Abstract The interplay between machine learning and quantum computing can lead to unprecedented prospects for both fundamental research and real-world applications. In this new field of quantum machine learning, quantum classifiers, i.e., quantum algorithms for solving classification problems, have attracted the most attention. However, despite the promising performance of these new models for classification tasks, they are complex black-box models. Therefore, like or even more so than classical machine learning models, their use in critical contexts such as medical diagnosis could be hampered without the support of explanations. This paper addresses this challenge by investigating the use of Local Interpretable Model-agnostic Explanations (LIME) for quantum classifiers, with a specific focus on the stability of the explanations. Stability is a key property for ensuring that explanations are consistent and trustworthy, especially when decisions impact sensitive domains. The paper conducts a systematic stability study of LIME in the quantum setting, validating whether a classical explainable AI technique can be meaningfully applied to quantum models. As shown in experiments involving different datasets belonging to critical applications, LIME provides explanations for quantum support vector machines and quantum neural networks that are not only interpretable but also stable. By highlighting the stability of explanations, these findings demonstrate the suitability of LIME as an explainability tool for quantum classifiers.

Blood is one of the most important and common types of physical evidence left at crime scenes and used by forensic investigators to determine the dynamics of violent crimes. Therefore, it is important to be able to detect blood stains at the crime scene but without contaminating it. For this reason, recently, classifying hyperspectral images has gained popularity in the procedures to detect blood at the crime scene with respect to traditional approaches based on chemical tests. However, defining a classifier that distinguishes between blood spectra and all the other possible ones requires to train machine learning models with all possible classes of substances left at a crime scene. Since this is no a realistic task, the current approaches perform a classification between blood spectra and a limited number of other substances. In order to overcome this limited vision, this paper proposes, for the first time, to detect blood at the crime scene using one-class classification methods that have the feature of being trained only with samples of the target class (in our case, blood class). In detail, the main goal of this paper is to compare several one-class classification methods and analyse their performance. As shown in the experimental session involving a publicly available hyperspectral-based bloodstain dataset, one-class classification methods result in accurate techniques to detect blood at the crime scene.

In the Noisy Intermediate-Scale Quantum (NISQ) era, efficient quantum circuit synthesis is essential for optimizing limited qubit resources and mitigating noise effects. Quantum Linear Reversible Circuits (QLRCs) are a fundamental class of circuits with applications in quantum compilation or quantum error correction. They are composed exclusively of CX and SWAP gates, which are among the noisiest operations on current quantum hardware. Therefore, their efficient synthesis is pivotal for executing QLRCs on NISQ processors, which have restricted connectivity between qubits. In this direction, we introduce a Reinforcement Learning (RL)-based synthesis approach using Proximal Policy Optimization (PPO) to synthesize QLRCs on generic topologies of up to five qubits. This work extends previous work that did not handle different topologies of quantum processors. Our method dynamically adapts to hardware constraints, learning optimal or near-optimal gate decompositions. We compare our approach against Qiskit’s synthesis methods for QLRCs, demonstrating that our RL-based synthesizer consistently achieves lower CX gate depth. These results highlight the potential of RL-driven quantum circuit synthesis as a powerful alternative to traditional heuristic-based techniques, paving the way for more efficient AI-based quantum compilation strategies in the NISQ era.

Recently, quantum computing has emerged as a new paradigm that promises to improve artificial intelligence techniques. One of the research fields that is certainly benefiting from this new computational paradigm is evolutionary optimization. In literature, efforts have been already made to run evolutionary algorithms on quantum computers using quantum effects such as superposition and entanglement to converge towards sub-optimal solutions of hard problems. However, the performance of these quantum evolutionary approaches is limited by the number of qubits available on current quantum devices. This limitation is more noticeable in the case of continuous optimization problems, where the search space is potentially infinite. This paper presents a recursive algorithmic structure that embodies a quantum evolutionary algorithm to overcome the limitations mentioned above. The result is an innovative and efficient approach in the context of quantum evolutionary optimization.

The nuclear shell model is one of the most adopted many-body methods for the description of atomic nuclei whose main ingredient is the effective Hamiltonian. One of the approaches widely used to derive it is of phenomenological type, where its matrix elements are considered parameters to be fixed to reproduce the experimental data. However, the number of parameters as well as the number of experimental data dramatically increases with the mass of the nuclei under investigation and the commonly adopted procedures such as least-square fitting become computationally prohibitive. Therefore, there is a strong emergence in finding alternative approaches to construct effective Hamiltonians for heavy nuclei. To pave the way in this direction, the proposed work exploits for the very first time Genetic Algorithms. Indeed, their ability to simultaneously examine and manipulate sets of possible solutions could be crucial to deal with the above issues. The suitability of Genetic Algorithms in computing effective Hamiltonians is evaluated experimentally for the p - shell interaction, where the obtained results, without any physical constraint on the parameters, outperform the current widely-adopted description represented by the Cohen and Kurath solution.

The regulation of traffic lights in a signalised urban network requires optimizing objective functions that represent performance indicators of one or more intersections (such as delay or queue length). In this scenario, evolutionary algorithms are adopted to find suitable approximate solutions, in cases when no deterministic algorithm for finding the exact solution is known. This paper attempts to further improve the performance of evolutionary approaches by using a hybrid quantum-classical genetic algorithm to find the optimal configuration of the green signal timing regulating the traffic flow across two interacting junctions. The adopted algorithm, run on IBM quantum computer simulators, is shown to be suitable for the optimization problem at hand. Indeed, the experimental results highlight some of the strengths of the proposed technique with respect to the purely evolutionary approach, and encourage the application of this approach to more complex and close-to-real application scenarios.

Quantum machine learning is a research area that explores the interplay of ideas from quantum computing and machine learning to speed up the time it takes to train or evaluate a machine learning model. Variational quantum classifiers are among the most widely used quantum models for supervised machine learning and base their operation on learning free parameters through conventional optimization algorithms. In spite of their potential benefits, there is a design issue making quantum variational classifiers not fully operative: the existence of exponentially vanishing gradients, known as “barren plateau landscapes”. A barren plateau is a trainability problem that occurs in optimization algorithms for quantum machine learning when the problem-solving space turns flat as the algorithm is run. In that situation, the algorithm cannot find the downward slope in what appears to be a featureless landscape and there's no clear path to the optimum of the cost function. In this challenging scenario, evolutionary optimization techniques can be a viable choice in solving the problem because of their gradient independence. This paper introduces a comparative study involving different evolutionary algorithms to assess their suitability in acting as optimizer for a specific quantum machine learning method, known as variational quantum classifier.

In recent years, quantum computing is finding strong application in the development of new evolutionary algorithms. This is mainly due to the intrinsic parallelism induced by this new computational paradigm, which fits well with the structure of population-based optimization algorithms. However, the pioneering nature of quantum hardware makes quantum computations still suffer from noise that affects their accuracy and precision. This study aims to analyze the effect of this noise on quantum genetic algorithms and to quantify how much this can destroy or enhance the guided search of these algorithms in the search space. In detail, the proposed paper introduces a systematic study that assess the effects of quantum noise in a genetic algorithm equipped with a quantum recombination operator, formally known as the Quantum Mating Operator, when applied to solving a well-known optimization problem such as the 01 knapsack.

Recently, a significant interest is arising in developing techniques capable of correcting errors in noisy quantum computations without using additional quantum resources. These techniques, known as mitigation methods, are aimed at post-processing the quantum outcome without working at quantum hardware level. Among the most error-prone operations on the current quantum devices, there is surely the quantum measurement. Currently, the most popular mitigation method for quantum measurement error consists of computing a so-called mitigation matrix to be applied on the results outputted by a quantum processor to transform them and make them closer to the ideal ones. In this paper, a new measurement error mitigation method based on a competent memetic algorithm is proposed to generate an appropriate mitigation matrix. As shown in the experimental session, the proposed measurement error mitigation method shows better results when compared with the conventional method and the state of the art among the evolutionary approaches.

Quantum computers can be a revolutionary tool to implement inference engines for fuzzy rule-based systems. In fact, the use of quantum mechanical principles can enable parallel execution of fuzzy rules and allow them to be used efficiently in complex contexts such as distributed and big data environments. This article introduces the very first quantum-based fuzzy inference engine that is capable of providing exponential acceleration in fuzzy rule execution compared to its classical counterpart, and allows a quantum computer to be programmed by fuzzy linguistic rules. The proposed inference engine was implemented using a quantum algorithm design scheme based on the oracle notion. This scheme allows the modeling of a fuzzy rule-based system as a Boolean function, the oracle, which is able to reconstruct the relationships between the antecedent and consequent parts of fuzzy rules, and can be efficiently computed on a quantum computer. The suitability of the proposed quantum algorithm for use as a fuzzy inference engine was tested in a typical benchmark scenario, such as that provided by inverted pendulum control.

Recently, Variational Quantum Circuits (VQCs) are attracting considerable attention among quantum algorithms thanks to their robustness to the noise characterizing the current quantum devices. In detail, VQCs involve parameterized quan-tum circuits to be trained by means of a classical optimizer that makes queries to the quantum device. VQCs play a key role in several applications including quantum classifiers where the Hilbert space is used as feature space. Currently, the most used classical optimizer to learn V QCs is the gradient descent method. However, the so-called barren plateaus issue causes gradients of cost functions to become exceedingly small as the dimension of the classification problem is increased. As consequence, gradient descent method could be not efficient in real-world classification problems. This paper proposes to apply Genetic Algorithms (GAs) to train VQCs used as quantum classifiers. As shown in the experiments, the application of GAs results in accurate solutions obtained with a reduced number of queries to quantum devices.

