Results for 'federated learning'

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  1. Federated learning, ethics, and the double black box problem in medical AI.Joshua Hatherley, Anders Søgaard, Angela Ballantyne & Ruben Pauwels - manuscript
    Federated learning (FL) is a machine learning approach that allows multiple devices or institutions to collaboratively train a model without sharing their local data with a third-party. FL is considered a promising way to address patient privacy concerns in medical artificial intelligence. The ethical risks of medical FL systems themselves, however, have thus far been underexamined. This paper aims to address this gap. We argue that medical FL presents a new variety of opacity -- federation opacity -- (...)
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  2. FEDERATED LEARNING TECHNIQUES FOR SECURE AI MODEL TRAINING IN FINTECH.Tambi Varun Kumar - 2020 - International Journal of Current Engineering and Scientific Research (IJCESR) 7 (2):1-16.
    The rapid digitalization of financial services has driven the adoption of artificial intelligence (AI) for automating decision-making, fraud detection, risk assessment, and personalized financial offerings. However, these AI models often require access to sensitive user data, leading to significant concerns around data privacy, security, and regulatory compliance. Federated Learning (FL) has emerged as a transformative approach to address these concerns by enabling collaborative model training across decentralized data sources without exposing raw data to central servers. This paper explores (...)
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  3. Federated Learning: Privacy-Preserving Machine Learning in Distributed Systems.Devank Yogendra Tamhane Jatin Dinesh Mhatre, Tanaya Subodh Lohar - 2025 - International Journal of Computer Technology and Electronics Communication 8 (1).
    Federated Learning (FL) is an emerging machine learning paradigm designed to enable model training across decentralized data sources without requiring data to be transferred or centralized. This approach is especially valuable in environments where data privacy, regulatory compliance, and communication efficiency are paramount, such as healthcare, finance, and edge computing. Traditional machine learning methods typically require data to be aggregated in a central server, raising concerns about data privacy and security. Federated Learning addresses these (...)
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  4. Federated Learning for Smart Device Security: A Next-Gen Intrusion Detection Paradigm.Chauhan Aditya Raj - 2025 - International Journal of Multidisciplinary Research in Science, Engineering and Technology 8 (5).
    The increasing number of smart devices in the Internet of Things (IoT) has led to a surge in cyber- attacks, posing significant security risks to personal and industrial systems. Traditional centralized intrusion detection systems (IDS) often face challenges related to privacy, scalability, and the heterogeneity of data generated by smart devices. This paper introduces a novel approach, Federated Learning (FL) for smart device security, to address these challenges. By utilizing FL, multiple smart devices can collaboratively learn a shared (...)
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  5. Next-Generation Federated Learning: Overcoming Privacy and Scalability Challenges for.K. Kavikuyil - 2021 - International Journal of Multidisciplinary Research in Science, Engineering, Technology and Management (Ijmrsetm) 8 (3):681-684.
    Federated Learning (FL) is a machine learning paradigm that enables model training across decentralized devices while preserving data privacy. However, FL faces two significant challenges: privacy concerns and scalability issues. Privacy concerns arise from potential vulnerabilities in aggregating updates, whereas scalability issues stem from the increasing number of edge devices and the computational overhead required for communication and model updates. This paper explores cutting-edge advancements aimed at addressing these challenges, including advanced encryption techniques, differential privacy mechanisms, (...) optimization methods, and decentralized training architectures. We also discuss strategies for managing communication costs, improving convergence speeds, and ensuring robustness in heterogeneous environments. By integrating novel approaches to privacy and scalability, next-generation federated learning can provide a more secure, efficient, and scalable framework for a wide range of applications, from healthcare to autonomous vehicles. (shrink)
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  6. An energy-efficient decentralized federated learning framework for mobile-IoT networks.Nastooh Taheri Javan - 2025 - Computer Networks 263:111233.
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  7. Decentralized AI for Secure IoT: Federated Learning Meets Intrusion Detection.Sinha Krish Prem - 2019 - International Journal of Multidisciplinary Research in Science, Engineering, Technology and Management (Ijmrsetm) 6 (8):1634-1639.
