Reviewerly/exHarmony huggingface dataset:
Preparation of Future Primary School Teachers for Modeling an Innovative Educational Environment in the Process of Studying Primary School Didactics

The article presents theoretical and methodological principles of training future primary school teachers to model an innovative educational environment. The relevance of the investigated issue in the context of modern requirements for the primary school teacher, in particular it’s projective and prognostic competences are proven. The substantive and practical components of training future primary school teachers for modeling an innovative educational environment in the process of studying primary school didactics have been determined. The essential aspects of the substantive component of the specified training are revealed: mastery by future teachers of the relevant conceptual and terminological apparatus (environment, environmental approach, educational environment, innovation, innovations in education, innovative educational environment, model, simulation); representation of modern psychological and pedagogical approaches to modeling the educational environment, principles of modeling stages. The principle of anticipation is characterized in the context of the implementation of modeling tasks of an innovative educational environment. Forms and methods of appropriate training are revealed - work with conceptual apparatus (tree of concepts, concept map, etc.), SWOT analysis, brainstorming, mini-projects, case method, etc.
Comparative Analysis of Transfer Learning-Based CNN Approaches for Recognition of Traffic Signs in Autonomous Vehicles

Traffic signs recognition has a crucial role in enhancing the safety and efficienty of autonomous vehicles (AVs). This AVs can contribute to a cleaner and healthier environment by improving fuel efficiency, minimizing travel distances, and deacreasing air pollution. Many artificial intelligence (AI) approaches contribute to develop AVs. Therfore, Convolutional Neural Networks (CNNs) have shown remarkable performance in image classification tasks for AVs, inculding traffic signs recognition. However, training deep CNNs from scratch for traffic sign recognition requires a significant amount of labeled data, which can be time-consuming and ressource-intensive to obtain. Transfer Learning, a technique that leverages pre-trained models on large-scale datasets,offers a promising solution by enabling the transfer of learned feautres from one task to another. This paper presents a comprehensive comparative analysis of three popular transfer learning based CNN approaches, namely ResNet, VGGNet, and MobileNet,for the recognition of traffic signs in the context of AVs.
Prediction on Illegal Drug Consumption Promotion from Twitter Data

Illegal drug consumption is one of the major problems faced by the world. With the increase in deaths from consumption of drugs, it is important to identify the users and understand why they use drugs. Because of the increased accessibility to the internet across the globe, many people are connecting to social media at a higher frequency. By extracting data from the twitter, we can predict if the user consumes illicit or semi-legal drugs. This can be used to track the spread of these posts in specific communities.
Automatic Detection of Generated Texts and Energy: Exploring the Relationship

The proliferation of artificial intelligence (AI) and natural language processing (NLP) technologies has enabled the generation of realistic and coherent texts, but it also raises concerns regarding the potential misuse of these technologies for generating misleading or malicious content. Automatic detection of generated texts is crucial in addressing this issue. This article provides a comprehensive examination of the relationship between the detection of generated texts and energy consumption, delving into the techniques, challenges, and opportunities for developing energyefficient algorithms for text detection.
Scientific Substantiation of Pedagogical Conditions for the Formation of Students' Social Competence

At the article, it was primed that it was appointed pedagogical conditions for the formation of social competence. The importance of improving the quality of professional training of students (scientific-pedagogical, practical, psycho-physiological, psychological) is evidently up to new skills in society, directed at the formation of social competence, as well as changing roles from the functions of a curator, curator and clerk from the traditional role of an expert mentor to the innovative role of a facilitator – the leader and participant in the educational process. It is described the creation in the student government of a special body that accepts the formation of the social welfare of students on the basis of the coordination of the students, the administration of the university, various social institutions, the development and implementation nominative social programs and projects included in various types of social practices. Propionated by the organization of the bodies of student government to the club «Startup» with the method of goal-directed formation of students’ social compe tence in the totality of social values, motives, knowledge, understanding, psychophysiological and psychological qualities.
Governance of internal audit and its role in adding value A field study at Al-Watania Cement Company (Aden) in the context of improving its environmental management

