Ricerc@Sapienza

Recent trends in scholarship publications, public debate on newspapers, and digital projects in the humanities seem to privilege the quantitative and computational approach to historical studies, as it is considered more objective, and therefore more correct. But some examples driven from direct experience in the field of digital epigraphy show how important the human component of any digital project still is for a whole and more correct comprehension of the traces of the past.

Computational intelligence and pattern recognition techniques are gaining more and more attention as the main computing tools in bioinformatics applications. This is due to the fact that biology by definition, deals with complex systems and that computational intelligence can be considered as an effective approach when facing the general problem of complex systems modelling. Moreover, most data available on shared databases are represented by sequences and graphs, thus demanding the definition of meaningful dissimilarity measures between patterns, which are often non-metric in nature.

Commentary on: Martino, A, Giuliani, A, Todde, V, Bizzarri, M, Rizzi, A, 2019, “Metabolic Networks Classification
Knowledge Discovery by Information Granulation” Computers in Biology and Chemistry, pp. 107187. DOI: 10.1016/j.
compbiolchem.2019.107187

Internet of Things (IoT) devices are starting to play a predominant role in our everyday life. Application systems like Amazon Echo and Google Home allow IoT devices to answer human requests, or trigger some alarms and perform suitable actions. In this scenario, any data information, related device and human interaction are stored in databases and can be used for future analysis and improve the system functionality.

In this paper we propose a novel evolutive agent-based clustering algorithm where agents act as individuals of an evolving population, each one performing a random walk on a different subset of patterns drawn from the entire dataset. Such agents are orchestrated by means of a customised genetic algorithm and are able to perform simultaneously clustering and feature selection.

Large scale deployments of Internet of Things (IoT) networks are becoming reality. From a technology perspective, a lot of information related to device parameters, channel states, network and application data are stored in databases and can be used for an extensive analysis to improve the functionality of IoT systems in terms of network performance and user services. LoRaWAN (Long Range Wide Area Network) is one of the emerging IoT technologies, with a simple protocol based on LoRa modulation.

The application of machine learning and soft computing techniques for function approximation is a widely explored topic in literature. Neural networks, evolutionary algorithms and support vector machines proved to be very effective, although these models suffer from very low level of interpretability by human operators. Conversely, Adaptive Neuro Fuzzy Inference Systems (ANFISs) demonstrated to be very accurate models featured by a considerable degree of interpretability. In this paper, a general framework for ANFIS training by clustering is proposed and investigated.

Quality of Service (QoS) management in IP networks today relies on static configuration of classes of service definitions and related forwarding priorities. Packets are actually classified according to the DiffServ architecture based on the RFC 4594, typically thanks to static configuration or filters matching packet features, at network access equipment. In this paper, we propose a dynamic classification procedure, referred to as Learning-powered DiffServ (L-DiffServ), able to detect the distinctive characteristics of traffic and to dynamically assign service classes to IP packets.

Palmprint verification is one of the most significant and popular approaches for personal authentication due to its high accuracy and efficiency. Using deep region of interest (ROI) and feature extraction models for palmprint verification, a novel approach is proposed where convolutional neural networks (CNNs) along with transfer learning are exploited. The extracted palmprint ROIs are fed to the final verification system, which is composed of two modules. These modules are (i) a pre-trained CNN architecture as a feature extractor and (ii) a machine learning classifier.

The goal of this special issue is to understand how and to what extent novel computational intelligence techniques based on the emerging end-to-end learning paradigm can be efficiently employed in Digital Audio, in the light of all aforementioned aspects.