Leveraging Stack4Things for Federated Learning in Intelligent Cyber Physical Systems

During the last decade, the Internet of Things acted as catalyst for the big data phenomenon. As result, modern edge devices can access a huge amount of data that can be exploited to build useful services. In such a context, artificial intelligence has a key role to develop intelligent systems (e.g., intelligent cyber physical systems) that create a connecting bridge with the physical world. However, as time goes by, machine and deep learning applications are becoming more complex, requiring increasing amounts of data and training time, which makes the use of centralized approaches unsuitable. Federated learning is an emerging paradigm which enables the cooperation of edge devices to learn a shared model (while keeping private their training data), thereby abating the training time. Although federated learning is a promising technique, its implementation is difficult and brings a lot of challenges. In this paper, we present an extension of Stack4Things, a cloud platform developed in our department; leveraging its functionalities, we enabled the deployment of federated learning on edge devices without caring their heterogeneity. Experimental results show a comparison with a centralized approach and demonstrate the effectiveness of the proposed approach in terms of both training time and model accuracy

L’ulteriore telematizzazione dei flussi degli atti nel processo civile

La razionalizzazione e l’incremento della telematizzazione del processo civile, in funzione della sua efficienza, sono stati un filo conduttore della riforma Cartabia: in questo saggio l’Autore esamina, tra legge delega e decreto delegato, le misure in tema di notifiche, depositi e fascicoli telematici, verificandone i limiti e le conseguenze, anche in termini di perfezionamento del passaggio in ambiente digitale delle attività di formazione, trasmissione e archiviazione degli atti processuali

Local anaesthesia efficacy as postoperative analgesia for open shoulder instability surgery. a prospective randomised controlled study

Background and objectives: The aim of present study was to evaluate for the first time, the clinical effect of local anaesthetic infiltration as postoperative analgesia in open shoulder surgery for anterior-inferior instability. The comparison of the local infiltration and interscalenic brachial plexus block to a control group test the local anaesthetic efficacy in this surgery. Methods: 78 patients scheduled for open shoulder surgery were enrolled and randomly assigned to one of three groups: local infiltration anaesthesia (LIA), interscalenic brachial plexus block (IBPB) and control (C). All patients received standardized general anaesthesia and all injections were performed with the same dose and volume of anaesthetic. The number boluses delivered by a PCA pump applied at the end of surgery and the visual analogue score (VAS) at 0, 2, 4, 6, 12, 18 and 24 hours after intervention were recorded. A patient satisfaction score was also assessed. Results: Mean bolus consumption of the rescue analgesic, compared to C, was significantly less both in the LIA and IBPB groups (P<0.05). The IBPB group showed VAS scores that were significantly better than C group at all time points (P<0.05). The VAS scores for LIA group were clinically comparable to IBPB, and only at the 2 and 6 hours, postoperative time points there were no significant differences found in respect to the C group. IBPB and LIA showed comparable patient satisfaction scores. Conclusion: The local anaesthetic infiltration as postoperative analgesia appears to be a clinically valid alternative, statistically comparable to IBPB, with no clinical meaningful adverse effects

A Novel Echo State Network Autoencoder for Anomaly Detection in Industrial Iot Systems

The Industrial Internet of Things (IIoT) technology had a very strong impact on the realization of smart frameworks for detecting anomalous behaviors that could be potentially dangerous to a system. In this regard, most of the existing solutions involve the use of Artificial Intelligence (AI) models running on Edge devices, such as Intelligent Cyber Physical Systems (ICPS) typically equipped with sensing and actuating capabilities. However, the hardware restrictions of these devices make the implementation of an effective anomaly detection algorithm quite challenging. Considering an industrial scenario, where signals in the form of multivariate time-series should be analyzed to perform a diagnosis, Echo State Networks (ESNs) are a valid solution to bring the power of neural networks into low complexity models meeting the resource constraints. On the other hand, the use of such a technique has some limitations when applied in unsupervised contexts. In this paper, we propose a novel model that combines ESNs and autoencoders (ESN-AE) for the detection of anomalies in industrial systems. Unlike the ESN-AE models presented in the literature, our approach decouples the encoding and decoding steps and allows the optimization of both the processes while performing the dimensionality reduction. Experiments demonstrate that our solution outperforms other machine learning approaches and techniques we found in the literature resulting also in the best trade-off in terms of memory footprint and inference time

