arXiv subjects

Allan Martins

Publications and source records attributed to Allan Martins.

4 recordsLinked to original sources

arXiv2026

Open-source emulation-based test environment to settle O-RAN-compliant trials

Experimental tools are a key factor in both academic and industrial research communities to create design evaluations of new networking technologies that involve troubleshooting or changing the planning of deployed networks. Physical Software-Defined Radio (SDR) experimental platforms enable a design solution for the quick prototyping of wireless communication systems. However, SDR-based experimental platforms incur high costs, which leads to scalability limitations in the experimental settings. Having said this, network simulators, emulators, and new testbeds have attracted increasing attention. Emulation-based research prototyping can be distinguished from real communication networks and SDR-based platforms by allowing a tradeoff between cost and flexibility. This paper examines the Mininet-RAN emulation tool, which, as well as Radio Access Network (RAN) modeling, provides a way to test Open RAN Intelligent Controller (RIC) services without the need to deploy an entire RAN infrastructure. The Mininet-RAN creates virtual network elements, such as hosts, L2/L3 devices, controllers, and links, by combining some of the best emulator features, hardware testbeds, and simulators. By running the current code of standard practice Unix/Linux network applications and network stack, the Mininet-RAN enables real-world network data traffic patterns to be delivered to the RIC, regarding the most significant aspect of the dynamic generation of wireless system's KPIs. We provide the basic code of Mininet-RAN for the first two O-RAN Alliance-defined use cases involving V2X and UAV. The xApps are being implemented in O-RAN SC near-RT RIC, with Mininet-RAN which provides a closed-loop validation environment.

cs.NI↗

arXiv2024

Organizing a Society of Language Models: Structures and Mechanisms for Enhanced Collective Intelligence

Recent developments in Large Language Models (LLMs) have significantly expanded their applications across various domains. However, the effectiveness of LLMs is often constrained when operating individually in complex environments. This paper introduces a transformative approach by organizing LLMs into community-based structures, aimed at enhancing their collective intelligence and problem-solving capabilities. We investigate different organizational models-hierarchical, flat, dynamic, and federated-each presenting unique benefits and challenges for collaborative AI systems. Within these structured communities, LLMs are designed to specialize in distinct cognitive tasks, employ advanced interaction mechanisms such as direct communication, voting systems, and market-based approaches, and dynamically adjust their governance structures to meet changing demands. The implementation of such communities holds substantial promise for improve problem-solving capabilities in AI, prompting an in-depth examination of their ethical considerations, management strategies, and scalability potential. This position paper seeks to lay the groundwork for future research, advocating a paradigm shift from isolated to synergistic operational frameworks in AI research and application.

cs.AI↗

arXiv2023

SNeL: A Structured Neuro-Symbolic Language for Entity-Based Multimodal Scene Understanding

In the evolving landscape of artificial intelligence, multimodal and Neuro-Symbolic paradigms stand at the forefront, with a particular emphasis on the identification and interaction with entities and their relations across diverse modalities. Addressing the need for complex querying and interaction in this context, we introduce SNeL (Structured Neuro-symbolic Language), a versatile query language designed to facilitate nuanced interactions with neural networks processing multimodal data. SNeL's expressive interface enables the construction of intricate queries, supporting logical and arithmetic operators, comparators, nesting, and more. This allows users to target specific entities, specify their properties, and limit results, thereby efficiently extracting information from a scene. By aligning high-level symbolic reasoning with low-level neural processing, SNeL effectively bridges the Neuro-Symbolic divide. The language's versatility extends to a variety of data types, including images, audio, and text, making it a powerful tool for multimodal scene understanding. Our evaluations demonstrate SNeL's potential to reshape the way we interact with complex neural networks, underscoring its efficacy in driving targeted information extraction and facilitating a deeper understanding of the rich semantics encapsulated in multimodal AI models.

cs.AI↗

arXiv2023

End-to-end Semantic Object Detection with Cross-Modal Alignment

Traditional semantic image search methods aim to retrieve images that match the meaning of the text query. However, these methods typically search for objects on the whole image, without considering the localization of objects within the image. This paper presents an extension of existing object detection models for semantic image search that considers the semantic alignment between object proposals and text queries, with a focus on searching for objects within images. The proposed model uses a single feature extractor, a pre-trained Convolutional Neural Network, and a transformer encoder to encode the text query. Proposal-text alignment is performed using contrastive learning, producing a score for each proposal that reflects its semantic alignment with the text query. The Region Proposal Network (RPN) is used to generate object proposals, and the end-to-end training process allows for an efficient and effective solution for semantic image search. The proposed model was trained end-to-end, providing a promising solution for semantic image search that retrieves images that match the meaning of the text query and generates semantically relevant object proposals.

cs.CV↗