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Healthcare Janbi et al. [[10]] propose, implement, and evaluate Imtidad, a reference architecture that provides Distributed Artificial Intelligence (AI) as a Service (DAIaaS) over the cloud, fog, and edge. The smartness that underpins smart cities and societies is defined by our ability to engage with our environments, analyze them, and make decisions, all in a timely manner [[1]]. Paradigms such as the Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) enable the generation of PPE models and the development of devices with more advanced capabilities such as monitoring, sensing the environment, and risk detection, among others. [Extracted from the article]

The urbanization problems we face may be alleviated using innovative digital technology. However, employing these technologies entails the risk of creating new urban problems and/or intensifying the old ones instead of alleviating them. Hence, in a world with immense technological opportunities and at the same time enormous urbanization challenges, it is critical to adopt the principles of responsible urban innovation. These principles assure the delivery of the desired urban outcomes and futures. We contribute to the existing responsible urban innovation discourse by focusing on local government artificial intelligence (AI) systems, providing a literature and practice overview, and a conceptual framework. In this perspective paper, we advocate for the need for balancing the costs, benefits, risks and impacts of developing, adopting, deploying and managing local government AI systems in order to achieve responsible urban innovation. The statements made in this perspective paper are based on a thorough review of the literature, research, developments, trends and applications carefully selected and analyzed by an expert team of investigators. This study provides new insights, develops a conceptual framework and identifies prospective research questions by placing local government AI systems under the microscope through the lens of responsible urban innovation. The presented overview and framework, along with the identified issues and research agenda, offer scholars prospective lines of research and development; where the outcomes of these future studies will help urban policymakers, managers and planners to better understand the crucial role played by local government AI systems in ensuring the achievement of responsible outcomes. [ABSTRACT FROM AUTHOR]

We propose a novel consensus notion, called “partial consensus,” for distributed Gaussian mixture probability hypothesis density fusion based on a decentralized sensor network, in which only highly weighted Gaussian components (GCs) are exchanged and fused across neighbor sensors. It is shown that this not only gains high efficiency in both network communication and fusion computation, but also significantly compensates the effects of clutter and missed detections. Two “conservative” mixture reduction schemes are devised for refining the combined GCs. One is given by pairwise averaging GCs between sensors based on Hungarian assignment and the other merges close GCs for trace minimal, yet, conservative covariance. The close connection of the result to the two approaches, known as covariance union and arithmetic averaging, is unveiled. Simulations based on a sensor network consisting of both linear and nonlinear sensors, have demonstrated the advantage of our approaches over the generalized covariance intersection approach. [ABSTRACT FROM AUTHOR]

This article presents a systematic review of studies on cognitive training programs based on artificial cognitive systems and digital technologies and their effect on executive functions. The aim has been to identify which populations have been studied, the characteristics of the implemented programs, the types of implemented cognitive systems and digital technologies, the evaluated executive functions, and the key findings of these studies. The review has been carried out following the PRISMA protocol; five databases have been selected from which 1889 records were extracted. The articles were filtered following established criteria, to give a final selection of 264 articles that have been used for the purposes of this study in the analysis phase. The findings showed that the most studied populations were school-age children and the elderly. The most studied executive functions were working memory and attentional processes, followed by inhibitory control and processing speed. Many programs were commercial, customizable, gamified, and based on classic tasks. Some more recent initiatives have begun to incorporate user-machine interfaces, robotics, and virtual reality, although studies on their effects remain scarce. The studies recognize multiple benefits of computerized neuropsychological stimulation and rehabilitation programs for executive functions in different age groups, but there is a lack of studies in specific population sectors and with more rigorous research designs. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a novel Ambient Intelligence based solution for shopping assistance. The core of the proposal is a CBR system developed for guiding and advising users in shopping areas. The CBR incorporates a neural based planner that identifies the most adequate plan for a given user based on user profile and interests. The RTPW neural network is based on the Kohonen one, and incorporates an interesting modification that allows a solution or a plan to be reached much more rapidly. Furthermore, once an initial plan has been reached, it is possible to identify alternatives by taking restrictions into account. The CBR system has been embedded within a deliberative agent and interacts with interface and commercial agents, which facilitate the construction of intelligent environments. This hybrid application, which works on execution time, has been tested and the results of the investigation and its evaluation in a shopping mall are presented within this paper. [Copyright &y& Elsevier]

