD) method. This learning process can effectively find good wide minima without being trapped in poor local ones. We present a novel account of how such effective deep learning emerges through the interactions of the SGD and the geometrical structure of the loss landscape. Rather than being a normal diffusion process (i.e. Brownian motion) as often assumed, we find that the SGD exhibits rich, complex dynamics when navigating through the loss landscape; initially, the SGD exhibits anomalous superdiffusion, which attenuates gradually and changes to subdiffusion at long times when the solution is reached. Such learning dynamics happen ubiquitously in different DNNs such as ResNet and VGG-like networks and are insensitive to batch size and learning rate. The anomalous superdiffusion process during the initial learning phase indicates that the motion of SGD along the loss landscape possesses intermittent, big jumps; this non-equilibrium property enables the SGD to escape from sharp local minima. By adapting the methods developed for studying energy landscapes in complex physical systems, we find that such superdiffusive learning dynamics are due to the interactions of the SGD and the fractal-like structure of the loss landscape. We further develop a simple model to demonstrate the mechanistic role of the fractal loss landscape in enabling the SGD to effectively find global minima. Our results thus reveal the effectiveness of deep learning from a novel perspective and have implications for designing efficient deep neural networks.
Automatic Scene Inference for 3D Object Compositing

We present a user-friendly image editing system that supports a drag-and-drop object insertion (where the user merely drags objects into the image, and the system automatically places them in 3D and relights them appropriately), post-process illumination editing, and depth-of-field manipulation. Underlying our system is a fully automatic technique for recovering a comprehensive 3D scene model (geometry, illumination, diffuse albedo and camera parameters) from a single, low dynamic range photograph. This is made possible by two novel contributions: an illumination inference algorithm that recovers a full lighting model of the scene (including light sources that are not directly visible in the photograph), and a depth estimation algorithm that combines data-driven depth transfer with geometric reasoning about the scene layout. A user study shows that our system produces perceptually convincing results, and achieves the same level of realism as techniques that require significant user interaction.
On the Decentralized Generation of theRSA Moduli in Multi-Party Settings

RSA cryptography is still widely used. Some of its applications (e.g., distributed signature schemes, cryptosystems) do not allow the RSA modulus to be generated by a centralized trusted entity. Instead, the factorization must remain unknown to all the network participants. To this date, the existing algorithms are either computationally expensive, or limited to two-party settings. In this work, we design a decentralized multi-party computation algorithm able to generate efficiently the RSA modulus.
Image Outpainting and Harmonization using Generative Adversarial Networks

Although the inherently ambiguous task of predicting what resides beyond all four edges of an image has rarely been explored before, we demonstrate that GANs hold powerful potential in producing reasonable extrapolations. Two outpainting methods are proposed that aim to instigate this line of research: the first approach uses a context encoder inspired by common inpainting architectures and paradigms, while the second approach adds an extra post-processing step using a single-image generative model. This way, the hallucinated details are integrated with the style of the original image, in an attempt to further boost the quality of the result and possibly allow for arbitrary output resolutions to be supported.
On metric regularity of Reed-Muller codes

In this work we study metric properties of the well-known family of binary Reed-Muller codes. Let $A$ be an arbitrary subset of the Boolean cube, and $\widehat{A}$ be the metric complement of $A$ -- the set of all vectors of the Boolean cube at the maximal possible distance from $A$. If the metric complement of $\widehat{A}$ coincides with $A$, then the set $A$ is called a {\it metrically regular set}. The problem of investigating metrically regular sets appeared when studying {\it bent functions}, which have important applications in cryptography and coding theory and are also one of the earliest examples of a metrically regular set. In this work we describe metric complements and establish the metric regularity of the codes $\mathcal{RM}(0,m)$ and $\mathcal{RM}(k,m)$ for $k \geqslant m-3$. Additionally, the metric regularity of the codes $\mathcal{RM}(1,5)$ and $\mathcal{RM}(2,6)$ is proved. Combined with previous results by Tokareva N. (2012) concerning duality of affine and bent functions, this establishes the metric regularity of most Reed-Muller codes with known covering radius. It is conjectured that all Reed-Muller codes are metrically regular.
Approximate Maximum Matching in Random Streams

