This paper presents a comprehensive review of current literature on drone detection and classification using machine learning with different modalities. This research area has emerged in the last few years due to the rapid development of commercial and recreational drones and the associated risk to airspace safety. Addressed technologies encompass radar, visual, acoustic, and radio-frequency sensing systems. The general finding of this study demonstrates that machine learning-based classification of drones seems to be promising with many successful individual contributions. However, most of the performed research is experimental and the outcomes from different papers can hardly be compared. A general requirement-driven specification for the problem of drone detection and classification is still missing as well as reference datasets which would help in evaluating different solutions.

Background YouTube is a valuable source of health-related educational material which can have a profound impact on people’s behaviors and decisions. However, YouTube contains a wide variety of unverified content that may promote unhealthy behaviors and activities. We aim in this systematic review to provide insight into the published literature concerning the quality of health information and educational videos found on YouTube. Methods We searched Google Scholar, Medline (through PubMed), EMBASE, Scopus, Direct Science, Web of Science, and ProQuest databases to find all papers on the analysis of medical and health-related content published in English up to August 2020. Based on eligibility criteria, 202 papers were included in our study. We reviewed every article and extracted relevant data such as the number of videos and assessors, the number and type of quality categories, and the recommendations made by the authors. The extracted data from the papers were aggregated using different methods to compile the results. Results The total number of videos assessed in the selected articles is 22,300 (median = 94, interquartile range = 50.5–133). The videos were evaluated by one or multiple assessors (median = 2, interquartile range = 1–3). The video quality was assessed by scoring, categorization, or based on creators’ bias. Researchers commonly employed scoring systems that are either standardized (e.g., GQS, DISCERN, and JAMA) or based upon the guidelines and recommendations of professional associations. Results from the aggregation of scoring or categorization data indicate that health-related content on YouTube is of average to below-average quality. The compiled results from bias-based classification show that only 32% of the videos appear neutral toward the health content. Furthermore, the majority of the studies confirmed either negative or no correlation between the quality and popularity of the assessed videos. Conclusions YouTube is not a reliable source of medical and health-related information. YouTube’s popularity-driven metrics such as the number of views and likes should not be considered quality indicators. YouTube should improve its ranking and recommender system to promote higher-quality content. One way is to consider expert reviews of medical and health-related videos and to include their assessment data in the ranking algorithm.

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The McEliece public key cryptosystem (PKC) is regarded as secure in the presence of quantum computers because no efficient quantum algorithm is known for the underlying problems, which this cryptosystem is built upon. As we show in this paper, a straightforward implementation of this system may feature several side channels. Specifically, we present a Timing Attack which was executed successfully against a software implementation of the McEliece PKC. Furthermore, the critical system components for key generation and decryption are inspected to identify channels enabling power and cache attacks. Implementation aspects are proposed as countermeasures to face these attacks.

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