Reviewerly/exHarmony huggingface dataset:
Development Of Reflexive Skills In Students - Future Teachers

The paper considers the problem of developing reflexive skills of studentsfuture teachers.Reflection is an important quality of a teacher necessary for effective professional activity, so it is necessary to develop reflexive skills at the stage of professional training.The authors suggest developing these skills through educational programs, as well as by creating the following pedagogical conditions: educational activities within individual educational program, educational dialogue in the course of training, subject-subject relations through educational interaction; training in the analysis of pedagogical activities from different positions.Reflexive skills are considered as a necessary and meaningful quality of a teacher, which is demonstrated through the ability to analyze and adequately perceive oneself, to determine and analyze the reasons of your behavior, as well as its performance parameters and mistakes, to understand your qualities in the present in comparison with the past and to forecast the prospects for further development, to understand the reasons for actions of another person in the process of interaction, to analyze experienced situations and to consider the actions of others within your behavioral strategies.The presented curricula reveal the content for the development of reflexive skills: theoretical bases of reflection, general method of reflection in educational activity, attitude to reflection as a method of interaction in educational and quasi-professional activity.The paper describes the results of diagnostics of students' reflexive skills of experimental and control groups.The experimental and control groups were compared using the Spearman correlation analysis.
Model-Driven Deep Learning Scheme for Adaptive Transmission in MIMO-SCFDE System

Adaptive transmission (AT) is considered as one of the critical technologies to enhance the effectiveness of communication systems. In this article, we propose a model-driven deep learning (DL) scheme for AT in multiple-input multiple-output single-carrier frequency-domain equalization (MIMO-SCFDE) systems, in which the adaptive modulation network (AMNet) and adaptive demodulation network (ADNet) are adopted to complete the modulation of the signal and the modulation recognition of the receiver. Under the target bit error rate (BER), the adaptive modulation (AM) scheme can adjust the modulation mode selection of different transmitting antennas adaptively according to the estimated channel information to improve the throughput. The features required by the AMNet are extracted from the received signal, and the labels are assigned according to the optimal modulation scheme got by analyzing the signal detection performance. Since the spectral correlation function has a powerful ability to suppress noise and the cyclic spectrum varies with the modulation mode, we take the preprocessed cyclic spectrogram as the input of ADNet to achieve the adaptive modulation recognition (AMR). Comparative experiments demonstrate that the proposed scheme gets better performance in terms of throughput and reliability in MIMO-SCFDE systems than the traditional scheme and the existing DL scheme.
Evaluating Case Study and Action Research Reports: Real-world Research in Cybersecurity

There is a growing number of scientific papers reporting on case studies and action research published each year. Consequently, evaluating the quality of pilling up research reports is becoming increasingly challenging. Several approaches for evaluation of quality of the scientific outputs exist however they appear to be fairly time-consuming and/or adapted for other research designs. In this paper, we propose a reasonably light-weight structure-based approach for evaluating case study and action research reports (SAE-CSAR) based on eight key parts of a real-world research report: research question, case description, data collection, data analysis, ethical considerations, results, discussion and limitations. To evaluate the feasibility of the proposed approach, we conducted a systematic literature survey of papers reporting on real-world cybersecurity research. A total of N = 102 research papers were evaluated. Results suggest that SAE-CSAR is useful and relatively efficient, and may offer a thought-provoking insight into the studied field. Although there is a positive trend for the inclusion of data collection, data analysis and research questions in papers, there is still room for improvement suggesting that the field of real-world cybersecurity research did not mature yet. The presence of a discussion in a paper appears to affect most its citation count. However, it seems that it is not uniformly accepted what a discussion should include. This paper explores this and other issues related to paper structure and provides guidance on how to improve the quality of research reports.
Private Browsing Forensic Analysis: A Case Study of Privacy Preservation in the Brave Browser

The Internet and its users are in continual growth. With it grows the number of organized crimes on the Internet and the potential for individuals to carry out illegal activities. These criminals have gained more awareness of private browsing facilities, and many have found a haven in privacy designed browsers that cover up their tracks and shield their nefarious actions. The development of these privacy features has proven to be a challenge for digital forensic investigators. They strive to perform a thorough analysis of web browsers to collect artefacts relating to illegal activity to be presented as evidence to the court of law and used to convict criminals. “Brave” browser is one of the most recent and fastest-growing private browsers that, up to this point, has not been studied in-depth, and its privacy preservation functionality remains unclear. In this paper, we studied Brave’s private browsing mode, examined its privacy-preserving and forensic data acquisition, and outlined the location and type of evidence available through live and post-mortem state analysis. The unique approach taken included a set of experiments that unveiled how the browser functions and showed the appropriate tools that could be utilized to extract leftover artefacts. Analysis of our results showed that despite Brave leaving no traces of browsing activity on the Hard Disk, visited URLs, images, keyword searches, and even cached videos were retrievable from the RAM, which shows that Brave is not entirely private.
Analysis of web platforms of learning management systems for distance education in the face of social isolation

