Reviewerly/exHarmony huggingface dataset:
Assessing digital maturity of schools: framework and instrument

The European Commission emphasises the need for educational institutions to integrate digital technologies in their teaching, learning and organisational practices. This study contributes to the field of digital transformation of schools by proposing and validating a Framework for Digitally Mature Schools (FDMS) and an instrument for assessing the level of schools’ digital maturity. The FDMS and the instrument recognise five areas divided into 38 elements described across five different levels in the form of a rubric. This assessment instrument enables schools to measure their current level of digital maturity and pinpoint the areas that require improvements. The developed Framework and instrument support educational institutions in the assessment, promotion and integration of digital technologies in their teaching, learning and organisational practices.
Board Games in Improving Pupils’ Speaking Skills: A Systematic Review

English is a fundamental language to learn as it is used worldwide. The teaching and learning of English has been emphasized in Malaysia as English plays a major role in global communication. However, speaking performance was recorded as poor and weak among pupils in ESL classrooms. Previous researchers explored a myriad of communicative language activities to improve speaking skill. Board games are employed as one of the most useful tools to improve speaking skills among pupils. This systematic review was conducted to examine pupils’ perceptions on the use of board games in learning and speaking, as well as the usefulness of board games in improving their speaking skill. A total of 35 articles, from the period 2017–2021, were accessed through three databases: Google Scholar, ERIC and SAGE Journals. The review was conducted systematically based on the guidelines from the PRISMA statement (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Based on the articles gathered, the results showed that the qualitative research design was mostly used to collect pupils’ opinions on the use of board games, while quantitative research design was mostly used to evaluate the usefulness of board games in improving speaking. Overall, the systematic review revealed that board games had several positive impacts in pupils’ speaking performances such as improving speaking competency, enhancing motivation in speaking and increasing interpersonal interaction among pupils. It is suggested that future studies could focus on investigating teachers’ opinions on the use of board games in teaching and speaking.
Special Section: Strategic Integration of Blockchain Technology into Organizations

Blockchain is an emerging technology that enables two or more entities to conduct secure transactions. After a blockchain transaction is executed, it cannot be altered because the transaction infor...
Modbus Access Control System Based on SSI over Hyperledger Fabric Blockchain

Security is the main challenge of the Modbus IIoT protocol. The systems designed to provide security involve solutions that manage identity based on a centralized approach by introducing a single point of failure and with an ad hoc model for an organization, which handicaps the solution scalability. Our manuscript proposes a solution based on self-sovereign identity over hyperledger fabric blockchain, promoting a decentralized identity from which both authentication and authorization are performed on-chain. The implementation of the system promotes not only Modbus security, but also aims to ensure the simplicity, compatibility and interoperability claimed by Modbus.
Dual Color Space Guided Sketch Colorization

Automatic sketch colorization is a challenging task in both computer graphics and computer vision since all the color, texture, shading generation have to be created based on the abstract sketch. Besides, it is a subjective task in painting process, which needs illustrators to comprehend drawing priori (DP), such as hue variation, saturation contrast and gray contrast and utilize them in the HSV color space which is closer to human visual cognition system. As such, incorporating supplementary supervision in the HSV color space may be beneficial to sketch colorization. However, previous methods improve the colorization quality only in the RGB color space without considering the HSV color space, often causing results with dull color, inappropriate saturation contrast, and artifacts. To address this issue, we propose a novel sketch colorization method, dual color space guided generative adversarial network (DCSGAN), that considers the complementary information contained in both the RGB and HSV color space. Specifically, we incorporate the HSV color space to construct dual color spaces for supervising our method with a color space transformation (CST) network that learns transformation from the RGB to HSV color space. Then, we propose a DP loss that enables the DCSGAN to generate vivid color images with pixel level supervision. Additionally, a novel dual color space adversarial (DCSA) loss is designed to guide the generator at global level to reduce the artifacts to meet audiences’ aesthetic expectations. Extensive experiments and ablation studies demonstrate the superiority of the proposed method over previous state-of-the-art (SOTA) methods.
Improved Salp Swarm Optimization Algorithm: Application in Feature Weighting for Blind Modulation Identification

