Reviewerly/exHarmony huggingface dataset:
Media Article Text Analysis in the Context of Distance Education: Focusing on South Korea

The impact of COVID-19 pandemic is far-reaching, encompassing the social, economic, and psychological aspects of the society. . In order to prevent the spread of COVID-19, various countries, including South Korea, have entered into long-term home care and distance learning systems. However, distance learning experiments conducted in many countries have raised whether face-to-face education can be replaced by distance learning. Therefore, this study analyzed public opinion, social perception, and field trends based on media reports on remote learning. For this purpose, 2,600 articles from 11 newspapers and four broadcasters related to remote learning were collected in this study. Based on this data, keyword trend analysis, topic modeling analysis, semantic network analysis, and emotion analysis were performed. This study provides valuable insights into the impacts and perceptions of remote learning during the COVID-19 pandemic. It highlights the complexities of remote education, from influencing educational policies to affecting students' and parents' emotional states. The findings underscore the potential of remote learning in driving educational innovation while also pointing out its role in widening educational disparities. This research offers essential perspectives for shaping future educational strategies in crisis situations, emphasizing a balanced approach to harness the benefits of remote learning while mitigating its challenges.
Real-Time Transcriptionist Based on Artificial Intelligence to Facilitate Learning for People with Hearing Disabilities in Virtual Classes

Schools have historically been ill-prepared to cater to the needs of deaf students at the elementary and secondary levels. This leads to communication difficulties that impact the learning process for each individual. During the recent COVID-19 pandemic, educational institutions for deaf students faced difficulties in providing effective teaching to children and youth. It is important to emphasize that education is fundamental for all individuals, without exception, as acquiring literacy skills enables them to lead a more fulfilling life. In this context, our research aims to investigate how the use of a computer tool can enhance communication for deaf students in a virtual environment. The methodology used involved the use of a checklist to gather data from each participant’s evaluation. The post-test yielded favorable results, thanks to the statistical analysis employed in the research. In conclusion, it has been determined that a real-time transcriber facilitates learning, leading to improved educational outcomes for deaf students.
Engineering Students' Acceptance of Augmented Reality Technology Integrated with E-Worksheet in The Laboratory Learning

The use of augmented reality (AR) technology in the field of education has emerged as a rapidly growing trend. However, there is an urgent need for more comprehensive research to determine the reactions of engineering students and their acceptance of this technology in laboratory learning. This study investigates the acceptance of integrated augmented reality with e-worksheet (IARE-W) among engineering students in the laboratory learning (IARE-W) among engineering students the electrical machines course (EMC). This research empirically uncovers the factors that influence it based on the technology acceptance model (TAM), specifically perceived ease of use (PEU) and perceived usefulness (PU). Acceptance is indicated by students’ attitudes toward the use. A survey-based quantitative research study using questionnaires was conducted to collect data, involving 102 students in the field of industrial electrical engineering. The partial least squares structural equation modeling (PLS-SEM) analysis was used to analyze the research data. The results demonstrated that engineering students had a highly positive attitude toward the use of the IARE-W in the EMC. Additionally, both PEU and PU had a positive and significant direct effect on engineering students’ attitudes toward using IARE-W. Furthermore, PEU also had a significant and positive indirect effect through PU as a mediating variable. These findings have significant implications for the development of engineering education and the integration of AR technology in laboratory learning contexts. The results of this study underscore the importance of taking into account PEU and PU in the design, development, and implementation of the IARE-W.
Risk Assessment and Distribution of Heavy Metals in Water and Sediments along Ibelebiri Axis of Kolo Creek, Ogbia Local Government Area, Niger Delta Region of Nigeria

