Smart-Pomodoro is an application aimed at improving efficiency, focus and time management in study sessions for children with Attention-Deficit Hyperactivity Disorder (ADHD). By using Smart-Pomodoro, caregivers can design customized study sessions, thus assisting children to mitigate their ADHD symptoms and influence their motivation. Therefore, the caregiver can set goal-oriented objectives and gamification strategies to make studying more engaging, enhancing self-efficacy, focus, and behaviour. The system also leverages the smartwatch's features to facilitate clear caregiver-child communication. Accordingly, Smart-Pomodoro employs a real-time, smartwatch-based and rule-driven approach to prompt children to plan and manage their study sessions and breaks. This unique feature delivers real-time feedback and gamification for children with ADHD, while increasing the caregiver's awareness of their condition and behaviour throughout the session. In the performed evaluation, parents of children with ADHD (from both primary and secondary) rated the system positively, praising its ease of use, usefulness, and effectiveness.

Purpose This paper aims to investigate the extent of digital intellectual capital disclosure (DICD) through the websites of Spanish public hospitals and analyze the organizational, governance, financial, and political determinants influencing such disclosure.Design/methodology/approach A quantitative approach is applied using content analysis of Spanish public hospitals' official websites to construct a digital intellectual capital disclosure index (DICDI). Subsequently, an econometric regression model is used to examine the impact of different variables - hospital size, board gender diversity, financial performance, indebtedness, and regional political ideology - on the level of DICD. Robustness is evaluated through alternative variable specifications and standard errors clustered at the regional level.Findings Spanish public hospitals display a moderate and heterogeneous level of DICD, with a primary focus on structural and relational capital. Hospital size, organizational complexity, gender diversity, and regional political ideology are positively associated with disclosure levels, while financial performance and indebtedness show a non-significant relationship. These associations remain stable across robustness checks (after controlling for regional clustering effects).Practical implications The findings offer a benchmark for hospital managers to assess their disclosure practices and can inform policymakers about designing incentives to foster greater accountability for intangible assets.Social implications Enhanced intellectual capital transparency can increase public trust and empower patients, allowing them to make more informed decisions.Originality/value This study provides one of the first comprehensive empirical analyses of the potential determinants of DICD in the Spanish public healthcare context. While this study builds upon established theoretical frameworks, its primary contribution lies in its contextual and empirical originality. By integrating multiple organizational, governance and political factors, it offers a nuanced view of hospitals' transparency strategies in a decentralized system, providing evidence that extends beyond traditional private-sector models.

The Smart-Pomodoro system is a wearable study session manager with a visually engaging web application organizer, designed to enhance children's time management, focus, and commitment to learning. It accomplishes this through the implementation of a user centred interface for caregivers and the incorporation of some gamified features and elements such as progress tracking, a objective-reward system, a point system and a virtual pet that reacts to the child's work and progress. The study time and the time management aspects are supported by motivational theories, optimized by the implementation of the Pomodoro technique into the child's work, transforming working into an interactive and pleasurable experience for the child. Additionally, it incorporates a system to increase the caregiver awareness, by analysing the child's state and the environment context using the smartwatch's sensors during work. This data can trigger various actions, enabling adaptive responses to different behaviours for a more personalized and effective learning experience, such as positive feedback, session duration changes and personalized questions to boost self-regulation, error correction and time management.

This paper aims to analyze the online transparency of Spanish higher education institutions and examine the factors that explain the degree of online transparency achieved by these institutions. To this end, this paper analyses the institutional websites of all Spanish universities and develops a global transparency index comprising of four dimensions (“E-information”, “E-Services”, “E-Participation” and “Navigability, Design and Accessibility”). This paper evidences that Spanish universities are aware of the importance of having a web page with adequate navigability, design and accessibility. In contrast, the “E-information” is the least valued dimension, particularly concerning the disclosure about Community services and Outcomes of teaching services. Moreover, the results show that internationality, leverage and size positively affect the online transparency in Spanish universities. From a practical point of view, our findings could be used by university’ managers, regulators and standard-setting bodies to improve the online transparency in universities.

