Reviewerly/exHarmony huggingface dataset:
A Framework for Improving Web Affordability and Inclusiveness

Today's Web remains too expensive for many Internet users, especially in developing regions. Unfortunately, the rising complexity of the Web makes affordability an even bigger concern as it stands to limit users' access to Internet services. We propose a novel framework and a fairness metric for rethinking Web architecture for affordability and inclusion. Our proposed framework systematically adapts Web complexity based on geographic variations in mobile broadband prices and income levels. We conduct a cross-country analysis of 99 countries, showing that our framework can better balance affordability and webpage quality while preserving user privacy. To adapt Web complexity, our framework solves an optimization problem to produce webpages that maximize page quality while reducing the webpage to a given target size.
INCORPORATING EDUCATIONAL SMARTPHONE APPS FOR TEACHING UKRAINIAN AS A FOREIGN LANGUAGE TO MEDICAL STUDENTS

This article explores the implementation of mobile applications in the training of future doctors studying the Ukrainian language. The authors conducted a descriptive survey to explore the types of language learning apps commonly used by foreign students studying Ukrainian at the Poltava State Medical University. Furthermore, the effectiveness of these applications for the development of language skills was identified and analyzed. The authors have demonstrated that most smartphone and tablet apps can be effectively used for learning new vocabulary, grammar, spelling rules, and developing writing and speaking skills in Ukrainian for foreign students. The descriptive survey indicated that students generally hold a positive attitude towards the use of apps for language learning. Among the students surveyed, grammar and vocabulary apps were found to be the most popular, while speaking and writing apps were less favored. The authors have also identified the advantages and disadvantages of using language learning apps. The article provides a careful analysis of the essence, principles, strengths, and weaknesses of mobile applications, as well as their pedagogical significance in the context of studying the Ukrainian language at medical universities. It emphasizes that educational mobile applications used by teachers of Ukrainian as a foreign language create an environment that facilitates information retention, promotes interaction between students, and develops their language skills. Mobile applications allow students to personalize their learning by choosing the content and pace, making them convenient for self-directed and distance learning. The data and results obtained from this study can be used to improve educational materials through mobile applications for learning Ukrainian as a foreign language. The findings contribute to research on mobile-assisted language learning and the design of educational apps by shedding light on students' preferences for acquiring different language skills.
ON OCCASION OF THE 300TH ANNIVERSARY: HRYHORII SKOVORODA`S PEDAGOGICAL IDEAS AS A PRECURSOR OF DIFFERENTIATED TEACHING

On 3rd December 2022, we celebrated the 300th anniversary of the birth of Hryhoriy Skovoroda – one of the greatest Baroque thinkers, poets, educators and writers of the XVIII century. His influence on the development of Ukrainian and worldwide pedagogy is difficult to overestimate, however, today we would like to stress the importance and relevance of his ideas by analysing them through the prism of a modern approach to education – differentiated teaching and learning. H. Skovoroda`s studying at Kyiv Mohyla Academy, where he had a chance to meet famous and prospective philosophers, thinkers, and activists of the time, and his experience of teaching and lecturing at famous Ukrainian educational centers shaped his views on teaching methods. Combined with his philosophic views these ideas prove to be relevant in the modern conditions. The article analyses the main principles of differentiated learning – principles of taking into account the individual characteristics of students, accessibility, systematicity, consistency, autonomy, and consciousness through their connection to Skovoroda`s ideas. The analysis showed that H. Skovoroda formulated some of the fundamental principles of personality-oriented learning, which were precursors for developing ideas of differentiated learning.
Global trends in disruptive technological change: social and policy implications for education

Purpose The purpose of this study is to identify global trends in disruptive technological change and map the social and policy implications, particularly as they relate to the educational ecosystem and main stakeholders across all levels of education. Design/methodology/approach The authors conducted a two-stage meta-analysis of 1,155 scholarly, peer-reviewed articles. The investigation involves a systematized literature review for data identification and collation adhering to defined selection criteria, and a network analysis to scrutinize data, consolidate information and unveil correlations and patterns from the literature review to produce a set of recommendations. Findings The study unveiled educational trends related to disruptive technologies and delineated four principal clusters representing how these technologies are transforming the education ecosystem. Additionally, a series of transversal aspects that reveal a societal vulnerability toward future prospects in the realms of ethics, sustainability, resilience, security, and policy were identified. Practical implications The findings spotlight an enlarging chasm between industry (and society at large) and conventional education, where many transformations triggered by disruptive technologies remain absent from teaching and learning systems. The study further offers recommendations and envisions potential scenarios, urging stakeholders to respond based on their positions concerning disruptive technologies. Originality/value Expanding from the meta-analysis of pertinent literature, this paper offers four collections of curated resources, four mini case studies and four scenarios for policymakers and local communities to consider, enabling them to plot courses for their optimal futures.
Upaya Peningkatan Kemampuan Guru dalam Pemanfaatan Perpustakaan Sekolah Sebagai Sumber Belajar dengan Menggunakan Metode Focus Group Discussion di Sekolah Dasar Negeri 1 Bade Kecamatan Klego Kabupaten Boyolali Tahun Pelajaran 2017/2018

