Recent research has demonstrated that blockchain-enabled robot swarms—where robots coordinate using blockchain technology—can secure robot swarms by neutralizing malicious and malfunctioning robots. This security is achieved through blockchain technology’s consistency properties. However, prior work addressed malfunctions at the information level, that is, it studied how to neutralize robots that stored information in the blockchain that did not correspond to the real-world state (i.e., it studied the oracle problem). In contrast, this study focuses on inconsistencies at the blockchain protocol level. We analyze how network partitions, which may arise from robots’ local-only communication capabilities, malfunctioning hardware, or external attacks, can lead to inconsistent information in a robot swarm. In order to mitigate these disruptions, we propose a decentralized approach to detect partitions and a corresponding response. We study our approach in a swarm robotics simulator, where we demonstrate its effectiveness in reducing blockchain inconsistencies.

Sleep disorders and their diagnosis are a significant public health concern. Automated sleep stage classification using deep learning models has shown promising results, but these models often lack transparency and interpretability. In this study, we propose an eXplainable Artificial Intelligence (XAI) approach to enhance the interpretability of cutting-edge deep learning sleep stage classification models. The proposed approach consists of a three-steps framework: (i) employing contrastive learning to order a neural network latent space based on input similarity; (ii) mining meaningful instances from that space; and (iii) explaining those instances by a customized XAI methodology. By doing this we are capable of extracting human-comprehensible insights about the model decision-making process, enhancing the applicability of the proposed approach in real-world clinical scenarios. The explanations provided point out high and low-representative sleep epochs of each sleep phase. These sleep epochs are analyzed considering both the single sleep epoch and the sequence of adjacent sleep epochs for the sleep phase classification.Our approach proved to maintain the original model performances, improve the model interpretability, and confirm that the network decision-making process is valid even from the perspective of a physician.

Abstract AI-based emotion recognition approaches may benefit from the integration of multimodal data, but their explainability and validation is still a critical challenge. Indeed, the limited neurophysiological understanding of novel multimodal features, e.g. brain-heart interaction, can be insufficient to assess whether the AI-extracted physiological insights (i.e., the model explanations) accurately reflect the real underlying physiological processes. To validate the explanations obtained by an AI-based model in this context, we introduce a novel framework that autonomously identifies the optimal explanations for a black-box model used in emotion recognition. Our approach leverages a convolutional neural network to process BHI features, which are derived from EEG and HRV data and rearranged as images. A model-agnostic methodology is employed to extract local explanations, which are then dynamically evaluated to select the most accurate for representing specific emotional states. The effectiveness of the proposed framework is evaluated across multiple classification tasks, including up to 9-level arousal and valence emotion classification, as well as nine discrete emotions classification, using the MAHNOB-HCI and DEAP datasets. The system achieved remarkable accuracy levels, consistently reaching approximately 97–98% across all tasks. Furthermore, our dynamic selection framework revealed that Integrated Gradients outperformed other state-of-the-art explainable AI approaches in reliably capturing global explanations.

Machine learning (ML) methods need to explain their reasoning to allow professionals to validate and trust their predictions, and employ those in real-world decision-making processes. To do so, explainable artificial intelligence (XAI) methods based on feature importance can be employed, even though those can be very computationally expensive. Moreover, it can be challenging to determine whether an XAI technique might introduce bias into the explanation (e.g., overestimating or underestimating the feature importance) in the absence of some reference feature importance measure or even some domain knowledge from which deriving an expected importance level for each feature. We address both these issues by (i) employing a counterfactualbased strategy, i.e. deriving a measure of feature importance by checking if some minor changes in one feature’s values significantly affect the ML model’s regression outcome, and (ii) employing both synthetic and real-world industrial data coupled with the expected degree of importance for each feature. Our experimental results show that the proposed approach (BoCSoRr) is more reliable and way less computationally expensive than DiCE, a well-known counterfactual-based XAI approach able to provide a measure of feature importance.

