Reviewerly/exHarmony huggingface dataset:
Organization of the Educational Process on Natural Science Training in Higher Education Institutions on the Basis of Innovation and Heuristics

The relentless informational variability in the field of natural sciences, today's need for the training of highly competent, versatile, apologetic future specialists, as well as the low motivation of psychology students to teach natural disciplines, was established by us in the process of their questioning, require the search for new, non-standard solutions in the organization of natural science training of future psychologists in institutions of higher education. We believe that one of the effective ways to solve this problem is the organization of the educational process with the natural scientific training of future psychologists in higher education institutions based on innovation and heuristic. To organize the educational process from the natural science training of future psychologists on the basis of innovation and eurestiality, there is a need to determine such ultra-innovation pedagogical technologies and teaching methods that will meet the requirements of the time, contribute to the formation of psychology students' motivation to study natural sciences and improve the quality and the effectiveness of natural science training. We have characterized pedagogical technologies, namely: coworking technology, barcamp technology, workshop technology, SMART technology, BYOD technology and such innovative methods as: lecture-visualization, video lecture, debates, round table, electronic brain assault , a laboratory experiment and a design-role-playing game, which, in our opinion, will contribute to the implementation of the educational process with the natural science training of future psychologists in higher education institutions based on innovation and heuristic.
Metadata based Classification Techniques for Knowledge Discovery from Facebook Multimedia Database

Classification is a parlance of Data Mining to genre data of different kinds in particular classes. As we observe, social media is an immense manifesto that allows billions of people share their thoughts, updates and multimedia information as status, photo, video, link, audio and graphics. Because of this flexibility cloud has enormous data. Most of the times, this data is much complicated to retrieve and to understand. And the data may contain lot of noise and at most the data will be incomplete. To make this complication easier, the data existed on the cloud has to be classified with labels which is viable through data mining Classification techniques. In the present work, we have considered Facebook dataset which holds meta data of cosmetic company’s Facebook page. 19 different Meta Data are used as main attributes. Out of those, Meta Data ‘Type’ is concentrated for Classification. Meta data ‘Type’ is classified into four different classes such as link, status, photo and video. We have used two favored Classifiers of Data Mining that are, Bayes Classifier and Decision Tree Classifier. Data Mining Classifiers contain several classification algorithms. Few algorithms from Bayes and Decision Tree have been chosen for the experiment and explained in detail in the present work. Percentage split method is used to split the dataset as training and testing data which helps in calculating the Accuracy level of Classification and to form confusion matrix. The Accuracy results, kappa statistics, root mean squared error, relative absolute error, root relative squared error and confusion matrix of all the algorithms are compared, studied and analyzed in depth to produce the best Classifier which can label the company’s Facebook data into appropriate classes thus Knowledge Discovery is the ultimate goal of this experiment.
Language Dynamism: A Cross-Cultural Analysis of Indonesian Political Leader Speech During Covid-19 Pandemic

Politicians achieve success thanks to the excellent use of rhetoric they use to convince audiences of the validity of their views, the use of elegant and persuasive language that is conveyed smoothly and carefully (Jones and Peccei (2004) as cited in Bayram (2010)). This research aims to find out how the speech strategy and communication style used by President Joko Widodo in his speech. And how it was when looking from cross-cultural perspective. The research started by Categorizing each phrase, sentence and sentences. Then analyzing the speaking strategy and style of communication used by President Joko Widodo by paying attention to every phrase, sentence, and sentences. Last, explaining from cross-cultural perspective. The result of this research shows that President Joko Widodo dominantly used argumentative strategy in both speeches. And for communication style analysis, it is found that President Jokowi used four different styles.
On the Robustness of Vision Transformers to Adversarial Examples

Recent advances in attention-based networks have shown that Vision Transformers can achieve state-of-the-art or near state-of-the-art results on many image classification tasks. This puts transformers in the unique position of being a promising alternative to traditional convolutional neural networks (CNNs). While CNNs have been carefully studied with respect to adversarial attacks, the same cannot be said of Vision Transformers. In this paper, we study the robustness of Vision Transformers to adversarial examples. Our analyses of transformer security is divided into three parts. First, we test the transformer under standard white-box and black-box attacks. Second, we study the transferability of adversarial examples between CNNs and transformers. We show that adversarial examples do not readily transfer between CNNs and transformers. Based on this finding, we analyze the security of a simple ensemble defense of CNNs and transformers. By creating a new attack, the self-attention blended gradient attack, we show that such an ensemble is not secure under a white-box adversary. However, under a black-box adversary, we show that an ensemble can achieve unprecedented robustness without sacrificing clean accuracy. Our analysis for this work is done using six types of white-box attacks and two types of black-box attacks. Our study encompasses multiple Vision Transformers, Big Transfer Models and CNN architectures trained on CIFAR-10, CIFAR-100 and ImageNet.
Real-Time FPGA Accelerated Stereo Matching for Temporal Statistical Pattern Projector Systems

