Reviewerly/exHarmony huggingface dataset:
Digital citizenship and its relevance for literacy education: perspectives of preservice teachers

This research examines preservice teachers' perspectives on digital citizenship and its relevance for literacy education. Digital citizenship has been explored in various ways in recent decades, primarily in the educational technology literature, and prominent themes of digital citizenship include the use of safely, responsibly, and productively using technology to engage in digital spaces. However, limited research has explored preservice teachers' perspectives on digital citizenship, particularly as relates to literacy education. This qualitative case study investigates the views of 111 preservice teachers enrolled in a teacher preparation program in the Midwestern United States on digital citizenship and its relevance for literacy education. Data analysis of participants' written reflections revealed four primary themes of digital citizenship that align with concepts, skills, and goals in literacy education: well-informed citizens, community engagement and activism, safety, and technological know-how. These themes also connect to digital literacies. Given the commonalities between digital citizenship and literacy education, explicit integration of digital citizenship curricula into literacy education can connect important and discrete digital literacy skills into more cohesive educational units that empower children to safely and productively utilize digital technologies to promote meaningful change in their communities.
Extending the Framework for Developing Intelligent Virtual Environments (FIVE) with Artifacts for Modeling Internet of Things Devices and a New Decentralized Federated Learning Based on Consensus for Dynamic Networks

One of the main lines of research in distributed learning in recent years is the one related to Federated Learning (FL). In this work, a decentralized Federated Learning algorithm based on consensus (CoL) is applied to Wireless Ad-hoc Networks (WANETs), where the agents communicate with other agents to share their learning model as they are available to the wireless connection range. When deploying a set of agents, it is essential to study whether all the WANET agents will be reachable before the deployment. The paper proposes to explore it by generating a simulation close to the real world using a framework (FIVE) that allows the easy development and modification of simulations based on Unity and SPADE agents. A fruit orchard with autonomous tractors is presented as a case study. The paper also presents how and why the concept of artifact has been included in the above-mentioned framework as a way to highlight the importance of some devices used in the environment that have to be located in specific places to ensure the full connection of the system. This inclusion is the first step to allow Digital Twins to be modeled with this framework, now allowing a Digital Shadow of those devices.
Short-Term Photovoltaic Power Prediction Using Nonlinear Spiking Neural P Systems

To ensure high-quality electricity, improve the dependability of power systems, reduce carbon emissions, and promote the sustainable development of clean energy, short-term photovoltaic (PV) power prediction is crucial. However, PV power is highly stochastic and volatile, making accurate predictions of PV power very difficult. To address this challenging prediction problem, in this paper, a novel method to predict the short-term PV power using a nonlinear spiking neural P system-based ESN model has been proposed. First, we combine a nonlinear spiking neural P (NSNP) system with a neural-like computational model, enabling it to effectively capture the complex nonlinear trends in PV sequences. Furthermore, an NSNP system featuring a layer is designed. Input weights and NSNP reservoir weights are randomly initialized in the proposed model, while the output weights are trained by the Ridge Regression algorithm, which is motivated by the learning mechanism of echo state networks (ESNs), providing the model with an adaptability to complex nonlinear trends in PV sequences and granting it greater flexibility. Three case studies are conducted on real datasets from Alice Springs, Australia, comparing the proposed model with 11 baseline models. The outcomes of the experiments exhibit that the model performs well in tasks of PV power prediction.
Solar Energy Forecasting With Performance Optimization Using Machine Learning Techniques

The objective of this paper is proposed a forecasting technique system constitute the data integrity phase using Machine Learning (ML) techniques and post-processing output optimization to improve the performance. The outcomes demonstrated that the Photo Voltaic (PV) energy manufacture neural network performed more accurate by reducing solar irradiance prediction errors of linear regression characteristics. Through the adoption of both the optimized method settings and chosen function of ML methods were evaluated for predicting the performance. The accuracy of solar radiation estimation could be assessed in terms of statistical error measurement and verification measurements. The approach was tested in two climates hot and cold and the findings demonstrated using absolute error percentage with a contribution of 6.3% and 4.7%, respectively to improve the performance of the system based on data-driven ML algorithms and analytical post-processing.
Digital competence profiles of first year, pre-service teachers. Analysis in the Catalan university system