The scarcity of a sufficiently large and representative hyperspectral image dataset is a substantial obstacle to the effective development of algorithms for remote sensing applications. Hyperspectral images can provide rich spectral information for various tasks, such as land cover classification, vegetation monitoring, and environmental assessment. However, the limited availability of diverse and well-annotated hyperspectral datasets hinders the development and optimization of these models in this domain. For this purpose, the generation of synthetic hyperspectral images has emerged as a pivotal area of research.This paper aims to introduce a preliminary analysis of various AI-based methodologies specifically crafted to generate synthetic PRISMA hyperspectral images derived from Sentinel-2 data. By exploring innovative approaches, this study aims to develop novel techniques for creating synthetic datasets, providing valuable insights into the potential of synthetic hyperspectral imagery for algorithm training and evaluation in the absence of extensive real-world hyperspectral datasets.

A methodology is presented to map the vegetation biodiversity based on the hypothesis of the spectral variation (SV) which has been proposed to assess the forest biodiversity by means of Earth Observation (EO) data. Hyperspectral data acquired by the PRecursore Iperspettrale della Missione Applicativa (PRISMA) mission of the Italian Space Agency to spectral signature with a high spectral resolution. The NDVI is computed from PRISMA data and used to identify pixels corresponding to vegetation cover. The spectral signatures at those pixels are then clusterized using the convolutional autoenconders technique and the final map with the location of pixels belonging to the different classes is produce. The methodology is applied to assess the vegetation biodiversity in National Parks of Gargano, Alta Murgia, Cilento-Vallo di Diano-Alburni, Appennino Lucano Val D’Agri Lagonegrese and Pollino, all located in Southern Italy.

Dense Information Retrieval (DIR) has recently gained attention due to the advances in deep learning-based word embedding. In particular, for historical languages such as Latin, a DIR task is appropriate although challenging, due to: (i) the complexity of managing searches using traditional Natural Language Processing (NLP); (ii) the availability of fewer resources with respect to modern languages; (iii) the large variation in usage among different eras. In this research, pre-trained transformer models are used as features extractors, to carry out a search on a Latin Digital Library. The system computes embeddings of sentences using state-of-the-art models, i.e., Latin BERT and LaBSE, and uses cosine distance to retrieve the most similar sentences. The paper delineates the system development and summarizes an evaluation of its performance using a quantitative metric based on expert’s per-query documents ranking. The proposed design is suitable for other historical languages. Early re sults show the higher potential of the LabSE model, encouraging further comparative research. To foster further development, the data and source code have been publicly released.

The goal of structural health monitoring is to continuously assess the structural integrity and performance of a building or structure over time. This is achieved by collecting data on various structural parameters and using this data to identify potential areas of concern or damage. A critical challenge involves some properties being severely damaged by recurrent variations of external factors. These variations in environmental and operational conditions (such as humidity, temperature, and traffic) can deflect the variability in structural behavior caused by structural damage and make it difficult to identify the damage of interest. In this paper, we present a study on how regression analysis and deep learning can be used to measure the influence of environmental factors on the structural behavior of the Leaning Tower of Pisa. Transparent linear regressors offer the benefit of being simple to understand and interpret. They can provide insights about the relationship between input an d target variables, as well as the relative importance of each input in forecasting the outcome. On the other hand, deep learning models are capable of learning nonlinear relationships between input and target variables. Definitively, in this work the accuracy-interpretability trade-off for structural health monitoring is discussed.

