Reviewerly/exHarmony huggingface dataset:
Neuroevolution Strategy for Time Series Prediction

Optimization is a concept, a process, and a method that all people use on a daily basis to solve their problems.The source of many optimization methods for many scientists has been the nature itself and the mechanisms that exist in it.Neural networks, inspired by the neurons of the human brain, have gained a great deal of recognition in recent years and provide solutions to everyday problems.Evolutionary algorithms are known for their efficiency and speed, in problems where the optimal solution is found in a huge number of possible solutions and they are also known for their simplicity, because their implementation does not require the use of complex mathematics.The combination of these two techniques is called neuroevolution.The purpose of the research is to combine and improve existing neuroevolution architectures, to solve time series problems.In this research, we propose a new improved strategy for such a system.As well as comparing the performance of our system with an already existing system, competing with it on five different datasets.Based on the final results and a combination of statistical results, we conclude that our system manages to perform much better than the existing system in all five datasets.
Development finance institutions and their role in increasing efficiency of capital flows

The article dwells upon modern approaches to determining the prospects of cooperation with specialized financial institutions for development in the interests of supporting economic growth.It is noted that the negative attitude to the prospects of cooperation with the financial aid institutions does not deny their potential.The modern economic relations give reasons to consider development finance institutions as a promising direction for rationalizing the financial openness of economies with an underdeveloped market environment.Involvement of development finance institutions into the economic policies, in the framework of special state programs, is able to extend the capacity of overcoming structural and functional defects of the underdeveloped financial sector that are immanent for the transitive economies.Cooperation with the financial institutions for development helps to overcome information asymmetry, to strengthen competition, to grow market institutions and implement non-speculative counter-cyclical measures for innovative development and industrialization.Paper analyses characteristics of cooperation between Ukraine and international development financial institutions.Also, author offers possible directions of maximizing the effectiveness of international financial support through the activities of national development finance institutions.The functionality and perspective directions of interaction of such institutions, tools and necessary prerequisites for raising their productive activity have been substantiated.Among this, the primary focus should be made not on the redistribution of financial resources, but rather on the intensification of attracting investment funds for the state economic development programs.Important functions of national development institutions at this stage are: establishing information and organizational support for international investment projects in Ukraine in order to develop the institutional environment of the financial sector, to restore investment attractiveness and to streamline the structure of financial openness.
Novel Methods for Forensic Multimedia Data Analysis: Part I

The increased usage of digital media in daily life has resulted in the demand for novel multimedia data analysis techniques that can help to use these data for forensic purposes. Processing of such data for police investigation and as evidence in a court of law, such that data interpretation is reliable, trustworthy, and efficient in terms of human time and other resources required, will help greatly to speed up investigation and make investigation more effective. If such data are to be used as evidence in a court of law, techniques that can confirm origin and integrity are necessary. In this chapter, we are proposing a new concept for new multimedia processing techniques for varied multimedia sources. We describe the background and motivation for our work. The overall system architecture is explained. We present the data to be used. After a review of the state of the art of related work of the multimedia data we consider in this work, we describe the method and techniques we are developing that go beyond the state of the art. The work will be continued in a Chapter Part II of this topic.
SOCIAL ADAPTATION OF PRESCHOOL CHILDREN WITH SPECIAL EDUCATIONAL NEEDS FOR INCLUSIVE LEARNING

The article investigates the substantive characteristics of the readiness of the social teacher, practical psychologist, educators, parents of children with special educational needs in the conditions of the pre-school educational institution. The definition of "social adaptation of children with special educational problems in the conditions of the institution of general educational institution " is analyzed. The main results of psychological and pedagogical coaching are determined: the awareness of specialists of the pre-school educational institution and parents of social adaptation importance for children with special educational needs as a requirement for their effective integration into the inclusive pre-school educational institution. Key words: inclusive learning, educational needs, social adaptation, coaching, tutors, pre-school education, inclusion.
Formalizing the LLL Basis Reduction Algorithm and the LLL Factorization Algorithm in Isabelle/HOL