Education is one of the sectors that were affected by the Covid-19 pandemic that forced citizens to keep us isolated in our homes, however, education had to adapt to circumstances and as a result Online Classes were implemented.in the different educational centers for which it is necessary to know the best online learning system to be able to implement in the institutions.In this work, a work methodology based on Analytic Hierarchy Process (AHP) was applied to be able to compare the different LMS shown in this article.In this case study, three different LMS platforms were evaluated.The results of this study indicated that the best LMS platform to implement in schools was Moodle.Based on these results, educational institutions could apply this LMS to their online teaching modality since it offers better characteristics compared to Chamilo and Google Classroom.
UTXO$$_{\textsf {ma}}$$: UTXO with Multi-asset Support

A prominent use case of Ethereum smart contracts is the creation of a wide range of user-defined tokens or assets by way of smart contracts. User-defined assets are non-native on Ethereum; i.e., they are not directly supported by the ledger, but require repetitive custom code. This makes them unnecessarily inefficient, expensive, and complex. It also makes them insecure as numerous incidents on Ethereum have demonstrated. Even without stateful smart contracts, the lack of perfect fungibility of Bitcoin assets allows for implementing user-defined tokens as layer-two solutions, which also adds an additional layer of complexity. In this paper, we explore an alternative design based on Bitcoin-style UTXO ledgers. Instead of introducing general scripting capabilities together with the associated security risks, we propose an extension of the UTXO model, where we replace the accounting structure of a single cryptocurrency with a new structure that manages an unbounded number of user-defined, native tokens, which we call token bundles. Token creation is controlled by forging policy scripts that, just like Bitcoin validator scripts, use a small domain-specific language with bounded computational expressiveness, thus favouring Bitcoin’s security and computational austerity. The resulting approach is lightweight, i.e., custom asset creation and transfer is cheap, and it avoids use of any global state in the form of an asset registry or similar. The proposed UTXO $$_{\textsf {ma}}$$ model and the semantics of the scripting language have been formalised in the Agda proof assistant.
Convolutional Multi-Spike Neural Network as Intelligent System Prediction for Control Systems

The evolution in the field of Artificial Intelligent (AI) with its training algorithms make AI very important in different aspect of the life. The prediction problem of behavior of dynamical control system is one of the most important issue that the AI can be employed to solve it. In this paper, a Convolutional Multi-Spike Neural Network (CMSNN) is proposed as smart system to predict the response of nonlinear dynamical systems. The proposed structure mixed the advantages of Convolutional Neural Network (CNN) with Multi -Spike Neural Network (MSNN) to generate the smart structure. The CMSNN has the capability of training weights based on a proposed training algorithm. The simulation results demonstrated that the proposed structure has the ability to predict the response of dynamical systems more powerful than with the CNN. The proposed structure is more powerful than the CNN by 28.33% in terms of minimizing the root mean square error.
Thai Character-Word Long Short-Term Memory Network Language Models with Dropout and Batch Normalization

Due to the emerging of Long Short-Term Memory neuron network (LSTM) which is a variation of deep neuron network, it is proven to be essential to the improvement of Natural Language Processing, especially Language Modelling.Many researches applied LSTM to model many well-defined languages and gain performance in term of accuracy.However, this new approach is rarely applied to Thai language.Unfortunately, the characteristic of Thai language is significantly different than other well-defined languages, particularly English or Latin-based languages.In this work, we applied LSTM in Language Modelling to predict the next word in the sequence.We designed seven variation of LSTM models and compared the result with word-level LSTM model.The experiment showed that character-word LSTM can improve the performance of Natural Language Modelling (NLM) on Thai dataset.Especially when using character-word LSTM with dropout value of 0.75 and batch normalization, the perplexity is lower than baseline word-level LSTM up to 21.10%.
AI and IP: Are Creativity and Inventorship Inherently Human Activities?

