The rapid proliferation of large language models (LLMs) has raised growing concerns about distinguishing between human-written and AI-generated text. This work addresses the task of detecting AI-generated content by evaluating the latent similarity between a given input text and an alternative response generated for the same prompt, either known or inferred. Accordingly, CLAID (Contrastive Learning for AI Detection) is proposed as a Siamese Neural Network architecture utilizing BERT encoders and contrastive loss to capture semantic similarity between text pairs. Unlike prior approaches that rely on explicit classification or domain-specific features, our method focuses on modeling pairwise similarity, enabling a flexible and model-agnostic detection framework. To evaluate the generalization capabilities of the system, a comprehensive multi-domain and multi-model benchmark comprising three diverse datasets (i.e., HC3, DAIGT, and OUTFOX), encompassing a wide range of text genres, prompt structures, and generative models, has been constructed. Experimental results show that the proposed model achieves near-perfect classification accuracy across both single-domain and mixed-domain scenarios, demonstrating strong robustness to domain shifts, prompt variability, and authorship ambiguity. The model also exhibits strong data efficiency, attaining high performance with minimal supervision.

In an era where image manipulation tools are widely accessible, detecting digital image forgery has become increasingly challenging. Image forgery detection is a critical concern in cybersecurity, particularly within IoT-based applications and digital forensics. This paper proposes an automated approach for image forgery detection, integrating Fuzzy C-Means clustering with TPOT, an automated machine learning framework, to optimize the detection pipeline. The approach is evaluated on four publicly available datasets: FIDAC, FID, CoMoFoD, and CASIA_V2. Experimental results demonstrate that the proposed approach outperforms conventional CNN and VGG models, achieving 96.83% accuracy on CASIA_V2 and 95.33% on CoMoFoD. These findings highlight the potential of AutoML-based solutions to enhance forgery detection.

The spread of misinformation and manipulative narratives is a significant challenge in today’s information landscape, shaping public opinion across diverse communities. Traditional framing detection methods are constrained by their post-hoc nature, limiting their ability to anticipate emerging framing tactics in real-time. To overcome these limitations, this work proposes a framework that combines Retrieval-Augmented Generation (RAG) models with Federated Prompt Tuning to generate and iteratively refine community-aware prompts. These prompts simulate how narratives derived from events, news, or factual statements might be reframed across different communities, enabling proactive identification of potentially manipulative content. Additionally, the framework exemplifies the exploitation of the framework to quantify the susceptibility of narratives to framing and support early warning systems for disinformation. Preliminary experiments conducted on a real-world dataset, including diverse community profiles, reveal the framework’s potential to combat the spread of disinformation and assess framing vulnerabilities in a timely and scalable manner.

Autonomous Driving (AD) entails vehicles that can perceive their surroundings and navigate without human intervention. This involves utilising a combination of sensors and algorithms to recognise obstacles, interpret traffic signals, and make driving decisions. While AD holds promise for transforming transportation by enhancing safety, reducing congestion, minimising pollution, and optimising efficiency, it poses technical challenges also. This work extends a novel approach to building an autonomous vehicle agent using Deep Reinforcement Learning (DRL) with Proximal Policy Optimisation (PPO) to navigate urban environments simulated by the CAR Learning to Act (CARLA) Simulator. The agent aims to maintain lane integrity and avoid collisions, even in adverse weather conditions. The proposed architecture integrates a 180-degree environmental view and various multimodal data inputs (RGB, segmentation, and depth camera inputs), extensively tested through experimentation. Notably, the integration of segmentation and depth data results in a 13% reduction in the collision rate, with the proposed agent achieving a total reward of 2510. This approach demonstrates significant progress over the previous framework, showcasing improved obstacle detection and collision avoidance accuracy. Moreover, these findings contribute to ongoing autonomous vehicle research, offering insights into effective strategies for developing robust and dependable driving agents capable of navigating urban environments and interacting with road infrastructure, contributing to advancements in Augmented Intelligence of Things (AIoT)-enabled autonomous driving.

In the evolving landscape of artificial intelligence, ensuring the robustness and explainability of machine learning models is valuable. This study presents an innovative method based on the Rough Set Theory and Principles of Justified Granularity to enhance the explainability of text-based classifiers, specifically in style-based news bias classification. The method helps understand why a classifier can be deceived with an Adversarial Attack. It leverages two levels of insight. The first level is independent of the specific classifier and consists of generating rules from a boundary region built with Rough Sets Theory starting from train data. The second level considers the behavior of a specific machine learning model in classifying manipulated observations and, starting from the classification results, constructs information granules of true positives and false negatives. These granules are representative of observations that deceived a classifier. By comparing boundary rules with information granules, it is possible to acquire actionable knowledge that is useful for making decisions on making a machine learning model more resilient. Results are evaluated with real data containing biased news. The success rate of adversarial examples generated using LLM to test classifiers on borderline cases, where minor textual changes cause false negatives, ranges from 45% to 68%.

The widespread utilization of social media for information consumption has significantly exacerbated the problem of information disorder. Recognizing the difficulty people face in discerning the truth, automated assistance is urgently needed. Current state-of-the-art approaches often involve fine-tuning existing models with contributions from domain knowledge bases. The black-box nature and interpretability issues of deep neural networks have increased interest in hybrid approaches, giving rise to deep neural fuzzy systems (DNFSs). This article presents the Hybrid Fact-Checking Framework leveraging a DNFS tailored to address the uncertainty inherent in fact verification tasks and enhance the reliability of model responses. The DNFS integrates a large language model with an adaptive neuro fuzzy inference system for automated fact verification. The framework utilizes relevant evidence from open-world and closed-world sources, leveraging deep language models and employing few-shot prompting without additional training to generate and justify verdicts. Including fuzzy rules and considering the trustworthiness and relevance of retrieved evidence enhances response reliability, thereby improving overall effectiveness and outcome interpretability. Experimental validations have been conducted on three publicly available datasets ranging in different domains of expertise: Climate-FEVER, SciFact, and FEVER. The results demonstrate that the proposed framework ensures better outcomes, transparency, and mindful decision-making.

In the contemporary era, the global proliferation of Internet of Things (IoT) devices exceeds 15 billion, serving functions from wearables to smart grid monitoring. These devices frequently manage sensitive data, underscoring the need for secure and reliable IoT networks leveraging blockchain technology. A key innovation of this study is an approach to mitigate vulnerabilities that quantum computing poses to blockchain-based IoT systems, which existing cryptographic methods cannot effectively address. Quantum computers could exploit these weaknesses to compromise key-pair generation and extract private keys from transaction signatures. To overcome this, the research introduces an optimized implementation of the post-quantum digital signature algorithm Dilithium-5, ensuring blockchain security and quantum readiness. These transaction signatures are designed for low-power, cost-effective microcontrollers, such as the ESP32, making the solution accessible for a wide range of IoT devices. In addition, the study includes a case study involving a post-quantum safe portable device for measuring blood oxygen levels and heart rate, illustrating the practical benefits and effectiveness of the proposed solution in enhancing IoT security against quantum threats. The results demonstrate that the proposed approach ensures quantum-resistant security while maintaining performance efficiency, making it suitable for real-world IoT applications.

Abstract High-performance computing (HPC) is crucial for artificial intelligence (AI) and deep learning (DL) but faces challenges related to scalability, data transfer costs, and security risks. Federated Learning (FL) enables collaborative model training without centralized data aggregation. However, FL introduces vulnerabilities, as exchanged models can be intercepted and manipulated, necessitating robust cryptographic protection. With the advent of quantum computing, traditional security mechanisms are at risk, requiring the adoption of Post-Quantum Cryptographic (PQC) algorithms. This study benchmarks three PQC digital signature algorithms: Falcon, SPHINCS+, and ML-DSA. Their execution time, memory usage, and computational efficiency are evaluated in a simulated FL setting. To extend the analysis, different cryptographic hash functions (SHA3-256, SHA3-512, and BLAKE3) are analyzed to assess hashing efficiency under varying computational loads. Both centralized and decentralized FL scenarios are simulated, incorporating PQC-based digital signatures at each phase of the communication pipeline to ensure model integrity and authenticity. The results provide insights into the trade-offs between security and computational overhead, guiding the selection of scalable cryptographic solutions for FL. Falcon and ML-DSA demonstrate minimal impact on computational performance, making them strong candidates for securing FL environments. Future research directions include the direct signing of DL models to enhance security and the integration of widely used FL libraries for more realistic evaluations. These advancements could improve the practical deployment of post-quantum security solutions in FL, ensuring resilience against emerging quantum threats.

The rapid evolution of synthetic voice generation and audio manipulation technologies poses significant challenges, raising societal and security concerns due to the risks of impersonation and the proliferation of audio deepfakes. This study introduces a lightweight machine learning (ML)-based framework designed to effectively distinguish between genuine and spoofed audio recordings. Departing from conventional deep learning (DL) approaches, which mainly rely on image-based spectrogram features or learning-based audio features, the proposed method utilizes a diverse set of hand-crafted audio features – such as spectral, temporal, chroma, and frequency-domain features – to enhance the accuracy of deepfake audio content detection. Through extensive evaluation and experiments on three well-known datasets, ASVSpoof2019, FakeAVCelebV2, and an In-The-Wild database, the proposed solution demonstrates robust performance and a high degree of generalization compared to state-of-the-art methods. In particular, our method achieved 89% accuracy on ASVSpoof2019, 94.5% on FakeAVCelebV2, and 94.67% on the In-The-Wild database. Additionally, the experiments performed on explainability techniques clarify the decision-making processes within ML models, enhancing transparency and identifying crucial features essential for audio deepfake detection. • Enhanced spoof audio detection via multi-feature integration. • Employed a lightweight ML framework for real-time applications. • Adopted subject-independent protocols to mitigate biometric bias. • Utilized Explainable AI (XAI) for transparent decision-making.

