Salvatore Gaglio

Università degli Studi di Palermo, Palermo, Sicily, Italy

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Publications (254)73.48 Total impact

  • Pietro Cottone · Salvatore Gaglio · Giuseppe Lo Re · Marco Ortolani
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    ABSTRACT: The growing popularity of Location-Based Services (LBSs) has boosted research on cheaper and more pervasive localization systems, typically relying on such monitoring equipment as Wireless Sensor Networks (WSNs), which allow to re-use the same instrumentation both for monitoring and for localization without requiring lengthy off-line training.
    No preview · Article · Apr 2016 · Engineering Applications of Artificial Intelligence
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    ABSTRACT: This paper aims at giving a contribution to the ongoing attempt to turn the theory of pattern-recognition into a rigorous science. In this article we address two problems which lie at the foundations of pattern-recognition theory: (i) What is a pattern? and (ii) How do we come to know patterns? In so doing much attention will be paid to tracing a non-arbitrary connection between (i) and (ii), a connection which will be ultimately based on considerations relating to Darwin's theory of evolution.
    Full-text · Conference Paper · Nov 2015
  • Adriano Manfrè · Ignazio Infantino · Filippo Vella · Salvatore Gaglio
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    ABSTRACT: The paper describes a novel approach to allow a robot to dance following musical rhythm. The proposed system generates a dance for a humanoid robot through the combination of basic movements synchronized with the music. The system made up of three parts: the extraction of features from audio file, estimation of movements through the Hidden Markov Models and, finally, the generation of dance. Starting from a set of given movements, the robot choices sequence of movements a suitable Hidden Markov Model, and synchronize them processing musical input. The proposed approach has the advantage that movement execution probabilities could be changed according evaluation of the dance execution in order to have an artificial creative system. In the same way, a choreographer could give more importance to some movements and/or exclude others, using the system as a co-creation tool. The approach has been tested on Aldebaran NAO humanoid using different genres of music, and experimentations was conduct at presence of real human dancers to have feedback of the goodness of the robot execution. Three professional judges expressed their evaluations about the following points: appropriateness of movements for a given musical genre; the precision to track the rhythm; the aesthetic impact of the whole sequence of movements; and the overall judgment of the robot performance. All the evaluations are very satisfying, and confirm that robot dance is realistic and aesthetically acceptable. The robustness and flexibility of the system allow us to embed the system in artificial creative system in future work. In the discussion we introduce some issues to pursuit this aim, using a previous proposed cognitive architecture based on needs and motivations.
    No preview · Article · Oct 2015 · Biologically Inspired Cognitive Architectures
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    Salvatore Gaglio · Giuseppe Lo Re · Marco Morana
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    ABSTRACT: Twitter is a popular social network which allows millions of users to share their opinions on what happens all over the world. In this work we present a system for real-time Twitter data analysis in order to follow popular events from the user’s perspective. The method we propose extends and improves the Soft Frequent Pattern Mining (SFPM) algorithm by overcoming its limitations in dealing with dynamic, real-time, detection scenarios. In particular, in order to obtain timely results, the stream of tweets is organized in dynamic windows whose size depends both on the volume of tweets and time. Since we aim to highlight the user’s point of view, the set of keywords used to query Twitter is progressively refined to include new relevant terms which reflect the emergence of new subtopics or new trends in the main topic. The real-time detection system has been evaluated during the 2014 FIFA World Cup and experimental results show the effectiveness of our solution.
    Full-text · Article · Sep 2015 · Computer Communications
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    Salvatore Gaglio · Giuseppe Lo Re · Marco Morana
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    ABSTRACT: Over the last 40 years, automatic solutions to analyze text documents collection have been one of the most attractive challenges in the field of information retrieval. More recently, the focus has moved towards dynamic, distributed environments, where documents are continuously created by the users of a virtual community, i.e., the social network. In the case of Twitter, such documents, called tweets, are usually related to events which involve many people in different parts of the world. In this work we present a system for real-time Twitter data analysis which allows to follow a generic event from the user's point of view. The topic detection algorithm we propose is an improved version of the Soft Frequent Pattern Mining algorithm, designed to deal with dynamic environments. In particular, in order to obtain prompt results, the whole Twitter stream is split in dynamic windows whose size depends both on the volume of tweets and time. Moreover, the set of terms we use to query Twitter is progressively refined to include new relevant keywords which point out the emergence of new subtopics or new trends in the main topic. Tests have been performed to evaluate the performance of the framework and experimental results show the effectiveness of our solution.
