Reviewerly/exHarmony huggingface dataset:
Educational practice as a component of the system of forming future teachers’ digital culture

The digital economy and the rapidly developing digital society have an impact on the training of modern teachers and put forward requirements for the level of development of their digital literacy, digital skills and, ultimately, the level of digital culture. However, current regulatory documents do not always reflect the current requirements for teachers’ digital competencies and do not offer tools to measure their level of formation. The authors analyzed the existing frameworks of digital competencies and offered a model of the modern school teachers’ digital culture, its technological component, for its introduction into the educational process of teacher training. The article describes the elements of the model, normative documents and theoretical provisions that underlie it. The first stage of digital culture formation is considered in educational (fact-finding) practice for curricula formed according to FSES 3++. The objectives of the practice are formulated, competencies are selected from the educational standard, its content, thematic planning and planned results in the form of a decomposition of digital skills from the model of the digital culture of the teacher are determined. This type of practice is being implemented at the university for the first time and sets the task of forming the foundation of the model, namely, general user ICT competence (digital literacy).
Deep Feature Attention and Aggregation for Real-Time Head Semantic Segmentation

Abstract This paper proposes a mobile CNN(Convolutional Neural Networks) architecture named FAANet(Feature Attention Aggregation Net), which focused on self-attention and feature aggregation for real time head semantic segmentation using limited computation resource. The proposed network is based on a single lightweight backbone cnn network and aggregates discriminative features through multiple different scale feature maps, and the different feature maps are fused using self-attention sub-block. Using the proposed multi-scale feature communication and propagation, FAANet substantially reduces the size of model, but still achieves big receptive field and strengthen the generalization ability, which achieves a balance between the computation cost and performance. Experiments on head segmentation dataset demonstrate the improved performance of FAANet with 10 times less FLOPs and 3times faster than the existing state-of-the-art fast semantic segmentation methods and the proposed method can achieve comparable accuracy. Specifically, when computed on one NVIDIA Titan X card, it can achieve 98.3% Mean IOU on our mixed test dataset with only 1.2 GFLOPs and 100 FPS while inferring on a 1024*1024 input image.
Meta analysis the use of e-modules based on research based learning models

Abstract This study aims to analyze the use of E-MODUL based on research based learning on student learning outcomes. The method of this study is a meta-analysis with a sample of 10 articles in national journals. Research meta-analysis shows that the effect of using E-MODUL based on reasear based learning on student learning outcomes is more effective. The results showed that the effect size on the effect of learning outcomes was carried out on 10 national journal samples categorized as medium. This means that the influence of the use of E-MODULES can improve student learning outcomes greater than using conventional learning.
Empirical Confidence Models for Supervised Machine Learning

We present a new methodology for assessing when data-based predictive models can be trusted. Particularly, we propose to learn a model from experimentation that determines, for a given labeled data set and a learning technique, when the model generated by the respective technique on the given data can be trusted to perform within specified accuracy limits. That is to say, we apply machine learning to machine learning: We repeatedly use a technique to generate models, referred as primary model, for a supervised regression problem. Based on the resulting model performance on a hold-out validation set, we then learn when the trained primary model can be expected to perform well and when there is a concern regarding the trustworthiness of that model.
Almost Uniform Sampling From Neural Networks

Given a length n sample from ${{\mathbb{R}}^d}$ and a neural network with a fixed architecture with W weights, k neurons, linear threshold activation functions, and binary outputs on each neuron, we study the problem of uniformly sampling from all possible labelings on the sample corresponding to different choices of weights. We provide an algorithm that runs in time polynomial both in n and W such that any labeling appears with probability at least${\left( {\frac{W}{{2ekn}}} \right)^W}$ for W < n. For a single neuron, we also provide a random walk based algorithm that samples exactly uniformly.
Validity of student worksheets based on inquiry based learning models assisted by tracker application

Abstract This study aims to determine the validation of student worksheets based on inquiry based learning models assisted by tracker application on momentum and impulse material of high school physics learning. This research method uses research and development with a model Plomp. A data collection instrument using a validity sheet previously validated bay three validators. The results of the validation analysis sheet show worksheet based on inquiry based learning models assisted by tracker application developed with a valid criteria.
A Mobile-based Assessment in Chemistry

