Reviewerly/exHarmony huggingface dataset:
Augmented Reality Technology as an Immersive Teaching Tool in the Middle School Biology Course

Based on the analysis of publications on the topic of digitalization of biological education, the article identifies four trends associated with the introduction of digital technologies and tools into the educational process: the formation of a blended learning model; transition to online learning; creation of an immersive educational environment; use of augmented reality technology. These trends are interconnected, and each of them affects the general biological education in Russia. On the one hand, digitalization contributes to an increase in the openness, flexibility of education, and an increase in the involvement of schoolchildren in the process of teaching biology in high school. On the other hand, it leads to the creation of a new educational situation, the inclusion of new factors in the system of biological education, which changes the nature of the relationship between its main participants. Digitalization introduces significant changes in the role of a biology teacher and a high school student in the process of teaching biology, which requires appropriate adaptation. The necessity of systematizing the educational and social effects of the introduction of immersive technologies in biological education and conducting longitudinal studies is substantiated. The design of the study was developed, which is based on the strategy of introducing immersive technologies in blended teaching of biology in high school.
Analysis of the initial education of chemistry teachers and their attitudes towards teaching in the Republic of Serbia

This research was aimed to analyse the current state in primary and secondary schools in the Republic of Serbia, regarding chemistry teachers? initial education and self-perceived competencies. As an instrument, a questionnaire of 20 questions was used, which was prepared in the form of a Google questionnaire. The questions were divided into five groups to examine data on the structure of chemistry teachers (gender, age, place and school where they teach), the level of education obtained and the method of acquisition and acquired competencies, and finally the personal opinion of chemistry teachers about the teaching profession, advantages, disadvantages and possible recommendations to his/her students in terms of choosing this profession as a possible career. The questionnaire was sent to 1537 schools and 497 chemistry teachers took part in the questionnaire. The results showed that chemistry teachers are predominantly women, mostly aged 36?55, full-time employed and with an average working experience of over 11 years. Most teachers have appropriate education and acquired competencies. They cite working with children as the biggest advantage of the teaching profession while as the biggest disadvantage, they report a constant increase in the amount of administration from year to year.
TWC Applications

This section contains a few of the many applications of TWC that have actually been implemented either at the request of a person or as a project from an agency or as a part of a journal publication. A few have been done as a scientific experiment, one was part of a thesis. There are very many more applications that were successfully run but are not presented in this section some of which are found in the bibliography. The applications found in this section represent the variety of types of problems that have been solved. TWC began when we asked the following questions: (1) If a series of events occur in time and physical space, are these events connected? (2) Is there an underlying process that generates what is manifest in time/space? TWC algorithms, as can be seen by the applications, answers these questions in the affirmative.
Towards Improved Performance of Text Detection Algorithms: Exploring the Impact of Automatic Image Classification and Blind Deconvolution

Purpose -This study aims to investigate the impact of the proposed BD-CRAFT, a variant of the CRAFT algorithm applying preprocessing steps as blurry or non-blurry image classification using Laplacian and a deblurring technique known as blind deconvolution, in improving the performance of the top three state-of-the-art scene text detection algorithms -SenseTime, TextFuseNet, and TencentAILab.Methodology -The researchers utilized the ICDAR 2013 Focused Scene Text Competition Challenge 2 dataset and the Intersection over Union (IoU) to determine the performance of the proposed BD-CRAFT.The IoU h-mean of the top three algorithms was compared against those of the modified versions.Results -Each algorithm variant significantly improves the overall h-mean and some of the precision and recall values.TextFuseNet + BD-CRAFT yields 93.55% h-mean, while the precision shows an impressive improvement of over 4% to increase the precision to 95.71%. 1700Meanwhile, TencentAILab + BD-CRAFT achieved an h-mean result of 94.77% with precision and recall improvement.Furthermore, SenseTime + BD-CRAFT ranked first with a very impressive 95.22% h-mean and showed a significant improvement of over 4%, which made it the top-ranked algorithm.Conclusion -Evidence shows that when BD-CRAFT is combined with other algorithms, their performances are improved; hence BD-CRAFT has a significant impact on the text detection performance of these algorithms.Recommendation -As possibilities for further studies, it would be interesting to investigate the other state-of-the-art algorithms for scene text detection that would also benefit from BD-CRAFT.Exploring other preprocessing techniques that can be incorporated into text detection algorithms in general, may be suitable.Research Implication -Though the performances of current state-of-the-art algorithms are already commendable, the use of image classification and blind deconvolution as preprocessing techniques helps the top-performing text detection algorithms perform better in natural scene images hence the proposed method can be utilized in improving scene text detection.
<scp>MACSum</scp>: Controllable Summarization with Mixed Attributes