Emotional intelligence (EI) is a basic concept in psychology that is gaining social importance. According to Goleman, it refers to the ability to be aware of and to handle one’s emotions in varying situations. Current research indicates that EI can be improved through training, leading to an increased awareness of how we can contribute to the emotional management. In this paper, a low-cost ElectroEncephaloGraphy (EEG) and PhotoPlethysmoGraphy (PPG) based proposal is introduced in order to assess the level of emotional intelligence among elderly people during cognitive stimulation sessions. Twenty-five older people , who were baby boomers (people born from 1946 to 1964), were recruited during 2 months, while they participated in a cognitive stimulation program. During those months, these participants were trained in different techniques of emotional management and they learned how to use several low-cost EEG and PPG devices. They were subjected to several emotional stimulation sessions where stress and anxiety scenarios were considered. By using our proposal, different supervised learning algorithms were evaluated in order to allow emotion detection, having the Support Vector Machine (SVM) technique as the one that reached better scores. Later, our solution supports emotional intelligence test and promising outcomes were achieved.

Collecting data allows researchers to store and analyze important information about activities, events, and situations. Gathering this information can also help us make decisions, control processes, and analyze what happens and when it happens. In fact, a scientific investigation is the way scientists use the scientific method to collect the data and evidence that they plan to analyze. Neuroscience and other related activities are set to collect their own big datasets, but to exploit their full potential, we need ways to standardize, integrate, and synthesize diverse types of data. Although the use of low-cost ElectroEncephaloGraphy (EEG) devices has increased, such as those whose price is below 300 USD, their role in neuroscience research activities has not been well supported; there are weaknesses in collecting the data and information. The primary objective of this paper was to describe a tool for data management and visualization, called MuseStudio, for low-cost devices; specifically, our tool is related to the Muse brain-sensing headband, a personal meditation assistant with additional possibilities. MuseStudio was developed in Python following the best practices in data analysis and is fully compatible with the Brain Imaging Data Structure (BIDS), which specifies how brain data must be managed. Our open-source tool can import and export data from Muse devices and allows viewing real-time brain data, and the BIDS exporting capabilities can be successfully validated following the available guidelines. Moreover, these and other functional and nonfunctional features were validated by involving five experts as validators through the DESMET method, and a latency analysis was also performed and discussed. The results of these validation activities were successful at collecting and managing electroencephalogram data.

Adapting the User Interface of an interactive application consists in modifying its different elements according to various levels of granularity.Adaptation aims at addressing specific needs, wishes, and requirements either of a particular user or a group of users.While user interface adaptation has been extensively studied, in particular for context awareness, one of the most widely used adaptation life cycles is Dieterich's survey of adaptation techniques.This survey considers only the execution part of the adaptation lifecycle and involves only one actor, user or system, in each adaptation stage.To overcome these shortcomings, we introduce GISATIE, a user interface adaptation life-cycle specifying which agents are involved in each adaptation stage: goals, initiative, specification, application, transition, interpretation, and evaluation.

Situational Awareness (SA) is the perception of our surrounding, comprehension of its meaning and projection of its status. The lack of SA is a causal factor in many military accidents. In this paper, Augmented Reality (AR) techniques are used to support SA and interaction in time-pressuring crisis situations. Firstly, existing military projects are reviewed. Then, the design of a military AR-based architecture as support to SA, called RAIOM (Augmented Reality for the identification of Military Objectives), is introduced and validated with real users considering traditional military scenarios. A distributed processing in a client-server architecture was implemented using an optical see-through AR glasses by the client side and a mini board by the server side. An AR application using the proposed AR architecture was deployed as an integrated proof of concept. The deployment was evaluated to measure the effectiveness, efficiency and usability of the AR software architecture in terms of three levels of SA such as perception, comprehension and projection. Different techniques were used in the evaluation such as User Testing, Thinking Aloud Protocol and SAGAT / SART. Also the User Experience (UX) was evaluated using UMUX questionnaire. The results were optimistic according to the degree of compliance of the SA got by the participants in the experiment.