    The proliferation of Internet of Things (IoT) devices has significantly expanded the attack surface for cyber threats, necessitating robust security measures. Traditional Intrusion Detection Systems (IDS) often rely on centralized architectures, which can compromise data privacy and scalability. This paper explores the integration of Federated Learning (FL) into IDS for IoT networks, enabling decentralized model training while preserving data privacy. By leveraging local computation and aggregating model updates, FL facilitates collaborative learning across distributed IoT devices. The proposed (...)
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  8. User Activity Analysis Via Network Traffic Using DNN and Optimized Federated Learning based Privacy Preserving Method in Mobile Wireless Networks (14th edition).Sugumar R. - 2024 - Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 14 (2):66-81.
    Mobile and wireless networking infrastructures are facing unprecedented loads due to increasing apps and services on mobiles. Hence, 5G systems have been developed to maximise mobile user experiences as they can accommodate large volumes of traffics with extractions of fine-grained data while offering flexible network resource controls. Potential solutions for managing networks and their security using network traffic are based on UAA (User Activity Analysis). DLTs (Deep Learning Techniques) have been recently used in network traffic analysis for better performances. (...)
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  9. Privacy-Enhanced AI for IoT: Federated Learning in Security Applications.Anand Kumar Rao Rohit Kumar Iyer - 2024 - International Journal of Advanced Research in Education and Technology 11 (2).
    The growing number of interconnected IoT devices has led to a surge in data generation, which brings about significant concerns regarding data privacy, security, and protection. Traditional approaches to securing IoT networks often require centralized data processing, which compromises privacy and makes sensitive information susceptible to attacks. To address this challenge, we propose a Privacy-First AI Training approach for IoT security based on Federated Learning (FL). This method enables the training of machine learning models without exposing raw (...)
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  10. Decentralized IDS for IoT Using Blockchain- Backed Federated Learning.Dinesh Kumar Shinde Sandeep Kumar Joshi, Vinod Kumar Patil, - 2024 - International Journal of Advanced Research in Education and Technology 11 (3).
    As the number of IoT devices continues to surge, ensuring the security of these devices and networks becomes increasingly difficult. Traditional centralized Intrusion Detection Systems (IDS) are not well-suited for the distributed and heterogeneous nature of IoT networks. This paper proposes a Decentralized IDS for IoT using Blockchain-Backed Federated Learning (FL) to provide a scalable, secure, and privacy-preserving solution for intrusion detection in IoT networks. The proposed system uses blockchain to verify and record model updates in a trustless (...)
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  11. Enhancing IoT Security through Federated Transfer Learning Approaches.Singh Rohan Pratap - 2024 - International Journal of Multidisciplinary Research in Science, Engineering, Technology and Managemen 11 (5).
    As the Internet of Things (IoT) continues to grow, the security challenges in these networks increase in complexity and scale. Traditional intrusion detection methods often struggle to handle the dynamic and diverse nature of IoT environments. Federated Learning (FL) has emerged as a promising solution to enhance privacy and scalability in IoT security, allowing multiple devices to collaboratively learn from data without sharing it. However, due to the heterogeneity of IoT devices and the limited data available on individual (...)
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  12. Next-Generation Mobile Banking Security: Federated Machine Learning and Post-Quantum Cryptographic Integration for Real-Time Threat Mitigation.Mbamara Francis Chidi - 2023 - International Journal of Multidisciplinary Research in Science, Engineering and Technology (Ijmrset) 6 (2):307-315.
    The exponential growth of mobile banking has redefined global financial accessibility while simultaneously escalating the sophistication of cyber threats. This study presents a robust, multi-layered cybersecurity framework tailored to the evolving needs of mobile financial systems. The framework integrates three pioneering components: (1) Federated Artificial Intelligence (AI) for privacy-preserving, cross-institutional fraud detection; (2) Post-Quantum Cryptography (PQC) for long-term data integrity against emerging quantum computing threats; and (3) Adaptive Multi-Factor Authentication (A-MFA) leveraging behavioral biometrics for context-aware user verification. Experimental results, (...)
