Information Retrieval (IR) increasingly relies on subjective, graded, and natural-language notions of relevance, motivating the development of reproducible test collections for ranking and recommendation. In affect-sensitive music domains, however, such resources with human-validated relevance signals remain scarce. We introduce CalmSet, a test collection for emotion-tagged music retrieval and recommendation in a therapeutic context for children with Autism Spectrum Disorder (ASD). CalmSet contains 432 modular music tracks instantiated from four purposefully composed base songs with controlled provenance, each formed by distinct combinations of seven active musical layers. Each track is annotated with ranked top-3 therapeutic intent labels and natural-language descriptions. Annotations are produced via a hybrid human-in-the-loop pipeline: CLAP proposes candidate intent labels, a large language model generates auxiliary semantic descriptions, and crowd workers provide ranked judgments without exposure to model outputs; final labels are aggregated using a Borda-based procedure. As initial baselines, we evaluate five one-vs-rest multi-label classifiers over CLAP audio embeddings, observing moderate micro-F1 scores (up to 0.60) but low exact-match accuracy (<0.10), while top-3 label overlap is substantially higher (Jaccard@3 up to 0.48), motivating graded-relevance evaluation. CalmSet supports both sparse (e.g., BM25) and dense audio–text retrieval models using therapeutic labels or natural-language descriptions as queries.

Children with Autism Spectrum Disorder (ASD) often face challenges with musical engagement due to unique sensory and neural processing needs.To address this, we introduce uCue, a musical interface designed to enhance active musical engagement.This interactive playback system offers modular arrangements of children's songs at an accessible tempo, enabling listeners to manipulate musical layers and create personalized renditions in real-time.Deployed in listening sessions with seven parent-child dyads, uCue facilitated self-expression through singing and gestures while fostering emotional regulation and sensory engagement.Participants quickly adapted to the interface, preferred soothing sounds, and expressed interest in more rhythmic layers over time.Our findings suggest that uCue has the potential to enhance musical interactions by allowing children to explore and control auditory experiences.We also discuss how uCue and data from its logs might support therapeutic goals in music therapy for children with ASD and provide design recommendations for similar technologies.

This paper proposes a new algorithm based on the clonal selection principle for the design of spreading codes for DS-CDMA. The algorithm follows a multi-objective approach, generating complex spreading codes with good autocorrelation as well as good cross-correlation properties. It also enables spreading code design with no restrictions on the number of users or code length. The algorithm maintains a repertoire of codes that are subject to cloning and undergo a process of affinity maturation to obtain better codes. Results indicate that the produces code sets that lie very close to the theoretical Pareto front.

This paper proposes a novel evolutionary approach to spreading code design in direct sequence code division multiple access (DS-CDMA). Specifically, a multiobjective evolutionary algorithm (EA) is used to generate complex spreading sequences that are optimized with respect to the average mean-square cross- and/or autocorrelation (CC and/or AC) properties. A theoretical model is developed in order to demonstrate the optimality of the generated codes. The proposed algorithm enables spreading code design with no constraints on the code length. Furthermore, it is possible to generate K/spl ges/N codes of length N with very little cost in correlation properties. This results in significant capacity enhancement in DS-CDMA systems.

The paper proposes a novel evolutionary approach to spreading code design in DS-CDMA. Specifically, a multiobjective evolutionary algorithm is used to generate complex spreading sequences that are optimized with respect to average mean square cross correlation and/or autocorrelation properties. A theoretical model is developed in order to demonstrate the optimality of the generated codes. The proposed algorithm enables spreading code design with no constraints on the code length. Furthermore, it is possible to generate K/spl ges/N codes of length N with very little cost in correlation properties. This results in significant capacity enhancement in DS-CDMA systems.