espanolEl presente trabajo tiene como principal objetivo presentar los resultados del analisis material y tecnico de una seleccion de ceramicas vidriadas del zocalo de azulejos del Patio de las Doncellas del Real Alcazar de Sevilla –Espana–, con el fin de ensayar materiales y procedimientos de reintegracion de alicatados mediante ceramica vidriada, respetando los criterios de diferenciacion y armonia. La metodologia se ha basado en el examen formal (trazado geometrico), estudio colorimetrico y analisis material –microscopia optica y SEM-EDX– mediante toma de muestras de ceramica vidriada. Los resultados muestran una alta calidad decorativa y tecnologica de los azulejos, realizados posiblemente en monococcion, destacando los lustres, elaborados tercera coccion reductora sobre vidriado blanco, situados en los sinos –estrellas–centrales de las ruedas, asi como los efectos de goniocromatismo observables en la superficie del, probable consecuencia del efecto reductor residual del combustible solido empleado en la coccion. EnglishThis work main aim is to show the results of material and technical analysis we performed in selected glazed ceramic tiles in Patio de las Doncellas at Real Alcazar of Seville –Spain– in order to test materials and procedures to reintegrate using glazed ceramic tiles according to differentiation and harmony criteria. The methodology is based on the formal examination – geometric design–, colorimetric study and scientific analysis –through optical microscopy and SEM-EDX– by sampling from glazed ceramic. The results show high decorative and technological quality tiles, possibly made in single firing. The most interesting tiles are called lustres, that are made in a third reducing firing on white glaze, located in the central sinos –stars– of the tracery decoration. Goniochromatic effects on the glaze surface are also significant, as a reducing firing effect.

El desarrollo de aplicaciones para dispositivos móviles es cada vez más imprescindible para cualquier empresa de software. Sin embargo, dicho desarrollo presenta retos adicionales frente al desarrollo tradicional de aplicaciones de escritorio. Las características y capacidades de interacción difieren de las interfaces de escritorio, lo cual plantea la necesidad de incorporar otras maneras distintas de interactuar en los dispositivos móviles. Otro aspecto relevante en el desarrollo de dichas aplicaciones es la fragmentación existente, donde tanto la variabilidad en los sistemas operativos que usan, y por lo tanto en las guías de diseño para dichos sistemas, como las propias características físicas de dichos dispositivos hacen que mantener desarrollos que estén dirigidos a distintas plataformas móviles sea complejo. En este trabajo se presenta una aproximación dirigida por modelos orientada a la generación de aplicaciones móviles que trata de aliviar los problemas anteriormente identificados, a través de una generación automática basada en un perfil UML y modelos habitualmente usados en el diseño de interfaces de usuario basadas en modelos. De esta manera, se persigue impulsar la reutilización de la mayor parte de los modelos creados en la generación para distintas plataformas. Actualmente, la implementación en los casos de estudio se ha centrado en el marco de trabajo de Android.

1 III-LIDI Instituto de Investigación en Informática, Facultad de Informática Universidad Nacional de La Plata (UNLP), Argentina {mjabasolo, degiusti, mnaiouf, ppesado}@lidi.info.unlp.edu.ar 2 Comisión de Investigaciones Científicas de la Provincia de Buenos Aires (CICPBA), Argentina 3 Facultad de Cs.Exactas.Universidad Nacional del Centrode la Pcia.de Bs.As.(UNICEN), Argentina 4 Universidad Técnica de Ambato, Ecuador 5 Instituto de Investigaciones Científicas y Técnicas para la Defensa (CITEDEF), Argentina 6 Universidad de las Islas Baleares (UIB), España 7 Universidad de Castilla-La Mancha (UCLM), España 8 Universidad Nacional del Sur (UNS), Argentina

This paper presents a proposal of a model for identification, selection and classification of Situational Awareness (SA) requirements together with the design of AR-based solutions. In this way, a three dimensional model that considers three main elements: SA phase, SA characteristics and interaction modality is introduced. This 3D-SA model facilitates the analysis, first, and the design later, of systems where the concept of SA is essential. The presented 3D-SA model tries to facilitate the use and application of AR-based solutions for SA for different kinds of stakeholders (developers, maintainers, etc.) as well as to cover a deficiency in the gap between SA requirements and SA design solutions. A particular use of the 3D-SA model in the military field is presented.

In modern software development, much time is devoted and much attention is paid to the activity of data modeling and the translation of data models into databases. This has motivated the proposal of different approaches and tools to support this activity, such as semiautomatic approaches that generate data models from requirements artifacts using text analysis and sets of heuristics, among other techniques. However, these approaches still suffer from important limitations, including the lack of support for requirements traceability, the poor support for detecting and solving conflicts in domain-specific requirements, and the considerable effort required for manually checking the generated models. This paper introduces DataMock, an Agile approach that enables the iterative building of data models from requirements specifications, while supporting traceability and allowing inconsistencies detection in data requirements and specifications. The paper also describes how the approach effectively allows improving traceability and reducing errors and effort to build data models in comparison with traditional, state-of-the-art, data modeling approaches.

