Reviewerly/exHarmony huggingface dataset:
Teaching with technology: A large-scale, international, and multilevel study of the roles of teacher and school characteristics

Providing high-quality instruction with technology has become more important than ever before. However, the instructional practices and the degree to which key skills, such as digital literacy and computational thinking, are emphasized in classrooms vary considerably between teachers, schools, and countries. The present study was aimed at explaining this variation in the frequency of teaching practices with technology and teachers' emphasis on developing students' computer and information literacy and computational thinking by key aspects of teacher motivation and expertise, school conditions and priorities, and countries' economy and innovation. Utilizing large-scale, representative data from the International Computer and Information Literacy Study (ICILS) 2018 (15,015 teachers in 1195 schools in eight countries), we performed multilevel structural equation modeling and regression trees and found that teacher motivation and collaboration were positively and consistently linked to teaching practices across countries. Besides, principals' expectations concerning the teaching with technology explained variation in Finnish and German schools. In three countries, teachers' professional development was related to their teaching practices. Finally, countries’ economic development and innovation explained variation in the teacher-level effects. Our study sheds new light on the possible factors related to teaching with technology and advances the field by taking a multilevel and international perspective on these factors.
Trends in education during the pandemic: modern online technologies as a tool for the sustainability of university education in the field of media and communication studies

The involuntary shutdown at universities during the COVID-19 pandemic and the impossibility of full-time teaching forced university teachers to look for other ways of communication strategies through Internet platforms with students and in everyday academic activities. The aim of this study is to reveal the attitudes and perception of university teachers in the field of Media and Communication Studies in Slovakia during two years of online home learning (2020 and 2021) when the first two waves of the COVID-19 pandemic took place. The aim was to show changes in the perception of the situation in terms of existing positives and negatives during this period as well as to point out a possible trend in the communication strategies used to ensure the sustainability of the education system. Due to the lockdown, the survey was only conducted in electronic format using online questionnaires. The participants were university teachers from the field of Media and Communication Studies. As this is a progressive field the study of which is carried out at the intersection of the interests of several scientific disciplines, it can be assumed that the conclusions can easily be transferred to social, economic, humanities and arts disciplines. When communicating and teaching students in 2020 and also in 2021, online teaching through systems such as Zoom or Google Meet and individual consultations by e-mail, chat and social networks dominated. As the results of the study indicate, modern online technologies contribute to the sustainability of the educational process during an emergency and will become an integral part of university education even after the end of the pandemic situation.
Automatic detection of breast cancer in ultrasound images using Mayfly algorithm optimized handcrafted features

The incidence rates of breast cancer in women community is progressively raising and the premature diagnosis is necessary to detect and cure the disease.To develop a novel automated disuse detection framework to examine the Breast-Ultrasound-Images (BUI).This scheme includes the following stages; (i) Image acquisition and resizing, (ii) Gaussian filter-based pre-processing, (iii) Handcrafted features extraction, (iv) Optimal feature selection with Mayfly Algorithm (MA), (v) Binary classification and validation. The dataset includes BUI extracted from 133 normal, 445 benign and 210 malignant cases. Each BUI is resized to 256×256×1 pixels and the resized BUIs are used to develop and test the new scheme. Handcrafted feature-based cancer detection is employed and the parameters, such as Entropies, Local-Binary-Pattern (LBP) and Hu moments are considered. To avoid the over-fitting problem, a feature reduction procedure is also implemented with MA and the reduced feature sub-set is used to train and validate the classifiers developed in this research.The experiments were performed to classify BUIs between (i) normal and benign, (ii) normal and malignant, and (iii) benign and malignant cases. The results show that classification accuracy of > 94%, precision of > 92%, sensitivity of > 92% and specificity of > 90% are achieved applying the developed new schemes or framework.In this work, a machine-learning scheme is employed to detect/classify the disease using BUI and achieves promising results. In future, we will test the feasibility of implementing deep-learning method to this framework to further improve detection accuracy.
Modeling daily global solar radiation using only temperature data: Past, development, and future