As artificial intelligence algorithms (AIs) become ever more powerful and sophisticated, they are becoming capable of generating audio and visual begs the question, can these AI-created works be granted copyright protection in the U.S.? Should they be granted the same copyright protections as human-created works?If AI-generated works are copyrightable, what are the legal and policy implications?Similarly, AIs can now devise novel and non-obvious solutions to known problems.The first patent applications have been filed in the U.K. and elsewhere, naming an AI as an inventor.Can AIs
Data Reduction for Optimizing Feature Selection in Modeling Intrusion Detection System

With the development and ease of access to internet networks, the potential for attacks and intrusions have increased. The intrusion detection system (IDS), an approach to overcome this problem, is grouped into two models: signature-based and anomaly-based. An anomaly-based IDS can be implemented by machine learning; one of the schemes in machine learning is data reduction. IDS datasets are usually obtained through a real-time process that has undefined proportional data. The purpose of data reduction is to speed up and optimize the process, improving accuracy, precision, and specifications. There are several methods to perform data reduction, one of which uses outlier detection techniques. Proper outlier detection has a positive impact on improving the classification results of machine learning. In this research, the outlier detection is done by a circle generated from the k -means clustering of all selected features. Two scenarios are designed for the evaluation: a circle generated from two points of the minimum and maximum cluster and median of all clusters. The formation of clusters conducted by k -means clustering determines the size and direction of the outlier circle so that it dynamically adjusts the distribution of data from the feature selection results. By employing the previous feature selection algorithms, the comparison is performed to evaluate the proposed method's performance. Our empirical results show that the second scenario can significantly improve the classification results in terms of accuracy, detection rate, and precision. The first and second experiments can increase the accuracy by 0.02%, and the third experiment is by 0.1%. The detection rate in the first, second, and third experiments increases by 0.01%, 0.02%, and 0.07. At the same time, precision increases by 0.04%, 0.02%, and 0.01%, correspondingly.
Post-Digital World, Pandemic and Higher Education

The article examines the prospects that open up for the implementation of the educational process in higher educational institutions, as well as the problems faced by the modern higher education system in the context of the transition to distance learning caused by the current epidemiological situation in the post-digital world. On the basis of the data from a student survey made during the COVID-19 pandemic, who studied using distance digital technologies, we have analyzed the current state of higher education. Consequently, the authors have concluded that the universities, implementing programs for the digitalization of the educational process and the creation of massive online courses, are actually away from the real social circles of the post-digital society. Given the results, the authors argue that digitalization is not a way to resolve the internal contradictions of higher education. Moreover, digitalization as a goal of higher education somewhat hides the real contradictions of the learning process.
Cross Complementary Fusion Network for Video Salient Object Detection

Recently, optical flow guided video saliency detection methods have achieved high performance.However, the computation cost of optical flow is usually expensive, which limits the applications of these methods in time-critical scenarios.In this article, we propose an end-to-end cross complementary network (CCNet) based on fully convolutional network for video saliency detection.The CCNet consists of two effective components: single-image representation enhancement (SRE) module and spatiotemporal information learning (STIL) module.The SRE module provides robust saliency feature learning for a single image through a pyramid pooling module followed by a lightweight channel attention module.As an effective alternative operation of optical flow to extract spatiotemporal information, the STIL introduces a spatiotemporal information fusion module and a video correlation filter to learn the spatiotemporal information, the inner collaborative and interactive information between consecutive input groups.In addition to enhancing the feature representation of a single image, the combination of SRE and STIL can learn the spatiotemporal information and the correlation between consecutive images well.Extensive experimental results demonstrate the effectiveness of our method in comparison with 14 state-of-the-art approaches.
Level of Readiness of Daily Secondary School Students for Use of Augmented Reality in Form 2 Science Textbooks

Augmented reality (AR) applications are an agent of change in how students learn by taking the learning process beyond the physical space of the classroom.AR applications have begun to be integrated into high school textbooks to enable students to visualize real phenomena from the textbooks to enhance the learning experience.This study sought to determine the level of readiness of daily secondary school students for the use of AR applications in Form 2 science textbooks.The findings indicate a high level of readiness among students to use AR applications (mean=3.92,SD=.439), and a moderate level of hindrance in AR applications (mean=3.17,SD=.652).Pearson correlation tests of the relationships among all of the variables suggested that the use of AR applications would be acceptable among secondary school students.This study provides an important indicator that the future educational environment in Malaysia must take into account and integrate elements of the latest technology.Educational institutions must move forward innovatively and adapt to aspects related to the curriculum and learning infrastructure creatively.
A Study on Improving Lecture Skill and Implementation of Anti-reset and Bampless Using 2-DOF-PID Controller and Python

