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The objective of this work is to perform a Systematic Mapping Study (SMS) on works that deal with the combined use of Multi-agent Systems (MAS) and Virtual Reality (VR). 83 relevant studies were found, which were categorized according to their purpose. This review led to establishing current and future lines of research on the subject.

Artificial Intelligence (AI) and its applications have undergone remarkable experimental development in the last decade and are now the basis for a large number of decision support systems [...]

One of the main purposes of financial technology solutions is to reduce the infrastructure costs of financial institutions. Adopting the use of FinTech technologies implies, in addition to a reduction in costs derived from physical entities, an improvement in user satisfaction, greater flexibility in terms of data access on different devices and more transparency in financial management. As a result, this paper proposes a blockchain-based scalable platform for investment recommendations. The platform proposed in this research serves as a decision support tool. A use case is included showing the process from the initial product search to the final investment recommendation that concludes in the optimal portfolio.

Edge Computing represents the activities of IoT (Internet of Things) devices at the border or limit of the network connected to the remote Cloud. The latest research in this field has intended to demonstrate that Edge Computing architectures are the optimal solution to minimising latency, improving privacy and reducing bandwidth costs in IoT-based scenarios. This article is a review of the Edge Computing technology and its reference architectures proposed by the Edge Computing Consortium, Intel-SAP, the FAR-Edge Project and the Industrial Internet Consortium for Industry 4.0. Moreover, this article presents a proposal for a tiered architecture with a modular approach that allows to manage the complexity of solutions not only for Industry 4.0 environments but also for other scenarios such as smart cities, smart energy, healthcare or precision agrotechnology. The main contributions of the proposed architecture reside in the security and privacy provided by blockchain technologies. Finally, the proposed reference architecture is tested by building an IoT platform in a smart agroindustry scenario to reduce bandwidth costs between the Edge and the Cloud.

Multi-agent systems integrate a great variety of artificial intelligence techniques from different fields, these systems have made it possible to create intelligent systems more efficiently. On the other hand, virtual reality applications are accepted as viable techniques in different areas such as visualization, simulation, design, and research. The combined use of these two technologies has led to the development of realistic and interactive applications. This work aims to do a Systematic Mapping Study (SMS) relying on the guidelines of Kitchenham and Petersen to analyze the state of the art of VR applications using multi-agent systems. Inclusion and exclusion criteria have been applied to identify relevant papers, 82 articles were selected and categorized according to the publication type, the research type, the asset type, and the purpose of the work. A complete review of the 82 selected articles was performed, based on the research questions that were established. This review made it possible to clarify the open lines of research that exist and to know where research in this field can be directed.

This article describes the development of a recommender system to obtain buy/sell signals from the results of technical analyses and of forecasts performed for companies operating in the Spanish continuous market. It has a modular design to facilitate the scalability of the model and the improvement of functionalities. The modules are: analysis and data mining, the forecasting system, the technical analysis module, the recommender system, and the visualization platform. The specification of each module is presented, as well as the dependencies and communication between them. Moreover, the proposal includes a visualization platform for high-level interaction between the user and the recommender system. This platform presents the conclusions that were abstracted from the resulting values.

The transport and logistics sector, by its nature, requires an intensive and efficient exchange of data for effective management and decision-making. The maritime sector requires a special treatment due to the complex and heterogeneous environment. The rapid adoption of IoT technologies in the naval industry will facilitate the effective management of primary services, including vessel tracking, emissions control, predictive maintenance, safety and welfare. This article describes IoT solutions adapted to the current needs of the sector. The proposals are aligned with the SMARTSEA project, which aims to develop an interactive MSc course on Maritime and Surveyor ICT/IoT systems, helping complete the market void in technical and maintenance specialists generated by the prompt expansion of the Smart Maritime & Surveying industry.

