Paolo Puliti

Signing individual fragments of an RDF graph

Being able to determine the provenience of statements is a fundamental step in any SW trust modeling. We propose a methodology that allows signing of small groups of RDF statements. Groups of statements signed with this methodology can be safely inserted into any existing triple store without the loss of provenance information since only standard RDF semantics and constructs are used. This methodology has been implemented and is both available as open source library and deployed in a SW P2P project.

A Generalized Approach to Consistency Based Belief Revision

This paper presents the basic ideas of a consistency-based approach to Belief Revision that allows the rejection of the newcoming information. This choice is justified for some application domains and leads to the definition of two distinct sets of formulas: the “Belief Base”, which is the eventually inconsistent collection of all the available pieces of information, and the “Reasoning Base” which is the consistent outcome of the revision process. The selection of the Reasoning Base, among the many possible maximally consistent subsets of the Belief Base, is induced by an ordering on the sentences that does not reflect necessarily the chronological order of the arrivals of the information.

Imaging approach to real-time tracking of submarine pipeline

The work presents a real-time underwater imaging system for identification and tracking of a submarine pipeline on a sequence of recorded images. The main novelty of this work relies on adopting an automatic approach that is entirely based on the analysis and interpretation of visual data, in spite of the various limitations upon the ability to image underwater objects. The analysis of the data is performed starting from image processing operations (like filtering, profile analysis, feature enhancement) implemented on a dedicated board. Then, the system employs an efficient dynamic process for recognizing the two contours of the pipeline. In each frame the system is able to determine the equations of the two straight lines corresponding to the pipeline contours. The system reaches satisfactory performances in real time operation: up to eight frames per second on a Pentium based PC. The results of this work are somewhat more meaningful as the input images were acquired by three cameras, mounted on a remotely operated vehicle travelling at one nautical mile an hour, without any attention either to illumination conditions or stability of cameras. This work is originated from the interest of Snamprogetti in enhancing the level of automation in submarine pipeline inspection.

Retina vascular network recognition

The analysis of morphological and structural modifications of the retina vascular network is an interesting investigation method in the study of diabetes and hypertension. Normally this analysis is carried out by qualitative evaluations, according to standardized criteria, though medical research attaches great importance to quantitative analysis of vessel color, shape and dimensions. The paper describes a system which automatically segments and recognizes the ocular fundus circulation and micro circulation network, and extracts a set of features related to morphometric aspects of vessels. For this class of images the classical segmentation methods seem weak. We propose a computer vision system in which segmentation and recognition phases are strictly connected. The system is hierarchically organized in four modules. Firstly the Image Enhancement Module (IEM) operates a set of custom image enhancements to remove blur and to prepare data for subsequent segmentation and recognition processes. Secondly the Papilla Border Analysis Module (PBAM) automatically recognizes number, position and local diameter of blood vessels departing from optical papilla. Then the Vessel Tracking Module (VTM) analyses vessels comparing the results of body and edge tracking and detects branches and crossings. Finally the Feature Extraction Module evaluates PBAM and VTM output data and extracts some numerical indexes. Used algorithms appear to be robust and have been successfully tested on various ocular fundus images.

A proposal for textual encoding based on semantic web tools

Purpose – The purpose of this paper is to investigate and prove the feasibility of a semantic web (SW) based approach to textual encoding. It aims to discuss benefits and novel possibilities with respect to traditional XML‐based approaches.Design/methodology/approach – The markup process can be seen as a task of knowledge representation where elements such as words, sentences and pages are instances of conceptual classes forming a semantic network. An ontology web language ontology for textual encoding has been developed, capturing structural and grammatical aspects. Different approaches and tools to query the encoded text are investigated.Findings – resource description framework (RDF) is powerful and expressive enough to fulfil tasks traditionally done in XML as well as to enable new possibilities such as collaborative and distributed textual encoding and the use of ontology‐based reasoning in text processing and querying. While the encoding of overlapping hierarchies through the use of existing approac...

Navigation with memory in a partially observable environment

Abstract The paper presents an architecture that allows the reactive visual navigation via an unsupervised reinforcement learning. This objective is reached using Q -learning and a hierarchical approach to the developed architecture. Using these techniques requires a deviation from the Partially Observable Markov Decision Processes (POMDP) and some innovations: heuristic techniques for generalizing the experience and for treating the partial observability; a technique for the speed adjournment of the Q function; the definition of a special reinforcement policy adequate for learning a complex task without supervision. The result is a satisfactory learning of the navigation assignment in a simulated environment.

Distributed knowledge revision/integration

We propose a distributed architecture for knowledge revisionintegration, where each element is conceived as a knowledgebased system able to exchange information with the others. since nodes can be affected by some degree of incompetence, part of the information running through the network may be incorrect and might cause contradictions in the knowledge base of some nodes. To manage these contradictions, each node is equipped with a belief revision module which makes it able to discriminate among more or less credible information and more or less reliable information sources. Our aim is that of comparing on a simulation basis the performances and the characteristics of this distributed system vs. those of a centralised architecture. We report here the fmt results of our experiments.

The Emergence of E.Learning

The innovation process, which took place in recent years, in Information and Communication Technology (ICT) had a strong impact on training activities. Namely the push towards knowledge commercialisation seems to be oriented to a setting which is free from both spatial and temporal constraints. This, in turn, implies a redefinition of the concept of Open Learning that, in its ideal representation, should be the solution to all distance learning problems. The new form of computer based knowledge involves a different approach to didactics, through the learning object practice, and, in particular, a careful reflection on technological equipment intended to: 1) assure accessibility to every user, 2) grant for the fruition of a useful product to all recipients, 3) assure a perfect integration between the knowledge content and the different devices employed. The aim of this contribution is to propose a systemic reflection on some emerging aspects in e-learning contexts, related to the role of usability, and to accessibility and cognitive processing in designing phases.

Self-Organizing Maps versus Growing Neural Gas in a Robotic Application

The paper proposes a method for visual based self-localisation of a mobile agent in indoor environment. The images acquired by the camera constitute an implicit topological representation of the environment. The environment is a priori unknown and so the implemented architecture is entirely unsupervised. To compare the performance of some self-organising neural networks, a similar neural network architecture of both Self-Organizing Map (SOM) and Growing Neural Gas (GNG) has been realized. Extensive simulations are provided to characterise the effectiveness of the GNG model in recognition speed, classification tasks and in particular topology preserving as compared to the SOM model. This behaviour depends on the following fact: a network (GNG) that adds nodes into map space can approximate the input space more accurately than a network with a predefined structure and size (SOM). The work shows that the GNG network is able to correctly reconstruct the environment topological map.

Knowledge-based approach to image interpretation

Abstract This paper presents an automatic interpretation system to support diagnostic biomedical image analysis. The system is organized in three modules: image segmentation; low level object interpretation; and high level biomedical object interpretation, in terms of clinical finding using a semantic model. The domain-specific knowledge used concerns the nailfold capillaries of the human finger, and is organized in a semantic network. User-defined semantic networks may be supplied to extend the system to other problem domains.