This paper deal with the design method of 2-DOF PID Controller.The PID controller is so popular and it has been using in the industrial sites, widely.However, when we change the switch from manual to automatic to control, the controller signal of setpoint value has discontinuities or has sharp changes in feedback loop due to the D-function in basic PID structure.To avoid these, they should introduce anti-wind, bumpless, and contioned transfer in PID controller.This paper introduces the design method of 2-DOF PID controller for avoiding those problems and suggests improving a lecture skill of the 2-DOF PID Control using Python.The Python is a good tool to teach and learn as teacher and students because it is free S/W and there is a good community.Finally, this paper provides on how we can teach and learn control engineering through implementation of 2-DOF PID Controller.
Investigating the Alignment Between the CELPIP-General Reading Test and the Canadian Language Benchmarks: A Content Validation Study

Seeking evidence to support content validity is essential to test validation. This is especially the case in contexts where test scores are interpreted in relation to external proficiency standards and where new test content is constantly being produced to meet test administration and security demands. In this paper, we describe a modified scale- anchoring approach to assessing the alignment between the Canadian English Language Proficiency Index Program (CELPIP) test and the Canadian Language Benchmarks (CLB), the proficiency framework to which the test scores are linked. We discuss how proficiency frameworks such as the CLB can be used to support the content validation of large-scale standardized tests through an evaluation of the alignment between the test content and the performance standards. By sharing both the positive implications and challenges of working with the CLB in high-stakes language test validation, we hope to help raise the profile of this national language framework among scholars and practitioners.
Current Problem of Education Management: Subjective Attitude of University Teachers to Changes in Higher Education

The beginning of the 2000s has been marked by the processes of large-scale reform of the higher education system in Russia.The implementation of the ideas of modernization has been entrusted to university teachers.The actual results of the announced transformations largely depend on how teachers perceive and treat the ongoing changes.The article considers the subjective attitude of teachers to the following changes in higher education: introduction of competency-based approach in higher education, reduction of classroom time and contact work with students, technologization of the learning process, introduction of performance-based contracts for teachers, and increase in the proportion of remote training (including full-time students).The research results have shown that most of the teachers who took part in the survey, regardless of the length of service in the university, are rather indifferent to the changes in the higher education system.The influence of most changes on the content and specifics of the teachers' work, on the quality of the teachers' work (the quality of the students' preparation), as well as on the professional development is assessed by the survey participants as insignificant.This indicates that most of the changes, according to the subjective opinion of the teachers who took part in the study, do not have significant impact on the real situation in higher education.The obtained empirical data are of interest for psychology, management and management of higher education.
Normalized Weighting Schemes for Image Interpolation Algorithms

Image interpolation algorithms pervade many modern image processing and analysis applications. However, when their weighting schemes inefficiently generate very unrealistic estimates, they may negatively affect the performance of the end-user applications. Therefore, in this work, the author introduced four weighting schemes based on some geometric shapes for digital image interpolation operations. Moreover, the quantity used to express the extent of each shape’s weight was the normalized area, especially when the sums of areas exceeded a unit square size. The introduced four weighting schemes are based on the minimum side-based diameter (MD) of a regular tetragon, hypotenuse-based radius (HR), the virtual pixel length-based height for the area of the triangle (AT), and the virtual pixel length for hypotenuse-based radius for the area of the circle (AC). At the smaller scaling ratio, the image interpolation algorithm based on the HR scheme scored the highest at 66.6% among non-traditional image interpolation algorithms presented. However, at the higher scaling ratio, the AC scheme-based image interpolation algorithm scored the highest at 66.6% among non-traditional algorithms presented, and, here, its image interpolation quality was generally superior or comparable to the quality of images interpolated by both non-traditional and traditional algorithms.
Research on Application of Artificial Intelligence in VR Games

Purposed to promote the development of artificial intelligence (AI) technology in the game field, this article adopts the literature analysis method and the case study method, analyzes the problems with virtual reality (VR) games and explores several popular VR game cases in China and abroad before summarizing the artistic characteristics of VR games. Artificial intelligence technology has brought a new creative mode to the game industry, while providing more suitable game content for players. Further, artificial intelligence technology can equip VR games with better game experience by analyzing players’ physiological and psychological information as well as players’ game strategies. Compared with traditional games, VR games can deliver better interactivity, deeper immersion and higher fidelity. However, VR games impose higher requirements on device data and space scale; in other words, they must meet specific requirements to provide better game experience. VR games also have monotonous game types and high prices. In the future, the integration of VR games with artificial intelligence would generate more profound game-player experiences and richer game contents.