In this paper, we study the problem of finding a maximum matching in the semi-streaming model when edges arrive in a random order. In the semi-streaming model, an algorithm receives a stream of edges and it is allowed to have a memory of $\tilde{O}(n)$ where $n$ is the number of vertices in the graph. A recent inspiring work by Assadi et al. shows that there exists a streaming algorithm with the approximation ratio of $\frac{2}{3}$ that uses $\tilde{O}(n^{1.5})$ memory. However, the memory of their algorithm is much larger than the memory constraint of the semi-streaming algorithms. In this work, we further investigate this problem in the semi-streaming model, and we present simple algorithms for approximating maximum matching in the semi-streaming model. Our main results are as follows. We show that there exists a single-pass deterministic semi-streaming algorithm that finds a $\frac{3}{5} (= 0.6)$ approximation of the maximum matching in bipartite graphs using $\tilde{O}(n)$ memory. This result significantly outperforms the state-of-the-art result of Konrad that finds a $0.539$ approximation of the maximum matching using $\tilde{O}(n)$ memory. By giving a black-box reduction from finding a matching in general graphs to finding a matching in bipartite graphs, we show there exists a single-pass deterministic semi-streaming algorithm that finds a $\frac{6}{11} (\approx 0.545)$ approximation of the maximum matching in general graphs, improving upon the state-of-art result $0.506$ approximation by Gamlath et al.
Blang: Bayesian declarative modelling of general data structures and inference via algorithms based on distribution continua

Consider a Bayesian inference problem where a variable of interest does not take values in a Euclidean space. These "non-standard" data structures are in reality fairly common. They are frequently used in problems involving latent discrete factor models, networks, and domain specific problems such as sequence alignments and reconstructions, pedigrees, and phylogenies. In principle, Bayesian inference should be particularly well-suited in such scenarios, as the Bayesian paradigm provides a principled way to obtain confidence assessment for random variables of any type. However, much of the recent work on making Bayesian analysis more accessible and computationally efficient has focused on inference in Euclidean spaces. In this paper, we introduce Blang, a domain specific language and library aimed at bridging this gap. Blang allows users to perform Bayesian analysis on arbitrary data types while using a declarative syntax similar to BUGS. Blang is augmented with intuitive language additions to create data types of the user's choosing. To perform inference at scale on such arbitrary state spaces, Blang leverages recent advances in sequential Monte Carlo and non-reversible Markov chain Monte Carlo methods.
Study on upper limit of sample sizes for a two-level test in NIST SP800-22

NIST SP800-22 is one of the most widely used statistical testing tools for pseudorandom number generators (PRNGs). This tool consists of 15 tests (one-level tests) and two additional tests (two-level tests). Each one-level test provides one or more $p$-values. The two-level tests measure the uniformity of the obtained $p$-values for a fixed one-level test. One of the two-level tests categorizes the $p$-values into ten intervals of equal length, and apply a chi-squared goodness-of-fit test. This two-level test is often more powerful than one-level tests, but sometimes it rejects even good PRNGs when the sample size at the second level is too large, since it detects approximation errors in the computation of $p$-values. In this paper, we propose a practical upper limit of the sample size in this two-level test, for each of six tests appeared in SP800-22. These upper limits are derived by the chi-squared discrepancy between the distribution of the approximated $p$-values and the uniform distribution $U(0, 1)$. We also computed a "risky" sample size at the second level for each one-level test. Our experiments show that the two-level test with the proposed upper limit gives appropriate results, while using the risky size often rejects even good PRNGs. We also propose another improvement: to use the exact probability for the ten categories in the computation of goodness-of-fit at the two-level test. This allows us to increase the sample size at the second level, and would make the test more sensitive than the NIST's recommending usage.
A sparse resultant based method for efficient minimal solvers

Many computer vision applications require robust and efficient estimation of camera geometry. The robust estimation is usually based on solving camera geometry problems from a minimal number of input data measurements, i.e. solving minimal problems in a RANSAC framework. Minimal problems often result in complex systems of polynomial equations. Many state-of-the-art efficient polynomial solvers to these problems are based on Gr\"obner bases and the action-matrix method that has been automatized and highly optimized in recent years. In this paper we study an alternative algebraic method for solving systems of polynomial equations, i.e., the sparse resultant-based method and propose a novel approach to convert the resultant constraint to an eigenvalue problem. This technique can significantly improve the efficiency and stability of existing resultant-based solvers. We applied our new resultant-based method to a large variety of computer vision problems and show that for most of the considered problems, the new method leads to solvers that are the same size as the the best available Gr\"obner basis solvers and of similar accuracy. For some problems the new sparse-resultant based method leads to even smaller and more stable solvers than the state-of-the-art Gr\"obner basis solvers. Our new method can be fully automatized and incorporated into existing tools for automatic generation of efficient polynomial solvers and as such it represents a competitive alternative to popular Gr\"obner basis methods for minimal problems in computer vision.
A Machine Learning Framework for Authorship Identification From Texts

