Reviewerly/exHarmony huggingface dataset:
Radial Basis Neural Network Trained Minimum Snap Trajectory for Quadrotor

This paper proposes radial basis neural network trained minimum snap trajectory for quadrotor unmanned aerial vehicle. The quadrotor is intended to fly over a set of waypoints with the least amount of jounce as possible. To do this, a minimum snap trajectory is created using velocity, acceleration, and jerk as boundary constraints. For the successful establishment of such a trajectory on real-world scenario, an accurate approximation approach is required. A compact design and a simple continuously differentiable data mapping are necessary for such an approximator. Radial basis neural networks are well-known for their qualities as universal approximators, making them ideal candidates for this task. As a result, the generated trajectory is trained using a radial basis neural network. Simulations are carried out using the MATLAB/SIMULINK software platform.
A One-class Model for Voice Replay Attack Detection

Replay attack poses a serious security concern for automatic speaker verification systems. Most of the existing replay detection methods cast the task to a binary classification problem. In this article, by analyzing distributions of genuine and replayed speech with a specifically designed database and summarizing the known artifacts in existing datasets, we show the potential shortcomings of the two-class approach in both discrimination and generalization, and discuss the advantage of the one-class approach. As a demonstration, we present our recent investigation on a novel one-class-based replay detection method, which models the discrepancy between the test speech and the enrollment speech by a Gaussian mixture model, thus casting the replay detection task to an out-of-distribution detection task.
Analysis of MCQs in Summative Exam in English: Difficulty Index, Discrimination Index and Relationship between them

This paper aims to assess the quality of a summative test items to improve its ability to measure students' knowledge acquisition. This test was used in the English subject for11th grade students. This study was administered at a Western district secondary school in Saudi Arabia. The test consisted of &nbsp;22 multiple-choice questions used to collect data from 94 students randomly. The Kuder-Richardson Formula 20 (KR-20) was used for test items, to determine the internal consistency reliability, which reaches a good reliability of α = 0.70. Difficulty and discrimination indices were used as well to evaluate the quality of the test. In addition, the relationships between difficulty and discrimination indices are measured. The difficulty index analysis showed that 50% of the items are in the average level, while the rest of the items fluctuate among too difficult, moderately difficult, and too easy levels. Moreover, the difficulty index analysis showed that 45.0% of the items are in the good level, while the other items ranged differently among poor, acceptable, and excellent levels. The Pearson correlation coefficient (r) to estimate the relationship between the difficulty index and the discrimination index has a value of (-0.936), which indicates that there is a statistically significant relationship at the level (α≤0.05) between the difficulty index and discrimination index of the multiple-choice question summative test. To enhance the quality of this test, to better assess students' knowledge acquisition, this study recommends that items with too difficult and too easy levels of difficulty index, and items with poor discrimination index are to be reviewed and modified by English experts. Moreover, reevaluation of the content validity by an English teacher could further improve its quality as well.
Deterministic optimal quantum cloning via a quantum-optical neural network

Quantum machine learning represents a promising approach to disclose novel possibilities for quantum information protocols. Thus, recent efforts have been dedicated to determine how to merge, or extend, machine learning approaches in the quantum domain. A relevant example is provided by a quantum-optical neural network (QONN), a class of physical systems that aim to represent the quantum-optical version of classical neural networks. This architecture was first introduced to identify the structure of optical circuits in analogy to their classical counterpart, showing the possibility of tackling different quantum tasks. However, the full potential of such an approach still needs to be explored. In this work, we provide a step forward in this direction by employing it to tackle the fundamental task of optical quantum cloning. The employed architecture has been successfully trained to perform deterministic universal quantum cloning on a photonic platform, achieving results that are very close to that of an optimal universal quantum cloner. Furthermore, we have verified numerically the robustness of this approach with respect to experimental imperfections relevant for future implementations. This work thus shows the capabilities and flexibility of QONNs, which might prove important for designing even more complex optical circuits.
Stay Healthy: Slovenian users' Opinions about the Covid-19 Contact-Tracing Mobile Application

