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It is foreseeable the popularity of the mobile edge computing enabled infrastructure for wireless networks in the incoming fifth generation (5G) and future sixth generation (6G) wireless networks. Especially after a ‘hard’ disaster such as earthquakes or a ‘soft’ disaster such as COVID-19 pandemic, the existing telecommunication infrastructure, including wired and wireless networks, is often seriously compromised or with infectious disease risks and should-not-close-contact, thus cannot guarantee regular coverage and reliable communications services. These temporarily-missing communications capabilities are crucial to rescuers, health-carers, or affected or infected citizens as the responders need to effectively coordinate and communicate to minimize the loss of lives and property, where the 5G/6G mobile edge network helps. On the other hand, the federated machine learning (FML) methods have been newly developed to address the privacy leakage problems of the traditional machine learning held normally by one centralized organization, associated with the high risks of a single point of hacking. After detailing current state-of-the-art both in privacy-preserving, federated learning, and mobile edge communications networks for ‘hard’ and ‘soft’ disasters, we consider the main challenges that need to be faced. We envision a privacy-preserving federated learning enabled buses-and-drones based mobile edge infrastructure (ppFL-AidLife) for disaster or pandemic emergency communications. The ppFL-AidLife system aims at a rapidly deployable resilient network capable of supporting flexible, privacy-preserving and low-latency communications to serve large-scale disaster situations by utilizing the existing public transport networks, associated with drones to maximally extend their radio coverage to those hard-to-reach disasters or should-not-close-contact pandemic zones.

Entropy concept is related to uncertainty and predictability of random time series. The estimated trend of such a parameter can provide useful information and possibly predict future behavior of a number of non-stationary noisy signals. The goal of this paper consists of analyzing the Covid19 signal made by the number of registered infections in Italy during the first four months of the pandemic epidemy (March-June 2020). Finally, some considerations are drawn after matching historical dates of some Covid-19 related Acts made by the Italian Government (i.e., lockdown and unlockdowns). Based on the obtained results, we could conjecture that the provisions have inducted people to a common behavior concerning local mobility during the lockdowns and the progressive unlockdowns of the quarantine period in Italy.

Biometrics has for objective to identify or verify the identity of an individual based on morphological or behavioral characteristics. A biometric system can be attacked by presenting a biometric data to the capture subsystem with the goal of interfering it, that is called a presentation attack. Covid, panther, shadow monster and dragon are the investigated presentation attacks associated to the Doddington Zoo Menagerie (which classify users in different categories considering their performance behavior when using biometric systems). In this work, we examined the robustness of each genuine class of the biometric menagerie against the proposed presentation attacks. The achieved experiments are applied to the keystroke dynamics modality. Owing to the adaptive strategy, we depicted each genuine category that is most vulnerable to a specific presentation attack class. We find that the impact of covid, panther, shadow monster and dragon attempts are more pronounced when compared to chameleons, worms, doves and phantoms classes respectively. The obtained results, point out that adding imposter labels to Doddington zoo may lead to a better assessment of biometric authentication systems and promotes the interpretation of their performances.

The wearables' market is rapidly evolving, with applications ranging from healthcare and activity monitoring to emerging domains such as drones and haptic helmets. Wearable-based contact tracing is gaining increased attention in the COVID-19 era for more efficient disease prevention. Therefore, it is of timely relevance to identify the leading existing wireless contact-tracing solutions and their suitability for wearables. Existing trade-offs of contact-tracing applications require a thorough analysis of technical capabilities, such as accuracy, energy consumption, availability, sources of errors when dealing with wireless channels, privacy challenges, and deterrents towards a large-scale adoption on the wearables market. Based on extensive literature research, we conclude that decentralized architectures generally offer a better place in a trade-off in terms of accuracy and user eagerness to adopt them, taking into account privacy considerations, compared to centralized approaches. Our paper provides a brief technical overview of the existing solutions deployed for contact tracing, defines main principles that affect the overall efficacy of digital contact tracing, and presents a discussion on the potential effect of wearables in tackling the spread of a highly contagious virus. acceptedVersion Peer reviewed

Worldwide, the elderly have suffered disproportionately from the effects of the COVID-19 pandemic, both in terms of their prognosis once contracted the disease and in terms of the preventative measures required for this demographic, who are at much higher risk than the rest of the population. In the “new normal”, the well-being of older adults (residing either in their own homes or in care homes) will be ideally monitored remotely. These measures would preserve the independence of individuals without compromising on their safety. In this paper we discuss aspects of the design and implementation of a resident monitoring system (RMS) with particular emphasis on overcoming the barriers for adoption among these populations, by addressing the aspects of usability, privacy and security at the core of the development of such a system. We discuss the current challenges of this research and future work on the RMS.