Authorship identification is a process in which the author of a text is identified. Most known literary texts can easily be attributed to a certain author because they are, for example, signed. Yet sometimes we find unfinished pieces of work or a whole bunch of manuscripts with a wide variety of possible authors. In order to assess the importance of such a manuscript, it is vital to know who wrote it. In this work, we aim to develop a machine learning framework to effectively determine authorship. We formulate the task as a single-label multi-class text categorization problem and propose a supervised machine learning framework incorporating stylometric features. This task is highly interdisciplinary in that it takes advantage of machine learning, information retrieval, and natural language processing. We present an approach and a model which learns the differences in writing style between $50$ different authors and is able to predict the author of a new text with high accuracy. The accuracy is seen to increase significantly after introducing certain linguistic stylometric features along with text features.
(Newtonian) Space-Time Algebra

The space-time (s-t) algebra provides a mathematical model for communication and computation using values encoded as events in discretized linear (Newtonian) time. Consequently, the input-output behavior of s-t algebra and implemented functions are consistent with the flow of time. The s-t algebra and functions are formally defined. A network design framework for s-t functions is described, and the design of temporal neural networks, a form of spiking neural networks, is discussed as an extended case study. Finally, the relationship with Allen's interval algebra is briefly discussed.
A Neural Model for Text Localization, Transcription and Named Entity Recognition in Full Pages

In the last years, the consolidation of deep neural network architectures for information extraction in document images has brought big improvements in the performance of each of the tasks involved in this process, consisting of text localization, transcription, and named entity recognition. However, this process is traditionally performed with separate methods for each task. In this work we propose an end-to-end model that combines a one stage object detection network with branches for the recognition of text and named entities respectively in a way that shared features can be learned simultaneously from the training error of each of the tasks. By doing so the model jointly performs handwritten text detection, transcription, and named entity recognition at page level with a single feed forward step. We exhaustively evaluate our approach on different datasets, discussing its advantages and limitations compared to sequential approaches. The results show that the model is capable of benefiting from shared features for simultaneously solving interdependent tasks.
Isoclinic Subspaces and Quantum Error Correction

We exhibit equivalent conditions for subspaces of an inner product space to be isoclinic, including a characterization based on the classical notion of canonical angles. We identify a connection with quantum error correction, showing that every quantum error correcting code is associated with a family of isoclinic subspaces, and we prove a converse for pairs of such subspaces. We also show how the canonical angles for isoclinic subspaces arise in the structure of the higher rank numerical ranges of the corresponding orthogonal projections.
Per-sample Prediction Intervals for Extreme Learning Machines

Prediction intervals in supervised Machine Learning bound the region where the true outputs of new samples may fall. They are necessary in the task of separating reliable predictions of a trained model from near random guesses, minimizing the rate of False Positives, and other problem-specific tasks in applied Machine Learning. Many real problems have heteroscedastic stochastic outputs, which explains the need of input-dependent prediction intervals. This paper proposes to estimate the input-dependent prediction intervals by a separate Extreme Learning Machine model, using variance of its predictions as a correction term accounting for the model uncertainty. The variance is estimated from the model's linear output layer with a weighted Jackknife method. The methodology is very fast, robust to heteroscedastic outputs, and handles both extremely large datasets and insufficient amount of training data.
Benchmark for Generic Product Detection: A Low Data Baseline for Dense Object Detection

Object detection in densely packed scenes is a new area where standard object detectors fail to train well. Dense object detectors like RetinaNet trained on large and dense datasets show great performance. We train a standard object detector on a small, normally packed dataset with data augmentation techniques. This dataset is 265 times smaller than the standard dataset, in terms of number of annotations. This low data baseline achieves satisfactory results (mAP=0.56) at standard IoU of 0.5. We also create a varied benchmark for generic SKU product detection by providing full annotations for multiple public datasets. It can be accessed at https://github.com/ParallelDots/generic-sku-detection-benchmark. We hope that this benchmark helps in building robust detectors that perform reliably across different settings in the wild.
LinCode -- computer classification of linear codes

We present an algorithm for the classification of linear codes over finite fields, based on lattice point enumeration. We validate a correct implementation of our algorithm with known classification results from the literature, which we partially extend to larger ranges of parameters.