In December 2019, a novel virus named COVID-19 emerged in the city of Wuhan, China. In early 2020, the COVID-19 virus spread in all continents of the world except Antarctica causing widespread infections and deaths due to its contagious characteristics and no medically proven treatment. The COVID-19 pandemic has been termed as the most consequential global crisis after the World Wars. The first line of defense against the COVID-19 spread are the non-pharmaceutical measures like social distancing and personal hygiene. The great pandemic affecting billions of lives economically and socially has motivated the scientific community to come up with solutions based on computer-aided digital technologies for diagnosis, prevention, and estimation of COVID-19. Some of these efforts focus on statistical and Artificial Intelligence-based analysis of the available data concerning COVID- 19. All of these scientific efforts necessitate that the data brought to service for the analysis should be open source to promote the extension, validation, and collaboration of the work in the fight against the global pandemic. Our survey is motivated by the open source efforts that can be mainly categorized as (a) COVID-19 diagnosis from CT scans, X-ray images, and cough sounds, (b) COVID-19 case reporting, transmission estimation, and prognosis from epidemiological, demographic, and mobility data, (c) COVID-19 emotional and sentiment analysis from social media, and (d) knowledge-based discovery and semantic analysis from the collection of scholarly articles covering COVID-19. We survey and compare research works in these directions that are accompanied by open source data and code. Future research directions for data-driven COVID-19 research are also debated. We hope that the article will provide the scientific community with an initiative to start open source extensible and transparent research in the collective fight against the COVID-19 pandemic.

In December 2019, a novel virus named COVID-19 emerged in the city of Wuhan, China. In early 2020, the COVID-19 virus spread in all continents of the world except Antarctica, causing widespread infections and deaths due to its contagious characteristics and no medically proven treatment. The COVID-19 pandemic has been termed as the most consequential global crisis since the World Wars. The first line of defense against the COVID-19 spread are the non-pharmaceutical measures like social distancing and personal hygiene. The great pandemic affecting billions of lives economically and socially has motivated the scientific community to come up with solutions based on computer-aided digital technologies for diagnosis, prevention, and estimation of COVID-19. Some of these efforts focus on statistical and Artificial Intelligencebased analysis of the available data concerning COVID-19. All of these scientific efforts necessitate that the data brought to service for the analysis should be open source to promote the extension, validation, and collaboration of the work in the fight against the global pandemic. Our survey is motivated by the open source efforts that can be mainly categorized as (a) COVID-19 diagnosis from CT scans, X-ray images, and cough sounds, (b) COVID-19 case reporting, transmission estimation, and prognosis from epidemiological, demographic, and mobility data, (c) COVID-19 emotional and sentiment analysis from social media, and (d) knowledge-based discovery and semantic analysis from the collection of scholarly articles covering COVID-19. We survey and compare research works in these directions that are accompanied by open source data and code. Future research directions for data-driven COVID-19 research are also debated. We hope that the article will provide the scientific community with an initiative to start open source extensible and transparent research in the collective fight against the COVID-19 pandemic.

Telecommunications networks need to guarantee that all node pairs involved in critical service communications are highly available. Here we adopt a novel approach to the problem of how to provide high levels of availability in an efficient manner. The basic idea is to embed at the physical layer a high availability set of links and nodes (termed the spine) in the network topology to support protection and routing in providing end-to-end availability. We first explore the spine concept through simple topologies illustrating the potential benefits of the approach in improving the overall network availability and the capability to support quality of resilience classes. Then, we study how the structural properties of a network topology can be used to determine heuristics to select a suitable spine and compare this with the case where all network components have the same availability. This is followed by a numerical based study comparing the heuristics with all possible spanning tree based spines for sample topologies. Our results demonstrate how to best design a physical network to support protection methods in achieving high levels of availability efficiently.

The problem of how to provide, in a cost efficient manner, high levels of availability and service differentiation in communication networks was investigated in [1-3]. The strategy adopted was to embed in the physical layer topology a high availability set of links and nodes (termed the spine). The spine enables through protection, routing and cross layer mapping, the provisioning of differentiated classes of resilience with varying levels of endto-end availability. Here we present an optimization model formulation of the spine design problem, considering link availability and the cost of upgrading link availability. The design problem seeks to minimize the cost while attaining a desired target flow availability. Extensive numerical results illustrate the benefits of modifying the availability of a subset of links of the network to implement quality of resilience classes.

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The availability of the resources in communication networks is critical, due to the impact that possible disruptions of communication services may have in the society. Therefore providing adequate levels of availability for every demand in a network is of paramount importance. In this work, we focus on the topological structure of a network to select a set of links that provide a high availability path to be used by the different end-to-end demands. This set of links constitutes a high availability structure (the spine) and is used as the working path for each demand. The backup path for each demand is edge-disjoint with the corresponding working path. This path pair provides end-to-end protection for critical service demands in the network. An exact formulation of the problem is presented and solved for small instances networks. A heuristic resolution approach with centrality measures is also put forward, with an experimental study comparing the exact and the approximate results.

In a recent series of papers [1][2][3] we studied the problem of how to provide both high levels of availability and service differentiation to traffic flows in a cost efficient manner. The basic idea developed was to embed at the physical layer a high availability set of links and nodes (termed the spine) in the network topology to support protection and routing in providing differentiated classes of resilience with varying levels of end-to-end availability. In this paper, we present an optimization model formulation of the spine design problem considering link availability and the cost of upgrading link availability. Numerical results show the advantages of modifying the availability of a subset of the network topology to provide QoR classes.

The rapid growth of social media content during the current pandemic provides useful tools for disseminating information which has also become a root for misinformation. Therefore, there is an urgent need for fact-checking and effective techniques for detecting misinformation in social media. In this work, we study the misinformation in the Arabic content of Twitter. We construct a large Arabic dataset related to COVID-19 misinformation and gold-annotate the tweets into two categories: misinformation or not. Then, we apply eight different traditional and deep machine learning models, with different features including word embeddings and word frequency. The word embedding models (FASTTEXT and word2vec) exploit more than two million Arabic tweets related to COVID-19. Experiments show that optimizing the area under the curve (AUC) improves the models' performance and the Extreme Gradient Boosting (XGBoost) presents the highest accuracy in detecting COVID-19 misinformation online.