Reviewerly/exHarmony huggingface dataset:
Influences of a Professional Development Program on Teachers' Attitudes Towards Educational Technology and Digital Learning Resources for Inclusion

The goal of this study was to examine the impact of a professional development program on teachers' attitudes towards educational technology and the perceived benefits of digital learning resources for inclusion. The content of the program focused on how technology can be used to create more inclusive learning environments. Findings indicate a decline in attitudes from the beginning to the end of the program. The authors explore several possible explanations for this decline, including the pressures of the Corona pandemic and a simultaneous national curriculum reform, a possible discrepancy between initial expectations and later experiences with the technology, and a possible reactance effect or “backlash” against the ambitious goals of the program. In addition, variables related to previous experience and teacher efficacy predicted attitudes to some degree, offering indications of factors to consider when preparing future professional development efforts in this area.
All in One

It is quite common in software house organizations for customers to request a detailed report on the key contributors who worked on their project, including a description of the work performed and the time devoted to the project. This document presents and describes the design and implementation of an application capable of satisfying these needs. For this, preliminary research was carried out on the main technologies, leading to the conclusion that, for this project, the best technologies would be NodeJS and Express, for the backend and Svelte and NeutralinoJS for the frontend, using TypeScript as the main programming language. In addition, this document presents all the necessary artefacts for the reproduction of said solution, divided into design and implementation artefacts, as well as an explanation of all decisions made for design and implementation.
An adaptive enhancement method for generic multi-modal high‐bit RAW grayscale image displaying on low-bit monitors

Aiming at the poor adaptability and robustness of existing pseudo-color methods, most of which can only deal with an 8-bit grayscale, an adaptive enhancement algorithm of high grayscale images based on priori knowledge was proposed. Firstly, aiming at the problem that the original RGB color space is not easy to adjust dynamically, the power adjustment was integrated into the constructed high-bit chromatogram, and a power adaptive adjustment function based on the brightness priority over the original grayscale image was designed. Secondly, aiming at the problems of over-exposure, under-exposure, and poor gradients in RGB space, an adaptive grayscale correction algorithm was designed according to the priori knowledge distribution of RGB perceived brightness. Finally, to guarantee the color balance of the enhanced image, a color balance correction algorithm based on CMY space was designed. To verify the effectiveness of this method, it was applied to the pseudo-color enhancement of 16-bit pipeline CR images, 14-bit infrared images, 24-bit pipeline DR weld images, 8-bit or 24-bit rail crack images, and 16-bit remote sensing images respectively. The subjective and objective experimental results show that the design method has stronger adaptability, which has obvious advantages compared with the existing advanced high grayscale image enhancement methods. The enhancement effect is more coordinated, the processing result is more in line with human visual perception, and the details and texture information of the original image can be better preserved.
An overview of Blockchain: Definitions, architecture, versions, applications and future directions

Blockchain technology has been gaining popularity in recent years, with many industries exploring its potential applications. This innovative technology has the potential to revolutionize the way we do business and interact with each other. It has emerged as a revolutionary innovation that promises to transform various industries by enabling secure, transparent, and decentralized transactions. The blockchain is a distributed ledger technology that uses cryptography to ensure the integrity and immutability of data. In this article, we will provide an overview of blockchain, including definitions, architecture, security, applications, and future directions
Formation of information space of knowledge learning foreign language

Modern trends in digitalization of education lead to the expansion of methods and means of teaching foreign language. This article is devoted to identifying the main components necessary for the formation of information space of knowledge in classes in foreign language, using advantages of training applications, as well as drawing up rules for the effective study of foreign language. It should be noted that the main way to obtain information in the modern world is Internet technology. By teaching foreign language, information technology helps to shape the skills and abilities of spoken language, as well as teach vocabulary and grammar, providing genuine interest and therefore efficiency. Moreover, learning applications develop skills that are important not only for foreign language. The task of teachers of modern digital education is to find the opportunity to use all sources and means of training in the educational process.
Intelligent recognition of English characters based on point cloud data model and artificial intelligence recognition

