Reviewerly/exHarmony huggingface dataset:
A rotationally invariant stochastic opposition-based learning using a beta distribution in differential evolution

A computational technique called opposition-based learning (OBL) has gained a lot of attention in the field of soft computing. Among the different forms of OBL, the iBetaCOBL variant, which is a stochastic OBL, has shown the best results in improving the performance of differential evolution (DE). However, this approach may not be effective in handling complex problems as it is susceptible to rotations in the coordinate system. To address this issue, we present a new and improved version of iBetaCOBL called iBetaCOBL-eig. This new technique uses an eigenvector-based multiple exponential crossover operator in the partial dimensional change method, making it rotationally invariant. We conducted experiments to evaluate the performance enhancements of DE using iBetaCOBL-eig on 29 challenging benchmark problems. Our results showed that the new algorithm is able to outperform not only ten strong OBL variants but also its previous version iBetaCOBL.
Reproducibility and transparency versus privacy and confidentiality: Reflections from a data editor

Transparency and reproducibility are often seen in opposition to privacy and confidentiality. Data that need to be kept confidential are seen as an impediment to reproducibility, and privacy would seem to inhibit transparency. I bring a more nuanced view to the discussion, and show, using examples from over 1,000 reproducibility assessments, that confidential data can very well be used in reproducible and transparent research. The key insight is that access to most confidential data, while tedious, is open to hundreds if not thousands of researchers. In cases where few researchers can consider accessing such data in the future, reproducibility services, such as those provided by some journals, can provide some evidence for effective reproducibility even when the same data may not be available for future research.
Design of quantum error correcting code for biased error on heavy-hexagon structure

Surface code is an error-correcting method that can be applied to the implementation of a usable quantum computer. At present, a promising candidate for a usable quantum computer is based on superconductor-specifically transmon. Because errors in transmon-based quantum computers appear biasedly as Z type errors, tailored surface and XZZX codes have been developed to deal with the type errors. Even though these surface codes have been suggested for lattice structures, since transmons-based quantum computers, developed by IBM, have a heavy-hexagon structure, it is natural to ask how tailored surface code and XZZX code can be implemented on the heavy-hexagon structure. In this study, we provide a method for implementing tailored surface code and XZZX code on a heavy-hexagon structure. Even when there is no bias, we obtain $$ 0.231 \%$$ as the threshold of the tailored surface code, which is much better than $$ 0.21 \%$$ and $$ 0.209 \%$$ as the thresholds of the surface code and XZZX code, respectively. Furthermore, we can see that even though a decoder, which is not the best of the syndromes, is used, the thresholds of the tailored surface code and XZZX code increase as the bias of the Z error increases. Finally, we show that in the case of infinite bias, the threshold of the surface code is $$ 0.264\%$$ , but the thresholds of the tailored surface code and XZZX code are $$ 0.296 \% $$ and $$ 0.328 \%$$ respectively.
Taylor series based dual-branch transformation for learned image compression

Nonlinear transformations (NTs) are a crucial element of learned image compression (LIC) as they eliminate correlations from images. However, there has been limited focus on developing efficient NTs. This paper aims to propose a design approach for efficient NTs. In this paper, firstly, we model the current NT methods with a unified formulation and find that the difference in the NTs lies in the representation of the scaling function. Subsequently, we analyze the essential elements of designing scaling functions from the perspective of the Taylor series of derivable functions to satisfy the conditions of the Taylor expansion. Lastly, we develop an efficient two-branch nonlinear transformation architecture based on the Taylor series of sinusoidal functions. We compare the performance of various existing nonlinear transformations in the same backbone network. The experimental results demonstrate that our method achieves advanced performance among existing NT methods without a significant increase in encoding and decoding time. For instance, our method achieves a 20.24% BD-rate reduction over BPG444 on the Kodak dataset. The generic capability of our method is validated through experimental results on multiple backbone architectures.
BE-ELM: Biological ensemble Extreme Learning Machine without the need of explicit aggregation