In modulation identification issues, like in any other classification problem, the performance of the classification task is significantly impacted by the feature characteristics. Feature weighting boosts the performance of machine learning algorithms, particularly the class of instance-based learning algorithms such as the Minimum Distance (MD) classifier, in which the distance measure is highly sensitive to the magnitude of features. In this paper, we propose an improved version of the Salp Swarm optimization Algorithm (SSA), called ISSA, that will be applied to optimize feature weights for an MD classifier. The aim is to improve the performance of a blind digital modulation detection approach in the context of multiple-antenna systems. The improvements introduced to SSA mainly rely on the opposition-based learning technique. Computer simulations show that the ISSA outperforms the SSA as well as the algorithms that derive from it. The ISSA also exhibits the best performance once it is applied for feature weighting in the above context.
A hyperchaotic cycloid map with attractor topology sensitive to system parameters

We propose herein a novel discrete hyperchaotic map based on the mathematical model of a cycloid, which produces multistability and infinite equilibrium points. Numerical analysis is carried out by means of attractors, bifurcation diagrams, Lyapunov exponents, and spectral entropy complexity. Experimental results show that this cycloid map has rich dynamical characteristics including hyperchaos, various bifurcation types, and high complexity. Furthermore, the attractor topology of this map is extremely sensitive to the parameters of the map. The x--y plane of the attractor produces diverse shapes with the variation of parameters, and both the x--z and y--z planes produce a full map with good ergodicity. Moreover, the cycloid map has good resistance to parameter estimation, and digital signal processing implementation confirms its feasibility in digital circuits, indicating that the cycloid map may be used in potential applications.
A Study on the Application of Interactive English-Teaching Mode under Complex Data Analysis

This research takes vocabulary learning in college English courses as an entry point and investigates the interrelationship between college students and electronic media, the presentation of interactive English-teaching content in college English, and the effect of different English-teaching modes on the effect of vocabulary learning through empirical methods in the form of network multimedia. This study provides ideas for an in-depth understanding of the incongruities that exist between interactive English teaching and environment, teacher and environment, and students and environment in the network multimedia environment and also brings thoughts on how to adapt interactive English teaching in college English to the development of modern information technology on the concept of interactive English teaching, interactive English-teaching methods, and interactive English-teaching modes. The concepts related to the adaptation of interactive English teaching based on multimedia, the compilation of the scale of adaptation of interactive English teaching based on multimedia and its influencing factors, and the construction of the model of influencing factors of adaptation of interactive English teaching based on multimedia, to a certain extent, enrich the theoretical system of learning adaptation. The design of the intervention model of adaptation of interactive English teaching based on multimedia broadens the intervention theory of learning adaptation; the study is also aimed at developing a multimedia-based interactive English-teaching adaptation intervention model and broadening the theory and method of learning adaptation.
Spectral Analysis of Random Processes

This chapter is dedicated to give a brief introduction to the frequency domain analysis of random processRandom processes. The Fourier transformFourier transform transfers a function $$ f(t) $$ from the time domain into the frequency domain. Please note that, $$ f(t) $$ should satisfy the Dirichlet conditions in order to have its Fourier transformFourier transform.
Weakly-Supervised Word-Level Pronunciation Error Detection in Non-Native English Speech

We propose a weakly-supervised model for word-level mispronunciation detection in non-native (L2) English speech. To train this model, phonetically transcribed L2 speech is not required and we only need to mark mispronounced words. The lack of phonetic transcriptions for L2 speech means that the model has to learn only from a weak signal of word-level mispronunciations. Because of that and due to the limited amount of mispronounced L2 speech, the model is more likely to overfit. To limit this risk, we train it in a multi-task setup. In the first task, we estimate the probabilities of word-level mispronunciation. For the second task, we use a phoneme recognizer trained on phonetically transcribed L1 speech that is easily accessible and can be automatically annotated. Compared to state-of-the-art approaches, we improve the accuracy of detecting word-level pronunciation errors in AUC metric by 30% on the GUT Isle Corpus of L2 Polish speakers, and by 21.5% on the Isle Corpus of L2 German and Italian speakers
Macroeconomic Digital Book Development: How are the Feasibility of Experts and Student Responses?