Summary: Objective: Recent advances in the field of biomedicine, specifically in the field of genomics, have led to an increase in the information available for conducting expression analysis. Expression analysis is a technique used in transcriptomics, a branch of genomics that deals with the study of messenger ribonucleic acid (mRNA) and the extraction of information contained in the genes. This increase in information is reflected in the exon arrays, which require the use of new techniques in order to extract the information. The purpose of this study is to provide a tool based on a mixture of experts model that allows the analysis of the information contained in the exon arrays, from which automatic classifications for decision support in diagnoses of leukemia patients can be made. The proposed model integrates several cooperative algorithms characterized for their efficiency for data processing, filtering, classification and knowledge extraction. The Cancer Institute of the University of Salamanca is making an effort to develop tools to automate the evaluation of data and to facilitate de analysis of information. This proposal is a step forward in this direction and the first step toward the development of a mixture of experts tool that integrates different cognitive and statistical approaches to deal with the analysis of exon arrays. The mixture of experts model presented within this work provides great capacities for learning and adaptation to the characteristics of the problem in consideration, using novel algorithms in each of the stages of the analysis process that can be easily configured and combined, and provides results that notably improve those provided by the existing methods for exon arrays analysis. Material and methods: The material used consists of data from exon arrays provided by the Cancer Institute that contain samples from leukemia patients. The methodology used consists of a system based on a mixture of experts. Each one of the experts incorporates novel artificial intelligence techniques that improve the process of carrying out various tasks such as pre-processing, filtering, classification and extraction of knowledge. This article will detail the manner in which individual experts are combined so that together they generate a system capable of extracting knowledge, thus permitting patients to be classified in an automatic and efficient manner that is also comprehensible for medical personnel. Results and conclusion: The system has been tested in a real setting and has been used for classifying patients who suffer from different forms of leukemia at various stages. Personnel from the Cancer Institute supervised and participated throughout the testing period. Preliminary results are promising, notably improving the results obtained with previously used tools. The medical staff from the Cancer Institute considers the tools that have been developed to be positive and very useful in a supporting capacity for carrying out their daily tasks. Additionally the mixture of experts supplies a tool for the extraction of necessary information in order to explain the associations that have been made in simple terms. That is, it permits the extraction of knowledge for each classification made and generalized in order to be used in subsequent classifications. This allows for a large amount of learning and adaptation within the proposed system. [Copyright &y& Elsevier]

The interaction of the atmosphere and the ocean has a profound effect on climate, while the uptake by the oceans of a major fraction of atmospheric CO2 has a moderating influence. By improving accuracy in the quantification of the ocean's CO2 budget, a more precise estimation can be made of the terrestrial fraction of global CO2 budget and its subsequent effect on climate change. An instance-based reasoning system, which incorporates a novel neural-based clustering and retrieval method has been developed for estimating the CO2 budget. This paper reviews the problem of measuring the ocean's CO2 budget, presents the model developed to resolve it and presents the obtained results. [ABSTRACT FROM AUTHOR]

A hybrid neuro-symbolic problem solving model is presented in which the aim is to forecast parameters of a complex and dynamic environment in an unsupervised way. In situations in which the rules that determine a system are unknown, the prediction of the parameter values that determine the characteristic behaviour of the system can be a problematic task. The system employs a case-based reasoning model to wrap a growing cell structures network, a radial basis function network and a set of Sugeno fuzzy models to provide an accurate prediction. Each of these techniques is used in a different stage of the reasoning cycle of the case-based reasoning system to retrieve, adapt and review the proposed solution to the present problem. This system has been used to predict the red tides that appear in the coastal waters of the north west of the Iberian Peninsula. The results obtained from experiments are presented. [Copyright &y& Elsevier]

Presents an approach to hybrid artificial intelligence problem solving. Aim of forecasting, in real time, the physical parameter values of a complex and dynamic environment; Determination of the characteristic behavior of the system; Development of a hybrid forecasting system.

An instance-based problem solving model is presented in which the aim is to forecast, in real time, the physical parameter values of a complex and dynamic environment: the ocean. The situations in which the rules that determine a system are unknown, the prediction of the parameter values that determine the characteristic behaviour of the system can be a problematic task. In such a situation it has been found that an instance-based reasoning system can provide a more effective means of performing such predictions than other connectionist or symbolic techniques. The instance-based reasoning system incorporates a radial basis function artificial neural network for the instance adaptation. The results obtained from experiments, in which the system operated in real time in the oceanographic environment, are presented. [ABSTRACT FROM AUTHOR]

An investigation is described into the application of artificial intelligence to forecasting in the domain of oceanography. A hybrid approach to forecasting the thermal structure of the water ahead of a moving vessel is presented which combines the ability of a case-based reasoning system for identifying previously encountered similar situations and the generalizing ability of an artificial neural network to guide the adaptation stage of the case-based reasoning mechanism. The system has been successfully tested in real time in the Atlantic Ocean; the results obtained are presented and compared with those derived from other forecasting methods. [ABSTRACT FROM AUTHOR]

In the era of Industry 4.0, pervasive adoption of communication technologies based on the Internet of Things represents a very strong requirement in several domains. In the smart grid domain, there is the need to overcome one of the main limitations of the current electric grid, allowing the use of heterogeneous devices capable of measuring, monitoring and exchanging information about grid components. For this reason, current literature often presents research activities about enabling internet of things (IoT) in smart grids; in particular, several proposals aim to realize interworking between IoT and smart grid communication standards, allowing exchange of information between IoT devices and the electrical grid components. Semantic interoperability should be achieved in an interworking solution in order to provide a common meaning of the data exchanged by heterogeneous devices, even if they belong to different domains. Until now, semantic interoperability remains an open challenge in the smart grid field. The paper aims to propose a novel solution of interworking between two of the most used communication systems in smart grids and IoT domains, i.e., IEC 61850 and oneM2M, respectively. A semantic interoperability solution is also proposed to be used in the interworking scheme here presented. [ABSTRACT FROM AUTHOR]