Abstract Controllable summarization allows users to generate customized summaries with specified attributes. However, due to the lack of designated annotations of controlled summaries, existing work has to craft pseudo datasets by adapting generic summarization benchmarks. Furthermore, most research focuses on controlling single attributes individually (e.g., a short summary or a highly abstractive summary) rather than controlling a mix of attributes together (e.g., a short and highly abstractive summary). In this paper, we propose MACSum, the first human-annotated summarization dataset for controlling mixed attributes. It contains source texts from two domains, news articles and dialogues, with human-annotated summaries controlled by five designed attributes (Length, Extractiveness, Specificity, Topic, and Speaker). We propose two simple and effective parameter-efficient approaches for the new task of mixed controllable summarization based on hard prompt tuning and soft prefix tuning. Results and analysis demonstrate that hard prompt models yield the best performance on most metrics and human evaluations. However, mixed-attribute control is still challenging for summarization tasks. Our dataset and code are available at https://github.com/psunlpgroup/MACSum.
Control through artificial neural networks of direct current motor

The main objective of this work is to present the methodology to control the speed of a DC motor experimentally through artificial neural networks of the NARX type (Nonlinear Autoregressive Neural Network with exogenous inputs). To achieve this, the artificial neural network was trained (ANN) on the Matlab platform, the speed of the motor was controlled in real-time with the LabVIEW software and a CompactRio data acquisition system, and it was possible for the speed of the motor to follow a constant reference, obtaining a steady state error less than 3 %.
The Postdigital School

Research into postdigital education has often been preoccupied with system-level infrastructure and policy, rather than the messy realities of the postdigital school. Schools have been criticized for being both too digital and not digital enough. They are held simultaneously to be insufficiently digital to prepare students for the future and increasingly dominated by commercial providers and data-mining algorithms. As a result, the postdigital school takes many forms (e.g., datafied, platformized), and the research that has examined them has tended to explore effects on students and teachers that are at best ambiguous.
National digital health legislation: lessons and recommendations

Abstract All Member States of the United Nations have committed to achieve the UN Sustainable Development Goals (SDGs) and Universal Health Coverage (UHC), but several policy and system challenges remain unaddressed. Technology innovation and digital health have the potential to find equitable, sustainable and rights-based solutions to many of these issues, but must be coupled with effective and appropriate national legislation. In the backdrop of today's global digital health landscape, this manuscript explores the urgent need for the national legislation on digital health and recommends a way forward for legislators to meaningfully address this need. It also presents direction for global and national stakeholders to coordinate their efforts towards supporting countries to enable meaningful digital health transformations. The recommendations below are informed by perspectives from international organizations, national parliaments, policy think tanks and civil society.
Definitions and Basic Properties

This chapter defines two-class functions and classification functions. A two-class function classifies input vectors into two classes, while a classification function classifies input vectors into m classes, where $$m>2$$ .
Functions with Continuous Variables

Classification functions in life science and engineering often have continuous variables. Such variables show length, width, weight, pressure, and temperature, etc.
Easily Reconstructable Functions

This chapter shows that simplification of SOPs gives the generalization ability. We show this in three steps. First, various classes of totally defined logic functions (SOPs) were generated. Second, minterms of the function are randomly selected to generate partially defined functions. And, third, from the partially defined functions, original functions are reconstructed by SOP minimization. We consider the Achilles heel functions, majority functions, monotone increasing cascade functions, functions generated from random SOPs, and monotone increasing random SOPs. As for machine learning methods, Naive Bayes, multi-level perceptron, support vector machine, JRIP, J48, and random forest are considered in addition to SOP minimization.
Intelligent Energy-Aware Thermal Exchange Optimization with Deep Learning Model for IoT-Enabled Smart Healthcare