Accurate determination on global solar radiation (Rs) is essential in many disciplines and sectors. This study comprehensively reviewed 78 existing models and developed 4 new models (N1-1∼N1-4) for estimating daily Rs using only temperature data. The best model was then applied to forecast daily Rs using temperature data in weather forecast. All models were calibrated and tested using data collected from 105 radiation stations scattered across China. Daily data of temperature were also collected from 703 additional meteorological stations to classify diurnal temperature range (ΔT) zones in China. The results showed that China could be divided into five ΔT zones. The newly developed model N1-4 was the most accurate among all models for various ΔT zones, with values of coefficient of determination, index of agreement, mean bias error, mean absolute bias error, root mean square error, and relative root mean square error in the ranges of 0.660–0.737, 0.888–0.919, −0.023–0.100 MJ m−2 d−1, 2.691–3.195 MJ m−2 d−1, 3.725–4.231 MJ m−2 d−1, and 0.224–0.352, respectively. The generalized coefficients of the N1-4 model were also derived and further used for estimating daily Rs in areas with or without Rs data when there are only temperature data. The model N1-4 was successfully applied to predict daily Rs in varying ΔT zones with forecast temperature data for a lead time of 1–8 days. This new model is recommended for forecasting daily Rs in zones with high ΔT for a long lead time and in zones with low ΔT for a short lead time.
Blockchain based secure and reliable Cyber Physical ecosystem for vaccine supply chain

Immunization has become essential for prevention of infectious diseases during pandemic situations across the globe, and unsafe vaccines are seriously affecting public health and economy of countries. However, issues such as falsified or substandard and expired vaccines are still exist due to complex global supply chains and the fact that present vaccine management systems are under the control of different entities in the vaccine supply chain. Therefore, the requirement to develop the Cyber Physical ecosystem that is more resilient, responsive and having smart track and trace is urgently required. To achieve these objectives, blockchain and Internet of Things (IoT) based vaccine supply chain is proposed in this work. The proposed system takes advantages of blockchain database to store the generated data through interactions among stakeholders and from IoT devices in vaccine supply chain. The proposed system is implemented on Hyperledger Fabric (HLF) blockchain framework through which the proposed system achieves resiliency, track and trace of vaccine circulation. The performance evaluations are obtained by testing a HLF with smart contracts. The evaluation is further driven by clients via Hyperledger Caliper, which is a bench mark tool used to measure the performance of blockchain solutions developed on Hyperledger. The results exhibit that the proposed system outperforms other benchmark schemes in terms of throughput and latency etc.
Using multivariate compositional data analysis (CoDA) and clustering to establish geochemical backgrounds in stream sediments of an onshore oil deposits area. The Agri River basin (Italy) case study

Establishing the natural background levels of chemical elements is very often extremely complicated. This is even more true especially for the more anthropized areas, where the concentration of an element in the environmental matrices is conditioned not only by the natural (geogenic) context but also by anthropic activities. In this study, the natural background levels (NBLs) of several chemical elements in the stream sediments of the High Agri River basin have been calculated using a combination of multivariate statistical analysis (MSA) and machine learning techniques in the frame of compositional data analysis (CoDA). Specifically, data clustering and robust principal component analysis (rPCA) have allowed us to recognize and isolate three different data populations, belonging to three different geological domains. A first population of data is clearly related to calcareous lithologies, while the second one is associated to siliciclastic lithologies. A third one is of mixed origin, partly linked to the volcanoclastic nature of the sediments and due to the presence of co-precipitation phenomena. The three data populations have been later separated into three different databases and, whereas needed, the outliers have been eliminated. Based on the obtained results, the NBLs have been calculated using the US-EPA's (United States Environmental Protection Agency) ProUCL software. The results derived from this new approach have been later compared with those obtained using the spectral analysis (S-A) method to evaluate its advantages and versatility. This approach allowed calculating more reliable natural background values.
Transformer-Based Graph Convolutional Network for Sentiment Analysis

Sentiment Analysis is an essential research topic in the field of natural language processing (NLP) and has attracted the attention of many researchers in the last few years. Recently, deep neural network (DNN) models have been used for sentiment analysis tasks, achieving promising results. Although these models can analyze sequences of arbitrary length, utilizing them in the feature extraction layer of a DNN increases the dimensionality of the feature space. More recently, graph neural networks (GNNs) have achieved a promising performance in different NLP tasks. However, previous models cannot be transferred to a large corpus and neglect the heterogeneity of textual graphs. To overcome these difficulties, we propose a new Transformer-based graph convolutional network for heterogeneous graphs called Sentiment Transformer Graph Convolutional Network (ST-GCN). To the best of our knowledge, this is the first study to model the sentiment corpus as a heterogeneous graph and learn document and word embeddings using the proposed sentiment graph transformer neural network. In addition, our model offers an easy mechanism to fuse node positional information for graph datasets using Laplacian eigenvectors. Extensive experiments on four standard datasets show that our model outperforms the existing state-of-the-art models.
Multi-objective optimization-based adaptive class-specific cost extreme learning machine for imbalanced classification

