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The Findable, Accessible, Interoperable and Reusable (FAIR) data principles were created to guide the improvement of research data (Wilkinson et al., 2016). As data curators and educators, we often see individual research groups and researchers establish their own unique data collection process, resulting in poor and inconsistent data documentation. At the conclusion of the project, while the data may be accessible and understood by members within the team, it is often not readily usable to anyone outside of those most closely associated with data collection and analysis. The root cause of this is the difficulty to document the pertinent information required to capture the context in which data was captured, processed, and presented. And even when this is attempted it tends to be static and non-machine actionable. As a result, the project data might be FAIR but it is not visible and the cost of re-use is too high as currently few protocols are machine actionable. The availability of context documentation will help other researchers understand and facilitate the re-use the data. Agri-Food Data Canada operates across multiple projects in different fields and run by different institutions. It is a natural environment to recognize the need of decentralized semantic definitions where each research group can influence, modify, or adjust the definition of the data while maintaining integrity of data objects (e.g., schema, data sets, catalogues) across the ecosystem. This practice paper describes the release of the first version of the Semantic Engine leveraging OCA, an architecture to document schemas optimized for decentralized collaboration and reproducibility. OCA leverages new technologies on self-addressing identifiers and enables content-based authority vs. location-based authority. We present here the first results of the Semantic Engine development and the future application.

Crime has been and still is one of the major threats and issues for every government and society around the world. Nowadays, a new generation of (cyber)criminals exploits modern and state-of-the-art technology and tools, especially social media channels, in order to achieve their malicious purposes. Thus, Law Enforcement Agencies (LEAs), analysts and security practitioners are facing the need to engage new methods and tools which can support them in the fight and prevention of crime by minimizing the prediction and response time. This paper focuses on two specific functionalities of a crime prediction and fighting software framework which assists LEAs to adopt and utilize emerging technologies such as Data mining and Big Data tools, Semantic Analysis, Visual Intelligence and more in the context of their everyday operations. In this light, the authors of this paper present an innovative Semantic Engine with a person fusion tool as well as an ontology visualization tool as parts of a discussed framework. Both of these tools are presented in detail in separate sections so that the readers can understand better their architecture and their integration into this common framework alongside with other tools. These two tools use state-of-the-art components and software libraries in order to offer robust and future proof solutions for LEAs. Also, they can be integrated and expanded in future research and commercial projects both as independent tools as well as parts of other integrated frameworks. [ABSTRACT FROM AUTHOR]

The objective of this work is twofold: (i) automatically setting up a landslide inventory using a state-of-the art semantic engine based on data mining on online news and (ii) evaluating if the automatically generated inventory can be used to validate a regional scale landslide warning system based on rainfall-thresholds. The semantic engine scanned internet news in real time in a 50 months test period. At the end of the process, an inventory of approximately 900 landslides was automatically set up for the Tuscany region (23,000 km 2 , Italy). Using a completely automated procedure, the inventory was compared with the outputs of the regional landslide early warning system and a good correspondence was found, e.g. 84% of the events reported in the news is correctly identified by the warning system. On the basis of the obtained results, we conclude that automatic validation of landslide models using geolocalized landslide events feedback is possible. The source of data for validation can be obtained directly from the Internet channel using an appropriate semantic engine dedicated to perform a monitoring of the Google News aggregator. Moreover, validation statistics can be used to evaluate the effectiveness of the predictive model and, if deemed necessary, an update of the rainfall thresholds could be performed to obtain an improvement of the forecasting effectiveness of the warning system. In the near future, the proposed procedure could operate in continuous time and could allow for a periodic update of landslide hazard models and landslide early warning systems with minimum or none human intervention. [ABSTRACT FROM AUTHOR]

Abstract: Having dedicated prior research to identifying a range of methods for detecting the hierarchical layers of language, which also incorporates unsupervised learning toolkits and a Corpus that represents the entire ‘Human Chorus’, this paper outlines the concepts behind providing the last piece in the jigsaw. This particular piece, which ultimately will provide the ability to decipher meaning without an exhaustive crib or primer, poses the perplexing problem of dealing with the seemingly arbitrary nature of this linguistic ‘veneer’. Techniques that, if perfected, will also have a profound impact on developing a universal translator for everyday translation issues and cryptopalaeography. [Copyright &y& Elsevier]