In recent years, Internet of Things (IoT) and advanced sensor technologies have gained considerable interest in linking different medical devices, patients, and healthcare professionals to improve the quality of medical services in a cost-effective manner. The evolution of the smart healthcare sector has considerably enhanced patient safety, accessibility, and operational competence while minimizing the costs incurred in healthcare services. In this background, the current study develops intelligent energy-aware thermal exchange optimization with deep learning (IEA-TEODL) model for IoT-enabled smart healthcare. The aim of the proposed IEA-TOEDL technique is to group the IoT devices into clusters and make decisions in the smart healthcare sector. The proposed IEA-TEODL technique constructs clusters using the energy-aware chaotic thermal exchange optimization-based clustering (EACTEO-C) scheme. In addition, the disease diagnosis model also intends to classify the collected healthcare data as either presence or absence of the disease. To accomplish this, the proposed IEA-TODL technique involves several subprocesses such as preprocessing, K-medoid clustering-based outlier removal, multihead attention bidirectional long short-term memory (MHA-BLSTM), and weighted salp swarm algorithm (WSSA). The utilization of outlier removal and WSSA-based hyperparameter tuning process assist in achieving enhanced classification outcomes. In order to demonstrate the enhanced outcomes of the IEA-TEODL approach, a wide range of simulations was conducted against benchmark datasets. The simulation results inferred the enhanced outcomes of the IEA-TEODL technique over recent techniques under distinct evaluation metrics.
Functions with Multi-valued Inputs

In practical applications, variables are often multi-valued. This chapter shows a method to represent functions by an SOP with multi-valued inputs.
Joint adjustment image steganography networks

Image steganography aims to achieve covert communication between two partners utilizing stego images generated by hiding secret images within cover images. Existing deep image steganography methods have been rapidly developed in this area. Such methods, however, usually generate the stego images and reveal the secret images using one-process networks, lacking sufficient refinement in these methods. Thus, the security and quality of stego and revealed secret images still have much room for promotion, especially for large-capacity image steganography. This paper proposes Joint Adjustment Image Steganography Networks (JAIS-Nets), containing a series of coarse-to-fine iterative adjustment processes, for image steganography. Our JAIS-Nets first proposes Cross-Process Contrastive Refinement (CPCR) adjustment method, using the cross-process contrastive information from cover-stego and secret-revealed secret image pairs, to iteratively refine the generated stego and revealed secret images, respectively. In addition, our JAIS-Nets further proposes Cross-Process Multi-Scale (CPMS) adjustment method, using the cross-process multi-scale information from different scales cover-stego and secret-revealed secret image pairs, to directly adjust and enhance the intermediate representations of the proposed JAIS-Nets. Integrating the proposed CPCR with CPMS methods, the proposed JAIS-Nets can jointly adjust the quality of the stego and revealed secret images at both the learning process and image scale levels. Extensive experiments demonstrate that our JAIS-Nets can achieve state-of-the-art performances on the security and quality of the stego and revealed secret images on both the regular and large capacity image steganography.
A histogram-driven generative adversarial network for brain MRI to CT synthesis

Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are commonly used tools for medical diagnostic assessment. Considering the ionizing radiation of CT imaging, estimating CT images from radiation-free MRI would be beneficial for medical diagnosis. Although state-of-art generative adversarial networks or end-to-end architecture models can generate realistic natural images, it is challenging to generate medical CT images with high signal-to-noise ratios and are paired with MRI. We propose a histogram-driven generative adversarial network (HisGAN) to address this issue, estimate CT images paired with MR, and develop a histogram-based dynamic scaling factor to facilitate learning different image styles. By employing an adversarial learning strategy to train the end-to-end generator, the generator better simulate the nonlinear mapping from source to target. For the generator, the proposed method applies multiple learnable parameters to adjust the overall weights of the dilated convolution layers to ensure sufficient expansive receptive fields for improved performance. Additionally, the method utilizes deep residual networks to train randomly smoothed generated images and employs adversarial loss to enhance the generation of the discriminator, achieving a balance between the generator and the discriminator. Our approach can synthesize image details at the pixel level in the target domain and has been evaluated using two datasets for MR to CT, T1 to T2, and Flair to T1ce modality synthesis tasks. The proposed method outperforms existing generative adversarial models applied to medical image synthesis.
Researching and Innovating from the 13+ Institute for Engineering Research and Innovation

Nowadays, the changes caused by globalization, the digital revolution, and the concept of environmental sustainability, among others, have created an imperative need to strengthen society’s socioeconomic, political, cultural, and environmental structure.
Manipify: An Automated Framework for Detecting Manipulators in Twitter Trends