Genetic Algorithms (GAs) are optimization methods that search near-optimal solutions by applying well-known operations such as selection, crossover and mutation. In particular, crossover and mutation are aimed at creating new solutions from selected parents with the goal of discovering better and better solutions in the search space. In literature, several approaches have been defined to create new solutions from the mating pool to try to improve the performance of genetic optimization. In this paper, the literature is enriched by introducing a new mating operator that harnesses the stochastic nature of quantum computation to evolve individuals in a classical genetic workflow. This new approach, named Quantum Mating Operator, acts as a multi-parent operator that identifies alleles' frequency patterns from a collection of individuals selected by means of conventional selection operators, and encodes them through a quantum state. This state is successively mutated and measured to generate a new classical chromosome. As shown by experimental results, GAs equipped with the proposed operator outperform those equipped with traditional crossover and mutation operators when used to solve well-known benchmark functions.

The Support Vector Machine (SVM) is a well-known supervised machine learning approach aimed at facing classification problems. Thanks to the exploitation of kernel functions, SVM succeeds to address also complex classification tasks involving non-linearly separable data. However, there are limitations to its success when the feature space becomes large, and the kernel functions become computationally expensive to estimate. Quantum-enhanced Support Vector Machine (QSVM) utilizes the properties of quantum computers to obtain an exponential speed up with respect to the conventional SVM by using the quantum state space as the feature space. Unfortunately, to benefit from these advantages, currently, it is necessary to have a background knowledge about quantum computing concepts and specific language skills. The goal of this paper is to introduce a web-based tool to make simple to all researchers, coming from several and heterogeneous scientific backgrounds, the application of QSVM to different real-world classification problems. To achieve this goal, the presented web tool involves data preprocessing techniques and a user-friendly interface. The suitability of the presented web tool is shown in an experimental session where QSVM is applied to solve well-known classification tasks.

Mobile applications in the area of human-centered applications are based on fuzzy logic have exhibited their effectiveness in managing intelligent environments, however the deployment of mobile fuzzy logic systems has been usually associated with dedicated hardware and software packages. Introducing openness for fuzzy logic systems offers exciting features such as system independence, simplicity, load balancing, and controlled resource allocation. On the other hand, while major cloud service providers support readymade commercial services for AI techniques such as for deep neural networks, there is no similar services for fuzzy logic systems. This study aims to develop a cloud-based fuzzy logic system under Microsoft Azure, employing Simpful as the cloud-side Python library and FML as data exchange standard. The developed cloud service is shown to effectively serve mobile phone applications for human monitoring purposes. Also in the present study, two types of fuzzy inference systems namely Mamdani and TSK have been utilized wherein both these systems have been compared on the basis of their processing time and accuracy of result. Results indicated that Mamdani fuzzy inference system outperformed TSK fuzzy inference system in terms of processing time by 0.456 seconds. Moreover, the detection accuracy of Mamdani system was found to be higher than that of TSK system by 6.82%.

Due to the built-in parallelism of quantum computing, there is an unexplored potential for some complex fuzzy logic computations to take the advantage of the future quantum computers. Recently, it has been introduced a novel representation of fuzzy sets and implementations of some basic fuzzy logic operators (union, intersection, alpha-cut and maximum) based on solving a Quadratic Unconstrained Binary Optimization (QUBO) problems, on a type of quantum computers known as quantum annealers. In this paper, this work is extended by presenting an implementation of centroid defuzzification on quantum annealer machines, based on binary quadratic model (BQM) but this time using Ising model. Having the basic operations and defuzzification implemented on quantum computers, this paper paves the way towards the implementation of a whole fuzzy inference engine on enhanced devices, such as quantum annealers.

Quantum computation is going to revolutionize the world of computing by enabling the design of massive parallel algorithms that solve hard problems in an efficient way, thanks to the exploitation of quantum mechanics effects, such as superposition, entanglement, and interference. These computational improvements could strongly influence the way how fuzzy systems are designed and used in contexts, such as Big Data, where computational efficiency represents a nonnegligible constraint to be taken into account. In order to pave the way toward this innovative scenario, this article introduces a novel representation of fuzzy sets and operators based on quadratic unconstrained binary optimization problems, so as to enable the implementation of fuzzy inference engines on a type of quantum computers known as quantum annealers.

Noisy Intermediate Scale Quantum (NISQ) devices are expected to demonstrate the real potential of quantum computing in solving hard problems. However, quantum noise that characterizes this kind of devices still remains an obstacle for their practical exploitation in real world scenarios. As a consequence, there is a strong emergence for error correction techniques aimed at making NISQ devices stable and fully operative. Unfortunately, current approaches for quantum error correction are prohibitive for NISQ devices because of the enormous multiplicative cost in resources that they require. For this reason, so-called quantum error mitigation methods are emerging as alternative approaches able to attenuate the quantum error as much as possible, without requiring a strong additional computational effort. Among the most error-prone operations, there is surely the quantum measurement. Conventionally, mitigation methods for quantum measurement error compute a so-called mitigation matrix capable of correcting results outputted by a quantum processor. In this paper, a new measurement error mitigation approach based on genetic algorithms is proposed to learn an appropriate mitigation matrix. As shown in the experimental session, the proposed measurement error mitigation method is comparable with or better than a conventional algebraic approach in terms of the Hellinger fidelity.

This paper introduces a study on fuzzy-based approaches aimed at addressing a crucial task in quantum computation: the evaluation of the similarity between quantum states. A quantum state is a mathematical entity that provides a probability distribution for the outcomes of each possible measurement of a quantum algorithm. Because quantum computers are still characterized by high noise in computation, output quantum states generated by quantum algorithms could be very far to be close to the ideal output quantum state computed by a noiseless quantum computer. As a consequence, there is a strong emergence for measures capable of assessing the similarity level of two quantum states, one ideal and the other real, to infer the quality of a quantum device in performing precise calculations and design appropriate quantum error correction schemes. This research proves that fuzzy methods are fully suitable to face this crucial challenge in a pioneering scenario such as that of quantum computing, as proved by their application on well-known quantum algorithms, such as Bernstein-Vazirani and Grover's algorithm.

Recently, Quantum Computing is entered in the so-called Noisy Intermediate-Scale Quantum (NISQ) era, where devices characterized by a few number of qubits are potentially able to overcome classical computers in performing specific tasks. However, noise in quantum operators still limits the size of quantum circuits that can be run in a reliable way. Consequently, there is a strong need for error mitigation approaches aimed at increasing reliability in quantum computation and making this paradigm really useful and productive in real world applications. In this paper, a fuzzy method, such as Fuzzy C-Means (FCM) clustering, has been used, for the very first time, to support the identification of matrices for error mitigation in quantum measurement. As shown in experiments, mitigation matrices identified with the support of FCM are able to strongly reduce error in computation when compared to mitigation matrices conventionally identified, like those used by IBM in its quantum library named Qiskit.

Fuzzy logic systems are customarily related to specific hardware or software systems. Nevertheless, it has been observed that distributed and cloud-based architectures of various intelligent systems are pouring intensifying attention. While the distributed architectures can potentially add values in developing fuzzy systems, a lack of standard methods and practices may limit their public use. This study aims to provide a standard solution for developing cloud-based service-oriented architectures for fuzzy logic systems, based on extending IEEE-1855 (2016) in the defining system and exchanging data. Experiments were performed employing simulation concerning collection, processing and monitoring of data in a distributed manner over the web. A real-time human activity recognition simulated scenario is also demonstrated through a cloud-based fuzzy system.

Quantum computing is a fascinating research area which promises a revolution in computing performance. Since the launch of the IBM Quantum Experience project in 2016, the research activities in this area are strongly increased. This project provides the public access to quantum processors composed of superconducting physical computing elements known as qubits. Unfortunately, qubits are sensitive to noise and, for this reason, quantum computation can be affected by errors. As a consequence, there is a strong emergence for so- called quantum error mitigation methods aimed to attenuate the quantum error as much as possible, without requiring a strong additional computational effort. Among the most error- prone operations, there is surely the quantum measurement. Conventionally, mitigation methods for quantum measurement error compute a so-called mitigation matrix capable of correcting results outputted by a quantum processor. In this paper, a new measurement error mitigation approach based on genetic algorithms whose fitness function uses Bhattacharyya distance is proposed to learn an appropriate mitigation matrix. As shown in the experimental session, the proposed measurement error mitigation method outperforms the traditional approach.

Bloodstain Pattern Analysis (BPA) is used by forensic officers to analyse bloodstains left at crime scenes. As a consequence, it has a crucial role in the investigations of bloody crimes. Currently, BPA activities are performed manually by leading to a slow and potentially imprecise analysis of the crime scenes. In order to overcome these issues, recently, some software tools have been developed in order to support forensic investigators in performing BPA activities in a more objective and fast way. However, only few approaches in literature use artificial intelligence methodologies to achieve this goal. Starting from this consideration, this paper presents a new intelligent tool based, for the first time, on the Density-Based Spatial Clustering of Application with Noise (DBSCAN) for supporting BPA activities. As shown in a case study, the proposed intelligent tool produces results that match with the ground truth.

Abstract Quantum computers have become reality thanks to the effort of some majors in developing innovative technologies that enable the usage of quantum effects in computation, so as to pave the way towards the design of efficient quantum algorithms to use in different applications domains, from finance and chemistry to artificial and computational intelligence. However, there are still some technological limitations that do not allow a correct design of quantum algorithms, compromising the achievement of the so-called quantum advantage. Specifically, a major limitation in the design of a quantum algorithm is related to its proper mapping to a specific quantum processor so that the underlying physical constraints are satisfied. This hard problem, known as circuit mapping, is a critical task to face in quantum world, and it needs to be efficiently addressed to allow quantum computers to work correctly and productively. In order to bridge above gap, this paper introduces a very first circuit mapping approach based on deep neural networks, which opens a completely new scenario in which the correct execution of quantum algorithms is supported by classical machine learning techniques. As shown in experimental section, the proposed approach speeds up current state-of-the-art mapping algorithms when used on 5-qubits IBM Q processors, maintaining suitable mapping accuracy.