Abstract&#x0D; &#x0D; This study aims to determine the ability of teachers to use the school library as a learning resource in SD Negeri 1 Bade, Klego District, Boyolali Regency. This research is a school action research. The research was conducted at SD Negeri 1 Bade, Klego District, Boyolali Regency. The research subjects were elementary school teachers from class I to class VI. The research procedure uses two cycles. Data analysis techniques using descriptive analysis techniques. The indicator of achievement of the success rate with a minimum score of 75% of teachers shows high activity in participating in Focus Group Discussions for the use of the School Library as a learning resource, at least 75% of teachers give a high response to coaching with the Focus Group Discussion for the use of the School Library as a learning resource and at least 75% of teachers are able to create learning scenarios by utilizing the School Library as a learning resource. The results of this study indicate that through Focus Group Discussions can improve Teacher Ability to Utilize School Libraries as Learning Resources, this is because through Focus Group Discussions they feel that they receive input and know the shortcomings in implementing Utilizing School Libraries as Learning Resources. Based on the results of the study, it can be seen that through Focus Group Discussion, the ability of teachers to use the school library as a learning resource at SD Negeri 1 Bade, Klego District, Boyolali Regency, 2017/2018 Academic Year. Thus, it can be concluded that through Focus Group Discussion can improve the ability of teachers to use the school library as a learning resource at SD Negeri 1 Bade, Klego District, Boyolali Regency.&#x0D; &#x0D; &#x0D;
Studying bias in visual features through the lens of optimal transport

Abstract Computer vision systems are employed in a variety of high-impact applications. However, making them trustworthy requires methods for the detection of potential biases in their training data, before models learn to harm already disadvantaged groups in downstream applications. Image data are typically represented via extracted features, which can be hand-crafted or pre-trained neural network embeddings. In this work, we introduce a framework for bias discovery given such features that is based on optimal transport theory; it uses the (quadratic) Wasserstein distance to quantify disparity between the feature distributions of two demographic groups (e.g., women vs men). In this context, we show that the Kantorovich potentials of the images, which are a byproduct of computing the Wasserstein distance and act as “transportation prices", can serve as bias scores by indicating which images might exhibit distinct biased characteristics. We thus introduce a visual dataset exploration pipeline that helps auditors identify common characteristics across high- or low-scored images as potential sources of bias. We conduct a case study to identify prospective gender biases and demonstrate theoretically-derived properties with experiments on the CelebA and Biased MNIST datasets.
Deep Q‐learning recommender algorithm with update policy for a real steam turbine system

Abstract In modern industrial systems, diagnosing faults in time and using the best methods becomes increasingly crucial. It is possible to fail a system or to waste resources if faults are not detected or are detected late. Machine learning and deep learning (DL) have proposed various methods for data‐based fault diagnosis, and the authors are looking for the most reliable and practical ones. A framework based on DL and reinforcement learning (RL) is developed for fault detection. The authors have utilised two algorithms in their work: Q‐Learning and Soft Q‐Learning. Reinforcement learning frameworks frequently include efficient algorithms for policy updates, including Q‐learning. These algorithms optimise the policy based on the predictions and rewards, resulting in more efficient updates and quicker convergence. The authors can increase accuracy, overcome data imbalance, and better predict future defects by updating the RL policy when new data is received. By applying their method, an increase of 3%–4% in all evaluation metrics by updating policy, an improvement in prediction speed, and an increase of 3%–6% in all evaluation metrics compared to a typical backpropagation multi‐layer neural network prediction with comparable parameters is observed. In addition, the Soft Q‐learning algorithm yields better outcomes compared to Q‐learning.
Teaching how to use English articles based on a cognitive approach at non-linguistic faculties in advanced level groups (B1+)

The aim of the study is to consider the essence and potential of using a cognitive approach in teaching students of non-linguistic faculties how to use English articles. The paper analyses the educational and methodological literature, as well as research in the field of psychology and linguistics, which makes it possible to justify the feasibility of introducing the step-by-step teaching of article forms based on a cognitive approach into pedagogical practice. The scientific novelty of the study lies in the hypothesis that teaching students of non-linguistic faculties how to use English articles can be carried out consistently on the basis of the proposed system of formation of ideas about a lexical unit, which correlates with the mental processes of abstraction, classification and concretisation. As a result of the study, the researchers proposed methodological recommendations for teaching the use of articles based on a cognitive approach for advanced level groups (B1+) learning English at non-linguistic faculties.
Deep neural architecture for natural language image synthesis for Tamil text using BASEGAN and hybrid super resolution GAN (HSRGAN)

