[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). · 3.59 Impact Factor
[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
[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. · 1.85 Impact Factor
[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).
[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.
[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.
[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
[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.
Journal of Telecommunications & Information Technology. 01/2013; 2013(4).
[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: Ambient Intelligence systems are typically characterized by the use of pervasive equipment for monitoring and modifying the environment according to users’ needs, and to globally defined constraints.Our work describes the implementation of a testbed providing the hardware and software tools for the development and management of AmI applications based on wireless sensor and actuator networks, whose main goal is energy saving for global sustainability. A sample application is presented that addresses temperature control in a work environment, through a multi-objective fuzzy controller taking into account users’ preferences and energy consumption.
[Show abstract][Hide abstract] ABSTRACT: The paper illustrates a system that implements a framework, which is oriented to the development of a modular knowledge base for a conversational agent. This solution improves the flexibility of intelligent conversational agents in managing conversations. The modularity of the system grants a concurrent and synergic use of different knowledge representation techniques. According to this choice, it is possible to use the most adequate methodology for managing a conversation for a specific domain, taking into account particular features of the dialogue or the user behavior. We illustrate the implementation of a proof-of-concept prototype: a set of modules exploiting different knowledge representation methodologies and capable of managing different conversation features has been developed. Each module is automatically triggered through a component, named corpus callosum, that selects in real time the most adequate chatbot knowledge module to activate.
[Show abstract][Hide abstract] ABSTRACT: In this paper a new intelligent system designed to support the researcher in the development of a workflow for bioinformatics experiments is presented. The proposed system is capable to suggest one or more strategies in order to resolve the selected problem and to support the user in the assembly of a workflow for complex experiments, using a a Knowledge base, representing the expertise about the application domain, and a Rule-Based system for decision-making activity. Moreover, the system can represent this workflow at different abstraction layers, freeing the user from implementation details and assisting him in the correct configuration of the algorithms. A sample workflow for protein complex extraction from protein-protein interaction network is presented in order to show the main features of the proposed workflow representation.
Sixth International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS); 01/2012
[Show abstract][Hide abstract] ABSTRACT: The paper illustrates the implementation and semantic enhancement of a domain-oriented Question-Answering system based on a pattern-matching chat bot technology, developed within an industrial project, named FRASI. The main difficulty in building a KB for a chat bot is to handwrite all possible question-answer pairs that constitute the KB. The proposed approach simplifies the chat bot realization thanks to two solutions. The first one uses an ontology, which is exploited in a twofold manner: to construct dynamic answers as a result of an inference process about the domain, and to automatically populate, off-line, the chat bot KB with sentences that can be derived from the ontology, describing properties and relations between concepts involved in the dialogue. The second one is to preprocess user sentences, and to reduce them to a simpler structure that can be referred to existing elements of the chat bot KB. The enhanced symbolic reduction of user questions and the automatic population of question templates in the chat bot KB from domain ontology have been implemented as two computational services (external modules).
Semantic Computing (ICSC), 2012 IEEE Sixth International Conference on; 01/2012
[Show abstract][Hide abstract] ABSTRACT: Many approaches have been adopted to solve the problem of image segmentation. Among them a noticeable part is based on graph theory casting the pixels as nodes in a graph. This paper proposes an algorithm to select clusters in the images (corresponding to relevant segments in the image) corresponding to the areas induced in the images through the search of the Minimum Spanning Tree (MST). In particular it is based on a clustering algorithm that extracts clusters computing a hierarchy of Minimum Spanning Trees. The main drawback of this previous algorithm is that the dimension of the cluster is not predictable and a relevant portion of found clusters can be composed by micro-clusters that are useless in the segments computation. A new algorithm and a new metric are proposed to select the exact number of clusters and avoid unmeaningful clusters.
Signal Image Technology and Internet Based Systems (SITIS), 2012 Eighth International Conference on; 01/2012
[Show abstract][Hide abstract] ABSTRACT: Ontologies are formal knowledge representation models. Knowledge organization is a fundamental requirement in order to develop Knowledge-Based systems. In this paper we present Data-Problem-Solver (DPS) approach, a new ontological paradigm that allows the knowledge designer to model and represent a Knowledge Base (KB) for expert systems. Our approach clearly distinguishes among the knowledge about a problem to resolve (answering the “what to do” question), the solver method to resolve it (answering the “how to do” question) and the type of input data required (answering the “what I need” question). The main purpose of the proposed paradigm is to facilitate the generalization of the application domain and the modularity and the expandability of the represented knowledge. The proposed DPS ontological approach is applied to the modelling of the knowledge about a bioinformatics application scenario: the protein complex extraction from a protein-protein interaction network.
2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB),; 01/2012