Reviewerly/exHarmony huggingface dataset:
Incivility in COVID-19 Vaccine Mandate Discourse and Moral Foundations: Natural Language Processing Approach

Vaccine hesitancy poses a substantial threat to efforts to mitigate the harmful effects of the COVID-19 pandemic. To combat vaccine hesitancy, officials in the United States issued vaccine mandates, which were met with strong antivaccine discourse on social media platforms such as Reddit. The politicized and polarized nature of COVID-19 on social media has fueled uncivil discourse related to vaccine mandates, which is known to decrease confidence in COVID-19 vaccines.This study examines the moral foundations underlying uncivil COVID-19 vaccine discourse. Moral foundations theory poses that individuals make decisions to express approval or disapproval (ie, uncivil discourse) based on innate moral values. We examine whether moral foundations are associated with dimensions of incivility. Further, we explore whether there are any differences in the presence of incivility between the r/coronaviruscirclejerk and r/lockdownskepticism subreddits.Natural language processing methodologies were leveraged to analyze the moral foundations underlying uncivil discourse in 2 prominent antivaccine subreddits, r/coronaviruscirclejerk and r/lockdownskepticism. All posts and comments from both of the subreddits were collected since their inception in March 2022. This was followed by filtering the data set for key terms associated with the COVID-19 vaccine (eg, "vaccinate" and "Pfizer") and mandates (eg, "forced" and "mandating"). These key terms were selected based on a review of existing literature and because of their salience in both of the subreddits. A 10% sample of the filtered key terms was used for the final analysis.Findings suggested that moral foundations play a role in the psychological processes underlying uncivil vaccine mandate discourse. Specifically, we found substantial associations between all moral foundations (ie, care and harm, fairness and cheating, loyalty and betrayal, authority and subversion, and sanctity and degradation) and dimensions of incivility (ie, toxicity, insults, profanity, threat, and identity attack) except for the authority foundation. We also found statistically significant differences between r/coronaviruscirclejerk and r/lockdownskepticism for the presence of the dimensions of incivility. Specifically, the mean of identity attack, insult, toxicity, profanity, and threat in the r/lockdownskepticism subreddit was significantly lower than that in the r/coronaviruscirclejerk subreddit (P<.001).This study shows that moral foundations may play a substantial role in the presence of incivility in vaccine discourse. On the basis of the findings of the study, public health practitioners should tailor messaging by addressing the moral values underlying the concerns people may have about vaccines, which could manifest as uncivil discourse. Another way to tailor public health messaging could be to direct it to parts of social media platforms with increased uncivil discourse. By integrating moral foundations, public health messaging may increase compliance and promote civil discourse surrounding COVID-19.
Educational Technology Based on Artificial Intelligence in Supporting Distance Learning in Arabic Language Learning in Higher Education

Nowadays, technological developments are increasingly widespread. In fact, the use of artificial intelligence-based technology is on the rise. Artificial intelligence technology is a managed tool that can facilitate human work. Artificial intelligence technology gets a high position in the educational process and even experiences development from time to time, namely from the school level to the college level. As the development of artificial intelligence technology began to be applied in the learning process. The purpose of this research is to find out that artificial intelligence technology can support distance learning in Arabic language learning in college and greatly help students in doing assignments. The method in this study is to use quantitative research methods where the data obtained is through the distribution of a questionnaire in which there are statements that will be answered by students. The questionnaire was distributed by utilizing the Google Form application. The results of this study explain that artificial intelligence technology is very suitable to be applied in the distance learning process and can support learning activities carried out in Arabic language learning in college. The conclusion of this research is that technology based on artificial intelligence is very appropriate to be used in the distance learning process, namely in Arabic language learning, because the use of intelligence technology is more effective and efficient and greatly helps humans in doing work. The limitation of this study is that researchers only conducted this research at the college level which in fact the learning process is carried out remotely. The researcher hopes that future researchers can develop the Arabic language learning process at a more advanced stage. This study also recommends to future researchers to make this research a reference in conducting research related to artificial intelligence technology.
Human Face Recognition and Facial Attribute Analysis Using Data Analytics Techniques in Smart Grid Using Image Processing

Multimedia face images appear in social networks, digital entertainment, and other applications, exhibiting dramatic pose, illumination, and expression variations, resulting in considerable performance degradation. Using deep visualization techniques, this study analyses the classic network VGG Face. First, we analyze features computed by neurons, looking at diversity, invariance, and discrimination characteristics. According to the conventional view, the robustness to transformation increases as the network gets deeper, but it turns out that the middle layer is the least robust. One of the most significant findings is that high-level features are correlated with complex face attributes that humans may not be able to describe verbally.
Naïve Bayes

