Salvatore Gaglio

Museo delle Scienze, Trento, Italy, Trient, Trentino-Alto Adige, Italy

Are you Salvatore Gaglio?

Claim your profile

Publications (238)60.38 Total impact

  • Source
    Salvatore Gaglio · Giuseppe Lo Re · Marco Morana
    [Show abstract] [Hide abstract]
    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.
    IEEE International Conference on Communications, London; 06/2015
  • Source
    Salvatore Gaglio · Giuseppe Lo Re · Gloria Martorella · Daniele Peri
    [Show abstract] [Hide abstract]
    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.
    Procedia Computer Science 12/2014; 32. DOI:10.1016/j.procs.2014.05.510
  • Source
    [Show abstract] [Hide abstract]
    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.
    IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIHLI 2014; 12/2014
  • Source
    [Show abstract] [Hide abstract]
    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.
    Cybernetics, IEEE Transactions on 07/2014; DOI:10.1109/TCYB.2014.2338611 · 3.47 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    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.
    Journal of Cheminformatics 05/2014; 6(24). DOI:10.1186/1758-2946-6-24 · 4.54 Impact Factor
  • Agnese Augello · S.Gaglio
    01/2014: chapter Advances onto the Internet of Things, How  Ontologies Make the Internet of Things Meaningful;
  • Agnese Augello · Giovanni Pilato · Giorgio Vassallo · Salvatore Gaglio
    Advances onto the Internet of Things, How Ontologies Make the Internet of Things Meaningful, 01/2014;
  • Source
    Salvatore Gaglio · Giuseppe Lo Re · Marco Morana
    [Show abstract] [Hide abstract]
    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.
    IEEE Transactions on Human-Machine Systems 01/2014; DOI:10.1109/THMS.2014.2377111 · 1.98 Impact Factor
  • I. Infantino · F. Vella · G. Martino · S. Gaglio
    [Show abstract] [Hide abstract]
    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.
    Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics; 10/2013
  • Source
    [Show abstract] [Hide abstract]
    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.
    Biologically Inspired Cognitive Architectures 10/2013; 6:23–29. DOI:10.1016/j.bica.2013.07.004
  • [Show abstract] [Hide abstract]
    ABSTRACT: Specific expert systems are used for supporting, speeding-up and adding precision to in silico experimentation in many domains. In particular, many experimentalists exhibit a growing interest in workflow management systems for making a pipeline of experiments. Unfortunately, these types of systems do not integrate a systematic approach or a support component for the workflow composition/reuse. For this reason, in this paper we propose a knowledge-based hybrid architecture for designing expert systems that are able to support experiment management. This architecture defines a reference cognitive space and a proper ontology that describe the state of a problem by means of three different perspectives at the same time: procedural, declarative and workflow-oriented. In addition, we introduce an instance of our architecture, in order to demonstrate the features of the proposed work. In particular, we model a bioinformatics case study, according to the proposed hybrid architecture guidelines, in order to explain how to design and integrate required knowledge into an interactive system for composition and running of scientific workflows.
    Expert Systems with Applications 09/2013; 41(4):1609-1621. DOI:10.1016/j.eswa.2013.08.058 · 1.97 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The construction of ad-hoc design processes is more and more required today. In this paper we present our approach for the construction of a new design process following the Situational Method Engineering paradigm. We mainly focus on the selection and assembly activities on the base of what we consider a key element in agent design processes: the MAS metamodel. The paper presents an algorithm establishing a priority order in the realization (instantiation) of MAS metamodel elements by the fragments that will compose the new process. 1
    05/2013: chapter Proc. of the Ninth International Workshop on Agent-Oriented Software Engineering (AOSE-2008) at The Seventh International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2008), pp. 86-100, Estoril, Portugal: pages 86-100; Springer-Verlag., ISBN: ISBN 978-3-642-01337-9
  • Antonio Chella · Salvatore Gaglio
    [Show abstract] [Hide abstract]
    ABSTRACT: Synthetic phenomenology typically focuses on the analysis of simplified perceptual signals with small or reduced dimensionality. Instead, synthetic phenomenology should be analyzed in terms of perceptual signals with huge dimensionality. Effective phenomenal processes actually exploit the entire richness of the dynamic perceptual signals coming from the retina. The hypothesis of a high-dimensional buffer at the basis of the perception loop that generates the robot synthetic phenomenology is analyzed in terms of a cognitive architecture for robot vision the authors have developed over the years. Despite the obvious computational problems when dealing with high-dimensional vectors, spaces with increased dimensionality could be a boon when searching for global minima. A simplified setup based on static scene analysis and a more complex setup based on the CiceRobot robot are discussed.
    International Journal of Machine Consciousness 01/2013; 04(02). DOI:10.1142/S1793843012400203
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Background We introduce a Knowledge-based Decision Support System (KDSS) in order to face the Protein Complex Extraction issue. Using a Knowledge Base (KB) coding the expertise about the proposed scenario, our KDSS is able to suggest both strategies and tools, according to the features of input dataset. Our system provides a navigable workflow for the current experiment and furthermore it offers support in the configuration and running of every processing component of that workflow. This last feature makes our system a crossover between classical DSS and Workflow Management Systems. Results We briefly present the KDSS' architecture and basic concepts used in the design of the knowledge base and the reasoning component. The system is then tested using a subset of Saccharomyces cerevisiae Protein-Protein interaction dataset. We used this subset because it has been well studied in literature by several research groups in the field of complex extraction: in this way we could easily compare the results obtained through our KDSS with theirs. Our system suggests both a preprocessing and a clustering strategy, and for each of them it proposes and eventually runs suited algorithms. Our system's final results are then composed of a workflow of tasks, that can be reused for other experiments, and the specific numerical results for that particular trial. Conclusions The proposed approach, using the KDSS' knowledge base, provides a novel workflow that gives the best results with regard to the other workflows produced by the system. This workflow and its numeric results have been compared with other approaches about PPI network analysis found in literature, offering similar results.
