Reviewerly/exHarmony huggingface dataset:
Avaliação do Processo para Embarcar uma Rede Neural Baseada em YOLO Utilizando um Acelerador de Hardware Dedicado

Object Detection is a challenging task in computer vision, but Deep Neural Networks (DNN) have made great progress in this area. This work presents the process and the results obtained in the attempts to embed a YOLO V3 model in a Neural Compute Engine, the Movidius Stick. Experiments were carried out with a Tensorflow model that is converted to Movidius (using OpenVINO) including an evaluation of the Movidius stick connected to a Raspberry Pi3. The application uses aerial images of power distribution towers captured by a drone. Although there are some fully operational networks for Neural Compute Engines, there are some difficulties in porting new networks to the platform, with gains in performance, but with losses in accuracy.
View Consistent 3D Face Reconstruction Using Siamese Encoder-Decoders

This paper addresses the problem of reconstructing the high-quality 3D face shape and high-fidelity texture from a single image. Traditional methods produce a 3D facial shape consistent with only the viewpoint of the input image. The shape and texture of un-exposed regions are estimated from a classic linear 3D Morphable Model (3DMM), leading to unsatisfactory results. Instead, we propose a novel method which is capable of inferring view-consistent shape and texture in unseen regions. Our method is based on a multi-task encoder-decoder framework which simultaneously learns a coarse 3DMM shape, a texture map and a normal map. To build the underlying nonlinear mapping between the input image and the whole shape/texture, we introduce siamese networks to learn a complete texture map and normal map by utilizing the multi-view images of the same person. Experiments show that our method produces more detailed results compared with the previous methods and can generate consistent and complete facial shape and texture.
WebFeatures: A Web Tool to Extract Features from Collaborative Content

The production from collaborative web content has grown in recent years. Thus, exploring the quality of these data repositories has also become relevant. This work proposes to develop a tool called WebFeature. Such system allows one to manage, extract, and share quality related feature sets from text, graph and article review. To accomplish this, diff erent types of metrics were implemented based on structure, style, and readability of the texts. In order to evalu- ate the WebFeature applicability, we presented a scenario with its main functionalities (creation of a feature set, extraction of features from a known dataset, and publishing the feature set). Our demon- stration shows that this framework can be useful for extracting features automatically, supporting quality prediction of collabo- rative contents, analyzing text characterization, and improving research reproducibility.
You May Like This Hotel Because ...: Identifying Evidence for Explainable Recommendations

Explainable recommendation is a good way to improve user satisfaction. However, explainable recommendation in dialogue is challenging since it has to handle natural language as both input and output. To tackle the challenge, this paper proposes a novel and practical task to explain evidences in recommending hotels given vague requests expressed freely in natural language. We decompose the process into two subtasks on hotel reviews: Evidence Identification and Evidence Explanation. The former predicts whether or not a sentence contains evidence that expresses why a given request is satisfied. The latter generates a recommendation sentence given a request and an evidence sentence. In order to address these subtasks, we build an Evidence-based Explanation dataset, which is the largest dataset for explaining evidences in recommending hotels for vague requests. The experimental results demonstrate that the BERT model can find evidence sentences with respect to various vague requests and that the LSTM-based model can generate recommendation sentences.
Dynamic Bayesian Network Model of a Country’s Economic Extension

This paper presents the studies’ results on the probability-determined models development based on Bayesian networks to estimate the economic development measure of Ukraine. Considering that one of the difficulties in the Bayesian networks development is the exponential increase in the parameters amount in conditional probability tables (CPT), this study proposes a technique for applying Noisy-MAX nodes to model economic processes taking into account the time component. It is shown that if the proportion of enterprises that implement innovations is increased now by 31%, while the share of profits of these enterprises increases by only 2%, at the next time step the measure of manufacturability and innovation of Ukraine will rise by 81% and will tend to the maximum.
Automation of Selection of a Pool of Graphical Interface Regression Tests for Multi Module Information Systems

Features of using the regression test selection method for automated testing of the graphical user interface in the development of information systems that consist of a set of modules are considered. The source of the need to create additional test environments required in the development of multi-module information systems that are using databases is specified. The three most popular approaches to organizing test environments – Copying, Scaling, and Scaling with synthetic data generation – are considered. The positive and negative sides are considered in terms of implementation, using, and resources spent on creating and maintaining resources, as well as in terms of the reliability of the results obtained in the process of testing models created using these approaches. The positive aspects of checking the quality of complex multi-module information systems from the point of view of the graphical user interface by various testing methods and, in particular, in the process of performing regression testing are presented. The positive aspects of using regression testing automation in conditions of lack of resources using various software platforms are indicated. The advantages of using the dynamic selection method for regression tests for automated testing are also given, as well as recommendations for implementing the selection method in existing and beginning projects.
Learning of classification models from group-based feedback