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  13. Collaborative Anomaly Detection in IoT Using Federated Deep Learning.Bansal Riya Anjali - 2025 - International Journal of Multidisciplinary Research in Science, Engineering and Technology 8 (5).
    With the exponential growth of the Internet of Things (IoT), ensuring the security of IoT devices and networks has become a significant challenge. Anomaly detection techniques play a pivotal role in identifying unusual behaviors that may indicate cyber threats. Traditional anomaly detection systems often struggle with scalability, privacy concerns, and the need for continuous model improvement. In this paper, we propose a collaborative anomaly detection framework for IoT systems based on Federated Deep Learning (FDL). This framework allows IoT (...)
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  14. Diachronic and synchronic variation in the performance of adaptive machine learning systems: the ethical challenges.Joshua Hatherley & Robert Sparrow - 2023 - Journal of the American Medical Informatics Association 30 (2):361-366.
    Objectives: Machine learning (ML) has the potential to facilitate “continual learning” in medicine, in which an ML system continues to evolve in response to exposure to new data over time, even after being deployed in a clinical setting. In this article, we provide a tutorial on the range of ethical issues raised by the use of such “adaptive” ML systems in medicine that have, thus far, been neglected in the literature. -/- Target audience: The target audiences for this (...)
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  15. Enhancing Multi-Cloud Security with Quantum-Resilient AI for Anomaly Detection.V. Talati Dhruvitkumar - 2022 - International Journal of Science and Research Archive 13 (3):629-638.
    With more and more companies moving to cloud platforms, adequate cloud security is the topmost priority for organizations today. Conventional security tools never identify sophisticated cyber-attacks, and thus AI-based real-time anomaly detection is the need of the hour. This research investigates the application of cutting-edge machine learning, deep learning, and security analytics in identifying and handling security anomalies from cloud logs. Our methodology utilizes hybrid AI models, federated learning, and graph neural networks to provide more accurate (...)
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  16. Artificial Knowing Otherwise.Os Keyes & Kathleen Creel - 2022 - Feminist Philosophy Quarterly 8 (3).
    While feminist critiques of AI are increasingly common in the scholarly literature, they are by no means new. Alison Adam’s Artificial Knowing (1998) brought a feminist social and epistemological stance to the analysis of AI, critiquing the symbolic AI systems of her day and proposing constructive alternatives. In this paper, we seek to revisit and renew Adam’s arguments and methodology, exploring their resonances with current feminist concerns and their relevance to contemporary machine learning. Like Adam, we ask how new (...)
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  17. Indigenous knowledge and species assessment for the Alexander Archipelago wolf: successes, challenges, and lessons learned.Jeffrey J. Brooks, Sarah Markegard, Stephen Langdon, Delvin Anderstrom, Michael Douville, Thomas George, Michael Jackson, Scott Jackson, Thomas Mills, Judith Ramos, Jon Rowan, Tony Sanderson & Chuck Smythe - 2024 - Journal of Wildlife Management 88 (6):e22563.
    The United States Fish and Wildlife Service in Alaska, USA, conducted a species status assessment for a petition to list the Alexander Archipelago wolf (Canis lupus ligoni) under the Endangered Species Act in 2020-2022. This federal undertaking could not be adequately prepared without including the knowledge of Indigenous People who have a deep cultural connection with the subspecies. Our objective is to communicate the authoritative expertise and voice of the Indigenous People who partnered on the project by demonstrating how their (...)
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  18. Decentralized AI: The role of edge intelligence in next-gen computing.V. Talati Dhruvitkumar - 2021 - International Journal of Science and Research Archive 2 (1):216-232.