Abstract Tamil is a language that has the most extended history and is a conventional language of India. It has antique origins and a distinct tradition. A study reveals that at the beginning of the twenty-first century, more than 66 million people spoke Tamil. In the present time, image synthesis from text emerged as a promising advancement in computer vision applications. The research work done so far in intelligent systems is trained in universal language but still has not achieved the desired development level in regional languages. Regional languages have a greater scope for developing applications and will enhance more research areas to be explored, ruling out the barrier. The current work using Auto Encoders failed at the point of providing vivid information along with essential descriptions of the synthesised images. The work aims to generate embedding vectors using a language model headed by image synthesis using GAN (Generative Adversarial Network) architecture. The proposed method is divided into two stages: designing a language model TBERTBASECASE model for generating embedding vectors. Synthesising images using Generative Adversarial Network called BASEGAN, the resolution has been improved through two-stage architecture named HYBRID SUPER RESOLUTION GAN. The work uses Oxford-102 and CUB-200 datasets. The framework efficiency has been measured using F1 Score, Fréchet inception distance (FID), and Inception Score (IS). Language and image synthesis architecture proposed can bridge the gap between the research ideas in regional languages.
Security estimation of LWE via BKW algorithms

Abstract The Learning With Errors (LWE) problem is widely used in lattice-based cryptography, which is the most promising post-quantum cryptography direction. There are a variety of LWE-solving methods, which can be classified into four groups: lattice methods, algebraic methods, combinatorial methods, and exhaustive searching. The Blum–Kalai–Wasserman (BKW) algorithm is an important variety of combinatorial algorithms, which was first presented for solving the Learning Parity With Noise (LPN) problem and then extended to solve LWE. In this paper, we give an overview of BKW algorithms for solving LWE. We introduce the framework and key techniques of BKW algorithms and make comparisons between different BKW algorithms and also with lattice methods by estimating concrete security of specific LWE instances. We also briefly discuss the current problems and potential future directions of BKW algorithms.
Design and Implementation of an Optimized High-Speed Vedic-Based Squarer Circuit Using Reversible Logic Gates

The primary purpose of this research is to investigate and evaluate the effectiveness of a 4 × 4 squarer circuit based on Vedic mathematics and reversible logic gates. A high-speed squarer circuit has been developed using Fredkin, Peres, BME, and HNG gates. The simulations are performed using the iSim simulator in Xilinx ISE 14.7. In addition, using several FPGA models, the delay, a fundamental aspect of a digital circuit, is also explored. The individual blocks of the existing circuits are implemented using different types of available reversible logic gates. Lastly, a comparison of the proposed circuit's different attributes with those of the existing designs such as gate count, the proportion of ancilla inputs, and garbage outputs and quantum cost are calculated and presented in a tabular form. Along with this, a brief comparison of FPGA hardware utilized and power consumed is also given. Finally, it is noted that as the quantum cost is greatly decreased by around 75%, also the delay is improved compared to the existing designs by 2.7%, and hence the proposed circuit becomes a viable candidate for high-speed applications.
Balancing software and training requirements for information security

Information security is one of the key areas of consideration to assure reliable and dependable information systems (IS). Achieving an appropriate level of IS security requires concurrent consideration of the technical aspects of IS and the human aspects related to the end users of IS. These aspects can be described in the form of information security requirements. We propose an approach that helps select and balance information security software requirements (iSSR) and information security training requirements (iSTR) according to the information security performance of end users. The approach was tested in an experiment involving 128 IS professionals. The results showed that using the proposed approach helps IS professionals with limited experience in information security make significantly better decisions regarding iSSR and iSTR.
Harnessing solar power: Predicting photovoltaic potential in fiche, oromia, ethiopia with artificial neural networks

