Ensuring AI safety and cybersecurity compliance for high-risk healthcare AI is complex under the EU AI Act, NIS2, and Cyber Resilience Act. This paper introduces GRAICE, a GenAI-based framework for automated remediation and continuous regulatory alignment, integrated into DELFOS, a clinical support tool for genetic diagnostics. By embedding GenAI agents into the AI lifecycle, the system replaces static audits with a continuous, evidence-driven compliance and cybersecurity continuum. Expected results include enhanced resilience, automated risk mitigation, and increased clinical trust.

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.

Over the last decade, clinical practice guidelines (CPGs) have become an important asset for daily life in healthcare organizations. Efficient management and digitization of CPGs help achieve organizational objectives and improve patient care and healthcare quality by reducing variability. However, digitizing CPGs is a difficult, complex task because they are usually expressed as text, and this often leads to the development of partial software solutions. At present, different research proposals and CPG-derived CDSS (clinical decision support system) do exist for managing CPG digitalization lifecycles (from modeling to deployment and execution), but they do not all provide full lifecycle support, making it more difficult to choose solutions or proposals that fully meet the needs of a healthcare organization. This paper proposes a method based on quality models to uniformly compare and evaluate technological tools, providing a rigorous method that uses qualitative and quantitative analysis of technological aspects. In addition, this paper also presents how this method has been instantiated to evaluate and compare CPG-derived CDSS by highlighting each phase of the CPG digitization lifecycle. Finally, discussion and analysis of currently available tools are presented, identifying gaps and limitations.

With years of frantic development, when release fast and release often was the mandatory rule for web technologies and services, the open source paradigm and online distribution repositories have imposed de facto standards for quality assessment in fast‐paced innovation processes. Nowadays, however, in pursuit of productivity, security, and user satisfaction, the industry is beginning, through the introduction of new standards such as ECMAScript 6 or web components, to consider software engineering mandates for web technologies. This article reports a quality model aligned with international standard ISO/IEC 25010, covering web components technology, which ultimately aims to improve adoption by the software engineering industry, traditionally wary of agile Internet practices, the open source paradigm, and public repositories. Our research also presents an experimentation platform on which end users have validated the quality properties, highlighting the implicit connection with the perceived quality. The key result of our research convinces us that user ratings are suitable as a testing mechanism for product quality and quality‐in‐use metrics in order to define an absolute scale of comparison for web component quality.

With years of frantic development, when release fast and release often was the mandatory rule for web technologies and services, the open source paradigm and online distribution repositories have imposed de facto standards for quality assessment in fast-paced innovation processes. Nowadays, however, in pursuit of productivity, security, and user satisfaction, the industry is beginning, through the introduction of new standards such as ECMAScript 6 or web components, to consider software engineering mandates for web technologies. This article reports a quality model aligned with international standard ISO/IEC 25010, covering web components technology, which ultimately aims to improve adoption by the software engineering industry, traditionally wary of agile Internet practices, the open source paradigm, and public repositories. Our research also presents an experimentation platform on which end users have validated the quality properties, highlighting the implicit connection with the perceived quality. The key result of our research convinces us that user ratings are suitable as a testing mechanism for product quality and quality-in-use metrics in order to define an absolute scale of comparison for web component quality.

Event-driven architectures are becoming more prevalent recently in multiple technological paradigms, especially in web applications, with message brokers being the cornerstone of these architectures. One of the most relevant implementations of these message brokers are content-based publish/subscribe systems. The performance of these systems is a critical factor for web engineering, since the web applications they support need to be reactive despite increases and fluctuations in workloads. However, an obstacle to the research of these systems is the lack of real and publicly available workloads, due to the privacy issue involved in disclosing the interests (subscriptions) of users and other commercial interests of the companies. In this paper we present a parameterizable automated system designed to syntactically translate workloads from different content-based publish/subscribe systems as a means to increase the availability of public workloads to solve the aforementioned problem. As a case study, we describe the evolution of a context-aware content-based publish/subscribe system (i.e. E-SilboPS) designed by the authors, which improves up to 5 times the performance of its previous version by reaching the maximum throughput limited by the physical resources of the hardware where it is deployed, as demonstrated by the conducted quantitative evaluation. Then, we validate the utility of the proposed automated workload generation system by using it to make the performance comparison between this new version E-SilboPS and one of the most cited publish/subscribe systems called PADRES, through a real trace of a massively multiplayer online game (MMOG) generated by the latter.