Learning of classification models in practice often relies on a nontrivial amount of human annotation effort. The most widely adopted human labeling process assigns class labels to individual data instances. However, such a process is very rigid and may end up being very time-consuming and costly to conduct in practice. Finding more effective ways to reduce human annotation effort has become critical for building machine learning systems that require human feedback.In this thesis, we propose and investigate a new machine learning approach - Group-Based Active Learning - to learn classification models from limited human feedback. A group is defined by a set of instances represented by conjunctive patterns that are value ranges over the input features. Such conjunctive patterns define hypercubic regions of the input data space. A human annotator assesses the group solely based on its region-based description by providing an estimate of the class proportion for the subpopulation covered by the region. The advantage of this labeling process is that it allows a human to label many instances at the same time, which can, in turn, improve the labeling efficiency.In general, there are infinitely many regions one can define over a real-valued input space. To identify and label groups/regions important for classification learning, we propose and develop a Hierarchical Active Learning framework that actively builds and labels a hierarchy of input regions. Briefly, our framework starts by identifying general regions covering substantial portions of the input data space. After that, it progressively splits the regions into smaller and smaller sub-regions and also acquires class proportion labels for the new regions. The proportion labels for these regions are used to gradually improve and refine a classification model induced by the regions. We develop three versions of the idea. The first two versions aim to build a single hierarchy of regions. One builds it statically using hierarchical clustering, while the other one builds it dynamically, similarly to the decision tree learning process. The third approach builds multiple hierarchies simultaneously, and it offers additional flexibility for identifying more informative and simpler regions. We have conducted comprehensive empirical studies to evaluate our framework. The results show that the methods based on the region-based active learning can learn very good classifiers from a very few and simple region queries, and hence are promising for reducing human annotation effort needed for building a variety of classification models.
Forming 5<sup>th</sup>-11<sup>th</sup>-Grade Pupils’ Soft Skills during the Russian Language Competitions

The paper summarizes experience of organizing competitions that promote formation of 5th-11th-grade pupils’ soft skills (XXI-century skills). The author identifies techniques to develop learners’ soft skills, such as creative and critical thinking, cooperativity and communication. Scientific originality of the study lies in the fact that the researcher describes the process of forming XXI-century skills taking into account the potential of modern educational environment. The research findings are as follows: it is proved that schoolchildren’s participation in the Russian language competitions largely promotes formation of their soft skills.
CURVEilance

It is an inevitable fact that social interaction nowadays is heavily mediated and distorted through the social network system. The presence of various media replicates our images to reproduce under a confirmation bias in our cognitive process. There has been a distorted gap between the original and reproduced images to reveal our identity in this process. The relationship form through this refracted self-image affects the user and the other users and eventually forms a complicated surveillance system.
Imitation of Compensator and Adaptive Processes in Biosystems

Introduction.The functioning efficiency of any biosystem (BS), regardless of its hierarchy level, depends on its adapt ability to changes in the environment or its state.Adaptive processes are implemented at the molecular, subcellular, cellular, tissue, organ, systemic and organismal levels.This adaptation can occur with the help of various mechanisms, has different dynamic characteristics, be accompanied by different energy costs and be more or less efficient.In this regard, it is expedient to identify as accurately as possible the type of adaptive process and assess its stressfulness for BS.In our opinion, such a classification can be carried out by formal markers of adaptive processes (by graphs) using mathematical methods of their description.The purpose of the paper is to develop a formalized classification of adaptive processes types based on mathematical modeling of their dynamics.Methods: structural and functional modeling using the approaches and mathematical apparatus of the theory of automatic control, using differential equations, probabilistic approaches and graphical analysis.Results.The structurally functional model of the adaptive process is presented as three connected subsystems.The output process of the first subsystem is described by an exponential function with a constraint, of the second -a logistic (S-shaped) function, of the thirdtheir difference, i.e. unimodal curve.The operation of such a system is considered for different ratios of the parameters of the transfer functions of the subsystems (conversion factors and time constants).It is proposed to use the output function of this system as an identifier of various types of adaptive reactions: adaptation, compensation, reparative regeneration and a pathological process.Criteria for definition of such a distinction are proposed using integral and dynamic characteristics of mathematical models.Conclusions.The proposed three-block diagram of the adaptive process makes it possible to perform its mathematical description in the simplest and most adequate form.
Dataset Expansion by Generative Adversarial Networks for Detectors Quality Improvement

