This article looks at how the Academic Rating Index (ARI) can be integrated with a variety of academic platforms to improve data collection and increase access to supervisors' academic records and competency. The ARI aims to address challenges faced by postgraduate students in South Africa, such as an increasing student-to-supervisor ratio, insufficient assessment of supervisor effectiveness, and limited digital forums for student participation. The ARI uses cross-platform data collection and powerful search functions to help students locate and interact with distinguished supervisors who share their research interests. Supervisors can assert and amend their profiles using the site, ensuring that the information is accurate and up to date. This study builds on previous research and emphasizes the importance of a central directory for academic supervisory expertise.
Aerial acoustic communication enables low-rate data exchange using audible or inaudible acoustic waves and has the advantage of operating on virtually all smart devices without additional hardware, unlike NFC, whose adoption remains limited by hardware and platform constraints. Standard microphones and speakers can therefore be used for both transmission and reception, making the technology an appealing and practical complement to existing wireless methods.However, commodity devices primarily support the audible band, much of which overlaps with speech and ambient noise, leaving only a narrow portion suitable for reliable communication. To address this limitation, the proposed approach utilizes a frequency region that is broadly supported across devices yet minimally influenced by everyday acoustic environments, thereby enhancing overall stability and robustness.Furthermore, this paper introduces a Zoom-FFT-based narrow-band acoustic communication technique that improves robustness and frequency resolution within this constrained spectrum. By exploiting its high-resolution spectral analysis, the system can reliably extract communication signals even in noisy indoor settings, supporting practical short-range data exchange applications across diverse usage scenarios.
Vision Transformers (ViTs) offer strong performance but face high computational costs from processing all tokens through their full depth. Standard ViTs lack adaptivity. This work introduces Adaptive Halting Transformer (AHT-ViT) to enhance efficiency by dynamically adjusting processing depth per token. AHT-ViT employs hierarchical ”planner” modules predicting token-specific target halting depths and an extremely parameter-efficient ”supervisor” mechanism (two shared parameters) generating per-layer halting scores. Tokens halt when their cumulative score crosses a threshold. A novel KL divergence-based loss, Ltarget depth, explicitly aligns executed halting distributions with planned depths. Evaluation on ImageNet, Places365, and CIFAR-100 using DeiT-S shows AHT-ViT achieves an improved accuracy-efficiency trade-off compared to its static baseline and demonstrates competitive performance against other adaptive methods (DynamicViT, A-ViT) evaluated under the same conditions, while significantly reducing FLOPs. Key hyperparameters were selected via grid search on a validation split.
This paper examines the impact of gamification in improving student engagement and learning outcomes in diverse educational contexts. Grounded in psychological theories, including Self-Determination Theory, Flow Theory, Constructivist Learning Theory, and Behaviorism, the review synthesizes evidence on how game-design elements, such as points, badges, leaderboards, challenges, and storytelling, influence motivation, academic performance, and retention. Case studies of widely used platforms like Duolingo, Classcraft, Kahoot, Minecraft, and ABCmouse illustrate practical implementations and highlight both benefits and challenges. While research generally supports the positive impact of gamification on engagement, findings are inconsistent across different age groups, disciplines, and cultural settings. Limitations include an excessive reliance on extrinsic rewards, limited long-term studies, and insufficient focus on accessibility and equity. The review highlights lessons learned and offers detailed recommendations for designing inclusive, adaptive, and pedagogically aligned gamified interventions. The findings show that although gamification offers considerable promise, its effectiveness depends greatly on thoughtful integration with sound instructional objectives and ongoing evaluation.
This paper presents a case study on developing and implementing metaverse-based VR simulations for training interprofessional health and human service students to work with vulnerable populations. Key challenges and strategies are discussed and include the need and justification for the simulation programs, the challenge of changing technology and educational trends over time and plans for enhancing existing programs and creating new programs and cases.
Cloud computing provides scalable, on-demand resources that support a wide range of services and applications. Efficient load balancing in cloud environments is critical when maintaining performance and quality of service. A hybrid Ant Colony Optimisation – Genetic Algorithm (ACO-GA) method is proposed for task scheduling in a hybrid cloud, implemented and evaluated using the CloudAnalyst simulator. The custom algorithm leverages ACO’s rapid local search for assigning workloads to virtual machines and GA’s global evolutionary search to diversify solutions. The ACO-GA is compared against Round Robin, pure ACO and pure GA strategies. Performance is measured by overall response time and data centre processing time. Simulation results indicate that the proposed ACO-GA outperforms the baseline strategies in both response time and data centre processing time, demonstrating that combining ACO’s pheromone-guided optimisation and GA’s genetic exploration leads to more balanced loads.
