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The SunoCaps dataset aims to provide an innovative contribution to music data. Expert description of human-made musical pieces, from the widely used MusicCaps dataset, are used as prompts for generating complete songs for this dataset. This Automatic Music Generation is done with the state-of-the-art Suno generator of audio-based music. A subset of 64 pieces from MusicCaps is currently included, with a total of 256 generated entries. This total stems from generating four different variations for each human piece; two versions based on the original caption and two versions based on the original aspect description. As an AI-generated music dataset, SunoCaps also includes expert-based information on prompt alignment, with the main differences between prompt and final generation annotated. Furthermore, annotations describing the main discrete emotions induced by the piece. This dataset can have an array of implementations, such as creating and improving music generation validation tools, training systems for multi-layered architectures and the optimization of music emotion estimation systems.

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BACKGROUND AND GOAL: Health information systems are increasingly sophisticated and developing them is a challenge for software developers. Software engineers usually make use of UML as a standard model language that allows defining health information system entities and their relations. However, working with health system requires learning HL7 standards, that defines and manages standards related to health information systems. HL7 standards are varied, however this work focusses on v2 and v3 since these are the most used one on the area that this work is being conducted. This works aims to allow modeling HL7 standard by using UML. METHODS: Several techniques based on the MDE (Model-Driven Engineering) paradigm have been used to cope with it. RESULTS: A useful reference framework, reducing final users learning curve and allowing modeling maintainable and easy-going health information systems. CONCLUSIONS: By using this approach, a software engineer without any previous knowledge about HL7 would be able to solve the problem of modeling HL7-based health information systems. Reducing the learning curve when working in projects that need HL7 standards.

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HL7 (Health Level 7) International is an organization that defines health information standards. Most HL7 domain information models have been designed according to a proprietary graphic language whose domain models are based on the HL7 metamodel. Many researchers have considered using HL7 in the MDE (Model-Driven Engineering) context. A limitation has been identified: all MDE tools support UML (Unified Modeling Language), which is a standard model language, but most do not support the HL7 proprietary model language. We want to support software engineers without HL7 experience, thus real-world problems would be modeled by them by defining system requirements in UML that are compliant with HL7 domain models transparently. The objective of the present research is to connect HL7 with software analysis using a generic model-based approach. This paper introduces a first approach to an HL7 MDE solution that considers the MIF (Model Interchange Format) metamodel proposed by HL7 by making use of a plug-in developed in the EA (Enterprise Architect) tool.

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e-Health Systems quality management is an expensive and hard process that entails performing several tasks such as analysis, evaluation, and quality control. Furthermore, the development of an e-Health System involves great responsibility since people's health and quality of life depend on the system and services offered. The focus of the following study is to identify the gap in Quality Characteristics for e-Health Systems, by detecting not only which are the most studied, but also which are the most used Quality Characteristics these Systems include. A strategic study is driven in this paper by a Systematic Literature Review so as to identify Quality Characteristics in e-Health. Such study makes information and communication technology organizations reflect and act strategically to manage quality in e-Health Systems efficiently and effectively. As a result, this paper proposes the bases of a Quality Model and focuses on a set of Quality Characteristics to enable e-Health Systems quality management. Thus, we can conclude that this paper contributes to implementing knowledge with regard to the mission and view of e-Health (Systems) quality management and helps understand how current researches evaluate quality in e-Health Systems.

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