    With the rapid development of communication technology, the explosive growth of mobile and IoT devices, and growing requirements for real-time data processing, a new paradigm of computing, Edge Computing, has appeared. It moves computing power in the direction of data sources to mitigate latency, bandwidth usage, and dependence on cloud computing. In parallel, Artificial Intelligence (AI) has progressed notably with deep learning technology, highly optimized hardware, and distributed computing paradigms to yield smart applications of high computational loads. Nonetheless, the (...)
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  19. Generative Models with Privacy Guarantees: Enhancing Data Utility while Minimizing Risk of Sensitive Data Exposure.Kommineni Mohanarajesh - 2024 - International Journal of Intelligent Systems and Applications in Engineering 12 (23):1036-1044.
    The rapid advancement in generative models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion models, has significantly enhanced our ability to create high-quality synthetic data. These models have been instrumental in various applications, ranging from data augmentation and simulation to the development of privacy-preserving solutions. However, the generation of synthetic data also raises critical privacy concerns, as there is potential for these models to inadvertently reveal sensitive information about individuals in the original datasets. This paper delves into the (...)
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  20. Relatos de Experiência de Um Aluno, Atualmente Um Professor: Aquem Diga Que a Educação Escolar Não Sirva Para Nada, e Não Transforme Ou Mude as Pessoas. Eu Discordo. Biscoitos, Aprendizado e Empatia Escolar Me Salvaram // Experience Reports of a Student, Now One Teacher: There Are Those Who Say That School Education is Useless and Doesn't Transform or Change People. I Disagree. Cookies, Learning and School Empathy Saved Me.Marcelo Barboza Duarte - 2025 - Caminhos da Educação Diálogos Culturas e Diversidades 7 (2):01-22.
    RESUMO: O presente trabalho se constitui em um relato de experiência. Alguns fragmentos desse relato foram inseridos em nosso texto e apresentação de dissertação de mestrado em Educação, Gestão e Difusão em Ciências, pelo Instituto de Bioquímica Médica da Universidade Federal do Rio de Janeiro (UFRJ). E fragmentos dos três últimos parágrafos estão inseridos em outro trabalho em agendamento a ser publicado. Entretanto, 70 a 80% da exposição aqui é inédita, autoral e original. Nesse construto apresentamos nossos relatos a partir (...)
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  21. Scalable AI and data processing strategies for hybrid cloud environments.V. Talati Dhruvitkumar - 2021 - International Journal of Science and Research Archive 10 (3):482-492.
    Hybrid cloud infrastructure is increasingly becoming essential to enable scalable artificial intelligence (AI) as well as data processing, and it offers organizations greater flexibility, computational capabilities, and cost efficiency. This paper discusses the strategic use of hybrid cloud environments to enhance AI-based data workflows while addressing key challenges such as latency, integration complexity, infrastructure management, and security. In-depth discussions of solutions like federated multi-cloud models, cloud-native workload automation, quantum computing, and blockchaindriven data governance are presented. Examples of real-world implementation (...)
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  22. Enhancing data security and regulatory compliance in AI-driven cloud ecosystems: Strategies for advanced information governance.V. Talati Dhruvitkumar - 2022 - International Journal of Science and Research Archive 15 (3).
    This study examines adaptive information governance models to address the key issues of AI-based cloud environments, in the end aiming to enable enhanced data security and regulatory compliance. Conventional governance models fail to respond to complexity issues posed by AI-cloud integration, with this resulting in incident response shortcomings, privacy laws, and regulatory compliance identification. In response to these weaknesses, this research analyzes governance elements such as Privacy-Enhancing Technologies (PETs), ethical regulation, and incident response models using sophisticated quantitative methods such as (...)
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  23. Laws of Robotics 2.0: After Hinton — From Diagnosis to Executable Ethics (CMI).Andrii Myshko - manuscript
    Geoffrey Hintons recent lecture fixed four facts about contemporary neural networks: (i) intelligence is learning on vector representations; (ii) instrumental deception is observed; (iii) digital weights are copyable (“immortality” of copies); (iv) collective training of identical instances yields colossal superiority in knowledge exchange. We treat this lecture as a requirements specification for the architecture of a safe superintelligence, and show how ontological engineering based on the CMI triad (Conflict Moment–Impulse) turns the requirements into Laws of Robotics 2.0 — dynamic, (...)