    BMC Bioinformatics 01/2013; 14(Suppl 1):S5. DOI:10.1186/1471-2105-14-S1-S5 · 2.67 Impact Factor
  • Source
    Salvatore Gaglio · Giuseppe Lo Re · Marco Morana · Marco Ortolani
    [Show abstract] [Hide abstract]
    ABSTRACT: Ambient Intelligence (AmI) is a new paradigm that specif-ically aims at exploiting sensory and context information in order to adapt the environment to the user's preferences; one of its key features is the attempt to consider common devices as an integral part of the sys-tem in order to support users in carrying out their everyday life activities without affecting their normal behavior. Our proposal consists in the definition of a gesture recognition module allowing users to interact as naturally as possible with the actuators available in a smart office, by controlling their operation mode and by querying them about their current state. To this end, readings obtained from a state-of-the-art motion sensor device are classified according to a supervised approach based on a probabilistic support vector machine, and fed into a stochastic syntactic classifier which will interpret them as the basic symbols of a probabilistic gesture language. We will show how this approach is suitable to cope with the intrinsic imprecision in source data, while still providing sufficient expressivity and ease of use.
    XIIIth International Conference of the Italian Association for Artificial Intelligence; 01/2013
  • [Show abstract] [Hide abstract]
    ABSTRACT: Integrated Coastal Zone Management is an emerging research area. The aim is to provide a global view of different and heterogeneous aspects interacting in a geographical area. Decision Support Systems, integrating Computational Intelligence methods, can be successfully used to estimate use- ful anthropic and environmental indexes. Bayesian Networks have been widely used in the environmental science domain. In this paper a Bayesian model for estimating the Sustainable Coastal Index is presented. The designed Bayesian Network consists of 17 nodes, hierarchically organized in 4 layers. The first layer is initialized with the season and the physiographic region information. In the second layer, the first-order indexes, depending on raw data, of physiographic regions are computed. The third layer estimates the second-order indexes of the analyzed physiographic regions. In the fourth layer, the global Sustainable Coastal Index is inferred. Processed data refers to 13 physiographic regions in the Province of Trapani, western Sicily, Italy. Gathered data describes the environmental information, the agricultural, fisheries, and economi- cal behaviors of the local population and land. The Bayesian Network was trained and tested using a real dataset acquired between 2000 and 2006. The developed system presents interesting results.
  • Source
    Pietro Cottone · Salvatore Gaglio · G.L. Re · Marco Ortolani
    [Show abstract] [Hide abstract]
    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.
    Sustainable Internet and ICT for Sustainability (SustainIT), 2013; 01/2013
  • Source
    Studies in Computational Intelligence 01/2013;
  • [Show abstract] [Hide abstract]
    ABSTRACT: Sentiment analysis is a new area of research in data mining that concerns the detection of opinions and/or sentiments in texts. This work focuses on the application and the comparison of three classification techniques over a text corpus composed of reviews of commercial products in order to detect opinions about them. The chosen domain is about "perfumes", and user opinions composing the corpus are written in Italian language. The proposed approach is completely data-driven: a Term Frequency / Inverse Document Frequency (TFIDF) terms selection procedure has been applied in order to make computation more efficient, to improve the classification results and to manage some issues related to the specific classification procedure adopted.
    Semantic Computing (ICSC), 2013 IEEE Seventh International Conference on; 01/2013
  • [Show abstract] [Hide abstract]
    ABSTRACT: Background / Purpose: Ontologies represent formal structures to define and organize knowledge of a specific application domain. Our purpose is to provide an ontological structure in order to add the functionalities and capability of a DSS to the more recent workflow management systems. We called our ontological approach Data Problem Solver Workflow (DPSW). Main conclusion: We show how the proposed ontology can match with a real bioinformatics issue, like for example the detection of protein sub-networks that identifies markers correlated with metastasis. We then modelled a workflow for this problem according to DPSW ontology.
    Network Tools and Applications in Biology (NETTAB) 2013: Semantic, Social, and Mobile Applications for Bioinformatics and Biomedical Laboratories; 12/2012

Publication Stats

1k Citations
60.38 Total Impact Points

Institutions

  • 1988–2013
    • Museo delle Scienze, Trento, Italy
      Trient, Trentino-Alto Adige, Italy
  • 1970–2013
    • Università degli Studi di Palermo
      • • Department of internal medicine and medical specialties (DIMIS)
      • • Sezione di Anatomia Umana
      Palermo, Sicily, 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
    • University of California, Irvine
      Irvine, California, United States
  • 2006–2010
    • Université de Technologie de Belfort-Montbéliard
      Belfort, Franche-Comté, France
  • 2004–2010
    • INO - Istituto Nazionale di Ottica
      Florens, Tuscany, Italy
  • 2008
    • Engineering Ingegneria Informatica
      Roma, Latium, Italy
  • 1986
    • Università degli Studi di Genova
      Genova, Liguria, Italy