Ricardo Flores Moyano

Noisy social text can be transformed into semantic representations, but evaluating the resulting artifacts is difficult when source records, ontology activations, graph resources, conformance reports, and model inputs are considered separately. The evaluation problem is whether their connections remain inspectable as these artifacts are generated and used. This study examines the above continuity using 1614 Reddit posts about artificial intelligence and technology. Ontology activations produce sibling Resource Description Framework (RDF) and numeric representations linked through stable identifiers. Explicit correspondence checks and controlled fault tests examine these connections, while Shapes Constraint Language (SHACL) checks assess conformance to declared graph constraints. Existing verification checks detect 15 of 22 injected continuity faults. The remaining seven are detected only by supplementary diagnostics. Separate human assessment of 120 validation texts evaluates concept presence and local polarity against the source text, distinguishing extraction quality from artifact continuity. Downstream classification provides secondary evidence of modest mean macro-F1 gains in agreement with inherited lexical labels, varying by model across three seeds on one fixed split. The contribution is an evaluation of governed semantic artifact continuity at the post and variable levels, making the connections among these artifacts and their failure conditions clear and inspectable.

The transition to Beyond fifth generation of wireless networks (B5G) and sixth generation of wireless networks (6G) exposes the severe interference and coverage limitations of conventional cell-centric architectures. To overcome these bottlenecks, this paper presents a scalable four-layer radio network planning framework that jointly optimizes the deployment of distributed active antenna arrays and passive reconfigurable intelligent surfaces (RISs). The proposed framework integrates a digital twin (DT) loop within an Open-RAN (O-RAN) architecture, employing multi-agent deep reinforcement learning (MADRL) and fractional programming (FP) for real-time joint active and passive beamforming optimization. Extensive Monte Carlo simulations in a dense urban environment demonstrate a 45% increase in spectral efficiency, a 30% reduction in uplink interference, and an 84% reduction in coverage holes compared to legacy 5G networks. Ultimately, these results provide network operators with a cost-effective, standards-compliant blueprint to extend non-line-of-sight (NLOS) coverage by 40% without incurring the prohibitive capital expenditure (CAPEX) of dense active hardware deployments. Furthermore, the proposed architecture demonstrates a competitive 10–15% margin of improvement in spectral efficiency over recent state-of-the-art DRL-based RIS frameworks.

This work introduces the MassSeg-Framework, a fully automatic two-stage pipeline for breast mass analysis in mammography that integrates YOLOv11-based detection with Chan–Vese ACM refinement to achieve accurate mass localization and segmentation with a lightweight computational footprint. The framework was trained and evaluated on two publicly available datasets using consistent experimental protocols. In the detection stage, YOLOv11-nano was the most effective architecture, with a confidence threshold of 0.4, achieving statistically significant mAP50 values of 0.862 and 0.709 on the dINbreast and dCBIS datasets, respectively. These results confirm that a moderate threshold preserves clinically relevant true-positive candidates, which is particularly important for screening-oriented settings where missed lesions are costly. In the segmentation stage, the proposed framework achieved mean DICE scores of 0.721 and 0.700 on the test sets of the same datasets, demonstrating consistent overlap with expert annotations. Compared with state-of-the-art approaches that commonly assume lesion-centered ROIs or rely on heavier backbones, the proposed pipeline addresses a more realistic scenario by performing automatic detection followed by segmentation while maintaining substantially lower computational requirements. This balance between performance and efficiency makes the MassSeg-Framework a promising tool for scalable mammography analysis, particularly in resource-constrained environments or high-throughput screening workflows that require rapid processing.

Software-defined networking (SDN) architecture enables flexible and centralized network management from the controller, making it increasingly attractive in deploying telecommunications services. However, despite the many benefits of SDN, the vulnerabilities inherent in its architecture must be considered, and potential attacks must be discarded. When this occurs, not only the technical areas are interested in the source of the problem, but also the organizational areas, since attacks can violate terms of service and lead to legal actions. Despite the shared interest in cybersecurity event information, forensics and incident response processes often operate independently, impacting the root cause determination. Considering this concern, an architectural evolution for digital forensics and incident response (DFIR) management is introduced. This paper presents an event filtering model that serves as a trigger for initialing the DFIR process, which involves the detection of unusual traffic and unexpected behavior of SDN elements. The proposal applies artificial intelligence technology and showcases the performance of the model and the presentation of a proprietary dataset obtained from OpenFlow traffic.

Machine learning is a branch of artificial intelligence that provides computers the ability to create or improve algorithms without being explicitly programmed by directly learning from data. It is widely used in automation or decision-making tasks in fields such as image or speech recognition, sentiment analysis, or self-driving cars. However, its application in the field of communication networks is limited by the lack of appropriate research resources, such as rich datasets for training or the definition of a standard set of features. In this context, a standard latent space dimension is proposed by performing an autoencoder-based dimensionality reduction process. Different network security datasets are projected onto a lower-dimensional space to determine a standard or convergent dimension. The convergent dimension is determined by identifying the threshold above which diminishing returns begin to occur in the autoencoder loss as the latent space dimension increases. The experimental validation showed that four machine learning classification models, trained with a standard latent space of ten dimensions, performed as well as the models that used the non-reduced versions of the datasets in terms of F1-score and accuracy. Furthermore, a Wilcoxon statistical test showed that the mean accuracy of all classification models trained with the standard latent space dimension had a difference of less than 0.0235 in comparison to the models trained with the original inputs. A negligible difference in accuracy is a significant outcome because researchers can use only the latent space to perform experiments with certainty that the performance of ML models will not be constrained.

The demand for mobile multimedia streaming services has been steadily growing in recent years. Mobile multimedia broadcasting addresses the shortage of radio resources but introduces a network error recovery problem. Retransmitting multimedia segments that are not correctly broadcast can cause service disruptions and increased service latency, affecting the quality of experience perceived by end users. With the advent of networking paradigms based on virtualization technologies, mobile networks have been enabled with more flexibility and agility to deploy innovative services that improve the utilization of available network resources. This paper discusses how mobile multimedia broadcast services can be designed to prevent service degradation by using the computing capabilities provided by multiaccess edge computing (MEC) platforms in the context of a 5G network architecture. An experimental platform has been developed to evaluate the feasibility of a MEC application to provide adaptive error recovery for multimedia broadcast services. The results of the experiments carried out show that the proposal provides a flexible mechanism that can be deployed at the network edge to lower the impact of transmission errors on latency and service disruptions.

Network slicing is a key element in 5G networks. It allows a mobile network operator (MNO) to offer multiple logical networks tailored to the requirements of different industry verticals over a shared infrastructure. From the point of view of an MNO, a mobile virtual network operator (MVNO) is a particular type of vertical network requiring not only the provision of logical networks with specific infrastructure resources but also customized management capabilities to allow the MVNO to offer network slice-based services to its clients over the infrastructure of multiple MNOs. However, the lack of flexibility to provide a customized and efficient network slice management system per tenant under the current proposals of the 3rd Generation Partnership Project (3GPP) and European Telecommunications Standards Institute (ETSI) limits the possibilities of MVNOs. This paper addresses this requirement by proposing a novel virtualized multidomain and multiclient orchestration system aligned with the functionalities of the network slicing management framework proposed by the 3GPP and ETSI. In particular, there are three contributions of this paper: (i) an extended service-based architecture is designed to provide an isolated management system to different MVNOs; (ii) a novel high-level slice instance template is defined to specify management and isolation requirements supporting network slicing as a service model; and (iii) finally, a teleoperated driving use case is described to showcase how our proposal provides an MVNO with an independent management system to orchestrate several network slices in cross-domain environments.