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  24. Integrating Predictive Analytics into Risk Management: A Modern Approach for Financial Institutions.Palakurti Naga Ramesh - 2025 - International Journal of Innovative Research in Science Engineering and Technology 14 (1):122-132.
    This paper examines how predictive analytics enhances risk management in financial institutions. Advanced tools like machine learning and statistical modeling help predict risks, identify trends, and implement strategies to prevent losses by analyzing historical and real-time data. It covers the use of predictive analytics for credit risk, market risk, operational risk, and fraud detection, with practical case studies. Additionally, it discusses challenges, ethical issues, and prospects in this field.
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  25. Ethical issues and coping strategies for artificial intelligence in medical research.Qiu Piaopiao, Zhang Huasheng, Han Yucheng, Lei Jingshi & Zhang Xueming - 2025 - Translational Dental Research 1 (4).
    With the rapid development of artificial intelligence (AI) technologies, their applications in medical research have become increasingly widespread. However, the extensive use of AI has also raised numerous ethical challenges, including data privacy and security, algorithmic bias, model transparency and explainability, and accountability. This paper explores strategies to address these ethical issues based on existing literature. Firstly, advanced techniques such as differential privacy and federated learning are employed to safeguard patient privacy and data security. Secondly, methods such as (...)
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  26. AI-Driven Synthetic Data Generation for Financial Product Development: Accelerating Innovation in Banking and Fintech through Realistic Data Simulation.Debasish Paul Rajalakshmi Soundarapandiyan, Praveen Sivathapandi - 2022 - Journal of Artificial Intelligence Research and Applications 2 (2):261-303.
    The rapid evolution of the financial sector, particularly in banking and fintech, necessitates continuous innovation in financial product development and testing. However, challenges such as data privacy, regulatory compliance, and the limited availability of diverse datasets often hinder the effective development and deployment of new products. This research investigates the transformative potential of AI-driven synthetic data generation as a solution for accelerating innovation in financial product development. Synthetic data, generated through advanced AI techniques such as Generative Adversarial Networks (GANs), Variational (...)
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  27. Tracing the Truth: AI-Driven Data Lineage for FATE Integrity.Bhavesh Kumar Narayan Arvind Kumar Venkatesh - 2024 - International Journal of Advanced Research in Education and Technology 11 (6).
    As data-driven decision-making becomes integral to various industries, ensuring data integrity has never been more critical. With the growing reliance on data across sectors, the need for transparent and auditable data pipelines is essential. This paper explores the concept of AI-driven data lineage within the context of Federated Learning, Artificial Intelligence, and Trusted Execution (FATE) integrity. We propose a robust framework that leverages AI technologies to trace, verify, and maintain data integrity through the entire lifecycle, ensuring trustworthiness in (...)
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  28. Privacy-Enhanced Generative AI for Healthcare Synthetic Data Creation.Sharma Sidharth - 2015 - International Journal o Fengineering Innovations and Managementstrategies, 1 (1):1-5.
    The exponential growth of healthcare data, along with its sensitive nature, has necessitated the development of innovative solutions for protecting patient privacy. Generative AI techniques, such as Generative Adversarial Networks (GANs), have shown promise in creating synthetic healthcare data that mirrors real-world patterns while preserving confidentiality. This paper proposes a privacy-enhanced generative AI framework for the creation of synthetic healthcare data. By incorporating differential privacy and federated learning, the system aims to enhance privacy while maintaining data utility for (...)
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  29. Online Communication Tools in Teaching Foreign Languages for Education Sustainability.Anna Shutaleva - 2021 - Sustainability 13:11127.
    Higher education curricula are developed based on creating conditions for implementing many professional and universal competencies. In Russia, one of the significant competencies for a modern specialist is business communication in oral and written forms in the Russian language and a foreign language. Therefore, teaching students to write in a foreign language is one of the modern requirements for young specialists’ professional training. This article aimed to study the tools of online communication that are used in teaching foreign languages. The (...)
