Improving Sustainability of Smart Cities through Visualization Techniques for Big Data from IoT Devices

Fostering sustainability is paramount for Smart Cities development. Lately, Smart Cities are benefiting from the rising of Big Data coming from IoT devices, leading to improvements on monitoring and prevention. However, monitoring and prevention processes require visualization techniques as a key component. Indeed, in order to prevent possible hazards (such as fires, leaks, etc.) and optimize their resources, Smart Cities require adequate visualizations that provide insights to decision makers. Nevertheless, visualization of Big Data has always been a challenging issue, especially when such data are originated in real-time. This problem becomes even bigger in Smart City environments since we have to deal with many different groups of users and multiple heterogeneous data sources. Without a proper visualization methodology, complex dashboards including data from different nature are difficult to understand. In order to tackle this issue, we propose a methodology based on visualization techniques for Big Data, aimed at improving the evidence-gathering process by assisting users in the decision making in the context of Smart Cities. Moreover, in order to assess the impact of our proposal, a case study based on service calls for a fire department is presented. In this sense, our findings will be applied to data coming from citizen calls. Thus, the results of this work will contribute to the optimization of resources, namely fire extinguishing battalions, helping to improve their effectiveness and, as a result, the sustainability of a Smart City, operating better with less resources. Finally, in order to evaluate the impact of our proposal, we have performed an experiment, with non-expert users in data visualization.This work has been co-funded by the ECLIPSE-UA (RTI2018-094283-B-C32) project funded by Spanish Ministry of Science, Innovation, and Universities and the DQIoT (INNO-20171060) project funded by the Spanish Center for Industrial Technological Development, approved with an EUREKA quality seal (E!11737DQIOT). Ana Lavalle holds an Industrial PhD Grant (I-PI 03-18) co-funded by the University of Alicante and the Lucentia Lab Spin-off Company

Fostering Sustainability through Visualization Techniques for Real-Time IoT Data: A Case Study Based on Gas Turbines for Electricity Production

Improving sustainability is a key concern for industrial development. Industry has recently been benefiting from the rise of IoT technologies, leading to improvements in the monitoring and breakdown prevention of industrial equipment. In order to properly achieve this monitoring and prevention, visualization techniques are of paramount importance. However, the visualization of real-time IoT sensor data has always been challenging, especially when such data are originated by sensors of different natures. In order to tackle this issue, we propose a methodology that aims to help users to visually locate and understand the failures that could arise in a production process.This methodology collects, in a guided manner, user goals and the requirements of the production process, analyzes the incoming data from IoT sensors and automatically derives the most suitable visualization type for each context. This approach will help users to identify if the production process is running as well as expected; thus, it will enable them to make the most sustainable decision in each situation. Finally, in order to assess the suitability of our proposal, a case study based on gas turbines for electricity generation is presented.This work has been co-funded by the ECLIPSE-UA (RTI2018-094283-B-C32) project funded by Spanish Ministry of Science, Innovation, and Universities and the DQIoT (INNO-20171060) project funded by the Spanish Center for Industrial Technological Development, approved with an EUREKA quality seal (E!11737DQIOT). Ana Lavalle holds an Industrial PhD Grant (I-PI 03-18) co-funded by the University of Alicante and the Lucentia Lab Spin-off Company

A smart data holistic approach for context-aware data analytics (AETHER-UA)

A smart data holistic approach for context-aware data analytics (AETHER-UA) is one of the four subprojects, developed in the University of Alicante, being part of the whole project AETHER. This project is being developed by four partners: (i) University of Malaga - Coordinator; (ii) University of Alicante, (iii) University of Castilla La-Mancha, and (iv) University of Seville. The project is funded by the Ministry of Science and Innovation. The main goal of this project is to advance towards a knowledge-based framework integrating novel solutions for data, process and business analytics. The research activities for designing and developing Aether will mainly focus on three main challenges: the characterization of the datasets, the improvement and automation of the algorithms, and the generation of mechanisms to enhance model explainability and interpretation of the results. The project is highly related to data processing, integration, analysis and modeling. More concretely, within the AETHER-UA project, several proposals are being developed for the modeling of user’s requirements for Machine Learning applications, the developing of a framework based on Model Driven Development (MDD) for eXplanable Artificial Intelligence and several approaches for the data bias analysis