Extreme learning machines (ELMs) are commonly adopted as base learners in ensemble methods due to unstable results and fast learning speed. However, most existing ensemble structures require explicit aggregation of ELM base learners’ results before the final decision. This study proposes a novel Biological Ensemble ELMs called BE-ELM from a biological perspective for the first time and exploit the superiority of assembling multiple ELM base learners in parallel without the need for explicit aggregation, and thus simplifying the learning procedure. BE-ELM’s structure is inspired by recent MIT neuroscience findings of brain learning mechanisms. The expression of the analytical solution of BE-ELM is similar to that of basic ELMs, which allows it to inherit their fast learning speed in the ensemble structure. Moreover, BE-ELM also has the added advantage of superior performance. Our theoretical analysis shows that BE-ELM consisting of multiple ELM base learners built on subsets of the original features is equivalent to a more complex ELM on the original feature space. We prove that BE-ELM, even without any explicit aggregation, can guarantee enhanced generalization capabilities. To confirm our findings, we conducted extensive experiments on various datasets. The results demonstrate that BE-ELM outperforms traditional ELMs and other state-of-the-art ensemble ELMs in terms of generalization performance on most datasets. These findings suggest that BE-ELM has the potential to improve prediction outcomes in practical applications.
Exploring modern trends in developing a digital educational environment for university: A case study of Ternopil Volodymyr Hnatiuk National Pedagogical University

This article delves into the contemporary tendencies shaping the development of digital educational environments in universities and proposes a model for their implementation in pedagogical institutions. The study analyzes the concept of educational environments, explores their components, and highlights the distinctive structural features. Additionally, it introduces the notion of a digital educational environment and elucidates its essential components for teachers’ professional training at Ternopil Volodymyr Hnatiuk National Pedagogical University (TNPU): technological, didactic, and social aspects. Notably, the article discusses the characteristics of this environment, such as its information-rich and open nature, digital transformation, and emphasis on social practices and collaboration. To evaluate the efficacy of the digital educational environment for teachers’ professional training, a comprehensive study was conducted with the participation of 432 master’s students across various disciplines at TNPU. Expert assessments were employed for statistical analysis, aiming to determine the significance level of each component’s indicators within the digital educational environment. The results highlight substantial changes observed in the technological and social dimensions of the university’s digital educational environment, which significantly influence the quality of teachers’ professional training.
Exploring the potential of online services in utilizing free software for educational activities

This article delves into the utilization of online services for studying and incorporating free software into educational settings. Free software embodies the principles of freedom and intellectual creativity, yet despite the existence of numerous software products within repositories, their integration into educational activities remains limited. Through questionnaires, analysis of open data, and reviewing existing research on the use of free software, this study identifies factors that facilitate or hinder its adoption in educational contexts. Drawing on the collected data, the article provides insights into the viability of incorporating free software into educational activities and showcases specific examples of its usage. Additionally, the study highlights the value of online resources in familiarizing educators and learners with free software, underscoring the central focus of this investigation. By shedding light on the potential benefits and challenges of leveraging free software in education and emphasizing the role of online services, this article contributes to the discourse surrounding the integration of free software in educational settings.
Improving Reliability for Detecting Anomalies in the MQTT Network by Applying Correlation Analysis for Feature Selection Using Machine Learning Techniques

Anomaly detection (AD) has captured a significant amount of focus from the research field in recent years, with the rise of the Internet of Things (IoT) application. Anomalies, often known as outliers, are defined as the discovery of anomalous occurrences or observations that differ considerably from the mainstream of the data. The IoT which is described as a network of Internet-based digital sensors that continuously generate massive volumes of data and use to communicate with one another theMessage Queuing Telemetry Transport (MQTT) protocol. Brute-force, Denial-of-Service (DoS), Malformed, Flood, and Slowite attacks are the most common in theMQTT network. One of the significant factors in IoT AD is the time consumed to predict an attack and take preemptive measures. For instance, if an attack is detected late, the loss of attack is irreversible. This paper investigates the time to detect an attack using machine learning approaches and proposes a novel approach that applies correlation analysis to reduce the training and testing time of these algorithms. The new approach has been evaluated on Random Forest, Decision Tree, Naïve Bayes, Multi-Layer Perceptron, Artificial Neural Network, Logistic Regression, and Gradient Boost. The findings indicate that the correlation analysis is significantly beneficial in the process of feature engineering, primarily to determine the most relevant features in the MQTT dataset. This is, to the best of our knowledge, the first study on MQTTset that reduces the prediction time for DoS 0.92 (95% CI −0.378, 2.22) reduced to 0.77 (95% CI −0.414, 1.97) and for Malformed 2.92 (95% CI −2.6, 8.44) reduced to 0.49 (95% CI −0.273, 1.25).
DSpamOnto: An Ontology Modelling for Domain-Specific Social Spammers in Microblogging