This chapter introduces the Naïve Bayes algorithm, a predictive model based on Bayesian analysis. The chapter starts with a thought problem involving a breathalyzer used by a police department. It demonstrates how Bayes’ Theorem can be used to estimate the probability that a driver is over the legal alcohol limit based on breathalyzer results. The concept of Naïve Bayes is illustrated with a toy data set of 15 observations, demonstrating how probabilities are calculated using counting. The assumption of conditional independence is explained, and the algorithm’s applicability to categorical and continuous predictors is discussed. The problem of zero probabilities and the need for Laplace smoothing to avoid issues with small sample sizes are explored. Naïve Bayes in KNIME is applied to a real-world example of predicting heart disease detection and identifying spam emails, achieving 85% and 99% accuracy, respectively, with test data. Despite its strong assumption of independence among predictors, Naïve Bayes proves to be a practical and efficient algorithm for classification tasks, especially when dealing with a large number of predictors. While its probability estimates may not always be precise, the classification results are often reliable.
Innovative Smart Grid Solutions for Fostering Data Security and Effective Privacy Preservation

Smart grids are crucial for the modernization and optimization of electrical power systems. The generation, distribution, and consumption of electricity are enhanced in terms of monitoring, controlling, and coordination. However, various privacy concerns are present in smart grids, such as identifiable information leakage and appliance usage pattern inference. With the digital transformation of the energy sector, protecting data and preserving privacy is vital for trust and reliability. Secure communication is crucial, requiring network infrastructure security, communication protocols, and intrusion detection and prevention mechanisms. In this work, we present the implementation of techniques like encryption algorithms, key management, and access control models for preserving privacy and enhancing data security in smart grid networks. The methods and techniques presented address secure metering infrastructure, meter data protection, and secure communication. The latest research includes threat detection, firmware and software updates, privacy-preserving data analytics, and blockchain for data security and privacy in smart grids. Robust security measures, privacy-enhancing techniques, and emerging technologies can mitigate risks and ensure data confidentiality, integrity, and availability while preserving privacy and trust. Further, the latest research improvements related to security and privacy in smart grids along with future research directions are discussed.
A Privacy Mitigating Framework for the Smart Grid Internet of Things Data

Smart Grid systems have the potential to meet the needs of modern system. The capacity of the smart grid is often underutilized since in most of the cases we cannot deal with security in an effective way. The issue that no single solution completely safeguards the smart grid environment remains even though various solutions have been put out for smart grid security. We propose an architecture to protect the data and privacy of smart grid connected through IoT. This technique functions by means of a privacy-monitoring component that watches unencrypted info and notifies the smart grid controller upon discovery. Mitigation Framework, which has two different implementation options. First mode, used to capture traffic before it enters the smart grid network and has the ability to accept or reject packets as it analyses them. Second mode, used only passively detect threats in this configuration because it does not stand in line with the flow. Although it warns the user, threats cannot be stopped the Mitigation Framework can be deployed inside a router. Mitigation Framework functions as a protection system that may identify risks regarding privacy of data, stop them, and notify smart grid controller.
Neural Networks

This chapter explores neural networks, focusing on their applications and underlying principles. Neural networks have gained immense popularity due to their flexibility and accuracy in supervised data mining tasks. They can effectively handle problems with categorical and continuous target variables, making them a versatile tool in predictive modeling. The chapter introduces the concept of artificial neural networks, which mimic the structure and function of human brain neurons. The mathematical model of a neuron, first proposed by McCulloch and Pitts, serves as the foundation for neural networks. However, early attempts to implement neural networks faced challenges, leading to a period of reduced interest. The breakthrough came in the 1980s with the development of algorithms like backpropagation, which enabled the estimation of weights in multilayer networks. The chapter discusses the learning process for neural networks, which involves adjusting the model weights iteratively to minimize an error function. Different activation functions are explored, each influencing the output of the neurons. Notably, the ReLU activation function enabled the development of deep learning models with three or more hidden layers. An example of a single-layer artificial neuron demonstrates the calculations with various activation functions. This is followed by an example of a multilayer perceptron, showcasing the real power of neural networks with multiple layers and nodes. Neural network applications using KNIME are illustrated in the context of credit screening and predicting used car prices. The chapter also emphasizes the importance of proper data preparation, including normalization and dealing with oversampling in the context of classification problems. Overfitting, a common challenge in neural networks, is discussed, and techniques to mitigate it are presented. The chapter provides a comprehensive overview of neural networks, highlighting their strengths and challenges. Neural networks offer great potential for complex and non-linear problems but require careful considerations in data preparation, model complexity, and validation to ensure reliable and accurate predictions.
Looking Back in Order to Move Forward: Lessons from COVID-19 for Teacher Education