The presented paper describes a hardware-accelerated field programmable gate array (FPGA)-based solution capable of real-time stereo matching for temporal statistical pattern projector systems. Modern 3D measurement systems have seen an increased use of temporal statistical pattern projectors as their active illumination source. The use of temporal statistical patterns in stereo vision systems includes the advantage of not requiring information about pattern characteristics, enabling a simplified projector design. Stereo-matching algorithms used in such systems rely on the locally unique temporal changes in brightness to establish a pixel correspondence between the stereo image pair. Finding the temporal correspondence between individual pixels in temporal image pairs is computationally expensive, requiring GPU-based solutions to achieve real-time calculation. By leveraging a high-level synthesis approach, matching cost simplification, and FPGA-specific design optimizations, an energy-efficient, high throughput stereo-matching solution was developed. The design is capable of calculating disparity images on a 1024 × 1024(@291 FPS) input image pair stream at 8.1 W on an embedded FPGA platform (ZC706). Several different design configurations were tested, evaluating device utilization, throughput, power consumption, and performance-per-watt. The average performance-per-watt of the FPGA solution was two times higher than in a GPU-based solution.
Preparation of Quantum Superposition Using Partial Negation

The preparation of a quantum superposition is the key to the success of many quantum algorithms and quantum machine learning techniques. The preparation of an incomplete or a non-uniform quantum superposition with certain properties is a non-trivial task. In this paper, an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -qubits variational quantum circuit using partial negation and controlled partial negation operators is proposed to prepare a quantum superposition from a given space of probability distributions. The speed of the preparation process and the accuracy of the prepared superposition has special importance to the success of any quantum algorithm. The proposed method can be used to prepare the required quantum superposition in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathscr {O}(n)$ </tex-math></inline-formula> steps and with high accuracy when compared with relevant methods in literature.
Three-Dimensional Fuzzy Control of Ultrasonic Cleaning

Abstract Consideration of ultrasonic cleaning as a process with distributed parameters enables reduction of power consumption. This approach is based on establishment of control over the process depending on fixed values of ultrasonic responses in set points. The initial intensity of radiators is determined using a three-dimensional (3D) interval type-2 fuzzy logic controller essentially created for processes with distributed parameters, as well as complex expert evaluation of the input data. The interval membership functions for the input and output data consider the space heterogeneity of ultrasonic cleaning. A rule base is formed, which is 2D and not dependent upon the number of input and output parameters. A model illustrating ultrasonic cleaning with a 3D interval type-2 fuzzy logic controller is designed. Comparative analysis of the output parameters of the proposed model and the traditional method indicates an increase in the energy efficiency by 41.17% due to application of only those ultrasonic radiators that are located next to the contamination.
A rational delegating computation protocol based on reputation and smart contract

Abstract The delegating computation has become an irreversible trend, together comes the pressing need for fairness and efficiency issues. To solve this problem, we leverage game theory to propose a smart contract-based solution. First, according to the behavioral preferences of the participants, we design an incentive contract to describe the motivation of the participants. Next, to satisfy the fairness of the rational delegating computation, we propose a rational delegating computation protocol based on reputation and smart contract. More specifically, rational participants are to gain the maximum utility and reach the Nash equilibrium in the protocol. Besides, we design a reputation mechanism with a reputation certificate, which measures the reputation from multiple dimensions. The reputation is used to assure the client’s trust in the computing party to improve the efficiency of the protocol. Then, we conduct a comprehensive experiment to evaluate the proposed protocol. The simulation and analysis results show that the proposed protocol solves the complex traditional verification problem. We also conduct a feasibility study that involves implementing the contracts in Solidity and running them on the official Ethereum network. Meanwhile, we prove the fairness and correctness of the protocol.
Population Diversity in Ant-inspired Optimization Algorithms

