Abstract
State-of-the-art symbolic music generation models have recently achieved remarkable output quality, yet explicit control over compositional features, such as tonal tension, remains challenging. We propose a novel approach that integrates a computational tonal tension model, based on tonal interval vector analysis, into a Transformer framework. Our method employs a two-level beam search strategy during inference. At the token level, generated candidates are re-ranked using model probability and diversity metrics to maintain overall quality. At the bar level, a tension-based re-ranking is applied to ensure that the generated music aligns with a desired tension curve. Objective evaluations indicate that our approach effectively modulates tonal tension, and subjective listening tests confirm that the system produces outputs that align with the target tension. These results demonstrate that explicit tension conditioning through a dual-level beam search provides a powerful and intuitive tool to guide AI-generated music. Furthermore, our experiments demonstrate that our method can generate multiple distinct musical interpretations under the same tension condition.

Abstract
We present a metadata enrichment framework for Music Encoding Initiative (MEI) files, featuring mid- to higher-level multimodal features to support content-driven (similarity) retrieval with semantic awareness across large collections. While traditional metadata captures basic bibliographic and structural elements, it often lacks the depth required for advanced retrieval tasks that rely on musical phrases, form, key or mode, idiosyncratic patterns, and textual topics. To address this, we propose a system that fosters the computational analysis and edition of MEI encodings at scale. Inserting extended metadata derived from computational analysis and heuristic rules lays the groundwork for more nuanced retrieval tools. A batch environment and a lightweight JavaScript web-based application propose a complementary workflow by offering large-scale annotations and an interactive environment for reviewing, validating, and refining MEI files' metadata. Development is informed by user-centered methodologies, including consultations with music editors and digital musicologists, and has been co-designed in the context of orally transmitted folk music traditions, ensuring that both the batch processes and interactive tools align with scholarly and domain-specific needs.

Abstract
Phrase segmentation is a fundamental preprocessing step for computational folk music similarity, specifically in identifying tune families within digital corpora. Furthermore, recent literature increasingly recognizes the need for tradition-specific frameworks that accommodate the structural idiosyncrasies of each tradition. In this context, this study presents a culturally informed adaptation of the established rule-based Local Boundary Detection Model (LBDM) algorithm to underrepresented Iberian folk repertoires. Our methodological enhancement expands the LBDM baseline, which traditionally analyzes rests, pitch intervals, and inter-onset duration functions to identify potential segmentation boundaries, by integrating a sub-structure surface repetition function coupled with an optimized peak-selection algorithm. Furthermore, we implement a genetic algorithm to maximize segmentation accuracy by weighting coefficients for each function while calibrating the meta-parameters of the peak-selection process. Empirical evaluation on the I-Folk digital corpus, comprising 802 symbolically encoded folk melodies from Portuguese and Spanish traditions, demonstrates improvements in segmentation F-measure of six and sixteen percentage points~(p.p.) relative to established baseline methodologies for Portuguese and Spanish repertoires, respectively.

Abstract

Abstract
The rapid growth of data traffic and evolving service demands are driving a shift from traditional network architectures to advanced solutions. While 5G networks provide reduced latency and higher availability, they still face limitations due to reliance on integrated hardware, leading to configuration and interoperability challenges. The emerging Open Radio Access Network (O-RAN) paradigm addresses these issues by enabling remote configuration and management of virtualized components through open interfaces, promoting cost-effective, multi-vendor interoperability. Network slicing, a key 5G enabler, allows for tailored network configurations to meet heterogeneous performance requirements. The main contribution of this paper is a private Standalone 5G network based on O-RAN, featuring a dynamic Data Radio Bearer Management xApp (xDRBM) for real-time metric collection and traffic prioritization. xDRBM optimizes resource usage and ensures performance guarantees for specific applications. Validation was conducted in an emulated environment representative of real-world scenarios. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

Abstract
Unmanned Aerial Vehicles (UAVs) are increasingly employed to enable wireless communications, serving as communications nodes. In previous work, we proposed the Sustainable multi-UAV Performance-aware Placement (SUPPLY) algorithm, which focuses on the energy-efficient placement of multiple UAVs acting as Flying Access Points (FAPs). We also developed the Multi-UAV Energy Consumption (MUAVE) simulator to evaluate UAV energy consumption. However, MUAVE was designed to compute the energy consumption for rotary-wing UAVs only. In this paper, we propose eMUAVE, an enhanced version of the MUAVE simulator that enables the evaluation of the energy consumption for both rotary-wing and fixed-wing UAVs. We then use eMUAVE to evaluate the energy consumption of rotary-wing and fixed-wing UAVs in reference and random networking scenarios. The results show that rotary-wing UAVs are typically more energy-efficient than fixed-wing UAVs when following SUPPLY-defined trajectories.