To improve the work efficiency and code quality of modern software development, users always reuse Application Programming Interfaces (APIs) provided by third-party libraries and frameworks rather than implementing from scratch. However, due to time constraints in software development, API developers often refrain from providing detailed explanations or usage instructions for APIs, resulting in confusion for users. It is important to categorize API reviews into different groups for easily usage. In this paper, we conduct a comprehensive study to evaluate the effectiveness of prompt-based API review classification based on various pre-trained models such as BERT, RoBERTa, BERTOverflow. Our experimental results show that prompts with complete context can achieve best effectiveness and the model RoBERTa outperforms other two models due to the size of training corpus. We also utilize the widely-used fine-tuning approach LoRA to evaluate that the training overhead can be significantly reduced (e.g., 50% reduction) without the loss of the effectiveness of classification.
This Research article explores the changing impact of AI (Artificial Intelligence) on automation capabilities of Copado, improving the deployment and change management in Salesforce DevOps. In this paper we have outlined the AI- based methodologies that automate the version control, optimize change management workflows, and improve accuracy of the deployments. The traditional Salesforce DevOps pipelines face many challenges like deployment errors, merge conflicts, roll back issues, and dependencies between the components. By implementing predictive analytics, machine learning, and automated risk assessment, Copado automation provides improved efficiency in deployments, decrease in errors, and optimized release velocity in complex salesforce environments. The results from integrating these AI-driven improvements across different salesforce instances highlight the critical value of integrating Artificial Intelligence in Salesforce DevOps Pipelines. This research paper shows Copado’s AI-powered automation as an important advancement towards scalable, robust, and adaptive Salesforce DevOps implementation.
Over the past decade, blockchain technology has undergone rapid development and is recognized as one of the pivotal information technologies driving industrial transformation. Today, blockchain technology offers a promising solution to the problems faced by corporate information systems. Namely, with the help of appropriate measures of anonymization and preservation of confidentiality, the blockchain enhances data security, reduces the risk of unauthorized access, and ensures user privacy in corporate systems. Blockchain technology increases transparency, allowing users to monitor and verify the content of information, assess the integrity of data stores. In recent years, blockchain has become a subject of interest for state governments, multinational corporations, and major financial institutions. Today, considerable attention is paid to the development of private (corporate) blockchains. This article examines the prospects and development of blockchain technology in corporate information systems. The study is aimed at providing additional clarity regarding the concept of blockchain applications in corporate information systems. Existing enterprise applications cannot operate seamlessly with traditional transactional requirements when integrating blockchain technology. Therefore, they will have to be modified with inclusion in the existing data storage for asynchronous interaction with distributed nodes of the blockchain. A scheme of the principle of interaction of nodes - storages that support data synchronization and their current states are proposed. This scheme is based on a blockchain structure centered around cloud storage as a corporate document circulation system. Consensus to include a new entry - Byzantine fault tolerance. Blockchain nodes receive a parametric weight for decision making.
This systematic literature review explores the transformative role of artificial intelligence (AI) chatbots in promoting sustainable tourism, particularly in the ecotourism sector. AI chatbots are pivotal in enhancing operational efficiency, fostering environmental responsibility, and improving tourist engagement. The study identifies their contributions to sustainability by optimizing resource use, reducing environmental impact, and educating tourists about local cultural and ecological practices. Despite these benefits, significant challenges such as data privacy concerns, infrastructural limitations, and cultural biases hinder widespread adoption. The findings emphasize the need for robust digital infrastructure, ethical frameworks, and culturally adaptive chatbot designs to overcome these barriers. By aligning technological innovation with sustainability goals, AI chatbots can significantly advance sustainable tourism practices. Future research should prioritize empirical analyses and inclusive strategies to maximize the potential of AI chatbots in fostering long-term sustainable development in ecotourism.