Finding a balance between exploration and exploitation is very important in the case of metaheuristics optimization, especially in the systems leveraging population of individuals expressing (as in Evolutionary Algorithms, etc.) or constructing (as in Ant Colony Optimization) solutions. Premature convergence is a real problem and finding means of its automatic detection and counteracting are of great importance. Measuring diversity in Evolutionary Algorithms working in real-value search space is often computationally complex, but feasible while measuring diversity in combinatorial domain is practically impossible (cf. Closest String Problem). Nevertheless, we propose several practical and feasible diversity measurement techniques dedicated to Ant Colony Optimization algorithms, leveraging the fact that even though analysis of the search space is at least an NP problem, we can focus on the pheromone table, where the direct outcomes of the search are expressed and can be analyzed. Besides proposing the measurement techniques, we apply them to assess the diversity of several variants of ACO, and closely analyze their features for the classic ACO. The discussion of the results is the first step towards applying the proposed measurement techniques in auto-adaptation of the parameters affecting directly the exploitation and exploration features in ACO in the future.
Scout-bot: Leveraging API Community Knowledge for Exploration and Discovery of API Learning Resources

&#x0D; &#x0D; &#x0D; Application Programming Interface (API) is a core technology that facilitates developers’ productivity by enabling the reuse of software components. Understanding APIs and gaining knowledge about their usage are therefore fundamental needs for developers. Here, API documentation plays a pivotal role in enabling developers to take full advantage of the benefits brought by APIs. The quality of API documentation has therefore become an important concern given the celerity and dynamics at which APIs are now being made available to users. This article aims at exploring existing research in the area of API documentation in order to identify the associated quality dimensions addressed by the literature. The research is carried out as a systematic mapping study where 103 research papers selected from the literature were reviewed and a total of 5 core quality dimensions were identified and analyzed. By focusing on the two most relevant quality dimensions (understandability and completeness), this article presents an approach to enable API users to explore, discover and learn about APIs through API topic issues discussed in Stack Overflow (SO). We demonstrate the feasibility of our approach through Scout-bot, our tool for exploration and discovery of API topic issues.&#x0D; &#x0D; &#x0D;
Improved Content-Based Image Retrieval Technique for Query Generation in Mobile Networks

Image database searching is in rapid growth with an advancement in multimedia technology. To manage these kinds of searches Content-Based Image Retrieval is an effective tool. In this paper, existing CBIR techniques are analyzed and a new technique has been proposed which works based on Region-Based Convolutional Neural Network (RCNN). In the proposed approach first of all image dataset is uploaded to cloud and features are stored in a storage. Then Query image is enhanced, uploaded and features are extracted. After this feature set is compared with dataset and matched images are extracted and ranked as the closest match. Using this proposed methodology, the accuracy and precision values are compared and validated and it is observed that the proposed methodology shows better results than the existing techniques.
An Adaptive E-learning System for Teaching Mobile Applications﻿

Support for various aspects of personalization and adaptation of the reviewed tools is not at the desired level of abstraction - very often depends on the specific way in which it is assumed that a learning object will be combined, to build a learning path for going through an e-course, or to achieve learning goals. This document describes an innovative and more abstract model and related software system for adaptive e-learning. A software prototype for adaptive teaching of the disciplines "Mobile Applications" and "Programming applications for mobile devices", taught at Plovdiv University "Paisii Hilendarski” is presented. Objectives are discussed, based on an extensive overview in the field of adaptive e-learning systems and the software implementation of the e-learning tool is presented. Results of specific tests with study activities will be presented in future scientific publications.
Arizona State University: A Learning Enterprise Supporting P-12 Education in the COVID-19 Pandemic

Abstract Although no one was fully prepared for a pandemic in 2020, Arizona State University was ready and able to respond. As the COVID-19 virus began to spread across the world, ASU began moving university students to remote learning environments. As K-12 schools began to close across the nation, ASU disseminated elementary- and secondary-level educational resources and created hybrid learning opportunities for students of all ages. Three distinct categories of action defined the university’s response to COVID-19: Direct provision of education to P-12 learners Provision of human and intellectual capital (people and ideas) to P-12 schools Curation and provision of free educational resources to learners, families, and schools Many of the existing long-term commitments being pursued by various ASU units had helped the university develop capabilities that could immediately be applied to help elementary and secondary learners and the education professionals serving them during the pandemic. Some of the immediate responses accelerated the university’s efforts to pursue long-term actions that could help both schools and P-12 students and families integrate remote learning and instruction into effective education models. ASU responded rapidly to the educational challenges COVID-19 presented because the university had a high degree of institutional readiness in at least three key areas of operational excellence and organizational culture: A core set of preexisting commitments and functional capabilities in the area of technology-enhanced instruction Strong existing partnerships with P-12 schools An institutional vision to universal learning that demands a university be ready and able to deliver instruction to all learners across many modalities This chapter only represents a moment in time, the beginning of the pandemic, and the actions taken to support and ameliorate consequences. Since this time, much has happened.
Highly accurate optical flow method based on volumetric segmentation for 3D PIV