This paper presents an efficient cyberphysical platform for the smart management of smart territories. It is efficient because it facilitates the implementation of data acquisition and data management methods, as well as data representation and dashboard configuration. The platform allows for the use of any type of data source, ranging from the measurements of a multi-functional IoT sensing devices to relational and non-relational databases. It is also smart because it incorporates a complete artificial intelligence suit for data analysis; it includes techniques for data classification, clustering, forecasting, optimization, visualization, etc. It is also compatible with the edge computing concept, allowing for the distribution of intelligence and the use of intelligent sensors. The concept of smart cities is evolving and adapting to new applications; the trend to create intelligent neighbourhoods, districts or territories is becoming increasingly popular, as opposed to the previous approach of managing an entire megacity. In this paper, the platform is presented, and its architecture and functionalities are described. Moreover, its operation has been validated in a case study where the bike renting service of Paris—Vélib' Métropole has been managed. This platform could enable smart territories to develop adapted knowledge management systems, adapt them to new requirements and to use multiple types of data, and execute efficient computational and artificial intelligence algorithms. The platform optimizes the decisions taken by human experts through explainable artificial intelligence models that obtain data from IoT sensors, databases, the Internet, etc. The global intelligence of the platform could potentially coordinate its decision-making processes with intelligent nodes installed in the edge, which would use the most advanced data processing techniques. [ABSTRACT FROM AUTHOR]

Human interaction has changed considerably with the emergence of the Internet. Today, a large percentage of daily communication takes place on instant messaging applications and social networks. In fact, there has been a considerable increase in the use of social networks because new social networks are being created for specific purposes, such as the search for employment or tourism. When the amount of content on a social network is large, it is necessary to help users find content of their interest. In this regard, artificial intelligence techniques can greatly facilitate the task of searching for relevant content. This paper presents a recommender system for a business and employment oriented social network, on which users are recommended job offers and other user profiles to follow. The presented system is based on virtual organizations of agents, and uses artificial neural networks to determine whether job offers and users should be recommended or not. The system has been evaluated on a real social network; its recommendations regarding job offers and user profiles have had a high acceptance rate. [ABSTRACT FROM AUTHOR]

Vagus nerve stimulation (VNS) is an adjuvant neuromodulation therapy for the treatment of refractory epilepsy. However, the mechanisms behind its effectiveness are not fully understood. Our aim was to develop a VNS protocol for the Genetic Audiogenic Seizure Hamster from Salamanca (GASH/Sal) in order to evaluate the mechanisms of action of the therapy. The rodents were subject to VNS for 14 days using clinical stimulation parameters by implanting a clinically available neurostimulation device or our own prototype for laboratory animals. The neuroethological assessment of seizures and general behavior were performed before surgery, and after 7, 10, and 14 days of VNS. Moreover, potential side effects were examined. Finally, the expression of 23 inflammatory markers in plasma and the left-brain hemisphere was evaluated. VNS significantly reduced seizure severity in GASH/Sal without side effects. No differences were observed between the neurostimulation devices. GASH/Sal treated with VNS showed statistically significant reduced levels of interleukin IL-1β, monocyte chemoattractant protein MCP-1, matrix metalloproteinases (MMP-2, MMP-3), and tumor necrosis factor TNF-α in the brain. The described experimental design allows for the study of VNS effects and mechanisms of action using an implantable device. This was achieved in a model of convulsive seizures in which VNS is effective and shows an anti-inflammatory effect. [ABSTRACT FROM AUTHOR]

Bilinear pooling is one of the most popular and effective methods for fine-grained image recognition. However, a major drawback of Bilinear pooling is the dimensionality of the resulting descriptors, which typically consist of several hundred thousand features. Even when generating the descriptor is tractable, its dimension makes any subsequent operations impractical and often results in huge computational and storage costs. We introduce a novel method to efficiently reduce the dimension of bilinear pooling descriptors by performing a Random Projection. Conveniently, this is achieved without ever computing the high-dimensional descriptor explicitly. Our experimental results show that our method outperforms existing compact bilinear pooling algorithms in most cases, while running faster on low computational power devices, where efficient extensions of bilinear pooling are most useful. [ABSTRACT FROM AUTHOR]

The NB-IoT benchmark dataset was used to detect botnet attack types using real traffic data gathered from nine commercial IoT devices. In [[7]], the authors implemented the RF tree algorithm, which achieved a very low prediction level (MSE = 0.01465) and a correlation R2 (R squared) level of 92.02% with the binary classification dataset, whereas the algorithm attained an R2 level of 89.35% with a multi-classification dataset. At present, the field of the Internet of Things (IoT) is one of the fastest-growing areas in terms of Artificial Intelligence (AI) and Machine Learning (ML) techniques. [Extracted from the article]