Supervised learning methods aimed at performing precise predictions by learning from labeled training data. Unfortunately, training data can contain noisy or wrong information, specially when they come from real-world applications. In this scenario, applying a so-called training set selection procedure on data can lead to improve the performance of the supervised learning methods used for classification or regression tasks. In literature, several training set selection techniques have been proposed, but, to the best of our knowledge, few software tools implement this procedure. Moreover, all of them require programming capabilities or software package installation what makes their use difficult for people without specific computer skills. This paper proposes the first web-based tool, named TSSweb, for performing an accurate selection of the training instances. Thanks to its web nature, TSSweb enables all researchers, coming from several and heterogeneous scientific backgrounds, to reduce own datasets so as to improve their analysis and reduce the execution time of their supervised learning models. As shown in the experimental session, TSSweb produces reduced datasets with a good quality as well as being user-friendly.

One of the main issues in machine learning is related to the quality of data used to efficiently train statistical models for classification/regression tasks. Among these issues, the presence of missing values in data sets is particularly prone in affecting the accuracy performance of learning methods. As a consequence there is a strong emergence of software tools aimed at supporting machine learning users in "filling-in" their data sets before inputting them to training algorithms. This paper bridges this gap by introducing a web-based tool for MIssing DAta imputation (MIDA) based on a novel supervised learning method, namely Generalized Boosted Incremental Non Parametric Imputation algorithm (G-BINPI), able to address the missing values issue in scenarios where a "missing at random" assumption occurs. The proposed approach enables machine learning users to remotely imputing their data sets by means of an intuitive graphical user interface. As highlighted in the experimental section, the proposed approach yields better performance than conventional approaches for missing data imputation on different benchmark data sets.

Motor therapies can be considered as one of the social challenges that have a great impact in children with autism. Traditionally, exercises and activities as therapies have been used to mitigate and to rehabilitate problems related to gross motor skills. Nevertheless, from the perspective of children, these therapies are often repetitive, boring and they need an extra motivation aspect due to the target: children with autism. From the point of interaction with the target population, the use of technology is a key issue in these therapies. In this scenario, a new software named JKinect which is based on RGB-D sensors and computer based games. JKinect helps children with gross motor problems and presents great flexibility in the therapy design and game sharing among specialists. Additionally, a new module for linking JKinect with both fuzzy systems based on the IEEE std 1855-2016 and the JFML library to support experts' decision making in therapies on the basis of fuzzy rules is also included.

Electroencephalography (EEG) headsets are wearable computing devices capable of recording electrical activity of the brain. These devices play a key role in the Brain-Computer Interfaces (BCIs) systems, i.e., systems capable of acquiring, processing and classifying EEG signals in order to control external devices such as wireless prosthetics. In spite of their crucial role, the current EEG headsets are very uncomfortable being composed of many wet electrodes. Hence, single-channel BCIs with dry electrodes are emerging like wearable devices more accepted by users. Unfortunately, this kind of device typically provides weaker and noisier signal that makes more challenging the classification task. This work is aimed at improving the quality of the classification of EEG signals, and in particular of Steady-State Visual Evoked Potentials (SSVEP), captured by single-channel EEG devices by using an evolutionary algorithm-based optimized version of Support Vector Machine (SVM). As shown by experimental results, the proposed approach improves on the state-of-the-art methods in terms of accuracy.

The currently observed developments in Artificial Intelligence (AI) and its influence on different types of industries mean that human-robot cooperation is of special importance. Various types of robots have been applied to the so-called field of Edutainment, i.e., the field that combines education with entertainment. This paper introduces a novel fuzzy-based system for a human-robot cooperative Edutainment. This co-learning system includes a brain-computer interface (BCI) ontology model and a Fuzzy Markup Language (FML)-based Reinforcement Learning Agent (FRL-Agent). The proposed FRL-Agent is composed of (1) a human learning agent, (2) a robotic teaching agent, (3) a Bayesian estimation agent, (4) a robotic BCI agent, (5) a fuzzy machine learning agent, and (6) a fuzzy BCI ontology. In order to verify the effectiveness of the proposed system, the FRL-Agent is used as a robot teacher in a number of elementary schools, junior high schools, and at a university to allow robot teachers and students to learn together in the classroom. The participated students use handheld devices to indirectly or directly interact with the robot teachers to learn English. Additionally, a number of university students wear a commercial EEG device with eight electrode channels to learn English and listen to music. In the experiments, the robotic BCI agent analyzes the collected signals from the EEG device and transforms them into five physiological indices when the students are learning or listening. The Bayesian estimation agent and fuzzy machine learning agent optimize the parameters of the FRL agent and store them in the fuzzy BCI ontology. The experimental results show that the robot teachers motivate students to learn and stimulate their progress. The fuzzy machine learning agent is able to predict the five physiological indices based on the eight-channel EEG data and the trained model. In addition, we also train the model to predict the other students’ feelings based on the analyzed physiological indices and labeled feelings. The FRL agent is able to provide personalized learning content based on the developed human and robot cooperative edutainment approaches. To our knowledge, the FRL agent has not applied to the teaching fields such as elementary schools before and it opens up a promising new line of research in human and robot co-learning. In the future, we hope the FRL agent will solve such an existing problem in the classroom that the high-performing students feel the learning contents are too simple to motivate their learning or the low-performing students are unable to keep up with the learning progress to choose to give up learning.

Sponsored by the IEEE Computational Intelligence Society, IEEE Std 1855 is aimed at defining a standard language, named Fuzzy Markup Language, capable of modeling fuzzy systems without considering hardware/software constraints and enabling, in this way, fuzzy systems sharing. In order to provide runnable fuzzy systems in accordance with IEEE Std 1855, recently, a Java library, named JFML, has been developed. Unfortunately, in spite of its benefits, JFML enables the modeling of fuzzy systems only for Java programmers. In order to overcome this drawback, this paper introduces a new open source fuzzy system software capable of enabling the modeling of fuzzy systems in accordance to IEEE Std 1855 enhancing JFML through a visual environment based on graphical user interfaces. Hence, VisualJFML represents a remarkable contribution to the literature since it allows modelling sharable fuzzy systems also to designers without programming skills. The user-friendly graphical interface provided by VisualJFML is shown by means of a case study dealing with the Iris classification problem.

Ordinal classification can be used for predicting an ordinal variable, i.e. a variable whose value exists on an arbitrary scale where the relative ordering between different values is significant. Ordinal classification problems are getting an important position in learning problems with examples such as studies on food quality or credit risks related to the financial obligations of a company. KEEL (Knowledge Extraction based on Evolutionary Learning) is an open source (GPLv3) Java software tool that can be used for a large number of different knowledge data discovery tasks. It contains a wide variety of computational intelligence algorithm implementations providing a good tool and scenario to assess and develop computational problems. Focusing on problems of nominal classification or regression, there is not a wide variety of software that addresses this type of problems. This work aims to facilitate the use of the fuzzy rule learning algorithm for ordinal classification (NSLVOrd) enabling its integration into the well-known software tool KEEL. The implementation and some instructions to execute NSLVOrd in KEEL are also detailed showing the ease of use for any KEEL user.

Steady-State Visual Evoked Potentials (SSVEPs) are electroencephalography (EEG) signals which, recently, have attracted a notable interest in the field of Brain Computer Interfaces (BCIs) due to their little training requirement. Similar to other EEG signals, SSVEPs are captured by means of EEG devices characterized by multiple wet electrodes. Unfortunately, these EEG devices are very uncomfortable for users to be worn. As a consequence, there is a strong interest in developing more comfortable single-channel EEG devices equipped with dry sensors. However, if, on one hand, these innovative EEG devices are less invasive and more simple to be used by an average user, on the other hand, the exploitation of a single dry sensor leads to the collection of a weaker and noisy signal which is more difficult to be opportunely processed and classified. The aim of this paper is to improve the performance of signal classification tasks of SSVEPs captured by single-channel EEG devices with dry sensors by logistic regression. As shown by experimental results, the proposed approach improves the state-of-the-art methods in terms of accuracy.

Mobile devices are always more diffused in the last years, allowing the users to perform several tasks: communication, web surfing, requiring web services. Given the high amount of sensitive data and operations related to these tasks, securing the mobile devices is becoming a very critical issue. As matter of the fact, malware attacks are on the rise and new mobile malware are continually generated with the aim of stealing private data and performing illegal activities. Since this new malware is mainly obtained by reusing existing malicious code, malware detection is supported by the study and the tracking of the mobile malware phylogeny. This paper proposes a malware phylogeny model obtained by a declarative Process Mining (PM) approach from the analysis of some running malware applications. The main idea is that the set of relations and recurring execution patterns among the syscalls of a running malware application can be modeled to obtain a malware fingerprint. The malware fingerprints are compared and classified by using a fuzzy clustering algorithm to recover the malware phylogeny map of all the considered malware families. The evaluation of the proposed approach is performed on a dataset of more than 4,000 infected applications across 39 malware families obtaining very encouraging results.

Fuzzy Rule-Based Systems (FRBSs) are powerful tools for handling uncertainty in complex real world problems. Unfortunately, designing an optimal set of fuzzy membership functions and rules is not an easy task. The complexity of this task increases furthermore when the design involves type-2 fuzzy memberships because of the higher number of parameters to identify. For this reason, researches have been carried out to automatically extract the most suitable parameters and the hidden rules for type-2 FRBSs by using historical data. However, very few approaches exist based on a hybrid optimization between global and local search. This paper is aimed at overcoming this gap by proposing to automatically generate type-2 FRBSs through the so-called memetic algorithms. As shown in the experiments, the exploitation of memetic algorithms leads to generate type-2 FRBSs more accurate than those obtained by well-known approaches in literature.

This paper introduces the first implementation of fuzzy rule-based systems for quantum computers. Quantum computers are incredibly powerful machines that use principles of quantum mechanics, such as superposition and entanglement, to process information in a more efficient way than classical computers. Currently, several companies and academic research groups are focusing their efforts on the development of quantum algorithms for artificial intelligence but, so far, no significant contribution in the area of the fuzzy inference for quantum machines has been proposed. This article bridges this gap by introducing a quantum implementation of a fuzzy rule-based system through a synergistic usage of the well-known Grover's algorithm and look-up tables. As shown by a case study, the behaviour of the quantum implementation of the fuzzy rule-based system is comparable to its classical version, but with a quadratic asymptotic speed-up than a fuzzy rule-based system implemented by a classical look-up table.

