Graph self-supervised learning (GSSL) has demonstrated strong potential for generating expressive graph embeddings without the need for human annotations, making it particularly valuable in domains with high labeling costs such as molecular graph analysis. However, existing GSSL methods mostly focus on node- or edge-level information, often ignoring chemically relevant substructures which strongly influence molecular properties. In this work, we propose Graph Semantic Predictive Network (GraSPNet), a hierarchical self-supervised framework that explicitly models both atomic-level and fragment-level semantics. GraSPNet […]

Ver mais

arXiv:2501.16178v3 Announce Type: replace-cross Abstract: In recent work on time-series prediction, Transformers and even large language models have garnered significant attention due to their strong capabilities in sequence modeling. However, in practical deployments, time-series prediction often requires operation in resource-constrained environments, such as edge devices, which are unable to handle the computational overhead of large models. To address such scenarios, some lightweight models have been proposed, but they exhibit poor performance on non-stationary sequences. In this paper, we […]

Ver mais

arXiv:2601.09736v1 Announce Type: new Abstract: Linear Genetic Programming (LGP) is a powerful technique that allows for a variety of problems to be solved using a linear representation of programs. However, there still exists some limitations to the technique, such as the need for humans to explicitly map registers to actions. This thesis proposes a novel approach that uses Q-Learning on top of LGP, Reinforced Linear Genetic Programming (RLGP) to learn the optimal register-action assignments. In doing so, we […]

Ver mais

For most of the internet era, commerce followed a predictable path. Consumers searched for a product, visited a website, compared options, and eventually checked out. The brand’s website was the center of gravity for discovery and purchase. That model is quietly evolving. Today, consumers increasingly discover and buy products directly inside content, communities, and AI-powered experiences—a shift I call moment commerce. Instead of pulling customers to a store or homepage, brands must meet them where attention already exists. […]

Ver mais

Training large-scale Neural Networks requires substantial computational power and energy. Federated Learning enables distributed model training across geospatially distributed data centers, leveraging renewable energy sources to reduce the carbon footprint of AI training. Various client selection strategies have been developed to align the volatility of renewable energy with stable and fair model training in a federated system. However, due to the privacy-preserving nature of Federated Learning, the quality of data on client devices remains unknown, posing challenges for […]

Ver mais

The global AI landscape has entered a crucial phase in 2026, where innovation is moving quickly from research to deployment. Throughout the year, the entire calendar is full of industry-shaping conferences and events that set the direction for Artificial Intelligence across emerging technologies, enterprise, and government. The Global AI Show in Riyadh 2026 is a flagship conference that will lead the lineup. It anchors the year by converging global AI leaders, builders, and policymakers at the heart of […]

Ver mais

arXiv:2604.22755v1 Announce Type: new Abstract: Reliable decision support in nuclear engineering requires traceable, domain-grounded knowledge retrieval, yet safety and risk analysis workflows remain hampered by fragmented documentation and hallucination when use pre-trained large language model (LLM) in specialized nuclear domains. To address these challenges, this paper presents RADIANT-LLM (Retrival-Augumented, Domain-Intelligent Agent for Nuclear Technologies using LLM), a multi-modal retrieval-augmented generation (RAG) framework designed for nuclear safety, security, and safeguards applications. The framework uses a local-first, model-agnostic architecture that […]

Ver mais

arXiv:2603.12300v1 Announce Type: new Abstract: The increasing adoption of server-side component-based web frameworks has introduced new application-layer attack surfaces that remain insufficiently understood at Internet scale. On 3 December 2025, a critical remote code execution vulnerability (CVE-2025-55182) in React Server Components, referred to as React2Shell, was publicly disclosed and subsequently observed being exploited in the wild. Despite its critical severity and a CVSS base score of 10.0, there is limited empirical understanding of how this vulnerability is exploited […]

Ver mais