This article provides critical perspectives on education technology integration in a teacher education context in a post-pandemic world. The authors—two early career teachers, one in a pre-school and one in an elementary school, and two elementary teacher education faculty members at a mid-sized public university—use the U.S. Department of Education’s 2016 guiding principles for educational technology in teacher education for analysis. The commentary evolves directly from and reflects the authors’ collective experience across the P-20 spectrum in education technology, with close attention paid to what was learned during the onset of the COVID-19 pandemic and subsequent pivot to remote learning in spring 2020, which altered the teacher education landscape in significant ways. This article makes curricular and pedagogical suggestions for P-20 educators and concludes with recommendations for future research at the intersection of technology integration and teacher education.
Meta-optic accelerators for object classification

A meta-optic platform for accelerating object classification is demonstrated. End-to-end design is used to co-optimize the optical and digital systems resulting in high-speed classifiers that are demonstrated for handwritten MNIST digits.
Nonlinear residual error compensation of a robot vision system using a rotary laser scanner

We design a rotary laser scanner integrated into an industrial robot. Then, the robot vision system is modeled and calibrated by scanning point cloud of spheres from different poses. A PR-DNN is presented to compensate nonlinear residual error and the RMSE can reach 0.0611mm.
Cluster Analysis

This chapter covers cluster analysis, a set of methods used for identifying groups of similar observations based on proximity measures. The chapter focuses on specific methods KNIME, a data analytics platform. Cluster analysis aims to find groups of objects that are similar within each group and distinct from objects in other groups. The number and composition of clusters can be challenging to determine, making cluster analysis an unsupervised and descriptive technique. Hierarchical clustering is a flexible method that can work with different distance measures and linkage types. It forms a tree-like structure of clusters, starting from individual observations and gradually merging them. On the other hand, K-means clustering, requires specifying the number of clusters beforehand and aims to minimize within-cluster variance by iteratively updating cluster centroids. Density-based clustering, like DBSCAN, can discover arbitrarily shaped clusters and is robust to outliers. Fuzzy clustering assigns probabilistic membership to clusters, allowing observations to belong partially to multiple clusters. Cluster validation is the process of evaluating the quality of clustering results. Internal validation uses metrics derived from the data, such as the Silhouette coefficient or within-cluster sum of squares. External validation compares the clustering results with external criteria or expected patterns. Overall, cluster analysis is a powerful technique for grouping similar observations, but it requires careful consideration of algorithm choice, parameter settings, and validation to produce meaningful and useful clusters.
Ensemble Models

Ensemble models in machine learning involve combining predictions from multiple diverse models to achieve improved accuracy and stability. This chapter explores various ensemble techniques and their benefits. The search for the best machine learning algorithm for a particular problem is an ongoing challenge. Studies have shown that no single algorithm performs best across all datasets. This has led to the concept of ensemble learning, where the predictions of multiple models are aggregated to produce a final estimate. The effectiveness of combining diverse independent estimates was first highlighted in “The Wisdom of Crowds.” A classic example by Sir Francis Galton demonstrated the power of combining individual estimates, leading to a more accurate prediction. Ensemble models are created using different approaches, such as employing multiple algorithms, varying model parameters, sampling different subsets of predictor variables, or sampling observations. The benefits of ensemble models lie in reduced variation and improved accuracy. Reduced variation ensures reliability in predictions with different data samples, allowing for a better understanding of the model’s performance with unseen data. Improved accuracy is achieved by combining independent predictions, which helps cancel out errors, resulting in better overall predictions. Bagging, Random Forests, AdaBoost, Gradient Tree Boosting, and XGBoost are discussed. These models are popular for their ability to handle different types of data and achieve state-of-the-art performance in various contexts. The chapter includes practical examples of ensemble modeling with continuous and binary targets. One example uses a KNIME workflow to predict used car prices using ordinary least regression (OLS) and Gradient Boosted Trees. Another example involves predicting credit status using XGBoost.
Classification and Regression Trees