The aim of the research is to identify internal audit governance and its role in adding value to companies in the context of improving their environmental Performance. The population of the study is comprised of Yemeni cement manufacturing companies in Aden, The study sample is Al-Watania Company. To achieve the objectives of the study, a questionnaire was developed to collect data by distributing it to a sample consisting of (the general manager, deputy general manager, department manager, department head, internal auditor, and accountant). The number of questionnaires distributed was (50), and the number of questionnaires recovered was (50), with a recovery rate of 100%. According to the study’s findings, there is a statistically significant impact of internal audit governance in adding value to companies in the context of improving their environmental performance, internal audit governance impacts adding value by (0.32), while the percentage (0.68) is due to other factors. Internal audit adds value to companies by raising the efficiency of management performance in decision-making and providing appropriate information at the right time. and Provide regular updates to the company’s management on environmental performance, especially when there are potential risks, and assist in responding to all risks, including environmental risks. The study suggested the need to adopt environmental auditing as a mandatory audit in all companies, especially industrial companies. Training internal auditors to develop their skills in the field of environment, procedures, and activities for environmental auditing.
Comparative Study of Amazigh Speech Recognition Systems Based on Different Toolkits and Approaches

The objective of this study is to evaluate and contrast the performance of different ASR approaches applied to the Amazigh language. Markovian modelling techniques, including Hidden Markov Models with Gaussian mixture distribution, Convolutional Neural Network, size of vocabulary, and lastly, the choice of decoder, whether Sphinx or HTK, by conducting a comprehensive analysis and comparison of these factors, this paper aims to provide valuable insights into the development of effective ASR systems for the Amazigh language. The findings will contribute to advancing the field of Amazigh ASR and aid in the selection of appropriate techniques and tools for future research and development efforts.
Sustainable MapReduce: Optimizing Security and Efficiency in Hadoop Clusters with Lightweight Cryptography-based Key Management

The exponential growth of big data has led to a significant increase in the volume and complexity of data being generated and stored. This trend has created a huge demand for secure storage and processing of big data. Cryptography is a widely used technique for securing data, but traditional cryptography algorithms are often too resource-intensive for big data applications. To address this issue, light weight cryptography algorithms have been developed that are optimized for low computational overhead and low memory utilization. This research paper explores the use of a new sustainable algorithm that utilizes a lightweight cryptographybased key management scheme to optimize MapReduce security and computational efficiency in Hadoop clusters. The proposed sustainable MapReduce algorithm aims to reduce memory and CPU allocation, thereby significantly reducing the energy consumption of Hadoop clusters. The paper emphasizes the importance of reducing energy consumption and enhancing environmental sustainability in big data processing and highlights the potential benefits of using sustainable lightweight cryptography algorithms in achieving these goals. Through rigorous testing and evaluation, the paper demonstrates the effectiveness of the proposed sustainable MapReduce algorithm in improving the energy efficiency and computational performance of Hadoop clusters, making it a promising solution for sustainable big data processing.
Cyber-attack crisis management in the context of energy companies

The aim of this research is to present and analyze a set of public or private energy companies that have been victims of cyber-attacks, while identifying various lessons that can guide other companies towards effective crisis management in response to this risk. To achieve this, we have adopted an exploratory approach that involves utilizing, in an original manner, the raw material derived from a collection of published articles and other documents that have addressed and presented the phenomenon of cyber-attacks on private and public energy companies. The main findings of this study demonstrate that, for effective management of a cyber-attack crisis, legislation must serve as a catalyst for anticipating and managing the crisis through innovative and ambitious draft texts. Additionally, companies must possess the necessary skills and expertise to respond to cyberattack crises, including the technical infrastructure and software required to counter cyber incidents. Furthermore, fostering a collaborative and multi-disciplinary team-building capacity led by a pilot with a comprehensive and unified vision, along with the ability to make prompt decisions, is of paramount importance.
Machine Learning for a Medical Prediction System “Breast Cancer Detection” as a use case

Breast cancer is a widespread and serious illness, highlighting the importance of an early detection tool that can provide prognostic information and suggest necessary lifestyle changes to prevent its advancement, also the environmental changes in our daily life have significantly enhance the chances of getting cancer at an early stage of our life. Machine learning has become an indispensable tool in addressing this pressing need, enhancing human capabilities and offering greater automation with reduced errors. In this article, a breast cancer detection and prediction system has been created, utilizing diverse machine learning models including KNN, LR, and XGBoost.
Optimal Deep Recurrent Neural Networks for IoT-enabled Human Activity Recognition in Elderly and Disabled Persons