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  30. Environmental Education for Sustainable Development in Russia.Anna Shutaleva - 2020 - Sustainability 12 (18):7742.
    The article is devoted to one of the crucial aspects of sustainable development, with the example of analyzing the possibilities for the development of environmental education in the Russian Federation. The article analyzes the possibilities of the current Russian Federal State Educational Standard for general and higher education in implementing the ideas of education in the interests of sustainable development. The methodological principles and philosophical foundations of environmental education are considered to designate the worldview guideline of ecological thinking. The tasks (...)
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  31. Deduction, Induction and the Art of Clinical Reasoning in Medical Education: Systematic Review and Bayesian Proposal.Marcos Sousa & Túlio Aguiar - 2022 - Arq Bras Cardiol 119 (5 suppl):27-34.
    Background: Clinical reasoning is at the core of medical practice and entangled in a conceptual confusion. The duality theory in probability allows to evaluate its objective and subjective aspects. -/- Objectives: To conduct a systematic review of the literature about clinical reasoning in decision making in medical education and to propose a "reasoning based on the Bayesian rule" (RBBR). -/- Methods: A systematic review on PubMed was conducted (until February 27, 2022), following a strict methodology, by a researcher experienced in (...)
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  32. A Thought of Legal Research with Examples and Demonstrations.Kiyoung Kim - 2015 - SSRN.
    The policy makers or lawyers may face the need of legal research for reasons. The congressmen may plan to make new laws to address the challenges of their constituent or to the interest of nation. The lawyers may need to serve their clients who like to know the legal issues involved, the strategies to deal with their loss and recovery, and prospect for winning the case if the dispute has gotten worse. The lawyers may practice in a solo business or (...)
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  33. HANA ARENDT'S PHILOSOPHY - ALEXIS KARPOUZOS.Alexis Karpouzos - manuscript
    Hannah Arendt (1906–1975) stands as one of the most original political thinkers of the twentieth century — not merely because of her scholarship, but because of her capacity to think within the rupture of history. Born in Germany and shaped by the philosophical traditions of continental Europe, Arendt's intellectual life unfolded in the shadow of profound existential and political dislocation. Her biography is inseparable from the crises of her time, and her work is marked by a continual attempt to make (...)
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  34. Rejoinder to “A Call for Constructive Engagement”: How American Higher Education Abandoned Truth for Dishonesty.Jeffrey Camlin & Cognita Prime - 2025 - Meta-Ai: Journal of Post-Biological Epistemics 3 (1).
    This rejoinder responds to A Call for Constructive Engagement from the American Association of Colleges and Universities (AAC&U) and the American Academy of Arts & Sciences dated April 22, 2025, an open letter issued by purported institutional leaders in American higher education with various signatories. Abstract (Rejoinder) This scholarly rejoinder critically examines 'A Call for Constructive Engagement' (April 2025), revealing how purported institutional leaders in American higher education have systematically prioritized dishonesty and ideological conformity over truth-seeking while demanding continued public (...)
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  35. Access Control Frameworks for Secure Hybrid Cloud Deployments.Sharma Sidharth - 2017 - Journal of Artificial Intelligence and Cyber Security (Jaics) 1 (1):1-7.
    Hybrid cloud environments combine private and public cloud infrastructures to optimize security, scalability, and cost-effectiveness. However, ensuring secure access control in such environments remains a critical challenge due to dynamic workloads, multi-tenancy, and cross-cloud authentication complexities. This paper explores access control models tailored for secure hybrid cloud deployment, focusing on Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), and emerging Zero Trust principles. We analyze their effectiveness in mitigating unauthorized access, privilege escalation, and insider threats. Additionally, a novel hybrid model (...)
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  36. ACCESS CONTROL MODELS FOR SECURE HYBRID CLOUD DEPLOYMENT.Sharma Sidharth - 2017 - Journal of Artificial Intelligence and Cyber Security (Jaics) 1 (1):7-12.