In developing nations like Ethiopia, especially in rural and semi-urban regions, reliable access to electricity through grid-based systems is often limited or unavailable due to inadequate energy infrastructure. To tackle this issue, photovoltaic (PV) technology plays a crucial role in converting solar energy into electricity, especially in remote regions. This study focuses on assessing the daily and monthly energy exposure of PV arrays located near Fiche town, Ethiopia. The study utilizes four network types - Feedforward Backpropagation (FFBP), Cascade Feedforward Backpropagation (CFBP), Error Backpropagation (EBP), and Levenberg-Marquardt (LR) - with eight input variables and a training-testing split of 70%-30% to estimate global solar radiation (GSR). Hidden and output layers are employed with a sigmoid and linear function as activation functions, respectively. The results reveal that the expected mean daily and monthly horizontal global solar radiation (HGSR) ranges between 3.282 kWh/m2/day ≤ HGSR ≤ 6.967 kWh/m2/day and 4.628 kWh/m2 ≤ HGSR ≤ 6.613 kWh/m2, respectively. Furthermore, the study determines that the daily energy delivered to the PV system and available from the PV array to load falls within the range of 0.656 kWh/m2 ≤ Ed ≤ 1.394 kWh/m2 and 0.567 kWh/m2 ≤ El ≤ 1.205 kWh/m2, respectively. The average results for the study period from 2016 to 2021 were 1.175 kWh/m2 and 1.015 kWh/m2 for the Ed and El, respectively. These results demonstrate that PV arrays near Fiche town have abundant solar energy resources, making them highly attractive for electricity generation. Furthermore, utilizing solar energy from PV arrays can help mitigate indoor pollution in the rural communities surrounding Fiche town.
Evaluating The Performance of Feature Extraction Techniques Using Classification Techniques

Dimensionality reduction techniques are widely used in machine learning to reduce the computational complexity of the model and improve its performance by identifying the most relevant features. In this research paper, we compare various dimensionality reduction techniques, including Principal Component Analysis(PCA), Independent Component Analysis(ICA), Local Linear Embedding(LLE), Local Binary Patterns(LBP), and Simple Autoencoder, on the Olivetti dataset, which is a popular benchmark dataset in the field of face recognition. We evaluate the performance of these dimensionality reduction techniques using various classification algorithms, including Support Vector Classifier (SVC), Linear Discriminant Analysis (LDA), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The goal of this research is to determine which combination of dimensionality reduction technique and classification algorithm is the most effective for the Olivetti dataset. Our research provides insights into the performance of various dimensionality reduction techniques and classification algorithms on the Olivetti dataset. These results can be useful in improving the performance of face recognition systems and other applications that deal with high-dimensional data.
Needs Analysis for a Web-Based Learning System to Develop Students’ Science Process Skills

The aim of this study was to explore the needs for the identification ofdesign elements of a web-based learning system that would help students develop their science process skills (SPS) to be used in science lessons. A descriptive survey method was adopted as the methodology of the study. Survey data was collected from a sample of 36 teachers and 196 middle school students (grades 5 to 8). The results of the study indicated that students had difficulty in learning science subjects. They thought that they could learn the subjects in the science course better with technological and educational support, and various teaching activities. Teachers stated that the web-based education system should meet the following requirements be simple and understandable, be relevant to real/everyday life, be an interactive system, which is rich in content and images, and include various multimedia elements such as simulations, animations, and 3D materials. On the other hand, many students stated that a web-based learning system that will help them learn the subjects in the science course should have course videos (educational videos), test/solved/sample questions, practice exams, accurate and reliable information. In addition, the students stated that this web-based learning system should be simple and understandable, have easy and rich content, be interactive and have a good technical infrastructure. Both teachers and students thought that “observing”, and “experimenting” skills would be improved the most in technology-enhanced learning environments. In addition, teachers stated that the benefits of developing students’ science process skills in real life would be “gaining a scientific perspective on events”, “awareness of the problem(s) around them”, “science literacy”, “enabling the student to access the information himself/herself”, “expanding perspectives on events”, and “able to solve the problems they encounter more easily”.
Exploiting Inconsistencies in Object Representations for Deepfake Video Detection

AI advancements in recent years have made it more simpler to fake digital images and videos, and much harder to spot forgeries. When spread maliciously, these forgeries not only threaten the integrity of our political systems but also present serious moral and legal quandaries. Deepfake videos are mostly generated frame-by-frame, leaving visible object-level inconsistencies in both temporal and spatial dimensions. In this paper, we propose a novel deepfake video detection method that exploits this important clue. Specifically, we extract object representations using vision transformers from video frames and then model the object-level coherence in both intra-frame and inter-frame manner. We propose that our model can learn object-level visual inconsistencies by excluding irrelevant context and focusing on key traces responsible for compromised video authenticity.
GAN Discriminator based Audio Deepfake Detection

Deepfake is a new technology that has emerged in recent times and is becoming one of the great challenges for society and individuals. In particular, scammers could use deepfake to start a phishing attack by cloning the victim’s voice and calling his related person with the fake audio. In this study, we propose a transfer learning model for detecting deepfake audio. This model introduces a method to leverage the discriminator of a GAN-based vocoder model to extract the front-end features of an unidentified audio sample, which helps the model to detect fake voices more easily. With a detection efficiency of up to 94%, we demonstrate that this transfer learning method is feasible.