Analysis of touch gestures for online child protection

AbstractThe growth of Internet and the pervasiveness of ICT have led to a radical change in social relationships. One of the drawbacks of this change is the exposure of individuals to threats during online activities. In this context, thetechno-regulationparadigm is inspiring new ways to safeguard legally interests by means of tools allowing to hamper breaches of law. In this paper, we focus on the exposure of individuals to specific online threats when interacting with smartphones. We propose a novel techno-regulatory approach exploiting machine learning techniques to provide safeguards against threats online. Specifically, we study a set of touch-based gestures to distinguish between underages or adults who is accessing a smartphone, and so to guarantee protection. To evaluate the proposed approach's effectiveness, we developed an Android app to build a dataset consisting of more than 9000 touch-gestures from 147 participants. We experimented bothsingle-viewandmulti-viewlearning techniques to find the best combination of touch-gestures able of distinguishing between adults and underages. Results show that the multi-view learning combining scrolls, swipes, and pinch-to-zoom gestures, achieves the best ROC AUC (0.92) and accuracy (88%) scores

Touchscreen gestures as images. A transfer learning approach for soft biometric traits recognition

Mobile devices such as smartphones and tablets are nowadays daily employed by more than 3 billion people, with an expected further worldwide penetration up to 5 billion users by 2025.1 Among the reasons for such astonishing growth, from the early years of mobile communications to the present day, there is the fact that such devices offer the chance to perform different tasks and access several services, such as taking pictures, socializing, finding road routes, or perform online payments with an extreme easiness. As a matter of fact, use of apps like Apple Pay or Google Pay in North America is believed to double between 2020 and 2025, although Asia’s market size will be significantly larger.2 Mobile devices are equipped with a variety of sensors, each designed for capturing specific signals like those related to the heart rate, or more basic ones related to touch gestures. Thanks to these abilities a vast number of applications is being developed for mobile platforms, ranging from activity tracker (Nweke et al., 2018) and healthcare (Shabut et al., 2018) to social recommendation (Gao et al., 2021). It has yet to be observed that most of the services which can be performed through mobile devices are typically accessed and used by providing sensitive and valuable data, such as passwords, credit card numbers, and so on. In this regard, resorting to biometric recognition systems seems a natural choice. Mobile devices’ sensors can be exploited to acquire discriminating traits, thus allowing to recognize the authorized users. Furthermore, the possibility of performing biometric recognition within mobile devices may come in handy to use them as authenticating tokens, providing the means to perform decentralized access control, thus exploiting mobile technology as authenticating means by combining their capabilities with biometric solutions. Within this research area, we also find soft biometrics, an active field of research that provides useful attributes to assist more complex ecosystems. It can improve the performance of biometric authentication systems, user experience in healthcare systems and smart spaces, and play a key role in access control systems. The results reported in the literature (Chai et al., 2019, Idrus et al., 2015, Jain et al., 2004, Park and Jain, 2010, Ranjan et al., 2017) indicate that the authentication performance can be improved by augmenting traditional biometric traits with soft biometric traits, especially when using gender and age

Adam or Eve? Automatic users’ gender classification via gestures analysis on touch devices

Gender classification of mobile devices’ users has drawn a great deal of attention for its applications in healthcare, smart spaces, biometric-based access control systems and customization of user interface (UI). Previous works have shown that authentication systems can be more effective when considering soft biometric traits such as the gender, while others highlighted the significance of this trait for enhancing UIs. This paper presents a novel machine learning-based approach to gender classification leveraging the only touch gestures information derived from smartphones’ APIs. To identify the most useful gesture and combination thereof for gender classification, we have considered two strategies: single-view learning, analyzing, one at a time, datasets relating to a single type of gesture, and multi-view learning, analyzing together datasets describing different types of gestures. This is one of the first works to apply such a strategy for gender recognition via gestures analysis on mobile devices. The methods have been evaluated on a large dataset of gestures collected through a mobile application, which includes not only scrolls, swipes, and taps but also pinch-to-zooms and drag-and-drops which are mostly overlooked in the literature. Conversely to the previous literature, we have also provided experiments of the solution in different scenarios, thus proposing a more comprehensive evaluation. The experimental results show that scroll down is the most useful gesture and random forest is the most convenient classifier for gender classification. Based on the (combination of) gestures taken into account, we have obtained F1-score up to 0.89 in validation and 0.85 in testing phase. Furthermore, the multi-view approach is recommended when dealing with unknown devices and combinations of gestures can be effectively adopted, building on the requirements of the system our solution is built-into. Solutions proposed turn out to be both an opportunity for gender-aware technologies and a potential risk deriving from unwanted gender classification

Dopamine transporter availability in motor subtypes of de novo drug-naïve Parkinson's disease

Tremor dominant (TD) and akinetic-rigid type (ART) are two motor subtypes of Parkinson's disease associated with different disease progression and neurochemical/neuropathological features. The role of presynaptic nigrostriatal dopaminergic damage is still controversial, poorly explored, and only assessed in medicated patients. In this study, we investigated with FP-CIT SPECT the striatal dopamine transporter (DAT) availability in drug-naïve PD patients with ART and TD phenotypes. Fifty-one de novo, drug-naïve patients with PD underwent FP-CIT SPECT studies. Patients were evaluated with Unified Parkinson's Disease Rating Scale (UPDRS) part III and Hoehn and Yahr scale (H&Y) and divided into ART (24/51) and TD (27/51) according to UPDRS part III. ART and TD patients were not different with regard to age, gender, and disease duration. However, compared to TD, ART patients presented higher UPDRS part III (p = 0.01) and H&Y (p = 0.02) and lower DAT availability in affected and unaffected putamen (p = 0.008 and p = 0.007, respectively), whereas no differences were found in caudate. Moreover, in the whole group of patients, rigidity and bradykinesia, but not tremor scores of UPDRS part III were significantly related to FP-CIT binding in the putamen. These results suggest that in newly diagnosed drug-naïve PD patients DAT availability might be different between ART and TD in relation to different disease severity

Art. 211 - (Pareri di precontenzioso dell'ANAC) - CODICE DEI CONTRATTI PUBBLICI. Commentario di dottrina e giurisprudenza

Art. 211 - (Pareri di precontenzioso dell'ANAC) - CODICE DEI CONTRATTI PUBBLICI. Commentario di dottrina e giurisprudenz