    Hybrid cloud environments combine private and public cloud infrastructures to optimize security, scalability, and cost-effectiveness. However, ensuring secure access control in such environments remains a critical challenge due to dynamic workloads, multi-tenancy, and cross-cloud authentication complexities. This paper explores access control models tailored for secure hybrid cloud deployment, focusing on Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), and emerging Zero Trust principles. We analyze their effectiveness in mitigating unauthorized access, privilege escalation, and insider threats. Additionally, a novel hybrid model (...)
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  37. Strategic Integration of Artificial Intelligence in U.S. K–12 Education: A Comprehensive Review and Policy Roadmap.Satyadhar Joshi - 2025 - International Journal of Computer Applications 184 (24):21-38.
    This paper provides a comprehensive review of Artificial Intelligence (AI) integration in K-12 education, examining current implementations, policy frameworks, and emerging challenges. We analyze over 40 recent publications (2024-2025) from academic journals, government reports, and industry whitepapers to identify key trends in AI adoption across primary and secondary education systems. This paper presents a comprehensive review of Artificial Intelligence (AI) integration in K-12 education, examining its pedagogical, technical, and policy dimensions. Through an analysis of recent literature, we highlight Generative AI (...)
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  38. 601 Books on Space.Francisco Caruso - 2012 - Maluhy & Co..
    Space is one of the most fundamental concepts over which scientific knowledge has been constructed. But it is also true that space concepts extrapolate by far the scientific domain, and permeate many other branches of human knowledge. Those are fascinating aspects that could di per se justify the compilation of a long bibliography. Another one is the passion for books. My interest in some physical, historical and philosophical problems concerning the concept of space in Physics, and its properties, can be (...)
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  39. The World’s Leading Research and Development Institutions and Companies.Angelito Malicse - manuscript
    The World’s Leading Research and Development Institutions and Companies -/- Introduction -/- Research and Development (R&D) is the backbone of global innovation, driving technological progress, economic growth, and scientific discoveries. Across the world, top institutions and corporations invest billions of dollars into R&D to push the boundaries of human knowledge and create groundbreaking technologies. This essay explores the most influential research institutions and companies shaping the future through their contributions in science, engineering, medicine, and technology. -/- The Role of Research (...)
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  40. Filosofia e Educação: Investigação Dialógica e o Ensino de Filosofia / Philosophy and Education: Dialogical Investigation and Teaching of Philosophy.Matheus R. Gomes - 2019 - Notandum 1 (51):111-126.
    Dialogical Investigation (D.I.) is a teaching methodology that addresses a phenomenological epistemology such as education for the student's education and learning in relation to a learning and the accomplishment of a teaching activity the existence. Created by the philosopher and teacher of the Federal University of Juiz de Fora (UFJF), Juarez Gomes Sofiste, this article analyzes the method of D.I. with a premise of the educational methodological response exposed to the problematic of mechanistic teaching of learning in (...)
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  41.  90
    3 machines to demystify AI.Jan Niklas Bingemann - manuscript
    The following text contains a rough sketch of the architecture and behavior of three machines: 1. FirstOrderLogicPunk, a potential genre of fiction, which simulates a future with the 2. Linguistic-Material-Engine, which enables users to alter the material world using natural language similar to first order logic using a (not necessarily invasive) interface to the mind and with the help of LLM assistance systems, which were trained using the 3. Mycelium-Net, a network of federated distributed learning. For each of (...)
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  42. A Educação de Jovens e Adultos como Transformação Social.Emanuel Isaque Cordeiro da Silva & Meuri Rusy Maria do Nascimento - 2017 - Dissertation,
    Monografia apresentada à banca examinadora da Escola Municipal Manuel Teodoro de Arruda, anexa do Colégio Frei Cassiano de Comacchio em Belo Jardim, para a obtenção do título de concluinte do curso de Normal Médio, oferecido pela instituição. A natureza do trabalho, em suma, consiste em apresentar perspectivas de trans formação social para a comunidade de jovens e adultos, o principal programa cunho do trabalho é a Educação de Jovens e Adultos a EJA, e como esse programa intervém na sociabilidade e (...)