An innovative mobile-based assessment application has been developed to practice pupils' digital literacy to encounter industrial revolution 4.0. The assessment has several advantages compared to traditional assessment using paper and pencil. This study conducted to analyze Indonesian pupils' experience and their perception of using a mobile-based assessment in chemistry subjects at school. Thirty-two secondary pupils participated in this study. They were not only asked by eleven reasoned closed-ended questions after the test but also observed along the assessment. The questions were asking for the design, the usability, the functionality, and the novelty of the assessment tool. Almost all pupils showed positive responses to the mobile-based assessment. Pupils stated that using mobile-based assessment is a new experience for them. The findings data suggested that a mobile-based assessment is an efficient and useful learning assessment tool in education. It might assist teachers in doing assessments effectively in the digital era.
A Dynamic Representation Solution for Machine Learning-Aided Performance Technology

This paper illuminates some root causes of confusion about dynamic representation in music technology and introduces a system that addresses this problem to provide context-dependent dynamics for machine learning-aided performance. While terms used for dynamic representations like forte and mezzo-forte have been extant for centuries, the canon gives us no straight answer on how these terms must be applied to literal decibel ranges. The common conception that dynamic terms should be understood as context-dependent is ubiquitous and reasonably simple for most human musicians to grasp. This logic breaks down when applied to digital music technologies. At a fundamental level, these technologies define all musical parameters using discrete numbers, rather than with continuous data, making it impossible for these technologies to make context-dependent decisions. The authors give examples in which this lack of contextual inputs in music technology often leads musicians, composers, and producers to ignore dynamics altogether as a concern in their given practice. The authors then present a system that uses an adaptive process to maximize its ability to hear relevant audio events, and which establishes its own definition for context-dependent dynamics for situations involving music technologies. The authors also describe a generative program that uses these context-dependent dynamic systems in conjunction with a Markov model culled from a living performer-composer as a choice engine for new music improvisations.
Updating Predictive Model by Concept Drift Detection in Semiconductor Package Test

In semiconductor manufacturing, it is difficult to maintain a high level of quality due to the miniaturization of the process and the large capacity of the product. Also the quality requirement of the customer is increasing. Observing and managing the quality is an essential element in the semiconductor manufacturing process. Because the data distribution changes as the manufacturing process and inspection conditions change, the predictive model generated from the previous data does not match the new data, so the relevant model must be updated. In this paper, we propose a method to determine the predictive model update by detecting the change of the importance of variables in the semiconductor package test. The proposed method can classify the lots efficiently and with high accuracy in a continuously changing data distribution.
Texture Synthesis and Style Transfer for Aesthetic Design Creation

Content rendering of the image by combining different styles has always been a challenging image processing task. The paucity of image representations and content separation from the respective semantic style of the images has been quite an arduous process. However, the recent advancements in image processing, computer vision and state-of-the-art models like VGGNet and ImageNet have helped to achieve human-like accuracy and efficiency in various tasks like generating artistic images, creating design patterns from content images, identifying objects within images, image recognition and segmentation, etc. One such application in this field is deep neural style transfer. It is an optimization technique in which the semantic content of one image is superimposed with the style of another image. Thus, this paper focuses on implementing deep neural style transfer to generate styled images as an application for interior graphic designing and apparel industries. The images generated can be personalized according to the users’ custom design and fashion styles.
Generating Graph from 2D Flowchart using Region-Based Segmentation

Visual content-based information retrieval intended message retrieval from infographics and their applications are gaining attention in many scientific domains. This seeks an understanding of the structure and correct semantics of infographics. Parsing flowchart and analyzing each element of infographics in the correct sequence is challenging. Using computer vision techniques, reconstruction of the structure in digital format is possible and can help to understand the features of the structure. This paper describes technical details to extract various elements of a 2D flowchart in a meaningful sequence, relate elements as connected in a flowchart and represent in conventional data structures. Region-based segmentation with adaptive thresholding and morphological operations are applied for feature extraction. The graph is generated by maintaining the semantics of the flowchart. The experimental results are evaluated for semantic correctness of flowchart and found that 90% of generated graphs are semantically correct as a flowchart.
Developing a Digital Scholarship Ecosystem

This research presents elements necessary to develop a Digital Scholarship Research Ecosystem for a university, college or research institution. Software systems, hardware, human resources and timelines are outlined with brief theoretical overviews and a pragmatic focus on 'open-source' (freely available) software, best-in-class applications and global best practices. Major digital scholarly system components in a larger digital ecosystem are discussed: Online Institutional Collection Repositories (D-SPACE), Online Research Data Repositories (DATAVERSE), Identity Management Systems (ORCID), Electronic Thesis and Dissertation Management Systems (VIREO), Academic Journal Systems (OJS3), Digitization Labs, User Interface Software (OMEKA). System assessment, synergistic possibilities and future directions are reviewed. This research arises from a successful five-year phased implementation of such a digital ecosystem for Texas State University Libraries, a large US university research library system. This scholarly ecosystem is suitable for any university, college, research institution or academic research library interested in setting up or building on such an infrastructure and enabling faculty and graduate students with their scholarly research online.
Single Image Blind Deblurring with Deep Recursive Networks

