Learning and Mining Player Motion Profiles in Physically Interactive Robogames

Physically-Interactive RoboGames (PIRG) are an emerging application whose aim is to develop robotic agents able to interact and engage humans in a game situation. In this framework, learning a model of players’ activity is relevant both to understand their engagement, as well as to understand specific strategies they adopted, which in turn can foster game adaptation. Following such directions and given the lack of quantitative methods for player modeling in PIRG, we propose a methodology for representing players as a mixture of existing player’s types uncovered from data. This is done by dealing both with the intrinsic uncertainty associated with the setting and with the agent necessity to act in real time to support the game interaction. Our methodology first focuses on encoding time series data generated from player-robot interaction into images, in particular Gramian angular field images, to represent continuous data. To these, we apply latent Dirichlet allocation to summarize the player’s motion style as a probabilistic mixture of different styles discovered from data. This approach has been tested in a dataset collected from a real, physical robot game, where activity patterns are extracted by using a custom three-axis accelerometer sensor module. The obtained results suggest that the proposed system is able to provide a robust description for the player interaction

Activity recognition in a Physical Interactive RoboGame

In this paper, we investigate the possibility of human physical activity recognition in a robot game scenario. Being able to recognize types of activity is essential to enable robot behavior adaptation to support player engagement. Also, the introduction of this recognition system will allow for development of better models for prediction, planning and problem solving in PIRGs that can foster human-robot interaction. The experiments reported on this paper were performed on data collected from real in-game activity, where a human player faces a mobile robot. We use a custom single tri-axial accelerometer module attached to the player’s chest in order to capture motion information. The main characteristic of our approach is the extraction of features from patterns found on the motion variance rather than on raw data. Furthermore, we allow for the recognition of unconstrained motion given that we do not ask the players to perform target activities before hand: all detectable activities are derived from the free player motion during the game itself. To the best of our knowledge, this is the first paper to consider activity recognition in a physical interactive robogame

Digital Twin in Naval Environment

A naval vessel is usually engaged in demanding operations that take place in a multifaceted environment. This requires a solid design of the ship as a platform and a prompt decision-making response. To support both the design and operation phases, digital tools and techniques have been widely implemented, along with a significant number of sensors and probes installed onboard. All of these features pave the way for the development of a Digital Twin model, which will be beneficial for the naval sector. In this work, relevant applications and a use case have been presented and discussed, with the goal of highlighting the added value and critical issues in the perspective of gathering them in a Digital Twin environment. The steps required to develop a shared reference digital architecture have been identified, as well as the gaps that need to be filled

A Psychometric Examination of the Coronavirus Anxiety Scale and the Fear of Coronavirus Disease 2019 Scale in the Italian Population

The coronavirus disease 2019 (COVID-19) outbreak has caused not only significant physical health problems but also mental health disorders. Anxiety and fear appear to be the main psychological symptoms associated with COVID-19. The aim of this study was to investigate whether anxiety and fear related to COVID-19 are influenced by sociodemographics and whether specific conditions, such as positivity for COVID-19 or death among relatives and friends, can further enhance these symptoms. In this cross-sectional study, 697 Italians responded to an online survey assessing sociodemographic information, the presence/absence of positive cases, or deaths due to COVID-19 among relatives or acquaintances. The Coronavirus Anxiety Scale (CAS) and Fear of COVID-19 Scale (FCS-19S) were administered in order to assess the levels of anxiety and fear associated with COVID-19. The data were collected in November 2020. Anxiety and fear scores were positively correlated. Both male and female subjects with higher CAS scores also displayed higher FCS-19S scores. The CAS and FCS-19S scores tended to increase with age, with older subjects exhibiting higher scores than younger subjects. Additionally, respondents with lower educational levels demonstrated higher scores on both the CAS and FCS-19S. Similarly, respondents living with older people and/or experiencing the death of one or more relatives due to COVID-19 exhibited corresponding outcomes. This study demonstrates how the levels of anxiety and fear, measured by CAS and FCS-19S associated with COVID-19, are influenced by gender, age, cohabitation status, educational levels, and the presence of positive cases or deaths due to COVID-19

Machine Learning and MADIT methodology for the fake news identification: the persuasion index

[EN] The phenomenon of fake news has grown concurrently with the rise of social networks that allow people to directly access news without the mediation of reliable sources. Recognizing news as fake is a difficult task for humans, and even tougher for a machine. This proposal aims to redesign the problem: from a check of truthfulness of news content, to the analysis of texts’ persuasion level. That is how information is introduced to the reader, assuming that fake news is aimed at persuading towards the reality of sense they intend to convey. M.A.D.I.T. methodology has been chosen. It is useful to describe how texts are built, overcoming the content/structure analysis level and stressing the study of Discursive Repertories: discursive modalities of reality of sense building, classified into real and fake news categories thanks to the Machine learning application. For the dataset building 7,387 news have been analysed. The results highlight different profiles of text building between the two groups: the different and typical discursive repertories allow to validate the methodological approach as a good predictor of the persuasion level of texts, not only of news, but also of information in domains such as the economic financial one (e.g. GameStop event).Orrù, L.; Moro, C.; Cuccarini, M.; Paita, M.; Dalla Riva, MS.; Bassi, D.; Da San Martino, G.... (2022). Machine Learning and MADIT methodology for the fake news identification: the persuasion index. En 4th International Conference on Advanced Research Methods and Analytics (CARMA 2022). Editorial Universitat Politècnica de València. 165-172. https://doi.org/10.4995/CARMA2022.2022.1508116517