The lack of regulations and oversight on Online Social Networks (OSNs) has resulted in the rise of social spam, which is the dissemination of unsolicited and low-quality content that aims to deceive and manipulate users. Social spam can cause a range of negative consequences for individuals and businesses, such as the spread of malware, phishing scams, and reputational damage. While machine learning techniques can be used to detect social spammers by analysing patterns in data, they have limitations such as the potential for false positives and false negatives. In contrast, ontologies allow for the explicit modelling and representation of domain knowledge, which can be used to create a set of rules for identifying social spammers. However, the literature exposes a deficiency of ontologies that conceptualize domain-based social spam. This paper aims to address this gap by designing a domain-specific ontology called DSpamOnto to detect social spammers in microblogging that targes a specific domain. DSpamOnto can identify social spammers based on their domain-specific behaviour, such as posting repetitive or irrelevant content and using misleading information. The proposed model is compared and benchmarked against well-proven ML models using various evaluation metrics to verify and validate its utility in capturing social spammers.
The Effect of Hyperparameter Optimization on the Estimation of Performance Metrics in Network Traffic Prediction using the Gradient Boosting Machine Model

Information and Communication Technology (ICT) has changed the way we communicate and access information, resulting in the high generation of heterogeneous data. The amount of network traffic generated constantly increases in velocity, veracity, and volume as we enter the era of big data. Network traffic classification and intrusion detection are very important for the early detection and identification of unnecessary network traffic. The Machine Learning (ML) approach has recently entered the center stage in network traffic accurate classification. However, in most cases, it does not apply model hyperparameter optimization. In this study, gradient boosting machine prediction was used with different hyperparameter optimization configurations, such as interaction depth, tree number, learning rate, and sampling. Data were collected through an experimental setup by using the Sophos firewall and Cisco router data loggers. Data analysis was conducted with R software version 4.2.0 with Rstudio Integrated Development Environment. The dataset was split into two partitions, where 70% was used for training the model and 30% for testing. At a learning rate of 0.1, interaction depth of 14, and tree number of 2500, the model estimated the highest performance metrics with an accuracy of 0.93 and R of 0.87 compared to 0.90 and 0.85 before model optimization. The same configuration attained the minimum classification error of 0.07 than 0.10 before model optimization. After model tweaking, a method was developed for achieving improved accuracy, R square, mean decrease in Gini coefficients for more than 8 features, lower classification error, root mean square error, logarithmic loss, and mean square error in the model.
Structural components of adolescent’s environmental awareness in a general education institution

The relevance of the problem of developing adolescents’ environmental awareness is conditioned upon the need to instil in students the concepts of nature management and nature conservation in a general education institution to develop healthy behaviour towards nature and its resources, and an appropriate level of value attitude towards natural and cultural monuments. The purpose of the research is to identify the main structural components, factors and conditions for the development of environmental awareness of adolescents in educational institutions, and to identify opportunities for using the latest information technologies for the development and promotion of environmental awareness. To comprehensively explore the subject of environmental awareness, the research uses analytical, statistical and comprehensive research methods. The main results of the study are to identify the types and specific features of the development of environmental awareness in adolescents. The research presents the criteria of environmental competence: cognitive, value and activity-practice. The structure of the ecological consciousness of pupils of general educational institutions based on cognitive, connotative, axiological and perceptual-emotional levels is determined. The research considers the possibilities of using information technologies that will help to develop a new programme of adolescent consciousness-raising, namely, modern systems for displaying and disseminating information, social networks, and online conferences and online courses. The practical significance of the research work lies in the establishment of a new programme for the development of a healthy attitude of adolescents towards the environment and the establishment of basic values that should be used to preserve existing natural and cultural monuments, and in classes on environmental awareness in secondary schools, training courses and out-of-school education.
The Italian School Ecosystems two years after the pandemic in the perceptions of schools’ principals and teachers – part 2 (a segmented analysis)

This article presents a detailed analysis of the influence of age and gender of the respondents (231 teachers and 153 principals), and, as well, of the geographical localization and level of the schools in which they operate, on the effects generated, two years later, by the digital shock undergone by the Italian school ecosystem at the beginning of March 2020 due to the pandemics. An overall positive perception of the introduction and use of technologies is confirmed, although it remains clearly distinct from that of the particularly critical period experienced. Inhomogeneities emerged among geographical areas and school levels. Among them, greater suffering of ICs (primary and middle schools) and, in general, of the schools in the South of Italy regarding infrastructures and connectivity was highlighted. Due to that and to a greater sense of unpreparedness also a greater sense of increased workload has been perceived in such area. In spite of this, and perhaps because of this, a greater enthusiasm by teachers to experiment, together with a more positive perception about the usefulness of technology (particularly in the female gender), was also observed, along with a demand for highly qualified continuous training. A gender gap was observed in the variation of the individual factor related to the teachers’/principals’ wellbeing (lower for males) and in the judgment towards technologies. We detected also a greater difficulty with the teachers’ age to become accustomed to technology, particularly in high schools. The text analysis of the answers given to open-ended questions made it possible to bring out the peculiarities of the technologies that allowed teachers to develop the mentioned positive perception, as well as to highlight the reasons why many technologies are shelved. The activities for which technologies are considered most useful also emerged, as well as future intentions of use and expectations about continuous training. Differences in expectations about the future integration and use of technologies among the respondents teaching at different school levels clearly emerged. Finally, future smart organization of the schools seems deemed necessary by all categories of teachers and principals.
RBEF: Ransomware Efficient Public Blockchain Framework for Digital Healthcare Application