Deep-sea object localization by underwater acoustic sensor networks is a current research topic in the field of underwater communication and navigation. To find a deep-sea object using underwater wireless sensor networks (UWSNs), the sensors must first detect the signals sent by the object. The sensor readings are then used to approximate the object's position. A lot of parameters influence localization accuracy, including the number and location of sensors, the quality of received signals, and the algorithm used for localization. To determine position, the angle of arrival (AOA), time difference of arrival (TDoA), and received signal strength indicator (RSSI) are used. The UWSN requires precise and efficient localization algorithms because of the changing underwater environment. Time and position are required for sensor data, especially if the sensor is aware of its surroundings. This study describes a critical localization strategy for accomplishing this goal. Using beacon nodes, arrival distance validates sensor localization. We account for the fact that sensor nodes are not in perfect temporal sync and that sound speed changes based on the medium (water, air, etc.) in this section. Our simulations show that our system can achieve high localization accuracy by accounting for temporal synchronisation, measuring mean localization errors, and forecasting their variation. The suggested system localization has a lower mean estimation error (MEE) while using RSSI. This suggests that measurements based on RSSI provide more precision and accuracy during localization. [ABSTRACT FROM AUTHOR]

Security in the blockchain has become a topic of concern because of the recent developments in the field. One of the most common cyberattacks is the so-called phishing attack, wherein the attacker tricks the miner into adding a malicious block to the chain under genuine conditions to avoid detection and potentially destroy the entire blockchain. The current attempts at detection include the consensus protocol; however, it fails when a genuine miner tries to add a new block to the blockchain. Zero-trust policies have started making the rounds in the field as they ensure the complete detection of phishing attempts; however, they are still in the process of deployment, which may take a significant amount of time. A more accurate measure of phishing detection involves machine-learning models that use specific features to automate the entire process of classifying an attempt as either a phishing attempt or a safe attempt. This paper highlights several models that may give safe results and help eradicate blockchain phishing attempts. [ABSTRACT FROM AUTHOR]

Abstract The growing amounts of online multimedia content challenge the current search, recommendation and information retrieval systems. Information in the form of visual elements is highly valuable in a range of web mining tasks. However, the mining of these resources is a difficult task due to the complexity and variability of images, and the cost of collecting big enough datasets to successfully train accurate deep learning models. This paper proposes a novel framework for the categorization of web pages on the basis of their visual content. This is achieved by exploring the joint application of a transfer learning strategy and metric learning techniques to build a Deep Convolutional Neural Network (DCNN) for feature extraction, even when training data is scarce. The obtained experimental results evidence that the proposed approach outperforms the state-of-the-art handcrafted image descriptors and achieves a high categorization accuracy. In addition, we address the problem of over-time learning, so the proposed framework can learn to identify new web page categories as new labeled images are provided at test time. As a result, prior knowledge of the complete set of possible web categories is not necessary in the initial training phase. [ABSTRACT FROM AUTHOR]

Summary: New solutions are required for the management of heterogeneous distributed sensor networks in order to address the problem of data quality and false data detection in wireless sensor networks (WSNs). In this paper, we present a nonlinear cooperative control algorithm based on game theory. Here, a new model is proposed for the automatic processing and management of information in heterogeneous distributed WSNs. We apply our algorithm to a case study with the aim of improving the quality of temperature data collected from indoor surfaces by a WSN. Unlike the classic unsupervised methods, in the proposed algorithm, it is not necessary to define the number of clusters beforehand. Once the game reaches the game equilibrium, the resulting number of clusters can be used as input for the unsupervised classification analysis. Anomalous temperature values are corrected according to their neighborhood, without modifying the temperature clusters. [ABSTRACT FROM AUTHOR]

Performing a Random Projection from the feature space associated to a kernel function may be important for two main reasons. (1) As a consequence of the Johnson–Lindestrauss lemma, the resulting low-dimensional representation will preserve most of the structure of data in the kernel feature space and (2) an efficient linear classifier trained on transformed data might approximate the accuracy of its nonlinear counterparts. In this paper, we present a novel method to perform Random Projections from the feature space of homogeneous polynomial kernels. As opposed to other kernelized Random Projection proposals, our method focuses on a specific kernel family to preserve some of the beneficial properties of the original Random Projection algorithm (e.g. data independence and efficiency). Our extensive experimental results evidence that the proposed method efficiently approximates a Random Projection from the kernel feature space, preserving pairwise distances and enabling a boost on linear classification accuracies. [ABSTRACT FROM AUTHOR]

The revolution generated by the Internet of Things (IoT) has radically changed the world; countless objects with remote sensing, actuation, analysis and sharing capabilities are interconnected over heterogeneous communication networks. Consequently, all of today's devices can connect to the internet and can provide valuable information for decision making. However, the data collected by different devices are in different formats, which makes it necessary to develop a solution that integrates comprehensive semantic tools to represent, integrate and acquire knowledge, which is a major challenge for IoT environments. The proposed solution addresses this challenge by using IoT semantic data to reason about actionable knowledge, combining next-generation semantic technologies and artificial intelligence through a set of cognitive components that enables easy interoperability and integration for both legacy systems and emerging technologies, such as IoT, to generate business value in terms of faster analytics and improved decision making. Thus, combining IoT environments with cognitive artificial intelligence services, COSIBAS builds an abstraction layer between existing platforms for IoT and AI technologies to enable cognitive solutions and increase interoperability across multiple domains. The resulting low-cost cross platform supports scalability and the evolution of large-scale heterogeneous systems and allows the modernization of legacy infrastructures with cognitive tools and communication mechanisms while reusing assets. [ABSTRACT FROM AUTHOR]