In this study, we present a new three-dimensional optical flow method based on volumetric segmentation for the velocity estimation of fluid flow. The proposed method uses a segmented smoothness term that is designed on the assumption that the particle velocity varies continuously in each segmented volume and discontinuously on the surfaces of the segmented volumes. Subsequently, the data term is proposed on the basis of the segmented volumes and the fluid mass conservation equation, which is derived from the Reynolds transport equation. In addition, the robust local level-set method is applied to segment the particle volume according to the velocity distribution of fluid flow. The proposed method is evaluated quantitatively on synthetic data and qualitatively on experimental data, and the velocity results are compared to the advanced 3D velocity estimation methods. The results indicate that the proposed method can obtain velocity fields with greater measurement accuracy for Tomo-PIV.
On BCH split metacyclic codes

<p style='text-indent:20px;'>Recently, Borello and Jamous have investigated some lower bounds on the dimension and minimum distance for dihedral codes, in analogy with the theory of BCH codes. In this paper, we extend some of their results to split metacyclic codes, that is, codes over semidirect products of cyclic groups.
Restriction of Fundamental Rights in a Context of Emergency: COVID-19 and the Aspect of Autonomy – Can Autonomy Be Limited?

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Next generation geophysical sensing: exploring a new wave of geophysical technologies for the energy transition

Disclaimer Text and Data Mining. Any automated analytical technique aimed at analysing text and data in digital form in order to generate information including - but not limited to - patterns, trends and correlations (so-called Text and Data Mining) may only be applied if it is done by a research organisation or cultural heritage institution solely for the purpose of (non-commercial) scientific research as mentioned in Clause 3 of Directive (EU) 2019/790 (the Digital Single Market Directive) and Clause 15n of the Dutch Copyright Act [Auteurswet]. Text and Data Mining for commercial purposes is not allowed; all rights in this respect are reserved pursuant to Clause 4 paragraph 3 of the Digital Single Market Directive and Clause 15o paragraph 1 of the Dutch Copyright Act.
Text-Image Retrieval With Salient Features

In recent years, deep learning has achieved remarkable results in the text-image retrieval task. However, only global image features are considered, and the vital local information is ignored. This results in a failure to match the text well. Considering that object-level image features can help the matching between text and image, this article proposes a text-image retrieval method that fuses salient image feature representation. Fusion of salient features at the object level can improve the understanding of image semantics and thus improve the performance of text-image retrieval. The experimental results show that the method proposed in the paper is comparable to the latest methods, and the recall rate of some retrieval results is better than the current work.
Design and Development of IoT-Based Tracking for Humans using Arduino

Technological advancement and innovation in the field of biometric identification technology and security of lives and properties have enabled the evolution of intelligent monitoring systems. In light of this, this paper intends to present the results of the hardware platform set up for the data collection of human tracking that would ultimately be integrated into the Kalman Filter Algorithm (KFA) to be developed in our further work. To provide a fast tool for creating a VLSI test bench, particularly for sensors, Arduino Mega 2560 was chosen and used as the heart of this hardware platform. As a consequence, its working principle and other applications were explored in this paper. Furthermore, since a two-dimensional Kalman Filter model would be developed, the positions and velocities of the object to be tracked (i.e., humans) were estimated in both x- and y-directions in a tabular form.
Value-based orientations as a normative-regulatory mechanism for the formation of professional worldview of future music teachers

The transition period of modern society can be observed in various aspects, particularly in the value-normative one. The study of the Genesis of the formation of personality’s value-based orientations in the current conditions of reforming educational sector in Ukraine and its integration into the European Higher Education Area are becoming prioritized. The research methodology is complex, combining theoretical and empirical methods. Theoretical study of the problem of values in philosophical-historical and psychological-pedagogical discourses creates a methodological basis for expanding the subject field of interdisciplinary branches of scientific knowledge: pedagogical axiology and philosophy of music education. The interdisciplinary context of the conceptual range of these disciplines makes it possible to modernize the value-based orientation in the content of art education. The problem of spiritual and value-based formation of the personality by means of music is actualized, which creates the prerequisites for the formation of a phenomenon of holistic professional worldview of future music teachers. The analysis of structural components of the outlined phenomenon shows that the normative and regulatory basis of professional worldview is a system of value-based orientations of the individual.