Following data ethics and respecting the clients’ privacy, the banking environment can use the client data that is available to them to offer personalized services to its clients. Intelligent recommender systems can support this attempt through specialized technological architectures. This article proposes the inclusion of CEBRA (CasE-Based Reasoning Application), a case-based reasoning system oriented to commercial banking, in a Fog Computing architecture coordinated by virtual agents. Throughout this article, the model of this architecture is presented and its life cycle is described, and improvements are proposed through the incorporation of several techniques in the retrieve and reuse phases, including the extraction of interests expressed by users on their social network profiles and collaborative filtering systems. A comprehensive case study has been carried out and a dataset of 60,000 cases has been generated to evaluate CEBRA. As a result, the Recommender System is presented, by including, the recommendation algorithm and a REST interface for its use. The recommendations are based on the user’s profile, previous ratings and/or additional knowledge such as the user’s contextual information. The proposal takes advantage of contextual information to support the promotion of banking and financial products, improving user satisfaction.

El Internet de las Cosas ha venido incorporándose a nuestras vidas de forma progresiva, trayendo consigo grandes beneficios para la humanidad como lo es disponer de infraestructuras y servicios más interconectados y eficientes, generación de empleo, reducción de costos operativos e incremento de ganancias. En este sentido, el desarrollo e instalación de sensores avanzados para recolección de datos, las soluciones informáticas de conexión remota y otras tecnologías disruptivas están marcando un proceso de transformación en la industria; dando inicio a lo que diversos sectores han denominado cuarta revolución industrial o Industria 4.0. En este artículo se presenta un modelo predictivo para la extracción de patrones utilizando técnicas de fusión de datos que permitan el diseño de un modelo de mantenimiento predictivo, a través de unenfoque de entrenamiento supervisado, realizar la clasificación de datos y probabilísticamentevalores predictivos., The Internet of Things has been incorporated into our lives progressively, bringing with it great benefits for humanity such as having more interconnected and efficient infrastructures and services, generating employment, reducing operating costs and increasing profits. In this sense, the development and installation of advanced sensors for data collection, remote connection computing solutions and other disruptive technologies are marking a process of transformation in the industry; giving rise to what several sectors have called the fourth industrial revolution or Industry 4.0. This article presents a predictive model for the extraction of patterns using data fusion techniques that allow the design of a predictive maintenance model, which allows using a supervised training approach, perform data classification and probabilistically predictive values.

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Visual and musical arts has been strongly interconnected throughout history. The aim of this work is to compose music on the basis of the visual characteristics of a video. For this purpose, descriptive music is used as a link between image and sound and a video fragment of film Fantasia is deeply analyzed. Specially, convolutional neural networks in combination with transfer learning are applied in the process of extracting image descriptors. In order to establish a relationship between the visual and musical information, Naive Bayes, Support Vector Machine and Random Forest classifiers are applied. The obtained model is subsequently employed to compose descriptive music from a new video. The results of this proposal are compared with those of an antecedent work in order to evaluate the performance of the classifiers and the quality of the descriptive musical composition.

The Internet of Things (IoT) ceased to be a novel technology to become part of daily life through the millions of sensors, devices and tools that measure, collect, process and transfer data. The need to exchange, process, filter and store this huge volume of data has led to the emergence of Edge Computing (EC). The purpose of this new paradigm is to solve the challenges of IoT such as localized computing, reducing latency in information exchange, balancing data traffic on the network and providing responses in real-time. In order to reduce the complexity in the implementation of EC architectures, Software Defined Networks (SDNs) and the related concept Network Function Virtualization (NFV) are proposed by different approaches. This paper addresses the characteristics and capabilities of SDNs and NFV and why can be successful an innovative integration between SDNs and EC for IoT scenarios.

Neuro-symbolic hybrid systems (NSHS) have been used in several research areas to obtain powerful intelligent systems. A systematic mapping study was conducted, searching studies published from January 2011 to May 2018 in three author databases defining four research questions and three search strings. With the results a literature review was made to generate a map with main trends and contributions about the use of NSHS in Industry 4.0. An evaluation rubric based on the work of Petersen et al. (2015) was applied too. In a first exploratory search 544 papers was found, but only 330 had relation with research theme. After this first classification a second filter was applied to identify repeated articles or which had not relevance for solve the research questions, obtaining 118. Finally, 50 primary studies was selected. This paper is a guide aimed at researching and obtaining evidence on the shortage of publications and contributions about the use of neuro symbolic hybrid systems applied in Industry 4.0 environment.