The application introduced within this paper, BCI Touch, is based on a prior knowledge base focused on the world of accessibility, within the field of information and communication technologies, EVA Facial Mouse application. Our main objective is to explore new paradigms of interaction, for the specific context of elder people with psycho-motor impairments. Something as routine and humdrum as the use of mobile devices can be an insurmountable barrier depending on the psycho-motor abilities of the user. Therefore, BCI Touch makes use of an innovative data source within the human-computer interaction field, such as brainwaves and brain activity patterns.

The term user experience (UX), even today, is a concept surrounded by ambiguity in its definition, which makes it difficult to be fully understood. There is a wide variety of interpretations around the UX concept, and although there have been attempts to develop a unified view of UX, there is still no common understanding of the nature and scope for the term "user experience". Benefits of a shared vision of the concept fall mainly on the relationship between research and industry. The effectiveness and efficiency of the UX study and learning have a direct impact on the relationship of consumers with the products and services available in the market. The challenge addressed in this article is to analyse the different definitions and interpretations of UX with the aim of deriving common knowledge about the meaning and scope of the term, through machine learning (ML) and natural language processing (NLP).

The use of computers in the e-Health domain is becoming increasingly common, since technology is present in most aspects of our lives. In the rehabilitation field in particular, some additional issues requiring the use of computer-assisted therapies arise. On the one hand, there is a scarce availability of rehabilitation specialists and centers to satisfy the growing demand of their services. This problem gets even magnified because of the ageing population. On the other hand, the huge opportunities that the new interaction devices can bring to rehabilitation smooth the path towards novel therapies. Nevertheless, even if a proper rehabilitation therapy is prescribed, it can fail because of the patient´s lack of motivation There are assorted motivation theories available in the literature to address this demotivation of patients. Unfortunately, there is no model or guide to put those theories into practice in computer-assisted rehabilitation. This paper is aimed at filling this gap by providing a model, namely Influence Awareness, to support the specification of motivation aspects in those applications used in computer-assisted rehabilitation. Furthermore, some guidelines are also provided, so that the designer can get some extra guidance on some heuristics about how to design motivation. The integration of motivation design into a model-based development process is presented by showing how this motivation model is integrated into a task model. Finally, to better illustrate our approach a case study based on a collaborative e-Health system is also included.

The development of app for mobile devices poses extra challenges to the ones already present in the development of the so-called traditional applications. The interaction capabilities differ from those found in desktop applications, which must be carefully considered to produce a natural way of interacting with mobile devices. Another relevant aspect in the development of such apps is fragmentation, given the variability in the operating systems they use, the guidelines that drive the development for each operating system and the hardware characteristics of the devices. In this work a model-driven approach for the generation of mobile apps is introduced aimed at contributing to address the issues previously discussed. This approach relies on a UML profile applied to a set of models, including models such as a task model. Currently, the implementation is focused in Android framework.

The lateral interaction in accumulative computation (LIAC) algorithm is a biologically inspired method that allows us to detect moving objects from image sequences acquired from fixed surveillance cameras. This method achieves excellent precision but requires a high processing time. Sequential implementation is too slow and cannot achieve real-time processing. In this paper, we present several improvements to the LIAC algorithm that increase its efficiency in terms of execution time and energy consumption. In particular, a GPU-based implementation delivers the same precision and is notably faster and more energy efficient than the sequential implementation.

Many researchers have explored the relationship between recurrent neural networks and finite state machines. Finite state machines constitute the best-characterized computational model, whereas artificial neural networks have become a very successful tool for modeling and problem solving. The neurally-inspired lateral inhibition method, and its application to motion detection tasks, have been successfully implemented in recent years. In this paper, control knowledge of the algorithmic lateral inhibition (ALI) method is described and applied by means of finite state machines, in which the state space is constituted from the set of distinguishable cases of accumulated charge in a local memory. The article describes an ALI implementation for a motion detection task. For the implementation, we have chosen to use one of the members of the 16-nm Kintex UltraScale+ family of Xilinx FPGAs. FPGAs provide the necessary accuracy, resolution, and precision to run neural algorithms alongside current sensor technologies. The results offered in this paper demonstrate that this implementation provides accurate object tracking performance on several datasets, obtaining a high F-score value (0.86) for the most complex sequence used. Moreover, it outperforms implementations of a complete ALI algorithm and a simplified version of the ALI algorithm—named “accumulative computation”—which was run about ten years ago, now reaching real-time processing times that were simply not achievable at that time for ALI.