The rapid adoption of online social media platforms has transformed the way of communication and interaction. On these platforms, discussions in the form of trending topics provide a glimpse of events happening around the world in real-time. Also, these trends are used for political campaigns, public awareness, and brand promotions. Consequently, these trends are sensitive to manipulation by malicious users who aim to mislead the mass audience. In this article, we identify and study the characteristics of users involved in the manipulation of Twitter trends in Pakistan. We propose “Manipify”-a framework for automatic detection and analysis of malicious users in Twitter trends. Our framework consists of three distinct modules: (1) user classifier, (2) hashtag classifier, and (3) trend analyzer. The user classifier module introduces a novel approach to automatically detect manipulators using tweet content and user behaviour features. Also, the module classifies human and bot users. Next, the hashtag classifier categorizes trending hashtags into six categories assisting in examining manipulators behaviour across different categories. Finally, the trend analyzer module examines users, hashtags, and tweets for hashtag reach, linguistic features, and user behaviour. Our user classifier module achieves 0.92 and 0.98 accuracy in classifying manipulators and bots, respectively. We further test Manipify on the dataset comprising 652 trending hashtags with 5.4 million tweets and 1.9 million users. The analysis of trends reveals that the trending panel is mostly dominated by political hashtags. In addition, our results show a higher contribution of human accounts in trend manipulation as compared to bots.
Utility-Driven Joint Caching and Bitrate Allocation for Real-Time Immersive Videos

Real-time immersive video demands high network bandwidth and low transmission delay. Limited communication resources make it time-consuming to deliver immersive videos in cloud service scenarios. To overcome this, we design a utility-driven <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">JOint Caching and Bitrate allocation (JOCB)</i> algorithm for the real-time immersive video to better utilize network and caching resources through the Mobile Edge Computing (MEC) technique. Firstly, we coin a concept, the unfreshness indicator, to reflect the obsolescence level of cached tiles in MEC. Secondly, we define the Quality of Immersive videos (QoI) to evaluate the users' experience, including content characteristics, unfreshness levels, and spatial and temporal quality loss. Thirdly, we formulate the system utility that increases effective quality at the cost of transmission loss. The utility optimization problem can be formulated as an integer programming problem and decomposed into the cache update subproblem and the viewing probability-based adaptive bitrate allocation subproblem, which are solved by the branch-and-bound algorithm and the greedy algorithm, respectively. We have implemented an immersive video transmission system to perform experiments. Both simulation and experimental results further imply that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">JOCB</i> can achieve utility maximization through balancing the transmission cost and the QoI.
PD2S: A Privacy-Preserving Differentiated Data Sharing Scheme Based on Blockchain and Federated Learning

With the rapid development of the Internet of Things (IoT), massive terminals and emerging applications bring a large amount of data. As an essential prerequisite for mining potential value from these data, data sharing is crucial and getting more and more attention. However, security and privacy concerns, and the lack of effective incentive mechanisms in the decentralized environment hinder the data owners from sharing their data. In this article, we propose a federated learning (FL) and blockchain-based privacy-preserving data sharing system, which solve the privacy leakage problem of FL and the scalability problem of blockchain by their technical complementarity. Considering the resource shortage and the heterogeneity of terminals, we first design a cross-layer architecture by exploiting the cloud–edge–terminal collaboration, and innovatively propose differential data sharing by dividing the providers into origin data providers and model providers. Then, we design a targeted incentive mechanism to maximize the utility of requesters and two types of data providers by formulating it into a two-stage Stackelberg game. Finally, a gradient-based algorithm is designed to get the optimal solution. Theoretical analysis and numerical simulations show that the proposed data-sharing system could eliminate privacy concerns and motivate more participants to share data. With 500 nodes, the differentiated data sharing scheme (DASC) is 1.72 s faster than the shared model scheme (SMC) and 2.59 s faster than SDC. In terms of utility of the requester, DASC is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.0 \times {10^{3}}$ </tex-math></inline-formula> higher than SDC, and 9.27 higher than SMC in average utility of providers.
Bayesian Dumbbell Diffusion Model for RGBT Object Tracking With Enriched Priors

RGBT tracking can be accomplished by constructing Bayesian estimators that incorporate fusion prior distributions for the visible (RGB) and thermal (T) modalities. Such estimators enable the computation of a posterior distribution for the variables of interest to locate the target. Incorporating rich prior information can improve the performance of predictors. However, current RGBT trackers face limited fusion prior data. To mitigate this issue, we propose a novel tracker, BD <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> Track, which employs a diffusion model. Firstly, this paper introduces a dumbbell diffusion model, and employ convolution networks and the dumbbell model to derive the fusion feature prior information from various index frames in the same tracking video sequence. Secondly, we propose a plug-and-play channel augmented joint learning strategy to derive the images prior distribution. This strategy not only homogeneously generates modality-relevant prior information but also increases the distance between positive and negative samples within the modality, while reducing the distance between modalities during fusion. Results demonstrate promising performance in the GTOT, RGBT234, LasHeR, and VTUAV-ST datasets, surpassing other state-of-the-art trackers.