A deep reinforcement learning (DRL) approach is applied, for the first time, to solve the routing, modulation, spectrum, and core allocation (RMSCA) problem in dynamic multicore fiber elastic optical networks (MCF-EONs). To do so, a new environment was designed and implemented to emulate the operation of MCF-EONs - taking into account the modulation format-dependent reach and intercore crosstalk (XT) - and four DRL agents were trained to solve the RMSCA problem. The blocking performance of the trained agents was compared through simulation to 3 baselines RMSCA heuristics. Results obtained for the NSFNet and COST239 network topologies under different traffic loads show that the best-performing agent achieves, on average, up to a four-times decrease in blocking probability with respect to the best-performing baseline heuristic method.

The lack of standards in the performance evaluation of new resource allocation algorithms in multicore fiber elastic optical networks (MCF-EONs) compromises the fairness when comparing them with the state of the art. This paper reviews the different transmission parameters, network parameters, performance metrics, and baselines used by the recent proposals to build a framework for future benchmarking of such algorithms according to the nature of the network operation, whether static or dynamic. This framework aims to provide standards regarding evaluation criteria, scenarios, and performance metrics, as well as recommendations concerning technology advances to promote methodology and reproducibility in further related studies.

Cable service providers face significant challenges in managing Hybrid Fiber–Coaxial (HFC) networks due to the growing demand for high-speed services. Ensuring high service availability is critical to preventing customer attrition. This study employs machine learning techniques, specifically Decision Tree and Random Forest models, for proactive fault detection in HFC networks using data from the Simple Network Management Protocol (SNMP). Two operational scenarios were considered: a network-wide model and node-specific models. The dataset for fault detection exhibited a severe class imbalance, with outage events being extremely rare. To address this, the Synthetic Minority Oversampling Technique (SMOTE), which generates synthetic samples of the minority class to balance the dataset, was applied. This significantly improved recall and F1-scores—the harmonic mean of precision and recall—while maintaining high precision. The results demonstrate that these machine learning algorithms achieve up to 98% accuracy, and the SMOTE-enhanced models provide more reliable detection of connectivity faults. This approach is highly effective for cable operators in maintaining quality of service, enabling proactive management of problems and enhancement of network performance.

Deepfakes, synthetic multimedia files generated by artificial intelligence, are drastically undermining digital credibility. Their ability to manipulate our perception of reality has created a new and complex battleground for disinformation, posing a critical threat to non-English-speaking audio with distinctive accents. Consequently, the objective of this study is to determine the human capacity to detect deepfake audio in Spanish with a Paraguayan accent through an experiment conducted with an Android application called ReFake (developed specifically for this research). In this experiment, 450 participants, aged 16–72, evaluated 10 audio samples of up to 15 s each, classifying them as authentic (belonging to Paraguayan journalists) or fake (generated with ElevenLabs). The findings suggests that human ear is more accurate than artificial intelligence (AI) at detecting vocal ‘naturalness’. This ability is influenced by generational age and educational level, with younger people and those with postgraduate degrees demonstrating greater performance. Conversely, gender and nationality do not influence detection, although the high prosodic quality of deepfakes still leads to errors in human judgment. Given these results, it is crucial to adapt and develop new strategies for a secure and resilient online ecosystem.