Most of the applications of wireless sensor networks require the continuous coverage of a region of interest. The irregular deployment of the nodes, or their failure, could result in holes in the coverage, thus jeopardizing such requirement. Methods to recover the sensing capabilities usually demand the availability of redundant full-fledged nodes, whose relocation should heal the holes. These solutions, however, do not consider the high cost of obtaining redundant, typically complex, devices, nor that they could in turn fail. In this work, we propose a bio-inspired and emergent approach toward hole detection and healing using a swarm of resource-constrained agents with reduced sensing capabilities, whose behavior draws inspiration from the concepts underlying blood coagulation. The swarm follows three rules: activation, adhesion, and cohesion, adapted from the behavior exhibited by platelets during the human healing process. Relying only on local and relative information, the mobile agents can detect the holes border and place themselves in locally optimal positions to temporarily restore the service. To validate the algorithm, we have developed a distributed, multi-process simulator. Experimental results show that the proposed method efficiently detects and heals the holes, outperforming two state-of-the-art solutions. It also demonstrates good robustness and flexibility to agent failure.

Evaluating and comparing text-to-image models is a challenging problem. Significant advances in the field have recently been made, piquing interest of various industrial sectors. As a consequence, a gold standard in the field should cover a variety of tasks and application contexts. In this paper a novel evaluation approach is experimented, on the basis of: (i) a curated data set, made by high-quality royalty-free image-text pairs, divided into ten categories; (ii) a quantitative metric, the CLIP-score, (iii) a human evaluation task to distinguish, for a given text, the real and the generated images. The proposed method has been applied to the most recent models, i.e., DALLE2, Latent Diffusion, Stable Diffusion, GLIDE and Craiyon. Early experimental results show that the accuracy of the human judgement is fully coherent with the CLIP-score. The dataset has been made available to the public.

The need for high recognition performance demands increasingly complex machine learning (ML) architectures, which might be extremely computationally burdensome to be implemented in real-world. This issue can be addressed by using an ensemble learning model to decompose the multi-class classification problem into many simpler binary classification problems, e.g. each binary classification problem can be handled via a simple multi-layer perceptron (MLP). The so-called one-versus-one (OVO) is a widely used multi-class decomposition schema in which each classifier is trained to distinguish between two classes. However, with an OVO schema each MLP is non-competent to classify instances of classes that have not been used to train it. This results in classification noise that may degrade the performance of the whole ensemble, especially when the number of classes grows. The proposed architecture employs a weighting mechanism to minimize the contribution of the non-competent MLPs and combine their outcomes to effectively solve the multi-class classification problem. In this work, the robustness to the classification noise introduced by non-competent MLPs is measured to assess in what conditions this translates in better classification accuracy. We test the proposed approach with five different benchmark data sets, outperforming both the baseline and one state-ofthe-art approach in multi-class decomposition algorithms.

Next generation cars will increase the passengers’ time for fun and relax, as well as the number of unknown passengers traveling together. A key functionality to improve the users’ experience is that of Temporary Mobile Social Networking (TMSN): where passengers form, for a limited-time, a mobile social group with common interests and activities, using their already available social network accounts. The goal of TMSN is to automatically redesign the users’ profiles and interfaces into a group-wise passengers’ profile and a common interface, by reducing isolation and enabling socialization. In this paper, a TMSN-inspired music selection is proposed and developed via the Spotify music streaming service. Early results are promising and encourage further developments towards the concept of in-car entertainment.

Anomaly detection in video streams with imbalanced data and real-time constraints is a challenging task of computer vision. This paper proposes a novel real-time approach for real-world video anomaly detection exploiting a supervised learning methodology. In particular, we present a deep learning architecture based on the analysis of contextual, spatial, and motion information extracted from the video. A data balancing strategy based on hard-mining and adaptive framerate is used to avoid overfitting and increase detection accuracy. The approach defines an extended taxonomy by differentiating anomalies in ”soft” and ”hard”. A novel anomaly detection score based on a sigmoidal function has been introduced to reduce false positive rate while maintaining a high level of true positive rate. The proposed methodology has been validated with a set of experiments on a well-known video anomaly dataset: UCF-CRIME. The experiments on the testbed demonstrate the impact of the contextual information and data balancing on the classification performances, considering only ”hard” anomalies during training and that the proposed model can achieve state-of-the-art performances while minimizing resource consumption.

