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Tux Machines


Programming Leftovers


Posted by Roy Schestowitz on Sep 17, 2023


Free, Libre, and Open Source Software Leftovers

Security Leftovers



Tkinter Change Label Text


↺ Tkinter Change Label Text


> The “config()” function can be applied on the label to change its text, while the “set()” is used on the StringVar variable to change Tkinter Label text.



Bounties Damage Open Source Projects


↺ Bounties Damage Open Source Projects


> Please don’t use bounties to incentivize Zig development.


> This blog post is inspired by this GitHub issue from three days ago: Support WASIX (see also)


> Here are some reasons why we believe bounties are a poor form of sponsorship when it comes to software development:


> • Bounties foster competition at the expense of cooperation. • Bounties are an utterly simplistic way of dealing with the business management side of creating software:[...]



[Old] The SSCCE: Short, Self-Contained, Correct (Compilable) Example


↺ The SSCCE: Short, Self-Contained, Correct (Compilable) Example


> If you are having a problem with some code and seeking help, preparing a Short, Self-Contained, Correct Example (SSCCE) is very useful. But what is an SSCCE?


> It is all in the name, really. Take a look at each part. The version prepared for others to see should be: Short (Small) - Minimise bandwidth for the example, do not bore the audience. Self-Contained - Ensure everything is included, ready to go. Correct - Copy, paste, (compile,) see is the aim. Example - Displays the problem we are trying to solve.



How CPython Implements and Uses Bloom Filters for String Processing


↺ How CPython Implements and Uses Bloom Filters for String Processing


> In our last discussion we learned all about bloom filters. It’s a unique data structure that provides membership queries in constant time while using a minimal quantity of memory. Primarily, you will find them being used in large scale and streaming applications where it is infeasible to keep all the data in memory. Examples include NoSQL databases, CDNs, load balancers, etc. However, it also has uses in some unexpected places. For instance, Python uses them in some of its string processing APIs. As string processing is one of the most common tasks in real-world code, it has to be fast. Therefore, the situations in which Python has used bloom filters and the way it has implemented them makes for an excellent case study.


> In this article we will examine in detail the places where CPython has used bloom filters. We will also cover the specific implementation detail of the bloom filter inside the string data structure of CPython and analyze how it works. So let’s get going!



Process Management in a Terminal


↺ Process Management in a Terminal


> Within a terminal you can start a process, such as updating your system packages. But what if you want to perform more tasks rather than wait for the update to finish? We can place processes in the background and let them continue to run while we run other processes in the foreground. It is possible to cancel paused processes. If needed, we can put the process back in the foreground.



Introduction to Topic Modelling in R and Python workshop


↺ Introduction to Topic Modelling in R and Python workshop


> Join our workshop on Introduction to Topic Modelling in R and Python, which is a part of our workshops for Ukraine series!


> [...]


> Description: This workshop offers an in-depth exploration of topic models, which allow extracting meaningful insights from extensive text corpora while minimizing the reliance on prior assumptions or annotated data. The workshop will start with the basics of text data preprocessing and progress to a general understanding of the underlying principles of topic modeling. It will cover a range of topic modeling techniques, such as Structural Topic Models, BiTerm, and Keyword Assisted Topic Models in R, and BERTopic in Python. We will explore the cases where each model is particularly promising. Participants will learn about the practical considerations when choosing a topic modeling algorithm, and how to apply these techniques to their own data. The lecture will be of interest to researchers and practitioners who are interested in extracting insights from large volumes of textual data, such as social media, news articles, or scientific publications.




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