    Full-text · Conference Paper · Jun 2015
  • S. Gaglio · G.L. Re · G. Martorella · D. Peri
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    ABSTRACT: Programming Wireless Sensor Networks (WSNs) is a complex task for which existing approaches adopt rigid architectures that are only suitable for specific application fields. In previous papers we introduced a programming methodology and a lightweight middleware based on high-level programming and executable code exchange for distributed processing on WSNs. In this paper, we show how high-level programming can be effectively used on WSNs to implement symbolic reasoning. In order to prove the feasibility of our approach, we present a Fuzzy Logic system where the value updates and the rule evaluations are performed in a distributed way. Through the proposed methodology, we discuss the development of an Ambient Intelligence application. In particular, we describe how the nodes of a WSN may compute an estimation of the user thermal comfort by exchanging symbolic rather than numerical data, and control an HVAC (Heating, Ventilation and Air Conditioning) system accordingly.
    No preview · Article · Mar 2015
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    Salvatore Gaglio · Giuseppe Lo Re · Gloria Martorella · Daniele Peri
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    ABSTRACT: The peculiar features of Wireless Sensor Networks (WSNs) suggest to exploit the distributed computing paradigm to perform complex tasks in a collaborative manner, in order to overcome the constraints related to sensor nodes limited capabilities. In this context, we describe a lightweight middleware platform to support the development of distributed applications on WSNs. The platform provides just a minimal general-purpose software layer, while the application components, including communication and processing algorithms, as well as the exchanged data, are described symbolically, with neither preformed syntax nor strict distinction between data and code. Our approach allows for interactive development of applications on each node, and requires no cross-compilation, a common practice that makes the development of WSN applications rigid and time-consuming. This way, tasks and behavior of each node can be modified at runtime, even after the network deployment, by sending the node executable code.
    Full-text · Article · Dec 2014 · Procedia Computer Science
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    ABSTRACT: Ambient Intelligence (AmI) systems are constantly evolving and becoming ever more complex, so it is increasingly difficult to design and develop them successfully. Moreover, because of the complexity of an AmI system as a whole, it is not always easy for developers to predict its behavior in the event of unforeseen circumstances. A possible solution to this problem might lie in delegating certain decisions to the machines themselves, making them more autonomous and able to self-configure and self-manage, in line with the paradigm of Autonomic Computing. In this regard, many researchers have emphasized the importance of adaptability in building agents that are suitable to operate in real-world environments, which are characterized by a high degree of uncertainty. In the light of these considerations, we propose a multi-tier architecture for an autonomic AmI system capable of analyzing itself and its monitoring processes, and consequently of managing and reconfiguring its own sub-modules to better satisfy users' needs. To achieve such a degree of autonomy and self-awareness, our AmI system exploits the knowledge contained in an ontology that formally describes the environment it operates in, as well as the structure of the system itself.
    Full-text · Conference Paper · Dec 2014
  • A. Fiannaca · M.L. Rosa · S. Gaglio · R. Rizzo · A. Urso
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    ABSTRACT: Today Workflow Management Systems (WFMS), like Taverna and Kepler, have a very important place in the everyday work of the scientist. These tools support the access to computational resources and act as interface for building complex data processing chains. The next step is to support decisions of the researcher on autonomously developing workflow parts guided by requirements of the scientist while she/he is working on the high-level goal of the experiment. To this aim, it is necessary an ontology to store the knowledge related to the experiments and tools used, and to make this knowledge available not only to the scientist, but also to a suitable artificial intelligent system. In this paper we present an ontological approach for knowledge organization in WFMS. The proposed approach is based on a previously developed ontology, called Data-Problem-Solution-to-Experiment (DPS2E); here we aim at matching the abstract workflow obtained by means of our ontology into a concrete set of processes in Taverna environment. As results, we instanced an ontology that uses Taverna components for making two different concrete workflows; then, we make them available in myExperiment repository.
    No preview · Conference Paper · Nov 2014
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    ABSTRACT: The paradigm of pervasive computing is gaining more and more attention nowadays, thanks to the possibility of obtaining precise and continuous monitoring. Ease of deployment and adaptivity are typically implemented by adopting autonomous and cooperative sensory devices; however, for such systems to be of any practical use, reliability and fault tolerance must be guaranteed, for instance by detecting corrupted readings amidst the huge amount of gathered sensory data. This paper proposes an adaptive distributed Bayesian approach for detecting outliers in data collected by a wireless sensor network; our algorithm aims at optimizing classification accuracy, time complexity and communication complexity, and also considering externally imposed constraints on such conflicting goals. The performed experimental evaluation showed that our approach is able to improve the considered metrics for latency and energy consumption, with limited impact on classification accuracy.