Natural data collected from the real world often exhibit the scale imbalance problem. A large object can produce much more loss values than a small object, causing the detector to favour large objects more, even though small objects dominant the dataset. This inclination inside detectors results in the performance degradation of small objects. To alleviate this problem, this paper proposes a new patch-level collage fashion data augmentation technique and a new global scheduler based on existing dynamic scale training paradigm. Our new data augmentation can generate collage images with uniform object scales for better augmentation effects. Additionally, our new global scheduler can adjust the strength between different data augmentations to adapt to different stages of the training process. Experiments demonstrate the effectiveness of our techniques. Codes at https://github.com/Andisyc/DataPool.
As distributed processing environments grow in complexity, accurate performance prediction models are essential to optimize system efficiency and resource allocation. However, modern computing workloads typically exhibit a wide variety of characteristics, which hinders optimized resource configurations. Diverse approaches have been suggested to tackle the challenge of workload characterization, employing various parameters for performance modeling in the process. To expand on this objective, this paper extensively surveys existing performance modeling methodologies and introduces a 5+1 layer classification model designed to enhance the accuracy of predictive models by classifying and reflecting on relevant modeling parameters. We conducted a systematic literature review to identify and analyze the role of six key layers: Big Data Framework, Performance, Hardware, Data, User Application, and Virtualization. Our findings reveal that while the Big Data Framework and Performance Layers are foundational, predictive accuracy improves when combined with complementary layers, especially the Data Layer, which highlights the impact of data characteristics such as size and distribution. The Hardware Layer provides critical insights into system limitations, while the emerging Virtualization Layer reflects the increasing importance of virtualized, potentially cloud-based environments. The proposed 5+1 layer classification model offers a structured approach to capture and explain the complexity of distributed analytical workflows, providing a nuanced framework for performance modeling. This layered classification model aims to support the development of more robust, adaptable, and generalizable prediction models for use in cloud-based systems.
Understanding diverse demographic groups presents a significant challenge in market research. In this paper, we introduce a novel system that integrates large language models with genetic algorithms to create synthetic personas capable of generating feedback that approximates real-world human responses. Our experimental evaluation demonstrates that synthetic personas not only exhibit age-differentiated technology usage patterns consistent with documented trends but also benefit from genetic algorithm optimization, which improves response accuracy from 60.4% to 78.5% on training questions and from 62.6% to 68.8% on hidden questions—outperforming human estimators. Moreover, the optimized personas achieve a 51.1% better correspondence with actual income distributions compared to random profiles. This approach makes it possible to rapidly generate feedback without requiring participants, facilitates iterative follow-ups, and systematically enhances demographic representativeness.
The devastating effects of the COVID-19 pandemic helped to shed light on the hidden challenges of global education systems. The lockdown period in turn presented reasons for ensuring preparedness for education and learning in times of crisis. The sudden switch to online learning on one hand enabled education and learning, but only to those who could access technology, while it also worsened existing inequalities between the affluent and those at the margins. This dynamic proved that technology alone cannot address the challenges of education and learning during pandemics and crisis, unless supported by other enablers. To address these challenges, this research explored the intersections of technology, infrastructure, collaboration, and community engagement in transforming education systems. Focusing on the African context, this study aims to identify context-specific strategies for building resilience and strengthening education systems. This research fills a critical knowledge gap in existing literature by providing context-specific case studies from Africa, a region previously underrepresented. Through in-depth analysis, this study uncovers the intricate challenges confronting education systems and presents actionable recommendations for harnessing technology, infrastructure, collaboration, and community engagement to drive transformative change in education. The findings emphasize the need for a multi-stakeholder approach, collective action, and shared responsibility among various stakeholders to mitigate pandemic and natural disaster disruptions.
This study addresses the limitations of Gaussian Mixture Models (GMMs) in clustering complex datasets and proposes Elliptical Mixture Models (EMMs) as a robust and flexible alternative. By adapting the Expectation-Maximization (EM) algorithm to handle elliptical distributions, the study introduces a novel computational framework that enhances clustering performance for data with irregular shapes and heavy tails. Leveraging the integration of R’s advanced statistical tools into Python workflows, this approach enables practical implementation of EMMs. Empirical evaluations on three datasets Rice, Customer Churn, and Glass Identification demonstrate the superiority of EMMs over GMMs across multiple metrics, including Weighted Average Purity, Dunn Index, Rand Index, and Silhouette Score. The re- search highlights EMMs as a valuable tool for advanced clustering tasks and provides insights into their potential applications in handling real-world datasets with complex covariance structures.
