The railway domain is regulated by rigorous safety standards to ensure that specific safety goals are met. Often, safety-critical systems rely on custom hardware-software components that are built from scratch to achieve specific functional and non-functional requirements. Instead, the (partial) usage of Commercial Off-The-Shelf (COTS) components is very attractive as it potentially allows reducing cost and time to market. Unfortunately, COTS components do not individually offer enough guarantees in terms of safety and security to be used in critical systems as they are. In such a context, RFI (Rete Ferroviaria Italiana), a major player in Europe for railway infrastructure management, aims at equipping track-side workers with COTS devices to remotely and safely interact with the existing interlocking system, drastically improving the performance of maintenance operations. This paper describes the first effort to update existing (embedded) railway systems to a more recent cyber-physical system paradigm. Our Remote Worker Dashboard (RWD) pairs the existing safe interlocking machinery alongside COTS mobile components, making cyber and physical components cooperate to provide the user with responsive, safe, and secure service. Specifically, the RWD is a SIL4 cyber-physical system to support maintenance of actuators and railways in which COTS mobile devices are safely used by track-side workers. The concept, development, implementation, verification, and validation activities to build the RWD were carried out in compliance with the applicable CENELEC standards required by certification bodies to declare compliance with specific guidelines.

Similarity neural networks (SNNs) are a novel neural network model designed to learn similarity measures for pairs of patterns, exploiting binary supervision. SNNs guarantee to compute non negative and symmetric measures, and show good generalization capabilities even if a small set of supervised pairs is used for training. The application of the new model to K-Means like semi-supervised clustering is investigated, introducing a technique that allows the algorithm to compute cluster centroids by means of Backpropagation on the input layer of the SNN, biased by a regularization function. The experiments carried out on some datasets from the UCI repository show that SNN based clustering almost always outperforms other methods proposed in the literature.

This paper presents an improved active shape model algorithm, that exploits auto associative neural networks (AANNs) to estimate the local feature models. The proposed technique aims at solving face feature localization tasks, nevertheless it can be used also in the more general case of object detection. Three main contributions are presented. The first one consists in the estimation of elliptic search areas by means of the training data. The second one is the use of AANNs as local feature detectors, since this network model is particularly suited to solve classification tasks with unbalanced classes. Finally, an optimized technique to set up the learning environment, needed to train the AANNs, is described. The performances of the proposed algorithm compare favorably with original ASMs and with two recent improved versions.

Recursive neural networks (RNNs) and graph neural networks (GNNs) are two connectionist models that can directly process graphs. RNNs and GNNs exploit a similar processing framework, but they can be applied to different input domains. RNNs require the input graphs to be directed and acyclic, whereas GNNs can process any kind of graphs. The aim of this paper consists in understanding whether such a difference affects the behaviour of the models on a real application. An experimental comparison on an image classification problem is presented, showing that GNNs outperforms RNNs. Moreover the main differences between the models are also discussed w.r.t. their input domains, their approximation capabilities and their learning algorithms.

Recursive neural networks are a powerful tool for processing structured data. According to the recursive learning paradigm, the input information consists of directed positional acyclic graphs (DPAGs). In fact, recursive networks are fed following the partial order defined by the links of the graph. Unfortunately, the hypothesis of processing DPAGs is sometimes too restrictive, being the nature of some real-world problems intrinsically cyclic. In this paper, a methodology is proposed, which allows us to process any cyclic directed graph. Therefore, the computational power of recursive networks is definitely established, also clarifying the underlying limitations of the model.

In this paper, we propose a general framework for graph matching which is suitable for different problems of pattern recognition. The pattern representation we assume is at the same time highly structured, like for classic syntactic and structural approaches, and of subsymbolic nature with real-valued features, like for connectionist and statistic approaches. We show that random walk based models, inspired by Google's PageRank, give rise to a spectral theory that nicely enhances the graph topological features at node level. As a straightforward consequence, we derive a polynomial algorithm for the classic graph isomorphism problem, under the restriction of dealing with Markovian spectrally distinguishable graphs (MSD), a class of graphs that does not seem to be easily reducible to others proposed in the literature. The experimental results that we found on different test-beds of the TC-15 graph database show that the defined MSD class "almost always" covers the database, and that the proposed algorithm is significantly more efficient than top scoring VF algorithm on the same data. Most interestingly, the proposed approach is very well-suited for dealing with partial and approximate graph matching problems, derived for instance from image retrieval tasks. We consider the objects of the COIL-100 visual collection and provide a graph-based representation, whose node's labels contain appropriate visual features. We show that the adoption of classic bipartite graph matching algorithms offers a straightforward generalization of the algorithm given for graph isomorphism and, finally, we report very promising experimental results on the COIL-100 visual collection.

In this paper, a new recursive neural network model, able to process directed acyclic graphs with labeled edges, is introduced, in order to address the problem of object detection in images. In fact, the detection is a preliminary step in any object recognition system. The proposed method assumes a graph-based representation of images, that combines both spatial and visual features. In particular, after segmentation, an edge between two nodes stands for the adjacency relationship of two homogeneous regions, the edge label collects information on their relative positions, whereas node labels contain visual and geometric information on each region (area, color, texture, etc.). Such graphs are then processed by the recursive model in order to determine the eventual presence and the position of objects inside the image. Some experiments on face detection, carried out on scenes acquired by an indoor camera, are reported, showing very promising results. The proposed technique is general and can be applied in different object detection systems, since it does not include any a priori knowledge on the particular problem.

The recursive paradigm extends the neural network processing and learning algorithms to deal with structured inputs. In particular, recursive neural network (RNN) models have been proposed to process information coded as directed positional acyclic graphs (DPAGs) whose maximum node outdegree is known a priori. Unfortunately, the hypothesis of processing DPAGs having a given maximum node outdegree is sometimes too restrictive, being the nature of some real-world problems intrinsically disordered. In many applications the node outdegrees can vary considerably among the nodes in the graph, it may be unnatural to define a position for each child of a given node, and it may be necessary to prune some edges to reduce the number of the network parameters, which is proportional to the maximum node outdegree. In this paper, we proposed a new recursive neural network model which allows us to process directed acyclic graphs (DAGs) with labeled edges, relaxing the positional constraint and the correlated maximum outdegree limit. The effectiveness of the new scheme is experimentally tested on an image classification task. The results show that the new RNN model outperforms the standard RNN architecture, also allowing us to use a smaller number of free parameters.