quantum computing

AI Agentic Architectures for Autonomous Data Engineering Pipelines

This study delves into the notion of AI agentic architectures for autonomous data engineering pipelines and investigates the potential benefits of intelligent agents in enhancing automation, resilience, and decision-making processes in contemporary data ecosystems.

Designing Self-Healing AI Agentic Systems: A Framework for Autonomous Detection and Response

A new scientific object – the Autonomous Recovery Efficiency Score (ARES) – is introduced – a quantitative measure of autonomous resilience, as well as a supporting foundation for future autonomous self-healing AI agentic infrastructure.

Design and Experimental Evaluation of a Quantum-Resilient Data Integrity Verification Framework Using AES-GCM, ECVRF, and ML-DSA

Cloud-based AI systems for scalable and intelligent software applications

The speed of cloud computing and artificial intelligence, which have transformed the way software applications are designed and deployed. The cloud-based AI systems provide a scalable, adaptable, and cost-efficient solution to build intelligent systems capable of processing large amounts of data and running complicated...

Active learning for interactive prompt clarification in safety-critical programmable logic controller code generation

A Memory-Efficient Tool for Bengali Parts of Speech Tagging

Reliability Beyond Accuracy in Crop Classification Benchmarks (Supplementary Materials)

Reliability Beyond Accuracy in Crop Classification Benchmarks (Supplementary Materials)Introduction: Near-perfect crop-label accuracy can conceal uncertainty, perturbation sensitivity, and weak explanations. This study evaluates these reliability dimensions without treating benchmark classification as agronomic recomme...

Reliability Beyond Accuracy in Crop Classification Benchmarks (Supplementary Materials)

Reliability Beyond Accuracy in Crop Classification Benchmarks (Supplementary Materials)Introduction: Near-perfect crop-label accuracy can conceal uncertainty, perturbation sensitivity, and weak explanations. This study evaluates these reliability dimensions without treating benchmark classification as agronomic recomme...

Are Predictive Models Epistemically Superior to Causal Ones?

This thesis investigates the complex and often contentious question of whether predictive models are epistemically superior to causal models. It challenges the simplistic dichotomy that often frames this debate, arguing that the epistemic superiority of a model is not an intrinsic property but is contingent upon the sp...

Are Predictive Models Epistemically Superior to Causal Ones?

This thesis investigates the complex and often contentious question of whether predictive models are epistemically superior to causal models. It challenges the simplistic dichotomy that often frames this debate, arguing that the epistemic superiority of a model is not an intrinsic property but is contingent upon the sp...

Can Democracy Survive in a Hyperconnected World?

This paper explores the complex and often contradictory relationship between hyperconnectivity and democracy. Drawing on novel theoretical frameworks inspired by quantum mechanics, network science, and temporal dynamics, we argue that the hyperconnected world is not simply an extension of the classical political landsc...

Can Democracy Survive in a Hyperconnected World?

This paper explores the complex and often contradictory relationship between hyperconnectivity and democracy. Drawing on novel theoretical frameworks inspired by quantum mechanics, network science, and temporal dynamics, we argue that the hyperconnected world is not simply an extension of the classical political landsc...