Digital transformation (DT) has become a strategic priority for public administrations, particularly due to the need to deliver more efficient and citizen-centered services and respond to societal expectations, ESG (Environmental, Social, and Governance) criteria, and the United Nations Sustainable Development Goals (UN SDGs). In this context, the main objective of this study is to propose an innovative methodology to automatically evaluate the level of digital transformation (DT) in public sector organizations. The proposed approach combines traditional assessment methods with Artificial Intelligence (AI) techniques. The methodology follows a dual approach: on the one hand, surveys are conducted using specialized staff from various public entities; on the other, AI-based models (including neural networks and transformer architectures) are used to estimate the DT level of the organizations automatically. Our approach has been applied to a real-world case study involving local public administrations in the Valencian Community (Spain) and shown effective performance in assessing DT. While the proposed methodology has been validated in a specific local context, its modular structure and dual-source data foundation support its international scalability, acknowledging that administrative, regulatory, and DT maturity factors may condition its broader applicability. The experiments carried out in this work include (i) the creation of a domain-specific corpus derived from the surveys and websites of several organizations, used to train the proposed models; (ii) the use and comparison of diverse AI methods; and (iii) the validation of our approach using real data. Based on the deficiencies identified, the study concludes that the integration of technologies such as the Internet of Things (IoT), sensor networks, and AI-based analytics can significantly support resilient, agile urban environments and the transition towards more effective and sustainable Smart City models.

The development of interactive analysis dashboards with georeferenced information derived from large volumes of data poses a significant challenge due to the lack of studies that establish a unified strategy for designing effective and functional dashboards. This limitation slows the development of analytical dashboards that require specific features, different from those offered by most tools available on the market. The objective of this study is to develop a procedure for implementing georeferenced analysis dashboards by identifying critical factors and the sequence in which they should be applied. Through a scoping review of the literature and subsequent content analysis, common patterns and recurring approaches in the design of georeferenced analysis dashboards were identified, providing insights aimed at systematizing the development process of these tools. As a result, the study presents a catalog of factors influencing the design and development of the foundational structure of georeferenced analysis dashboards, along with a procedure for their effective implementation. The primary contribution of this research is to provide a framework for the creation of georeferenced dashboards that support decision-making, offering development teams a catalog of key factors and an implementation sequence that facilitates the effective design of these tools, enabling the eventual automation of the process through artificial intelligence.

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The field of human activity recognition has evolved significantly, driven largely by advancements in Internet of Things (IoT) device technology, particularly in personal devices. This study investigates the use of ultra-wideband (UWB) technology for tracking inhabitant paths in home environments using deep learning models. UWB technology estimates user locations via time-of-flight and time-difference-of-arrival methods, which are significantly affected by the presence of walls and obstacles in real environments, reducing their precision. To address these challenges, we propose a fingerprinting-based approach utilizing received signal strength indicator (RSSI) data collected from inhabitants in two flats (60 m ^2 and 100 m ^2 ) while performing daily activities. We compare the performance of convolutional neural network (CNN), long short-term memory (LSTM), and hybrid CNN+LSTM models, as well as the use of Bluetooth technology. Additionally, we evaluate the impact of the type and duration of the temporal window (future, past, or a combination of both). Our results demonstrate a mean absolute error close to 50 cm, highlighting the superiority of the hybrid model in providing accurate location estimates, thus facilitating its application in daily human activity recognition in residential settings.