In the current constantly changing business and economic environment, partners (i.e., individuals and/or enterprises) create Collaborative Networks to join efforts and undertake new projects together, thus allowing them to face business opportunities that would not be possible if attempted by them individually. In this situation, an assignment problem arises, since these projects involve the performance of a group of tasks or processes (named roles) that have to be distributed among the partners. Specifically, this problem, called the Role-Partner Allocation (RPA) problem in Collaborative Networks is a two-sided matching problem with lower and upper quotas on the partner’s side, and incomplete and partially ordered preference lists on both sides. A matching problem, and thus also the RPA problem, should be solved by a centralized matching scheme. However, allocations in Collaborative Networks continue to be mainly created by ad hoc arrangements, which takes a long time and is hard work. Looking for a reliable and faster way of distributing roles among partners in a Collaborative Network, the existing centralized matching schemes expected to solve the RPA problem (e.g., DA algorithm, SOSM, CA-QL algorithm, and EADAM) are studied in this paper, concluding that none of them obtain a matching that properly meets the requirement of the RPA problem. Therefore, a new centralized matching scheme to solve the RPA problem is proposed, discussed and exemplified.

Cloud computing has been consolidated as a support for the vast majority of current and emerging technologies. However, there are some barriers that prevent the exploitation of the full potential of this technology. First, the major cloud providers currently put the onus of implementing the mechanisms that ensure compliance with the desired service levels on cloud consumers. However, consumers do not have the required expertise. Since each cloud provider exports a different set of low-level metrics, the strategies defined to ensure compliance with the established service-level agreement (SLA) are bound to a particular cloud provider. This fosters provider lock-in and prevents consumers from benefiting from the advantages of multi-cloud environments. This paper presents a solution to the problem of automatically translating SLAs into objectives expressed as metrics that can be measured across multiple cloud providers. First, we propose an intelligent knowledge-based system capable of automatically translating high-level SLAs defined by cloud consumers into a set of conditions expressed as vendor-neutral metrics, providing feedback to cloud consumers (intelligent tutoring system). Secondly, we present the set of vendor-neutral metrics and explain how they can be measured for the different cloud providers. Finally, we report a validation based on two use cases (IaaS and PaaS) in a multi-cloud environment formed by leading cloud providers. This evaluation has demonstrated that, thanks to the complementarity of the two solutions, cloud consumers can automatically and transparently exploit the multi-cloud in many application domains, as endorsed by the cloud experts consulted in the course of this study.

As the number of cyber-attacks on financial institutions has increased over the past few years, an advanced system that is capable of predicting the target of an attack is essential. Such a system needs to be integrated into the existing detection systems of financial institutions as it provides them with proactive controls with which to halt an attack by predicting patterns. Advanced prediction systems also enhance the software design and security testing of new advanced cyber-security measures by providing new testing scenarios supported by attack forecasting. This present study developed a model that forecasts future network-based cyber-attacks on financial institutions using a deep neural network. The dataset that was used to train and test the model consisted of some of the biggest cyber-attacks on banking institutions over the past three years. This provided insight into new patterns that may end with a cyber-crime. These new attacks were also evaluated to determine behavioral similarities with the nearest known attack or a combination of several existing attacks. The performance of the forecasting model was then evaluated in a real banking environment and provided a forecasting accuracy of 90.36%. As such, financial institutions can use the proposed forecasting model to improve their security testing measures.

Innovative instructional courses in university-level distance learning are key to improving the quality of education, due to their great ability to facilitate higher education to students with mobility limitations or problems in reconciling their professional and academic activity. However, university distance education presents a great challenge: students often feel lost, have issues with the technology, and experience lack of engagement. All these factors, among others, can result in increased dropout rates or lack of understanding and commitment. This is the context that provides the framework for the project we set out here, and whose goal is to design and analyse a gamification model for university-level distance learning, where game choice is based on skill type and the learning objectives to be attained. Using gamification does not guarantee success, as the results in terms of dropout and interaction will depend on how it is undertaken. We addressed this question via an exhaustive prior analysis, which guided the subsequent experimental design, and has allowed us to assess, through analysis of real experiments which reveal the lessons learned, its effectiveness in different areas of study and types of subject The method followed in our model is based on the application of gamification techniques in 4 subjects from different fields of knowledge. The total sample was made up of 150 students and the results were compared with those obtained in the previous course without applying the model. This gives interesting results with respect to aspects that might be linked to encouraging interactivity or permanence in distance university students, such as an increase in interaction with students in the classroom, and training resources, a decrease in the dropout rate, an increase in the number of passes, and developmental achievement in creative problems. It thereby satisfies the goals set out for the research and offers the first clues as to how to continue work on the most important points.

Cloud computing has established itself as the support for the vast majority of emerging technologies, mainly due to the characteristic of elasticity it offers. Auto-scalers are the systems that enable this elasticity by acquiring and releasing resources on demand to ensure an agreed service level. In this article we present FLAS (Forecasted Load Auto-Scaling), an auto-scaler for distributed services that combines the advantages of proactive and reactive approaches according to the situation to decide the optimal scaling actions in every moment. The main novelties introduced by FLAS are (i) a predictive model of the high-level metrics trend which allows to anticipate changes in the relevant SLA parameters (e.g. performance metrics such as response time or throughput) and (ii) a reactive contingency system based on the estimation of high-level metrics from resource use metrics, reducing the necessary instrumentation (less invasive) and allowing it to be adapted agnostically to different applications. We provide a FLAS implementation for the use case of a content-based publish–subscribe middleware (E-SilboPS) that is the cornerstone of an event-driven architecture. To the best of our knowledge, this is the first auto-scaling system for content-based publish–subscribe distributed systems (although it is generic enough to fit any distributed service). Through an evaluation based on several test cases recreating not only the expected contexts of use, but also the worst possible scenarios (following the Boundary-Value Analysis or BVA test methodology), we have validated our approach and demonstrated the effectiveness of our solution by ensuring compliance with performance requirements over 99% of the time.