This paper presents a novel approach for predicting and explaining instances of Information Disorder. The paper reports two significant findings: i) the use of structures of opposition to describe relationships between instances of Information Disorder, and ii) the development of an explainable prediction method that combines Fuzzy Rough Sets and TOPSIS with these structures. The findings have the potential to assist analysts and decision-makers in gaining a deeper understanding of the phenomenon of Information Disorder. The results are based on real data and demonstrate promising applications for future research.

Abstract Precision agriculture is evolving toward a contemporary approach that involves multiple sensing techniques to monitor and enhance crop quality while minimizing losses and waste of no longer considered inexhaustible resources, such as soil and water supplies. To understand crop status, it is necessary to integrate data from heterogeneous sensors and employ advanced sensing devices that can assess crop and water status. This study presents a smart monitoring approach in agriculture, involving sensors that can be both stationary (such as soil moisture sensors) and mobile (such as sensor-equipped unmanned aerial vehicles). These sensors collect information from visual maps of crop production and water conditions, to comprehensively understand the crop area and spot any potential vegetation problems. A modular fuzzy control scheme has been designed to interpret spectral indices and vegetative parameters and, by applying fuzzy rules, return status maps about vegetation status. The rules are applied incrementally per a hierarchical design to correlate lower-level data (e.g., temperature, vegetation indices) with higher-level data (e.g., vapor pressure deficit) to robustly determine the vegetation status and the main parameters that have led to it. A case study was conducted, involving the collection of satellite images from artichoke crops in Salerno, Italy, to demonstrate the potential of incremental design and information integration in crop health monitoring. Subsequently, tests were conducted on vineyard regions of interest in Teano, Italy, to assess the efficacy of the framework in the assessment of plant status and water stress. Indeed, comparing the outcomes of our maps with those of cutting-edge machine learning (ML) semantic segmentation has indeed revealed a promising level of accuracy. Specifically, classification performance was compared to the output of conventional ML methods, demonstrating that our approach is consistent and achieves an accuracy of over 90% throughout various seasons of the year.

The capacity to create “fake” videos has recently raised concerns about the reliability of multimedia content. Identifying between true and false information is a critical step toward resolving this problem. On this issue, several algorithms utilizing deep learning and facial landmarks have yielded intriguing results. Facial landmarks are traits that are solely tied to the subject’s head posture. Based on this observation, we study how Head Pose Estimation (HPE) patterns may be utilized to detect deepfakes in this work. The HPE patterns studied are based on FSA-Net, SynergyNet, and WSM, which are among the most performant approaches on the state-of-the-art. Finally, using a machine learning technique based on K-Nearest Neighbor and Dynamic Time Warping, their temporal patterns are categorized as authentic or false. We also offer a set of experiments for examining the feasibility of using deep learning techniques on such patterns. The findings reveal that the ability to recognize a deepfake video utilizing an HPE pattern is dependent on the HPE methodology. On the contrary, performance is less dependent on the performance of the utilized HPE technique. Experiments are carried out on the FaceForensics++ dataset that presents both identity swap and expression swap examples. The findings show that FSA-Net is an effective feature extraction method for determining whether a pattern belongs to a deepfake or not. The approach is also robust in comparison to deepfake videos created using various methods or for different goals. In the mean the method obtain 86% of accuracy on the identity swap task and 86.5% of accuracy on the expression swap. These findings offer up various possibilities and future directions for solving the deepfake detection problem using specialized HPE approaches, which are also known to be fast and reliable.

The paper presents and evaluates an approach based on Rough Set Theory, and some variants and extensions of this theory, to analyze phenomena related to Information Disorder. The main concepts and constructs of Rough Set Theory, such as lower and upper approximations of a target set, indiscernibility and neighborhood binary relations, are used to model and reason on groups of social media users and sets of information that circulate in the social media. Information theoretic measures, such as roughness and entropy, are used to evaluate two concepts, Complexity and Milestone, that have been borrowed by system theory and contextualized for Information Disorder. The novelty of the results presented in this paper relates to the adoption of Rough Set Theory constructs and operators in this new and unexplored field of investigation and, specifically, to model key elements of Information Disorder, such as the message and the interpreters, and reason on the evolutionary dynamics of these elements. The added value of using these measures is an increase in the ability to interpret the effects of Information Disorder, due to the circulation of news, as the ratio between the cardinality of lower and upper approximations of a Rough Set, cardinality variations of parts, increase in their fragmentation or cohesion. Such improved interpretative ability can be beneficial to social media analysts and providers. Four algorithms based on Rough Set Theory and some variants or extensions are used to evaluate the results in a case study built with real data used to contrast disinformation for COVID-19. The achieved results allow to understand the superiority of the approaches based on Fuzzy Rough Sets for the interpretation of our phenomenon.

Abstract The spreading of machine learning (ML) and deep learning (DL) methods in different and critical application domains, like medicine and healthcare, introduces many opportunities but raises risks and opens ethical issues, mainly attaining to the lack of transparency. This contribution deals with the lack of transparency of ML and DL models focusing on the lack of trust in predictions and decisions generated. In this sense, this paper establishes a measure, namely Congruity, to provide information about the reliability of ML/DL model results. Congruity is defined by the lattice extracted through the formal concept analysis built on the training data. It measures how much the incoming data items are close to the ones used at the training stage of the ML and DL models. The general idea is that the reliability of trained model results is highly correlated with the similarity of input data and the training set. The objective of the paper is to demonstrate the correlation between the Congruity and the well-known Accuracy of the whole ML/DL model. Experimental results reveal that the value of correlation between Congruity and Accuracy of ML model is greater than 80% by varying ML models.

The microgrid (MG) is a complicated cyber-physical system that operates based on interactions between physical processes and computational components, which make it vulnerable to varied cyber-attacks. In this article, the impact of data integrity attack (DIA) has been considered, as one of the most dangerous cyber threats to MGs, on the steady-state operation of hybrid microgrids (HMGs). Additionally, a novel method based on the sequential hypothesis testing approach is proposed to detect DIA on the renewable energy sources’ metering infrastructure and improve the data security within the HMGs. The proposed method generates a binary sample, which is used to compute a test statistic that is further used against two thresholds to decide among three alternatives. The performance of the suggested method is examined using an IEEE standard test system. The results illustrated the acceptable performance of the proposed methodology in detection of DIAs. In addition, to evaluate the effect of DIA on the operation of the HMGs, DIAs with different severities are launched on the measured power generation of renewable energy resources like wind turbines. The results showed that a successful DIA on renewable units can severely affect the operation of electric grids and cause serious damage.

In online learning, the dropout phenomenon is a relevant issue to address with practical solutions. Several data sets stimulate original, and resolutive data analysis approaches, demonstrating the importance of the dropout phenomenon. This study proposes a novel approach to predicting massive online open course (MOOC) students at risk of dropout stressing the need to consider the temporal dimension in the data log. The proposal aims to build a data-driven decision support system able to identify students at risk of dropout based on the conceptualization of such students' behavior and its evolution along the time dimension. The primary theoretical model behind the proposed method is the formal concept analysis, and its temporal extension (i.e., temporal concept analysis) for analyzing timestamped data and carrying out a timed lattice. The main result of the paper is a method to extract behavioral patterns of MOOC students at risk of dropout. Such patterns are defined as Time-based Behavior Rules extracted from the aforementioned timed lattice obtained through the preprocessing of MOOC platform log files. The resulting rule set can be easily integrated for implementing educational DSS, as shown in the last part of the paper. The conducted experiments reveal promising results in terms of F-score and students' monitoring time.

In this article, we introduce a variant of the adaptive network-based fuzzy inference system (ANFIS). The proposed variant does not use backpropagation and grid partitioning, but the least-squares method with fractional Tikhonov regularization. The fractional regularization is a generalization of the standard regularization and is applied here to the learning process of the ANFIS scheme for the first time. This results in a simpler rule base, with a low number of rules, allowing to handle problems with many input variables with relatively low computational time while keeping high accuracy. We present new theoretical results on the fractional Tikhonov regularization. Such results are the basis for a formal discussion on how much the choice of a different architecture, resulting in a different matrix in the least-squares minimization, could affect the accuracy. We perform several numerical experiments on benchmark examples, first to assess the impact of the fractional regularization on the accuracy and then to compare our results against the most recent ones reported in the literature by other ANFIS-like or neuro-fuzzy systems. The numerical results show the good performance of the proposed approach.

Interconnectivity and smart automation of Internet of Things in recent times have led to the concept of Industry 4.0. Together with the improvement in productivity and new business models, employment conditions should take advantage of these new technologies. Safety in the workplace is one of the most sensitive topics on matters that needs targeted and accurate solutions. The safety can be guaranteed by investigating the attention states of the workers, and in particular, their drowsiness levels. Several technologies have faced this problem by using biometrics, but how many of them are applicable in a real-case-use scenario of Industry 4.0? This article aims to answer this question by discussing available data and methods that can be used in specific workplaces. We highlight their limitations and accuracy to sketch out the recent literature that may contribute to worker safety in Industry 4.0. Finally, we point out a gap that needs to be filled in order to implement these strategies on a large scale.