BACKGROUND:Human Emotion Recognition (HER) has been a popular field of study in the past years. Despite the great progresses made so far, relatively little attention has been paid to the use of HER in autism. People with autism are known to face problems with daily social communication and the prototypical interpretation of emotional responses, which are most frequently exerted via facial expressions. This poses significant practical challenges to the application of regular HER systems, which are normally developed for and by neurotypical people. OBJECTIVE:This study reviews the literature on the use of HER systems in autism, particularly with respect to sensing technologies and machine learning methods, as to identify existing barriers and possible future directions. METHODS:We conducted a systematic review of articles published between January 2011 and June 2023 according to the 2020 PRISMA guidelines. Manuscripts were identified through searching Web of Science and Scopus databases. Manuscripts were included when related to emotion recognition, used sensors and machine learning techniques, and involved children with autism, young, or adults. RESULTS:The search yielded 346 articles. A total of 65 publications met the eligibility criteria and were included in the review. CONCLUSIONS:Studies predominantly used facial expression techniques as the emotion recognition method. Consequently, video cameras were the most widely used devices across studies, although a growing trend in the use of physiological sensors was observed lately. Happiness, sadness, anger, fear, disgust, and surprise were most frequently addressed. Classical supervised machine learning techniques were primarily used at the expense of unsupervised approaches or more recent deep learning models. Studies focused on autism in a broad sense but limited efforts have been directed towards more specific disorders of the spectrum. Privacy or security issues were seldom addressed, and if so, at a rather insufficient level of detail.

In this article, we present CuentosIE (TalesEI: chatbot of tales with a message to develop Emotional Intelligence), an educational chatbot on emotions that also provides teachers and psychologists with a tool to monitor their students/patients through indicators and data compiled by CuentosIE. The use of “tales with a message” is justified by their simplicity and easy understanding, thanks to their moral or associated metaphors. The main contributions of CuentosIE are the selection, collection, and classification of a set of highly specialized tales, as well as the provision of tools (searching, reading comprehension, chatting, recommending, and classifying) that are useful for both educating users about emotions and monitoring their emotional development. The preliminary evaluation of the tool has obtained encouraging results, which provides an affirmative answer to the question posed in the title of the article.

The ageing of today’s society, according to demographic and epidemiological data, presents a significant increase in the elderly population. Following the experienced pandemic, research in telemedicine to improve the lives of elderly people through a comprehensive program developed by multidisciplinary teams has become a top priority. This enables the provision of remote healthcare services, facilitating access to specialists, disease monitoring, medication management, and health indicator tracking to address the medical, social, and emotional needs of elderly individuals. This study proposes a sensor-based approach to identify activity patterns without prior labels. The system architecture responsible for collecting data from the monitored user in the assisted living facility consists of a beacon, multiple anchors, and various sensors for motion, opening and closing, temperature, and humidity. The experimentation was carried out with distinct activities such as sleeping, eating, taking medication, walking, showering, and brushing teeth, inferred from the identified patterns. This approach offers an automatic and objective way to understand the routines and behaviours of older individuals, thereby improving their care and attention through personalized interventions tailored to their individual needs. Furthermore, it lays the groundwork for future research on the detection and monitoring of changes in activities over time, identifying possible signs of impairment or changes in the health of elderly people.

Autism Spectrum Disorder (ASD) is a developmental disability primarily characterized by challenges in social interaction and communication. Due to the unknown etiology of ASD, numerous computational psychiatry research studies have been carried out to identify pertinent features and uncover hidden correlations to detect this type of disability at an early stage. The aim of this ongoing project is to present the initial tests carried out on autistic children by analysing their conversations or writings to assess their social skills in order to find indicators for the most personalised intervention possible. This model would consist of the most advanced machine learning algorithms and Natural Language Processing techniques (e.g. Transformers or ChatGPT). The paper concludes by presenting a case study that utilized autism data to verify the efficacy of our proposed model, demonstrating remarkably promising findings.