Aging is related to a decrease in the ability to execute activities of day-to-day routine and decay in physical exercise, which affect mental and physical health. Elderly patients or people can depend on a human activity recognition (HAR) system, which monitors the activity interventions and patterns if any critical event or behavioral changes occur. A HAR system incorporated with the Internet of Things (IoT) environment might allow these people to live independently. While the number of groups of activities and sensor measurements is enormous, the HAR problem could not be resolved deterministically. Hence, machine learning (ML) algorithm was broadly applied for the advancement of the HAR system to find the patterns of human activity from the sensor data. Therefore, this study presents an Optimal Deep Recurrent Neural Networks for Human Activity Recognition (ODRNN-HAR) on Elderly and Disabled Persons technique in the IoT platform. The intension of the ODRNN-HAR approach lies in the recognition and classification of various kinds of human activities in the IoT environment. Primarily, the ODRNN-HAR technique enables IoT devices to collect human activity data and employs Z -score normalization as a preprocessing step. For effectual recognition of human activities, the ODRNN-HAR technique uses the DRNN model. At the final stage, the optimal hyperparameter adjustment of the DRNN model takes place using the mayfly optimization (MFO) algorithm. The result analysis of the ODRNN-HAR algorithm takes place on benchmark HAR dataset, and the outcomes are examined. The comprehensive simulation outcomes highlighted the improved recognition results of the ODRNN-HAR approach in terms of different measures.
Design of a specialized search engine for university students dedicated to education and environment

The aim of this study is to introduce a new specialized search engine that helps university students learn about environmental issues and improve their environmental literacy. Our search engine collects information from environmental documents and scientific articles from trusted sources. After intensive word processing, it provides a list of different contexts for the terms queried, depending on the chosen field, allowing students to refine their online search. In a single operation, students can find phrases and paragraphs using multiple related terms. This model aims to generate maximum output with semantic value using minimum user input, thanks to the new search mechanism on which it is based. The search engine is optimized for environmental education, allowing students to access environmental information in their preferred language. Our work is structured as follows: first, we motivate the need for a specialized environmental education search engine. Then, we discuss the context and construction of our specialized search engine for environmental education. Finally, we review the proposed solution and conclude with future work.
Improving green supply chain resilience through Blockchain innovation: A risk mitigation perspective

The increasing popularity of cryptocurrencies has brought to light the underlying technology known as Blockchain. This innovation is a decentralized information management system that allows for the recording of digital asset transactions without the need for institutional intermediaries. The potential of Blockchain to revolutionize the management of companies, particularly in the context of supply chain risk management, is immense due to the complex and vulnerable nature of the supply chain. This article aims to highlight the impact of the Blockchain on the risks observed in the Supply Chain by examining how this innovation can strengthen the resilience of these chains in times of increased threat and uncertainty. Our study, which utilizes a bibliometric analysis, highlights the role of Blockchain technology in mitigating various risks, including those related to fraud, errors, loss of information, and supply chain failure. It should be noted that the implementation of environmental supply chain practices requires the commitment and participation of all stakeholders involved in the supply chain, as well as favorable policies and incentives from governments and institutions to encourage the adoption of sustainable practices. The key features of Blockchain, including real-time transparency, traceability, and automated smart contract management, enable risk mitigation and contribute to the overall resilience of green supply chains.
The calculated uncertainty of scientific discovery: From Maths to Deep Maths

Throughout history, diverse Maths have underpinned numerous important natural and physical science discoveries. In their initial development and application, these Maths were often incompletely or imperfectly understood, with constants and “fudge factors” needed to account for statistical uncertainties to advance a scientific discipline. Some polymaths have acted as philosophers in support of new ways of thinking, based on their novel discoveries about the natural and physical world. Deep Maths integral to artificial intelligence (AI), machine learning and deep learning (DL), are also subject to human imperfections (i.e., computational errors, operator assumptions) and stochastic uncertainties (i.e., modeling biases, convergence optimizers). Mathematicians and domain experts can collaborate to increase AI model accuracy by improving training data quality (i.e., curating, reducing dimensionality), mitigating human and machine biases, and understanding data contexts prior to query. Since the advent of DL and through the design of multilayered feedforward neural networks then large language models, scientists have applied advanced AI computing capabilities to push the limits of this technology trend. Recently, AI's capacity to uncover newly modeled insights has been hyped beyond the proven limits of DL model accuracy. History has witnessed the acceptance of new knowledge (primarily by peers) based on the accuracy and/or reproducibility of empirical observations and on varied interpretations of mathematical proofs. Societal enthusiasm for science or technology insertion is often limited by the general public's understanding of the underlying Maths and Deep Maths, and related human fears and concerns of displacement (i.e., lost jobs, ecological impact, less privacy, etc.). Today's proponents of societal progress based on new discoveries and technologies are motivated by a range of influences (i.e., humanity, control, security, profit, etc.), creating additional uncertainties that can deflect initial scientific enthusiasm and/or delay widespread adoption.
Scalable Summarization for Knowledge Graphs with Controlled Utility Loss