Modern neural network algorithms for object detection tasks require a large la-belled dataset for training. In a number of practical applications creation and an-notation of large data, collections requires considerable resources which are not always available. One of the solutions to this problem is creation of artificial images containing the object of interest. In this work the use of generative adversarial networks (GAN) for generation of images of target objects is proposed. It is demonstrated experimentally that GAN’s allows to create new images on the basis of the initial collection of real images on background images (not containing objects), which simulate real images accurately enough. Due to this, it is possible to create a new training collection containing a greater variety of training examples, which allows to achieve higher precision for detection algorithm. In our setting, GAN training does not require more data than is required for direct detector training. The proposed method has been tested to teach a network for detecting unmanned aerial vehicles (UAVs).
Visualization of the Stress-Strain State of Shell Structures Using Virtual and Augmented Reality Technologies

The paper describes a mathematical model of changes in the geometry of thin-shell structures for visualization of the analysis data on their stress-strain state (SSS). Based on this mathematical model, a visualization module for shell SSS visualization using VR and AR technologies was developed. The interactive visualization environment Unity 2019.3 and C# programming language were used. The interactive visualization module makes a 3D image of a shell structure and visualizes the SSS either through heat maps over the shell or through the changes in the shell geometry on the basis of the shell type, its geometric characteristics, and SSS analysis data (transferred to the visualization module by means of a JSON file). While working on the visualization module, the authors developed a system of components that makes it possible to visualize any 3D surface with coordinate axes (including numbers with a pitch determined automatically), visualize heat maps with a graduated scale, visualize a mesh over the graph to improve the perception of the surface deformations. The middle surface can also be deformed on the basis of SSS analysis data. This solution increases the efficiency of the work of specialists in civil engineering and architecture and can be used when training specialists in courses on thin-shell structures and procedural geometry.
Research on the hardware detection device of the IR46 intelligent IoT electric energy meter

Abstract Based on the characteristics and design concept of the IR46 intelligent IoT electric energy meter (hereinafter referred to as the IoT electric energy meter), from the perspective of verifying its design, practicability, and quality, a hardware detection device solution that satisfies the IoT electric energy meter is proposed. It satisfies its multi-core modular hardware detection requirements, can effectively guarantee the network access detection of IoT electric energy meters, improve detection efficiency, and reduce the difficulty of development of electric energy meter manufacturers. A reasonable hardware detection device solution is provided for the implementation of IR46 electric energy meters in China.
The Reform of Publishing Format and the Reconstruction of Editing Power in the Era of Intelligence

Intelligence represents the future direction of media development.The combination of intelligent technology and publishing media brings penetration and deep application, which makes the content production from the original manual process into a highly automated machine workflow.This eliminates the production relations and inherent boundaries of the traditional publishing field, changes the thinking mode and working mode of the relevant subjects in the original publishing ecological chain, and reshapes the publishing process and industrial ecology.Under the background of integrating new demands, new formats, new technologies and new talents, the prospect and analysis of publishing formats in the intelligent era and the establishment of an analysis framework of editorial power in the intelligent era are helpful for us to grasp the development trend of intelligent publishing and build an intelligent publishing format in line with ecological development.Therefore, starting from the future intelligent publishing scene, this paper discusses the reconstruction and reconstruction of the publishing content value by media intelligence, expounds the concept of editing power in the intelligent era, and from the development trend of publishing formats in the intelligent era, the ability development of editors, the main body of publishing industry, is viewed.The research shows that: the editing work in the intelligent era is not only limited to editing and proofreading work, but also closely linked with the times and integrated with the development of technology; the editing ability in the intelligent era is composed of learning ability, new human-computer cooperation quality and competency of intelligent work field; editors in intelligent era must develop their own core literacy to improve digital students in intelligent era.
Depth-Aware Arbitrary Style Transfer Using Instance Normalization