The aim of this article is to highlight the benefits of implementing an active learning methodology in classes delivered to engineering degree students in Spain. The proposed teaching methodology has been designed for subjects with assessable practices and a high number of students in Computer Engineering degrees. The methodology is based on the redefinition of roles in the work assessment process where collaborative learning methods are incorporated. The idea of this approach is that students work closely together to learn from each other and that they are responsible for their own learning and that of fellow classmates. Moreover, they learn from participating in the process of assessing the work of their peers. This research describes the implementation of this technology and its promising results. Students increase their activity because they assume the role of solver of the proposed assignment in pair programming and also as evaluator of those of others due peers’ protocol. The proposed methodology gives them a more enriching understanding of the contents of the subjects as well as the need for greater intensity in the acquisition of such knowledge in the subject.

Background and objectives The examination of the fundus allows to evaluate retinal the microcirculation in vivo. We assess the reliability and validity of ALTAIR software, and to evaluate its clinical relevance by the association of thickness, area and length of the retinal vessels with other measures of vascular structure and function, target organ damage and cardiovascular risk. Methods Cross-sectional study involving a total of 250 subjects aged 62 ± 9 years, 51 % males. In a random subsample of 60 subjects (118 retinographies), we estimated the intraobserver, interobserver and interdevice intraclass correlation coefficients (ICC) of the measurements of retinal vascular thickness, area and length in 3 concentric circles. Concurrent validity was assessed with all 250 subjects (495 retinographies), analysing the relationship to age, blood pressure, target organ damage, vascular structure and function, and cardiovascular risk. Results Of the sample, 69 % were diagnosed with hypertension and 17 % with diabetes. Intraobserver ICC ranged from 0.640 for venous length to 0.906 for arterial area. Interobserver ICC ranged from 0.809 for arterial length to 0.916 for venous area, and interdevice ICC for arteriovenous ratio (AVR) was 0.887, thickness of arteries 0.590 and vein thickness 0.677. We found a moderate correlation between retinal vascular parameters and vascular structure and function, and target organ damage. In multiple linear regression analysis, the association with blood pressure, albumin/creatinine ratio, carotid intima-media thickness and cardiovascular risk is maintained. Conclusion The ALTAIR tool has been useful for analysing the thickness, area and length of retinal vessels, with adequate reliability and a concomitant association of retinal vessel measurements with other cardiovascular parameters and cardiovascular risk. Therefore, in addition to thickness, the area and length of retinal vessels could also play a role in the prediction of cardiovascular risk.

Today’s globalized and highly competitive world market has broadened the spectrum of requirements in all the sectors of the agri-food industry. This paper focuses on the dairy industry, on its need to adapt to the current market by becoming more resource efficient, environment-friendly, transparent and secure. The Internet of Things (IoT), Edge Computing (EC) and Distributed Ledger Technologies (DLT) are all crucial to the achievement of those improvements because they allow to digitize all parts of the value chain, providing detailed information to the consumer on the final product and ensuring its safety and quality. In Smart Farming environments, IoT and DLT enable resource monitoring and traceability in the value chain, allowing producers to optimize processes, provide the origin of the produce and guarantee its quality to consumers. In comparison to a centralized cloud, EC manages the Big Data generated by IoT devices by processing them at the network edge, allowing for the implementation of services with shorter response times, and a higher Quality of Service (QoS) and security. This work presents a platform oriented to the application of IoT, Edge Computing, Artificial Intelligence and Blockchain techniques in Smart Farming environments, by means of the novel Global Edge Computing Architecture, and designed to monitor the state of dairy cattle and feed grain in real time, as well as ensure the traceability and sustainability of the different processes involved in production. The platform is deployed and tested in a real scenario on a dairy farm, demonstrating that the implementation of EC contributes to a reduction in data traffic and an improvement in the reliability in communications between the IoT-Edge layers and the Cloud.