Feeder reconfiguration is an effective way to reduce power losses of distribution network. In this way, configuration of distribution system is changed in order to achieve possible minimum losses, while electricity demand of consumers has to be provided. Consumers’ power demand has an important role in feeder reconfiguration because any change in demand affects power losses directly. Whereas load demand has a variable and stochastic nature because of its dependence on consumption pattern and accuracy of forecasted load amounts. Accordingly, reconfiguration models should be robust enough against load uncertainty and variations. Thus, this article presents an efficient robust model for reconfiguration of distribution feeders under uncertain and variable loads. The proposed reconfiguration model is robust enough and efficient, in which its implementation is relatively simple. The results show higher efficiency and lower complexity of the proposed model compared to existing robust reconfiguration approaches.

This paper presents a comprehensive model for representing and reasoning on situations to support decision makers in Intelligence analysis activities. The main result presented in the paper stems from a work of refinement and abstraction of previous results of the authors related to the use of Situation Awareness and Granular Computing for the development of analysis methods and techniques to support Intelligence. This work made it possible to derive the characteristics of the model from previous case studies and applications with real data, and to link the reasoning techniques to concrete approaches used by intelligence analysts such as, for example, the Structured Analytic Techniques. The model allows to represent an operational situation according to three complementary perspectives: descriptive, relational and behavioral. These three perspectives are instantiated on the basis of the principles and methods of Granular Computing, mainly based on the theories of fuzzy and rough sets, and with the help of further structures such as graphs. As regards the reasoning on the situations thus represented, the paper presents four methods with related case studies and applications validated on real data.

Precision agriculture employs IoT devices to smartly monitoring plant vegetation and support food production. Precision agriculture is highly required to improve product quality and better suit the requirements of the market. Among the IoT devices, Unmanned Aerial Vehicles (UAVs), can be equipped with many sensors that allow precise assessments of plant stress by flying over the plots. Notwithstanding the great benefits introduced, IoT devices may suffer from some issues. Many devices provide data in different formats on the same task, therefore they need solutions to integrate data and support a more thorough crop monitoring. This paper introduces a multitier architecture to deal with IoT-based intelligent monitoring, as well as an implementation of the architecture through multiagent modeling of the IoT devices for precision agriculture. The introduced model allows data acquisition from various sources (i.e., IoT devices), an ontology-based integration of data provided by the devices and a knowledge integration process to deal with domain-specific applications.

One of the most important sources of revenue for social media platforms, like, Twitter, Facebook, Reddit, etc., is advertising. An effective social media advertising plan moves people from awareness and interest in desire and action. Despite the potentiality, campaigns and marketing strategies should be improved. One of the challenges is to identify the right target audience at the right time, considering both communities of interests and locations and the development of these conditions along the timeline. This is crucial to create the right communication strategy and the right advertising message. This paper proposes a context-aware ad-targeting methodology using time, locations, and inferring users' interests by analyzing published content. The method relies on a fuzzy extension of Triadic Formal Concept Analysis for identifying Location-based and Content-based communities of users. Then, a task of community fusion takes place, named Join, for matching a target audience. The matching may be tuned for identifying a wide or narrow community and implementing a fine-grained ad targeting. Experimental results are given.

In recent years, unmanned vehicles (UVs) have been largely employed in many applications. They, enhanced with computer vision and artificial intelligence, can autonomously recognize targets in an environment and detect events occurring in a real-world scenario. The employment of cooperative UVs can provide multiple interpretations supporting a multiperspective view of the scene. However, UV multiple interpretations often diverge, therefore, UVs need to find an agreed interpretation of the scenario. To this purpose, this paper proposes a novel consensus-based approach to lead multi-UV systems to find agreement on what they observe and build a group situation-based description of the scenario. UVs are modeled as experts in a group decision making problem that must decide on which situations best describe the scenario. First, the approach allows each UV to build high-level situations from the detected events through a fuzzy-based event aggregation. The event aggregation is modeled with a fuzzy ontology which allows each UV to express preferences on the situations. Then, a collective interpretation of situations is achieved by consensing each UV interpretation. Finally, consensus and proximity measures support the evaluation of the final group decision reliability. The assessed consensus reflects how much the collective scenario interpretation fits each UV perspective. The proximity measures support the detection of reliable and unreliable UVs to serve many tasks (i.e., mission replanning, damaged UV detection, etc.).

This article presents a new interactive method to analyze and assess hypotheses, and its application to terrorism events. The method combines probability, fuzzy, and rough set theories and supports decision makers and analysts of counterterrorism in the analysis of intelligence information by using behavioral models of known terrorist groups. Starting from intelligence information about possible attack patterns, the proposed method uses two parameters allowing derivation and analysis of a wide range of hypotheses, and their assessment on the basis of different support levels of evidence. The evaluation of results has been done on real data relating to five years (2012-2016) of terrorist activities extracted from the Global Terrorism Database.

This paper studies active relocalization of 6D camera pose from a single reference image, a new and challenging problem in computer vision and robotics. Straightforward active camera relocalization (ACR) is a tricky and expensive task that requires elaborate hand-eye calibration on precision robotic platforms. In this paper, we show that high-quality camera relocalization can be achieved in an active and much easier way. We propose a hand-eye calibration free approach to actively relocating the camera to the same 6D pose that produces the input reference image. We theoretically prove that, given bounded unknown hand-eye pose displacement, this approach is able to rapidly reduce both 3D relative rotational and translational pose between current camera and the reference one to an identical matrix and a zero vector, respectively. Based on these findings, we develop an effective ACR algorithm with fast convergence rate, reliable accuracy and robustness. Extensive experiments validate the effectiveness and feasibility of our approach on both laboratory tests and challenging real-world applications in fine-grained change monitoring of cultural heritages.

A great impetus for the study of resilience in critical infrastructures (CIs) is found in the large number of initiatives and international research programmes from U.S., EU, and Asia. Politicians, decision makers, and citizens are now aware of the drastic consequences that can have the cascading effects of an adverse event in these large scale infrastructures. However, the study of resilience in CIs is challenging for several reasons, among which their large scale and interdependencies. We have to consider also that adverse events, e.g., attacks, natural hazards, or man-made disasters, suddenly occur and evolve rapidly, giving us little time to take decisions and react to them. Approximate reasoning and rapid decision making have to be considered requirements for resilience analysis of CIs. The main result presented in this paper relates to a systemic integration of granular computing (GrC) and resilience analysis for CIs. Each phase of our approach presents distinctive aspects but, overall, we argue the merit of this paper consists in the originality of the study, being this the first work that combines GrC and resilience analysis of CIs. This paper reports an illustrative example that shows how to apply our results, and a discussion on the necessary contextualizations and extensions of the GrC results to be better adapted for CIs resilience.

Visual tracking supported by unmanned aerial vehicles (UAVs) has generated a lot of interest in recent years, especially in application domains such as surveillance, search for missing persons and traffic monitoring. The major challenges in visual tracking with small UAVs arise in the form of target representation, target appearance change, target detection and localization in real time computation. Reliable target detection depends on factors such as occlusions, image noise, illumination and pose changes, or image blur that may compromise the object labeling. To mitigate these issues, this paper proposes a hybrid solution: along with the tracked objects, scenes are completely depicted by adding contextual information, i.e., data describing places, natural features, or in general points of interest. Each scenario indeed is semantically described by ontological statements that define the context and then, by inference, support the object tracking task in the object identification and labeling. The synergy between the tracking methods and semantic modeling can bridge the object labeling gap, enhancing the scene understanding and awareness when alarming situations are discovered. Experimental results are promising and confirm the applicability of the proposed framework in supporting drones in object identification and critical situation detection tasks.

Summary Social media is assuming a crucial role in purchasing decisions, and most companies are using social media for marketing. Making sense of the unstructured information content shared by users along the time is an emerging challenge. We advise that the dynamic nature with which trends and user's interests evolve along the timeline requires the revising of well‐assessed methods to address, for instance, recommendation provisioning, information retrieval, and so on. Time‐awareness is crucial to more effectively estimate user's interests in the future to better address social media marketing attempting to increase the traction, for instance, posting the right message at the right time. This work defines time‐aware collaborative filtering for estimating users' interest along the time in Twitter. It uses text analysis services to semantically annotate tweets' content and to track concepts considering post frequencies along the time. A model‐based approach implementing K‐Nearest Neighbors is used to estimate user's similarity representing their profile by sampling user's interest with three different techniques: Vectorial Representation, Symbolic Aggregation Approximation, and Median. We show the experimental results comparing these techniques and performing model training in different time windows. The proposed approach is used to address some social media marketing questions.

Although fuzzy preference relations (FPRs) are among the most commonly used preference models in group decision making (GDM), they are not free from drawbacks. First of all, especially when dealing with many alternatives, the definition of FPRs becomes complex and time consuming. Moreover, they allow to focus on only two options at a time. This facilitates the expression of preferences but let experts lose the global perception of the problem with the risk of introducing inconsistencies that impact negatively on the whole decision process. For these reasons, different preference models are often adopted in real GDM settings and, if necessary, transformation functions are applied to obtain equivalent FPRs. In this paper, we propose fuzzy rankings, a new approximate preference model that offers a higher level of user-friendliness with respect to FPRs while trying to maintain an adequate level of expressiveness. Fuzzy rankings allow experts to focus on two alternatives at a time without losing the global picture so reducing inconsistencies. Conversion algorithms from fuzzy rankings to FPRs and backward are defined as well as similarity measures, useful when evaluating the concordance between experts’ opinion. A comparison of the proposed model with related works is reported as well as several explicative examples.