A deep learning approach for pressure ulcer prevention using wearable computing

Abstract In recent years, statistics have confirmed that the number of elderly people is increasing. Aging always has a strong impact on the health of a human being; from a biological of point view, this process usually leads to several types of diseases mainly due to the impairment of the organism. In such a context, healthcare plays an important role in the healing process, trying to address these problems. One of the consequences of aging is the formation of pressure ulcers (PUs), which have a negative impact on the life quality of patients in the hospital, not only from a healthiness perspective but also psychologically. In this sense, e-health proposes several approaches to deal with this problem, however, these are not always very accurate and capable to prevent issues of this kind efficiently. Moreover, the proposed solutions are usually expensive and invasive. In this paper we were able to collect data coming from inertial sensors with the aim, in line with the Human-centric Computing (HC) paradigm, to design and implement a non-invasive system of wearable sensors for the prevention of PUs through deep learning techniques. In particular, using inertial sensors we are able to estimate the positions of the patients, and send an alert signal when he/she remains in the same position for too long a period of time. To train our system we built a dataset by monitoring the positions of a set of patients during their period of hospitalization, and we show here the results, demonstrating the feasibility of this technique and the level of accuracy we were able to reach, comparing our model with other popular machine learning approaches

Human- or object-like? Cognitive anthropomorphism of humanoid robots

Across three experiments (N = 302), we explored whether people cognitively elaborate humanoid robots as human- or object-like. In doing so, we relied on the inversion paradigm, which is an experimental procedure extensively used by cognitive research to investigate the elaboration of social (vs. non-social) stimuli. Overall, mixed-model analyses revealed that full-bodies of humanoid robots were subjected to the inversion effect (body-inversion effect) and, thus, followed a configural processing similar to that activated for human beings. Such a pattern of finding emerged regardless of the similarity of the considered humanoid robots to human beings. That is, it occurred when considering bodies of humanoid robots with medium (Experiment 1), high and low (Experiment 2) levels of human likeness. Instead, Experiment 3 revealed that only faces of humanoid robots with high (vs. low) levels of human likeness were subjected to the inversion effects and, thus, cognitively anthropomorphized. Theoretical and practical implications of these findings for robotic and psychological research are discussed

An 1H NMR study of the cytarabine degradation in clinical conditions to avoid drug waste, decrease therapy costs and improve patient compliance in acute leukemia

Cytarabine, the 4-amino-1-(β-D-arabinofuranosyl)-2(1H)-pyrimidinone, (ARA-C) is an antimetabolite cytidine analogue used worldwide as key drug in the management of leukaemia. As specified in the manufacturers' instructions, once the components-sterile water and cytarabine powder-are unpackaged and mixed, the solution begins to degrade after 6 hours at room temperature and 12 hours at 4°C. To evaluate how to avoid wasting the drug in short-term, low-dose treatment regimens, the reconstituted samples, stored at 25°C and 4°C, were analyzed every day of the test week by reversed-phase HPLC and high-field NMR spectroscopy. All the samples remained unchanged for the entire week, which corresponds to the time required to administer the entire commercial drug package during low-dose therapeutic regimens. The drug solution was stored in a glass container at 4°C in an ordinary freezer and drawn with sterile plastic syringes; during this period, no bacterial or fungal contamination was observed. Our findings show that an cytarabine solution prepared and stored in the original vials retains its efficacy and safety and can, therefore, be divided into small doses to be administered over more days, thus avoiding unnecessary expensive and harmful waste of the drug preparation. Moreover, patients who require daily administration of the drug could undergo the infusion at home without need to go to hospital. The stability of the aliquots would help decrease hospitalization costs