Assessing the impact of the awareness level on a co-operative game

Context: When playing a co-operative game, being aware of your collaborators (where they are playing, what they are doing, the abilities they have, etc.) is essential for achieving the game's goals. This led to the definition of Gamespace Awareness in order to guide in the identification of the awareness needs in the form of a compilation of the awareness elements that a co-operative game should feature. Objective: Gamespace Awareness does not establish how much awareness information players must be provided with. This constitutes the main motivation for this work: to assess the impact of different levels of Gamespace Awareness elements on a co-operative game. Method: A multiplayer action game was developed that supports three different awareness configurations, each one featuring different awareness levels (high, medium and low). The impact of these awareness levels was measured as regards game score, time, players’ happiness while playing, enjoyment and perceived usefulness. Several techniques such as subjective surveys and facial expression analysis were used to measure these factors. Results: The analysis of the results shows that the higher the awareness, the better the game score. However, the highest level of player happiness was not achieved with the most awareness-enabled configuration; we found that the players’ enjoyment depends not only on their awareness level but also on their expertise level. Finally, the awareness elements related to the present and the future were the most useful, as could be expected in a multiplayer action game. Conclusions: The results showed that the medium level awareness obtained the best results. We therefore concluded that a certain level of awareness is necessary, but that excessive awareness could negatively affect the game experience

The New Era of Business Intelligence Applications: Buildingfrom a Collaborative Point of View

Collaborative business intelligence (BI) iswidely embraced by enterprises as a way of making themost of their business processes. However, decision mak-ers usually work in isolation without the knowledge or thetime needed to obtain and analyze all the available infor-mation for making decisions. Unfortunately, collaborativeBI is currently based on exchanging e-mails and documentsbetween participants. As a result, information may be lost,participants may become disoriented, and the decision-making task may not yield the needed results. The authorspropose a modeling language aimed at modeling andeliciting the goals and information needs of participants ofcollaborative BI systems. This approach is based on inno-vative methods to elicit and model collaborative systemsand BI requirements. A controlled experiment was per-formed to validate this language, assessing its under-standability, scalability, efficiency, and user satisfaction byanalyzing two collaborative BI systems. By using theframework proposed in this work, clear guideless can beprovided regarding: (1) collaborative tasks, (2) their par-ticipants, and (3) the information to be shared among them.By using the approach to design collaborative BI systems,practitioners may easily trace every element needed in thedecision processes, avoiding the loss of information andfacilitating the collaboration of the stakeholders of suchprocesses

A novel approach to identify the brain regions that best classify ADHD by means of EEG and deep learning

Objective: Attention-Deficit Hyperactivity Disorder (ADHD) is one of the most widespread neurodevelopmental disorders diagnosed in childhood. ADHD is diagnosed by following the guidelines of Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). According to DSM-5, ADHD has not yet identified a specific cause, and thus researchers continue to investigate this field. Therefore, the primary objective of this work is to present a study to find the subset of channels or brain regions that best classify ADHD vs Typically Developing children by means of Electroencephalograms (EEG). Methods: To achieve this goal, we present a novel approach to identify the brain regions that best classify ADHD using EEG and Deep Learning (DL). First, we perform a filtering and artefact removal process on the EEG signal. Then we generate different subsets of EEG channels depending on their location on the scalp (hemispheres, lobes, sets of lobes and single channels) and using backward and forward stepwise feature selection methods. Finally, we feed the DL neural network with each set, and compute the f1-score. Results and Conclusions: Based on the obtained results, the Frontal Lobe (FL) (0.8081 f1-score) and the Left Hemisphere (LH) (0.8056 f1-score) provide more significant information detecting individuals with ADHD, than using the entire set of EEG Channels (0.8067 f1-score). However, when combining the Temporal, Parietal and Occipital Lobes (TL, PL, OL), better results (0.8097 f1-score) were obtained compared with using only the FL and LH subsets. The best performance was obtained using Feature Selection Methods. In the case of the Backward Stepwise Feature Selection method, a combination of 14 EEG channels yielded a 0.8281 f1-score. Similarly, using the Forward Stepwise Feature Selection method, a combination of 11 EEG channels yielded a 0.8271 f1-score. These findings hold significant value for physicians in the quest to better understand the underlying causes of ADHD.This work has been co-funded by the BALLADEER (PROMETEO / 2021 / 088) project, a Big Data analytical platform for the diagnosis and treatment of Attention Deficit Hyperactivity Disorder (ADHD) featuring extended reality, funded by the Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital (Generalitat Valenciana) and the AETHER-UA project (PID2020-112540RB-C43), a smart data holistic approach for context-aware data analytics: smarter machine learning for business modelling and analytics, funded by Spanish Ministry of Science and Innovation. Javier Sanchis is part of the Program for the Promotion of R+D+I (UAIND20-03B); Vicerrectorado de Investigación y Transferencia de Conocimiento de la Universidad de Alicante. Sandra García-Ponsoda holds a predoctoral contract granted by ValgrAI - Valencian Graduate School and Research Network of Artificial Intelligence and the Generalitat Valenciana, and co-funded by the European Union

Measuring attention of ADHD patients by means of a computer game featuring biometrical data gathering

