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Variety of Big data is a significant impediment for anyone who wants to search inside a large-scale structured dataset. For example, there are millions of tables available on the Web, but the most relevant search result does not necessarily match the keyword-query exactly due to a variety of ways to represent the same information. Here we describe Hybrid.AI, a learning search engine for large-scale structured data that uses automatically generated machine learning classifiers and Unified Famous Objects (UFOs) to return the most relevant search results from a large-scale Web tables corpora. We evaluate it over this corpora, collecting 99 queries and their results from users, and observe significant relevance gain. 

Dipstick-based urinalysis is routinely used for detection of early signs of such pregnancy complications, as preeclampsia and gestational diabetes. Usually it is done in doctor’s office using an automatic dipstick analyzer. Here we present a novel smartphone-based colorimeter and demonstrate its application to the measurements of glucose and protein concentrations in biological samples. The key innovations of our approach was to combine powerful image processing encoded into a mobile phone application with a low cost 3D printed sample holder that allowed to control lighting conditions and significantly improved sensitivity. Different solutions with protein and glucose concentrations ranging from 0 to 2000 mg/dL were prepared and analyzed using our system. The smartphone-based colorimeter always correctly classified the corresponding reagent strip pads, what confirms that it can be used as a low cost alternative for commercial dipstick analyzers.