Managing water distribution networks via pumps scheduling programs is a multi-objective optimization problem with dynamic and various site-specific challenges. Metaheuristics-based approaches, with respect to mathematical solvers, offer data-driven strategies for manageable and adaptive control. Some evolutionary approaches are suitable for multi-criteria decision making and decentralized coordination on programmable logic controllers. This paper focuses on the development of a testbed and an early assessment of an approach based on NSGA-II and Pseudo-Weights. The experimental studies are based on a physically developed case study, and on a scalable case study with realistic water demand and source patterns. The testbed has been publicly released.

This work presents the results of a simulated analysis concerning algorithms of self-coordination of a swarm of Unmanned Surface Vehicles (USV) for the mitigation of plastic pollution in oceans. The analysis is based on real scenarios provided by the Copernicus Marine Service. The scenario includes the localization of plastics on the sea surface and their movement in time based on the sea surface currents. A swarm intelligence algorithm is used for the decentralized coordination of the USV swarm. Results are presented on a study area located in the northern Tyrrhenian sea between Corsica and the Tuscan coast, in the period of July 2016.

This work presents an application of the Perceptually Important Points (PIP) technique for the analysis of VLF time series. The aim of the analysis is to detect anomalies with respect to the normal variations of the data trends. Such anomalies could reveal possible radio precursors of the earthquake. Since 2009, several radio receivers have been installed throughout Europe in order to realize the INFREP European radio network for studying the VLF (10–50 kHz) and LF (150–300 kHz) radio precursors of earthquakes. The time series used for experiments was collected during the Dodecanese islands earthquakes ($\text{MW}=5.6$and$\text{MW}=5.7$) occurred on January 30, 2020.

The analysis of credit card transactions allows gaining new insights into the spending occurrences and mobility behavior of large numbers of individuals at an unprecedented scale. However, unfolding such spatiotemporal patterns at a community level implies a non-trivial system modeling and parametrization, as well as, a proper representation of the temporal dynamic. In this work we address both those issues by means of a novel computational technique, i.e. computational stigmergy. By using computational stigmergy each sample position is associated with a digital pheromone deposit, which aggregates with other deposits according to their spatiotemporal proximity. By processing transactions data with computational stigmergy, it is possible to identify high-density areas (hotspots) occurring in different time and days, as well as, analyze their consistency over time. Indeed, a hotspot can be permanent, i.e. present throughout the period of observation, or intermittent, i.e. present only in certain time and days due to community level occurrences (e.g. nightlife). Such difference is not only spatial (where the hotspot occurs) and temporal (when the hotspot occurs) but affects also which people visit the hotspot. The proposed approach is tested on a real-world dataset containing the credit card transaction of 60k users between 2014 and 2015.

This paper focuses on the problem of coordinating multiple UAVs for distributed targets detection and tracking, in different technological and environmental settings. The proposed approach is founded on the concept of swarm behavior in multi-agent systems, i.e., a self-formed and self-coordinated team of UAVs which adapts itself to mission-specific environmental layouts. The swarm formation and coordination are inspired by biological mechanisms of flocking and stigmergy, respectively. These mechanisms, suitably combined, make it possible to strike the right balance between global search (exploration) and local search (exploitation) in the environment. The swarm adaptation is based on an evolutionary algorithm with the objective of maximizing the number of tracked targets during a mission or minimizing the time for target discovery. A simulation testbed has been developed and publicly released, on the basis of commercially available UAVs technology and real-world scenarios. Experimental results show that the proposed approach extends and sensibly outperforms a similar approach in the literature.