Currently, there are many character string segmentation algorithms, and these algorithms all have the common advantages of good versatility. However, they require a lot of calculations, path searches, etc., which lead to complex algorithm structures and high time and space complexity. This paper proposes an intelligent recognition model of English characters based on point cloud data model and artificial intelligence recognition. Moreover, this paper designs a data point reduction algorithm to eliminate and filter data points in the curve and surface fitting process, and uses comparative experiments to verify that the algorithm can reduce the number of data points used for fitting curves and surfaces while ensuring fitting accuracy and improve fitting efficiency. In addition, this paper uses the processed point cloud data and the extracted and parsed two-dimensional engineering drawing data to improve the expression of the B-spline curve with the theoretical support of the B-spline curve and the B-spline surface. Finally, this paper designs a control experiment to analyze the performance of the model. The research results show that the model constructed in this paper has certain reliability.
Error Correction Using Quantum Computation

Quantum Error Correction (QEC) is an important technique for protecting quantum information against decoherence and errors. This involves the design and implementation of algorithms and techniques to minimize error rates and increase the stability of quantum circuits. One of the key parameters in QEC is the distance of the error- correcting code, which determines the number of errors that can be corrected. Another important parameter is the error probability, which quantifies the likelihood of errors occurring in the quantum system. In this context, the goal of a simulation sweeps like the one performed in the code is to study the performance of the QEC code for different values of the distance and error probability, and to optimize the code for maximum accuracy. By varying these parameters and observing the performance of the code, researchers can gain insights into how to design better codes and improve the reliability of quantum computing systems. We also discuss the challenges that need to be addressed for quantum computing to realize its potential in solving practical Error-correction problems.
Unknown Traffic Recognition Based on Multi-Feature Fusion and Incremental Learning

Accurate classification and identification of Internet traffic are crucial for maintaining network security. However, unknown network traffic in the real world can affect the accuracy of current machine learning models, reducing the efficiency of traffic classification. Existing unknown traffic classification algorithms are unable to optimize traffic features and require the entire system to be retrained each time new traffic data are collected. This results in low recognition efficiency, making the algoritms unsuitable for real-time application detection. To solve the above issues, we suggest a multi-feature fusion-based incremental technique for detecting unknown traffic in this paper. The approach employs a multiple-channel parallel architecture to extract temporal and spatial traffic features. It then uses the mRMR algorithm to rank and fuse the features extracted from each channel to overcome the issue of redundant encrypted traffic features. In addition, we combine the density-ratio-based clustering algorithm to identify the unknown traffic features and update the model via incremental learning. The cassifier enables real-time classification of known and unknown traffic by learning newly acquired class knowledge. Our model can identify encrypted unknown Internet traffic with at least 86% accuracy in various scenarios, using the public ISCX-VPN-Tor datasets. Furthermore, it achieves 90% accuracy on the intrusion detection dataset NSL-KDD. In our self-collected dataset from a real-world environment, the accuracy of our model exceeds 96%. This work offers a novel method for identifying unknown network traffic, contributing to the security preservation of network environments.
CSFL: Cooperative Security Aware Federated Learning Model Using The Blockchain

Abstract Federated learning (FL) is a focus of research in the area of privacy protection since it does not have the privacy issues that arise from data concentration. Although its emergence has attracted widespread attention from academia and industry, existing works on FL still face security challenges. FL can be considered as a cooperative-based task to achieve global model sharing. However, the model raises issues of cooperative security, such as free-riding and poisoning attacks. Therefore, we focus on the behavior of participants with strong cooperative relationships and build a Cooperative Security-aware Federated Learning model using blockchain. In addition, we propose a credit-based economic model including profit and punishment mechanisms to ensure fairness and security among participants. Furthermore, for data privacy, we develop a participation permission strategy to protect the privacy of participants through proxy re-encryption and homomorphic encryption. Finally, the simulation results of the real datasets show that the proposed scheme achieves a good performance in security and accuracy.
A Visual Analytics Approach Applying for Discovering Knowledge from Multivariate Datasets of Stakeholders Feedback in the University

In accrediting training quality, assessing the educational quality of a university is an extremely important task to affirm the prestige of the university to the society. Areas of assessment include training program, teaching and learning activities, capacity of graduate student, and the reputation of the university in society. This research studies the characteristics of the survey dataset structures to form data variables and analyzes the relations between data variables to build non-spatial and temporal multidimensional model (nSTM), applies the establishment of visual graphs according to the data variables representing the datasets with vertical bars indicating the values of the data variables being visually represented. This research conducts experiments on the dataset structures surveying the areas of a university. With this scientific approach, it will help university administrators to build policies to improve in all aspects and especially improve the quality of university education.
A novel stochastically stable variational Bayesian Kalman filter for spacecraft attitude estimation