This paper aims to provide a systemic vision of the role and specifics of a Covid-19 contact-tracing mobile application, which can be presented as a data-driven mobile application for citizens in urban data management processes and is essential for a sustainable urban ecosystem to inform the user about contact with a potentially infected person. The study aims to reveal the behaviour of adults when using such an application. We also attempt to discover how the Covid 19 pandemic has changed communication and why users use mobile phones and their features, such as Bluetooth and GPS. The research focused on Slovenia and was conducted during the Covid-19 pandemic in April 2021. Five hundred adults were surveyed. The mixed-method survey investigates the determinants of adult citizens’ behaviour in the Republic of Slovenia concerning the #Ostanizdrav application from three perspectives (installed app, activated app, and app used to report infection). In addition, the study examines the barriers and levers to using a contact tracing application to combat the pandemic in Slovenia. The second part of the study involves developing a Deep Learning-based predictive model trained on the data collected due to the survey.&#x0D;
Optimal Selection of Parameters for Production of Multiwall Carbon Nanotubes (MWCNTs) by Electrolysis in Molten Salts using Machine Learning

The production and use of carbon nanotubes (CNTs) have become extremely wide within the last decade. Hence, the high interest in producing non-expensive and quality CNTs has motivated many research projects. This research considers the design and development of new technology for producing MWCNTs by electrolysis in molten salts using non-stationary and stationary current regimes. The electrolysis is simple, ecological, economical, and flexible, and it offers possibilities for accurate control of various parameters, such as applied voltage, current density, or temperature. We infer the underlying relationship between the parameters and the quality of the experimentally produced MWCNTs by using explainable tree-based Machine Learning (ML) models. We train several models in a supervised manner, whereas in model covariates, we use the parameters of the MCWNTs, and as a target variable, the quality of the produced MWCNT. Domain experts label all the experimental examples in our data set. Controlling these parameters enables high-yield production and, particularly important, obtaining MWCNTs, which are up to ten times cheaper than other existing technologies.
Psychological and pedagogical aspects concerning professional self-determination of student youth

The consideration of the psychological and pedagogical aspects of the professional selfdetermination of students in the field is presented in the context of solving the problem of individual reconciliation of intrapersonal conflicts and social and professional needs. It is noted that the effectiveness of the solution of the problem requires a systematic approach, the pedagogical leadership of this process, the appropriate training of teachers for it, the consideration of the age and individual characteristics of the individual, the consideration of socioeconomic conditions and the main need to enhance the developmental effectiveness of the pedagogical process. This is due to the creation and implementation of qualitatively new methods in educational practice. These methods should be based not on the mechanism of external reinforcement, but on reflexive-willed mechanisms, mechanisms of empathy and positive emotional evaluation, appealing primarily to self-awareness and to theperson's creative attitude to social norms and values. It is noted that the consequence of the development of the individual is the emergence in the individual of a positive motivation for professional self-determination. The creation of an educational system for favorable conditions for thedevelopment of the personality is a mechanism for activating the internal psychological resources of the individual, which in turn will allow thedetermination of certain personal qualities of the individual, namely, the ability to self-realization in a certain professional sphere, actualizing inthe future achieving a significant life goal - trajectory, the construction of a professional career and its implementation. The implementation of the preparation of young people for further independent living in the social environment, the formation of life and professional self-determination, as a result, will determine the effectiveness of the processes of self-change and self-education.
Correlation Between GitHub Stars and Code Vulnerabilities

In the software industry, open-source repositories are widely utilized to speed up software development. GitHub is a big source of open-source repositories and offers users to star the code repository. Stars are used in GitHub to represent appreciation and popularity. Studies have revealed that repositories may be of lower quality and may have vulnerabilities that hackers may exploit. It is not known whether the popularity of the GitHub repositories in terms of stars confirms the security and invulnerability of the program code. This paper analyzed the correlation between stars of GitHub’s code repositories and the vulnerabilities in their code by using static code analyzer. The study examined the vulnerabilities in ten popular C++ source repositories on GitHub and discovered 3487 vulnerabilities in the dataset, which were split into four categories based on severity. There was not a single repository in the dataset that was free of flaws. On the detected vulnerabilities, a Kruskal-Wallis H test reveals a significant difference between the different code repositories of the dataset. The Spearman's rank correlation coefficient test found no correlation between repositories’ stars and the frequency of vulnerabilities, implying that the popularity of code repositories on GitHub in terms of high star ratings does not imply their security integrity. Overall, the findings suggest that code repositories should be thoroughly evaluated before being used in software development. The novelty of this paper resides in the development of new knowledge as well as the study pattern that may be used to other investigations.
Blockchain-enabled Edge Computing Framework for Hierarchic Cluster-based Federated Learning