The importance of data security is currently increasing owing to the number of data transactions that are continuously taking place. Large amounts of data are generated, stored, modified and transferred every second, signifying that databases require an appropriate capacity, control and protection that will enable them to maintain a secure environment for so much data. Big Data is becoming a prominent trend in our society, and increasing amounts of data, including sensitive and personal information, are being loaded into NoSQL and other Big Data technologies for analysis and processing. However, current security approaches do not take into account the special characteristics of these technologies, leaving sensitive and personal data unprotected and consequently risking considerable financial losses and brand damage. In this paper, we focus on NoSQL document databases and present a proposal for the design and implementation of security policies in this type of databases. We first follow the concept of security by design in order to propose a metamodel that allows the specification of both the structure and the security policies required for document databases. We also define an implementation model by analysing the implementation features provided by a specific NoSQL document database management system (MongoDB). Having obtained the design and implementation models, we follow the model-driven development philosophy and propose a set of transformation rules that allow the automatic generation of the final implementation of security policies. We additionally provide a technological solution in which the Eclipse Modelling Framework environment is employed in order to implement both the design metamodel (Emfatic) and the transformations (Epsilon, EGL). Finally, we apply the proposed framework to a case study carried out in the airport domain. This proposal, in addition to saving development time and costs, generates more robust solutions by considering security by design. This, therefore, abstracting the designer from both specific aspects of the target tool and having to choose the best strategies for the implementation of security policies.

The scientific and technological evolution that has taken place in the field of human activity recognition, while enormous, is possibly only the tip of the iceberg of the possibilities that we are currently facing and will continue to experience in the foreseeable future. Much of this innovation has also been driven by the rise of IoT technology at the personal device level which has enabled convenient application in the fields of assisting living, ambient intelligence, and e-health. This paper presents a proposal that is part of an ongoing project for the deployment of sensors in supervised housing, with the challenges and opportunities that this implies. At the moment, different technical possibilities are being assessed for the implementation of the schemes, both at macro and micro level, which are described in the proposed architecture detailed in the solutions presented in this paper. The special characteristics of the individuals, usually with different types of disabilities, who live in these homes make this exciting project, marked with a very high social component, a very big challenge for their inclusion in society.

The problem of lack of data remains a major drawback regardless of the continuous evolution of machine learning and deep learning models. This paper presents a modular and scalable architecture for the prediction and treatment of autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD). With this architecture, therapists will be able to collect data from individuals from anywhere and at anytime, thanks to mobile devices, which will enable personalised monitoring. One of the main objectives of this ongoing project, which has a very widespread international projection within the framework of social inclusion, is the creation of a new collection of data due to the lack of data in this area. As a result, we will be able to place it in important repositories and specialised journals and thereby make it available to the scientific community. This architecture has been evaluated with several supervised and unsupervised machine learning algorithms in order to identify possible candidates for ASD/ADHD diagnosis. The initial results are very encouraging despite the small volume of data because they allow the possibility of developing personalised dashboards, which specialists can adapt to the personalised treatment of each individual according to the indicators obtained.

Nowadays, millions of users are using devices to access Internet services, and the growth of this is just exponential. Simultaneously, mobile cloud computing (MCC) tries to distribute computation from mobile devices, solving the limitations of them with the capacity of cloud computing (CC). There are also other approaches that share MCC and computation in the local devices. CC still confronts several challenges, one of which is quality of services (QoS), which is especially crucial in health-care applications. Therefore one of the main contributions of this chapter is a review of the literature of QoS of MCC and the most novel and current applications, mainly in the health area. We also discuss all the challenges and opportunities that MCC applications are facing currently as well as the current status related to performance evaluation and QoS in MCC. The challenge for future work is to integrate the different systems adapting the new technologies both in communications and in data analysis.