In this article, a novel Fog Computing solution is proposed, developed in the area of fintech. It integrates predictive systems in the process of delivery of personalized customer services for the recommendation of the products of a banking entity. The motivation behind this research is to improve aspects of customer support services, especially, achieve greater security, increased transparency and agility of processes as well as reduce entity management costs. The presented architecture includes fog nodes where data are processed by light intelligent agents allowing for the implementation of contextual recommendation systems together with the configuration of a Case Based Reasoning in the Cloud layer to improve the efficiency of the whole system over the time. The recommendation system is the cornerstone of architecture operating on banking products, such as mortgages, loans, retirement plans, etc., and it is developed by a hybrid method of recommendation: collaborative filtering combined with content-based filtering. The article analyzes the presented architecture while performing a verification and simulation of the data in the context of commercial banking. For this purpose, it shows the use of the proposed system of recommendations that represent the different communication channels as well as the possible devices. The proposed architecture offers the opportunity to improve the customer service in the bank’s physical channels and at the same time generate technological support to improve the resolution capacity of office managers, allowing employees to adopt a more versatile and flexible role. It also allows the evolution of the banking services model in offices while the processes that support it to follow a one-stop shop approach.

Context: Smart Energy is a disruptive concept that has led to the emergence of new energy policies, technology projects, and business models. The development of those models is driven by world capitals, companies, and universities. Their purpose is to make the electric power system more efficient through distributed energy generation/storage, smart meter installation, or reduction of consumption/implementation costs. This work approaches Smart Energy as a paradigm that is concerned with systemic strategies involving the implementation of innovative technological developments in energy systems. However, many of the challenges encountered under this paradigm are yet to be overcome, such as the effective integration of solutions within Smart Energy systems. Edge Computing is included in this new technology group. Objective: To investigate developments that involve the use of Edge Computing and that provide solutions to Smart Energy problems. The research work will be developed using the methodology of systematic mapping of literature, following the guidelines established by Kitchenham and Petersen that facilitate the identification of studies published on the subject. Results: Inclusion and exclusion criteria have been applied to identify the relevant articles. We selected 80 papers that were classified according to the type of publication (journal, conferences, or book chapter), type of research (conceptual, experience, or validation), type of activity (implement, validate, analyze) and asset (architecture, framework, method, or models). Conclusion: A complete review has been conducted of the 80 articles that were closely related to the questions posed in this research. To reach the goal of building Edge Computing architectures for Smart Energy environments, several lines of research have been defined. In the future, such architectures will overcome current problems, becoming highly energy-efficient, cost-effective, and capacitated to process and respond in real-time.

Synaesthesia is a neurological phenomenon in which stimulation of one sense triggers an involuntary experience in a secondary sense. This paper draws on this phenomenon to generate a system that creates music using colors as a synaesthete input. The system optimizes a function based on the Tonal Interval Space by applying the Particle Swarm Optimization algorithm to automatically generate a tonal melody. The final result is a musical line that is evaluated in terms of musical quality.

Prediction is the way to optimize the maintenance task by determining the correct moment to interview, repair or replace equipment which the most difficult decision for companies in Industry 4.0 environment. This research present MASPI. I is a multiagent system based on advantages of virtual organization. The goal of MASPI is to be a reference model for making predictions about data captured by sensors installed in equipment or industrial machines. The capability of MASPI is evaluated by applying it to SCANIA trucks dataset, using machine learnings algorithms to obtain the prediction and compare their accuracy.