Single image deblurring aims to retore the latent sharp images from the corresponding blurred ones, which is a highly ill-posed problem. In this paper, we present a deep recursive network based on generatetive adversarial networks (GANs) that restores sharp images in an end-to-end manner where blur is caused by various sources, our recursive neural network can greatly reduce the computation and complexity of the model. Then we use the least square discriminator to prevent the gradient from disappearing and make the training process more stable. We also add an adversarial loss to make the generated images look more realistic and a perceptual loss to generated better image. Experimental results have shown that our proposed method produces better performance and faster time.
Application of Block Chain in Digital Copyright Protection

Artificial intelligence, big data and block chain, three new trend is more and more frequently in people's life, the copyright protection of domain blocks also emerge in the chain that a new science and technology, the technology in improving the quality of the copyright protection at the same time reduces the cost of copyright protection, has played an important role in the field of copyright protection, and publishing staff work efficiency has been greatly improved. This technology has more transparency and centrality than traditional publishing technology, and cannot be changed at will. As a result, all aspects of copyright protection technology have changed greatly. Block chain technology for the improvement of copyright protection mechanism is not only an unprecedented opportunity, but also a serious challenge to be faced. On the basis of a brief introduction to the principle of block chain, this paper expounds the application of domestic block chain in digital copyright protection, analyzes the shortcomings of current domestic digital copyright protection, expounds the impact on copyright protection and gives solutions, and provides beneficial inspiration and reference for future research and application of block chain.
ЕКСПЕРИМЕНТАЛЬНА ПЕРЕВІРКА ФОРМУВАННЯ ІНФОРМАЦІЙНОЇ КОМПЕТЕНТНОСТІ МАЙБУТНЬОГО ПЕДАГОГА ПРОФЕСІЙНОГО НАВЧАННЯ У ЗАКЛАДАХ ВИЩОЇ ОСВІТИ

&#x0D; &#x0D; &#x0D; Training teachers is an important task of modern pedagogy and requires new approaches to the organization of the educational process. The use of information and communication technologies for the purpose of forming the information competence of the future vocational teacher is an important element of the formation of his professional competence. The article describes the stages of conducting a pedagogical experiment to develop the information competence of a future teacher of vocational training in domestic higher education institutions. The organization and methodology of the experimental study are described in detail, its results are given, the methods of diagnostics are described and so on.&#x0D; &#x0D; &#x0D;
The Method of Organizing a Service-oriented User Interface for Multi-agent Information and Control Systems

In this paper we describe a proposed method for organizing a service-oriented interface for multi-agent information and control systems, focused on solving large-scale problems, based on distributed computing. We dwell on the detailed description of the architecture and elements of the user interface, based on the structure of the solved task parameters. The translation of these descriptions into a graphical representation within the framework of a dynamically generated visual shell of the user is proposed.
Exploring and improving industry-academia communication in software engineering

Background: Despite the mutual benefit of the industry-academia partnership, the level of joint work is still low. For this reason, the interest in connecting research and practice has increased recently in the academic community. Objective: This research aims to design and apply approaches to improve the knowledge exchange between academic researchers and software engineering practitioners. Methodology: This work can be seen from a design science perspective. Following the design-science paradigm, the knowledge regarding a phenomenon is obtained through the design and evaluation of solutions that apply in a specific context. Consequently, this research work proposes and evaluates approaches to bridge the communication gap. Results: Two approaches have been explored and partially evaluated. The SERP-taxonomy architecture that can be used to describe and link research results and industry challenges, and rapid reviews to foster communication between industry and academia. Conclusion: This thesis will provide empirical evidence of the application of collaborative approaches to improve industry-academia communication and get closer research and practice.
A False Negative Cost Minimization Ensemble Methods for Customer Churn Analysis