Style transfer is the process of rendering one image with some content in the style of another image, representing the style. Recent studies of Liu et al. (2017) show that traditional style transfer methods of Gatys et al. (2016) and Johnson et al.(2016) fail to reproduce the depth of the content image, which is critical for human perception. They suggest to preserve the depth map by additional regularizer in the optimized loss function, forcing preservation of the depth map. However these traditional methods are either computationally inefficient or require training a separate neural network for each style. AdaIN method of Huang et al. (2017) allows efficient transferring of arbitrary style without training a separate model but is not able to reproduce the depth map of the content image. We propose an extension to this method, allowing depth map preservation by applying variable stylization strength. Qualitative analysis and results of user evaluation study indicate that the proposed method provides better stylizations, compared to the original AdaIN style transfer method.
Simulation in the 4th Industrial Revolution

Simulation itself is not a new notion; it has been there since the invention of computers (before the 4th Industrial Revolution). However, with the arrival of the 4th Industrial Revolution, where things change at breakneck speed, the importance of simulation is even now more amplified. In this chapter, we will give examples of the various types of simulations available as well as some examples, and describe the benefits of simulation, especially in the context of the 4th Industrial Revolution. The following examples are covered: controller design, mechanical systems, manufacturing systems, transport systems, physically responding simulations, and virtual reality simulation.
Implementasi Model Peta Konsep dalam Peningkatan Kemampuan Bercerita Siswa SDN 69 Kota Banda Aceh

Implementing the concept map learning model is one of the efforts to improve the ability of grade fives tudents in telling the Prophets stories at SDN (public elementary school) 69 in Banda Aceh. In this study, the concerns were on the teacher’s activity in the implementation of the concept map learning model, the students’ activity in the concept map learning model, and the improvement of the students' story telling skills within the concept map learning model. The subject of this study was the improvement of storytelling skills by using the concept map model. This study employed a three-cycle classroom action research, in which each cycle consisted of planning, implementation, observation, and reflection. Data were collected by oral test and observation, and analyzed quantitatively.The results of the study showed that there was an increase in the students’ storytelling test results, including prior to the intervention, and in all three cycles. The classical average score reached 60.62 in cycle I,63.87 incycle II, and increased to 75.87 in cycle III. The results achieved in cycle III have fulfilled the average score target of 73, meaning that thestudents’ ability in telling story had improved after using the concept map model. This improvement also suggested that the concept map learning model was successful, as it was further reflected by the increase of the teacher’s and the students’ activities. Theobservation results revealed that the teacher’s activity in cycle I was 67.5%, rose to 75% in cycleII, and to 92.5% in cycle III. In terms of the students’ activity, in cycle I the proportion was 59.37%, and then rose respectively to 74.47% in cycle II, and 85.97% in cycle III.
IMPLEMENTATION OF STAD LEARNING STRATEGIES (STUDENTS-TEAM-ACHIEVEMENT-DIVISION) TO IMPROVE STUDENT'S ABILITY IN ENGLISH

The research aims to improve student's ability in english by using the stad learning strategy implementation (students-team-achievement-division). The design of this research is classroom action research, in which a researcher and collaborator take part actively in the class to conduct teaching and make observations during the activity. In this study, researcher used cooperative learning type STAD or Students-Team-Achivement-Division. In the type of STAD cooperative learning, students will be divided into groups of 4-6 people who prioritize class heterogeneity in academic achievement. The researchers provided a lesson, and students in the group are expected to master the lesson. The results of the research showed that the STAD type cooperative learning is effective in improving students' ability in English. The implementation of cycle 1 and cycle 2 showed an increase in student achievement. There were 9 (45%) students who were successful and 11 (55%) of them failed. Then, the success increased to 20 (100%) students in cycle 2. In other words, all students can achieve learning achievement in cycle 2.
The Study on Simulation of Parallel Circuit Resistance in Motor

The stall phenomenon which happens in loaded motor is unqualification in application.Meanwhile it may measure the maximum property of motor in manufacture.So the phenomenon of multiple circuit is analyzed to find a simulation of electrical state to predict the maximum currency and torque which is a necessary method to be proceeded up to now before design.We find that the simulation fits well to the reference.The conditions of multiple circuit with t=6s, U=12V result in the biggest stall force according to rotation to change time and voltage.Then it is t=8s, U=10V; t=10s, U=8V and t=12s, U=6V in turns.As for torque it is t=6s, U=12V; t=8s, U=10V; t=6s, U=12V and t=12s, U=6V in truns.When the voltage decreases resistance declines the rotation will decline in the end.