These days, the use of digital healthcare has been growing in practice. Getting remote healthcare services without going to the hospital for essential checkups and reports is easy. It is a cost-saving and time-saving process. However, digital healthcare systems are suffering from security and cyberattacks in practice. Blockchain technology is a promising technology that can process valid and secure remote healthcare data among different clinics. However, ransomware attacks are still complex holes in blockchain technology and prevent many healthcare data transactions during the process on the network. The study presents the new ransomware blockchain efficient framework (RBEF) for digital networks, which can identify transaction ransomware attacks. The objective is to minimize transaction delays and processing costs during ransomware attack detection and processing. The RBEF is designed based on Kotlin, Android, Java, and socket programming on the remote process call. RBEF integrated the cuckoo sandbox static and dynamic analysis application programming interface (API) to handle compile-time and runtime ransomware attacks in digital healthcare networks. Therefore, code-, data-, and service-level ransomware attacks are to be detected in blockchain technology (RBEF). The simulation results show that the RBEF minimizes transaction delays between 4 and 10 min and processing costs by 10% for healthcare data compared to existing public and ransomware efficient blockchain technologies healthcare systems.
Privacy-Enhancing Digital Contact Tracing with Machine Learning for Pandemic Response: A Comprehensive Review

The rapid global spread of the coronavirus disease (COVID-19) has severely impacted daily life worldwide. As potential solutions, various digital contact tracing (DCT) strategies have emerged to mitigate the virus’s spread while maintaining economic and social activities. The computational epidemiology problems of DCT often involve parameter optimization through learning processes, making it crucial to understand how to apply machine learning techniques for effective DCT optimization. While numerous research studies on DCT have emerged recently, most existing reviews primarily focus on DCT application design and implementation. This paper offers a comprehensive overview of privacy-preserving machine learning-based DCT in preparation for future pandemics. We propose a new taxonomy to classify existing DCT strategies into forward, backward, and proactive contact tracing. We then categorize several DCT apps developed during the COVID-19 pandemic based on their tracing strategies. Furthermore, we derive three research questions related to computational epidemiology for DCT and provide a detailed description of machine learning techniques to address these problems. We discuss the challenges of learning-based DCT and suggest potential solutions. Additionally, we include a case study demonstrating the review’s insights into the pandemic response. Finally, we summarize the study’s limitations and highlight promising future research directions in DCT.
A Deep-Learning-Based GPS Signal Spoofing Detection Method for Small UAVs

The navigation of small unmanned aerial vehicles (UAVs) mainly depends on global positioning systems (GPSs). However, GPSs are vulnerable to attack by spoofing, which causes the UAVs to lose their positioning ability. To address this issue, we propose a deep learning method to detect the spoofing of GPS signals received by small UAVs. Firstly, we describe the GPS signal dataset acquisition and preprocessing methods; these include the hardware system of the UAV and the jammer used in the experiment, the time and weather conditions of the data collection, the use of Spearman correlation coefficients for preprocessing, and the use of SVM-SMOTE to solve the spoofing data imbalance. Next, we introduce a PCA-CNN-LSTM model. We used principal component analysis (PCA) of the model to extract feature information related to spoofing from the GPS signal dataset. The convolutional neural network (CNN) in the model was used to extract local features in the GPS signal dataset, and long short-term memory (LSTM) was used as a posterior module of the CNN for further processing and modeling. To minimize randomness and chance in the simulation experiments, we used the 10-fold cross-validation method to train and evaluate the computational performance of our spoofing machine learning model. We conducted a series of experiments in a numerical simulation environment and evaluated the proposed model against the most advanced traditional machine learning and deep learning models. The results and analysis show that the PCA-CNN-LSTM neural network model achieved the highest accuracy (0.9949). This paper provides a theoretical basis and technical support for spoofing detection for small-UAV GPS signals.