We present an algorithm for graph isomorphism and subgraph isomorphism suited for dealing with large graphs. A first version of the algorithm has been presented in a previous paper, where we examined its performance for the isomorphism of small and medium size graphs. The algorithm is improved here to reduce its spatial complexity and to achieve a better performance on large graphs; its features are analyzed in detail with special reference to time and memory requirements. The results of a testing performed on a publicly available database of synthetically generated graphs and on graphs relative to a real application dealing with technical drawings are presented, confirming the effectiveness of the approach, especially when working with large graphs.

The field of Natural Language Processing (NLP) has undergone a significant transformation with the introduction of Transformers. From the first introduction of this technology in 2017, the use of transformers has become widespread and has had a profound impact on the field of NLP. In this survey, we review the open-access and real-world applications of transformers in NLP, specifically focusing on those where text is the primary modality. Our goal is to provide a comprehensive overview of the current state-of-the-art in the use of transformers in NLP, highlight their strengths and limitations, and identify future directions for research. In this way, we aim to provide valuable insights for both researchers and practitioners in the field of NLP. In addition, we provide a detailed analysis of the various challenges faced in the implementation of transformers in real-world applications, including computational efficiency, interpretability, and ethical considerations. Moreover, we highlight the impact of transformers on the NLP community, including their influence on research and the development of new NLP models.

Biometric authentication systems are quite vulnerable to sophisticated spoofing attacks. To keep a good level of security, reliable spoofing detection tools are necessary, preferably implemented as software modules. The research in this field is very active, with local descriptors, based on the analysis of microtextural features, gaining more and more popularity, because of their excellent performance and flexibility. This paper aims at assessing the potential of these descriptors for the liveness detection task in authentication systems based on various biometric traits: fingerprint, iris, and face. Besides compact descriptors based on the independent quantization of features, already considered for some liveness detection tasks, we will study promising descriptors based on the joint quantization of rich local features. The experimental analysis, conducted on publicly available data sets and in fully reproducible modality, confirms the potential of these tools for biometric applications, and points out possible lines of development toward further improvements.

The paper discusses the performance of a graph matching algorithm tailored for dealing with large graphs without using information about the topology of the graphs to be matched. The algorithm, presented in more details in other papers (and publicly available on the WEB as VF), is now discussed with reference to its computational complexity and memory requirements.The performance analysis is carried out by theoretically characterizing the matching time and the required memory in the best and worst cases. The theoretical analysis is completed by tests on a database of graphs randomly generated.The algorithm is compared with the one proposed by J.R. Ullmann: experimental results confirmed the theoretical expectations, highlighting the overall efficiency of the algorithm. Some results obtained by researchers who recently used the algorithm in application domains requiring a massive use of graph matching techniques are finally reported.