While robots are more and more deployed among people in public spaces, the impact of cyber-security attacks is significantly increasing. Most of consumer and professional robotic systems are affected by multiple vulnerabilities and the research in this field is just started. This paper addresses the problem of automatic detection of anomalous behaviors possibly coming from cyber-security attacks. The proposed solution is based on extracting system logs from a set of internal variables of a robotic system, on transforming such data into images, and on training different Autoencoder architectures to classify robot behaviors to detect anomalies. Experimental results in two different scenarios (autonomous boats and social robots) show effectiveness and general applicability of the proposed method.

Radiology reports are a valuable source of textual information used to improve clinical care and support research. In recent years, deep learning techniques have been shown to be effective in classifying radiology reports. This article investigates the use of deep learning techniques with attention mechanisms to achieve better performance in the classification of radiology reports. We focus on various Natural Language Processing approaches, such as LSTM with Attention, BERT, and GPT-4, evaluated on a chest tomography report dataset regarding neoplastic diseases collected from an Italian hospital. In particular, we compare the results with a previous machine learning system, showing that models based on attention mechanisms can achieve higher performance. The Attention Mechanism allows us to identify the most relevant bits of text used by the model to make its predictions. We show that our model achieves state-of-the-art results on the hierarchical classification of radiology reports. Moreover, we evaluate the performance of GPT-4 on the classification of these reports in a zero-shot setup through prompt engineering, showing interesting results even with a small context and a non-English language. Our findings suggest that deep learning techniques with attention mechanisms may be successful in the classification of radiology reports even in non-English languages for which it is not possible to leverage on large text corpus.

During 2020 and 2021, managing limited healthcare resources and hospital beds has been a fundamental aspect of the fight against the COVID-19 pandemic. Predicting in advance the length of stay, and in particular identifying whether a patient is going to stay in the hospital longer or less than a week, can provide important support in handling resources allocation. However, there have been significant changes in terms of containment measures, virus diffusion, new treatments, vaccines, and new variants of SARS-CoV-2 during the last period. These changes pose several conceptual drift issues that can limit the usefulness of machine learning in this context. In this work, we present a machine learning system trained and tested using data from more than 6000 hospitalised patients in northern Italy, distributed over almost two years of pandemic. We show how machine learning can be effective even by analysing data over this long period of time, also exploiting a model that predicts the patient's outcome in terms of discharge or death. Furthermore, learning from data that also consider deceased patients is a common issue in predicting the length of stay because they have severe conditions similar to patients with a long stay period, but may actually have a very short duration of hospitalisation. For this purpose, we present a method for handling data from alive and deceased patients, exploiting more patient records, increasing the robustness of the model and its performance in this task. Finally, we investigate the features that are most relevant to the prediction of the simplified length of stay.

Recognising the goal of an agent from a trace of observations is an important task with many applications. The state-of-the-art approach to goal recognition (GR) relies on the application of automated planning techniques. We study an alternative approach, called GRNet, where GR is formulated as a classification task addressed by machine learning. GRNet is primarily aimed at solving GR instances more accurately and more quickly by learning how to solve them in a given domain, which is specified by a set of propositions and a set of action names. The goal classification instances in the domain are solved by a Recurrent Neural Network (RNN). The only information required as input of the trained RNN is a trace of action labels, each one indicating just the name of an observed action. A run of the RNN processes a trace of observed actions to compute how likely it is that each domain proposition is part of the agent's goal, for the problem instance under consideration. These predictions are then aggregated to choose one of the candidate goals. An experimental analysis confirms that GRNet achieves good performance in terms of both goal classification accuracy and runtime, obtaining better results w.r.t. a state-of-the-art GR system over the considered benchmarks. Moreover, such a state-of-the-art system and GRNet can be combined achieving higher performance than with each of the two integrated systems alone.

Artificial Intelligence and Natural Language Processing techniques can have a very significant impact on the e-learning sector, with the introduction of chatbots, automatic correctors, or scoring systems. However, integrating such technologies into the business environment in an effective way is not a trivial operation, and it not only requires realising a model with good predictive performance, but also it requires the following: (i) a proper study of the task, (ii) a data collection process, (iii) a real-world evaluation of its utility. Moreover, it is also very important to build an entire IT infrastructure that connects the AI system with the company database, with the human employees, the users, etc. In this work, we present a real-world system, based on the state-of-the-art BERT model, which implements an automatic scoring system for open-ended questions written in Italian. More specifically, these questions pertain to the workplace safety courses which every worker must attend by law, often via e-learning platforms such as the one offered by Mega Italia Media. This article describes how our system has been designed, evaluated, and finally deployed for commercial use with complete integration with the other services provided by the company.