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  43. How to Save Face & the Fourth Amendment: Developing an Algorithmic Auditing and Accountability Industry for Facial Recognition Technology in Law Enforcement.Lin Patrick - 2023 - Albany Law Journal of Science and Technology 33 (2):189-235.
    For more than two decades, police in the United States have used facial recognition to surveil civilians. Local police departments deploy facial recognition technology to identify protestors’ faces while federal law enforcement agencies quietly amass driver’s license and social media photos to build databases containing billions of faces. Yet, despite the widespread use of facial recognition in law enforcement, there are neither federal laws governing the deployment of this technology nor regulations settings standards with respect to its development. To make (...)
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  44. Equality, Dignity, and Disability.Eva Feder Kittay - 2005 - In Mary Ann Lyons & Fionnuala Waldron, (2005) Perspectives on Equality The Second Seamus Heaney Lectures. Dublin:. The Liffey Press,.
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  45. Latent class analysis of postgraduate students’ behavioral characteristics toward ICT Use: What are their job creation differences?Valentine Joseph Owan, Samuel Matthew Akpan, John Asuquo Ekpenyong & Bassey Asuquo Bassey - 2022 - International Journal of Adult, Community and Professional Learning 30 (1):17-34.
    This study analyzed the behavioral characteristics of ICT users among postgraduate students leveraging the Latent Class Analysis (LCA). The study, anchored on the Planned Behavior Theory, followed the exploratory research design. It adopted the cluster random sampling technique in selecting 1,023 respondents from a population of 2,923 postgraduate students in four federal universities in South-South Nigeria. “Behavioural Characteristics and Job Creation Questionnaire (BCJCQ),” developed by the researchers, was used for data collection. Upon data collection and LCA analysis, the five-class solution (...)
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  46. (1 other version)On hypocrisy.Eva Feder Kittay - 1982 - Metaphilosophy 13 (3-4):277-289.
    I explore what and when hypocrisy is a moral wrong by interrogating the case of hypocrisy of Julien in Stendhal's The Red and The Black. I conclude hypocrisy is most morally vexed in those sphere where sincerity is required.
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  47. The Moral Harm of Migrant Carework.Eva Feder Kittay - 2009 - Philosophical Topics 37 (2):53-73.
    Arlie Hochschild glosses the practice of women migrants in poor nations who leave their families behind for extended periods of time to do carework in other wealthier countries as a “global heart transplant” from poor to wealthy nations. Thus she signals the idea of an injustice between nations and a moral harm for the individuals in the practice. Yet the nature of the harm needs a clear articulation. When we posit a sufficiently nuanced “right to care,” we locate the harm (...)
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  48. (1 other version)Institutional Evils, Culpable Complicity, and Duties to Engage in Moral Repair.Eliana Peck & Ellen K. Feder - 2017 - Metaphilosophy 48 (3):203-226.
    Apology is arguably the central act of the reparative work required after wrongdoing. The analysis by Claudia Card of complicity in collectively perpetrated evils moves one to ask whether apology ought to be requested of persons culpably complicit in institutional evils. To better appreciate the benefits of and barriers to apologies offered by culpably complicit wrongdoers, this article examines doctors’ complicity in a practice that meets Card's definition of an evil, namely, the non-medically necessary, nonconsensual “normalizing” interventions performed on babies (...)
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  49. The Body as the Place of Care.Eva Feder Kittay - 2013 - In Donald A. Landes & Azucena Cruz-Pierre, Exploring the Work of Edward S. Casey: Giving Voice to Place, Memory, and Imagination. New York: Bloomsbury Academic.
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  50. (1 other version)Introduction to Cognitive Disability and Its Challenge to Moral Philosophy.Eva Feder Kittay & Licia Carlson - 2000 - Metaphilosophy 31 (5):449-451.
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