Abstract
Silent motors are an excellent strategy to combat noise pollution. Still, they can pose risks for pedestrians who rely on auditory cues for safety and reduce driver awareness due to the absence of the familiar sounds of combustion engines. Sound design for silent motors not only tackles the above issues but goes beyond safety standards towards a user-centered approach by considering how users perceive and interpret sounds. This paper examines the evolving field of sound design for electric vehicles (EVs), focusing on Acoustic Vehicle Alerting Systems (AVAS). The study analyzes existing AVAS, classifying them into different groups according to their design characteristics, from technical concerns and approaches to aesthetic properties. Based on the proposed classification, an (adaptive) sound design methodology, and concept for AVAS are proposed based on state-of-the-art technologies and tools (APIs), like Wwise Automotive, and integration through a functional prototype within a virtual environment. We validate our solution by conducting user tests focusing on EV sound perception and preferences in rural and urban environments. Results showed participants preferred nature-like and melodic sounds with a wide range of frequencies, emphasizing 1000Hz, in rural areas, for the AVAS. For the interior experience, melodic, reliable, and relaxing sounds with a frequency range from 200Hz to 500Hz. In urban areas, melodic, futuristic, but not overpowering sounds (80Hz to 700Hz) with balanced frequencies at high speeds were chosen for the car's exterior. In the interior, melodic, futuristic, and combustion engine-like sounds with a low frequencies background and higher frequencies at high speeds were also preferred. © 2025 SAE International. All Rights Reserved.

Abstract
Sound design plays an important role in serious games, influencing user experience and learning outcomes. However, deriving general principles and best practices remains challenging, as most literature relies on case-based studies in different application domains. Through a systematic review of the literature, 21 studies were analyzed to address two key questions: 1) what types of serious games and application domains incorporate sound design? and 2) what sound design strategies are implemented to enhance user experience and learning outcomes? The findings show that serious games have mainly focused on education, healthcare, and training, using sound to enhance motivation (50%), cognition (32%), and knowledge acquisition (18%). Furthermore, sound design strategies fulfill distinct roles: sound effects enhance feedback and engagement, background music influences motivation and cognitive processing, ambient sounds support navigation and emotional regulation, and dialogue facilitates knowledge acquisition. The findings highlight the need for further research to establish standardized sound design principles to optimize user experience and learning outcomes in serious games.

Abstract
Monodic folk music has traditionally been preserved in physical documents. It constitutes a vast archive that needs to be digitized to facilitate comprehensive analysis using AI techniques. A critical component of music score digitization is the transcription of lyrics, an extensively researched process in Optical Character Recognition (OCR) and document layout analysis. These fields typically require the development of specific models that operate in several stages: first, to detect the bounding boxes of specific texts, then to identify the language, and finally, to recognize the characters. Recent advances in vision language models (VLMs) have introduced multimodal capabilities, such as processing images and text, which are competitive with traditional OCR methods. This paper proposes an end-to-end system for extracting lyrics from images of handwritten musical scores. We aim to evaluate the performance of two state-of-the-art VLMs to determine whether they can eliminate the need to develop specialized text recognition and OCR models for this task. The results of the study, obtained from a dataset in a real-world application environment, are presented along with promising new research directions in the field. This progress contributes to preserving cultural heritage and opens up new possibilities for global analysis and research in folk music.

Abstract
This study introduces a methodological framework for constructing virtual ironic environments through the deliberate mismatching of emotional profiles in music and imagery. We conducted a statistical analysis of "happy/joy" and "angry" samples from two independent datasets to identify significant acoustic and visual features. These feature profiles were translated into mid-level semantic prompts to guide AI-based generation of visual and musical content. Our findings reveal distinct emotional signatures: happy music exhibits higher rhythmic onset rates and greater spectral variability, whereas angry music is characterized by a higher spectral centroid and more stable dissonance. Visually, joyful images are brighter and more symmetrical, while angry images feature darker hues and concentrated color distributions. Furthermore, mid-level perceptual descriptors generate the most coherent content, and we employed them to build a spectrum of virtual environments, including Sarcastic (joyful visuals + angry music) and Kind Ironic (angry visuals + happy music) spaces. This work establishes a new, data-driven approach to affective computing and speculative virtual design, grounded in the formal principle of audiovisual dissonance.

Abstract
This study introduces and evaluates a new methodology for cross-cultural ethnomusicological analysis of symbolic music. We investigate music similarity in popular traditions rooted in oral transmission by identifying shared patterns at scale across multiple hierarchies. The novelty of our approach lies in expanding musical similarity phyloanalysis-typically adopting alignment metrics that compare entire scores-to structurally aware phrases and macrostructure (i.e., form) alignment. Additionally, we explore patterns derived from multiple representations (chromatic interval, diatonic interval, rhythmic ratios, and a combination of them) to facilitate the exploration of stylistic affinities across musical genres and traditions. Our method is tested on a new dataset of 600 Galician and Irish popular music scores, which includes expert annotations for 21 genres (four shared between the two traditions) and detailed phrase information, all made available as open-access data. We use the genre separation ratio to examine how alignment strategies capture stylistic structure, providing insights that support musicological exploration across genres and traditions. The resulting phylogenetic trees and distance matrices reveal relationships among traditions, genres, and scores, facilitating the exploration of cross-cultural influences and enabling the identification of shared patterns at multiple hierarchies.

Abstract
Accurately estimating workload runtime is a longstanding goal in computer systems, and plays a key role in efficient resource provisioning, latency minimization, and various other system management tasks. Runtime prediction is particularly important for managing increasingly complex distributed systems in which more sophisticated processing is pushed to the edge in search of better latency. Previous approaches for runtime prediction in edge systems suffer from poor data efficiency or require intensive instrumentation; these challenges are compounded in heterogeneous edge computing environments, where historical runtime data may be sparsely available and instrumentation is often challenging. Moreover, edge computing environments often feature multi-tenancy due to limited resources at the network edge, potentially leading to interference between workloads and further complicating the runtime prediction problem. Drawing from insights across machine learning and computer systems, we design a matrix factorization-inspired method that generates accurate interference-aware predictions with tight provably-guaranteed uncertainty bounds. We validate our method on a novel WebAssembly runtime dataset collected from 24 unique devices, achieving a prediction error of 5.2% -- 2x better than a naive application of existing methods.