Pollution arising from uncontrollable combustion processes, including artisanal refineries are common in communities through which Kolo Creek traverses. Previous studies have centered on the effect of oil and gas production on Kolo Creek at shell flow station at Imiringi. Therefore, the aim of this study is to investigate the risk and distribution of heavy metals (Zn, Ni, Cr, Cu, Pb, Co, and Cd) in water and sediments along Ibelebiri axis of Kolo creek in Ogbia L.G.A., Niger Delta region of Nigeria. Water and sediment samples were collected at locations upstream, within and downstream at Ibelebiri community, and also from a control site. Generally, heavy metal concentrations showed a decreasing trend of Cu˃Zn˃Pb˃Cr˃Cd˃Co˃Ni for water samples. The mean concentration of these metals: Ni, Cr, Zn and Cu recorded in this study was below international recommended limits. But, Pb, Cd levels in locations LC1, LC3 and LC4 are above the international recommended limits. The heavy metals in the sediments showed a decreasing trend of Zn˃Pb˃Cu˃Co˃Cr˃Ni˃Cd. The concentrations of these metals Cd, Pb, Ni, Cr, Cu and Zn are below the permissible limit set by Department of Petroleum Resources (DPR). However, Co values at locations LC3 and LC4 are slightly above the DPR target value of 20 mg/kg for sediments. Using the geoaccumulation index (Igeo) classification, the sediments across the four locations are uncontaminated with Cd, Cr, Cu, Zn, Co and Ni. But, the sediments in locations LC1, LC3 and LC4 are moderately contaminated with Pb. Similarly, the contamination factor (CF) classification, revealed low contamination of Cd, Cr, Cu, Zn, Ni and Co across the four locations; moderate contamination of Pb in the four locations. Finally, principal component analysis (PCA) identified two major factors.
Characterisation, axial anisotropy, and formation conditions of celestine minerals from the Jabal Eghei (Nuqay) late Neogene – Pleistocene volcanic province, southeastern edge of the Sirt Basin, southern Libya: Constraints on the mineralogical geothermometer – ERRATUM

Characterisation, axial anisotropy, and formation conditions of celestine minerals from the Jabal Eghei (Nuqay) late Neogene – Pleistocene volcanic province, southeastern edge of the Sirt Basin, southern Libya: Constraints on the mineralogical geothermometer – ERRATUM
Proving the existence of Euclidean knight’s tours on n × n × ⋯ × n chessboards for n &lt; 4

The Knight's Tour problem consists of finding a Hamiltonian path for the knight on a given set of points so that the knight can visit exactly once every vertex of the mentioned set. In the present, we provide a 5-dimensional alternative to the well-known statement that it is not ever possible for a knight to visit once every vertex of $C(3,k):=\{\underbrace{\{0,1,2\} \times \{0,1,2\}\times \cdots \times \{0,1,2\}}_\textrm{\textit{k}-times}\}$ by performing a sequence of $3^k-1$ jumps of standard length, since the most accurate answer to the original question actually depends on which mathematical assumptions we are making at the beginning of the game when we decide to extend a planar chess piece to the third dimension and above. Our counterintuitive outcome follows from the observation that we can alternatively define a 2D knight as a piece that moves from one square to another on the chessboard by covering a fixed Euclidean distance of $\sqrt{5}$ so that also the statement of Theorem 3 in [Erde, J., Golénia, B., &amp; Golénia, S. (2012), The closed knight tour problem in higher dimensions, The Electronic Journal of Combinatorics, 19(4), #P9] does not hold anymore for such a Euclidean knight, as long as a 2 × 2 × ⋯ × 2 chessboard with at least 2&lt;sup&gt;7&lt;/sup&gt; cells is given. Moreover, we construct a classical closed knight's tour on $C(3,4)-\{(1,1,1,1)\}$ whose arrival is at a distance of 2 from $(1,1,1,1)$, and then we show a closed Euclidean knight's tour on $\{\{0,1\}\times\{0,1\}\times\{0,1\}\times\{0,1\}\times\{0,1\}\times\{0,1\}\times\{0,1\}\}\subseteq \mathbb{Z}^7$.
On Preventing and Mitigating Cache Based Side-Channel Attacks on AES System in Virtualized Environments

