Artificial intelligence (AI)

The rapid adoption of electric vehicles (EVs) introduces new challenges in managing residential charging demand, where uncoordinated behavior can increase energy costs, stress local grids, and reduce system efficiency. Deep Reinforcement Learning (DRL) has emerged as a powerful framework for developing intelligent, data-driven charging strategies that adapt to dynamic user behavior, time-varying electricity prices, and renewable generation. This paper presents a comprehensive survey of DRL techniques applied to home EV charging.

The Moroccan Monuments Dataset (MMD) is a large-scale, high-quality benchmark designed for semantic segmentation and visual analysis of Moroccan architectural heritage. It comprises 2,686 RGB images collected from historical cities including Fez, Rabat, Marrakech, Meknes, and Tetouan, representing diverse monument types such as mosques, madrasas, royal gates, and mausoleums. Each image is manually annotated at the pixel level into five semantic classes: roof, door, window, wall, and background.

The Model Context Protocol (MCP) enables seamless context sharing between AI agents, yet the performance implications of different sharing strategies remain unexplored. This paper presents the first comprehensive empirical evaluation of five context sharing strategies: Broadcast (BC), Publish-Subscribe (PS), Pull-on-Demand (PD), Hierarchical Caching (HC), and Hybrid Adaptive (HA) in multi-agent MCP systems.

The invariant shape descriptor tool is a methodology which can identify the basics shapes of an image. There are three basic shapes rectangle, triangle and circle. These basic shapes have a circularity value by which we can identify them. If a image either translated or rotated or sheared then it's surrounding shapes are changed but the basic shapes may exist within it. So, invariant shape descriptor tool can identify the basic shapes still the image variant in nature. This kind study applied over alphabets here.

The results underscore the impressive potential of the new mechanism, highlighting classifiers with accuracy above 95\%, and achieving throughput gains with peaks exceeding five times that of the CC traditionally embedded in most systems, which promises to enhance unforeseen high-error communication in the future.

This dataset contains synthetic GPS trajectories of maritime vessels for evaluating AI-based spoofing detection on Maritime Autonomous Surface Ships (MASS). The dataset includes vessel identifiers (MMSI), timestamps, positions (latitude and longitude), speed, course, heading, calculated motion parameters, predicted positions, prediction errors, and labels indicating normal or spoofed data. It is intended to support research in autonomous maritime navigation, cybersecurity, and AI-driven anomaly detection.

This paper presents a comprehensive English cybersecurity question-answer dataset designed to serve as a knowledge base for Retrieval-Augmented Generation (RAG) systems and cybersecurity support applications. The dataset comprises 2,650 unique question-answer pairs covering three critical domains: cybersecurity (40%), cloud computing (35%), and IT support (25%). This resource addresses the need for high-quality, domain-specific knowledge bases that can power AI-driven cybersecurity assistance systems.

Data set including a vast number of generic codes in python.