In this paper, a novel architecture of Recurrent Neural Network (RNN) is designed and experimented. The proposed RNN adopts a computational memory based on the concept of stigmergy. The basic principle of a Stigmergic Memory (SM) is that the activity of deposit/removal of a quantity in the SM stimulates the next activities of deposit/removal. Accordingly, subsequent SM activities tend to reinforce/weaken each other, generating a coherent coordination between the SM activities and the input temporal stimulus. We show that, in a problem of supervised classification, the SM encodes the temporal input in an emergent representational model, by coordinating the deposit, removal and classification activities. This study lays down a basic framework for the derivation of a SM-RNN. A formal ontology of SM is discussed, and the SM-RNN architecture is detailed. To appreciate the computational power of an SM-RNN, comparative NNs have been selected and trained to solve the MNIST handwritten digits recognition benchmark in its two variants: spatial (sequences of bitmap rows) and temporal (sequences of pen strokes).

Modern cities are growing ecosystems that face new challenges due to the increasing population demands. One of the many problems they face nowadays is waste management, which has become a pressing issue requiring new solutions. Swarm robotics systems have been attracting an increasing amount of attention in the past years and they are expected to become one of the main driving factors for innovation in the field of robotics. The research presented in this paper explores the feasibility of a swarm robotics system in an urban environment. By using bio-inspired foraging methods such as multi-place foraging and stigmergy-based navigation, a swarm of robots is able to improve the efficiency and autonomy of the urban waste management system in a realistic scenario. To achieve this, a diverse set of simulation experiments was conducted using real-world GIS data and implementing different garbage collection scenarios driven by robot swarms. Results presented in this research show that the proposed system outperforms current approaches. Moreover, results not only show the efficiency of our solution, but also give insights about how to design and customize these systems.

In Turkey, the increasing tension, due to the presence of 3.4 million Syrian refugees, demands the formulation of effective integration policies. Moreover, their design requires tools aimed at understanding the integration of refugees despite the complexity of this phenomenon. In this work, we propose a set of metrics aimed at providing insights and assessing the integration of Syrian refugees, by analyzing a real-world call detail record (CDR) dataset including calls from refugees and locals in Turkey throughout 2017. Specifically, we exploit the similarity between refugees' and locals' spatial and temporal behaviors, in terms of communication and mobility in order to assess integration dynamics. Together with the already known methods for data analysis, we use a novel computational approach to analyze spatio-temporal patterns: computational stigmergy, a bio-inspired scalar and temporal aggregation of samples. Computational stigmergy associates each sample with a virtual pheromone deposit (mark). Marks in spatiotemporal proximity are aggregated into functional structures called trails, which summarize the spatiotemporal patterns in data and allow computing the similarity between different patterns. According to our results, collective mobility and behavioral similarity with locals have great potential as measures of integration, since they are: 1) correlated with the amount of interaction with locals; 2) an effective proxy for refugee's economic capacity, and thus refugee's potential employment; and 3) able to capture events that may disrupt the integration phenomena, such as social tension.

A key aspect of a sustainable urban transportation system is the effectiveness of transportation policies. To be effective, a policy has to consider a broad range of elements, such as pollution emission, traffic flow, and human mobility. Due to the complexity and variability of these elements in the urban area, to produce effective policies remains a very challenging task. With the introduction of the smart city paradigm, a widely available amount of data can be generated in urban spaces. Such data can be a fundamental source of knowledge to improve policies because they can reflect the sustainability issues underlying the city. In this context, we propose an approach to exploit urban positioning data based on stigmergy, a bio-inspired mechanism providing scalar and temporal aggregation of samples. By employing stigmergy, samples in proximity with each other are aggregated into a functional structure called trail. The trail summarizes relevant dynamics in data and allows matching them, providing a measure of their similarity. Moreover, this mechanism can be specialized to unfold specific dynamics. Specifically, we identify high-density urban areas (i.e. hotspots), analyze their activity over time, and unfold anomalies. Furthermore, by matching activity patterns, a continuous measure of the dissimilarity with respect to the typical activity pattern is provided. This measure can be used by policy makers to evaluate the effect of policies and change them dynamically. As a case study, we analyze taxi trip data gathered in Manhattan from 2013 to 2015.