ADHD is a neurodevelopmental disorder diagnosed mainly in children, marked by inattention and hyperactivity-impulsivity. The symptoms are highly variable, such as different ages of onset and potential comorbidities, contributing to frequent misdiagnoses. Professionals note a gap in modern diagnostic tools, making accurate identification challenging. To address this, recent studies recommend gamification for better ADHD diagnosis and treatment, though further research is essential to confirm its efficacy. This work aims to create a serious game, namely “Attention Slackline", to assess attention levels. The game, designed with expert input, requires players to concentrate on a specific point to recognize specific patterns while managing distractions. A controlled experiment tested its precision, and results were compared with established attention tests by a correlation analysis. Statistical analysis confirmed the game's validity, especially in tracking attention through correct responses and errors. Preliminary evidence suggests that “Attention Slackline" may serve as a credible instrument for the assessment of attentional capacities in individuals with ADHD, given that its outcomes have been empirically shown to correlate with those derived from a well-established attention assessment methodology.This work has been co-funded by the BALLADEER (PROMETEO/2021/088) project, a Big Data analytical plat-form for the diagnosis and treatment of Attention Deficit Hyperactivity Disorder (ADHD) featuring extended reality, funded by the ,Conselleria de Innovación, Universidades Ciencia y Sociedad Digital (Generalitat Valenciana), Grant RED2022-134656-T funded by MCIN/AEI/10.13039/501100011033 and Program for the Promotion of R + D + I (UAIND20-03B) by Vicerrectorado de Investigación y Transferencia de Conocimiento de la Universidad de Alicante, and by the AETHER-UA project (PID2020-112540RB-C43), a smart data holistic approach for context-aware data analytics: smarter machine learning for business modelling and analytics, funded by Spanish Ministry of Science and Innovation

Ambient Intelligence Environment for Home Cognitive Telerehabilitation

Higher life expectancy is increasing the number of age-related cognitive impairment cases. It is also relevant, as some authors claim, that physical exercise may be considered as an adjunctive therapy to improve cognition and memory after strokes. Thus, the integration of physical and cognitive therapies could offer potential benefits. In addition, in general these therapies are usually considered boring, so it is important to include some features that improve the motivation of patients. As a result, computer-assisted cognitive rehabilitation systems and serious games for health are more and more present. In order to achieve a continuous, efficient and sustainable rehabilitation of patients, they will have to be carried out as part of the rehabilitation in their own home. However, current home systems lack the therapist’s presence, and this leads to two major challenges for such systems. First, they need sensors and actuators that compensate for the absence of the therapist’s eyes and hands. Second, the system needs to capture and apply the therapist’s expertise. With this aim, and based on our previous proposals, we propose an ambient intelligence environment for cognitive rehabilitation at home, combining physical and cognitive activities, by implementing a Fuzzy Inference System (FIS) that gathers, as far as possible, the knowledge of a rehabilitation expert. Moreover, smart sensors and actuators will attempt to make up for the absence of the therapist. Furthermore, the proposed system will feature a remote monitoring tool, so that the therapist can supervise the patients’ exercises. Finally, an evaluation will be presented where experts in the rehabilitation field showed their satisfaction with the proposed system.This work was partially supported by Spanish Ministerio de Economía y Competitividad/FEDER under TIN2016-79100-R grant. Miguel Oliver holds an FPU scholarship (FPU13/03141) from the Spanish Government

Applying Thematic Analysis to define an Awareness Interpretation for Collaborative Computer Games

Abstract. Collaborative computer games have evolved from single-player to massively multiplayer games, usually involving collaboration to achieve team goals. As a consequence of such evolution, these players should be provided with feedback that enables them to perform collaborative tasks with other team members. The objective of this work is the analysis of current awareness interpretations in order to develop a new one that guides stakeholders while collect the needs of such games. This analysis has been conducted by means of a step-bystep Thematic Analysis of current interpretations that led us to extract the most relevant awareness elements defined in existing interpretations. This has resulted in the definition of Gamespace Awareness, a new interpretation based on a combination of the previously analyzed ones, which is suitable for collaborative computer games. Gamespace Awareness combines the potential awareness elements needed for collaborative computer games, making it possible to identify the awareness requirements of these games from the very beginning

Association of CD247 polymorphisms with rheumatoid arthritis: a replication study and a meta-analysis

Given the role of CD247 in the response of the T cells, its entailment in autoimmune diseases and in order to better clarify the role of this gene in RA susceptibility, we aimed to analyze CD247 gene variants previously associated with other autoimmune diseases (rs1052237, rs2056626 and rs864537) in a large independent European Caucasian population. However, no evidence of association was found for the analyzed CD247 single-nucleotide polymorphisms (SNPs) with RA and with the presence/absence of anti-cyclic citrullinated polypeptide. We performed a meta-analysis including previously published GWAS data from the rs864537 variant, revealing an overall genome-wide significant association between this CD247 SNP and RA with anti-CCP (OR = 0.90, CI 95% = 0.87-0.93, Poverall = 2.1×10−10). Our results show for first time a GWAS-level association between this CD247 polymorphism and RA risk