Cloud computing aims to cut costs through a reduction in spending on equipment, infrastructure, and software by applying the multi-tenancy feature. Despite all the benefits of multi-tenancy, it is still a source of risk in cloud computing. Cloud adoption may be hampered by security concerns if suitable cloud-based security solutions are not available. Moreover, virtualization that enables multi-tenancy, considered one of the main components of a cloud, introduces major security risks and does not offer appropriate isolation between different instances running on the same physical machine. In this paper, we present a preliminary idea that may support the development of new countermeasures for a particular type of threat, namely cache-based side-channel attacks that target cache memories in virtualized environments. Attackers specifically target virtual machines in this type of attack to create many side channels and gather sensitive data. Additionally, this research offers preliminary concepts to aid in developing of solutions or defenses that enable us to identify unusual activity that could point to attacks associated with multi-tenancy, as well as security measures that preserve the benefits of multi-tenancy while lowering security concerns.
Towards Achieving the Sustainable Development Goals (SDGs): The Impact of Debts on Income Inequality in Africa

The Agenda for Sustainable Development provides a framework for the creation of a better and sustainable future for all. The Sustainable Development Goals (SDGs) focus on addressing global challenges, including income inequality. Among others, the SDGs also commit countries to work towards reducing income inequality and as such, lowering income inequality is one of the key issues in Africa. However, given the high levels of debt on the continent, reducing income inequality could be a challenge. Majority of the literature examines the link between debts and economic growth with little attention paid to the impact of debts on income inequality. Moreover, not much is known on whether the relationship between debt and income inequality is non–linear. This study therefore contributes to the existing research efforts by investigating the threshold effects of debts using data from 24 African countries by relying on the sample splitting and threshold estimation approach. The results reveal that, while debts generally dampen income inequality, unbridled debt accumulation above the estimated thresholds does not decrease income inequality, indicating that the income inequality–reducing impact holds at lower levels of debt–to–GDP ratio. This evidence is insensitive to different indicators of income inequality and debts. Findings from this study show how increasing indebtedness contributes to widening income inequality in Africa.
Enhancing digital competencies in schools: The role of ICT coordinators

This article discusses the growing importance of digital competencies in education, specifically focusing on the role of technical infrastructure in schools and staff support strategies. The discussion aims at the introduction of the ICT coordinator role in Czech Republic schools. Analysis indicates that schools with an ICT coordinator exhibit improved technology access for students and teachers, not solely determined by computer quantity per pupil. Noteworthy differences lie in the flexibility and availability of technology, with ICT coordinator-aided schools having more mobile computers, mobile labs, and more vital facilitation of BYOD methods. In conclusion, while recognizing the partial nature of school technology equipment data, the article concludes that training teachers and implementing the ICT coordinator role positively impact ICT technology use in schools. Recommendations based on Czech Republic research include establishing the ICT coordinator position, providing financial incentives, and reducing direct teaching loads. Future research should track changes in ICT equipment during ICT coordinators’ tenures and explore technology deployment dynamics, emphasizing collaboration between ICT coordinators, school management, and individual teachers.
A Comprehensive Exploration of the Jordan Tourism Board's 2023 Strategic Planning, News, and Social Media

The tourism industry is one of constant changesin its nature, as it operates within a world that has become increasingly globalized with vast opportunities through the digital age. The study is anchored in two core research questions, determine the prevailing themes and narrations about 2023 news and posts that are related to Visit Jordan and secondly to find out how those narrations were constructed and what implications does it have for Jordan’s tourism industry. Content analysis of Visit Jordan’s news articles and social media posts in 2023 concerning major attractions and forms of tourism in Jordan is another topic that the study explores. However, cultural aspects emerge as the main focus of this type of tourism including such sites as Jerash, Petra, etc. Besides, architectural and heritage, natural tourism, adventure, outdoor tourism, festive and religious tourism, events, and seasons tourism, plus the diversification of Jordan’s provision.
Learning Framework for Joint Optimal Node Placement and Resource Management in Dynamic Fog Environment