• Internet developers use an implicit quality model for web components. • Web components can be endowed with an explicit quality model based on ISO 25010. • The relationship between implicit / explicit models can be validated by end-users. • Quality of web components can be predicted based in explicit metrics. This article presents the application of a mixed method research in software engineering, developing an innovative quality model for web components. The research applies the qualitative analysis of open surveys in order to determine a set of internal/static and external/dynamic metrics of an empirical quality model. Subsequently, Delphi Method quantitatively analyses, by consensus amongst the members of a panel of experts, the level of coverage that these metrics offer of the product quality and quality in use characteristics included in standard ISO/IEC 25,000. Aiming a better understanding of the industry, the method applies to the software quality domain of web components developed in open repositories, where the fragmentation in the development processes has led to organic quality models of an implicit and collaborative nature. As a result of the research, a mixed methods methodology, exploratory sequential design, has been obtained that allows not only the assessment of web components hosted on public repositories, but also the systematisation of the coverage analysis amongst characteristics and metrics, essential for the development of software quality models in heterogeneous development environments.

Context: Measuring the Software Development Process (SDP) supports organizations in their endeavor to understand, manage, and improve their development processes and projects. In the last decades, the SDP has evolved to meet the market needs and keep abreast of modern technologies and infrastructures. These changes in the development processes have increased the importance of the measurement and caused changes in the measurement process and the used measures. Objective: This work aims to develop a solution to support the measurement activities throughout the process lifecycle. Method: Study the current state of the art to identify existing gaps. Then, propose a solution to support the process measurement throughout the SDP lifecycle. Results: The proposed solution consists of two main components: (i) Measurement lifecycle, which defines the measurement activities throughout the SDP lifecycle, (ii) Measurement definition metamodel (MDMM), which supports the measurement lifecycle and its integration into the process lifecycle. Conclusion: This proposal allows organizations to define, manage, and improve their processes; the proposed information model supports the unification of the measurement concepts and vocabulary. The defined measurement lifecycle provides a comprehensive guide for the organizations to establish the measurement objectives and carry out the necessary activities to achieve them. The proposed MDMM supports and guides the engineers in the complete and operational definition of the measurement concepts.

Purpose Universities are continually transforming its structure and governance in response to the new social, environmental and economic challenges. Particularly, there has recently been a growing academic interest for measuring sustainable practices of higher education institutions (HEI) aiming to monitor and reduce their carbon emissions, as well as transform them into more sustainable organizations. More recent studies began to focus also on the sustainable performance of distance education Universities. So it became crucial to evaluate their sustainability practices through sustainability assessment tools with the aim of improving their sustainability performance and boosting their role as agents of academic, social and economic change. The purpose of this study is to assess and compare holistically sustainability implementation in two similar distance learning universities and to evaluate their advantages and disadvantages. Design/methodology/approach One of the most rigorous and internationally used sustainability assessment tools was used – the Sustainability Tracking, Assessment and Rating System, to evaluate and compare sustainability implementation in two distance universities, one from Spain and another from Portugal: the Madrid Open University and Universidade Aberta. Indicators of both universities were compared and ways of improvement in both universities were widely discussed. Findings The results of this research show that there is a similar pattern in both universities. Both have low performance in campus operations and low levels of community participation but good performance in sustainability courses and programmes offer. The results of both institutions were compared and allowed a learning process for improvement. Originality/value This research hopes to contribute to the continuous research about the usefulness of sustainability assessment tools in particular when applied to distance universities at the time that offers new paths to carry out improved sustainable practices in crucial areas of interest such as research, administration, education and resource-saving. This research also highlights the value of distance learning universities and their ability to be more sustainable after the advent of COVID-19.

BlockChain Technology (BCT) has appeared with strength and promises an authentic revolution on business, management, and organizational strategies related to utilization of advanced software systems.In fact, BCT promotes a decentralized architecture to process management and the collaborative work between entities when these ones are working together in a business process.This paper aims to know what proposals exist to improve any stage of business process management using BCT because this technology could provide benefits in this management.For this purpose, this paper presents a systematic literature review in area of Collaborative Business Processes (CBP) in BCT domain to identify opportunities and gaps for further research.This paper concludes there is a rapid and growing interest of public bodies, scientific community and software industries to know opportunities that BCT offers to improve CBP management in a decentralized manner.However, although the topic is in early stages, there are very promising lines of research and relevant open issues, but there also is lack of scientific rigor in validation process into the different studies.