The automatic generation of music is an emergent field of research that has attracted the attention of countless researchers. As a result, there is a broad spectrum of state of the art research in this field. Many systems have been designed to facilitate collaboration between humans and machines in the generation of valuable music. This research proposes an intelligent system that generates melodies under the supervision of a user, who guides the process through a mechanical device. The mechanical device is able to capture the movements of the user and translate them into a melody. The system is based on a Case-Based Reasoning (CBR) architecture, enabling it to learn from previous compositions and to improve its performance over time. The user uses a device that allows them to adapt the composition to their preferences by adjusting the pace of a melody to a specific context or generating more serious or acute notes. Additionally, the device can automatically resist some of the user&rsquo, s movements, this way the user learns how they can create a good melody. Several experiments were conducted to analyze the quality of the system and the melodies it generates. According to the users&rsquo, validation, the proposed system can generate music that follows a concrete style. Most of them also believed that the partial control of the device was essential for the quality of the generated music.

In an Industry 4.0 environment, the data generated by sensors networks requires machine learning and data analysis techniques. Thus, organizations face both new opportunities and challenges, one of them is predictive analysis using computer tools capable of detecting patterns in the analyzed data from the same rules that can be used to formulate predictions. The Heating, Ventilation and Air Conditioning Systems (HVAC) control in an important number of industries: indoor climate, air’s temperature, humidity and pressure, creating an optimal production environment. In accordance, a case study is presented, in it a HVAC dataset was used to test the performance of the equipment and observe whether it maintains temperatures in an optimal range. The aim of this paper is making use of machine learning algorithms for the design of predictive models in the Industry 4.0 environment, using the previously mentioned dataset.

This work presents an innovative agent based platform specially designed to simulate human activity at any urban area environment (buildings, apartments, houses, offices, gardens, parks, small residential areas, etc.) and manage data from sensors. The platform uses 3D models of the environment to perform accurate simulations, while simultaneously showing relevant and high quality data. This chapter also presents the platform case studies that have been conducted and that demonstrates their technical and conceptual validity, where, by merging data obtained by multiple sensors with information of the subject’s activities and simulation data, the platform can extract and store information on typical situations in a real environment, and also observe possible technological or architectural barriers for people with disabilities.

The distribution of services on multi-agent systems allows it to reduce to the agents their computational load. The functionality of the system does not reside in the agents themselves, however it is ubiquitously distributed so that allows you to perform tasks in parallel avoiding an additional computational cost to the elements in the system. The distribution of services that offers SCODA (Distributed and Specialized Agent Communities) allows an intelligent management of these services provided by agents of the system and the parallel execution of threads that allow to respond to requests asynchronously, which implies an improvement in the performance of the system at both the computational level as the level of quality of service in the control of these services. The comparison carried out in the case of study that is presented in this paper demonstrates the existing improvement in the distribution of services on systems based on SCODA.

Organizations of agents based on Virtual Organizations needs to be supported by a coordinated effort that explicitly determines how the agents should be organized and carry out the actions and tasks assigned to them. The interactions of a multi-agent system cannot be only related to the agent and the communication skills, but also to the concepts of organizational engineering. Moreover, nowadays there is a clear trend towards using methods and tools that can help to develop and simulate virtual organizations by means of multiagent systems (MAS). Simulation is used for several purposes ranging from work flow to system's procedures representation. The contribution from agent based computing to the field of computer simulation mediated by ABS (Agent Based Simulation) is a new paradigm for the simulation of complex systems that require a high level of interaction between the entities of the system. The main goal of this work is a new global coordination model for an agent organization in a simulation context. The innovation of this work consists of the dynamic and adaptive planning capability to distribute tasks among the agent of the organization. The middleware used for simulation makes it possible to visualize the emergent agent behaviour and the entity agent.

Information fusion in a context-aware system is understood as a process that assembles assessments of the environment based on its goals. Advantages of intelligent approaches such as Multi-Agent Systems (MAS) and the use of Wireless Sensor Networks (WSN) within the information fusion process are emerging, especially in context-aware scenarios. However, it has become critical to propose improved and efficient ways to handle the enormous quantity of data provided by these approaches. Agents are a suitable option because they can represent autonomous entities by modeling their capabilities, expertise and intentions. In this sense, virtual organizations of agents are an interesting option/possibility because they can provide the necessary capacity to handle open and heterogeneous systems such as those normally found in the information fusion process. This paper presents a new framework that defines a method for creating a virtual organization of software and hardware agents. This approach facilitates the inclusion of context-aware capabilities when developing intelligent and adaptable systems, where functionalities can communicate in a distributed and collaborative way. Several tests have been performed to evaluate this framework and preliminary results and conclusions are presented.