Due to the explosive growth of semantic data over recent years, extracting critical and representative information from knowledge graphs (KGs), i.e., KG summarization is a challenging task under real-world resource constraints such as memories and response time. We present scalable utility-driven algorithms for summarizing large-scale KGs based on the features of entities. Specifically, we propose the notion of utility for KG summaries and develop the SiFS algorithm for efficient lossless KG summarization. Second, we present the highly scalable KG summarization algorithm ScFS for generating lossy summaries by user defined utility threshold. The experiments over real-world and synthetic datasets show that SiFS achieves more compact summary sizes and significant reductions in execution time. Moreover, ScFS outperforms state-of–the–art summarization methods significantly in summary size and is about two orders of magnitude better in terms of speed. Third, we present a query evaluation algorithm over KGs based on SiFS and ScFS. The experiments results show that the proposed summaries facilitate efficient and high-quality KG queries.
DMIS: Dual Model Index Structure for Enhanced Performance on Complexly Distributed Datasets

Recently learned index was proposed to improve index performance, where error-driven methods are widely adopted. However, when applied to datasets with complex data distributions, the method may produce overdispersed models. Specifically, data sets with complex distributions are highly irregular and difficult to describe using parameterized distribution laws, which ultimately affects the performance of the learned index. Aiming to the issue, we propose a Dual Model Index Structure (DMIS) that combines the learned index and traditional index to better handle complex datasets. Meanwhile, we propose an evaluation model that measures the compatibility of the data distribution with the learned index and explore a classification method to categorize datasets as either learning-friendly or non-learning-friendly. Our evaluation results demonstrate that the DMIS architecture improves performance by about 1.6 times compared to the state-of-the-art learned index and performs better under various workloads. The DMIS model for partitioning learning-friendly data enhances the model's universality, efficiently improves the data index's efficiency, and reduces the number of bottom segments of the index. Our work effectively mitigates the impact of dataset differences on learned indexes.
Evaluating Prompt-Based Question Answering for Object Prediction in the Open Research Knowledge Graph

Recent investigations have explored prompt-based training of transformer language models for new text genres in low-resource settings. This approach has proven effective in transferring pre-trained or fine-tuned models to resource-scarce environments. This work presents the first results on applying prompt-based training to transformers for scholarly knowledge graph object prediction. Methodologically, it stands out in two main ways: 1) it deviates from previous studies that propose entity and relation extraction pipelines, and 2) it tests the method in a significantly different domain, scholarly knowledge, evaluating linguistic, probabilistic, and factual generalizability of large-scale transformer models. Our findings demonstrate that: i) out-of-the-box transformer models underperform on the new scholarly domain, ii) prompt-based training improves performance by up to 40% in relaxed evaluation, and iii) tests of the models in a distinct domain reveals a gap in capturing domain knowledge, highlighting the need for increased attention and resources in the scholarly domain for transformer models.
MIRS: [MASK] Insertion Based Retrieval Stabilizer for Query Variations

Pre-trained Language Models (PLMs) have greatly pushed the frontier of document retrieval tasks. Recent studies, however, show that PLMs are vulnerable to query variations, i.e., queries containing misspellings or word re-ordering of original queries, and etc.. Despite the increasing interest to robustify the retriever performance, the impact of the query variations is not fully exploited. To effectively address this problem, this paper revisits the Masked-Language Modeling (MLM) and proposes a robust fine-tuning algorithm, termed [MASK] Insertion based Retrieval Stabilizer (MIRS). The proposed algorithm differs from existing methods via the injection of [MASK] tokens into query variations and further encouraging the representation similarity between the pair of original queries and their variations. In comparison to MLM, the traditional [MASK] substitution-then-prediction is less emphasized in MIRS. Additionally, an in-depth analysis of our algorithm is also provided to reveal: (1) the latent representation (or semantic) of the original query forms a convex hull, while the impact of the query variation is then quantified as a "distortion" to this hull via deviating the hull vertices; and (2) inserted [MASK] tokens play a significant role in enlarging the intersection between the newly-formed hull (after variations) and the original one, thereby preserving more semantic from original queries. With the proposed [MASK] injection, MIRS exhibits a relative 1.8 MRR@10 absolute point enhancement on average in the retrieval accuracy, verified using 5 baselines across 3 public datasets with 4 types of query variations. We also provide intensive ablation studies to investigate the hyperparameter sensitiveness, to breakdown the model into individual components to manifest their efficacy, and further, to evaluate the out-of-domain model generalizability.