Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Earth Observation in Support of Operational Maritime Surveillance

Abstract not availableJRC.G-Institute for the Protection and the Security of the Citizen (Ispra

Assessing cyber challenges of maritime navigation

This paper provides a close investigation into the landscape of both cyber threats and actual incidents in the maritime sector, identifying the cyber trends and challenges as they relate to safe navigation and marine shipping. As an important subset of cyber threats that impact many maritime systems, the vulnerabilities of satellite navigation systems, in particular the Global Positioning System (GPS), receive special attention. For this article, a systematic literature review was conducted, complemented by the research and analysis of a specific spoofing event. Analyzing available resources, we might summarize that a shift in mind-set is essential to direct more attention and resources toward cybersecurity as well as the necessity for manufacturers to improve the cybersecurity of their products, as shipping systems currently remain vulnerable to cybercriminals. There is a need for multiple positioning, navigation, and timing (PNT) systems onboard maritime vessels to complement GPS-only navigation. The use of multiple satellite navigation constellations, public as well as private, in combination with the terrestrial components of an enhanced LOng-RAnge Navigation (eLoran) system and ports\u27 laser-based aid system for berthing and docking should provide the shipping industry with the direly needed increased protection from cyber-attackers for the foreseeable future

MARITIME AWARENESS FOR COUNTER-PIRACY IN THE GULF OF ADEN

Maritime awareness is a keystone of counter-piracy activities, as they are nowadays unfortunately called for in the Gulf of Aden and the Western Indian Ocean. There are a number of space-based systems that can be used to obtain knowledge of shipping and ship traffic patterns beyond coastal range, e. g. Satellite AIS, LRIT and satellite SAR. Based on data gathered during a trial in 2010, this paper analyses the capabilities of these systems when used to obtain a fused maritime picture. It is concluded that all data sources contribute to the maritime picture, but that in particular for Satellite AIS the update rates need to increase to enable accurate fusion of the non-cooperative data

The SUMO Ship Detector Algorithm for Satellite Radar Images

Search for Unidentified Maritime Objects (SUMO) is an algorithm for ship detection in satellite Synthetic Aperture Radar (SAR) images. It has been developed over the course of more than 15 years, using a large amount of SAR images from almost all available SAR satellites operating in L-, C- and X-band. As validated by benchmark tests, it performs very well on a wide range of SAR image modes (from Spotlight to ScanSAR) and resolutions (from 1–100 m) and for all types and sizes of ships, within the physical limits imposed by the radar imaging. This paper describes, in detail, the algorithmic approach in all of the steps of the ship detection: land masking, clutter estimation, detection thresholding, target clustering, ship attribute estimation and false alarm suppression. SUMO is a pixel-based CFAR (Constant False Alarm Rate) detector for multi-look radar images. It assumes a K distribution for the sea clutter, corrected however for deviations of the actual sea clutter from this distribution, implementing a fast and robust method for the clutter background estimation. The clustering of detected pixels into targets (ships) uses several thresholds to deal with the typically irregular distribution of the radar backscatter over a ship. In a multi-polarization image, the different channels are fused. Azimuth ambiguities, a common source of false alarms in ship detection, are removed. A reliability indicator is computed for each target. In post-processing, using the results of a series of images, additional false alarms from recurrent (fixed) targets including range ambiguities are also removed. SUMO can run in semi-automatic mode, where an operator can verify each detected target. It can also run in fully automatic mode, where batches of over 10,000 images have successfully been processed in less than two hours. The number of satellite SAR systems keeps increasing, as does their application to maritime surveillance. The open data policy of the EU’s Copernicus program, which includes the Sentinel-1 satellite, has hugely increased the availability of SAR images. This paper aims to cater to the consequently expected wider demand for knowledge about SAR ship detectors

Ship classification in high and very high resolution satellite SAR imagery

To serve the security of the maritime domain, ship self-reporting systems provide information on the cooperative vessels. However, non-reporting ships should be also monitored. Satellite images can be used to detect and classify non-reporting ships. Synthetic Aperture Radar (SAR) offers monitoring capabilities regardless of clouds or daylight, and hence it is used for satellite global monitoring. Different satellite SAR systems are deployed, from European ones such as Sentinel-1, to national ones such as TerraSAR-X, presenting very diverse characteristics from their coverage to their image resolution. In this paper, two ship classification methods are presented, a method developed for use on high (20 m) resolution SAR images (Sentinel-1 dataset), and a method developed for use on very high (3 m) resolution ones (TerraSAR-X dataset). In a cross-application experiment, both methods are evaluated on both datasets. The exercise quantifies the methods’ performance across resolutions, highlighting their pros and cons in this challenging application