A promising research area in the field of group decision making (GDM) is the study of interpersonal influence and its impact on the evolution of experts' opinions. In conventional GDM models, a group of experts express their individual preferences on a finite set of alternatives, then preferences are aggregated and the best alternative, satisfying the majority of experts, is selected. Nevertheless, in real situations, experts form their opinions in a complex interpersonal environment where preferences are liable to change due to social influence. In order to take into account the effects of social influence during the GDM process, we propose a new influence-guided GDM model based on the following assumptions: experts influence each other and the more an expert trusts in another expert, the more his opinion is influenced by that expert. The effects of social influence are especially relevant to cases when, due to domain complexity, limited expertise or pressure to make a decision, an expert is unable to express preferences on some alternatives, i.e., in presence of incomplete information. The proposed model adopts fuzzy rankings to collect both experts' preferences on available alternatives and trust statements on other experts. Starting from collected information, possibly incomplete, the configuration and the strengths of interpersonal influences are evaluated and represented through a social influence network (SIN). The SIN, in its turn, is used to estimate missing preferences and evolve them by simulating the effects of experts' interpersonal influence before aggregating them for the selection of the best alternative. The proposed model has been experimented with synthetic data to demonstrate the influence driven evolution of opinions and its convergence properties.

This paper investigates the possibility of learning a trading rule looking at the relationship between market indicators and decisions undertaken regarding entering or quitting a position. As means to achieve this objective, we employ a long short-term memory machine, due its capability to relate past and recent events. Our solution is a first step in the direction of building a model-free robot, based on deep learning, able to identify the logic that links the market mood given by technical indicators to the undertaken investment decisions. Although preliminary, experimental results show that the proposed solution is viable and promising.

In this paper, a fuzzy-based paradigm for data compression aimed at reducing the computational burden of data analysis in smart grids (SGs) is proposed. In the SG context, it is challenging achieving an efficient use of the channel communication bandwidth and a reduced need of the storage space for operational data. Thus, we discuss a fuzzy-based mathematical kernel which transforms the data into a new domain where their cardinality can be sensibly reduced, consequently allowing the development of more efficient data analysis algorithms. Detailed numerical results obtained from several test systems are presented and discussed in order to demonstrate the effectiveness of the proposed approach to handle SG operation problems.

Visual object tracking with unmanned aerial vehicles (UAVs) plays a central role in the aerial surveillance. Reliable object detection depends on many factors such as large displacements, occlusions, image noise, illumination and pose changes or image blur that may compromise the object labeling. The paper presents a proposal for a hybrid solution that adds semantic information to the video tracking processing: along with the tracked objects, the scene is completely depicted by data from places, natural features, or in general Points of Interest (POIs). Each scene from a video sequence is semantically described by ontological statements which, by inference, support the object identification which often suffers from some weakness in the object tracking methods. The synergy between the tracking methods and semantic technologies seems to bridge the object labeling gap, enhance the understanding of the situation awareness, as well as critical alarming situations.

Formal Concept Analysis (FCA) and its fuzzy extension have been widely used to arrange data into a lattice that is an effective data structure useful to address several aims, such as: data mining, ontology learning and merging, and so on. In literature it is possible to distinguish two main approaches to address fuzzy FCA implementation: the one-sided threshold and the fuzzy closure one. This work focuses on a specific definition of one-sided threshold algorithm and fuzzy closure one. Specifically, it shows that these methods can be unified, since the one-sided threshold approach can be seen as a specialization of the fuzzy closure. The lattice generated using one-sided fuzzy threshold approach is a substructure of the lattice generated using the fuzzy closure approach. In addition, an experimentation has been performed on both implementations of the fuzzy FCA, one-sided threshold and fuzzy closure. In particular, the results are compared in terms of running time and number of extracted fuzzy concepts by varying the t-norm function Łukasiewicz, Gödel, and Product.

We present a methodology to support urban planners and decision makers in obtaining a good awareness of how city assets (points of interest) are perceived by a community, and on the impact and influence that this collective perception can have on other city assets and city issues such as mobility, environment, security. The methodology employees Fuzzy Cognitive Maps and Fuzzy sets. Fuzzy Cognitive Maps are used to model the relationships between elements of mental representations that different communities have with regards to city issues. The concept of signature as relation between two fuzzy sets is adopted, in analogy to what proposed by Yager and Reformat [1], to characterize a point of interest. Different signatures are subsequently grouped to characterize an area and adopted, in combination with sentiment analysis, to derive a measure of collective perception on the quality of the area. This measure is used to activate some qualitative concept of a Fuzzy Cognitive Map and perform what-if analysis. The methodology has been applied to a sample of three POIs (representing three attractions of the city of Salerno) by using data gathered from the Web and involving some real citizens. Our preliminary results are encouraging with regards to the possibilities offered by our approach of enforcing city decision makers with a good awareness on how changes in the perception of quality of urban areas can influence other city related issues.

Nowadays, many research works are moving toward the definition of models for human decision support systems within business process executions. Existing solutions, in general, do not take into account the context in which such processes run but they provide rigid models that could erroneously support decision-making activities when a different context needs to be considered. This work focuses on the definition of a framework to support and trace human decision-making activities, in business processes, when more heterogeneous decision-makers have to find a consensus to select one alternative among the others. One class of such processes is that of Innovation Processes. In particular, the main result described here is a Context-aware Fuzzy Linguistic Consensus Model, based on Fuzzy Logic, Semantic Web technologies and Reinforcement Learning, that considers heterogeneous decision makers with different levels of influence (assigned by considering their past decisions) in the context where the decision activity takes place.

We present an application of Granular Computing and Three-way decisions to intelligence analysis. In particular we extend the Analysis of Competing Hypotheses with an additional perspective devoted to support analysts in reasoning with groups of hypotheses that can be equivalent on the basis of partial and incomplete evidence, and in classifying these groups of hypotheses with respect to a decisional attribute of interest for the analyst, such as dangerous or safe. Creating and reasoning with granules and multi-level granular structures give to our approach an added value when dealing with a large number of evidence and hypotheses. Three-way decision making offers the possibility of a rapid understanding of how granules of hypotheses approximate a class of dangerous hypotheses, with clear benefits when analysts have to take decision on classifying a group of hypotheses or setting a proper level of attention to group of equivalent hypotheses.

The papers in this special section focus on advanced techniques for reliable cloud storage. Cloud computing has emerged as a disruptive technology and new computing paradigm in which IT resources (hardware, software, applications, data storage, computing capacity, etc.) are provided as services over the Internet, through "everything-as-a-service". It builds on the foundations of distributed computing, grid computing, virtualization, service orientation, etc. Cloud storage, which is one of the most attractive cloud services, offers numerous benefits from both the technology and functionality perspectives such as increased availability, flexibility, functionality and costs. However, Cloud environment needs to manage data center virtualization, lower cost and boost reliability by consolidating systems in the cloud.

The papers in this special section focus on the topic of industrial sensing intelligence. Examines the sensor and communications technologies that support these services and reports on industrial applications for their use.

Wireless Mesh Networks (WMNs) are attracting a lot of attention from wireless network researchers. Node placement problems have been investigated for a long time in the optimization field due to numerous applications in location science. In our previous work, we evaluated WMN-GA system which is based on Genetic Algorithms (GAs) to find an optimal location assignment for mesh routers. In this paper, we evaluate the performance of two different distributions of mesh clients for two WMN architectures considering throughput, delay and energy metrics. For simulations, we used ns-3 and Optimized Link State Routing (OLSR). We compare the performance for normal and uniform distributions of mesh clients by sending multiple Constant Bit Rate (CBR) flows in the network. The simulation results show that for both distributions, the throughput of Hybrid WMN is higher than I/B WMN architecture. The delay of Hybrid WMN is a lower compared with I/B WMN. The delay for Hybrid WMN is almost the same for both distributions. However for I/B WMN, the delay is lower for Uniform distribution. For Normal distribution, the energy decreases sharply, because of the high density of nodes. For Uniform distribution, the remaining energy is higher compared with Normal distribution.

Functional Magnetic Resonance Imaging provides researchers with an effective and non-invasive tool to understand cerebral functions and correlate them with brain activities. With the ever increasing diffusion of the Internet such images may be exchanged in several ways, thus allowing new research and medical services. On the other hand, ensuring the security of exchanged fMRI data becomes a main concern, due to the special characteristics arising from strict ethics, legislative and diagnostic implications. So it is very important to prevent unauthorized manipulation and misappropriation of such images. The risks are increased when dealing with open environments like the Internet. For this reason, security mechanisms which ensure protection of such data are required. In this paper we introduce a watermarking scheme explicitly designed for this kind of images. In particular, such a scheme belongs to the category of fragile reversible watermarking. The validity of this scheme has been demonstrated through testing. Finally, by using the proposed scheme, we show how to create a distributed security solution that models a multi-domain environment, for ensuring authenticity and integrity of such images.

