833 papers
This research presents an AI-BIM integrated framework utilizing Random Forest and XGBoost models to enable rapid, real-time energy performance prediction for Algerian residential buildings, thereby overcoming the limitations of traditional simulation tools during early-stage design.
This paper proposes the Quantum Stateless AI Data Center (QS-AIDC) architecture, which utilizes Algorithmic Topological Resonance theory, Zero-Payload I/O, and Proof of Resonance consensus to achieve permanent O(1) complexity, eliminate data storage, and drastically reduce energy consumption by replacing traditional stateful computing with non-local, deterministic reconstruction.
This paper presents a design science study introducing a governance framework for AI-assisted software delivery that utilizes a canonical requirements model, a dual-vendor build-and-review loop, and executable documentation to achieve high traceability and compliance across two independent production systems while redefining the business analyst's role.
The paper introduces EVE, a variational neuron that treats uncertainty as a computational primitive by propagating distributional latent states, demonstrating that this neuron-level approach improves uncertainty calibration and performance in both dense MLPs and Transformer architectures while maintaining competitive point accuracy.
This study demonstrates that the large language model Gemini 3.5 Flash can achieve reliable automated scoring of handwritten middle school mathematical problem-posing tasks when using a zero-shot prompting approach with a structured rubric, particularly for whole number problems and when utilizing revised OCR text inputs.
This paper proposes MBRD-Tor, a three-stage deep learning framework that addresses the challenges of protocol homogenization, encryption noise, and extreme class imbalance in the Tor network by fusing multi-scale behavioral signatures, employing an ensemble-based quality estimation for adaptive weighting, and utilizing GAN-generated synthetic features to achieve superior malicious service detection performance.
This systematic benchmarking study demonstrates that frontier large multimodal models, particularly Gemini 2.5 Pro and GPT-4o, significantly outperform both time-pressured human experts and traditional NLP baselines in extracting critical perineural invasion status from unstructured gastrointestinal pathology reports, while a novel failure mode taxonomy provides actionable guidance for model selection based on specific error signatures.
This paper proposes a hedonic potential-game framework for stable and budget-feasible coalition formation in clustered federated learning, proving the existence of Nash-stable partitions and deriving welfare efficiency guarantees that are empirically validated to outperform equal-surplus sharing on CIFAR-10 datasets.
This paper establishes that in delayed multi-agent correction systems, verification latency imposes an inescapable accuracy floor determined by the Dottie number, creates a budget ceiling for performance gains, and reverses the optimal strategy from concentrating to spreading correction resources beyond a specific threshold.
This paper proposes a resilient hybrid defense mechanism combining Adversarial Training and Gaussian Data Augmentation to significantly restore the accuracy of Network Intrusion Detection Systems against Fast Gradient Sign Method and Carlini & Wagner adversarial attacks, improving post-attack performance from as low as 26.49% to 96.57%.