Fuzzy Logic Systems have been successfully used in a wide range of real-world problems. They can include a priori expert knowledge and represent systems for which it is not possible to obtain a mathematical model. The standard IEEE Std 1855™-2016 was established to provide the fuzzy community with a unique and well-defined tool allowing a fuzzy logic system design completely independent from the specific hardware/software. Recently, the library Java Fuzzy Markup Language (JFML) offers a complete implementation of the standard, however, the actual version of the JFML does not support the development of fuzzy inference systems on specific types of hardware. The aim of this paper is to develop an interoperability module to design and running FLS for embedded systems in JFML, concretely for Arduino boards. In addition, a communication protocol between JFML and Arduino boards is also defined, removing the limited computing capacity usually offered by embedded systems. A case study with a wall-following fuzzy controller to manage a mobile robotic in two environments is developed in order to illustrate the potential of the new interoperability module.

Several studies focused on single human activity recognition, while the classification of group activities is still under-investigated. In this paper, we present an approach for classifying the activity performed by a group of people during daily life tasks at work. We address the problem in a hierarchical way by first examining individual person actions, reconstructed from data coming from wearable and ambient sensors. We then observe if common temporal/spatial dynamics exist at the level of group activity. We deployed a Multimodal Deep Learning Network, where the term multimodal is not intended to separately elaborate the considered different input modalities, but refers to the possibility of extracting activity-related features for each group member, and then merge them through shared levels. We evaluated the proposed approach in a laboratory environment, where the employees are monitored during their normal activities. The experimental results demonstrate the effectiveness of the proposed model with respect to an SVM benchmark.

Automatic movement-prothesis control aims to increase the quality of life for patients with diseases causing temporary or permanent paralysis or, in the worst case, the lost of limbs. This technology requires the interaction between the user and the device through a control interface that detects the user's movement intention. Basing on the Motor-Imagery theory, many researchers have explored a wide variety of Classifiers to identify patients' physiological signals from many different sources in order to detect patients' moves intentions. We here propose a novel approach relying on the use of a Weightless Neural Network-based classifier, whose design lends itself to an easy hardware implementation. Additionally, we employ a non-invasive light weight and easy donning EEG-helmet in order to provide a portable controller interface. The developed interface is connected to a robotic hand for controlling open/close actions. We compared the proposed classifier with state of the art classifiers by showing that the proposed method achieves similar performance and contemporaneously represents a viable and practicable solution due to its portability on hardware devices, which will permit its direct implementation on the helmet board.

In the last years, the growing adoption of cloud-based multi-tiers systems has strongly increased the levels of resource sharing among companies, improving the enterprise efficiency, thanks to a refined business dynamism and a rapid decrease in costs. However, in spite of their advantages, this new business model highlights the emergence of new computational approaches aimed at the distribution and the optimization of resources sharing along so-called multi-tenants system, i.e., cloud-based architecture where a single instance of software runs on a single server and serves multiple companies (tenants). This paper faces this challenging gap by proposing an auto-scaling cloud computing multi-tenancy architecture where process mining and fuzzy-based load-balancing systems synergistically interact to provide an improved and optimized resource management distribution. A case study is carried out to show the proposed architecture in operation.

Fuzzy Systems are an efficient instrument to create efficient and transparent models of the behavior of complex dynamic systems such as autonomous humanoid robots. The human interpretability of these models is particularly significant when it is applied to the cognitive robotics research, in which the models are designed to study the behaviors and produce a better understanding of the underlying processes of the cognitive development. From this research point of view, this paper presents a comparative study on training fuzzy based system to control the autonomous navigation and task execution of a humanoid robot controlled in a soccer scenario. Examples of sensor data are collected via a computer simulation, then we compare the performance of several fuzzy algorithms able to learn and optimize the humanoid robot's actions from the data.

IYEEE Std 1855 is the first IEEE standard technology developed in the area of fuzzy logic. Its main characteristic is the interoperability, a design feature that enables system designers to develop fuzzy inference engines without taking into account the hardware/software constraints imposed by the specific architecture on which the system will be deployed. Thanks to this feature, a fuzzy system can be integrated into different types of architectures without any need to carry out porting strategies. This feature is particularly crucial in the area of embedded systems where, for each kind of device, a variety of applications, communication protocols, software libraries and programming tools, exists. In this context, ArduinoTMtechnology represents one of the most popular architectures, thanks to its ease of development and prototyping. This paper shows how the native extendability feature of IEEE Std 1855 enables the design of a fuzzy rule-based systems in fully interoperable fashion on ArduinoTMarchitectures and, as a consequence, allows designers to focus on fuzzy concepts, without any need to consider the hardware/software details related to the specific ArduinoTMsystem.

Smart home systems have become increasingly widespread in the last few years. State-of-the-art smart home architectures concentrate on modeling the user physical behavior and on discovering possible behavioral pattern, while they provide very little personalization of the services based on the individual cognitive characteristics. In this direction, the analysis of the user activity on social networks offers a reliable and efficient way to obtain psychological traits of a human being in a manner that can be easily integrated with smart systems. In this paper, we outline a robot based architecture that blends ubiquitous computing and personality analysis to provide a custom-tailored system. An entertainment recommender scenario with a social robot is then analyzed as a case study.

This paper explores the applications of fuzzy logic inference systems as an instrument to perform linguistic analysis in the domain of prosodic prominence. Understanding how acoustic features interact to make a linguistic unit be perceived as more relevant than the surrounding ones is generally needed to study the cognitive processes needed for speech understanding. It also has technological applications in the field of speech recognition and synthesis. We present a first experiment to show how fuzzy inference systems, being characterised by their capability to provide detailed insight about the models obtained through supervised learning can help investigate the complex relationships among acoustic features linked to prominence perception.

A robotic system that is designed to coexist with humans has to adapt its behavioral and social interaction parameters not only with respect to the task it is supposed to accomplish, but also with respect to the human being it is interacting with by profiling her habits, preferences, and personality. This is particularly relevant in the domain of assistive robotics where the behavioral adaptability has been shown to enhance the users' acceptability of a robot. In this work, we propose a neuro-fuzzy-Bayesian system able to adapt the robot proxemics behavior with respect to the human users' personality and the action she is currently performing. The user's personality is evaluated according to the Big-Five factors model and the activity recognition is obtained by classifying data from a wearable device through the use of a Bayesian Network classifier. As shown by a statistical study, the proposed framework is capable of computing the most appropriate robot proxemics behavior in order to improve human feeling in interacting with artificial agents, such as robots.

Optimal Reactive Power Flow (ORPF) is one of the most fundamental and widely used tools in modern smart grids for improving the power system performance in terms of both operation cost and quality of service. This result is obtained by identifying the optimal set points of the voltage controllers, which improve the bus voltage profiles, minimise the regulation costs, and satisfy the power equipment constraints. In this context, the application of traditional optimisation methods is often complicated by the presence of multiple local minima, which could affect the performance and the convergence of iterative methods. To address this issue the adoption of evolutionary based paradigms has been recognised as one of the most promising enabling methodologies. Armed with such a vision, in this paper a novel solution method based on Search Group Algorithm (SGA) is proposed to solve the ORPF problem. As demonstrated by detailed experimental studies, which have been performed on IEEE 57-bus and 118-bus test power systems, the proposed evolutionary approach outperforms state-of-the-art evolutionary algorithms.

Memetic Algorithms (MAs) are a family of metaheuristics which combines global search approaches with local search techniques. Thanks to this hybridization, MAs have emerged as a powerful tool for tackling hard optimization problems in a more efficient way than their `no hybrid' counterparts. However, despite the success of MAs, it is still vital to note that the design of MAs raises a number of important issues which must be addressed to achieve a suitable hybridization of global and local search approaches resulting in a so-called Competent Memetic Algorithm (CMA). Currently, no software tools exist to automatically design CMAs and, as a consequence, all the hybridization choices are responsibility of human designers, which suffer from slowness and propensity to make mistakes. In order to bridge this design gap, this paper introduces jMeme, a Java library enabling an automatic development of CMAs so as to free practitioners of the responsibility for the design choices. As shown by experiments involving well-known benchmark functions, the competent design of MAs performed by jMeme yields better performance than MAs designed conventionally.

The k-nearest neighbor (k-NN) algorithm is one of the most well-known supervised classifiers due to its ease of use and good performance. However, in spite of its popularity, k-NN suffers from some drawbacks such as high computational complexity, high storage requirements, and low noise tolerance. Prototype selection is a successful technique aimed at addressing aforementioned issues by reducing the size of training datasets without deprecating, but improving, the classification accuracy. Recently, evolutionary algorithms have been successfully applied to the optimisation of accuracy and size of reduction of prototype selection because of their innate exploration and exploitation capabilities in visiting the space of solutions of a problem. However, so far, all the evolutionary approaches for prototype selection are based on a so-called multi-objective “a priori” technique, where multiple objectives are aggregated together into a single objective through a weighted combination. This paper proposes to apply, for the first time, an “a posteriori” algorithm, namely SPEA2, to prototype selection problem in order to explicitly deal with both objectives and offer a better trade-off between classification and reduction performance. As shown in the experimental section, the application of SPEA2 allows to hold high accuracy in nearest neighbour classification with a significant reduction of training data thanks to the discovery of higher quality solutions than those detected by a conventional “a priori” approach.