Wireless Mesh Networks (WMNs) are attracting a lot of attention from wireless network researchers. Node placement problems have been investigated for a long time in the optimization field due to numerous applications in location science. In our previous work, we implemented WMN-GA system which is based on Genetic Algorithms (GAs) and used it for node placement problem in WMNs. In this paper, we evaluate the performance of two different distributions of mesh clients for two WMN architectures considering PDR, throughput, delay and energy metrics. For simulations, we used ns-3, Optimized Link State Routing (OLSR) and TCP. We compare the performance for normal and uniform distributions of mesh clients by sending multiple Constant Bit Rate (CBR) flows in the network. The simulation results show that the PDR is almost the same for both distributions and architecture. However, the throughput is better for normal distribution and Hybrid WMN architecture. For both distributions, the delay is better for Hybrid WMN architecture. But, the energy is better for uniform distribution.

Social media and networks are used by millions of people to share with their friends across the world: tastes, opinions, ideas, etc. The volume and the speed at which these data are produced make it a challenging task to discover meaningful patterns in the data. Nevertheless, very interesting business goals could be achieved collecting these data and performing analytics on social media data streams, such as: addressing marketing strategies, targeting advertisements, and so forth. We emphasize that there is a need to investigate and define suitable knowledge mining approaches to go beyond explicitly available metadata by analyzing unstructured data to provide intelligent analytics services. Specifically, in this paper we provide first results on applying OLAP analysis to multidimensional Tweet streams.

Emotions govern all the human actions and play a key role in decision-making processes. Capturing sentiments and opinions hidden in the written (natural) language is a key activity which attracts both the scientific community, by leading to many novel challenges, and the business world, by supporting market behavior and prediction. Sentiment analysis and Sentic Computing are two interrelated research trends that, by exploiting the common sense in the natural language, try to distill human feelings in the textual data.

Microblog service has attracted much attention in big data analysis. Twitter statistics remark that the average number of tweets per day is greater than 100 million and many thousands of them happen every minute. It's an urgent challenge to face with such a large amount of collected tweets. This work defines an online query-focused Twitter summarization framework. It crawls and semantically indexes tweets exploiting wikification service. When a user's query is submitted, the system filters out the most relevant tweets. Then, a summarization algorithm browses the knowledge structure extracted by performing a Fuzzy Formal Concept Analysis on the given filtered tweets. Experimental results reveal good performances.

With the proliferation of different heterogeneous biomedical data sources and with the growing amount of their content available over the Web, there is, on one side, the need to support mashing and data integration and, on the other side, the more urgent need to relate literature and research results that are often enclosed in unstructured textual documents. Nowadays, ontologies have been used as a common access knowledge layer playing a crucial role to support categorized access to the information resources. Moreover, manual construction of a domain-specific ontology and content categorization is a labor intensive and a time-consuming process. This work focuses on the development of a novel biomedical ontology-driven multi-facets visualization to support categorized access to heterogeneous and unstructured biomedical data sources (e.g., PubMed, WikiGenes). Specifically, the framework relies on: knowledge extraction methodology, to automatically extract ontology exploiting the Fuzzy Formal Concept Analysis theory; and ontology matching strategy to find relation between extracted ontology and the available ones in the field of biomedicine (e.g., Ontology of Gene and Genomes, Gene Ontology, Protein Ontology). The evaluation will be shown in terms of Precision and Recall by using biomedical ontology concepts as input query to the multi-facets visualization engine.

Situation identification is a complex task that is usually employed in order to sustain the work of Decision Support Systems in several and heterogeneous application scenarios like, for instance, Emergency Management, Safety and Security. Typically, situation awareness systems gather and process raw sensor data by means of different techniques. In this context, it is fundamental to exploit qualitative sensor data in order to guarantee the reliability of the situation identification task results. The consolidation of Internet of Things and the growth of the Linked Sensor Data ecosystem provide us with different degrees of availability and, sometimes, redundancy of sensor observations that could be conflicting. This could be caused by sensor failures due to contextual factors, malicious attacks, faults. This paper proposes an approach based on Fuzzy Consensus to assess data coming from a group of redundant sensors and provide reliable observations to be exploited for situation identification. Lastly, Granular Computing paradigm is adopted to handle multigranularity of information, i.e., To manage observations assessed in different linguistic term sets.

The concept of smart microgrid (SMG) is widely recognized as one of the most promising and enabling technologies for smart grids. In this context, the development of integrated and decentralized frameworks for executing complex control and monitoring applications is still in its infancy and needs to be researched. To address this issue, this paper conceptualizes a self-organizing computing framework, based on self-organizing agents, for solving the fundamental control and monitoring problems of an SMG without the need for centralized data acquisition and processing. Simulation results obtained for an 18-bus test network are presented and discussed in order to demonstrate the significance and validity of the proposed new framework.

Workplace learning can be conceived as the set of processes related to learning and training activities at work. Typically, workplace learning includes formal, informal, and non-formal learning activities. Having a control on the whole learning process of each worker is a complex task. Indeed we have to align individual learning paths, real workers' needs (for instance in terms of tasks or projects to accomplish), career plans, and other organizational needs to activate virtuous knowledge flows. In order to accomplish this complex task a comprehensive framework is needed. This paper provides the definition of the aforementioned framework by exploiting semantic technologies in order to model (by means of Ontologies), represent, extract, and share knowledge within organizations. Although the proposed framework allows a wide range of workplace learning experiences, it mainly focuses on informal learning, on the ways it can be sustained by exploiting organizational resources, and on the capability of linking individual and organizational learning in the context of a widely accepted knowledge flow model like socialization, externalization, composition, and internalization.

People using smartphones to connect to the Internet for day-life activities has overtaken the number of people using canonical PCs. This lead to a huge quantity of security threats that usually tend to penetrate the defenses of a smartphone in order to gain control of its resources. Differently, energy-based attacks have the objective of increasing the energy consumption of the victim device. It is important to highlight that this objective could be possibly achieved by just activating the system's defenses as a consequence of canonical attacks and letting the system defenses detect and (try to) defeat them. These activities consume additional energy and could led the mobile device to its complete uselessness. In this paper, an energy-based attack based on soliciting hardware-level encoding/decoding functions through properly crafted multimedia files is analyzed and its impact evaluated. Such kind of attacks are performed without accessing the device by taking advantage of the new HTML5 functionalities. A series of experiments have been performed in order to understand which are the codecs that have a more relevant impact on energy consumption, and, as a consequence, that make the attack more effective.

Serendipitous Learning is the learning process occurring when hidden connections or analogies are unexpectedly discovered, mostly during searching processes (for instance on the Web) which are typical for informal learning activities, especially accomplished at the workplace context. Moreover, serendipitous processes have high probability to occur in the contexts where learners have high autonomy, more chances to intervene in different activities and to interact with resources and people. This paper proposes an approach based on the Social Semantic Web vision to sustain and improve Serendipitous Learning. The proposed approach considers two connected ontology layers to model knowledge by using several SemanticWeb vocabularies like SIOC, Dublin Core, SKOS, and so on. The SKOS role is particularly relevant because it allows connections among heterogeneous resources, also across multiple communities. The proposed approach models the above-mentioned connections at the conceptual level and facilitate learners in discovering them and following unexpected paths.

In this paper, we propose a decentralized and self-organizing solution framework aimed at addressing economic dispatch (ED) analysis in a distributed scenario. In particular we will demonstrate that, under some hypotheses, the solution of the ED analysis can be obtained by computing proper weighted averages of the variable of interests. To compute these global quantities we propose the deployment of a network of cooperative dynamic agents solving distributed average consensus problems. Thanks to this decentralized/nonhierarchical paradigm, all the basic operations needed to solve the economic dispatch problem could be easily processed by the agents. Simulation results obtained on the 118 and 300 bus IEEE test networks are presented and discussed in order to prove the effectiveness of the proposed framework.

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.

We present our preliminary results in the definition of a model and knowledge based techniques to support creativity by establishing consonance relationships between a worker and the organization. The model is based on the Viable Systems Approach (VSA) and links this theory with Creativity and Open innovation via the definition of proper consonance and resonance relationships. VSA is an interdisciplinary approach grounded on systems thinking and resource-based theory, and focused on methodologies to govern relations among supra-systems and subsystems. Leveraging on the systems perspective of the creativity proposed by Csikszentmihalyi, we defined a way to analyse and understand how the variety introduced by workers in organization can lead to novel and useful products and services. We present also our preliminary results on how a well-defined set of knowledge based methodologies and techniques, resulting from the ARISTOTELE research project, can be applied to this purpose in the context of a Gene lore model. We believe our proposal, i.e. the application of VSA methods combined with knowledge based techniques, can lead to substantial advantages in several areas of Computational Creativity.

This paper discusses a new computational scheme based on functional networks and applies it to the problem of classification and quantification of gas species in a mixture. A generalized functional network as a new classifier is proposed to improve the potentialities of the standard functional network classifier. Both methodology and learning algorithm are derived. The performance of this new classifier is examined by using experimental applications. A comparative study with the most common classification algorithms is carried out by showing the high-quality performance of the proposed classifier. The classifier interacts with some quantifiers, again based on functional networks and finite differences. The scheme of the quantifiers was previously proposed for single gas exposure applications and is here extended to the multigas case. Numerical results show that our approach behaves quite satisfactorily.