This paper presents the core component of a solution based on agent technology specifically adapted for the classification of SOAP messages. The messages can carry out attacks that target the applications providing Web Services. One of the most common attacks requiring novel solutions is the denial of service attack (DoS), caused for the modifications introduced in the XML of the SOAP messages. The specifications of existing security standards do not focus on this type of attack. This article presents an advanced mechanism of classification designed in two phases incorporated within a CBR-BDI Agent type. This mechanism classifies the incoming SOAP message and blocks the malicious SOAP messages. Its main feature involves the use of decision trees, fuzzy logic rules and neural networks for filtering attacks. These techniques provide a mechanism of classification with the self-adaption ability to the changes that occur in the patterns of attack. A prototype was developed and the results obtained are presented in this study. This paper presents the core component of a solution based on agent technology specifically adapted for the classification of SOAP messages. The messages can carry out attacks that target the applications providing Web Services. One of the most common attacks requiring novel solutions is the denial of service attack (DoS), caused for the modifications introduced in the XML of the SOAP messages. The specifications of existing security standards do not focus on this type of attack. This article presents an advanced mechanism of classification designed in two phases incorporated within a CBR-BDI Agent type. This mechanism classifies the incoming SOAP message and blocks the malicious SOAP messages. Its main feature involves the use of decision trees, fuzzy logic rules and neural networks for filtering attacks. These techniques provide a mechanism of classification with the self-adaption ability to the changes that occur in the patterns of attack. A prototype was developed and the results obtained are presented in this study.

Introduction The fundus examination is a non-invasive evaluation of the microcirculation of the retina. The aim of the present study is to develop and validate (reliability and validity) the ALTAIR software platform (Automatic image analyser to assess retinal vessel calibre) in order to analyse its utility in different clinical environments. Methods and analysis A cross-sectional study in the first phase and a prospective observational study in the second with 4 years of follow-up. The study will be performed in a primary care centre and will include 386 participants. The main measurements will include carotid intima-media thickness, pulse wave velocity by Sphygmocor, cardio-ankle vascular index through the VASERA VS-1500, cardiac evaluation by a digital ECG and renal injury by microalbuminuria and glomerular filtration. The retinal vascular evaluation will be performed using a TOPCON TRCNW200 non-mydriatic retinal camera to obtain digital images of the retina, and the developed software ( ALTAIR ) will be used to automatically calculate the calibre of the retinal vessels, the vascularised area and the branching pattern. For software validation, the intraobserver and interobserver reliability, the concurrent validity of the vascular structure and function, as well as the association between the estimated retinal parameters and the evolution or onset of new lesions in the target organs or cardiovascular diseases will be examined. Ethics and dissemination The study has been approved by the clinical research ethics committee of the healthcare area of Salamanca. All study participants will sign an informed consent to agree to participate in the study in compliance with the Declaration of Helsinki and the WHO standards for observational studies. Validation of this tool will provide greater reliability to the analysis of retinal vessels by decreasing the intervention of the observer and will result in increased validity through the use of additional information, especially in the areas of vascularisation and vessel branching patterns. Trial registration number Clinical Trials.gov Identifier: NCT02087605.

This article presents a case study in which the THOMAS architecture is applied in order to obtain a multi-agent system (MAS) that can provide recommendations and guidance in a shopping mall. THOMAS is made up of a group of related modules that are well-suited for developing systems in other highly volatile environments similar to a shopping mall. Because the development of this type of system is complex, it is essential to thoroughly analyze the intrinsic characteristics of typical environment applications, and to design all of the system components at a very high level of abstraction.