Abstract
Deep-learning beat-tracking algorithms have achieved remarkable accuracy in recent years. However, despite these advancements, challenges persist with musical examples featuring complex rhythmic structures, especially given their under-representation in training corpora. Expanding on our prior work, this paper demonstrates how our user-centred beat-tracking methodology effectively handles increasingly demanding musical scenarios. We evaluate its adaptability and robustness through musical pieces that exhibit rhythmic dissonance, while maintaining ease of integration with leading methods through minimal user annotations. The selected musical works-Uruguayan Candombe, Colombian Bambuco, and Steve Reich's Piano Phase-present escalating levels of rhythmic complexity through their respective polyrhythm, polymetre, and polytempo characteristics. These examples not only validate our method's effectiveness but also demonstrate its capability across increasingly challenging scenarios, culminating in the novel application of beat tracking to polytempo contexts. The results show notable improvements in terms of the F-measure, ranging from 2 to 5 times the state-of-the-art performance. The beat annotations used in fine-tuning reduce the correction edit operations from 1.4 to 2.8 times, while reducing the global annotation effort to between 16% and 37% of the baseline approach. Our experiments demonstrate the broad applicability of our human-in-theloop strategy in the domain of Computational Ethnomusicology, confronting the prevalent Music Information Retrieval (MIR) constraints found in non-Western musical scenarios. Beyond beat tracking and computational rhythm analysis, this user-driven adaptation framework suggests wider implications for various MIR technologies, particularly in scenarios where musical signal ambiguity and human subjectivity challenge conventional algorithms.

Abstract
The entrepreneurial performance of new ventures operating within the sustainable open innovation paradigm remains underexplored, particularly in terms of how specific sustainability-oriented practices translate into measurable performance outcomes. Prior research has largely examined sustainability, entrepreneurship, and open innovation in isolation, offering limited empirical evidence on their combined effects at the early venture stage. To address this gap, this study analyzes panel data from 407 new ventures incubated in science and technology parks, employing regression-based panel data analysis to examine the relationships between sustainable practices, open innovation engagement, and entrepreneurial performance. The findings suggest that new ventures widely adopt sustainable materials and energy as key strategies, which significantly influence entrepreneurial performance. In contrast, support from local communities does not have a statistically significant impact. Among the sociodemographic factors tested, only the number of years participating in open innovation networks shows a significant effect on entrepreneurial performance. Theoretically, this study advances sustainable open innovation literature by empirically integrating sustainability practices into entrepreneurship performance models. From a managerial perspective, the findings offer actionable insights for entrepreneurs and incubator managers, highlighting which sustainability strategies and network engagements are most likely to yield performance benefits in new ventures.

Abstract
Early fault detection remains a critical challenge in predictive maintenance (PdM), particularly within critical infrastructure, where undetected failures or delayed interventions can compromise safety and disrupt operations. Traditional anomaly detection methods are typically reactive, relying on real-time sensor data to identify deviations as they occur. This reactive nature often provides insufficient lead time for effective maintenance planning. To address this limitation, we propose a novel two-stage early detection framework that integrates time series forecasting with anomaly detection to anticipate equipment failures several hours in advance. In the first stage, future sensor signal values are predicted using forecasting models; in the second, conventional anomaly detection algorithms are applied directly to the forecasted data. By shifting from real-time to anticipatory detection, the framework aims to deliver actionable early warnings, enabling timely and preventive maintenance. We validate this approach through a case study focused on metro train systems, an environment where early fault detection is crucial for minimizing service disruptions, optimizing maintenance schedules, and ensuring passenger safety. The framework is evaluated across three forecast horizons (1, 3, and 6 hours ahead) using twelve state-of-the-art anomaly detection algorithms from diverse methodological families. Detection performance is assessed using five performance metrics. Results show that anomaly detection remains highly effective at short to medium horizons, with performance at 1-hour and 3-hour forecasts comparable to that of real-time data. Ensemble and deep learning models exhibit strong robustness to forecast uncertainty, maintaining consistent results with real-time data even at 6-hour forecasts. In contrast, distance- and density-based models suffer substantial degradation at longer horizons (6-hours), reflecting their sensitivity to distributional shifts in predicted signals. Overall, the proposed framework offers a practical and extensible solution for enhancing traditional PdM systems with proactive capabilities. By enabling early anomaly detection on forecasted data, it supports improved decision-making, operational resilience, and maintenance planning in industrial environments.

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Abstract

Abstract
Context. Software testing is a critical aspect of the software development lifecycle, yet it remains underrepresented in academic curricula. Despite advances in pedagogical practices and increased attention from the academic community, challenges persist in effectively teaching software testing. Understanding these challenges from the teachers' perspective is crucial to aligning education with industry needs. Objective. To analyze the characteristics, practices, tools, and challenges of software testing courses in higher education, from the perspective of educators, and to assess the integration of recent pedagogical approaches in software testing education. Method. A structured survey consisting of 52 questions was distributed to 143 software testing educators across Western European universities, resulting in 49 valid responses. The survey explored topics taught, course organization, teaching practices, tools and materials used, gamification approaches, and teacher satisfaction. Results. The survey revealed significant variability in course content, structure, and teaching methods. Most dedicated software testing courses are offered at the master's level and are elective, whereas testing is introduced earlier in less specialized (NST) courses. There is low adoption of formal guidelines (e.g., ACM, SWEBOK), limited integration of non-functional testing types, and a high diversity in textbooks and tools used. While modern practices like Test-Driven Development and automated assessment are increasingly adopted, gamification and active learning approaches remain underutilized. Teachers expressed a need for improved and more consistent teaching materials. Conclusion. The study highlights a mismatch between academic practices and industry expectations in software testing education. Greater integration of standardized curricula, broader adoption of modern teaching tools, and increased support for teachers through high-quality, adaptable teaching materials are needed to enhance the effectiveness of software testing education.