The likelihood calculation of a vast number of particles forms the computational bottleneck for the particle filter in applications where the observation model is complicated, especially when map or image processing is involved. In this paper, a numerical fitting approach is proposed to speed up the particle filter in which the likelihood of particles is analytically inferred/fitted, explicitly or implicitly, based on that of a small number of so-called fulcrums. It is demonstrated to be of fairly good estimation accuracy when an appropriate fitting function and properly distributed fulcrums are used. The construction of the fitting function and fulcrums are addressed respectively in detail. To avoid intractable multivariate fitting in multi-dimensional models, a nonparametric kernel density estimator such as the nearest neighbor smoother or the uniform kernel average smoother can be employed for implicit likelihood fitting. Simulations based on a benchmark one-dimensional model and multi-dimensional mobile robot localization are provided. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Over a billion people around the world are disabled, among whom 253 million are visually impaired or blind, and this number is greatly increasing due to ageing, chronic diseases, and poor environments and health. Despite many proposals, the current devices and systems lack maturity and do not completely fulfill user requirements and satisfaction. Increased research activity in this field is required in order to encourage the development, commercialization, and widespread acceptance of low-cost and affordable assistive technologies for visual impairment and other disabilities. This paper proposes a novel approach using a LiDAR with a servo motor and an ultrasonic sensor to collect data and predict objects using deep learning for environment perception and navigation. We adopted this approach using a pair of smart glasses, called LidSonic V2.0, to enable the identification of obstacles for the visually impaired. The LidSonic system consists of an Arduino Uno edge computing device integrated into the smart glasses and a smartphone app that transmits data via Bluetooth. Arduino gathers data, operates the sensors on the smart glasses, detects obstacles using simple data processing, and provides buzzer feedback to visually impaired users. The smartphone application collects data from Arduino, detects and classifies items in the spatial environment, and gives spoken feedback to the user on the detected objects. In comparison to image-processing-based glasses, LidSonic uses far less processing time and energy to classify obstacles using simple LiDAR data, according to several integer measurements. We comprehensively describe the proposed system's hardware and software design, having constructed their prototype implementations and tested them in real-world environments. Using the open platforms, WEKA and TensorFlow, the entire LidSonic system is built with affordable off-the-shelf sensors and a microcontroller board costing less than USD 80. Essentially, we provide designs of an inexpensive, miniature green device that can be built into, or mounted on, any pair of glasses or even a wheelchair to help the visually impaired. Our approach enables faster inference and decision-making using relatively low energy with smaller data sizes, as well as faster communications for edge, fog, and cloud computing. [ABSTRACT FROM AUTHOR]

Social media, such as Twitter and Facebook, allow the creation, sharing and exchange of information among people, companies and brands. This information can be used for several purposes, such as to understand consumers and their preferences. In this direction, the sentiment analysis can be used as a feedback mechanism. This analysis corresponds to classifying a text according to the sentiment that the writer intended to transmit. A basic sentiment classifier determines the sentiment polarity (negative, neutral or positive) of a given text at the document, sentence, or feature/aspect level. Advanced types may consider other elements like the emotional state (e.g. angry, sad, happy), affective states (e.g. pleasure and pain), motivational states (e.g. hunger and curiosity), temperaments, among others. In general, there are two main approaches to attribute sentiment to tweets: based on knowledge; or based on machine learning algorithms. In the latter case, the learning algorithm requires a pre-classified data sample to determine the class of new data. Typically, the sample is pre-classified manually, making the process time consuming and reducing its real time applicability for big data. This paper proposes a polarity analysis framework for Twitter messages, which combines both approaches and an automatic contextual module. To assess the performance of the proposed framework, four text datasets from the literature are used. Five different types of classifiers were considered: Naïve Bayes (NB); Support Vector Machines (SVM); Decision Trees (J48); and Nearest Neighbors (KNN). The results show that the proposal is a suitable framework to automate the whole polarity analysis process, providing high accuracy levels and low false positive rates. [ABSTRACT FROM AUTHOR]

The recent viral outbreaks have had a significant impact on interpersonal relationships, particularly in enclosed spaces. Detecting and preventing the transmission of diseases such as COVID-19 has become a top priority. These diseases are typically identifiable through the symptoms they cause in humans. However, the collection of personal and health data for use in Artificial Intelligence models can give rise to ethical, security, and privacy issues. Therefore, it is necessary to have architectures that maintain the principles of Trustworthy Artificial Intelligence by design. This work proposes a decentralised architecture based on Federated Learning for symptomatic disease detection using the edge computing paradigm, storing the information in the device that collected it, and the foundations of Trustworthy Artificial Intelligence. The architecture is designed to be robust, secure, transparent, and responsible while maintaining data privacy. The proposed approach can be used with medical information capture systems with different user profiles. [Display omitted] • FBAC architecture designed to provide robustness and advocate data transparency. • Device networks that capture biometric parameters while protecting privacy rights. • Distributed computation without a central node, reducing information exchange times. • Resilience scales with the number of nodes, reducing the chance of information loss. • FBAC aims for fairness during model training and data privacy in data transport. [ABSTRACT FROM AUTHOR]