Computers and artificial intelligence play a key role in the production of artwork through the designing of synthetic agents that are able to reproduce the capabilities of human artists in assembling high-quality artefacts such as paintings and sculptures. In this context, music composition represents one of the art disciplines that can greatly benefit from the appropriate use of computational intelligence, as witnessed by the large number of research activities performed in this field over the recent years. Nevertheless, the automatic composition of music is far from being completely and precisely perfected due to the intrinsic virtuosity that characterizes human musicians' capabilities. This paper reduces this gap with the proposal of an intelligent scheme for the efficient composition of melodies based on a musical method that is inspired by and strongly characterized by human virtuosity: the unfigured bass technique. In particular, we formulate this music composition technique as an optimization problem and solve it with an adaptive multiagent memetic approach comprising diverse metaheuristics, the composer agents that cooperate to create high-quality four-voice pieces of music starting from a bass line as input. A collection of experimental studies on the famous Bach's four-voice chorales showed that the cooperation among different optimization strategies yields improved performance over the solutions obtained by conventional and hybrid evolutionary algorithms.

Source-code plagiarism detection in programming, concerns the identification of source-code files that contain similar and/or identical source-code fragments. Fuzzy clustering approaches are a suitable solution to detecting source-code plagiarism due to their capability to capture the qualitative and semantic elements of similarity. This paper proposes a novel Fuzzy-based approach to source-code plagiarism detection, based on Fuzzy C-Means and the Adaptive-Neuro Fuzzy Inference System (ANFIS). In addition, performance of the proposed approach is compared to the Self- Organising Map (SOM) and the state-of-the-art plagiarism detection Running Karp-Rabin Greedy-String-Tiling (RKR-GST) algorithms. The advantages of the proposed approach are that it is programming language independent, and hence there is no need to develop any parsers or compilers in order for the fuzzy-based predictor to provide detection in different programming languages. The results demonstrate that the performance of the proposed fuzzy-based approach overcomes all other approaches on well-known source code datasets, and reveals promising results as an efficient and reliable approach to source-code plagiarism detection.

Fuzzy Cognitive Maps (FCMs) are a very simple and powerful technique for simulation and analysis of dynamic systems. In spite of their wide applicability in different domain areas, the manual development of FCMs suffers from several drawbacks such as the human difficulty to deal with systems characterised by a large number of variables. Therefore, several evolutionary learning approaches aimed at automatically building FCM models by using historical data have been developed over years. Nevertheless, there is no a formal and complete comparison able to evaluate the performance of evolutionary algorithms in learning FCMs. Consequently, the goal of this paper is to bridge this experimental gap by performing a multiple statistical procedure able to compare the best known nature-inspired algorithms-based learning methods for FCM models.

Inferring Gene Regulatory Networks (GRNs) from expression data is one of the most challenging topic in computational biology. Indeed, the reasoning about GRN behaviours is a crucial biological task useful to provide an significant support for the identification of genetic diseases and the estimation of the effects of medications. Over years, several approaches have been applied to infer GRNs, most of them are based on deterministic and crisp-based algorithms. However, the intrinsic imprecise nature of the gene regulation makes these approaches as inefficient and characterized by a low accuracy. Starting from this consideration, in this work, we propose to use Fuzzy Cognitive Maps to model the complex behaviour of GRNs and to learn FCMs models of GRNs by means of an innovative evolutionary algorithm: the Big Bang-Big Crunch algorithm. As shown through a statistical comparison, the proposed approach outperforms other evolutionary learning methods in inferring GRNs representing, as a consequence, a breakthrough approach in this fascinating and challenging domain.

Bloodstain pattern analysis (BPA) is a forensic discipline that plays a key role in performing a reconstruction of the crime scene related to blood shedding events. By studying the distribution, size and shape of bloodstains, BPA supports worldwide investigation agencies (US FBI, Italian Carabinieri and so on) in identifying the dynamics of a certain act of violence and evaluating the credibility of statements provided by a witness, a victim, or a suspect. However, in spite of its importance, this forensic discipline is still mainly based on manual approaches, making the analysis of a crime scene long, tedious and potentially imperfect. This paper is aimed at presenting a proposal for a robotic framework to automate the BPA in all its steps. In particular, the robotic framework is composed of an Unmanned Aerial Vehicle (UAV) capable of navigating the crime scene, detecting bloodstains, computing the points of origin and preparing a technical report describing the bloody event.

Fuzzy cognitive maps (FCMs) form an important class of models for describing and simulating the behavior of dynamic systems through causal reasoning. Owing to their abilities to make the symbolic knowledge processing simple and transparent, FCMs have been successfully used to model the behavior of complex systems originating from numerous application areas, such as economy, politics, medicine, and engineering. However, the design of FCMs necessarily involves domain experts to develop a graph-based model composed of a collection of system's concepts and causal relationships among them. Consequently, since humans exhibit an intrinsic factor of subjectivity and are only able to efficiently develop small-size graph-based models, there is a legitimate need to devise methods capable of automatically learning FCM models from data. This research addresses this need by introducing a competent memetic algorithm to generate FCM models from available historical data, with no human intervention. Extensive benchmarking tests performed on both synthetic and real-world data quantify the performance of the competent memetic method and emphasize its suitability over the models obtained by conventional and noncompetent hybrid evolutionary approaches in terms of accuracy, approximation ability, and convergence speed. Moreover, the proposed approach is shown to be scalable due to its capability to efficiently learn high-dimensional FCM models.

In recent years, several ontology-based systems have been developed for data integration purposes. The principal task of these systems is to accomplish an ontology alignment process capable of matching two ontologies used for modeling heterogeneous data sources. Unfortunately, in order to perform an efficient ontology alignment, it is necessary to address a nested issue known as ontology meta-matching problem consisting in appropriately setting some regulating parameters. Over years, evolutionary algorithms are appeared to be the most suitable methodology to address this problem. However, almost all of existing approaches work with a single function to be optimized even though a possible solution for the ontology meta-matching problem can be viewed as a compromise among different objectives. Therefore, approaches based on multi-objective optimization are emerging as techniques more efficient than conventional evolutionary algorithms in solving the meta-matching problem. The aim of this paper is to perform a systematic comparison among well-known multi-objective Evolutionary Algorithms (EAs) in order to study their effects in solving the meta-matching problem. As shown through computational experiments, among the compared multi-objective EAs, OMOPSO statistically provides the best performance in terms of the well-known measures such as hypervolume, Δ index and coverage of two sets.

Internet has opened new interesting scenarios in the fields of commerce and marketing. In particular, the idea of e-commerce has enabled customers to perform their transactions in a faster and cheaper way than conventional markets, and it has allowed companies to increase their sales volume thanks to a world-wide visibility. However, one of the problems that can strongly affect the performance of any e-commerce portal is related to the quality and validity of ratings provided by customers in their past transactions. Indeed, these reviews are used to determine the extent of customers acceptance and satisfaction of a product or service and they can affect the future selling performance and market share of a company. As a consequence, an efficient analysis of customer feedback could allow e-commerce portals to improve their selling capabilities and revenue. This paper introduces an innovative computational intelligence framework for efficiently learning review ratings in e-commerce by addressing different issues involved in this significant task: the dimension and imprecision of ratings data. In particular, we integrate the techniques of Singular Value Decomposition (SVD), Fuzzy C-Means (FCM) and ANFIS and, as shown in experimental results, this synergetic approach yields better learning performance than other rating predictors based on a conventional artificial neural network and FCM algorithm.

During the last years, electronic markets (e-markets) are emerging as a new idea of economy, where trade transactions can be performed by buyers and sellers even if they are separated by geographic boundaries, time differences or distance barriers. Unfortunately, in this on-line trade environment, the probability of large-scale fraud and deceit is higher than traditional commerce because of the lack of face-to-face communications. For this reason, reputation systems, which enable to assess the trustiness level of transacting parties, are becoming a fundamental component of any current e-market portal. In this paper, we propose a new fuzzy logic based reputation system capable of efficiently assessing transacting parties through the exploitation of 1) a fuzzy trust model which takes into account a set of metrics reflecting the trust human perception both on seller and buyer side and, furthermore, it does not miss to consider past transactions; 2) a fuzzy based reputation aggregation taking into account credibility concept to discriminate false trust values. As shown by performed experiments, the proposed reputation system yields better performance than that used by one of the most known e-markets, eBay®.

The introduction of Fuzzy Markup Language (FML) in 2004 has initiated an important trend in Computational Intelligence research: the application of new web technologies to create more flexible and hardware independent environment for deploying "fuzzy ideas". FML allows researchers and engineers to focus on problem solving activities bypassing additional difficulties related to programming or physical equipment constraints. From that moment on, many researches have been using FML and other XML-based languages for modeling and developing fuzzy systems. However, in spite of their hardware interoperability, XML languages are able to model a fuzzy system in static way and, consequently, they do not provide any support for modelling "evolving" and temporal-based fuzzy systems, as such Timed Automata based Fuzzy Controllers. To address this deficiency, this paper introduces an extension of FML called FMLScript. It is based on a scripting language concept and allows for modeling XML-based systems that can dynamically modify their configurations. As the consequence, a better expressive power can be achieved when compared with static modelling approaches. This is shown in a case study involving a smart grid control.

Ontology alignment systems are software tools aimed at producing a set of correspondences, called alignment, between two heterogeneous ontologies in order to bring them in a mutual agreement. Performing this task is an essential step to allow the exchange of information between people, organizations and web applications using ontologies for representing their view of the world. Currently, in spite of several ontology alignment systems have been developed, there is no a robust solution that seems capable of producing alignments with the same high quality on different alignment task instances. Mainly, this weakness of ontology alignment systems is due to the dependence of their behavior on a set of specific instance parameters. This work proposes to improve performance of a well-known memetic algorithm based ontology alignment system by adaptively regulating its specific instance parameters through a FML-based fuzzy tuning. The validity of our proposal is shown by aligning ontologies belonging to two well-known OAEI datasets and by performing a Wilcoxon's signed rank test which highlights that our proposal statistically outperforms its not fuzzy adaptive counterpart.