The use of books today is inseparable from the rapid development of technology, one of the book developments that attracts a lot of interest from teachers is digital books. Digital books themselves are very suitable for complex and memorizing learning such as macroeconomics courses, the reason digital books are suitable for macroeconomic learning is that they can be carried anytime and anywhere. This study aims to see the feasibility of the product being developed, namely a digital book on macroeconomic material. The feasibility of the digital book made is divided into two, namely the feasibility of the experts and the feasibility of the sample (students). This research is an R&amp;D type with the selected development model, namely the ADDIE development model. The sampling technique used is simple random sampling with a sample of 36 active students of economic education study program. There are 3 data collection instruments used including media and material expert validation instruments which have 10 statements and student response instruments totaling 28 statements. Analysis of the data used in the form of descriptive statistics. The results of material validation for both validators were 42 (very good) and 37 (good), then for media experts, a score of 38 (good) and 39 (good), while for student responses to the material aspect, a score of 29.25 (worth it) was obtained. , media aspect is 52.5 (very worth it), output aspect is 9.75 (worth it), and for all aspects is 91.5 (very worth it). Based on the results that have been obtained from the validation and small group trials, it shows that digital books on macroeconomic material are appropriate to be used as complementary learning resources in macroeconomics lectures.
Self-supervised Learning for Reading Activity Classification

Reading analysis can relay information about user's confidence and habits and can be used to construct useful feedback. A lack of labeled data inhibits the effective application of fully-supervised Deep Learning (DL) for automatic reading analysis. We propose a Self-supervised Learning (SSL) method for reading analysis. Previously, SSL has been effective in physical human activity recognition (HAR) tasks, but it has not been applied to cognitive HAR tasks like reading. We first evaluate the proposed method on a four-class classification task on reading detection using electrooculography datasets, followed by an evaluation of a two-class classification task of confidence estimation on multiple-choice questions using eye-tracking datasets. Fully-supervised DL and support vector machines (SVMs) are used as comparisons for the proposed SSL method. The results show that the proposed SSL method is superior to the fully-supervised DL and SVM for both tasks, especially when training data is scarce. This result indicates the proposed method is the superior choice for reading analysis tasks. These results are important for informing the design of automatic reading analysis platforms.
Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation

News recommendation is critical for personalized news access.Most existing news recommendation methods rely on centralized storage of users' historical news click behavior data, which may lead to privacy concerns and hazards.Federated Learning is a privacy-preserving framework for multiple clients to collaboratively train models without sharing their private data.However, the computation and communication cost of directly learning many existing news recommendation models in a federated way are unacceptable for user clients.In this paper, we propose an efficient federated learning framework for privacy-preserving news recommendation.Instead of training and communicating the whole model, we decompose the news recommendation model into a large news model maintained in the server and a light-weight user model shared on both server and clients, where news representations and user model are communicated between server and clients.More specifically, the clients request the user model and news representations from the server, and send their locally computed gradients to the server for aggregation.The server updates its global user model with the aggregated gradients, and further updates its news model to infer updated news representations.Since the local gradients may contain private information, we propose a secure aggregation method to aggregate gradients in a privacy-preserving way.Experiments on two real-world datasets show that our method can reduce the computation and communication cost on clients while keep promising model performance.
Analysis of the Mental Health of Urban Migrant Children Based on Cloud Computing and Data Mining Algorithm Models

With the rapid development of internet technology, the amount of data generated is also increasing day by day. As a kind of distributed computing, cloud computing has been widely used in the analysis of massive data. With the development of China’s economic construction, the integration of urban and rural areas is constantly improving, and the migrant children in the city are also focused on. After moving into the city, migrant children not only face the pressure from the society but also face the pressure from life, which inevitably affects the physical and mental health of urban migrant children. The education of urban migrant children is also a focus that needs attention. How to integrate into the education environment of urbanization and adjust the learning pressure in the process of education is also worthy of our attention. Therefore, this article analyzes the current status of urban migrant children’s mental health based on cloud computing and data mining algorithm models. Based on the current research status of urban migrant children and the standards of mental health, this paper conducts a survey of middle and high school students in a certain city through questionnaires, then builds a data mining algorithm model to analyze the survey data, and explores the differences in the grades of students’ social identity and the differences in mental health between migrant children and urban children. According to the survey, most of the psychological performances of urban migrant children are very vague. At the same time, there are also some phenomena such as poor adaptability, bad mood, and inferiority complex. During the study period, there are situations such as unwilling to communicate with others, weariness, sensitivity, anxiety, and hostility. The overall incidence of the situation is relatively high in big cities, while the situation of urban children is relatively small.
A Security and Privacy Scoring System for Contact Tracing Apps