Like many other countries around the world, Catalonia is currently facing an urgent need for digitally competent teachers. Understanding the profile of first-year, pre-service teachers in relation to their teacher digital competence (TDC) can help design effective training programmes for future educators. The present study aims to measure the TDC profile of first-year, pre-service teachers along with the demographic variables related. A quantitative approach with a not experimental design was conducted in the 2018–19 academic year, to perform a cluster analysis. Three TDC groups emerge from a sample of 947 participants: Confident (n = 525), Average (n = 311), and Realistic (n = 111). Two variables show significant differences among groups: age and access to university. Both younger students and those coming from vocational training should be guided intensively during their initial training. On the other hand, gender and type of studies are distributed similarly across the three profiles. These findings have implications for developing pre-service teachers’ digital competence. In particular, the findings of this study highlight the need for all Catalan universities to develop a critical and common perspective, considering personal and professional factors of TDC as a key aspect for designing and implementing training in the curriculum.
Dark Light Image-Enhancement Method Based on Multiple Self-Encoding Prior Collaborative Constraints

The purpose of dark image enhancement is to restore dark images to visual images under normal lighting conditions. Due to the ill-posedness of the enhancement process, previous enhancement algorithms often have overexposure, underexposure, noise increases and artifacts when dealing with complex and changeable images, and the robustness is poor. This article proposes a new enhancement approach consisting in constructing a dim light enhancement network with more robustness and rich detail features through the collaborative constraint of multiple self-coding priors (CCMP). Specifically, our model consists of two prior modules and an enhancement module. The former learns the feature distribution of the dark light image under normal exposure as an a priori term of the enhancement process through multiple specific autoencoders, implicitly measures the enhancement quality and drives the network to approach the truth value. The latter fits the curve mapping of the enhancement process as a fidelity term to restore global illumination and local details. Through experiments, we concluded that the new method proposed in this article can achieve more excellent quantitative and qualitative results, improve detail contrast, reduce artifacts and noise, and is suitable for dark light enhancement in multiple scenes.
Digital and Social-Civic Skills in Future Primary Education Teachers: A Study from the Didactics of Social Sciences for the Improvement of Teacher Training in Competences

The use of technology, especially among young people, is providing new possibilities, including in the academic field, and requires teacher training through the development of skills and competences. At this point, Social Science Didactics plays a fundamental role, as it prepares future teachers to teach social knowledge in order to achieve useful and meaningful learning for students and society. Using an online questionnaire, structured with the Likert scale, which had previously been validated and published by Professors Peart, Gutiérrez-Esteban, and Cubo-Delgado, 156 students of the Degree in Primary Education of the subject Didactics of Social Sciences at the University of Granada (Spain) participated (academic year 2023–2024), with the aim of investigating the digital and socio-civic competences of trainee teachers in order to seek ways to improve their training. The results were processed in the IBM SPSS Statistics 25 programme, carrying out a descriptive statistical analysis, considering the mode and the variance ratio. The participating students mainly use digital environments to communicate with acquaintances and, although they know and value a democratic society, they do not exercise their citizenship on the Internet. This makes it even more necessary to train future teachers in digital competences, based on digital and socio-civic skills, as only in this way will they be able to train citizens capable of facing the challenges of the knowledge society.
Research on course teaching evaluation and academic early warning based on data mining

Based on the development requirements of modern vocational education and the development trend of information-based teaching, the purpose of the paper is to build a three-dimensional and diversified teaching evaluation method by introducing data mining technology, use appropriate data mining algorithms to summarize the knowledge as well as master rules, predict the learning status of students, timely carry out early warning and teaching intervention, and explore a teaching evaluation and academic early warning method suitable for vocational education courses through practical classroom practice so as to improve curriculum mastery level of the students and optimize the quality of personnel training technical skills.
Song Emotion Intelligence Analysis for Psychological Stress Relief

In today's digital and networked era, multimedia content such as images, audio, and video have become an important part of the data transmitted on the Internet's information superhighway. How to manage the stress in the college student population is directly related to the future life and development of college students. In this article, based on domestic and international data mining technology, the authors designed an intelligent analysis system for college students' mental health, pre-processed the data, then analysed these data in detail by using the outlier analysis algorithm in clustering algorithm, and finally mined the intrinsic connection between the psychological problems and attributes by using the Apriori association rule algorithm, so as to provide the decision makers with a reliable basis. This study puts forward reasonable solutions and suggestions for the problems existing in the psychological level of contemporary college students.
Unsupervised Graph Anomaly Detection with Discriminative Embedding Similarity for Viscoelastic Sandwich Cylindrical Structures