Achieving semantic interoperability is an essential task for all distributed and open knowledge based systems. Currently, the best technology recognized for fulfilling this complex task is represented by ontologies. Unfortunately, in turn, the power of ontological representation is reduced by the semantic heterogeneity problem which affects two ontologies when they are characterized by terminological and conceptual discrepancies. The most solid solution to overcome this problem is to perform an ontology alignment process capable of leading two heterogeneous ontologies into a mutual agreement by detecting a set of correspondences between them. All ontology alignment processes based on evolutionary approaches developed so far perform an evaluation of the produced alignments based on multi-objectives "a priori" approaches. This paper proposes to apply NSGA II to ontology alignment problem in order to overcome the well-known drawbacks of "a priori" methods. As shown in the experimental section, the application of NSGA II allows to improving semantic interoperability by finding high quality solutions that are not detected by "a priori" approaches.

Ambient Intelligence (AmI) is a new paradigm in information technology aimed at empowering people's capabilities by the means of digital environments that are sensitive, adaptive, and responsive to human needs, habits, gestures, and emotions. This futuristic vision of daily environment will enable innovative human-machine interactions characterized by pervasive, unobtrusive and anticipatory communications. Such innovative interaction paradigms make ambient intelligence technology a suitable candidate for developing various real life solutions, including in the health care domain. This survey will discuss the emergence of ambient intelligence (AmI) techniques in the health care domain, in order to provide the research community with the necessary background. We will examine the infrastructure and technology required for achieving the vision of ambient intelligence, such as smart environments and wearable medical devices. We will summarize of the state of the art artificial intelligence methodologies used for developing AmI system in the health care domain, including various learning techniques (for learning from user interaction), reasoning techniques (for reasoning about users' goals and intensions) and planning techniques (for planning activities and interactions). We will also discuss how AmI technology might support people affected by various physical or mental disabilities or chronic disease. Finally, we will point to some of the successful case studies in the area and we will look at the current and future challenges to draw upon the possible future research paths.

The psychological overcharge issue related to human inadequacy to maintain a constant level of attention in simultaneously monitoring multiple visual information sources makes necessary to develop enhanced video surveillance systems that automatically understand human behaviors and identify dangerous situations. This paper introduces a semantic human behavioral analysis (HBA) system based on a neuro-fuzzy approach that, independently from the specific application, translates tracking kinematic data into a collection of semantic labels characterizing the behavior of different actors in a scene in order to appropriately classify the current situation. Different from other HBA approaches, the proposed system shows high level of scalability, robustness and tolerance for tracking imprecision and, for this reason, it could represent a valid choice for improving the performance of current systems.

Interoperability is a key problem in agent-based systems where different interacting computational entities negotiate to achieve a common goal. In last years, this interoperability issue has been faced by exploiting the concept of ontology that enables a single agent to model its knowledge by means of a semantic description of a domain of interest. However, ontology ability to enable a full interoperability can be limited by the so-called semantic heterogeneity problem which arises when some discrepancies exist among ontologies modeling the knowledge related to different agents. As consequence, in order to enable an effective knowledge exchange, an ontology alignment process is necessary to lead proprietary ontologies to a mutual agreement. Recently, some studies have successfully investigated the suitability of memetic algorithms to solve this complex task. However, memetic algorithms are influenced by some design issues arising from the different choices that can be taken to implement them. The aim of this paper is to compare the performances yielded by different memetic ontology alignment systems in order to individuate the most suitable hybrid evolutionary approach which enables a strong agent interoperability. The comparison among the considered approaches is performed by applying a statistical multiple comparison procedure on a collection of ontologies belonging to the well-known Ontology Alignment Evaluation Initiative (OAEI) benchmarks.

Ontologies are recognized as a fundamental component for enabling interoperability across heterogeneous systems and applications. Indeed, they try to fit a common understanding of concepts in a particular domain of interest to support the exchange of information among people, artificial agents, and distributed applications. Unfortunately, because of human subjectivity, various ontologies related to the same application domain may use different terms for the same meaning or may use the same term to mean different things, raising the so-called heterogeneity problem. The ontology alignment process tries to solve this semantic gap by individuating a collection of similar entities belonging to different ontologies and enabling a full comprehension among different actors involved in a given knowledge exchanging. However, the complexity of the alignment task, especially for large ontologies, requires an automated and effective support for computing high-quality alignments. The aim of this paper is to propose a memetic algorithm to perform an efficient matching process capable of computing a suboptimal alignment between two ontologies. As shown by experiments, the memetic approach is more suitable for ontology alignment problem than a classical evolutionary technique such as genetic algorithms. © 2012 Wiley Periodicals, Inc.

This paper introduces a new type of TSK-based neuro-fuzzy approach and its application to modeling highly dynamic systems. In details, our proposal performs an adaptive supervised learning on a collection of time series in order to create a so-called Timed Automata Based Fuzzy Controller, i.e. an evolvable fuzzy controller whose dynamic features yield high performances in variable structure systems representation. The adaptive learning is accomplished by merging together theories from the area of times series analysis such as the Adaptive Piecewise Constant Approximation method, with a well-known neuro-fuzzy framework, the Adaptive Neuro Fuzzy Inference System. As will be shown in our experiments, where our proposal has been tested on a Fuzz-IEEE 2011 Fuzzy Competition dataset, this approach reduces the output error measure and achieves a better performance than a standard application of the ANFIS algorithm when applied to highly dynamic systems.

Born primarily as means to model knowledge, ontologies have successfully been exploited to enable knowledge exchange among people, organizations and software agents. However, because of strong subjectivity of ontology modeling, a matching process is necessary in order to lead ontologies into mutual agreement and obtain the relative alignment, i.e., the set of correspondences among them. The aim of this paper is to propose a memetic algorithm to perform an automatic matching process capable of computing a suboptimal alignment between two ontologies. To achieve this aim, the ontology alignment problem has been formulated as a minimum optimization problem characterized by an objective function depending on a fuzzy similarity. As shown in the performed experiments, the memetic approach results more suitable for ontology alignment problem than other evolutionary techniques such as genetic algorithms.

The use of computers in the production of artifacts has drawn the attention of both artists and computer scientists. Among the different art disciplines, music is one of the arts that most benefited from the use of computers. There are many works which demonstrate the great synergy between these two fields. In this paper we will focus on a specific music composition problem: the figured bass problem, in which we have to automatically generate a 4 voice piece of music, starting from an input the bass line. To solve this problem we use a hybrid strategy, in which different metaheuristics cooperate to find high quality solutions. The cooperation is controlled by means of the combination of fuzzy control and knowledge obtained through Data Mining. As will be shown in the experimental results section, this hybrid strategy is capable of finding musical solutions with an acceptable quality and never discordant which, according to experts, are sound and adhere to scholastic rule.

The rapid changes in modern knowledge, due to exponential growth of information sources, are complicating learners' activity. For this reason, novel approaches are necessary to obtain suitable learning solutions able to generate efficient, personalized and flexible learning experiences. From this point of view, the use of different cooperative intelligent agents can be exploited to analyze learner's preferences and generate high quality learning presentations which provide attractive learning solutions. In particular, to achieve this goal this paper exploits an ontological representation of the learning environment and an adaptive memetic algorithm based on a cooperative multi-agent framework. In this framework different agents analyze the e-learning instance and solve it in a parallel way, cooperating among them. This cooperation is performed by jointly exploiting data mining, via fuzzy decision trees, together with a decision making framework exploiting fuzzy methodologies. As will be shown in the experimental results section, this multi-agent strategy is capable of speeding up the convergence to high-quality personalized e-learning experiences.

In this paper we present the preliminary results of application of Fuzzy Markup Language (FML) to suspect a non-common disease. Under non-common diseases we understand rare diseases. From the broad point of view this problem belongs to the computer-assisted decision support in medical diagnostics and can be supported by fuzzy logic controllers. We can use conventional methods to diagnose a rare disease if it can be exhibited by outstanding symptoms. For example, there are several search machines and data banks that allow to find a rare disease clearly exhibited by a patient's symptoms/signs. But it is very difficult to diagnose a rare disease if it masks as a common disease. Diagnostic of rare diseases is connected with lack, uncertainty and imprecision of knowledge, medical mistake and even medical failure. Additionally, very often a common disease is also established with some degree of belief, thus, the expressions such as "it is possible that a patient has a particular disease" rather often present in the daily medical practice. It is clear that if we would know the common diseases, then deviations from them can be considered as a sign of non-common diseases. In this paper we investigate such deviations with the help of FML. We show how FML mechanism can be adjusted to suspect a rare disease, and discuss the appropriateness of the available operators.

Estimating the number of people in a scene is a very relevant issue due to the possibility of using it in a large number of contexts where it is necessary to automatically monitor an area for security/safety reasons, for economic purposes, etc. The large number of people counting approaches available in the literature can be roughly abscribed to two categories: direct approaches and indirect ones. In the first category there are methods that first detect people and then count them; differently, the indirect methods face the counting problem by establishing a relation between some scene features and the estimated number of people. Some recent comparative evaluations carried out in the framework of the PETS initiative have demonstrated that the indirect methods tends to be more robust than direct ones, above all when they are used in very crowded conditions. In this paper, we analyze the behavior of an indirect approach that is based on a trainable estimator that does not require an explicit formulation of a priori knowledge about the perspective and density effects present in the scene at hand. In particular, we investigate on the way the counting accuracy in different crowding conditions is affected by the choice of the trainable estimator.

The large-scale deployment of the Smart Grid paradigm will support the evolution of conventional electrical power systems toward active, flexible and self-healing web energy networks composed of distributed and cooperative energy resources. In a Smart Grid platform, the optimal coordination of distributed voltage controllers is one of the main issues to address. In this field, the application of traditional control paradigms has some disadvantages that could hinder their application in Smart Grids where the constant growth of grid complexity and the need for massive pervasion of Distribution Generation Systems (DGSs) require more scalable, more flexible control and regulation paradigms. To try and overcome these challenges, this paper proposes the concept of a decentralized non-hierarchical voltage regulation architecture based on intelligent and cooperative smart entities. The distributed voltage controllers employ traditional sensors to acquire local bus variables and mutually coupled oscillators to assess the main variables that characterize the operation of the global Smart Grid. These variables are then amalgamated by a novel fuzzy inference engine, named Timed Automata based Fuzzy Controllers, in order to identify proper control actions aimed at improving the grid voltage profile and reducing power losses.