The massive pervasion of distributed generators in modern smart grids makes hierarchical voltage control paradigms unsuitable. Fuzzy logic and fuzzy agent based architecture is considered to be a promising research approach to address voltage control problem in smart grids. Following the said approach, this paper proposes decentralized and non-hierarchal voltage control architecture based on cooperative fuzzy agents. Similarly to self-organizing biological populations, the voltage control is achieved by the local interaction of fuzzy agents as that of the theory distributed consensus. This allows control agents to decide that when reactive power flow injection in the network is more useful in case of global network conditions. Simulation results obtained on the 30 bus IEEE test network are presented and discussed in order to prove the effectiveness of the proposed solution.

Workforce Management (WFM) is becoming a core decisional approach for optimizing different enterprise processes such as operational activities needed to maintain a high production rate. However, in order to solve complex optimization problems it is necessary to analyze and deal with a plethora of distributed and semantically different information defining the collection of criteria from which enterprise activities depend. For this reason, this paper introduces a novel WFM system that, by using an ontological representation of knowledge related to the different aspects of an enterprise activity, exploits a multi-criteria decision making approach for selecting the most suitable strategies to face WFM issues.

Workforce Management (WFM) is becoming a core decisional approach for optimizing different enterprise processes such as operational activities needed to maintain a high production rate. However, in order to solve complex optimization problems it is necessary to analyze and deal with a plethora of distributed and semantically different information defining the collection of criteria from which enterprise activities depend. For this reason, this paper introduces a novel WFM system that, by using an ontological representation of knowledge related to the different aspects of an enterprise activity, exploits a multi-criteria decision making approach for selecting the most suitable strategies to face WFM issues.

Situation awareness computing employs sensor networks to collect large amounts of heterogeneous data in different and complex environments. The rapid development and deployment of sensor technology stress the problem related to the availability of too much and heterogeneous data. Last trend emphasizes the semantic annotation of acquired sensor data. Semantic sensor data provides machine understandable contextual information. In particular, the availability of semantic sensor data allows situation awareness in several application domains. This paper introduces a swarm-based approach to semantic web reasoning in order to identify situations. On one hand, fuzzy control has been employed in order to face with uncertainty of happening situations. On the other hand, Situation Theory has been used in order to model situation awareness. A multi agent swarm architecture enables to monitor complex environments by using spatially distributed autonomous sensors. An application scenario for bank intrusion detection has been described.

Context aware computing as well as wearable and ubiquitous computing often attain with pattern recognition on incoming sensor data. Recognizing more (useful) contexts requires more information about the context, and thus more sensors and better recognition algorithms. In order to enable logic inference on incoming data, the proposed work assumes that incoming data are represented by means of semantic languages (e.g., RDF, OWL, etc.). Nevertheless, in a context aware computing purely logic-based reasoning on context may not be enough. So, the work introduces soft computing techniques to approximate context recognition. Specifically, this paper introduces an approach to context analysis and recognition that relies on f-SPARQL[1] tool, that is a flexible extension of SPARQL. In particular, in this work a JAVA implementation of f-SPARQL and the integrated support for fuzzy clustering and classification are discussed. This tool is exploited in the architecture that foresees some task oriented agents in order to achieve context analysis and recognition in order to identify critical situations. Finally, a simple application scenario and preliminary experimental results have been described.

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.

Context aware computing attains environments monitoring by means of sensors in order to provide relevant information or services according to the identified context. Nowadays, ad hoc wireless sensor networks for medical purposes are playing an increasing role within healthcare. Specifically, Body Sensor Networks (BSN) and Wireless Sensor Network, are being designed for prophylactic and follow-up monitoring of patients e.g., at home, at hospital, and so on. This work defines a framework aimed at proactively providing personalized healthcare services by performing sensor data analysis in order to recognize specific user's context. In particular, the approach is strongly based on the synergy between semantic formalisms and soft computing techniques. Semantic Web formalisms are exploited to model healthcare services and context. Soft computing techniques are applied in order to support activity of unsupervised context analysis and semantic service matchmaking. Specifically, Fuzzy Logic enable us to automatically characterize the context and to consequently find the set of healthcare services among the available ones that approximately meet the user's context. Experimental results shows performance in terms of services matchmaking.

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.

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.

In the area of ambient intelligence there is a need to address user needs according with context features. Recently, the synergy between context aware computing and collaborative filtering is leading to enhance recommender systems with capabilities always nearer to user needs. Specifically, in the domain of tourism it is useful to proactively suggest right sets of attractive locations, events and so on. This work defines a context aware recommender system aimed at suggesting pertinent points of interest (POIs) to tourists. In particular, the approach is strongly based on the synergy between soft computing and data mining techniques. The general framework integrates user profiles, history of social networking and POIs data. Then by defining collaborative filtering approach on the history meaningful POIs are extracted. Indeed, soft computing techniques are mainly applied in order to support activity of unsupervised users and POIs classification. On the other hand, data mining techniques are exploited in order to extract rules able to associate user profile and context features with an eligible set of recommendable POIs. Experimental results show performance in terms of recommendations accuracy.

In an era in which organizations increasingly consider the competencies of their employees as a crucial resource, human management becomes a key activity for improving staff and business performances. Important is also knowing who knows what inside the organization, so that project teams are assembled as the right mix of skill, knowledge and workforce abilities. At the same time, trust, an essential component, related to understanding interpersonal and group behavior, is an indisputable prerequisite for organizational effectiveness, in terms of social-cognitive capital, global competency as well as economic exchange and social and political stability. This paper defines an approach to support competency-based management by providing recommendations about the reliability of a worker in terms of trust information and own competencies. The approach lies on an agent-based architecture which supervises the Human Resources Management (HRM). Task-oriented agents monitor the employees' profiles and capabilities by maintaining update the competencies and the trusts in the organizational social network. Particularly an agent endowed by fuzzy reasoning capabilities provides recommendation about workers' competencies in HRM decision-making processes.

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.

Applying best available evidences to clinical decision making requires medical research sharing and (re)using. Recently, computer assisted medical decision making is taking advantage of Semantic Web technologies. In particular, the power of ontologies allows to share medical research and to provide suitable support to the physician's practices. This paper describes a system, named ODINO (Ontological Disease kNOwledge), aimed at supporting medical decision making through semantic based modeling of medical knowledge base. The system defines an ontology model able to represent relations between medical disease and its symptomatology in a qualitative manner by using fuzzy labels. Medical knowledge is defined according with physician experts members of INMP (National Institute for Health Migration and Poverty). The main aim of ODINO is to provide an effective user interface by using ontologies and controlled vocabularies and by allowing faceted search of diseases. In particular, this work mashes the capabilities of Description Logic reasoners and information retrieval techniques in order to answer to physician's requests. Some experimental results are given in the field of dermatological diseases.

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.

Situation awareness is crucial factor in decision-making. It involves monitoring and identification of relationships among objects in collaborative dynamic environments. In the domain of Airport Security one of the main needs is to support the security operator to manage in real-time risk scenarios in the airside. This work relies on a cognitive approach to model the awareness ontology and introduces an agent-based architecture to address the problem. In particular, in order to model situation awareness the work instantiates the generic Situation Theory Ontology(STO) in the specific domain of airport security. Furthermore, some task-oriented agents allow to distribute the information in order to achieve better performances.

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.

Nowadays, in enterprise environments there is a wide and consolidated utilization of software for the human resource management providing functionalities like organizational management, personnel development, training event management, etc. that lay upon a competencies repository mostly populated through expensive and inefficient data entry activities. The new trends in Web 2.0 see a paradigm namely Enterprise 2.0, for supporting business activities within organizations. Web 2.0 is mainly exploited to sustain collaboration, information exchange and knowledge sharing. This work introduces an agent-based framework for the dynamic refinement of employees' competencies profiles by analyzing and monitoring collaborative activities executed through Enterprise 2.0 tools (e.g. corporate blogs, enterprise wikis, etc.). A fuzzy extension of Formal Concept Analysis model supports the elicitation of implicit knowledge and the content structuring into a conceptual representation. The resulting concept-based organization of initial user-generated content will be exploited to provide automatic hints to human resources (HR) managers in order to support them in making safer decisions that involve employees' competencies.

The current “semantic” generation of Web strongly lies in sharing knowledge rather than linkages among digital resources. The Semantic Web represents an effective infrastructure based on ontologies, languages and tools to enhance visibility of knowledge on the net. The imprecise nature of knowledge often requires fuzzy techniques to coherently represent the imprecise and uncertain information of the real world. It is indubitable that many decision making problems within business, industrial and web applications are solved by fuzzy approaches, especially by exploiting fuzzy control. In order to integrate fuzzy knowledge in the Semantic Web, appropriate formal schemas are introduced for describing fuzzy data types and uncertainty information. In particular, this paper presents an OWL-based upper ontology, called OWL-FC (Ontology Web Language for Fuzzy Control) which provides a set of ontological constructs for defining semantic specification of Fuzzy Control. The OWL-FC ontology represents a straightforward contribute to support automation in discovery, usage and interoperability among a large number of fuzzy controls; built-in markups for the fuzzy controls enable the natural integration in description logics-based reasoners and guarantee the specification of fuzzy concepts which do not depend on the application domain.