Imbalanced classification is a challenging task in the fields of machine learning and data mining. Cost-sensitive learning can tackle this issue by considering different misclassification costs of classes. Weighted extreme learning machine (W-ELM) takes a cost-sensitive strategy to alleviate the learning bias towards the majority class to achieve better classification performance. However, W-ELM may not achieve the optimal weights for the samples from different classes due to the adoption of empirical costs. In order to solve this issue, multi-objective optimization-based adaptive class-specific cost extreme learning machine (MOAC-ELM) is presented in this paper. To be specific, the initial weights are first assigned depending on the class information. Based on that, the representation of the minority class could be enhanced by adding penalty factors. In addition, a multi-objective optimization with respect to penalty factors is formulated to automatically determine the class-specific costs, in which multiple performance criteria are constructed by comprehensively considering the misclassification rate and generalization gap. Finally, ensemble strategy is implemented to make decisions after optimization. Accordingly, the proposed MOAC-ELM is an adaptive method with good robustness and generalization performance for imbalanced classification problems. Comprehensive experiments have been performed on several benchmark datasets and a real-world application dataset. The statistical results demonstrate that MOAC-ELM can achieve competitive results on classification performance.
Analysis of Augmentation Methods for RF Fingerprinting under Impaired Channels

Cyber-physical systems such as autonomous vehicle networks are considered to be critical infrastructures in various applications. However, their mission critical deployment makes them prone to cyber-attacks. Radio frequency (RF) fingerprinting is a promising security solution to pave the way for "security by design" for critical infrastructures. With this in mind, this paper leverages deep learning methods to analyze unique fingerprints of transmitters so as to discriminate between legitimate and malicious unmanned vehicles. As RF fingerprinting models are sensitive to varying environmental and channel conditions, these factors should be taken into consideration when deep learning models are employed. As another option, data acquisition can be considered; however, it is infeasible since collecting samples of different circumstances for the training set is quite difficult. To address such aspects of RF fingerprinting, this paper applies various augmentation methods, namely, additive noise, generative models and channel profiling. Out of the studied augmentation methods, our results indicate that tapped delay line and clustered delay line (TDL/CDL) models seem to be the most viable solution as the accuracy to recognize transmitters can significantly increase from 74% to 87.94% on unobserved data.
Blockchain and supply chain finance: a critical literature review at the intersection of operations, finance and law

Abstract In the current environment, where the Covid-19 pandemic has exposed the vulnerabilities of the incumbent paper-based trade and supply chain finance systems, digital transformation pledges to alleviate the friction on international trade. Here, we provide a timely review of state-of-the-art industry applications and theoretical perspectives on the use of blockchain as the medium toward digitalisation for supply chain finance systems. We argue that blockchain technology has an innovation promoting role in supply chain finance solutions through reducing inefficiencies and increasing visibility between different parties, which have hitherto constituted the main challenges in this sphere. Based on a review of the academic literature as well as an analysis of the industrial solutions that have emerged, we identify and discuss the financial, operational and legal challenges encountered in supply chain financing and the promise of blockchain to address these limitations. We discuss the bottlenecks as well as the benefits of blockchain and identify some necessary conditions required for the emergence of blockchain-enabled trade and supply chain financing, such as the establishment of co-opetition among supply chain actors, integration with IoT systems for data quality, and reform of regulatory and legal frameworks. We conclude by identifying promising research directions about the implementation process, inviting further research into the transformation of business models toward a more collaborative nature.
Experimental Investigation of Azimuth- and Sensor-Based Control Strategies for a PV Solar Tracking Application

Today, the world continuously investigates the promising potential of advanced and optimized technologies for harvesting green renewable energy sources, such as a solar tracking system (STS). This study presents an actual implementation of a single-axis solar tracking system (SAST), where an azimuth control scheme is developed to precisely follow the sun’s orientation. This is demonstrated by computing in real-time the optimal azimuth angle based on the actual altitude, date, and time using an embedded microcontroller. At the same time, experimental comparisons using the azimuth-based control method compared with the proportional, integral, derivative (PID) control scheme and fixed-tilt PV systems (FTPV) are derived. Experimental results show that the developed azimuth solar tracker can autonomously follow the sun’s orientation primarily from time and location-based information and independent from the actual solar irradiance. Nevertheless, the electrical energies gained from all three tested systems revealed that using the sensor-based STS would increase the net energy production by 12.68% compared to the azimuth-based SAST, with an energy production increase of 7.7%. In addition, energy consumption by the azimuth-based STS is 65% greater than the energy consumption of a sensor-based STS due to the continuous movements of the tracker, regardless of the increase or change in power production within short periods.
Using Digital Tools to Teach Soft Skill-Oriented Subjects to University Students during the COVID-19 Pandemic