In the detection of slipping anomalies in viscoelastic sandwich cylindrical structures (VSCS), conventional methods may encounter challenges due to the extremely rare and weak nature of slipping signals. This study focuses on normal signals and introduces unsupervised graph representation learning (UGRL) with discriminative embedding similarity for VSCS's detection. UGRL involves data preprocessing, model embedding, and matrix reconstructing. Association graphs are constructed based on sample similarities for yielding adjacency and attribute matrices. Subsequently, the matrices undergo embedding and reconstruction via various network modules to enhance graph data characterization. Detection indicators are derived by calculating embedding similarities and reconstruction errors, and thresholds are constructed using these indicators to enable efficient anomaly detection. The experiments in VSCS slipping dataset effectively indicate the superiority of the proposed method.
Artificial Intelligence and the Modelling of Teachers Competencies

This research explores educators' perceptions of their role in shaping competencies and also presents the key challenges and measures perceived by educators in the AI system development landscape.The study employs a partial least squarestructural equation modelling (PLS-SEM) as the primary analytical tool.A set of hypotheses was derived from the literature, formulated and tested to substantiate the relationship between educators' attitudes and competencies.The findings highlight a significant positive relationship between teachers' attitudes toward AI and competence development, with the most pronounced impact on educational management competencies.Thus, teachers' positive attitudes toward AI statistically significant influence cognitive, fundamental and educational management competencies, underscoring the critical role of instilling positive attitude towards AI among educators to foster the development of multifaceted competencies.Furthermore, the research highlights key challenges to integrating AI into education, including the imperative for professional development for educators and ensuring equitable access to educational resources and technology.From a practical point of view, the study advocates for initiatives aimed at bridging the digital divide and integrating AI education into curricula as indispensable policy measures.The originality of our research lies in the unitary approach of the relationship between positive attitudes of teachers toward artificial intelligence and a series of competencies, such as cognitive, fundamental and educational management.Although the study provides valuable information, it has certain limitations.The findings are based on educators' perceptions and attitudes, which may vary among individuals, the study scope is limited to a specific region, and the generalisability of findings to other settings may require further investigation.This research contributes to the existing literature by exploring the relationship between teachers' attitudes toward AI and the development of their skills, with the help of an exploratory model using structural equations, a model that has the teachers in the foreground.
Adversarial attacks and defenses in explainable artificial intelligence: A survey

Explainable artificial intelligence (XAI) methods are portrayed as a remedy for debugging and trusting statistical and deep learning models, as well as interpreting their predictions. However, recent advances in adversarial machine learning (AdvML) highlight the limitations and vulnerabilities of state-of-the-art explanation methods, putting their security and trustworthiness into question. The possibility of manipulating, fooling or fairwashing evidence of the model’s reasoning has detrimental consequences when applied in high-stakes decision-making and knowledge discovery. This survey provides a comprehensive overview of research concerning adversarial attacks on explanations of machine learning models, as well as fairness metrics. We introduce a unified notation and taxonomy of methods facilitating a common ground for researchers and practitioners from the intersecting research fields of AdvML and XAI. We discuss how to defend against attacks and design robust interpretation methods. We contribute a list of existing insecurities in XAI and outline the emerging research directions in adversarial XAI (AdvXAI). Future work should address improving explanation methods and evaluation protocols to take into account the reported safety issues.
In Conversation with Sir David Spiegelhalter and Professor Sylvia Richardson

Sir David Spiegelhalter and Professor Sylvia Richardson are two eminent statisticians of our time who have made pioneering contributions to Statistics and Data Science with careers spanning over more than four decades. They have a long and celebrated legacy built through foundational research in Bayesian statistics, impactful collaborations, steadfast professional service and superb scientific communications. They have won many prestigious awards and recognitions throughout their distinguished careers. During my sabbatical in 2022 at the University of Cambridge I had the honor of sitting down for a conversation with these two remarkable individuals. We discussed early career influences and digressions, research philosophy, role of mentors and advice for the future generation. We hope this conversation with David and Sylvia will inspire many future statisticians.
Emerging Directions in Bayesian Computation