    Full-text · Article · Jul 2014 · Cybernetics, IEEE Transactions on
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    ABSTRACT: Background In many experimental pipelines, clustering of multidimensional biological datasets is used to detect hidden structures in unlabelled input data. Taverna is a popular workflow management system that is used to design and execute scientific workflows and aid in silico experimentation. The availability of fast unsupervised methods for clustering and visualization in the Taverna platform is important to support a data-driven scientific discovery in complex and explorative bioinformatics applications. Results This work presents a Taverna plugin, the Biological Data Interactive Clustering Explorer (BioDICE), that performs clustering of high-dimensional biological data and provides a nonlinear, topology preserving projection for the visualization of the input data and their similarities. The core algorithm in the BioDICE plugin is Fast Learning Self Organizing Map (FLSOM), which is an improved variant of the Self Organizing Map (SOM) algorithm. The plugin generates an interactive 2D map that allows the visual exploration of multidimensional data and the identification of groups of similar objects. The effectiveness of the plugin is demonstrated on a case study related to chemical compounds. Conclusions The number and variety of available tools and its extensibility have made Taverna a popular choice for the development of scientific data workflows. This work presents a novel plugin, BioDICE, which adds a data-driven knowledge discovery component to Taverna. BioDICE provides an effective and powerful clustering tool, which can be adopted for the explorative analysis of biological datasets.
    Full-text · Article · May 2014 · Journal of Cheminformatics
  • Pietro Cottone · Salvatore Gaglio · Marco Ortolani
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    ABSTRACT: Extracting knowledge from a great amount of collected data has been a key problem in Artificial Intelligence during the last decades. In this context, the word "knowledge" refers to the non trivial new relations not easily deducible from the observation of the data. Several approaches have been used to accomplish this task, ranging from statistical to structural methods, often heavily dependent on the particular problem of interest. In this work we propose a system for knowledge extraction that exploits the power of an ontology approach. Ontology is used to describe, organise and discover new knowledge. To show the effectiveness of our system in extracting and generalising the knowledge embedded in data, we have built a system able to pick up some strategies in the solution of complex puzzle game.
    No preview · Article · Mar 2014
  • S. Gaglio · G. Martorella
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    ABSTRACT: Ambient Intelligence (AmI) defines a scenario involving people living in a smart environment enriched by pervasive sensory devices with the goal of assisting them in a proactive way to satisfy their needs. In a home scenario, an AmI system controls the environment according to a user's lifestyle and daily routine. To achieve this goal, one fundamental task is to recognize the user's activities in order to generate his daily activities profile. In this chapter,we present a simpleAMI system for a home scenario to recognize and predict users' activities.With this predictive capability, it is possible to anticipate their actions and improve their quality of life. Our approach uses a Hidden Markov Model (HMM) to recognize activities and deal with the intrinsic uncertainty of sensory information. The concepts of this domain have been formally defined to allow a higher-level system to enrich its knowledge base.
    No preview · Article · Mar 2014
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    Salvatore Gaglio · Giuseppe Lo Re · Marco Morana
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    ABSTRACT: In this paper, we present a method for recognizing human activities using information sensed by an RGB-D camera, namely the Microsoft Kinect. Our approach is based on the estimation of some relevant joints of the human body by means of the Kinect; three different machine learning techniques, i.e., K-means clustering, support vector machines, and hidden Markov models, are combined to detect the postures involved while performing an activity, to classify them, and to model each activity as a spatiotemporal evolution of known postures. Experiments were performed on Kinect Activity Recognition Dataset, a new dataset, and on CAD-60, a public dataset. Experimental results show that our solution outperforms four relevant works based on RGB-D image fusion, hierarchical Maximum Entropy Markov Model, Markov Random Fields, and Eigenjoints, respectively. The performance we achieved, i.e., precision/recall of 77.3% and 76.7%, and the ability to recognize the activities in real time show promise for applied use.
    Full-text · Article · Jan 2014 · IEEE Transactions on Human-Machine Systems
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    A. Augello · S. Gaglio · G. Oliveri · G. Pilato
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    ABSTRACT: Mathematical patterns are an important subclass of the class of patterns. The main task of this paper is examining a particular proposal concerning the nature of mathematical patterns and some elements of the cognitive structure an agent should have to recognize them. © 2014, International Workshop on Artificial Intelligence and Cognition.
    Full-text · Article · Jan 2014
  • Agnese Augello · S.Gaglio