Federated learning implements decentralized machine learning tasks without exposing users' private data. However, in practical scenarios, intelligent devices data pertain to different fields are non-independent and identically distributed (non-IID), which leads to a decrease in the accuracy of the global model. In addition, if there are untrusted devices participated in federated learning, the global model accuracy will be decreased. To address the above-mentioned issues, in this paper, we propose a blockchain-enabled hierarchic cluster-based federated learning in edge computing framework to improve the accuracy of the global model and ensure the local model credibility. Firstly, we propose the hierarchic cluster-based federated learning (HCFL) algorithm, which realizes hierarchically aggregation based on user cosine similarity to improve global model accuracy. Moreover, blockchain technology is enabled in the proposed HCFL algorithm to verify the local model gradient from IDs before global aggregation. Moreover, incentive mechanism is proposed to dynamically adjust reward of IDs for promote IDs train trusted models. Finally, simulation results demonstrate the efficiency and performance of the blockchain-enabled hierarchic cluster-based federated learning framework.
Signal Processing in Twin-field Quantum Key Distribution

Twin-field quantum key distribution can break through the linear bound, and promises higher key rate over longer distance. But there are two main challenges of signal processing when the twin-field protocol is implemented. One is remote generation of twin fields, the other is perfect compensation of the fast phase drift. Here, we introduce the typical methods and strategies to deal with these challenges by analysis of recent experiments, especially the latest twin-field experiment over 833.8 km fiber.
Using artificial intelligence for predictive eye-tracking analysis to evaluate photographs

The goal of the study was to determine how close the eye-tracking results predicted by the AI model are to actual measurements and whether they can be used in scientific research or in real business cases. The study was based on a carefully prepared photo database of 30 photos of varying complexity and colour. The photos were shown to 110 participants (age and gender evenly distributed), and eye-tracking device (Tobii X120) was used to mea-sure how the photos were viewed. In comparison, the same photos were tested using an AI-based application (Expoze.io). The final results show the comparison between the heatmaps and transparent gaze visualisations of the collected data with the two used measurement methods. Suggestions are made in which cases and how the two described methods should be used.
Implementation of Edunesia Learning Media Platform Based on Mobile Ionic Typescript

In the era of the Internet of Things as it is today, especially in the world of education, where information can be accessed quickly, can be accessed from anywhere and anytime, it will be very helpful in the learning process and interaction between teachers and students. One method that we can apply is the existence of an application that can bridge the learning process between students and teachers, so that distance and time are no longer an obstacle, especially during this COVID-19 pandemic, such as when there is a communication PSBB between teachers and students at school. Some use Whatsapp Groups, the use of Whatsapp groups is considered less well organized, both from the material presented or the tasks given. This also supports the Industrial Revolution 4.0 where we try to prepare from an early age so that the learning process and interaction between students and teachers, apart from face-to-face, can also get used to doing distance learning. With the resources and limitations of schools, it is not an easy and cheap job to make such an application. For this reason, we dream of presenting an educational platform that can automatically initiate applications that can support today's digital learning processes or methods. Schools will be able and able to make their respective school applications easily and quickly. To answer this problem, the author tries to solve it by presenting an educational platform that can be used to create school applications independently. Edunesia platform produces applications needed for learning media or material delivery, while the applications produced by this platform include Web Admin (content manager), Teacher Android Mobile Application , and Student Android Mobile Apps. The resulting application is complete with features that support learning activities from home, such as learning videos, student and teacher consultations, Online Classrooms (Video Conference).
A STUDY ON THE EXECUTIVE TRAINING AND DEVELOPMENT PROGRAMME AT WOVV TECHNOLOGIES

Training and development is a necessary factor in the attainment of organizational objectives of any organization.In every organization exists some form training and development programme.So by keeping these things in mind WovV Technology (Vadodara, Gujarat) had decided to conduct Training and development programme at the workplace and to analyze that what are the effects on the Employees those are working there.The manpower has to be trained and developed in terms of quality and quantity so that they can effectively and efficiently control the commanding height of our profit.The business organization has risen to this by providing the national Council for management education and training and the center for management development schemes.It is important because it directs training and development towards organization need and aspiration.In Nigeria Breweries Plc there is already a formalized system of staff training and development in the organization to help it attain it objectives.This project paper is set for highlight the problems of staff training and development in the company and also to offer solutions to these problems.
Destruction of stego images formed by adaptive embedding methods with dictionary learning methods