Bayesian models are powerful tools for studying complex data, allowing the analyst to encode rich hierarchical dependencies and leverage prior information. Most importantly, they facilitate a complete characterization of uncertainty through the posterior distribution. Practical posterior computation is commonly performed via MCMC, which can be computationally infeasible for high-dimensional models with many observations. In this article, we discuss the potential to improve posterior computation using ideas from machine learning. Concrete directions are explored in vignettes on normalizing flows, statistical properties of variational approximations, Bayesian coresets and distributed Bayesian inference.
Outlier detection using flexible categorisation and interrogative agendas

Categorization is one of the basic tasks in machine learning and data analysis. Building on formal concept analysis (FCA), the starting point of the present work is that different ways to categorize a given set of objects exist, which depend on the choice of the sets of features used to classify them, and different such sets of features may yield better or worse categorizations, relative to the task at hand. In their turn, the (a priori) choice of a particular set of features over another might be subjective and express a certain epistemic stance (e.g. interests, relevance, preferences) of an agent or a group of agents, namely, their interrogative agenda. In the present paper, we represent interrogative agendas as sets of features, and explore and compare different ways to categorize objects w.r.t. different sets of features (agendas). We first develop a simple unsupervised FCA-based algorithm for outlier detection which uses categorizations arising from different agendas. We then present a supervised meta-learning algorithm to learn suitable (fuzzy) agendas for categorization as sets of features with different weights or masses. We combine this meta-learning algorithm with the unsupervised outlier detection algorithm to obtain a supervised outlier detection algorithm. We show that these algorithms perform at par with commonly used algorithms for outlier detection on commonly used datasets in outlier detection. These algorithms provide both local and global explanations of their results.
Quantum Annealing-driven Branch and Bound for the single machine total weighted number of tardy jobs scheduling problem

In the paper we present a new approach to solving NP-hard problems of discrete optimization adapted to the architecture of quantum processors (QPU, Quantum Processor Unit) implementing hardware quantum annealing. This approach is based on the use of the quantum annealing metaheuristic in the exact branch and bound algorithm to compute the lower and upper bounds of the objective function. To determine the lower bound, a new method of defining the Lagrange function for the dual problem (the generalized discrete knapsack problem) was used, the value of which is calculated on the QPU of a quantum machine. In turn, to determine the upper bound, we formulate an appropriate task in the form of binary quadratic programming with constraints. Despite the fact that the results generated by the quantum machine are probabilistic, the hybrid method of algorithm construction proposed in the paper, using alternately a CPU and QPU, guarantees the optimal solution. As a case study we consider the NP-hard single machine scheduling problem with minimizing the weighted number of tardy jobs. The performed computational experiments showed that optimal solutions were already obtained in the root of the solution tree, and the values of the lower and upper bounds differ by only a few percent.
Feature selection based on dataset variance optimization using Hybrid Sine Cosine – Firehawk Algorithm (HSCFHA)

Feature selection plays a pivotal role in preprocessing data for machine learning (ML) models. It entails choosing a subset of pertinent features to enhance the model’s accuracy and minimize overfitting. Wrapper methods based on metaheuristics are one approach to feature selection, leveraging the predictive accuracy of a learning algorithm to form a condensed set of features. Traditionally, this method uses K-Nearest Neighbour (KNN) for maximizing accuracy as its cost function. However, this approach often yields less than optimal results in large sample spaces and demands considerable computational resources. To circumvent the shortcomings of this approach, this work proposes a novel metaheuristic algorithm, termed the Hybrid Sine Cosine Firehawk Algorithm. Furthermore, a novel feature selection technique is designed that uses this hybrid algorithm to eliminate insignificant and redundant features by incorporating the minimization of dataset variance in the cost function. Additionally, the hybridization of multiple metaheuristic algorithms produces the best features of each algorithm to improve the exploration ability. The proposed technique is tested on 22 University of California Irvine datasets containing low, medium and high dimensional datasets and compared to the traditional KNN-based approach. The technique is also compared with other state-of-the-art metaheuristic techniques, namely Particle Swarm Optimizer, Grey Wolf Optimizer, Whale Optimization Algorithm, Hybrid Ant Colony Optimizer and Improved Binary Bat Algorithm. The results show significant improvements over previous techniques in terms of minimal loss in essential data while reducing the size of the raw data in considerably less time, as well as a well-balanced confusion matrix.