Another Word For It Patrick Durusau on Topic Maps and Semantic Diversity

January 3, 2019

Getting Started with… Middle Egyptian [Middle Egyptian Code Talker?]

Filed under: Cybersecurity,Hacking,Hieroglyphics — Patrick Durusau @ 9:29 pm

Getting Started with… Middle Egyptian by Patrick J. Burns.

Middle Egyptian, sometimes referred to as Classical Egyptian, refers to the language spoken at Egypt from the beginning of the second millennium BCE to roughly 1300 BCE, or midway through the New Kingdom. It is also the written, hieroglyphic language of this period and so the medium in which the classical Egyptian literature of this period is transmitted. Funerary inscriptions, wisdom texts, heroic narratives like the “Tale of Sinuhe” or the “Shipwrecked Sailor,” and religious hymns have all come down to us in Middle Egyptian hieroglyphic. We also have papyri from this period written in a cursive script known as hieratic. The “middle” separates this phase of the Egyptian language from that of the previous millennium, or Old Egyptian (for example, the “pyramid” texts), and Late Egyptian, which begins in the second half of the New Kingdom and lasts until roughly 700 BCE with the emergence of Demotic. …

It’s been years since I seriously looked at a Middle Egyptian grammar or text but as a hobby, you could do far worse.

For hackers it offers the potential to keep records only you can read.

I don’t mean illegible, we can all do that, but written in a meaningful script but decodeable only by you.

Even better, you can take known religious texts, quotations for your notes. Various law enforcement agencies can hire (hope they charge top dollar) experts to translate your notes. Standard Middle Egyptian religious texts. Maybe that’s your thing. No way to prove otherwise.

The other upside is your support for the publishing of Middle Egyptian grammars, readers, and payments to Middle Egyptian experts by authorities for translation of standard texts. Bes will see the humor in such payments.

Enjoy!

May 8, 2018

2,000+ New Egyptian Hieroglyphs Coming Soon! [Code Talker Security?]

Filed under: Hieroglyphics,Security — Patrick Durusau @ 7:37 pm

Soon You May Be Able to Text with 2,000 Egyptian Hieroglyphs by Sarah E. Bond.

From the post:

Collaborations among Egyptologists and digital linguistics promise global visualizations of what was written on inscriptions, papyri, wall paintings, and other sources of Hieroglyphs. It may also allow for more popular knowledge of Egyptian Hieroglyphs and encourage its assimilation into popular language-learning apps like Duolingo.

Over 2,000 new Hieroglyphs may soon be available for use on cell phones, computers, and other digital devices. The Unicode Consortium recently released a revised draft of standards for encoding Egyptian Hieroglyphs. If approved, the available Hieroglyphs will provide greater access and global uniformity for Egyptologists, covering a much longer period of Hieroglyphic usage than ever before. The proposal is part of a larger effort between the Unicode Consortium, ancient linguists, font designers, and the federal government to attempt to study, preserve, and then digitally represent ancient and endangered languages through the use of computer code.

Certainly a boon for Egyptologists but don’t miss the opportunity to use Egyptian from different historical periods as a secure language.

Before you say: “Security through obscurity is a bad idea,” remember that Navajo code talkers worked quite well during World War II.

Moreover, in adapting an ancient language to a modern context, you can shift the meaning of words such that standard dictionaries and tools aren’t useful.

Being always mindful of the question: How long does this message need to remain secure? Messages about an action are of little value once an action is public. Events replace hopes and aspirations.

Enjoy!

May 19, 2017

SketchRNN model released in Magenta [Hieroglyphs/Cuneiform Anyone?]

Filed under: Cuneiform,Hieroglyphics,Neural Networks — Patrick Durusau @ 7:14 pm

SketchRNN model released in Magenta by Douglas Eck.

From the post:

Sketch-RNN, a generative model for vector drawings, is now available in Magenta. For an overview of the model, see the Google Research blog from April 2017, Teaching Machines to Draw (David Ha). For the technical machine learning details, see the arXiv paper A Neural Representation of Sketch Drawings (David Ha and Douglas Eck).

To try out Sketch-RNN, visit the Magenta GitHub for instructions. We’ve provided trained models, code for you to train your own models in TensorFlow and a Jupyter notebook tutorial (check it out!)

The code release is timed to coincide with a Google Creative Lab data release. Visit Quick, Draw! The Data for more information. For versions of the data pre-processed to work with Sketch-RNN, please refer to the GitHub repo for more information.

We’ll leave you with a look at yoga poses generated by moving through the learned representation (latent space) of the model trained on yoga drawings. Notice how it gets confused at around 10 seconds when it moves from poses standing towards poses done on a yoga mat. In our arXiv paper A Neural Representation of Sketch Drawings we discuss reasons for this behavior.

The paper, A Neural Representation of Sketch Drawings mentions:


ShadowDraw [17] is an interactive system that predicts what a finished drawing looks like based on a set of incomplete brush strokes from the user while the sketch is being drawn. ShadowDraw used a dataset of 30K raster images combined with extracted vectorized features. In this work, we use a much larger dataset of vector sketches that is made publicly available.

ShadowDraw is described at: ShadowDraw: Real-Time User Guidance for Freehand Drawing as:


We present ShadowDraw, a system for guiding the freeform drawing of objects. As the user draws, ShadowDraw dynamically updates a shadow image underlying the user’s strokes. The shadows are suggestive of object contours that guide the user as they continue drawing. This paradigm is similar to tracing, with two major differences. First, we do not provide a single image from which the user can trace; rather ShadowDraw automatically blends relevant images from a large database to construct the shadows. Second, the system dynamically adapts to the user’s drawings in real-time and produces suggestions accordingly. ShadowDraw works by efficiently matching local edge patches between the query, constructed from the current drawing, and a database of images. A hashing technique enforces both local and global similarity and provides sufficient speed for interactive feedback. Shadows are created by aggregating the top retrieved edge maps, spatially weighted by their match scores. We test our approach with human subjects and show comparisons between the drawings that were produced with and without the system. The results show that our system produces more realistically proportioned line drawings.

My first thought was the use of such techniques to assist in copying hieroglyphs or cuneiform as such or perhaps to assist in the practice of such glyphs.

OK, that may not have been your first thought but you have to admit it would make a rocking demonstration!

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