This chapter discusses Classification and Regression Trees, widely used in data mining for predictive analytics. The chapter starts by explaining the two principal types of decision trees: classification trees and regression trees. In a classification tree, the dependent variable is categorical, while in a regression tree, it is continuous. The first section discusses classification trees, using an example of customer targeting in a marketing campaign. The chapter emphasizes that classification trees are “automatic” models, as they select independent variables by searching for optimal splits based on measures of purity or entropy. The second section covers regression trees, illustrating their application in predicting continuous target variables using an example of head acceleration measurements from simulated motorcycle accidents. The chapter explores the development of classification trees, explaining how splitting nodes are continued until they are pure or no further splits are possible. It emphasizes the importance of pruning to avoid overfitting, which can lead to poor generalization with unseen data. The author discusses different pruning techniques, including pre-pruning and post-pruning. Pre-pruning involves setting stopping rules during tree growth, while post-pruning involves trimming the tree after it is fully grown. The strengths and weaknesses of decision trees are highlighted. The interpretability and intuitiveness of decision trees are listed as strengths, while the risk of overfitting and sensitivity to minor data changes are cited as weaknesses. Overall, this chapter provides a comprehensive overview of decision trees, their applications, and essential considerations for creating accurate and robust models using this popular data mining technique.
Deep learning aided Variational Hilbert Quantitative Phase Imaging

This work discusses the use of neural networks to determine the result of quantitative phase measurement via 2D Hilbert transform. We propose two approaches for solving fringe pattern-based phase demodulation problem aiding quantitative phase imaging technology.
Computational 3D surface microscopy from terabytes of data with a self-supervised reconstruction algorithm

We present a computational 3D profilometric microscope employing an array of 54 cameras and 3-axis scanning to produce multi-TB datasets per sample. Using stereo and sharpness cues, our self-supervised reconstruction algorithm generates 6-gigapixel reconstructions with micron-scale resolution across &gt;110 cm 2 .
Simple method for microscope lens calibration using grid lines.

We propose a simple method to calibrate a microscope lens using a grid lines pattern. We get four calibration patterns from each image, reducing the number of required images. In the final result, most of the spherical aberration is eliminated.
Conditional generative modelling based fringe pattern normalization

In this article, we propose a generative adversarial network based fringe pattern normalization method. We investigate the method's effectiveness under various noise levels by evaluating root mean square error (RMSE) and structural similarity index measure (SSIM).
Deep Learning-based Cryptanalysis on Double Random Phase Encoding

We overview deep learning-based cryptanalysis schemes on DRPE, which find the weakness of the DRPE by classifying the DRPE images. We show the classification performance is almost the same as the original image.
Evaluating rhyme annotations for large corpora

Abstract Recent methods have been proposed to produce automatic rhyme annotators for large rhymed corpora. These methods, such as Baley (2022b) greatly reduce the cost of annotating rhymed material, allowing historical linguists to focus on the analysis of the rhyme patterns. However, evidence for the quality of those annotations has been anecdotal, consisting of a handful of individual poem case studies. This paper proposes to address the issue: first, we discuss previously proposed metrics that evaluate the quality of an annotator’s output against a ground-truth annotation (List, Hill, and Foster; 2019) and we propose an alternative metric that is better suited to the task. Then, sampling from Baley’s published annotated corpus and re-annotating it by hand, we use the sample to demonstrate the lacunae in the original approach and show how to fix them. Finally, the hand-annotated sample and source code are published as additional data, so that other researchers can compare the performance of their own annotators.
Improving risk management – combining security and cyber insurance practices

Cyber insurance will help you deal with the costs of recovering from a breach. But it can also play a deeper role. Insurance companies now demand that organisations demonstrate their security preparedness, and this means having a detailed and accurate picture of your infrastructure and IT estate. The tools you use to obtain this picture can also give you the insight you need to improve your readiness.
Encryption blind spots: shining a light on cloud network traffic

Monitoring for and detecting malicious activity on your networks is being made harder by the increasing volume of encrypted traffic. This encryption exists for good reasons, particularly when cloud services are in play, but threat actors are exploiting its inscrutability to gain access to networks and loiter for extended periods. However, extended Berkeley Packet Filter (eBPF) network analysis technology offers the possibility to gain full visibility into encrypted traffic across VM or container workloads.
Investigation of Vibrations in Dry Friction Pair using PNRP Methods

Methods of periodical non-stationary random processes (PNRP) we applied to analysis of vibration time series of dry friction pair. Three different stages of surface frictional destruction during experiment were detected using spectral and correlation properties of vibration signals.