Background: With recent improvements in fog computing, it is now feasible to offer faster response time and better service delivery quality; however, the impending challenge is to place the fog nodes within the environment optimally. A review of existing literature showcases that consideration of joint problems such as fog node placement and resource management are less reported. Irrespective of different available methodologies, it is noted that a learning scheme facilitates better capability to incorporate intelligence in the network device, which can act as an enabling technique for superior operation of fog nodes. Objective: The prime objective of the study is to introduce simplified and novel computational modelling toward the optimal placement of fog nodes with improved resource allocation mechanisms concerning bandwidth. Method: Implemented in Python, the proposed scheme performs predictive operations using the Deep Deterministic Policy Gradient (DDPG) method. Markov modelling is used to frame the model. OpenAI Gym library is used for environment modelling, bridging communication between the environment and the learning agent. Result: Quantitative results indicate that the proposed scheme performs better than existing schemes by approximately 30%. Conclusion: The prime innovative approach introduced is the implementation of a reinforcement learning algorithm with a Markov chain towards enriching the predictive analytical capabilities of the controller system with faster service relaying operations a. a This article is an extension of our paper entitled “Computational Framework for Node Placement and Bandwidth Optimization in Dynamic Fog Computing Environments" presented at INDICON- 2022, CUSAT, 24-27 November 2022.
Multi-Objective Reinforcement Learning Based on Decomposition: A Taxonomy and Framework

Multi-objective reinforcement learning (MORL) extends traditional RL by seeking policies making different compromises among conflicting objectives. The recent surge of interest in MORL has led to diverse studies and solving methods, often drawing from existing knowledge in multi-objective optimization based on decomposition (MOO/D). Yet, a clear categorization based on both RL and MOO/D is lacking in the existing literature. Consequently, MORL researchers face difficulties when trying to classify contributions within a broader context due to the absence of a standardized taxonomy. To tackle such an issue, this paper introduces multi-objective reinforcement learning based on decomposition (MORL/D), a novel methodology bridging the literature of RL and MOO. A comprehensive taxonomy for MORL/D is presented, providing a structured foundation for categorizing existing and potential MORL works. The introduced taxonomy is then used to scrutinize MORL research, enhancing clarity and conciseness through well-defined categorization. Moreover, a flexible framework derived from the taxonomy is introduced. This framework accommodates diverse instantiations using tools from both RL and MOO/D. Its versatility is demonstrated by implementing it in different configurations and assessing it on contrasting benchmark problems. Results indicate MORL/D instantiations achieve comparable performance to current state-of-the-art approaches on the studied problems. By presenting the taxonomy and framework, this paper offers a comprehensive perspective and a unified vocabulary for MORL. This not only facilitates the identification of algorithmic contributions but also lays the groundwork for novel research avenues in MORL.
Analysis of English Reading Teaching Strategies based on the Thematic Meaning in Senior High Schools

Thematic meaning inquiry provides a new way of thinking and direction for English reading teaching. The degree of exploring the meaning of the theme influences the depth of students’ discourse comprehension, the height of students’ thinking development and the effect of students’ language learning. Combined with senior high school English teaching cases, this paper discusses how to explore the thematic meaning through in-depth interpretation of texts. Under the guidance of the activity-based approach to English learning, this paper expounds an effective approach to reading teaching design based on thematic meaning exploration, aiming to lead students to read texts in depth, explore the thematic meaning and connotation of texts, and highlight the educational value of English subjects.
Characteristics and Development Trends of Network Mass Incidents in Colleges and Universities