NoSQL technologies have become a common component in many information systems and software applications. These technologies are focused on performance, enabling scalable processing of large volumes of structured and unstructured data. Unfortunately, most developments over NoSQL technologies consider security as an afterthought, putting at risk personal data of individuals and potentially causing severe economic loses as well as reputation crisis. In order to avoid these situations, companies require an approach that introduces security mechanisms into their systems without scrapping already in-place solutions to restart all over again the design process. Therefore, in this paper we propose the first modernization approach for introducing security in NoSQL databases, focusing on access control and thereby improving the security of their associated information systems and applications. Our approach analyzes the existing NoSQL solution of the organization, using a domain ontology to detect sensitive information and creating a conceptual model of the database. Together with this model, a series of security issues related to access control are listed, allowing database designers to identify the security mechanisms that must be incorporated into their existing solution. For each security issue, our approach automatically generates a proposed solution, consisting of a combination of privilege modifications, new roles and views to improve access control. In order to test our approach, we apply our process to a medical database implemented using the popular document-oriented NoSQL database, MongoDB. The great advantages of our approach are that: (1) it takes into account the context of the system thanks to the introduction of domain ontologies, (2) it helps to avoid missing critical access control issues since the analysis is performed automatically, (3) it reduces the effort and costs of the modernization process thanks to the automated steps in the process, (4) it can be used with different NoSQL document-based technologies in a successful way by adjusting the metamodel, and (5) it is lined up with known standards, hence allowing the application of guidelines and best practices.

Different fields such as linguistics, teaching, and computing have demonstrated special interest in the study of sign languages (SL). However, the processes of teaching and learning these languages turn complex since it is unusual to find people teaching these languages that are fluent in both SL and the native language of the students. The teachings from deaf individuals become unique. Nonetheless, it is important for the student to lean on supportive mechanisms while being in the process of learning an SL. Bidirectional communication between deaf and hearing people through SL is a hot topic to achieve a higher level of inclusion. However, all the processes that convey teaching and learning SL turn difficult and complex since it is unusual to find SL teachers that are fluent also in the native language of the students, making it harder to provide computer teaching tools for different SL. Moreover, the main aspects that a second language learner of an SL finds difficult are phonology, non-manual components, and the use of space (the latter two are specific to SL, not to spoken languages). This proposal appears to be the first of the kind to favor the Costa Rican Sign Language (LESCO, for its Spanish acronym), as well as any other SL. Our research focus stands on reinforcing the learning process of final-user hearing people through a modular architectural design of a learning environment, relying on the concept of phonological proximity within a graphical tool with a high degree of usability. The aim of incorporating phonological proximity is to assist individuals in learning signs with similar handshapes. This architecture separates the logic and processing aspects from those associated with the access and generation of data, which makes it portable to other SL in the future. The methodology used consisted of defining 26 phonological parameters (13 for each hand), thus characterizing each sign appropriately. Then, a similarity formula was applied to compare each pair of signs. With these pre-calculations, the tool displays each sign and its top ten most similar signs. A SUS usability test and an open qualitative question were applied, as well as a numerical evaluation to a group of learners, to validate the proposal. In order to reach our research aims, we have analyzed previous work on proposals for teaching tools meant for the student to practice SL, as well as previous work on the importance of phonological proximity in this teaching process. This previous work justifies the necessity of our proposal, whose benefits have been proved through the experimentation conducted by different users on the usability and usefulness of the tool. To meet these needs, homonymous words (signs with the same starting handshape) and paronyms (signs with highly similar handshape), have been included to explore their impact on learning. It allows the possibility to apply the same perspective of our existing line of research to other SL in the future.

In the complex study to obtain indicators in the autism spectrum disorder it is very common to perform many and very complex tasks. Often, these tasks require the completion of a series of forms and surveys that are even more complex and tedious, which means that the accuracy of the reports is not always satisfactory. In this paper, we propose a general architecture based on machine learning techniques and data mining for prediction of the main indicators in the diagnosis and intervention of the autistic spectrum disorder. The main idea of this approach is to replace those print documents by mobile tests, tablet or smartphones tests through games, store them in databases and analyse them. Furthermore, very often these last two steps are not undertaken with the lack of quantitative and qualitative analysis that could be generated. Finally, the presented architecture is oriented to data collection with the objective of the creation of large specialized datasets.