The Naive Bayes (NB) algorithm is widely recognized for its efficiency and simplicity in classification tasks, particularly in domains with high-dimensional data. While the Gaussian Naive Bayes (GNB) model assumes a Gaussian distribution for continuous features, this assumption often limits its applicability to real-world datasets with non-Gaussian characteristics. To address this limitation, we introduce an enhanced Naive Bayes framework that incorporates stable distributions to model feature distributions. Stable distributions, with their flexibility in handling skewness and heavy tails, provide a more realistic representation of diverse data characteristics. This paper details the theoretical integration of stable distributions into the NB algorithm, the implementation process utilizing R and Python, and an experimental evaluation across multiple datasets. Results indicate that the proposed approach offers competitive or superior classification accuracy, particularly when the Gaussian assumption is violated, underscoring its potential for practical applications in diverse fields.
There are already dominant ideas that influence our education, policies, and practice. These dominant societal knowledge systems still produce inequalities in our education and society. Using Postcolonial Theory, the study focused on colonial rule's impact on colonized societies, cultures, and identities and explored how colonialism's effects continue to shape the world in contemporary times. This study sought to critique the dominant knowledge narratives perpetuating social injustice. It has amplified the voices and experiences of those often silenced or marginalized by the dominant knowledge narratives. The study has also identified how the dominant knowledge system has produced inequality and marginalization and suggested more inclusive, equitable, and socially just knowledge through a curriculum studies approach.
Blockchain technology holds immense transformative potential; however, its adoption is accompanied by significant challenges. This research explores the opportunities and obstacles associated with blockchain, with a particular focus on its application within STC Pay in Saudi Arabia. Based on user feedback collected from a questionnaire distributed to 100 participants, the study delves into critical areas such as participant demographics, potential benefits, challenges encountered, proposed solutions, and general feedback. The findings emphasize the significance of blockchain in enhancing security, transparency, and efficiency while also identifying key barriers that must be addressed for its successful adoption. Key challenges, including scalability, privacy concerns, regulatory compliance, and interoperability, are highlighted as major hurdles to integrating blockchain technology effectively. Addressing these issues requires clear communication, adaptable policies, and targeted strategies that consider the unique needs of different stakeholders. By fostering collaboration among regulatory authorities, management teams, and users, the research proposes actionable solutions to overcome these obstacles. The study also underscores the importance of user feedback in shaping the adoption process. Perspectives from diverse groups, including IT professionals, management, and end-users, are crucial for aligning technological advancements with practical needs. The comprehensive analysis offers a balanced understanding of both opportunities and challenges, providing a roadmap for effective blockchain integration.In conclusion, this research provides valuable insights into blockchain's potential to revolutionize digital payments, particularly within STC Pay. By addressing existing challenges through strategic approaches, the findings contribute to advancing the adoption of blockchain technology while emphasizing its role in fostering innovation and driving efficiency in modern financial systems.
This study aims to create language units for an Arabic speaker synthesizer based on the predefined schemes for the syllabic structures of Arabic. The focus of this research is to design a system for spoken language assistance for the blind people in Arab countries. Simple verbs, names and particles are within our reach. Moreover, with only 84 subsyllables, we can produce speech at different levels of complexity, including syllable, word, sentence, or text level based on the different schemes devised.
This study evaluates the performance of the Stable Distribution Naive Bayes Classifier on the well-known IRIS dataset, comparing it against the traditional Naive Bayes Classifier. The Stable Distribution Classifier, well-suited for data with heavy tails and skewness, consistently achieves superior accuracy, especially when handling outliers and nonstandard samples. This study conducted 18 feature combinations of Iris Versicolor and Iris Virginica across varying parameter configurations (𝛼, 𝛼), demonstrating the stable model’s robustness under constrained sample sizes. A significant technical contribution involves integrating R’s specialized stable package into Python, enabling the direct application of professional fitting and PDF functions for precise analysis. Representative results from key feature combinations further illustrate its practical advantages. Additionally, five additional datasets—Wine, Social Network Ads, Diabetes, Electrical Grid Stability Simulated, and Vehicle Silhouettes—further demonstrate the Stable Distribution Classifier’s broad applicability across diverse domains. This research further confirms that the Stable Distribution Naive Bayes Classifier is a robust and accessible alternative, offering enhanced predictive performance over models traditionally based on Gaussian distribution assumptions.