The LLL basis reduction algorithm was the first polynomial-time algorithm to compute a reduced basis of a given lattice, and hence also a short vector in the lattice. It approximates an NP-hard problem where the approximation quality solely depends on the dimension of the lattice, but not the lattice itself. The algorithm has applications in number theory, computer algebra and cryptography. In this paper, we provide an implementation of the LLL algorithm. Both its soundness and its polynomial running-time have been verified using Isabelle/HOL. Our implementation is nearly as fast as an implementation in a commercial computer algebra system, and its efficiency can be further increased by connecting it with fast untrusted lattice reduction algorithms and certifying their output. We additionally integrate one application of LLL, namely a verified factorization algorithm for univariate integer polynomials which runs in polynomial time.
Estimation of Unconfined Compression Test (UCT) parameters using digital image analysis

Abstract Digital image analysis is a method of studying digital image for obtaining significant information and has been widely used in engineering field. In this research, digital image analysis is used to estimate related parameters from unconfined compression test. Soil sample is prepared and tested until failure. During testing, vertical deformation are recorded manually, and video are captured. Static images are extracted from the video and analysis on those images are performed. The measurement from digital images are compared with manual measurement. It was found that, digital image analysis on sample produce close result when compared with manual measurement. This suggest that this method might be suitable as an alternative for manual measurement. Method for determining Poisson’s ratio using digital image is also proposed.
Research on Cultivation of College English Autonomous Learning Ability in the New Media Era

Due to the rapid development of information technology and the increasing requirements of modern society for college students' comprehensive English application ability, it has become an inevitable trend to apply online new media to college English teaching to improve students' autonomous learning ability. Based on the relevant theories of autonomous learning and new media, this paper analyzes the current situation of college English autonomous learning in the new media era, explores the construction of the teaching mode of college English autonomous learning, and puts forward some suggestions on how to help students improve their autonomous learning ability.
App Development for the Social Good: Teaching a Socially Conscious Mobile App Development in an Upper-Level Computer Science Course

She is the director of the first REU program hosted by JMU during summer 2017.She designed and delivered courses on mobile development that involved critical research challenges of mobile computing area.She has mentored over 10
Hierarchical regularization networks for sparsification based learning on noisy datasets

We propose a hierarchical learning strategy aimed at generating sparse representations and associated models for noisy datasets.The hierarchy follows from approximation spaces identified at successively finer scales.For promoting model generalization at each scale, we also introduce a novel, projection-based penalty operator across multiple dimension, using permutation operators for incorporating proximity and ordering information.The paper presents a detailed analysis of stability and approximation properties in the reconstruction Reproducing Kernel Hilbert Spaces (RKHS) with emphasis on optimality and consistency of predictions, and behavior of error functionals associated with the produced sparse representations.Results show the data reduction and modeling capabilities of our approach on both synthetic (univariate and multivariate) and multivariate real datasets.For demonstrating the quality of our results, we compare the performance with multiple variants of Gaussian Processes with respect to performance measures of optimal smoothing and generalization.Being a sparsity driven approach, we also compare our results with widely successful sparse algorithms such as Relevance Vector Machines (RVM) and greedy approaches like Orthogonal Matching Pursuit (OMP).The superior performance of our approach on majority of experiments, as compared to the optimized implementation of these widely used algorithms, clearly justify the usefulness of the proposed approach.
Beyond Possession Existence: Duration and Co-Possession

This paper introduces two tasks: determining (a) the duration of possession relations and (b) co-possessions, i.e., whether multiple possessors possess a possessee at the same time.We present new annotations on top of corpora annotating possession existence, and experimental results.Regarding possession duration, we derive the time spans we work with empirically from annotations indicating lower and upper bounds.Regarding co-possessions, we use a binary label.Cohen's kappa coefficients indicate substantial agreement, and experimental results show that text is more useful than the image for solving these tasks.
Stable Prediction via Leveraging Seed Variable

In this paper, we focus on the problem of stable prediction across unknown test data, where the test distribution is agnostic and might be totally different from the training one. In such a case, previous machine learning methods might exploit subtly spurious correlations in training data induced by non-causal variables for prediction. Those spurious correlations are changeable across data, leading to instability of prediction across data. By assuming the relationships between causal variables and response variable are invariant across data, to address this problem, we propose a conditional independence test based algorithm to separate those causal variables with a seed variable as priori, and adopt them for stable prediction. By assuming the independence between causal and non-causal variables, we show, both theoretically and with empirical experiments, that our algorithm can precisely separate causal and non-causal variables for stable prediction across test data. Extensive experiments on both synthetic and real-world datasets demonstrate that our algorithm outperforms state-of-the-art methods for stable prediction.
IDENTIFICAÇÃO DE PARTÍCULAS EM ALTAS ENERGIAS UTILIZANDO MÉTODOS DE APRENDIZADO DE MÁQUINA