Abstract In the context of nonlinear systems, variational Bayesian filters play an important and alternative role to sampling‐based Monte Carlo methods. Although they have been used in conjunction with derivatives of Kalman filters, their behavior, both in terms of convergence, stability, and a solution without a stepwise approximation procedure, has not been well explored. In this paper, we propose a novel, stochastically stable, stepwise approximation‐free variational Bayesian Kalman filter (hence, termed as VBKF) for nonlinear systems. The proposed filter, unlike other posterior distribution estimation methods, is very flexible with the number of parameters that should be known a priori, offers an excellent convergence rate, stochastic stability, and more specifically in its ability to handle non‐Gaussian, unknown, inaccurate and drifting noise models. Furthermore, one of the key advantages of the proposed solution is that it is stepwise approximation‐free, and as such, less computationally demanding. We also prove that the proposed filter is stochastically stable using the Posterior Cramér–Rao Lower Bound. In addition to the stability, by using Lyapunov function, we show that the estimation errors are exponentially bounded by the mean square error under certain conditions. In addition to this, we also demonstrate the practical applicability of the proposed approach by developing a novel filter for estimating the attitude of spacecrafts using star cameras and gyro‐sensors. We then evaluate the efficacy of the novel filter for attitude estimation, and thus that of the VBKF, using a set of detailed simulations mimicking different initialization errors that are common in attitude estimation. Our results show that the proposed approach offers an excellent convergence rate within conventional bounds, better root mean square error when compared against several of the common, and state‐of‐the‐art methods.
Microblog topic detection using evolutionary clustering and social network information

Summary The messages in microblogs, like Twitter, present a wealth of information around the hot topics and public opinions. Topic detection and tracking enables a better understanding of the incoming information from social networks. Many of the studies performed in this field consider a set of predefined number of topics (clusters) which remain constant during the detection process. Such approaches are not efficient when data is dynamic and cumulative. In addition, nonparametric evolutionary topic models do not have an accurate performance on short texts due to the data deficiency. In this article, we propose an evolutionary clustering model based on the Bayesian nonparametric Dirichlet process mixture model, which automatically determines the number of clusters. To improve the algorithm performance when dealing with short social media texts, temporal information of tweets and other social network parameters are used in a weighted combination to augment the text similarity in detecting topics. Model evaluation is performed on the data gathered from the Twitter during a 2.5‐month period. Results reveal that the social network information can promote the topic detection performance. The proposed model outperforms previous study in terms of topic coherence and clustering performance, leading to a better clustering on short texts.
Tangible Data Exploration: Creating Card Games for Sensemaking

Participatory sensing has largely focused on the collection and dissemination of data, paying little attention to the fact that people often struggle to use the data and relate it to their daily lives. However, data is strongly connected to the context, environment and community within which it is gathered, whether by human or machine. Thus, those who are local to the data - especially those who participate in its collection - can bring valuable insights and can themselves gain value from interpreting it. This paper offers an approach for designing interactive narrative games with civic datasets, which are designed to make data easier to interact with, discuss and share in informal settings. It describes the planning and execution of a public data exploration event based around these activities. This work will be of use to people who are designing civic interfaces to support participatory sensemaking from data, especially in informal settings.
Exploring Pre-scoring Clustering for Short Answer Grading

Automatic short answer grading is a topic that has gained significant popularity recently, especially due to developments in natural language processing. While automated grading in computer supported assessment tasks traditionally imposed significant restrictions on the answer format (e.g., multiple choice questions), automated short answer grading could enable assessment scalability with very few answer format limitations and thereby increase the assessment tasks’ validity. Here, ‘short answer’ refers to a text of up to, approximately, 10 sentences. However, automatic solutions require a lot of pre-graded material. In this paper, several pre-trained machine learning models were utilized to explore pre-scoring clustering for short answer grading of text in Croatian. The aim of this approach is to shorten the process of manual short answer grading by clustering similar answers, facilitating the development of automatic grading solutions. The described approach was evaluated on a dataset containing graduate students’ answers in Croatian to six questions related to cyber security topics. The obtained results are promising and show how increases in cluster purity, normalized mutual information, Rand index, and adjusted Rand index measures can be achieved by finetuning a pre-trained model.
Medical Data in the Digital Era - Legal Challenges Related to Providing Information Security, Applying GDPR and Respecting the Professional Secrecy

