A control oriented model for manual-pick warehouses

Nowadays the low cost of wireless communications allows the real time monitoring of the state of manual-pick warehouse systems making possible the real time control of these systems. At this aim, an approach based on a two-level model is presented in this paper. At the lower level, Petri nets are used to build online a model representing the active missions state and to detect conflicts among resources. At the upper level, logical expressions are used to add constraints for a single mission or item. The model is proved to be effective for online monitoring, scheduling and rescheduling of warehouse activities.     

ECHO: a digital library for historical film archives

Easy access to large information collections is of great importance in many aspects of everyday life. However, limitations in information and communication technologies have so far prevented the average person from taking much advantage of existing resources. Historical documentaries held by national archives constitute some of the most precious yet least accessible cultural information. The ECHO project has facilitated accessibility to this type of precious information by developing a digital library (DL) service for historical films belonging to large national audiovisual archives.     

Pattern recognition in stained HEp-2 cells: Where are we now?

Indirect Immunofluorescence (IIF) images are increasingly being used for the diagnosis of autoimmune diseases. However, the analysis of this kind of images has until now reached a comparatively low level of automation, if compared with other medical imaging techniques. The Special Issue on the Analysis and Recognition of Indirect Immunofluorescence Images of the Pattern Recognition journal aims at providing a comprehensive evaluation of the state of the art for the staining pattern classification problem, through the adoption of a common experimental protocol and the testing of all the methods on a publicly available dataset.     

A query expansion and user profile enrichment approach to improve the performance of recommender systems operating on a folksonomy

In this paper we propose a query expansion and user profile enrichment approach to improve the performance of recommender systems operating on a folksonomy, storing and classifying the tags used to label a set of available resources. Our approach builds and maintains a profile for each user. When he submits a query (consisting of a set of tags) on this folksonomy to retrieve a set of resources of his interest, it automatically finds further “authoritative” tags to enrich his query and proposes them to him. All “authoritative” tags considered interesting by the user are exploited to refine his query and, along with those tags directly specified by him, are stored in his profile in such a way to enrich it. The expansion of user queries and the enrichment of user profiles allow any content-based recommender system operating on the folksonomy to retrieve and suggest a high number of resources matching with user needs and desires. Moreover, enriched user profiles can guide any collaborative filtering recommender system to proactively discover and suggest to a user many resources relevant to him, even if he has not explicitly searched for them.     

Optimal multicast routing with quality of service constraints

We consider the problem of optimal multicast routing with Quality of Service constraints motivated by the requirements of interactive continuous media communication, e.g., real-time teleconferencing. We concentrate on distributed algorithms for determining a tree over the network topology, rooted at the source and spanning the intended destinations. Quality of Service requirements for interactive continuous media typically impose constraints on some metric over the individual paths from the source to each destination, usually in the form of an upper bound on the delay. Thus, we focus on the problem of minimizing the cost of the tree while at the same time satisfying a common constraint over individual source-destination paths. We have shown that this problem is intractable, but have also devised centralized polynomial time heuristics that perform well. Here we present distributed algorithms to minimize tree cost while satisfying the constraints on the paths from the source to each destination.     

Suboptimal supervisory control of Petri nets in presence of uncontrollable transitions via monitor places

This paper deals with the problem of enforcing generalized mutual exclusion constraints (GMEC) on place/transition nets with uncontrollable transitions. An efficient control synthesis technique, which has been proposed in the literature, enforces GMEC constraints by introducing monitor places to create suitable place invariants. The method has been shown to be maximally permissive and to give a unique control structure in the case that the set of legal markings is controllable. This paper investigates on and formally shows that the class of controllers obtained by this technique may not have a supremal element for uncontrollable specifications. Moreover, it is shown that the family of monitor places enforcing an uncontrollable specification can be parameterized with respect to the solution of a linear system of equation. An algorithm to obtain such parameterization is presented here.     

A multi-scale template method for shape detection with bio-medical applications

In this paper we present a novel methodology based on non-parametric deformable prototype templates for reconstructing the outline of a shape from a degraded image. Our method is versatile and fast and has the potential to provide an automatic procedure for classifying pathologies. We test our approach on synthetic and real data from a variety of medical and biological applications. In these studies it is important to reconstruct accurately the shape of the object under investigation from very noisy data. Here we assume that we have some prior knowledge about the object outline represented by a prototype shape. Our procedure deforms this shape by means of non-affine transformations and the contour is reconstructed by minimizing a newly developed objective function that depends on the transformation parameters. We introduce an iterative template deformation procedure in which the scale of the deformation decreases as the algorithm proceeds. We compare our results with those from a Gaussian Mixture Model segmentation and two state-of-the-art Level Set methods. This comparison shows that the proposed procedure performs consistently well on both real and simulated data. As a by-product we develop a new filter that recovers the connectivity of a shape.

Efficient energy-based embedding models for link prediction in knowledge graphs

We focus on the problem of link prediction in Knowledge Graphs, with the goal of discovering new facts. To this purpose, Energy-Based Models for Knowledge Graphs that embed entities and relations in continuous vector spaces have been largely used. The main limitation in their applicability lies in the parameter learning phase, which may require a large amount of time for converging to optimal solutions. In this article, we first propose an unified view on different Energy-Based Embedding Models. Hence, for improving the model training phase, we propose the adoption of adaptive learning rates. We show that, by adopting adaptive learning rates during training, we can improve the efficiency of the parameter learning process by an order of magnitude, while leading to more accurate link prediction models in a significantly lower number of iterations. We extensively evaluate the proposed learning procedure on a variety of new models: our result show a significant improvement over state-of-the-art link prediction methods on two large Knowledge Graphs, namely WordNet and Freebase.     

Development of an auxiliary system for the execution of vascular catheter interventions with a reduced radiological risk; system description and first experimental results

Vascular catheterization is a common procedure in clinical medicine. It is normally performed by a specialist using an X-ray fluoroscopic guide and contrast-media. In the present paper, an image-guided navigation system which indicates a path providing guidance to the desired target inside the vascular tree is described with the aim of reducing the exposure of personnel and patients to X-rays during the catheterization procedure. A 3D model of the patient vascular tree, reconstructed with data collected by an angiography before starting the intervention, is used as a guide map instead of fluoroscopic scans. An accurate spatial correspondence between the body of the patient and the 3D reconstructed vascular model is established and, by means of a position indicator installed over the catheter tip, the real-time position/orientation of the tip is indicated correctly. This paper describes the system and the operational procedures necessary to use the proposed method efficiently during a catheter intervention. Preliminary experimental results on a phantom are also reported.