In the field of high energy physics, massive amounts of data are currently being produced, requiring technological tools to support their research and discoveries.This work aims to compare two techniques of machine learning: decision trees and neural networks.The supposition based on related works shows that neural networks develop better in classification work among other applications.Based on this, the classification of subatomic 1 particles at high energies studied from the databases of the great hadrons collider by CERN, came to an unequal conclusion when using such methods and in the end comparing them.In the end, both had very similar results.
Pose Registration of 3D Face Images

In computer vision, face analysis is a hot topic in various domains. It is very challenging to establish a robust face recognition system, including different variations of faces such as expression, pose, and occlusion. The problem arises when the system considers any pose-variant images. Pose variation issues are not handled correctly in 2D domain, whereas 3D data easily handles the pose variation issue. There exist different registration techniques for aligning 3D pose-variant data. In this chapter, we have discussed the need for registration, followed by different registration techniques. Before registration, the pose should be detected in any system, so we have discussed some new pose detection techniques. After that, we have given a brief description of currently proposed face registration techniques; those are mainly worked with 3D face data. Experimentally we have illustrated the importance of registration in any face recognition system. We have considered some of the well-known 3D face databases for validation of the mentioned techniques and also put forward some discussions on the relevant issues.
Learning to Select Base Classes for Few-shot Classification

Few-shot learning has attracted intensive research attention in recent years. Many methods have been proposed to generalize a model learned from provided base classes to novel classes, but no previous work studies how to select base classes, or even whether different base classes will result in different generalization performance of the learned model. In this paper, we utilize a simple yet effective measure, the Similarity Ratio, as an indicator for the generalization performance of a few-shot model. We then formulate the base class selection problem as a submodular optimization problem over Similarity Ratio. We further provide theoretical analysis on the optimization lower bound of different optimization methods, which could be used to identify the most appropriate algorithm for different experimental settings. The extensive experiments on ImageNet, Caltech256 and CUB-200-2011 demonstrate that our proposed method is effective in selecting a better base dataset.
Mastering the Methods of Cognition of Natural Phenomena As an Important Component of the Natural Science Functional Literacy of a Primary Schoolchild

The article discusses the features of building the process of familiarizing elementary schoolchildren with scientific methods of cognition&#x0D; (observation, experience) and their application to obtain new knowledge, solve educational, practical and educational-cognitive problems. This direction of educational activity of schoolchildren implements meta-subject learning outcomes in accordance with the requirements of the Federal State Educational Standard of Primary General Education, and also forms the basis of natural-scientific functional literacy among younger schoolchildren. On the basis of the author’s study, the principles of adapting scientific methods of cognition for use as ways of organizing learning activities are determined; the main stages of the formation of the ability of students to use scientific methods of cognition in students are identified.
The Young Teacher Preparing to Work With Gifted Children in the Continuing Education System

The article is devoted to the preparation of young teachers to work with gifted children in the system of continuing education. The author outlines that activity of a young teacher with gifted children is a very responsible job that constantly requires the teacher to master professional skills, develop personal qualities, flexibility, emotional stability, and the ability to create individual and personalized programs designed for all types of pupils, including gifted children. The author comes to the conclusion that preparation of young teachers to work with smart and gifted children should be focused on improve their theoretical knowledge on the specifics of a gifted child’s psychology , as well as on the formation of professionally important qualities necessary to ensure the effectiveness of the educational process, which contributes to the improvement of skills, increase the general and psychological and pedagogical culture of a young teacher for the development of professional competence and ownership of the professional component in the system of continuing education.
Implementation of Neural Network in Classifying the Product Quality for the ABS Metallization