The processing of medical data in electronic form poses many challenges in terms of the security of these resources and ensuring patient privacy. This paper presents new dimensions of healthcare data processing, such as EHR, eHealth, mHealth, IoT or Big Data, in the perspective of the challenges of enforcing legal regulations to ensure the security of such healthcare information resources while also guaranteeing cross-border interoperability of these solutions. The technical and organizational measures applied by the controllers, as well as the legal requirements, should be consistent and comprehensive, considering not only the challenges related to the protection of personal data, but also cybersecurity and the protection of professional secrets in health care. In this context, ensuring the integrity and availability of information is also important as protecting its confidentiality.
Hybrid Agile Approach in Software Engineering Education – A Case Study

By definition, software engineering is a systematic approach to software development. However, to succeed in today’s dynamic business environment, development teams must adapt and respond to change quickly. To prepare students for their role in real-world software development teams of which they will soon be a part, teachers attempt to create as realistic a project environment as possible. The software development education model presented in this paper is based on the hybrid agile approach. It combines both the agile approach, which is suitable for dealing with changes in requirements, and the planning-oriented, systematic approach, which is traditionally used in software engineering. In addition, the students’ attitude towards this hybrid agile model is investigated.
Data Acquisition and Corpus Creation for Phishing Detection

Detecting phishing attacks is not straightforward, since there are many obstacles that derive from language complexity and technical aspects. Studying phishing attacks and other related issues heavily relies on computer datasets, i.e. digital corpora that reflect these linguistic and technical intricacies. Diverse studies using phishing datasets have been performed, but mainly for the English language. Research for other languages is scarce, and especially for not widely spoken languages. For the Croatian language there is an evident lack of corpora that are essential for diverse analyses and for constructing models that are capable of recognizing phishing attacks and protecting users. These datasets are necessary for natural language processing and building machine learning workflows, where results largely depend on corpora that must be specifically crafted for this purpose. Therefore, creating high-quality domain-specific corpora is of great importance in the domain of information security. Such corpora can be employed for teaching purposes in various courses in higher education, and could be analyzed in numerous ways in order to understand the underlying principles of phishing attack strategies. The aim of this paper is to demonstrate the entire process of data acquisition and corpus creation for the phishing detection domain. In addition, an analysis of the corpus is presented with regard to different aspects, such as descriptive attributes, terminology characteristics, metadata and language.
Augmenting Student Education Using the RealityScan Application for Generating 3D Content

Augmented reality in education is surging in popularity in schools worldwide. Through augmented reality (AR), educators are able to improve learning outcomes through increased engagement and interactivity. Augmented Reality (AR) in education features aspects that enhance learning abilities like problem-solving, collaboration, and creation to better prepare students for the future. It is also good for traditional pedagogy focused on technical knowledge and proficiencies.An analysis is made on the innovative technology for generating 3D models - the RealityScan application by Epic Games for iOS. Several characteristics and functionalities of the application are described and tested. Methodologies and workshops based on them are created and presented.A process of including the aforementioned technologies in the curriculum and easing the students into using such tools in their future professional career is created and partially implemented. Several benefits of becoming familiar with such technologies are analyzed. A conclusion is made on several critical points that have to be addressed when implementing the necessary steps for improving the education system using AR.
Exploring Pie Charts and Part-To-Whole Alternatives: Eye-tracking Approach

In the field of business information visualization (BIV), there are numerous options for displaying data. One popular yet debated choice is the use of pie charts, as their effectiveness and efficiency have been called into question. Despite their widespread use, there is limited empirical research on the effectiveness and efficiency of pie charts compared to other display types. Additionally, existing research primarily focuses on measuring user impact through accuracy and time, neglecting the impact on users’ physiological attention and cognitive resources. This study aims to fill this gap by comparing the use of pie charts to seven alternative data representation types in a part-to-whole task The study employed a randomized, within-subject experiment with 21 participants, utilizing eye-tracking technology to evaluate user performance (accuracy and time) and cognitive effort (eye fixation). The results showed that pie charts were more accurate than bar charts but required more cognitive effort than stacked bars and treemaps. Donut charts required the most cognitive effort among all tested data representations. The study highlighted that time to complete the task may not be the best indicator of user experience and accuracy while elevating the importance of accounting for users’ cognitive resources.