AskXML >>> from askxml import AskXML >>> conn = AskXML(‘file.xml’) # get an SQL cursor object >>> c = conn.cursor() >>> results = c.execute(“SELECT color FROM fruit WHERE _text LIKE ‘% kiwi'”) >>> for row in results.fetchall(): >>> print(row) [(‘green’), (‘dark green’)] # cleanup >>> c.
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Craig talked last post about our project to reorganize our whole Python codebase. This entails a lot of architectural challenges – deciding where to put each file, prioritizing which files and classes to split, and so on – which Carter will talk about more in the final post of this series.
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This cheatsheet is intended to run down the typical steps performed when conducting a web application penetration test. I will break these steps down into sub-tasks and describe the tools I recommend using at each level.
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The Google Ngram viewer is a fun/useful tool that uses Google’s vast trove of data scanned from books to plot word usage over time. Take, for example, the word Python (case sensitive):
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dejavu fits the unmet need of being a modern Web UI for Elasticsearch. Existing UIs were either built with a legacy UI and have left much to be desired from a Ux perspective or have been built with server side page rendering techniques (I am looking at you, Kibana).
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In my previous blog post, I explored some of the early ways of word embeddings and their shortcomings. The purpose of this post is to explore one of the most widely used word representations in the natural language processing industry today.
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SketchCode is a deep learning model that takes hand-drawn web mockups and converts them into working HTML code. It uses an image captioning architecture to generate its HTML markup from hand-drawn website wireframes.
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