Physical activity level (PAL) in older adults can enhance healthy aging, improve functional capacity, and prevent diseases. It is known that human annotations of PAL can be affected by subjectivity and inaccuracy. Recently developed smart devices can allow a non-invasive, analytic, and continuous gathering of physiological signals. We present an innovative computational system fed by signals of heartbeat rate, wrist motion and pedometer sensed by a smartwatch. More specifically, samples of each signal are aggregated by functional structures called trails. The trailing process is inspired by stigmergy, an insects’ coordination mechanism, and is managed by computational units called stigmergic receptive fields (SRFs). SRFs, which compute the similarity between trails, are arranged in a stigmergic perceptron to detect a collection of micro-behaviours of the raw signal, called archetypes. A SRF is adaptive to subjects: its structural parameters are tuned by a differential evolution algorithm. SRFs are used in a multilayer architecture, providing further levels of processing to realize macro analyses in the application domain. As a result, the architecture provides a daily PAL, useful to detect behavioural shift indicating initial signs of disease or deviations in performance. As a proof of concept, the approach has been experimented on three subjects.

A significant phenomenon in microblogging is that certain occurrences of terms self-produce increasing mentions in the unfolding event. In contrast, other terms manifest a spike for each moment of interest, resulting in a wake-up-and-sleep dynamic. Since spike morphology and background vary widely between events, to detect spikes in microblogs is a challenge. Another way is to detect the spikiness feature rather than spikes. We present an approach which detects and aggregates spikiness contributions by combination of spike patterns, called archetypes. The soft similarity between each archetype and the time series of term occurrences is based on computational stigmergy, a bio-inspired scalar and temporal aggregation of samples. Archetypes are arranged into an architectural module called Stigmergic Receptive Field (SRF). The final spikiness indicator is computed through linear combination of SRFs, whose weights are determined with the Least Square Error minimization on a spikiness training set. The structural parameters of the SRFs are instead determined with the Differential Evolution algorithm, minimizing the error on a training set of archetypal series. Experimental studies have generated a spikiness indicator in a real-world scenario. The indicator has enhanced a cloud representation of social discussion topics, where the more spiky cloud terms are more blurred.

Regional innovation is more and more considered an important enabler of welfare. It is no coincidence that the European Commission has started looking at regional peculiarities and dynamics, in order to focus Research and Innovation Strategies for Smart Specialization towards effective investment policies. In this context, this work aims to support policy makers in the analysis of innovation-relevant trends. We exploit a European database of the regional patent application to determine the dynamics of a set of technological innovation indicators. For this purpose, we design and develop a software system for assessing unfolding trends in such indicators. In contrast with conventional knowledge-based design, our approach is biologically-inspired and based on self-organization of information. This means that a functional structure, called track, appears and stays spontaneous at runtime when local dynamism in data occurs. A further prototyping of tracks allows a better distinction of the critical phenomena during unfolding events, with a better assessment of the progressing levels. The proposed mechanism works if structural parameters are correctly tuned for the given historical context. Determining such correct parameters is not a simple task since different indicators may have different dynamics. For this purpose, we adopt an adaptation mechanism based on differential evolution. The study includes the problem statement and its characterization in the literature, as well as the proposed solving approach, experimental setting and results.

In this paper we propose a novel algorithm for adaptive coordination of drones, which performs collaborative target detection in unstructured environments. Coordination is based on digital pheromones released by drones when detecting targets, and maintained in a virtual environment. Adaptation is based on the Differential Evolution (DE) and involves the parametric behaviour of both drones and environment. More precisely, attractive/repulsive pheromones allow indirect communication between drones in a flock, concerning the availability/unavailability of recently found targets. The algorithm is effective if structural parameters are properly tuned. For this purpose DE combines different parametric solutions to increase the swarm performance. We focus first on the study of the principal parameters of the DE, i.e., the crossover rate and the differential weight. Then, we compare the performance of our algorithm with three different strategies on six simulated scenarios. Experimental results show the effectiveness of the approach.

