Leaving Your Mark: Thermal Signatures of Passwords Left on Keyboards

We leave evidence of where we’ve been and what we’ve done all over the place. From footprints to DNA, we seldom act without a trace. Our keyboards are no exception. I’m not talking just about fingerprints, DNA, food particles, or skin oils. I’m talking about heat. I saw a movie or maybe a television show […]
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Clustering data using k-means in ML.NET

Microsoft recently released a preview of a machine learning framework for .NET developers—ML.NET. I needed to perform a clustering analysis from existing data in one of my applications. This is a pretty common machine learning task, so I decided to document the basic approach in this article. We’ll use the well-worn iris data set from […]
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ML.NET—an open source, cross-platform, machine learning framework for .NET

One of the products Microsoft introduced at their 2018 Build conference was an open source, cross-platform machine learning framework for .NET developers. ML.NET will …allow .NET developers to develop their own models and infuse custom ML into their applications without prior expertise in developing or tuning machine learning models. Democratisation of machine learning—-i.e. enabling people […]
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LINQ: The Good, The Bad, and The Poorly Optimized (Part 2)

Part 2: Controlling Parameter Optimization with Plan Guides For Part 1 of this series, see LINQ: The Good, The Bad, and The Poorly Optimized. T-SQL supports a variety of “hints”. They are hints in the same sense that your mother used to hint that it’s time to wash up and come to dinner. In other […]
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LINQ: The Good, The Bad, and The Poorly Optimized (Part 1)

Part I: LINQ and sp_executesql Numerous questions raised in numerous forums mention that LINQ to SQL wraps queries in a call to the stored procedure sp_executesql. There seems to be agreement that this is done to ensure the caching of the query and query parameterization (if, indeed, parameters are supplied). Some advantages to sp_executesql, like […]
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