The climate in the European Union and the enlarged European Region is a determinant of the COVID-19 case fatality ratio

Climate could influence the COVID-19 pandemic, but while no evidence has been advanced on the influence of colder climates, some studies have provided data to support a possible heat-related protective factor. The objective is to verify whether areas with a Cold Temperate Climate (TC) have a higher Case Fatality Ratio (CFR) for COVID-19 than areas with a Cold Climate (CC) or with a Mediterranean Climate (MC) in the European Union and the Enlarged European Region. Countries or regions were subdivided into 3 groups according to the Köppen climate classification system: TC (Cfa, Cfb and Cfc in the Köppen system); MC (Csa, Csb); CC (D and E in the Köppen system). The total number of cases and the total number of deaths were detected on 13 August 2020 on the COVID-19 Map-Johns Hopkins Coronavirus Resource Center-the CFR was thus calculated by area. Living in TC areas is strongly associated with risk of a high Case Fatality Ratio for COVID-19, OR for MC =0.42, IC 95% 0.41-0.43; OR for CC=0.33, IC 95% 0.33-0.35. The results are confirmed in the EU, OR per MC=0.85, CI 95% 0.84-0.87; OR per CC=0.63, IC 95% 0.61-0.65.The study found that the IC in a humid temperate climate is associated with higher CFR with respect to the coldest and warmest temperate climates in Europe. This does not appear to be the only determinant of the pandemic

Managing the Consequences of Oncological Major Surgery: A Short- and Medium-Term Skills Assessment Proposal for Patient and Caregiver through M.A.D.I.T. Methodology

The effects of cancer surgery and treatment harm patients' life and working ability: major causes of this can be intensified by the postoperative symptoms. This study, the first part of the HEAGIS project (Health and Employment after Gastrointestinal Surgery), proposes a method to assess patients and caregivers' competences in dealing with postoperative course and the related needs to improve the adequate competences. In this observational study, an ad hoc structured interview was conducted with 47 patients and 15 caregivers between the third and fifteenth postoperative day. Oesophageal (38%), esophagogastric junction (13%), gastric (30%), colon (8%) and rectum (11%) cancer patients were considered. Computerized textual data analysis methodology was used to identify levels of competences. Text analysis highlighted three different levels (low, medium and high) of four specific types of patients and caregivers' competences. In particular, the overall trend of the preview of future scenarios and use of resource competences was low. Less critical were situation evaluation and preview repercussion of own actions' competences. Caregivers' trends were similar. The Kruskal-Wallis test did not distinguish any differences in the level of competences related to the characteristics of the participants. Patients and caregivers are not accurate in planning the future after surgery, using personal beliefs rather than referring to physicians, and not recognizing adequate resources. The medium-low competences' trend leads to unexpected critical situations, and patients could not deal with them in a maximally effective way. Both patients and caregivers should be taken over by healthcare professionals to improve patients' competences and make the curative surgery effective in daily life

Is the Inversion in the Trend of the Lethality of the COVID-19 in the Two Hemispheres due to the Difference in Seasons and Weather?

The climate has an influence on the COVID-19 virus lethality. The aim of this study is to verify if the summer weather coincided with the decrease of the Case Fatality Ratio (CFR) in Europe and if, on the contrary, an inverse trend was observed in Australia and New Zealand. To verify our hypothesis, we considered the largest European countries (Germany, UK, France, Italy, and Spain), plus Belgium and the Netherlands. Furthermore, we compared these countries with Australia and New Zealand. For each country considered, we have calculated the CFR from the beginning of the pandemic to May 6th and from May 6th to September 21st (late summer in Europe, late winter in the southern hemisphere). The CFRs were calculated from the John Hopkins University database. According to the results, in all European countries, a progressive decrease in CFR is observed. A diametrically opposite result is found in Australia where, on the contrary, the CFR is much higher at the end of September (at the end of winter) than on May 6th (mid-autumn), and the risk of dying if we count the infection is higher in September. In New Zealand, there are no statistically significant differences between the two surveys. The present study was based on public access macro data

Gastrointestinal Coronavirus disease 2019: epidemiology, clinical features, pathogenesis, prevention, and management

Introduction: The new Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is the etiologic agent of coronavirus disease 2019. Some authors reported evidences that patients with SARS-CoV-2 infection could have a direct involvement of the gastrointestinal tract, and in symptomatic cases, gastrointestinal symptoms (diarrhea, nausea/vomiting, abdominal pain) could be very common. Area covered: In this article, we reviewed current published data of the gastrointestinal aspects involved in SARS-CoV-2 infection, including prevalence and incidence of specific symptoms, presumptive biological mechanism of GI infection, prognosis, clinical management and public health related concerns on the possible risk of oral-fecal transmission. Expert opinion: Different clues point to a direct virus infection and replication in mucosal cells of the gastrointestinal tract. In vitro studies showed that SARS-CoV-2 could enters into the gastrointestinal epithelial cells by the Angiotensin-Converting enzyme 2 membrane receptor. These findings, coupled with identification of viral RNA found in stools of patients, clearly suggest that a direct involvement of gastrointestinal tract is very likely. This can justify most of the gastrointestinal symptoms but also suggest a risk for an oral fecal route for transmission, additionally or alternatively to the main respiratory route