February 2026

We introduce an abelian group structure on the positive real numbers via the operation a ⊗κ b = exp(κ ln a ln b) for a parameter κ > 0. The transformation Tκ (x) = ln(κ ln x) establishes a group iso- morphism (M>1κ , ⊗κ ) ∼= (R, +), enabling harmonic analysis on the scale group. We define generalized zeta functions ζκ (s) = ∑ n−⊗κ s and prove ζκ (s) = ζ(κ ln s) [11 , 13]. […]

Ver mais

Dyslexia impacts 5–15% of school-aged children globally, but automated screening mechanisms to detect it are rare, and such tools are relatively scarce in non-Latin scripts. The work introduces a bilingual deep learning model for dyslexia preliminary diagnosis through digitalized handwriting samples in both English and Arabic. Two computational methods were employed and compared systematically: the page-oriented classification strategy and the character-oriented classification method. For Arabic, an EnhancedCNN architecture is proposed to classify whole-page scans end-to-end by coping with […]

Ver mais

This study proposes a novel distributionally robust portfolio optimization framework based on Wasserstein generative modeling, aiming to address the challenges of distributional uncertainty, tail risk, and structural drift in financial markets. The model integrates Wasserstein distance-based robust optimization with generative adversarial learning to jointly enhance risk control and return stability. Specifically, a Wasserstein generative adversarial network is employed to reconstruct the latent distribution of asset returns, enabling the capture of non-Gaussian features and tail dependencies in complex market […]

Ver mais

Social media platforms have established themselves as relevant sources of real-time information for urban traffic analysis. This study proposes an intelligent framework for the classification and spatiotemporal analysis of traffic incidents based on data flows constructed for controlled validation, based on real reports from platforms such as X and Telegram. The approach integrates adaptive machine learning and incremental density-based clustering. An Adaptive Random Forest (ARF) incremental classifier is used to identify the type of incident, allowing for continuous […]

Ver mais

Cybercriminals have increasingly leveraged sophisticated techniques to bypass traditional signature-based detection systems through the use of Ransomware-as-a-Service (RaaS) platforms, double and triple extortion strategies, and advanced evasion mechanisms. As a result, ransomware attacks have reached unprecedented levels. Using this systematic evaluation framework, we examine the current state and effectiveness of machine learning (ML) and deep learning (DL) approaches for ransomware detection, addressing critical gaps in existing research methodologies while providing comprehensive recommendations for future research. The study analyses […]

Ver mais

Shadow AI—the unsanctioned use of artificial intelligence tools, models, or services within organisational processes—introduces governance, security, and privacy risks that extend beyond traditional shadow IT. This communication proposes a practical framework to (i) define and classify shadow AI use cases, (ii) detect shadow AI activity through multi-layer technical signals, and (iii) govern risk through an obligations-to-evidence mapping that supports compliance and auditability. The framework aims to balance innovation and productivity with proportionate controls, offering clear remediation paths (block, […]

Ver mais

In the era of connected and cyber-physical systems, firmware and software releases management in industries like IoT, smart grids, medical devices, UAVs, satellites, and vehicles has been growing more complex. These systems require constant, secure, and reliable updates to keep up with increasing user expectations and regulatory requirements. However, the growing complexity of release pipelines, as well as increasing security risks and tight regulatory demands, make it a challenging process. The emergence of Generative AI (GenAI), and Large […]

Ver mais

To address staining variability in digital pathology, we formulate normalization as a variational inverse problem balancing structural fidelity and chromatic alignment. We propose Screened Poisson Normalization (SPN), a convex model in CIE L∗a∗b∗ space governed by the modified Helmholtz equation. By coupling a first-order gradient consistency term with a zeroth-order chromatic anchor, SPN yields a screened reconstruction controlled by a single screening parameter λ. Beyond establishing well-posedness in H1(Ω) and Lipschitz stability, we derive a property crucial for […]

Ver mais

Assessing skill levels from videos of human activities is critical for applications in sports coaching, surgical training, and workplace safety. Existing approaches typically assign a global skill score to a video, failing to localize where and how skilled performers differ from novices. We propose SkillDiff, a framework that quantifies fine-grained skill differences between paired demonstration videos at the temporal segment level. Our method first aligns expert and novice videos temporally through a learned alignment module, then computes per-segment […]

Ver mais

Machine learning operations (MLOps) practices have reached a notable level of maturity within general-purpose software engineering. Pipelines are standardized, monitoring is automated, and deployment patterns are well rehearsed. Yet when these same practices are transferred into clinical environments, their adequacy becomes less certain. The healthcare domain introduces operational and ethical pressures that conventional MLOps frameworks were not originally designed to address.Clinical AI systems operate under constraints that extend beyond typical production settings. Stringent data protection regimes — including […]

Ver mais