The primary objective of this research is to develop hybrid decision tree induction methods based on the decision tree C4.5 algorithm and ensemble methods, taking into account cost-sensitivity for the purpose of minimizing either misclassification cost, false negative cost or false positive cost. This paper proposed two cost-sensitive learning methods by modifying the model weight of AdaBoost.M1 for churn analysis in the telecommunication industry. Method 1 applies the ratio of false negative cost over true negative cost to make the weight of false negative heavier than the weight of false positive. While Method 2 combines error rate weighting with false negative cost weighting in order to let examples have heavier weight values for future training in the next learning cycle. The proposed methods have been evaluated with a series of experiments to prove its ability to reduce either false negative cost or misclassification costs. Microsoft Azure Machine Learning Telco Customer Churn and IBM Watson Studio Telecommunication Customer Churn datasets, which include the cost value for each instance, are used for the experiments. The proposed Method 1 able to obtain the lowest false negative cost comparing with the original AdaBoost.M1.
Data Returns and Reliability Metrics From the Indian Deep Ocean Wave Measurement Buoys

Abstract Reliable performance is the key requirement for deep ocean wave measurement buoys as failures lead to loss of data and expensive reinstallations. From more than 0.37 million demanding offshore wave measurement buoy-hours, the data returns from the buoys operating in the Bay of Bengal and the Arabian Sea were 88% and 95%, respectively. The mean time between failures of the buoys was 1.8 years in the Bay of Bengal and 3.8 years in the Arabian Sea, and the mean time to restore was 90 and 68 days, respectively. The offshore failure events, the technical developments for improving the performance, and the strategies adopted to mitigate the failures are presented in detail. The significance of the technical features incorporated for improving the reliability of the wave measurement buoy subsystems is quantitatively represented through fault tree modeling and simulations based on standards and the field failure data.
Using Lean Method in English Language Classes

New approaches, methods, and tools are necessary for the implementation of the modern management system in educational organizations. The main purpose of this chapter is to determine using the lean method in foreign language teaching. The lean method is new in educational systems, but it is used in different spheres of industries all over the world. This chapter shows the history, its implementation, experiments, and how it can be used and developed further. Lean in education will create a clear understanding of its importance on lean process and increase the motivations of administrations, teachers, and students.
Controversial Issues on Corpus Delicti in the Field of Art in the Criminal Legislation of the Russian Federation

The choice of the subject of research — the field of scientific knowledge related equally to both criminal law and the limitless world of art — is determined by the goal of finding out the content of modern criminal law policy in the field of art in Russia. In the paper, the author considers the main elements of art crimes existing in the national criminal law through the prism of the specifics of the art sphere, and reveals some shortcomings of legislative structures establishing responsibility for the corresponding crimes. The author expresses her position regarding the possibility and necessity of considering art as an independent and separate object of criminal law protection in criminal law, which is justified, among other things, by the close attention required by modern legislative and law enforcement bodies to the phenomenon of art crimes.
Towards Web-based Etymological Hanzi Learning

Modern-day Chinese characters, or Hanzi, originate from the ancient oracle-bone scripts (甲骨文). Such etymological relationship creates unique opportunities for Hanzi learning. This work proposes to use Web-based tools and the latest machine learning techniques to scale-up and enhance etymological Hanzi learning. By sharing our implementation details from launching an interactive sketch-based learning exhibition, we hope education-AI becomes more widely incorporated into today’s commercial Web applications. Our demo video can be found at: https://youtube/1met8Uk5pA0
3D Path Planning Based on Improved Particle Swarm Optimization Algorithm

Aiming at the problem that the particle swarm optimization (PSO) algorithm easy to fall into the local optimum. Combining the advantages of local search of bacterial foraging optimization algorithm (BFO), this paper introduces the chemotaxis and dispersal operation of the bacterial foraging algorithm into the PSO algorithm to obtain hybrid algorithm. This paper applies the hybrid algorithm to 3D path planning. The simulation results show that the hybrid algorithm effectively improves the PSO algorithm’s defects that are easy to fall into local optimal, improves the optimization efficiency and accuracy of the algorithm, and shows good performance in 3D path planning.
Asymptotic properties of statistical estimators using multivariate Chi-squared measurements

This paper studies the problem of estimating a parameter vector from measurements having a multivariate chi-squared distribution. Maximum likelihood estimation in this setting is unfeasible because the multivariate chi-squared distribution has no closed form expression. The typical approach to go around this consists in considering a sub-optimal solution by replacing the chi-squared distribution with a normal one. We investigate the theoretical properties of this approximation as the number of measurements approach infinity. More precisely, we show that this approximation is strongly consistency, asymptotically normal and asymptotically efficient. We consider a source localization problem as a case study.