Abstract
Objectives: To describe baseline clinical characteristics of adult patients with COVID-19. Methods: We conducted a scoping review of the evidence available at LitCovid, until March 23th, 2020, and selected articles that reported the prevalence of socio-demographic characteristics, symptoms and co-morbidities in adults with COVID-19. Results: In total, 1 572 publications were published on LitCovid. We have included 56 articles in our analysis, with 89% conducted in China, and 75% contained inpatients. Three studies were conducted in North America and one in Europe. Participants age ranged from 28 to 70 years, with balanced gender distribution. Proportion of asymptomatic cases were from 2 to 79%. The most common reported symptoms were fever [4-99%], cough [4-92%], dyspnoea/shortness of breath [1-90%], fatigue 4-89%], myalgia [3-65%], and pharyngalgia [2-61%], while regarding co-morbidities we found cardiovascular disease [1-40%], hypertension [0-40%] and cerebrovascular disease [1-40%]. Such heterogeneity impairs the conduction of meta-analysis. Conclusions: The infection by COVID-19 seems to affect people in a very diverse manner and with different characteristics. With the available data it is not possible to clearly identify those at higher risk of being infected with this condition. Furthermore, the evidence from countries other than China is, at the day, too scarce.

Abstract
Online applications now routinely replicate their data at multiple sites around the world. In this paper we present ATLAS, the first state-machine replication protocol tailored for such planet-scale systems. ATLAS does not rely on a distinguished leader, so clients enjoy the same quality of service independently of their geographical locations. Furthermore, clientperceived latency improves as we add sites closer to clients. To achieve this, ATLAS minimizes the size of its quorums using an observation that concurrent data center failures are rare. It also processes a high percentage of accesses in a single round trip, even when these conflict. We experimentally demonstrate that ATLAS consistently outperforms state-of-the-art protocols in planet-scale scenarios. In particular, ATLAS is up to two times faster than Flexible Paxos with identical failure assumptions, and more than doubles the performance of Egalitarian Paxos in the YCSB benchmark.

Abstract
Today’s cloud computing techniques are becoming unsustainable for real time applications as very low latency is required with billions of connected devices. New paradigms are arising; the one that offers an integrated solution for extending cloud resources to the edge of the network and addresses current cloud issues is Fog Computing. Performing Fog Computing brings a set of challenges such as: provisioning edge nodes to perform task volumes downloaded from the Cloud; placing task volumes on edge nodes; resource management on edge nodes; need for a new programming model; programming, resource management, data consistency service discovery challenges; privacy and security and improving the quality of service (QoS) and user experience (QoE). This paper aims at introducing the Fog Computing concept and it presents a literature review on the way it is applied: context-sensitive applications and context-sensitive mobile service platforms. The result of the study is presented as the current research challenges for context aware mobile applications in Fog Computing infrastructure.

Abstract
Monitoring current communication networks and services is an increasingly complex task as a result of a growth in the number and variety of components involved. Moreover, different perspectives on network monitoring and optimisation policies must be considered to meet context-dependent monitoring requirements. To face these demanding expectations, this article proposes a semantic-based approach to support the flexible configuration of context-aware network monitoring, where traffic sampling is used to improve efficiency. Thus, a semantic layer is proposed to provide with a standard and interoperable description of the elements, requirements and relevant features in the monitoring domain. On top of this description, semantic rules are applied to make decisions regarding monitoring and auditing policies in a proactive and context-aware manner. Use cases focusing on traffic accounting and traffic classification as monitoring tasks are also provided, demonstrating the expressiveness of the ontology and the contribution of smart SWRL rules for recommending optimised configuration profiles.

Abstract
During this still increasing mobile devices proliferation age, much of human-computer interaction involves text input, and the task of typing text is provided via virtual keyboards. In a mobile setting, energy consumption is a key concern for both hardware manufacturers and software developers. Virtual keyboards are software applications, and thus, inefficient applications have a negative impact on the overall energy consumption of the underlying device. Energy consumption analysis and optimization of mobile software is a recent and active area of research. Surprisingly, there is no study analyzing the energy efficiency of the most used software keyboards and evaluating the performance advantage of its features. In this paper, we studied the energy performance of five of the most used virtual keyboards in the Android ecosystem. We measure and analyze the energy consumption in different keyboard scenarios, namely with or without using word prediction. This work presents the results of two studies: one where we instructed the keyboards to simulate the writing of a predefined input text, and another where we performed an empirical study with real users writing the same text. Our studies show that there exist relevant performance differences among the most used keyboards of the considered ecosystem, and it is possible to save nearly 18% of energy by replacing the most used keyboard in Android by the most efficient one. We also showed that is possible to save both energy and time by disabling keyboard intrinsic features and that the use of word suggestions not always compensate for energy and time.

Abstract
The significant evolution of smartphones has given ordinary people the power to create good-quality content which can then be spread, by the press, over multiple platforms. Citizens are almost always the first ones to arrive at a breaking news location and can provide the initial images of the scene. However, existing crowdsourced tools and platforms are predominantly centralized and are usually fed with unreliable and untrustworthy information. This work introduces a Crowd Journalism ecosystem whose core is a video marketplace web tool based on an organization-level decentralized system that can store, visualize, rate, and execute transactions of live-made videos. Smart contracts ensure that all the transactions are transparent and secure. This approach to Crowd Journalism exploits the inherent features of a blockchain such as offering trustful, anonymized, and immutable transactions, which has the potential to revolutionize the way news content is shared and commercially exploited.