    No preview · Chapter · Jan 2014
  • Agnese Augello · Giovanni Pilato · Giorgio Vassallo · Salvatore Gaglio

    No preview · Chapter · Jan 2014
  • I. Infantino · F. Vella · G. Martino · S. Gaglio
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    ABSTRACT: This paper describes a technique to reconstruct the volumes of the human body. For this purpose, are introduced mathematical objects able to represent 3d shapes, called super quadrics. These objects are positioned in the space according the captures made by a Microsoft Kinect device and are composed to represent the volumes of the human body. The employment of quaternions provides a relevant speedup for the rotation of the volumes and allows to follow the human movements in real time and reduced computational cost.
    No preview · Conference Paper · Oct 2013
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    Pietro Cottone · Salvatore Gaglio · Giuseppe Lo Re · Marco Ortolani
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    ABSTRACT: Current energy demand for appliances in smart homes is nowadays becoming a severe challenge, due to economic and environmental reasons; effective automated approaches must take into account basic information about users, such as the prediction of their course of actions. The present proposal consists in recognizing user daily life activities by simply relying on the analysis of environmental sensory data in order to minimize energy consumption by guaranteeing that peak demands do not exceed a given threshold. Our approach is based on information theory in order to convert raw data into high-level events, used to represent recursively structured activities. Experiments based on publicly available datasets and consumption models are provided to show the effectiveness of our proposal.
    Full-text · Conference Paper · Oct 2013
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    ABSTRACT: According to Gärdenfors, the theory of conceptual spaces describes a level of representation present in some cognitive agents between a sub-conceptual and a symbolic level of representation. In contrast to a large part of contemporary philosophical speculation on these matters for which concepts and conceptual content are propositional, conceptual spaces provide a geometric framework for the representation of concepts. In this paper we introduce an algebra for the manipulation of different conceptual spaces in order to formalise the process whereby an artificial agent rearranges its internal conceptual representations as a consequence of its perceptions, which are here rendered in terms of measurement processes.
    Full-text · Article · Oct 2013 · Biologically Inspired Cognitive Architectures

Publication Stats

1k Citations
73.48 Total Impact Points

Institutions

  • 1970-2015
    • Università degli Studi di Palermo
      • • Department of internal medicine and medical specialties (DIMIS)
      • • Department of experimental medicine and clinical neurosciences
      • • Dipartimento di Ingegneria Chimica, Gestionale, Informatica, Meccanica (DICGIM))
      • • Sezione di Anatomia Umana
      Palermo, Sicily, Italy
  • 2004-2014
    • INO - Istituto Nazionale di Ottica
      Florens, Tuscany, Italy
  • 1988-2013
    • Museo delle Scienze, Trento, Italy
      Trient, Trentino-Alto Adige, Italy
  • 2000-2012
    • National Research Council
      • Institute for High Performance Computing and Networking ICAR
      Oristany, Sardinia, Italy
  • 2010
    • Université Paris-Est Marne-la-Vallée
      Champs, Île-de-France, France
    • Université de Technologie de Belfort-Montbéliard
      Belfort, Franche-Comté, France
  • 2008
    • Engineering Ingegneria Informatica
      Roma, Latium, Italy
  • 1986
    • Università degli Studi di Genova
      Genova, Liguria, Italy