Abstract
The segmentation of the foveal avascular zone (FAZ) in optical coherence tomography angiography (OCTA) images plays a crucial role in diagnosing and monitoring ocular diseases such as diabetic retinopathy (DR) and age-related macular degeneration (AMD). However, accurate FAZ segmentation remains challenging due to image quality and variability. This paper provides a comprehensive review of FAZ segmentation techniques, including traditional image processing methods and recent deep learning-based approaches. We propose two novel deep learning methodologies: a multitask learning framework that integrates vessel and FAZ segmentation, and a conditionally trained network that employs vessel-aware loss functions. The performance of the proposed methods was evaluated on the OCTA-500 dataset using the Dice coefficient, Jaccard index, 95% Hausdorff distance, and average symmetric surface distance. Experimental results demonstrate that the multitask segmentation framework outperforms existing state-of-the-art methods, achieving superior FAZ boundary delineation and segmentation accuracy. The conditionally trained network also improves upon standard U-Net-based approaches but exhibits limitations in refining the FAZ contours.

Abstract

Abstract

Abstract

Abstract
With the growing popularity of DevOps culture among companies and the corresponding increase in Microservices architecture development-both known to boost productivity and efficiency in software development-an increasing number of organizations are aiming to integrate them. Implementing DevOps culture and best practices can be challenging, but it is increasingly important as software applications become more robust and complex, and performance is considered essential by end users. By following the Design Science Research methodology, this paper proposes an iterative framework that closely follows the recommended DevOps practices, validated with the assistance of expert interviews, for implementing DevOps practices into Microservices architecture software development, while also offering a series of tools that serve as a base guideline for anyone following this framework, in the form of a theoretical use case. Therefore, this paper provides organizations with a guideline for adapting DevOps and offers organizations already using this methodology a framework to potentially enhance their established practices.

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.