The surface treatment conditions of a plastic surface are related to the quality of finished products. Usually, more than 20 causes dominate the success of electroplating for acrylonitrile butadiene styrene (ABS). Thus, the quality control is very complicated and challenging. Even nowadays, most of the production quality still relies on the operator's experience and intuition. This research takes a company of water hardware in Taiwan as the research object. We propose a revolutionary concept of quality management, combining artificial intelligence and surface treatment process altogether. We set up a parameter monitoring system during production to predict the quality of ABS metallization using neural network models such as artificial intelligence forms the basis of the intelligent manufacturing system. It can be used as a quality control tool to improve quality yield and industrial competitiveness. Totally 13 operational parameters (causes) and one quality parameter (consequence) of the electroplating tanks were collected from time to time to build the NN models. Interestingly, we finally find the fuzzy NN model performs better than the precise NN model. We conclude this is resulting from the limitation and vagueness of data.
The Method of Clearing Printed and Handwritten Texts from Noise

The article reviews the existing methods and algorithms for clearing printed and handwritten texts from noise and proposes an alternative approach. Among the solutions analyzed, a group of methods based on adaptive threshold conversion is distinguished. Our method for clearing print and handwritten documents from noise is based on using of a convolutional neural network ensemble with a U-Net architecture and a multi-layer perceptron. Using consequently a convolutional neural network and a multilayer perceptron demonstrates high efficiency in small training sets. As a result of applying our method to the entire test sample, an image cleaning degree of 93% was achieved. In the future, these methods can be introduced in libraries, hospitals, news companies where people work with non-digitized papers and digitization is needed.
To the problem of reduction of dispositional terms of scientific theory

This article examines the problem of reduction of dispositional terms in the scientific theory. Dispositional terms are the predicated, expressing proclivity of the body for having a certain response in particular circumstances. The difference between dispositional predicated and other descriptive terms consists in the fact that for their identification it is essential to know an empirical factor that invokes manifestation of a dispositional trait. Since disposition cannot be observed directly, it requires carrying out an experiment to reveal the needed quality of a subject. It is established that for dispositional predicates, definition should be viewed as a particular case of reduction. Such conclusion is substantiated by the fact that the two-sided reduction sentence represents a special case of a reduction pair of sentences. In constant clarification of the meaning of dispositional term empirically, the set of reference using reduction sentences is more convenient, since it can be augmented with a new sentence that describes the additional verification conditions. Presence in the language of observation of dispositional predicates, which could not be determined in a usual way, and possibility of their reduction underline nonequivalence of the methodological operations of reduction and definition in the substantive scientific theories, which also represents an argument for further research of reduction in natural sciences, social sciences and humanities.
Por uma história intelectual da arquivologia, da biblioteconomia e da museologia desde uma perspectiva transversal

In this article is presented the history of the scientific evolution of archival science, library science and museum studies.The intellectual movements that influenced or crossed the three areas are emphasized, asking common questions and problems.At the end, contemporary perspectives are analyzed.It is concluded that the three areas started from a more simplified study perspective and focused on more concrete elements, moving, over time, towards aspects more
FlowChroma - A Deep Recurrent Neural Network for Video Colorization

We develop an automated video colorization framework that minimizes the flickering of colors across frames. If we apply image colorization techniques to successive frames of a video, they treat each frame as a separate colorization task. Thus, they do not necessarily maintain the colors of a scene consistently across subsequent frames. The proposed solution includes a novel deep recurrent encoder-decoder architecture which is capable of maintaining temporal and contextual coherence between consecutive frames of a video. We use a high-level semantic feature extractor to automatically identify the context of a scenario including objects, with a custom fusion layer that combines the spatial and temporal features of a frame sequence. We demonstrate experimental results, qualitatively showing that recurrent neural networks can be successfully used to improve color consistency in video colorization.
Principles of formation of research and educational objects of innovative purpose

An analysis of global trends in the development of education and science shows that a modern university has ceased to be a place for transferring knowledge from a teacher to a student in the form of lectures.Today, the university fulfi lls the task of forming intelligence in the form of knowledge, innovation, competencies, being at the same time a space for researchers, teachers, and students to meet and work together.This article discusses the main functional components of modern Research and Educational objects.The analysis revealed a common unifying link in the relationship of these blocks.The result of studying domestic and foreign experience in the design and construction of educational centers was the identifi cation of the basic principles of their formation.