Radical changes in education occurred in 2020 during the pandemic. The need to fully switch to a distance-learning mode required rethinking the approaches to the organization of the learning process. Despite the fact that Russian universities were already using digital learning tools quite extensively at the beginning of the pandemic—those were seen as auxiliary, supporting tools. Within a short period, online learning made it necessary to design educational programs from a digital-technologies viewpoint, as traditional teaching methods had lost some of their functionality in the distance-learning mode. First of all, the changes affected the disciplines focused on the formation of soft skills, such as communication skills, group interaction, and managing people. Another problem of digitalization of all aspects of our lives is the huge amount of readily available information. In this regard, developing the students’ systemic thinking and augmenting their ability to find and properly use information became an important alternative to acquisition of factual knowledge. This article summarizes the experience of the educational process at one of the leading Russian universities, National University of Science and Technology (NUST) “MISIS” during the COVID-19 pandemic based on the analysis of the degree of application of digital tools in online and hybrid learning. In this article, we present the description of methodology approaches to the use of digital tools for soft skill development, using the example of teaching specific disciplines “Systems Thinking and Theory of Constraints” and “Life Cycle of Corporations and Change Management” in the master’s program in Corporate Finance taught at NUST “MISIS”.
Digital Investigation of Cybercrimes Based on Big Data Analytics Using Deep Learning

Big Data Analytics has become an important paradigm that can help digital investigators to investigate cybercrimes as well as provide solutions to malware and threat prediction, detection and prevention at an early stage. Big Data Analytics techniques can use to analysis enormous amount of generated data from new technologies such as Social Networks, Cloud Computing and Internet of Things to understand the committed crimes in addition to predict the new coming severe attacks and crimes in the future. This chapter introduce principles of Digital Forensics and Big Data as well as exploring Big Data Analytics and Deep Learning benefits and advantages that can help the digital investigators to develop and propose new techniques and methods based on Big Data Analytics using Deep Learning techniques that can be adapted to the unique context of Digital Forensics as well as support performing digital investigation process in forensically sound and timely fashion manner.
Passing the Data Baton : A Retrospective Analysis on Data Science Work and Workers

Data science is a rapidly growing discipline and organizations increasingly depend on data science work. Yet the ambiguity around data science, what it is, and who data scientists are can make it difficult for visualization researchers to identify impactful research trajectories. We have conducted a retrospective analysis of data science work and workers as described within the data visualization, human computer interaction, and data science literature. From this analysis we synthesis a comprehensive model that describes data science work and breakdown to data scientists into nine distinct roles. We summarise and reflect on the role that visualization has throughout data science work and the varied needs of data scientists themselves for tooling support. Our findings are intended to arm visualization researchers with a more concrete framing of data science with the hope that it will help them surface innovative opportunities for impacting data science work.<;/p> <;p><;b>Data availability:<;/b> <;uri>https://osf.io/z2xpd/?view_only=87fa24be486a473884adb9ffbe8db4ec<;/uri>
Minimal Adversarial Examples for Deep Learning on 3D Point Clouds

With recent developments of convolutional neural net-works, deep learning for 3D point clouds has shown significant progress in various 3D scene understanding tasks, e.g., object recognition, semantic segmentation. In a safety-critical environment, it is however not well understood how such deep learning models are vulnerable to adversarial examples. In this work, we explore adversarial attacks for point cloud-based neural networks. We propose a unified formulation for adversarial point cloud generation that can generalise two different attack strategies. Our method generates adversarial examples by attacking the classification ability of point cloud-based networks while considering the perceptibility of the examples and ensuring the minimal level of point manipulations. Experimental results show that our method achieves the state-of-the-art performance with higher than 89% and 90% of attack success rate on synthetic and real-world data respectively, while manipulating only about 4% of the total points.
Cumulative Learning

An important feature of human learning is the ability to continuously accept new information and unify it with existing knowledge, a process that proceeds largely automatically and without catastrophic side-effects. A generally intelligent machine (AGI) should be able to learn a wide range of tasks in a variety of environments. Knowledge acquisition in partially-known and dynamic task-environments cannot happen all-at-once, and AGI-aspiring systems must thus be capable of cumulative learning: efficiently making use of existing knowledge while learning new things, increasing the scope of ability and knowledge incrementally—without catastrophic forgetting or damaging existing skills. Many aspects of such learning have been addressed in artificial intelligence (AI) research, but relatively few examples of cumulative learning have been demonstrated to date and no generally accepted explicit definition exists of this category of learning. Here we provide a general definition of cumulative learning and describe how it relates to other concepts frequently used in the AI literature.