The huge growth of data on the Web and the requirement of semantic content analysis make the knowledge management and data mining very difficult activities. The knowledge elicitation, codification, and storage need not trivial techniques to improve formal information structuring on the Internet. Ontologies provide conceptualization and processing knowledge, sharing of consolidate understanding, reusing of domain knowledge codification for many Web applications. Manual construction of a domain-specific ontology is an intensive and time-consuming process, which requires an accurate domain expertise, because of structural and logical difficulties in the definition of concepts, as well as conceivable relationships. At the same time, the ontology visualization process requires similar endeavors to support ontology management, exploration, and browsing. This work describes an automatic method for ontology design from the content analysis of Web resources. The approach exploits a fuzzy extension of formal concept analysis model for structuring the elicited knowledge, viz. concepts and relations embedded in the resources content. Final result is an effective ontology visualization through a navigable, facet-based view of the built ontology across the extracted concepts and their own population. Furthermore, the approach proposes a simple labeling of ontology concepts through a sketched and intuitive process. © 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.

No abstract available.

In this study, we introduce a certain knowledge-guided scheme of fuzzy clustering in which domain knowledge is represented in the form of so-called viewpoints. Viewpoints capture a way in which the user introduces his/her point of view at the data by identifying some representatives, which, being treated as externally introduced prototypes, have to be included in the clustering process. More formally, the viewpoints (views) augment the original, data-based objective function by including the term that expresses distances between data and the viewpoints. Depending upon the nature of domain knowledge, the viewpoints are represented either in a plain numeric format (considering that there is a high level of specificity with regard to how one establishes perspective from which the data need to be analyzed) or through some information granules (which reflect a more relaxed way in which the views at the data are being expressed). The detailed optimization schemes are presented, and the performance of the method is illustrated through some numeric examples. We also elaborate on a way in which the clustering with viewpoints enhances fuzzy models and mechanisms of decision making in the sense that the resulting constructs reflect the preferences and requirement that are present in the modeling environment.

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.

The success of today enterprises depends on their capability to respond on demand to challenging customers' request. At the same time, the enterprise marketplace is characterized by high dynamicity and heterogeneous requests. This makes difficult to provide in house cost-effective services able to meet them and, in many cases, the only solution is to aggregate different services at this purpose. In this context, the availability of an efficient service discovery mechanism is a key feature in order to enable the sketched context. In this paper, we present an architectural pattern for web service discovery following the principle of separation of concerns and based on the definition of discovery queries driven by the customers' needs.

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 recent years, the success of Semantic Web is strongly related to the diffusion of numerous distributed ontologies enabling shared machine readable contents. Ontologies vary in size, semantic, application domain, but often do not foresee the representation and manipulation of uncertain information. Here we describe an approach for automatic fuzzy ontology elicitation by the analysis of web resources collection. The approach exploits a fuzzy extension of Formal Concept Analysis theory and defines a methodological process to generate an OWL-based representation of concepts, properties and individuals. A simple case study in the Web domain validates the applicability and the flexibility of this approach.

Nowadays, the emphasis on Web 2.0 is specially focused on user generated content, data sharing and collaboration activities. Protocols like RSS (Really Simple Syndication) allow users to get structured web information in a simple way, display changes in summary form and stay updated about news headlines of interest. In the e-Learning domain, RSS feeds meet demand for didactic activities from learners and teachers viewpoints, enabling them to become aware of new blog posts in educational blogging scenarios, to keep track of new shared media, etc. This paper presents an approach to enrich personalized e-learning experiences with user-generated content, through the RSS-feeds fruition. The synergic exploitation of Knowledge Modeling and Formal Concept Analysis techniques enables the definition and design of a system for supporting learners in the didactic activities. An agent-based layer supervises the extraction and filtering of RSS feeds whose topics are specific of a given educational domain. Then, during the execution of a specific learning path, the agents suggest the most appropriate feeds with respect to the subjects in which the students are currently engaged in.

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.

This paper describes a system for supporting the user in the discovery of semantic Web services, taking into account personal requirements. Goal is to model an ad-hoc service request by filtering semantic specifications rather than the exploitation of strict syntax formats. Adaptive agent-based techniques help the user to compose his Web service request, exploiting the semantic annotation of the browsed Web resources. This annotation reflects concepts or ontological terms that are relevant for the user services request formulation. Once the request is formulated, the system returns the list of semantic Web services that match the query input and output concepts.

Internet has become a huge repository of information and knowledge, based on the sharing of the electronic documents. Last trends in knowledge management focus on the knowledge representation based on the document content. In fact, most accustomed approaches achieve the document understanding by analyzing the “portions of information'' in the document which describe the content, through techniques of text parsing and extraction. This paper presents an alternative approach that departs from the consolidated techniques of document management and focuses on the logical structure of a PDF document as a discriminating source of document knowledge. The main idea is based on the fact, when the reader looks at a paper, his first perception is related to the layout of the document. The analysis of layout, typesetting, paginating, and graphical arrangement of a document provides interesting information about its content understanding; in general, the documents that are in the same category present similar page layout, fonts, and figures arrangement. In this sense, this work presents an alternative way to deal with documents recognition and understanding, through the analysis of the layout of electronic PDF documents and their classification. © 2008 Wiley Periodicals, Inc.

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.

The evolution of the Semantic Web promises infrastructures for the semantic interoperability of Web Services. Hindrances in the service discovery, composition and execution are often of syntactic nature: the difficulty in the interpretation of inputs, outputs or other nontrivial statements does not favor to find eligible advertised services which appropriately meet the consumer's demand. This paper deals with the semantic matchmaking focusing on the ontology mismatch problem: concepts appearing in the services description are compared at semantic level in order to profit by the semantic similarity existing among entity classes (i.e. concepts). The approach is based on a multi-agent architecture and exploits fuzzy techniques to represent the multi-granular capabilities of a web service. The semantic similarity among concepts supports the clustering of the advertised services and improves the quality of the retrieved results, given a request.

Nowadays, in Europe tunnel safety is a problem of great interest. Recent tragic events have led community and national politicians to start a process of legislation harmonization planning investments toward new technologies designed to improve tunnel safety. The purpose of this paper is to present an innovative system aiming at monitoring and steering vehicular flows nearby tunnels with high accidents risk rate. Based on the integration of IC, video, agent and soft computing technologies, the system's mission is to reduce the risk of accidents which occur inside tunnel through the adaptive generation of speed limits and information to the users approximating tunnels. Strict real-time requirements of the applicative context impose our goal being achieved through a parallel hierarchical fuzzy controller (HFC) implemented by means of a fuzzy control agents network (FCAN) allowing the system to gain higher performances. The fuzzy controller is divided into several sub controllers conceived separately. Some of these, adopt in turn a hierarchical prioritized structure.

Web information may currently be acquired by activating search engines. However, our daily experience is not only that web pages are often either redundant or missing but also that there is a mismatch between information needs and the web's responses. If we wish to satisfy more complex requests, we need to extract part of the information and transform it into new interactive knowledge. This transformation may either be performed by hand or automatically. In this article we describe an experimental agent-based framework skilled to help the user both in managing achieved information and in personalizing web searching activity. The first process is supported by a query-formulation facility and by a friendly structured representation of the searching results. On the other hand, the system provides a proactive support to the searching on the web by suggesting pages, which are selected according to the user's behavior shown in his navigation activity. A basic role is played by an extension of a classical fuzzy-clustering algorithm that provides a prototype-based representation of the knowledge extracted from the web. These prototypes lead both the proactive suggestion of new pages, mined through web spidering, and the structured representation of the searching results. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 1101–1122, 2007.

The semantic vision of the Web involves the processing of data by automated tools as well as by people, where the association of meaning with content, facilitates the search, the interoperability and the composition of several services. The Semantic Web forms a new scenario, where advanced methods and techniques are developed for the description, the retrieval and filtering of Web-based content. In the light of existing challenges and open issues concerning the actual cyberspace, this study proposes an approach for binding the "semantic" facet with the usual textual one, that together constitutes a typical web page, or specifically, a semantic web document. Through the use of unsupervised learning, we offer a new alternative of organizing web documents which emphasizes a direct separation between the syntactic and semantic facets of the web information. In this study, we discuss a collaborative proximity-based fuzzy clustering and show how this type of clustering is used to discover a structure of web information by a prudent reliance on the structures in the spaces of semantics and data. The method focuses on the reconciliation between the two separated facets of web information and a combination of results leading to a comprehensive data organization. The information arranged in this manner can provide an integral description of web resources, becoming in this manner an essential technique for the next generation of Web search engines.

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".

Abstract The explosive growth of the Web and the consequent exigency of the Web personalization domain have gained a key position in the direction of customization of the Web information to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user's navigational behavior (usage data) in correlation with other information collected in the Web context, namely, structure, content, and user profile data. This work presents an agent‐based framework designed to help a user in achieving personalized navigation, by recommending related documents according to the user's responses in similar‐pages searching mode. Our agent‐based approach is grounded in the integration of different techniques and methodologies into a unique platform featuring user profiling, fuzzy multisets, proximity‐oriented fuzzy clustering, and knowledge‐based discovery technologies. Each of these methodologies serves to solve one facet of the general problem (discovering documents relevant to the user by searching the Web) and is treated by specialized agents that ultimately achieve the final functionality through cooperation and task distribution.

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.

The rapid growth of Web resources makes very difficult the task of Web search engines. Nevertheless powerful search crawlers have been developed to aid in locating unfamiliar document (by means of category, contents or subject based approaches), often queries return inconsistent results. The main lack of Web searching is in the deduction capability: nowadays Web searching put much attention in matching user's queries that are too weak to cope with the user's expressiveness. First attempts in extending searching towards deduction capability are essentially based on two-valued logic and standard probability theory. The complexity of the problem (8.4 million of Web sites), the features of the space domain (unstructured data, immature standards) demand a strong deviation from this trend. This work presents some results stemmed from a research projects where different technologies (in particular mobile agents and approximate reasoning) have been merged into an operational architecture suitable for Web searching/Web discovering. This paper discusses a different approach to Web searching where the input to the retrieval process is described through a Web page. The system reacts to this kind of query by returning a set of Web pages that reflect a similar context and deals with related arguments.