Contact tracing applications have flooded the marketplace, as governments worldwide have been working to release apps for their citizens. These apps use a variety of protocols to perform contact tracing, resulting in widely differing security and privacy assurances. Governments and users have been left without a standard metric to weigh these protocols and compare their assurances to know which are more private and secure. Although there are many ways to approach a quantitative metric for privacy and security, one natural way is to draw on the methodology used by the well-known common vulnerability scoring system (CVSS). For privacy, we applied consensus principles for contract tracing as a basis for comparing their relative privacy practices. For security, we performed attack modeling to develop a rubric to compare the security of respective apps. Our analysis shows that centralized Bluetooth with added location functionality has low privacy and security, while non-streaming GPS scored high in security and medium in privacy. Based on our methodology, only two apps were given a high ranking of privacy: Canada’s Covid Alert and Germany’s Corona Warn-App. They both used the Google/Apple Notification Framework as the basis for their design. To achieve comparable privacy, we recommend that future projects follow their examples in the following ways: minimizing the amount of data they collect and holding it for the shortest possible length of time; only having features necessary for the app’s main function; and releasing design details so that users can make informed decisions.
Privacy-Preserving Solutions in Blockchain-Enabled Internet of Vehicles

Blockchain, a promising technology that has matured and nowadays is widely used in many fields, such as supply chain management, smart grids, agriculture and logistics, has also been proposed for the Internet of Vehicles (IoV) ecosystem to enhance the protection of the data that roadside units and vehicles exchange. Blockchain technology can inherently guarantee the availability, integrity and immutability of data stored in IoV, yet it cannot protect privacy and data confidentiality on its own. As such, solutions that utilise this technology have to consider the adoption of privacy-preserving schemes to address users’ privacy concerns. This paper provides a literature review of proposed solutions that provide different vehicular services using blockchain technology while preserving privacy. In this context, it analyses existing solutions’ main characteristics and properties to provide a comprehensive and critical overview and identifies their contribution in the field. Moreover, it provides suggestions to researchers for future work in the field of privacy-preserving blockchain-enabled solutions for vehicular networks.
Noise-Boosted Backpropagation Learning of Feedforward Threshold Neural Networks for Function Approximation

Aiming to ensure the feasibility of the backpropagation training of feedforward threshold neural networks, each hidden unit layer is designed to be composed of a sufficiently large number of hard-limiting activation functions that are excited by mutually independent external noise components and the weighted inputs simultaneously. The application of noise to nondifferentiable activation functions enables a proper definition of the gradients, and the injected noise is treated as a network parameter that can be adaptively updated by a stochastic gradient descent learning rule. This noise-boosted backpropagation learning process is found to converge to a nonzero optimized level of noise, indicating that the injected noise is beneficial both for the learning and for the ensuing retrieval phase. For minimizing the total error energy of the function approximation in the designed threshold neural network, the proposed noise-boosted backpropagation learning method is proven to be better than directly injecting noise into network inputs or weight coefficients. The Lipschitz continuous property of the noise-smoothed activation function in the hidden unit layer is demonstrated to guarantee the local convergence of the learning process. Beyond the Gaussian injected noise, the optimal noise type is also numerically solved for training the designed threshold neural network. Test experiments for approximating nonlinear functions and real-world datasets verify the feasibility of this noise-boosted backpropagation algorithm in the threshold neural network. These results not only extend the analysis of the beneficial effects of noise similar to stochastic resonance and exploited here to the universal approximation capabilities of threshold neural networks, but also allow backpropagation training of neural networks with a much wider family of nondifferentiable activation functions.
Semi-supervised consensus clustering based on closed patterns

Semi-supervised consensus clustering, also called semi-supervised ensemble clustering, is a recently emerged technique that integrates prior knowledge into consensus clustering in order to improve the quality of the clustering result. In this article, we propose a novel semi-supervised consensus clustering algorithm extending the previous work on the MultiCons multiple consensus clustering approach. By using closed pattern mining technique, the proposed Semi-MultiCons algorithm manages to generate a recommended consensus solution with a relevant inferred number of clusters k based on ensemble members with different k and pairwise constraints. Compared with other semi-supervised and/or consensus clustering approaches, Semi-MultiCons does not require the number of generated clusters k as an input parameter, and is able to alleviate the widely reported negative effect related to the integration of constraints into clustering. The experimental results demonstrate that the proposed method outperforms state of the art semi-supervised consensus clustering algorithms.