Counteraction to sensitive information leakage that processed by state and private organizations is topical task today. Of special interest are methods for prevention data leakage by usage of hidden (steganographic) communication channels by attackers. Despite wide range of proposed steganalysis methods for detection of embedded messages, theirs performance highly depends on prior information about used embedding methods. As an example, we may mention modern stegdetectors for digital images, which are based on cover rich models and deep convolutional neural networks. Therefore, the stego image destruction methods are widely applied as preventive action.Modern methods for stego image destruction are based on widespread image denoising methods, like median filter and lossy compression. The limitation of such methods is significant changes of image's statistical features that may disclosure the steganalysis process to attacker. Therefore, development of stego images processing methods that provide reliable destruction of embedded data, and preserving cover image statistical features is needed. The paper is aimed at performance evaluation of applying the novel methods of spectral analysis, namely dictionary learning, for solving this tasks. The obtained results showed limitation of state-of-the-art methods for destruction of stego image formed by adaptive embedding methods, namely considerable changes of image's statistical parameters. The proposed method allows preserving both minimal changes of a \ac{ci} parameters, and ratio of survived bits of embedded message (less than $7\%$). This makes proposed solution an attractive candidate for reliable destruction of stego images formed by novel embedding methods. However, practical usage of proposed solution requires further improvement of dictionary learning methods, namely decreasing of computation complexity of dictionary forming procedure.
Systems of Linear Restrictions Over a Finite Field

&#x0D; &#x0D; &#x0D; This paper considers the problem of recovering an unknown vector based on partial information presented in the form of certain linear dependencies. Such problem is an alternative to the standard one of solving a system of polynomial equations over a finite field, which arises in the context of algebraic cryptanalysis of stream ciphers, and it models a situation when it’s not possible to formulate specific equations with an unknown vector, but certain restrictions on linear dependencies with this vector. To formalize such linear dependencies, the notation of the system of linear restrictions over a finite field is introduced, and the problem of recovering the unknown vector is replaced by the problem of solving the system of linear restrictions over a finite field. In this paper, we researched some properties of this problem using its equivalent forms and important partial cases.&#x0D; &#x0D; &#x0D;
Basic concepts, approaches and fundamentals of cyber threat intelligence

&#x0D; &#x0D; &#x0D; This article is designed to introduce readers to Cyber Threat Intelligence (CTI). Various Threat Hunting (TH) techniques and sources of information about threats were mentioned. Commercial tools and open source software for cyber threat hunting are also described.&#x0D; &#x0D; &#x0D;
A Literature Review of Object Detection using YOLOv4 Detector

Abstract: Object detection is an advanced form of image classification where a neural network predicts objectsin an image and points them out in the form of bounding boxes. Compared to the approach taken by object detection algorithms before YOLO, which repurpose classifiers to perform detection, YOLO proposes the use of an end-to-end neural network that makes predictions of bounding boxes and class probabilities all at once. Object detection not solely includes classifying and recognizing objects in an image but also includes localizing those objects and attracts bounding boxes around them. Its applicationincludes field like face detection, detecting vehicles, autonomous vehicles and pedestrians on streets.
IMPROVING STUDENTS’ READING THROUGH COMIC STRIPS MEDIA IN WEBTOON APPLICATION

The aims of this research is to improve the students’ reading skill of the Tenth grade of SMAN 4 Bangkalan by using comic strips in webtoon application. This research is collaborative classroom action research. It aimed to discover and put into action various reading skills improving measures. The research procedure was an action research that consisted of four key phases: planning, implementing/ acting, observing, and reflecting. The researcher and teacher collaboratively recognized certain obstacles andweaknesses of the reading learning process, identified some problems, and planned and carried out the proposed actions. The result of the cycle shows that the implementation of comic strip improves the students’ reading skill, especially understanding the content of the text, vocabulary mastery, and motivation to read by providing the students kinds of reading activities. The success indicator of the implementation were that (1) the students were able to understand the content of the English text, by finding the main idea and specific information, (2) the students were more enthusiastic in doing reading activities, and (3) the students vocabulary mastery was improved. Furthermore, the findings were also supported by the means of the students’ reading score which improved from 62 in preliminary test to 76 in cycle test.