We use fuzzy logic for proposing a classification model which divides a geographic map into isoreliable zones. This classification is based on a software tool called FUZZY-SRA (spatial reliability analysis) integrated in a geographical information system (GIS) of the PROCIDA island, located near Naples (Italy). The GIS is realized with technology of the Environmental Systems Research Institute and the logical operations, for processing the linguistic approximations and the classification of the zones, are defined in the context of an algebraic structure, already known in literature.

As information nowadays represents a key factor in every successful business, and Internet offers literally tons of available data concerning almost every area of human activity, there emerges a real necessity for good search tools that can obtain high quality information in a short time. Nowadays finding useful information can be an overwhelming task. As a result, consumers need effective mechanisms for searching the Web. This paper describes LearnMiner, an open, agent-based platform designed for advanced didactic resource discovery. A central role is played by deductive agents, equipped with a similarity-based inference mechanism. This issue enables to bridge the gap between the requested information and the available information on the net, considering the "similarity" as a weaker relation for undiscernibility.

Although advanced and innovative technologies have been used to retrieve information, the need of advanced, new techniques for Web information retrieval is extant. One of the main difficulties is to return accurate information that fully describes the human request. Standard Web search engines provide two mechanisms to handle a user's query: to input the search keywords or to restrict the searching within a certain topic. These approaches appear too weak to cope with such a vast amount of information and with such a rich human expressiveness. Our approach to Web information retrieval consists in merging different available technologies and paradigms: mobile computation is exploited as effective approach to obtain updated results by dispatching the agents directly on Web resources. Agents are equipped with deductive behavior so to be autonomous in applying inference-based reasoning. A similarity model is embedded into the reasoning engine in order to correctly process the approximation level established at user level side.

This paper presents an extension of SLD resolution towards approximate reasoning. The proposed refutation procedure overcomes failures in the unification process by exploiting similarity relation defined between predicate and constant symbols. This enables to compute approximate solutions, with an associated approximation degree, when failures of the exact inference process occur. In this paper we outline the main ideas of this approach and we present an extended PROLOG interpreter, named SiLog, which implements this inference procedure.

Software agents are programs designed to perform tasks autonomously. Mail agents attempt to provide useful functions about electronic mail (E-mail) services, such as information filtering, gathering, and scheduling. The diffusion of the Internet has multiplied the amount of data and the number of information sources so the qualities that made E-mail so popular are now becoming a problem (e.g. the volume of junk or spam mail). Industrial as well as academic research has faced this problem in terms of automated filtering methods in order to distinguish, at the receiver-side, legitimate E-mail from spamming. We describe an alternative approach: our mail system is able to find, at the sender-side, "appropriate" destinations for a message by triggering a spidering process on (a portion of) the Web. This process performs distributed computation using mobile agents: by applying similarity-based reasoning on information extracted from the Web pages of potential addressees, the agents are able to compute a numeric value in [0, 1] which provides, for any address, a measure of the "interest" in receiving the E-mail.

We propose a genetic-level clustering methodology able to cluster objects represented by R/sup p/ spaces. The unsupervised cluster algorithm is based on a fuzzy clustering c-means method that searches the best fuzzy partition of the universe assuming that the evaluation of each object respect to some features is unknown, but knowing that it belongs to circular region of R/sup 2/ space.

We present an open, concurrent model of adaptive hypermedia; all its functionality and data are completely distributed on a web of autonomous actors. The model that enables the system to interact adaptively is a fuzzy stereotype-based user model. Its presentation and application are exploited in the cooperative actor-based environment, generated from the hypermedia model. The merge of these two paradigms offers a new concurrent and fuzzy approach in the adaptive hypermedia system domain.

Technological advances in processor power, networking, telecommunications and multimedia are stimulating the development of applications requiring parallel and distributed computing. This new perspective is enticing research into new design methodologies that view the software as an "intelligent" collection of agents that interact by coordinating knowledge-based processes. We present an actor-based workflow architecture that would fit naturally into distributed heterogeneous environments. The actors combine object-oriented and functional programming in order to make the management of concurrency easier for the user.

The main goal of evolutionary computation is to provide a near optimal technique between exploration and exploitation of a search space. This approach is based on a genetic “engine” that operates the search of the optimal solution via biological-based assumptions. Selection of the optimal maintenance interventions activity, that can be tackled with success thanks to an evolutionary approach able to correct the distresses on the road pavement, is a very complex task. This paper presents an experimental architecture that improves the evolutionary aspect with additional benefits deriving from a synergistic combination of other powerful techniques, in particular neural networks and fuzzy logic. The best rules for managing pavement maintenance activities, developed through a genetic selection, are judged by a neural network. By an appropriate introduction of simple and efficient fuzzy identifiers, the features of the distress to treat can be described in an efficient and natural way. We describe the main advantages arising from this hybrid approach discussing the applicability of the method with experimental results. © 2000 John Wiley & Sons, Inc.

The market for parallel and distributed computing systems keeps growing. Technological advances in processor power, networking, telecommunication and multimedia are stimulating the development of applications requiring parallel and distributed computing. An important research problem in this area is the need to find a robust bridge between the decentralisation of knowledge sources in information-based systems and the distribution of computational power. Consequently, the attention of the research community has been directed towards high-level, concurrent, distributed programming. This work proposes a new hypermedia framework based on the metaphor of the actor model. The storage and run-time layers are represented entirely as communities of independent actors that cooperate in order to accomplish common goals, such as version management or user adaptivity. These goals involve fundamental and complex hypermedia issues, which, thanks to the distribution of tasks, are treated in an efficient and simple way.

Although constraint programming has attracted much attention in logic programming, nowadays the importance to integrate constraints with imperative programming is widely acknowledged. In particular, in artificial intelligence domains, the benefits deriving from merging constraint-based programming with object-oriented paradigms seem to be still more attractive because of the lack of ‘pure’ AI languages in supporting structured representations. This work presents the extension of the Java language towards finite domain constraint programming. This extension has been possible thanks to a high-level approach to low-level resource management: the sleeper mechanism. As practical results, this paper shows how Java programmers can develop meaningful applications in which finite domain constraints have been extensively used, as in the design of visual, interactive, user-interface environments in a client–server architecture. Copyright © 1999 John Wiley & Sons, Ltd.

This paper describes the OPLA language, an object-oriented (OO) logic programming language layered on Common Lisp Object System. OPLA allows the benefits of the OO approach to the design, organization and realization of large logic programs thanks to an OO programming environment. Thanks to a deep link with the underlying CLOS language, OPLS offers important features of the OO paradigm, such as multiple inheritance, multi-methods (multi-argument discrimination) and constraint-based control flow mechanism. These characteristics play an important role in OO Prolog programming, improving the readability of the code and augmenting the maintainability of the applications. This paper presents the OPLA language and some experiences resulting from the design and implementation of graphic interfaces which have been realized in OPLA itself. The OPLA programming style facilities the management of some difficult problems which are basic issues of user-interface development, such as the management of graphical constraints on windows, the distinction of specifying the graphics of objects from their behaviour, and law-based delegation. The implementation methodology which was adopted to realize the bulk of the OPLA system is completely independent of hardware constraints; it exploits an incremental, abstract implementation technique based on a high-level generator mechanism. Our approach has been evaluated both in terms of software design and in terms of overall performances. The novel features of OPLA are quantitatively discussed by reporting their impact in the realization of the significant applications.

This work originated from the challenge to strengthen a C-like language compiler developed to support the compilation of sleepers, which are tools which allow complete access to the run-time stack in a delayed non-local execution protocol. Sleepers use a specialized form of procedure call, for which the gain in time execution and space memory allocation becomes a crucial need. These two objectives have been attained thanks to a methodology which generalizes the traditional distinction between tail-calls and normal-calls, introducing the orthogonal distinction between calls in which the calling environment is needed after the call site and calls in which it is not. These two dichotomies divide the space of calls into four classes. The strategy described in this paper is a simple and general framework which can be used to optimize the call-procedure statement in C compilers. The paper discusses optimization techniques appropriate to each class in turn, providing code details for SPARC and ALPHA processors.

Abstract We present the implementation of a Prolog system composed of interpreter and compiler. The originality of our work consists in the adoption of a new framework to realize the main components of the system. The framework relies on new mechanisms, called sleepers. With their help we have developed a complete Prolog interpreter in which all the control activities, from backtracking up to last‐call optimization, are performed by the sleeper mechanism. We have also produced a Prolog compiler by using a philosophy and tactics that are completely independent of hardware constraints; it exploits an incremental and abstract implementation technique, based on a delayed non‐local execution protocol. Our approach to Prolog system implementation has been extremely useful both in terms of software design and overall performance.

Proposes a new framework to implement a complete logic programming system. This framework is composed of a set of new mechanisms intended to give high level and safe means to manage a computation history. Upon this framework the authors developed a logic programming environment prototype, called MxLog, featuring a complete Prolog-II interpreter and its debugging environment. This prototype has been realised following a new implementation philosophy independent of hardware constraints and based on an incremental strategy, expressed in terms of agents in a sequential open system.>