The italian quaternary volcanism

The peninsular and insular Italy are punctuated by Quaternary volcanoes and their rocks constitute an important aliquot of the Italian Quaternary sedimentary successions. Also away from volcanoes themselves, volcanic ash layers are a common and frequent feature of the Quaternary records, which provide us with potential relevant stratigraphic and chronological markers at service of a wide array of the Quaternary science issues. In this paper, a broad representation of the Italian volcano logical community has joined to provide an updated comprehensive state of art of the Italian Quaternary volcanism. The eruptive history, style and dynamics and, in some cases, the hazard assessment of about thirty Quaternary volcanoes, from the north ernmost Mt. Amiata, in Tuscany, to the southernmost Pantelleria and Linosa, in Sicily Channel, are here reviewed in the light of the substantial improving of the methodological approaches and the overall knowledge achieved in the last decades in the vol canological field study. We hope that the present review can represent a useful and agile document summarising the knowledege on the Italian volcanism at the service of the Quaternary community operating in central Mediterranean area

Outcomes of COVID-19 patients treated with continuous positive airway pressure outside ICU

Aim We aim at characterizing a large population of Coronavirus 19 (COVID-19) patients with moderate-to-severe hypoxemic acute respiratory failure (ARF) receiving CPAP outside intensive care unit (ICU), and ascertaining whether the duration of CPAP application increased the risk of mortality for patients requiring intubation. Methods In this retrospective, multicentre cohort study, we included COVID-19 adult patients, treated with CPAP outside ICU for hypoxemic ARF from March 1 st to April 15th, 2020. We collected demographic and clinical data, including CPAP therapeutic goal, hospital length of stay (LOS), and 60- day in-hospital mortality. Results The study includes 537 patients with a median age of 69 (IQR, 60-76) years. Males were 391 (73%). According to predefined CPAP therapeutic goal, 397 (74%) patients were included in full treatment subgroup, and 140 (26%) in the do-not intubate (DNI) subgroup. Median CPAP duration was 4 (IQR, 1-8) days, while hospital LOS 16 (IQR, 9-27) days. Sixty-day in-hospital mortality was overall 34% (95%CI, 0.304-0.384), and 21% (95%CI, 0.169-0.249) and 73% (95%CI, 0.648-0.787) for full treatment and DNI subgroups, respectively. In the full treatment subgroup, in-hospital mortality was 42% (95%CI, 0.345-0.488) for 180 (45%) CPAP failures requiring intubation, while 2% (95%CI, 0.008- 0.035) for the remaining 217 (55%) patients who succeeded. Delaying intubation was associated with increased mortality [HR, 1.093 (95%CI, 1.010-1.184)]. Conclusions We described a large population of COVID-19 patients treated with CPAP outside ICU. Intubation delay represents a risk factor for mortality. Further investigation is needed for early identification of CPAP failures

The Italian Draft Law on the \u2018Provisions Concerning the Safeguarding of the Intangible Cultural Heritage\u2019

Intangible cultural heritage in Italy is still in need of a unified approach, capable of providing reliable criteria for identifying its assets and for indicating timescales and means by which they should be safeguarded. In the continued absence of up-to-date, ad hoc state legislation (since the content of those laws which do implement international Conventions is too generic in nature to be sufficiently effective), the Regions have proceeded to act in a somewhat scattered manner, giving rise to an extremely fragmented and very disorderly regulatory framework. The draft law N. 4486, "Provisions Concerning the Safeguarding of the Intangible Cultural Heritage", presented on 12th May 2017 at the Chamber of Deputies of the Italian Republic - as the result of the work of an interdisciplinary and inter-university research team coordinated by Marco Giampieretti, who has drafted the final text with the collaboration of Simona Pinton - seeks to fill the serious void that exists in Italian legal system by aligning it to the principles of international and European law, by redirecting the relevant State and Regional legislation, and by satisfying the fundamental requirements of the national community