Nowadays there is a clear trend towards using methods and tools that can help the development of Simulation Systems. In this regard, Multiagent System (MAS) is a key technology that allows simulating user behavior. When the user is the focus of the simulation, Ambient Intelligent (AmI) increases in importance. AmI is an emerging multidisciplinary area based on ubiquitous computing that aims to adapt the environment to the needs of the user. Moreover, Cloud Computing is revolutionizing the services provided through the Internet, continually adapting itself in order to maintain the quality of its services. This study presents a multiagent based middleware for the simulation of agent behavior in cloud environments, and uses this information to adapt to the environment as AmI proposed. The main challenge of this work is the design and development of a new middleware that makes possible the communication between the technology in charge of the development of MAS and the technology in charge of the simulation, visualization and analysis of the behavior of the agents using the potential of cloud computing. The platform makes the computation and communication possible by following the principles proposed by AmI. [ABSTRACT FROM AUTHOR]

Highlights: [•] Intelligent composition of business processes based on multi-agent systems (IPCASCI). [•] A tool to automatically build web services from the composition of the existing ones. [•] Web service discovery to build a business process through composition. [ABSTRACT FROM AUTHOR]

Gene expression profiles are composed of thousands of genes at the same time, representing the complex relationships between them. One of the well-known constraints specifically related to microarray data is the large number of genes in comparison with the small number of available experiments or cases. In this context, the ability of design methods capable of overcoming current limitations of state-of-the-art algorithms is crucial to the development of successful applications. This paper presentsgene-CBR, a hybrid model that can perform cancer classification based on microarray data. The system employs a case-based reasoning model that incorporates a set of fuzzy prototypes, a growing cell structure network and a set of rules to provide an accurate diagnosis. The hybrid model has been implemented and tested with microarray data belonging to bone marrow cases from forty-three adult patients with cancer plus a group of six cases corresponding to healthy persons. [ABSTRACT FROM AUTHOR]

The optimization of energy use in family homes and public buildings is an ongoing topic of discussion. State-of-the-art research has almost always focused on reducing the consumption of heating systems, air-conditioning or lighting. Despite their importance, user-related variables, such as comfort, are normally not included in the optimization process. These aspects should be considered to be able to effectively minimize energy consumption. Thus, there is a need for a comprehensive energy optimization approach, one that will consider both climatological factors and user behaviour. Learning about user behaviour is key to effective optimization. In this work, the proposed architecture's capacity to organize Virtual Agent Organizations (VAO) allows it to adapt to highly variable user behavior and preferences. This agent methodology has the ability to manage Wireless Sensor Networks (WSNs), Artificial Neural Networks (ANN) and Case-Based Reasoning (CBR) to obtain user preferences and predict their behaviour in the home or building. The proposed approach has been tested in two different buildings, a traditional-construction house and a modular home, obtaining savings of 30.16% and 13.43%, respectively. These results validate the proposed mixed approach of temperature adjustment algorithms together with the extraction of user behavior patterns for the establishment of a threshold based on preferences. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) should not be seen only as a cost reduction mechanism for manufacturing companies; instead, it should be seen as the basis for transition to a new business model that monetizes the data from an intelligent ecosystem. In this regard, deciphering the operation of the value creation system and finding the balance between the digital strategy and the deployment of technological platforms, are the main motivations behind this research. To achieve the proposed objectives, systems theory has been adopted in the conceptualization stage, later, fuzzy logic has been used to structure a subsystem for the evaluation of input parameters. Subsequently, system dynamics have been used to build a computational representation and later, through dynamic simulation, the model has been adjusted according to iterations and the identified limits of the system. Finally, with the obtained set of results, different value creation and capture behaviors have been identified. The simulation model, based on the conceptualization of the system and the mathematical representation of the value function, allows to establish a frame of reference for the evaluation of the behaviour of IoT ecosystems in the context of the connected home. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is a powerful technology with an increasing popularity and applications in areas ranging from marketing to banking and finance, from agriculture to healthcare and security, from space exploration to robotics and transport, and from chatbots to artificial creativity and manufacturing. Although many of these areas closely relate to the urban context, there is limited understanding of the trending AI technologies and their application areas--or concepts--in the urban planning and development fields. Similarly, there is a knowledge gap in how the public perceives AI technologies, their application areas, and the AI-related policies and practices of our cities. This study aims to advance our understanding of the relationship between the key AI technologies (n = 15) and their key application areas (n = 16) in urban planning and development. To this end, this study examines public perceptions of how AI technologies and their application areas in urban planning and development are perceived and utilized in the testbed case study of Australian states and territories. The methodological approach of this study employs the social media analytics method, and conducts sentiment and content analyses of location-based Twitter messages (n = 11,236) from Australia. The results disclose that: (a) digital transformation, innovation, and sustainability are the most popular AI application areas in urban planning and development; (b) drones, automation, robotics, and big data are the most popular AI technologies utilized in urban planning and development, and; (c) achieving the digital transformation and sustainability of cities through the use of AI technologies--such as big data, automation and robotics--is the central community discussion topic. [ABSTRACT FROM AUTHOR]