With the leapfrog progress of mobile internet technology, the network mass incidents in colleges and universities are increasingly concerned by many parties. The characteristics of network mass incidents in colleges and universities include the particularity of participants, the diversity of inducement causes, emotionalization and irrationalization, the non-confrontational nature of event and the extensive range of impact. With social development, the potential trends of future network mass incidents in colleges and universities include the probability and quantity of occurrences will further increase, the breadth and intensity of impact will be further enhanced, the organizational form will become more complex, the coping strategies of management department will affect the development trend of the event, the increase in uncertainty brought by the development of new technologies.
Refining AI Vision: How Retrieval-Augmented Generation Transforms Image Captioning in Large…

&lt;strong&gt; Refining AI Vision: How Retrieval-Augmented Generation Transforms Image Captioning in Large Language&amp;nbsp;Models &lt;/strong&gt; Leveraging External Knowledge to Enhance the Descriptive Capabilities of AI&amp;nbsp;Systems Author: Vaibhav Khobragade (0009–0009–8807–5982) &lt;strong&gt; Introduction &lt;/strong&gt; Large Language Models (LLMs) are artificial intelligence models that are trained on massive amounts of text data in order to generate human-like
Teachers’ Learning Reinforcement: Effects on Students’ Motivation, Self Efficacy and Academic Performance

Teachers play an important role in shaping the educational experiences and achievements of students. As education continues to evolve, it is essential to explore innovative approaches that can enhance teaching effectiveness and improve student outcomes. This study was conducted to determine the teachers’ learning reinforcement and its effects to student’s motivation, self-efficacy and academic performance. A total of thirty five (35) classroom teachers and two hundred sixty (262) Criminology students of Southern Capital Colleges participated in the study during the academic year 2022-2023. Weighted Mean, Standard Deviation, Frequency Count and Percentage, and Spearman Rho Correlation Coefficient were the statistical tools used in the study. The teachers consistently employed various reinforcement strategies, demonstrating their commitment to supporting students’ educational experiences. Students exhibited positive self-efficacy beliefs, indicating confidence in their abilities to succeed and overcome challenges. It also displayed satisfactory levels of motivation, showing a genuine interest in challenging course content and a focus on deep understanding. Furthermore, there were significant relationships between teachers’ learning reinforcement and students’ self-efficacy, motivation, and academic performance. Effective learning reinforcement positively influenced students’ beliefs in their abilities, motivating them to persist and excel in their studies. An emphasis on the importance of implementing effective learning reinforcement in educational settings is paramount to enhance students’ educational experiences and promote academic success.
Exploring the Latest Trends in Artificial Intelligence Technology: A Comprehensive Review

Artificial intelligence (AI) has become increasingly pervasive across various domains, including smartphones, social media platforms, search engines, and autonomous vehicles, among others. This study undertakes a scoping review of the current landscape of AI technologies, following the PRISMA framework, with the aim of identifying the most advanced technologies utilized in different domains of AI research. Three reputable journals within the artificial intelligence and machine learning domain, namely the Journal of Artificial Intelligence Research, the Journal of Machine Learning Research, and Machine Learning, were selected for this review. Articles published in 2022 were scrutinized against certain criteria: the technology must be tested against comparable solutions, employ commonly approved or well-justified datasets, and demonstrate improvements over comparable solutions. A crucial aspect of technology development identified in this review is the processing and exploitation of data collected from diverse sources. Given the highly unstructured nature of data, technological solutions should minimize the need for manual intervention by humans. The review indicates that creating labeled datasets is a labor-intensive process, leading to increased research focus on solutions leveraging unsupervised or semi-supervised learning technologies. Efficient updating of learning algorithms and the interpretability of predictions emerge as key considerations in the development of AI technologies. Moreover, in real-world applications, ensuring safety and providing explainable predictions are imperative before widespread adoption can be achieved. Thus, this review underscores the importance of addressing these factors to facilitate the responsible and effective integration of AI technologies into various domains.