Antimalware applications represent one of the most important research topic in the area of information security threat. Indeed, most computer network issues have malwares as their underlying cause. As a consequence, enhanced systems for analyzing the behavior of malwares are needed in order to try to predict their malicious actions and minimize eventual computer damages. However, because the environments where malwares operate are characterized by high levels of imprecision and vagueness, the conventional data analysis tools lack to deal with these computer safety applications. This work tries to bridge this gap by integrating semantic technologies and computational intelligence methods, such as the Fuzzy Ontologies and Fuzzy Markup Language (FML), in order to propose an advanced semantic decision making system that, as shown by experimental results, achieves good performances in terms of malicious programs identification.

Learning is a critical support mechanism for industrial and academic organizations to enhance the skills of employees and students and, consequently, the overall competitiveness in the new economy. The remarkable velocity and volatility of modern knowledge require novel learning methods offering additional features as efficiency, task relevance and personalization. Computational Intelligence methodologies can support e‐Learning system designers in two different aspects: (1) they represent the most suitable solution able to support learning content and activities, personalized to specific needs and influenced by specific preferences of the learner and (2) they assist designers with computationally efficient methods to develop “in time” e‐Learning environments. This article attempts to achieve both results by exploiting an ontological representations of learning environment and memetic approach of optimization, integrated into a cooperative distributed problem solving framework. This synergy enables multi‐island memetic approach managing a collection of models and processes for adapting an e‐Learning system to the learner expectations and to formulate objectives in an effective and dynamic intelligent way. More precisely, our proposal exploits ontological representations of learning environment and a memetic distributed problem‐solving approach to generate the best learning presentation and, at the same time, minimize the computational efforts necessary to compute optimal learning experiences.

The theory of fuzzy cognitive maps (FCMs) is a powerful approach to modeling human knowledge that is based on causal reasoning. Taking advantage of fuzzy logic and cognitive map theories, FCMs enable system designers to model complex frameworks by defining degrees of causality between causal objects. They can be used to model and represent the behavior of simple and complex systems by capturing and emulating the human being to describe and present systems in terms of tolerance, imprecision, and granulation of information. However, FCMs lack the temporal concept that is crucial in many real-world applications, and they do not offer formal mechanisms to verify the behavior of systems being represented, which limit conventional FCMs in knowledge representation. In this paper, we present an extension to FCMs by exploiting a theory from formal languages, namely, the timed automata, which bridges the aforementioned inadequacies. Indeed, the theory of timed automata enables FCMs to effectively deal with a double-layered temporal granularity, extending the standard idea of B-time that characterizes the iterative nature of a cognitive inference engine and offering model checking techniques to test the cognitive and dynamic comportment of the framework being designed.

Over recent years, there has been increasing interest of the research community towards evolutionary algorithms, i.e., algorithms that exploit computational models of natural processes to solve complex optimization problems. In spite of their ability to explore promising regions of the search space, they present two major drawbacks: 1) they can take a relatively long time to locate the exact optimum and 2) may sometimes not find the optimum with sufficient precision. Memetic Algorithms are evolutionary algorithms inspired by both Darwinian principles and Dawkins' notion of a meme, able not only to converge to high-quality solutions, but also search more efficiently than their conventional evolutionary counterparts. However, memetic approaches are affected by several design issues related to the different choices that can be made to implement them. This paper introduces a multiagent-based memetic algorithm which executes in a parallel way different cooperating optimization strategies in order to solve a given problem's instance in an efficient way. The algorithm adaptation is performed by jointly exploiting a knowledge extraction process together with a decision making framework based on fuzzy methodologies. The effectiveness of our approach is tested in several experiments in which our results are compared with those obtained by nonadaptive memetic algorithms. The superiority of the proposed strategy is manifest in the majority of cases.

In these last decades, both industrial and academic organizations have used extensively different learning methods to improve humans' capabilities and, as consequence, their overall performance and competitiveness in the new economy context. However, the rapid change in modern knowledge due to exponential growth of information sources is complicating learners' activity. At the same time, new technologies offer, if used in a right way, a range of possibilities for the efficient design of learning scenarios. For that reason, novel approaches are necessary to obtain suitable learning solutions which are able to generate efficient, personalized, and flexible learning experiences. From this point of view, computational intelligence methodologies can be exploited to provide efficient and intelligent tools to be able to analyze learner's needs and preferences and, consequently, personalize its knowledge acquirement. This paper reports an attempt to achieve these results by exploiting an ontological representation of learning environment and an adaptive memetic approach, integrated into a cooperative multi-agent framework. In particular, a collection of agents analyzes learner preferences and generate high-quality learning presentations by executing, in a parallel way, different cooperating optimization strategies. This cooperation is performed by jointly exploiting data mining via fuzzy decision trees, together with a decision-making framework exploiting fuzzy methodologies.

A Network Intrusion Detection System is a network monitoring framework that tries to detect malicious network activity such as port scans, denial of service or other attempts to crack computer network environments. The main aim of intrusion detection is to identify unauthorized use, misuse, and abuse of computers by external penetrators. In real life, however, temporal changes in network intrusion patterns and characteristics tend to invalidate the usability of existing intrusion detection systems. In order to solve this drawback, our paper introduces a novel kind of fuzzy controller, known as Timed Automata-based Fuzzy Controllers, and it presents a data mining approach able to learn the most suitable controller that manages, in efficient way, the computer network dynamism and support networks' administrators to prevent eventual damages coming from unauthorized network intrusion.

Evolutionary Algorithms are a collection of optimization techniques that take their inspiration from natural selection and survival of the fittest in the biological world and they have been exploited to try to resolve some of the more complex NP-complete problems. Nevertheless, in spite of their capability of exploring and exploiting promising regions of the search space, they present some drawbacks and, in detail, they can take a relatively long time to locate the exact optimum in a region of convergence and may sometimes not find the solutions with sufficient precision. Memetic Algorithms are innovative meta-heuristic search methods that try to alleviate evolutionary approaches' weaknesses by efficiently converging to high quality solutions. However, as shown in literature, memetic approaches are affected by several design issues related to the different choices that can be made to implement them. This paper introduces a multi-agent based memetic algorithm which executes in a parallel way different cooperating optimization strategies in order to solve a given problem's instance in an efficient way. The algorithm adaptation is performed by jointly exploiting a knowledge extraction process, based on fuzzy decision trees, together with a decision making framework based on fuzzy methodologies. The effectiveness of our approach is tested in several experiments in which our results are compared with those obtained by some non-adaptive memetic algorithms.

Learning is a mechanism to acquire new knowledge and to enhance individual skills in industrial and academic environments. In particular, employing learning methods in an industrial context supports the overall business competitiveness in the new economy. Currently, the e-Learning systems provide a simple “digitalization” of the learning process where the focus is on the educational resources, which are only an input of the whole learning process, and on their presentation (delivery). Computational Intelligence methodologies can overcome current learning systems limitations attaining to personalize learning content and activities to specific preferences of the learner and to assist designers with computationally efficient methods to develop “in time” e-Learning environments. This paper shows how to achieve both results exploiting an ontological representation of learning environment and memetic approach of optimization, integrated into a cooperative distributed problem solving framework.

During the past several years, fuzzy control has emerged as one of the most suitable and efficient methods for designing and developing complex systems in environments characterized by high level of uncertainty and imprecision. Nowadays, this methodology is used to model systems in several applications domains which range from industrial machineries to financial decisions support systems. Nevertheless, in spite of the usefulness of fuzzy control, one of its drawbacks comes from the lack of the temporal concept that is crucial in many systems characterized from a discontinuous nonlinear behaviour. In particular, in its standard vision, fuzzy control is not able to represent Variable-Structure systems, i.e., systems that change their configuration (knowledge base) or their behaviour (rule base) over time. To overcome these drawbacks, this paper extends fuzzy control idea by considering a theory from formal languages: timed automata. This novel synergic approach achieves a twofold advantage by representing a system in qualitative and linguistic way and introducing a novel switching control concept able to maximize system's performances and robustness.

In an enterprise context, competencies are often dispersed across different teams. A specific need within the enterprise could not be satisfied only because of a lack of awareness about real competencies owned by employees. The aforementioned problem involves two main critical aspects: the difficulty to manage employees' competencies in order to constantly keep them up-to-date and the ability to agilely share employees' profiles across the organization in order to support competency finding. This work proposes an approach to relax the above critical points by integrating a semantic web-based educational system within a social network system applied to the enterprise context. The integration glue is provided by using and harmonizing several existing upper ontologies also furthering semantic interoperability.

Nowadays most people can get enough energy to maintain one-day activity, while few people know whether they eat healthily or not. It is quite important to analyze nutritional facts for foods eaten for those who are losing weight or suffering chronic diseases such as diabetes. This paper proposes a novel type-2 fuzzy ontology, including a type-2 fuzzy food ontology and a type-2 fuzzy markup language (FML)-based ontology, for diet assessment. In addition, we also present a type-2 FML (FML2) to describe the type-2 fuzzy ontology and the FML2-based diet assessment agent, including a type-2 knowledge engine, a type-2 fuzzy inference engine, a diet assessment engine, and a semantic analysis engine. In the proposed approach, first, the nutrition facts of various kinds of food are collected from the Internet and the convenience stores. Next, the domain experts construct the type-2 fuzzy ontology, and then the involved subjects are requested to input the different food eaten. Finally, the proposed FML2-based diet assessment agent displays the diet assessment of the food eaten based on the constructed type-2 fuzzy ontology. Using the generated semantic analysis, people can obtain health information about what they eat, which can lead to a healthy lifestyle and healthy diet. Experimental results show that the proposed approach works effectively where the proposed system can provide a diet health status, which can act as a reference to promote healthy living. © 2010 Wiley Periodicals, Inc.