The study of sign languages (SL) has generated great interest in the fields of linguistics, computing and teaching. Teaching and learning SL are complex tasks, since they are rarely taught by people who are fluent both in SL and the native language of the students. These lessons taught by deaf people are irreplaceable, but it is appropriate for the student to have support mechanisms in the process. This paper presents a SL learning reinforcement tool that uses phonological proximity to improve the obtained results. The methodology used consisted of mapping the phonological parameters of each sign in the lexicon to numerical values and then constructing a matrix where each sign is compared with the others by means of a classic measure of similarity. As far as the authors know, this is the first time that this type of proposal is made for Costa Rican SL (LESCO, for its Spanish acronym), incorporating phonological proximity in a reinforcement tool. The operation of a graphical software tool is explained, showing grouped concepts and reproducing the signs, interfacing with the computerized avatar of the already operational International Platform for Sign Language Edition (PIELS, for its Spanish acronym), expanding its current functionality by learning signs with similar handshapes. For these purposes, homonyms, paronyms, and polysemy are explored, to the extent that these concepts apply to SL. The incorporation of phonological proximity to the tool is explored, in order to reinforce LESCO learning, offering the possibility of using the same approach in any other SL.

Nowadays, artificial intelligence techniques are applied in many fields such as industry, natural language processing, or medicine. In this chapter, we have focused on the health area. Within the field of health, artificial intelligence acts in different ways, from predicting any type of disease or dysfunctions in order to establish correlations between different measures collected. Here, we have addressed the application of data mining and machine learning techniques for early detection in autism spectrum disorder. Our approach consists of an architecture based on five steps: (1) search and selection of data sources; (2) application of extract, transform, and load process; (3) creation of the data warehouse and data mart; (4) application of machine learning techniques with the aim of extracting relevant information from selected information sources in order to correctly classify the new data; and (5) visualization of results. In order to test our methodology, we present a case study using three autism datasets with patients of different ages. We used different machine learning techniques in order to compare the results obtained and to find out the best techniques depending on the input data and indicators collected. Finally, we have created dashboards using two of the most significant tools (i.e., Power BI and Tableau) in order to graphically analyze the results.

Nowadays, the increasing demand of water for electricity production, agricultural and industrial uses are directly affecting the reduction of available quality water for human consumption in the world. Efficient and sustainable maintenance of water reservoirs and supply networks implies a holistic strategy that takes into account, as much as possible, information from the stages of water usage. Next,-generation decision-making software tools, for supporting water management, require the integration of multiple and heterogeneous data sources of different knowledge domains. In this regard, Linked Data and Semantic Web technologies enable harmonization of different data sources, as well as the efficient querying for feeding upper-level Business Intelligence processes. This work investigates the design, implementation and usage of a semantic approach driven by ontology to capture, store, integrate and exploit real-world data concerning water supply networks management. As a main contribution, the proposal helps with obtaining semantically enriched linked data, enhancing the analysis of water network performance. For validation purposes, in the use case, a series of data sources from different measures have been considered, in the scope of an actual water management system of the Mediterranean region of Valencia (Spain), throughout several years of activity. The obtained experience shows the benefits of using the proposed approach to identify possible correlations between the measures such as the supplied water, the water leaks or the population.

About 15% of the world’s population suffers from some form of disability. In developed countries, about 1.5% of children are diagnosed with autism. Autism is a developmental disorder distinguished mainly by impairments in social interaction and communication and by restricted and repetitive behavior. Since the cause of autism is still unknown, there have been many studies focused on screening for autism based on behavioral features. Thus, the main purpose of this paper is to present an architecture focused on data integration and analytics, allowing the distributed processing of input data. Furthermore, the proposed architecture allows the identification of relevant features as well as of hidden correlations among parameters. To this end, we propose a methodology able to integrate diverse data sources, even data that are collected separately. This methodology increases the data variety which can lead to the identification of more correlations between diverse parameters. We conclude the paper with a case study that used autism data in order to validate our proposed architecture, which showed very promising results.