• A dynamic and complete platform for energy management systems in a microgrid. • Microgrid's players representation using a multi-agent system. • A single-board computer platform easy to deploy. • A real case scenario using an office building with five agents. Microgrids bring advantages to end-users and to the smart grid environment. However, adequate management software, enabling bringing to the field new energy management concepts, is not available yet. Small, single-tasked, software is usually proposed and tested while a clear overall system architecture for microgrid management required to take full advantage of the microgrids' potential. Previous publications usually focus on energy-related problems and do not provide an efficient and viable solution for players' representation and microgrid operation. This paper proposes a complete architecture for a microgrid management system based on a multi-agent approach – µGIM – allowing the easy implementation of different energy strategies. The µGIM agents can independently manage local resources while able to collaborate and/or compete with other agents. Designed to run in single-board computers, µGIM agents are light-weighted and easily deployed in buildings. To demonstrate these capabilities, the paper details and presents a microgrid deployment using µGIM solution. [ABSTRACT FROM AUTHOR]

• Arithmetic averaging (AA) and geometric averaging (GA) are compared. • AA performs better in fusing variables while GA performs better in fusing PDFs. • Multitarget density fusion in the presence of false alarms and missed detection is studied. • A hybrid fusion rule is proposed combining AA and GA for multitarget density fusion. • GA is comparably more accurate but less robust compared to AA. Two fundamental approaches to information averaging are based on linear and logarithmic combination, yielding the arithmetic average (AA) and geometric average (GA) of the fusing data, respectively. In the context of multi-sensor target tracking, the two most common formats of data to be fused are random variables and probability density functions, namely v -fusion and f -fusion, respectively. In this work, we analyze and compare the second-order statistics (including variance and mean square error) of AA and GA in terms of both v -fusion and f -fusion. The case of weighted Gaussian mixtures representing multitarget densities in the presence of false alarms and missed detections (whose weight sums are not necessarily unit) is also considered, the result of which turns out to be significantly different from that of a single target. In addition to exact derivation, exemplifying analyses and illustrations are also provided. [ABSTRACT FROM AUTHOR]

Highlights • We focus on the problem of monitoring and control of IoT-blockchain networks. • We develop a new adaptive control algorithm that uses the properties of the IoT network. • We explore the effects of using hashmaps for speed up searches in big data. • We present an architecture capable of integrating the above highlights. Abstract IoT network generates a large amount of data. This means that the monitoring and control of these networks and the transfer of packets from the IoT network to the server can cause communications to collapse. On the other hand, due to the large volume of data stored in the databases the monitoring of the IoT network needs very powerful servers to have a high degree of efficiency. This paper presents a novel adaptive closed-loop control system and speed up searches model to improve the monitor and control efficiency in IoT networks, specially those which are based in blockchain. The non linear control model under consideration includes a new way to evaluate the optimal number of blocks that should be at the queue of the miners' network in order to make the process efficient through the use of queuing theory. Also, a new system to speed up searches is presented by using hashmaps, which makes the monitoring process faster, reliable and efficient. The efficiency of the presented approach is illustrated by a numerical case study. [ABSTRACT FROM AUTHOR]

Abstract A new era of cleaner distributed generators, like wind and solar, dispersed along the distribution network are gaining great importance and contributing to the environment and political goals. However, the variability and intermittency of those generators pose new complexities and challenges to the network planning. This research paper proposes an innovative stochastic methodology to deal with the expansion planning of large distribution networks in a smart grid context with high penetration of distributed renewable energy sources and considering the seasonal impact. Also, new power lines locations and types, the size and the location of energy storage systems are considered in the optimization. A distribution network with 180 buses located in Portugal considering high distributed generators penetration is used to illustrate the application of the proposed methodology. The results demonstrate the advantage of the stochastic model when compared with a deterministic formulation, avoiding the need for larger investments in new lines and energy storage systems. Highlights • Seasonal and daily periods impact effect in the long-term DN expansion planning. • Two-stage stochastic model in a smart grid context with high RES penetration. • Optimal ESS size and location and optimal type and location of new lines. • Optimal investment expansion planning. [ABSTRACT FROM AUTHOR]

This paper presents a joint trajectory smoothing and tracking framework for a specific class of targets with smooth motion. We model the target trajectory by a continuous function of time (FoT), which leads to a curve fitting approach that finds a trajectory FoT fitting the sensor data in a sliding time-window. A simulation study is conducted to demonstrate the effectiveness of our approach in tracking a maneuvering target, in comparison with the conventional filters and smoothers. Note to Practitioners—Estimation, such as automatically tracking and predicting the movement of an aircraft, a train, or a bus, plays a key role in our daily life. In this paper, we provide a new approach for the online estimation of the target trajectory function by means of fitting the time-series observation, which accommodates the lack of quantifiable knowledge about the target motion and of the statistical property of the sensor observation noise. The resulting trajectory function can be used to infer either the past or the present state of the target. Engineering-friendly strategies are provided for computationally efficient implementation. The proposed approach is particularly appealing to a broad range of real-world targets that move in smooth courses, such as passenger aircraft and ships. [ABSTRACT FROM AUTHOR]