In Ambient Intelligence (AmI) vision, people should be able to seamlessly and unobtrusively use and configure the intelligent devices and systems in their ubiquitous computing environments without being cognitively and physically overloaded. In other words, the user should not have to program each device or connect them together to achieve the required functionality. However, although it is possible for a human operator to specify an active space configuration explicitly, the size, sophistication, and dynamic requirements of modern living environment demand that they have autonomous intelligence satisfying the needs of inhabitants without human intervention. This work presents a proposal for AmI fuzzy computing that exploits multiagent systems and fuzzy theory to realize a long-life learning strategy able to generate context-aware-based fuzzy services and actualize them through abstraction techniques in order to maximize the users' comfort and hardware interoperability level. Experimental results show that proposed approach is capable of anticipating user's requirements by automatically generating the most suitable collection of interoperable fuzzy services.

Thanks to the advances of voltage regulator (VR) technologies and haptic systems, virtual simulators are increasingly becoming a viable alternative to physical simulators in medicine and surgery, though many challenges still remain. In this study, a pervasive visual-haptic framework aimed to the training of obstetricians and midwives to vaginal delivery is described. The haptic feedback is provided by means of two hand-based haptic devices able to reproduce force-feedbacks on fingers and arms, thus enabling a much more realistic manipulation respect to stylus-based solutions. The interactive simulation is not solely driven by an approximated model of complex forces and physical constraints but, instead, is approached by a formal modeling of the whole labor and of the assistance/intervention procedures performed by means of a timed automata network and applied to a parametrical 3-D model of the anatomy, able to mimic a wide range of configurations. This novel methodology is able to represent not only the sequence of the main events associated to either a spontaneous or to an operative childbirth process, but also to help in validating the manual intervention as the actions performed by the user during the simulation are evaluated according to established medical guidelines. A discussion on the first results as well as on the challenges still unaddressed is included.

Ambient Intelligence (AmI) is born as a computer paradigm that deals with a new world where computing devices are spread everywhere in order to make wider the interaction between human beings and information technology and put together a dynamic computational-ecosystem capable of satisfying the users requirements. However, the AmI systems are more than a simple integration among computer technologies, indeed, their design can strongly depend upon psychology and social sciences aspects able to describe and analyze the human being status during the system's decision making. Consequently, from a computational point of view, an AmI system can be considered as a distributed cognitive framework composed by a collection of intelligent entities capable of modifying their behaviours by taking into account the user's cognitive status in a given time. This paper introduces a novel methodology of AmI systems' design that exploits multi-agent paradigm and a novel extension of Fuzzy Cognitive Maps theory benefiting on the theory of Timed Automata in order to create a collection of dynamical intelligent agents that use cognitive computing to define actions' patterns able to maximize environmental parameters as, for instance, user's comfort or energy saving.

In the last years several computing frameworks based on the ubiquitous and embedding properties have been designed and realized. These systems, characterized by the interconnection of several devices embedded into a micro-environment and interacting among them in order to achieve a common goals, offer new fascinating challenges to face as, for instance, the interoperability and safety problems. The automotive environments are a typical sample of ubiquitous and embedded systems, in fact, modern cars can be considered as mobile computer network composed of intelligent devices capable of controlling the mechanical and hydraulic car components. This study presents an advanced integrated development environment modeling FNM-based fuzzy controllers useful to design an efficient electronic stability program (ESP) to be reprogrammed on different hardware without additional effort. In this scenario, multi-agent paradigm and transparent fuzzy control methodology represent the natural technologies exploited to achieve the proposed aims.

e-Learning is a critical support mechanism for industrial and academic organizations to enhance the skills of employees and students and, consequently, the overall competitiveness in the new economy. The remarkable velocity and volatility of modern knowledge require novel learning methods offering additional features as efficiency, task relevance and personalization. The main aim of adaptive eLearning is to support content and activities, personalized to specific needs and influenced by specific preferences of the learner. This paper describes a collection of models and processes for adapting an e-Learning system to the learner expectations and to formulate objectives in a dynamic intelligent way. Precisely, our proposal exploits ontological representations of learning environment and a memetic optimization algorithm capable of generating the best learning presentation in an efficient and qualitative way.

Membrane Computing (or P-System theory) is a recent area of Natural Computing, the field of computer science that works with computational techniques inspired by nature and natural systems. Particularly, Membrane computing investigates models of computation inspired by the structure and functioning of biological cells focusing attention on their distributed and parallel transformations. Different software applications which have been developed in imperative languages, like Java, or in declaratives languages, as Prolog, work in the framework of Membrane Computing systems. These applications simulate the behavior of P-System focusing on details about computational power of different Membrane devices without exploiting the distributed nature of simulated cellular structures. This paper presents a parallel and distributed application, based on Multi-Agent System technology, able to simulate Membrane Computing devices. The aim is to show how the theoretical distributed nature of P-Systems can be mapped into a real distributed Multi-Agent System in order to achieve two important goals: 1) to define a theoretical computational model for Multi-Agent System architectures; 2) to design a software application able to simulate Membrane Computing devices in a real fashion by exploiting the distributed nature of Multi-Agent System technology.

Ambient intelligence (AmI) is considered as the composition of three emergent technologies: ubiquitous computing, ubiquitous communication and intelligent user interfaces. The aim of integration of aforesaid technologies is to make wider the interaction between human beings and information technology equipment through the usage of an invisible network of ubiquitous computing devices composing dynamic computational-ecosystems capable of satisfying the users' requirements. Many works focus the attention on the interaction from users to devices in order to allow an universal and immediate access to available content and services provided by the environment. This paper, vice versa, focuses on the reverse interactions, from devices to users, in order to realize a collection of autonomous control services able to minimize the human effort. In particular, by merging computational intelligence methodologies with standard Web technologies we show how ubiquitous devices will be able to find the suitable set of 'intelligent' services in a transparent way.

In this work, we present last extensions done on H2ML, a new approach to model human interaction models inside intelligent environments at different abstraction levels. H2ML has been defined by considering the exigency to design Ambient Intelligence (Ami) applications, by assuring transparency, uniformity and abstractness in bridging multiple sensors properties to flexible and personalized actuators that interact with the human choosing appropriate media communication strategies. Within this aim, H2ML can be viewed as a methodological approach, based on markup languages and fuzzy theory, that provides semantic representation to human attitudes acquired by the intelligent environment. H2ML works in bi-directional way: if from an one hand it builds semantic models of human behavior, from the other hand enables to impact on the most suitable control strategy to perform at actuator side, conciliating the action with the most suitable media "facilitator".

Ambient intelligence (AmI, shortly) gathers best results from three key technologies, ubiquitous computing, ubiquitous communication, and intelligent user friendly interfaces. The functional and spatial distribution of tasks is a natural thrust to employ multi-agent paradigm to design and implement AmI environments. Two critical issues, common in most of applications, are (1) how to detect in a general and efficient way "context" from sensors and (2) how to process contextual information in order to improve the functionality of services. In this work we experiment a framework where hybrid techniques (distributed fuzzy control, mobile agents, fuzzy rules induction algorithms) are mixed to gain flexibility and uniformity

This paper describes a multilayer architecture for flexible, efficient and uniform utilization of control activities for intelligent environment. The multilayer organization is not a composition of heterogeneous techniques merged together to achieve a final objective but a rethinking of several complex issues, such as sensor utilization, hardware transparency and independency, reconfigurable services under the vision of the recent applications of the semantic Web. It is important to realize that Web searching is only one potential application of semantic Web technologies, other important applications of the semantic Web will include decision support systems, information sharing, knowledge discovery, automation, integration of distributed embedded systems (e.g., sensors), negotiation for goods and resources (e.g., for e-commerce or defense logistics), business development, and administration. As common feature of these applications, we find transformation of data from targeting human consumption to being machine readable. Our multilayer architecture employs markup-based technologies to transform rough information on sensors, actuators and services towards "smart data". We use this approach in the design of ambient intelligent systems.

The evolution of the microprocessor industry, combined with the reduction on cost and increase of efficiency, gives rise to new scenario for ubiquitous computing where humans trigger seamlessly activities and tasks using unusual (often imperceptible) interfaces according to physical space and context. Many problems must be faced: adaptivity, hybrid control strategies, system (hardware) integration, and ubiquitous networking access. In this paper, a solution that attempts to provide a flexible and dependable solution to these complicated problems is illustrated. First, an extensible markup language (XML)-derived technologies is proposed to define fuzzy markup language (FML), a markup language skilled for defining detailed structure of fuzzy control independent from its legacy representation. FML is essentially composed of three layers: 1) XML in order to create a new markup language for fuzzy logic control; 2) document type definition in order to define the legal building blocks; and 3) extensible stylesheet language transformations in order to convert a fuzzy controller description into a specific programming language. Then an agent-based framework designed for providing proactive services in domotic environments, is presented. The agent architecture, exploiting mobile computation, is able to maximize the fuzzy control deployment for the natively FML representation by performing an efficient distribution of pieces of the global control flow over the different computers. Agents are also used to capture user habits, to identify requests, and to apply the artefact-mediated activity through an adaptive fuzzy control strategy. The architecture adopts interoperability techniques that, combined with sophisticated control facilities, represent an efficient experience for adaptive domotic framework.

From a technologic point of view, the problem of fuzzy control deals with the real implementation of a controller on a specific hardware. Today, the market of micro-controller offers different solutions able to implement a fuzzy controller varying from application domains to programming language support. Considering the integration issue, made easier from the cheap network infrastructure, there is the need to empower practical approaches suitable to support various and different components ruled by advanced (fuzzy) control strategies. In this work we first present a general Web-based architecture that supports a high integration of heterogeneous and increasingly complex control systems, and then we focus on a Takagi-Sugeno-Kang (TSK) fuzzy model able to reproduce the thermal behaviour of mineral-oil-filled power transformers for implementing a protective overload system. The TSK fuzzy model, working on the load current waveform and on the top oil temperature (TOT), gives an accurate global prediction of the hot-spot temperature (HST) pattern. In order to validate the usefulness of the approach suggested herein, some data cases, derived from various laboratory applications, are presented to measure the accuracy and robustness of the proposed fuzzy model.