Programming Leftovers

posted by Roy Schestowitz on Sep 12, 2022

• Game of Trees 0.75 released

  Stefan Sperling (stsp@) noted the release of version 0.75 of Game of Trees: [...]

• You Can't Do That: Abstracting over Ownership in Rust with Higher-Rank Type Bounds. Or Can You?

  Let's set the stage first: The problem I'm talking about relates to abstracting over borrows and owned values when combined with functions or something that uses higher-kinded trait bounds. In other words: one wants to create an API where it's possible to either borrow or clone out of some input value. Think of a generic function that can produce both a String and a &str.

• Running a Shortest Splitline Algorithm on the UK - and other mapping adventures

  The Splitline Algorithm is, conceptually, very simple:

  1) Divide the entire map in half based on population.

  2) Repeat (1) for each half.

  3) Once you have reached the target number of voters in each segment, stop.

• Recursive-length Prefix (RLP)/Simple Serialize (SSZ)

  The problem: data needs to be encoded/decoded over the wire, but also for hash verification (a transaction is signed by signing the RLP hash of the transaction data, blocks are identified by the RLP hash of their header). Additionally, for some cases, there should be support for efficient encoding of the merkle tree data structure.

• How does SPVM resolve the problems of Perl numeric operations? | Yuki Kimoto Perl Blog - Perl Club [blogs.perl.org]

  How does SPVM resolve the problems of Perl numeric operations?

  I hear Perl have the problems of numeric operation.

  I realized this problems, and try to resolve them using SPVM. (SPVM is yet experimental release).

• Tales Tips and Tricks: GSoC 2022 Final Report

  So here we have it, the end of my Google Summer of Code journey. A few more than a hundred days have passed, and I can already tell that the seeds have been sown for me to keep collaborating with open source software from here on out.

  My project’s primary goal was to create unit tests using KUnit for the AMDGPU driver focusing on code used by GPUs from the same generation of the GPU “RX 580” (DCE 11.2). We predicted that KUnit would have some limitations in regards to testing GPU’s drivers, so we expected to see some collaboration in that sense. Finally, we knew that I would be working in parallel with people writing tests for newer generations of GPUs (DCN). I planned to keep track of my weekly progress in my blog, trying to create an introductory material that could help future newcomers.

  For starters, this project was completely different from what I had in mind, given that it was far from an individual experience with the Linux Kernel community; it was actually a team effort to introduce unit testing to the AMD display driver in a way that would encourage the community to spread KUnit into other GPU drivers.

• Update: xor-kpa.py Version 0.0.6 | Didier Stevens

  This is an update for my tool to perform XOR known plaintext attacks: xor-kpa.py.

  The tool has been updated for Python 3, and 3 new plaintext have been added, all for Cobalt Strike configurations.

• Update: hex-to-bin.py Version 0.0.6 | Didier Stevens

  This is a small update: when non-hexadecimal characters are found, they are listed before an exception is raised.

• RObservations #38: Visualizing Average Delay Times On TTC Subway Stations | bensstats

  Any Torontonian who has commuted regularly on the TTC has probably experienced their fair share of delays on the subway. Having experienced a few recently I was inspired to visualize the average delay times across all stops on the subway. What are the stations with the longest delays on average this past year? Could we make a nice visual with it?

• Dirk Eddelbuettel: RcppArmadillo 0.11.2.4.0 on CRAN: Bugfix and Deprecation

  Armadillo is a powerful and expressive C++ template library for linear algebra and scientific computing. It aims towards a good balance between speed and ease of use, has a syntax deliberately close to Matlab, and is useful for algorithm development directly in C++, or quick conversion of research code into production environments. RcppArmadillo integrates this library with the R environment and language–and is widely used by (currently) 1016 packages other packages on CRAN, downloaded 26.2 million times (per the partial logs from the cloud mirrors of CRAN), and the CSDA paper (preprint / vignette) by Conrad and myself has been cited 493 times according to Google Scholar.

• Assess Performance of the Classification Model - finnstats

  Assess Performance of the Classification Model, We can evaluate a classification model’s effectiveness using a metric called the Matthews correlation coefficient (MCC).

• Professional Shiny App UI and Layouts with imola and shiny.fluent - R programming

  Shiny as a web framework is known for its combination of power, simplicity, and flexibility. It’s a convenient tool for app development at all levels, from proof of concept to production-grade Shiny apps. With additional packages like {imola} and {shiny.fluent}, anyone can create professional Shiny app UIs with custom layout options.

  These characteristics make Shiny an easy-to-pick-up technology, helping teams and R users – without web development experience – to create applications and automate tasks fast. But soon we realize that this simplicity comes with a cost: boilerplate applications that look basic and monotonous.

• How to Concatenate Strings in R

  We can perform string concatenation in R language using the paste() and cat() methods.

• Notes of a Dabbler - Exploring OMPR with HiGHS solver

  There is a class of software for modeling optimization problems referred to as algebraic modeling systems which provide a unified interface to formulate optimization problems in a manner that is close to mathematical depiction and have the ability to link to different types of solvers (sparing the user from solver specific ways of formulating the problem). Both commercial and open source options are available. GAMS and AMPL are examples of commercial options. The popular open source options are JuMP in Julia and Pyomo in python. I have typically used Pyomo in Python but have explored using it from R. I recently became aware of algebraic modeling system in R provided by OMPR package developed by Dirk Schumacher.

• How to Apply AI to Small Data Sets? - finnstats

  How to Apply AI to Small Data Sets? Data science and artificial intelligence collaborate to better gather, classify, analyze, and interpret data.

  But all we constantly hear about is employing AI to comprehend huge data sets.

  This is because individuals can typically understand small data sets, therefore using AI to analyze and interpret them is not necessary.

• Visualizing OLS Linear Regression Assumptions in R | by Trevor French | Trevor French | Sep, 2022 | Medium

  While most of the time it’s sufficient to programmatically validate your model assumptions, sometimes it’s helpful to visualize them. Here are a few quick ways you can do just that.

• Global vs. local assignment operators in R (‘<<-’ vs. ‘<-’) | by Trevor French | Trevor French | Sep, 2022 | Medium

  Understanding the difference between local and global assignment operators in R can be tricky to get your head around. Here’s an example which should clear things up.

• How to Find Optimal Clusters in R? - Data Science Tutorials

  How to Find Optimal Clusters in R, K-means clustering is one of the most widely used clustering techniques in machine learning.

  With the K-means clustering technique, each observation in a dataset is assigned to one of K clusters.

  The ultimate goal is to have K clusters in which the observations are relatively similar to one another within each cluster and considerably dissimilar from one another within different clusters.