Stay at home order during the COVID-19 helps flatten the curve but ironically, instigate mental health problems among the people who have Substance Use Disorders. Measuring the electrical activity signals in brain using off-the-shelf consumer wearable devices such as smart wristwatch and mapping them in real time to underlying mood, behavioral and emotional changes play striking roles in postulating mental health anomalies. In this work, we propose to implement a wearable, {\it On-device Mental Anomaly Detection (OMAD)} system to detect anomalous behaviors and activities that render to mental health problems and help clinicians to design effective intervention strategies. We propose an intrinsic artifact removal model on Electroencephalogram (EEG) signal to better correlate the fine-grained behavioral changes. We design model compression technique on the artifact removal and activity recognition (main) modules. We implement a magnitude-based weight pruning technique both on convolutional neural network and Multilayer Perceptron to employ the inference phase on Nvidia Jetson Nano; one of the tightest resource-constrained devices for wearables. We experimented with three different combinations of feature extractions and artifact removal approaches. We evaluate the performance of {\it OMAD} in terms of accuracy, F1 score, memory usage and running time for both unpruned and compressed models using EEG data from both control and treatment (alcoholic) groups for different object recognition tasks. Our artifact removal model and main activity detection model achieved about $\approx$ 93\% and 90\% accuracy, respectively with significant reduction in model size (70\%) and inference time (31\%).

At the moment when this article is written, a pandemic disease is attacking our lives, our style of living and our economy. The present work uses this occasion for focusing the attention on the importance to make available a digital copy of our knowledge, history and habits. The slower passing of time inside own residence let the individual to rediscover natural indoor activities, like reading a book or watching a documentary, and try to mentally escape by a virtual visit in a museum or a city. The first evidence coming out from these sites is mainly the limits of this technology for appreciating the artworks, even inside 3D environments, and, probably the most important, the lack of standardization in terms of accessibility and quality of the products. The present work focuses the attention only on one of the aspects of the processes for studying and documenting an artwork: the data acquisition and preprocessing data fusion. For approaching these steps, an out-of-the-market 3D technology based on the combination of several laser sources will be described: the description of this kind of systems is the pretext for analyzing the main differences with the available devices and techniques today largely used in Cultural Heritage environment, but especially for highlighting how the research can try to unify the gamification with diagnostic and restoration support in this sector.

Link/channel scheduling is a process by which different channels within the allowed set of frequencies or different timeslots from a single channel are assigned to achieve minimum interference to the neighbouring nodes and higher efficiency. Existing channel scheduling approaches are often evaluated under the assumption of random mobility model. These approaches tend to average out interfering and non-interfering situations and the end results are not purely applicable to a consistent interference situation, such as the COVID-19 isolation centers. Therefore, in this paper, for a given interference level, we analyze the performance of the Interference and Priority aware Coexistence (IPC) algorithm. To this end, a controlled interference generation model is designed that can ensure consistent interference among a given number of Wireless Body Area Networks (WBANs). The performance is assessed in terms of delay, delivery, reuse, energy consumption, and throughput. Under controlled interference, the results show that the IPC algorithm guarantees better results as compared to existing link scheduling techniques.

The world has faced the devastating outbreak of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), or COVID-19, in 2020. Research in the subject matter was fast-tracked to such a point that scientists were struggling to keep up with new findings. With this increase in the scientific literature, there arose a need for organizing those documents. We describe an approach to organize and visualize the scientific literature on or related to COVID-19 using machine learning techniques so that papers on similar topics are grouped together. By doing so, the navigation of topics and related papers is simplified. We implemented this approach using the widely recognized CORD-19 dataset to present a publicly available proof of concept. Maksim Ekin Eren, Nick Solovyev, Edward Raff, Charles Nicholas, and Ben Johnson. 2020. COVID-19 Kaggle Literature Organization. In ACM Sym-posium on Document Engineering 2020 (DocEng 20), September 29-October2, 2020, Virtual Event, CA, USA.ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3395027.3419591

The rapid spread of the Coronavirus SARS-2 is a major challenge that led almost all governments worldwide to take drastic measures to respond to the tragedy. Chief among those measures is the massive lockdown of entire countries and cities, which beyond its global economic impact has created some deep social and psychological tensions within populations. While the adopted mitigation measures (including the lockdown) have generally proven useful, policymakers are now facing a critical question: how and when to lift the mitigation measures? A carefully-planned exit strategy is indeed necessary to recover from the pandemic without risking a new outbreak. Classically, exit strategies rely on mathematical modeling to predict the effect of public health interventions. Such models are unfortunately known to be sensitive to some key parameters, which are usually set based on rules-of-thumb. In this paper, we propose to augment epidemiological forecasting with actual data-driven models that will learn to fine-tune predictions for different contexts (e.g., per country). We have therefore built a pandemic simulation and forecasting toolkit that combines a deep learning estimation of the epidemiological parameters of the disease in order to predict the cases and deaths, and a genetic algorithm component searching for optimal trade-offs/policies between constraints and objectives set by decision-makers. Replaying pandemic evolution in various countries, we experimentally show that our approach yields predictions with much lower error rates than pure epidemiological models in 75% of the cases and achieves a 95% R² score when the learning is transferred and tested on unseen countries. When used for forecasting, this approach provides actionable insights into the impact of individual measures and strategies.