Background In December 2019, COVID-19 broke out in Wuhan, China, leading to national and international disruptions in health care, business, education, transportation, and nearly every aspect of our daily lives. Artificial intelligence (AI) has been leveraged amid the COVID-19 pandemic; however, little is known about its use for supporting public health efforts. Objective This scoping review aims to explore how AI technology is being used during the COVID-19 pandemic, as reported in the literature. Thus, it is the first review that describes and summarizes features of the identified AI techniques and data sets used for their development and validation. Methods A scoping review was conducted following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews). We searched the most commonly used electronic databases (eg, MEDLINE, EMBASE, and PsycInfo) between April 10 and 12, 2020. These terms were selected based on the target intervention (ie, AI) and the target disease (ie, COVID-19). Two reviewers independently conducted study selection and data extraction. A narrative approach was used to synthesize the extracted data. Results We considered 82 studies out of the 435 retrieved studies. The most common use of AI was diagnosing COVID-19 cases based on various indicators. AI was also employed in drug and vaccine discovery or repurposing and for assessing their safety. Further, the included studies used AI for forecasting the epidemic development of COVID-19 and predicting its potential hosts and reservoirs. Researchers used AI for patient outcome–related tasks such as assessing the severity of COVID-19, predicting mortality risk, its associated factors, and the length of hospital stay. AI was used for infodemiology to raise awareness to use water, sanitation, and hygiene. The most prominent AI technique used was convolutional neural network, followed by support vector machine. Conclusions The included studies showed that AI has the potential to fight against COVID-19. However, many of the proposed methods are not yet clinically accepted. Thus, the most rewarding research will be on methods promising value beyond COVID-19. More efforts are needed for developing standardized reporting protocols or guidelines for studies on AI.

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Geolocation information is not only crucial in conventional crime investigation, but also increasingly important for digital forensics as it allows for the logical fusion of digital evidence that is often fragmented across disparate mobile assets. This, in turn, often requires the reconstruction of mobility patterns. However, real-time surveillance is often difficult and costly to conduct, especially in criminal scenarios where such process needs to take place clandestinely. In this paper, we consider a vehicular tracking scenario and we propose an offline post hoc vehicular trace reconstruction mechanism that can accurately reconstruct vehicular mobility traces of a target entity by fusing the corresponding available visual and radio-frequency surveillance data. The algorithm provides a probabilistic treatment to the problem of incomplete data by means of Bayesian inference. In particular, we realize that it is very likely that a reconstructed route of a target entity will contain gaps (due to missing trace data), so we try to probabilistically fill these gaps. This allows law enforcement agents to conduct off-line tracking while characterizing the quality of available evidence.

The accurate estimation of underwater Visible Light Communication (VLC) channel conditions is challenging due to its widespread attenuation and scattering effects. The channel attenuation is a linear function of frequency and causes exponential signal power loss whereas due to the scattering effect, numerous photons are statistically generated as light beams strike water molecules and there arise security concerns. Assuming realistic underwater conditions, this paper investigates the security performance of a typical Non-Orthogonal Multiple Access (NOMA)-assisted underwater VLC system. It consists of a Floating Vehicle Transmitter (FVT), equipped with multiple Light Emitting Diodes (LEDs) to transmit the signal to two legitimate near-end and far-end Underwater Vehicles (UVs) in presence of an active/passive eavesdropper. The Channel State Information (CSI) of each transmitting link is estimated with the use of a Minimum Mean Square Error (MMSE) technique. Furthermore, we propose a LED selection mechanism to select an LED that can achieve the highest secrecy rate defined under the constraints of known and unknown CSI of legitimate and/or eavesdropping links. Using the Successive Interference Cancellation (SIC) technique, a novel closed-form secrecy outage probability expressions for the conventional single-LED and multi-LED NOMA-VLC links for both known and unknown CSI scenarios is derived. The security performance of the proposed multi-LED NOMA-VLC system is compared with the conventional single-LED NOMA-VLC system under the effect of air bubbles for both fresh and salty water. Finally, we verify the validity of the numerical results through Monte-carlo simulation analysis.INDEX TERMS Non-orthogonal multiple access, physical layer security, secrecy capacity, secrecy outage probability and underwater visible light communication.

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International audienceVehicular networks have attracted significant attention in the context of traffic control and management applications. However, vehicular networks also have important applications in crime prevention and criminal investigations. This paper presents a system for passively tracking a target vehicle whose driver is assumed to be a "person of interest." The tracking system relies on the dynamic recruitment of neighboring vehicles of the target as agents. A mobility prediction algorithm is used to probabilistically predict the target's future movement and to adjust the tracking process. Combining agent-based tracking and mobility prediction enables a target vehicle to be passively localized and tracked in an efficient manner

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