Situation-aware service recommendation for mobile devices is aimed at proactively pushing personalized suggestions to users, presenting them unseen or unknown services. A challenging area in the field is that of recommendation schemes emerging from users' collective behavior. When we consider a mobile user, for instance, the recommendation process can be based on social events that can arise from collective positioning information. In this scenario, we discuss a collaborative multi-agent scheme for event detection, in which fuzzy representations are employed to cope with the approximation typical of implicit and aggregated information. More specifically, the first level of information processing is managed by marking agents leaving marks in the environment which are associated with users' positioning. The accumulation of marks enables a fuzzy information granulation process, managed by event agents, in which relevant events can emerge. Finally, a fuzzy inference level, managed by situation agents, deduces user situations from the underlying events.

Situation awareness is a promising approach to recommend to a mobile user the most suitable resources for a specific situation. However, determining the correct user situation is not a simple task since users have different habits that may affect the way in which the situations arise. Thus, an appropriate tuning aimed at adapting the situation recognizer to the specific user is desirable to make a resource recommender more effective. In this paper, we show how this objective can be achieved by collecting data during the interaction of the user with the mobile device and using this context history to personalize the resource recommender by a genetic algorithm. To describe our approach, we adopt a recently proposed resource recommender which exploits fuzzy linguistic variables to manage the inherent vagueness of some contextual parameters. Experimental results on a real business case show that the responsiveness and modeling capabilities of the recommender increase, thus validating the proposed approach.

Nowadays, a huge quantity of resources for mobile users are made available on the most important marketplaces. Further, handheld devices can accommodate plenty of these resources, such as applications, documents and web pages, locally. Thus, to search for resources suitable for specific circumstances often requires a considerable effort and rarely brings to a completely satisfactory result. A tool able to recommend suitable resources at the right time in each situation would be of great help for the mobile users and would make the use of the handheld devices less boring and more attractive. To this aim, new levels of granularity, together with some degree of self-awareness, are needed to assist mobile users in managing and using resources. In this paper, we propose an efficient situation-aware resource recommender (SARR), which helps mobile users to timely locate resources proactively. Situations are determined by a semantic reasoner that exploits domain knowledge expressed in terms of ontologies and semantic rules. This reasoner works in synergy with a fuzzy engine, which is in charge of handling the vagueness of some conditions in the semantic rules, computing a certainty degree for each inferred situation. These degrees are used to rank the situations and consequently to assign a priority to the resources associated with the specific situations. The application of SARR to two real business cases is also shown and discussed.

Today's mobile Internet service portals offer thousands of services and mobile devices can host plenty of applications, documents and web URLs. Hence, for average mobile users there is an increasing cognitive burden in finding the most appropriate service among the many available. On the other hand, methodologies such as bookmarks and resource tagging require a great arranging effort to handle increasing resources. To help mobile users in managing and using this personal information space, new levels of granularity should be introduced in the organization of services, together with some degree of self-awareness. This paper proposes a situation-aware service recommender that helps locating services proactively. In the recommender, a semantic layer determines one or more user current situations by using domain knowledge expressed in terms of ontology and semantic rules. A fuzzy inference layer manages the vagueness of some contextual condition of these rules and outputs an uncertainty degree for each situation. Based on this degree, the recommender proposes a set of specific resources.

Most clustering algorithms partition a data set based on a dissimilarity relation expressed in terms of some distance function. When the nature of this relation is conceptual rather than metric, distance functions may fail to adequately model dissimilarity. For this reason, we propose to extract dissimilarity relations directly from the data. We exploit some pairs of patterns with known dissimilarity to build a TS fuzzy system, which models the dissimilarity relation between any pair of patterns. The resulting dissimilarity matrix is input to a new unsupervised fuzzy relational clustering algorithm, which partitions the data set based on the proximity of the vectors containing the dissimilarity values between a pattern and all the patterns in the data set. Experimental results to confirm the validity of our approach are shown and discussed.