Modern software development paradigms, including Service-Oriented Architecture (SOA), tend to make use of available services e.g., web service Application Programming Interfaces (APIs) to generate new software. Thus, for the further advancement of SOA, the development of accurate automatic tasks, such as service discovery and composition, is necessary. Most of these automated tasks rely heavily on web service metadata annotation. The lack of machine-readable documentation and structured metadata reduces the accuracy and volume of automatic data annotation, negatively affecting the performance of automated SOA tasks. This study aims to propose automatic code completion for improving web service-based systems by identifying and capturing service usage collected from public repositories that share Open Source Software (OSS). To this end, a Data-Centric Service Code Generation (DCServCG) model is proposed to improve old-fashioned, general-purpose code generators that neglect essential service-based code characteristics e.g., sequence overlap and bias issues. DCServCG takes advantage of the data-centric concept, i.e., conditional text generation, to overcome the mentioned issues. We have evaluated the approach from the point of view of language modeling metrics. The obtained results indicate that the usage of the data-centric approach reduces perplexity by 1.125. Moreover, the DCServCG model uses de-noising and conditional text generation, which is trained on the transformer by distilling the knowledge, DistilGPT2 (82M parameters) trained faster and its perplexity is 0.363 lower than ServCG (124M parameters) without de-noising and conditional text generation, which lower perplexity value indicates better model generalization performance. [ABSTRACT FROM AUTHOR]

Due to fire protection regulations, a minimum number of fire extinguishers must be available depending on the surface area of each building, industrial establishment or workplace. There is also a set of rules that establish where the fire extinguisher should be placed: always close to the points that are most likely to be affected by a fire and where they are visible and accessible for use. Fire extinguishers are pressure devices, which means that they require maintenance operations that ensure they will function properly in the case of a fire. The purpose of manual and periodic fire extinguisher checks is to verify that their labeling, installation and condition comply with the standards. Security seals, inscriptions, hose and other seals are thoroughly checked. The state of charge (weight and pressure) of the extinguisher, the bottle of propellant gas (if available), and the state of all mechanical parts (nozzle, valves, hose, etc.) are also checked. To ensure greater safety and reduce the economic costs associated with maintaining fire extinguishers, it is necessary to develop a system that allows monitoring of their status. One of the advantages of monitoring fire extinguishers is that it will be possible to understand what external factors affect them (for example, temperature or humidity) and how they do so. For this reason, this article presents a system of soft agents that monitors the state of the extinguishers, collects a history of the state of the extinguisher and environmental factors and sends notifications if any parameter is not within the range of normal values.The results rendered by the SmartFire prototype indicate that its accuracy in calculating pressure changes is equivalent to that of a specific data acquisition system (DAS). The comparative study of the two curves (SmartFire and DAS) shows that the average error between the two curves is negligible: 8% in low pressure measurements (up to 3 bar) and 0.3% in high pressure (above 3 bar). [ABSTRACT FROM AUTHOR]

Gene selection (or feature selection) from DNA-microarray data can be focused on different techniques, which generally involve statistical tests, data mining and machine learning. In recent years there has been an increasing interest in using hybrid-technique sets to face the problem of meaningful gene selection; nevertheless, this issue remains a challenge. In an effort to address the situation, this paper proposes a novel hybrid framework based on data mining techniques and tuned to select gene subsets, which are meaningfully related to the target disease conducted in DNA-microarray experiments. For this purpose, the framework above deals with approaches such as statistical significance tests, cluster analysis, evolutionary computation, visual analytics and boundary points. The latter is the core technique of our proposal, allowing the framework to define two methods of gene selection. Another novelty of this work is the inclusion of the age of patients as an additional factor in our analysis, which can leading to gaining more insight into the disease. In fact, the results reached in this research have been very promising and have shown their biological validity. Hence, our proposal has resulted in a methodology that can be followed in the gene selection process from DNA-microarray data. [ABSTRACT FROM AUTHOR]

The gradual depletion of energy resources makes it necessary to optimize their use and to reuse them. Although great advances have already been made in optimizing energy generation processes, many of these processes generate energy that inevitably gets wasted. A clear example of this are nuclear, thermal and carbon power plants, which lose a large amount of energy that could otherwise be used for different purposes, such as heating greenhouses. The role of GreenVMAS is to maintain the required temperature level in greenhouses by using the waste energy generated by power plants. It incorporates a case-based reasoning system, virtual organizations and algorithms for data analysis and for efficient interaction with sensors and actuators. The system is context aware and scalable as it incorporates an artificial neural network, this means that it can operate correctly even if the number and characteristics of the greenhouses participating in the case study change. The architecture was evaluated empirically and the results show that the user’s energy bill is greatly reduced with the implemented system. [ABSTRACT FROM AUTHOR]

At present, the domotization of homes and public buildings is becoming increasingly popular. Domotization is most commonly applied to the field of energy management, since it gives the possibility of managing the consumption of the devices connected to the electric network, the way in which the users interact with these devices, as well as other external factors that influence consumption. In buildings, Heating, Ventilation and Air Conditioning (HVAC) systems have the highest consumption rates. The systems proposed so far have not succeeded in optimizing the energy consumption associated with a HVAC system because they do not monitor all the variables involved in electricity consumption. For this reason, this article presents an agent approach that benefits from the advantages provided by a Multi-Agent architecture (MAS) deployed in a Cloud environment with a wireless sensor network (WSN) in order to achieve energy savings. The agents of the MAS learn social behavior thanks to the collection of data and the use of an artificial neural network (ANN). The proposed system has been assessed in an office building achieving an average energy savings of 41% in the experimental group offices. [ABSTRACT FROM AUTHOR]