Podcasts about caesar cipher

There has been a series of significant issues with a voter roll algorithm in the state of New York, interfering with a fair election process. Yes, you read that right: an dangerous algorithm was found in the New York State Board of Elections voter roll. Dr. Jerome Corsi talks with Andrew Paquette, formerly of the New York Citizens' Audit, who discovered the algorithm: "Voters in New York State are identified by two identification numbers.  This study has discovered strong evidence that both numbers have been algorithmically manipulated to produce steganographically concealed record attribute information. One of the several algorithms discovered has been solved. It first utilizes a mechanism nearly identical to the simple ‘Caesar Cipher' to change the order of a group of ID numbers. Then, it interlaces them the way a deck of cards is arranged to create a ‘stacked deck'. The algorithmic modifications create hidden structure within voter ID numbers. The structure can be used to covertly tag fraudulent records for later use." (Paquette, Research Abstract, https://www.researchgate.net/publication/370835885_The_Caesar_cipher_and_stacking_the_deck_in_New_York_State_voter_rolls).The question is: WHY is there an algorithm in the New York State Board of Elections voter roll, who put it there and to what end?Dr. Corsi and Paquette go in-depth to answer the question on The Truth CentralIf you like what we are doing, please support our Sponsors:Get RX Meds Now: https://www.getrxmedsnow.comMyVitalC https://www.thetruthcentral.com/myvitalc-ess60-in-organic-olive-oil/Swiss America: https://www.swissamerica.com/offer/CorsiRMP.phpGet Dr. Corsi's new book, The Assassination of President John F. Kennedy: The Final Analysis: Forensic Analysis of the JFK Autopsy X-Rays Proves Two Headshots from the Right Front and One from the Rear, here: https://www.amazon.com/Assassination-President-John-Kennedy-Headshots/dp/B0CXLN1PX1/ref=sr_1_1?crid=20W8UDU55IGJJ&dib=eyJ2IjoiMSJ9.ymVX8y9V--_ztRoswluApKEN-WlqxoqrowcQP34CE3HdXRudvQJnTLmYKMMfv0gMYwaTTk_Ne3ssid8YroEAFg.e8i1TLonh9QRzDTIJSmDqJHrmMTVKBhCL7iTARroSzQ&dib_tag=se&keywords=jerome+r.+corsi+%2B+jfk&qid=1710126183&sprefix=%2Caps%2C275&sr=8-1Join Dr. Jerome Corsi on Substack: https://jeromecorsiphd.substack.com/Visit The Truth Central website: https://www.thetruthcentral.comGet your FREE copy of Dr. Corsi's new book with Swiss America CEO Dean Heskin, How the Coming Global Crash Will Create a Historic Gold Rush by calling: 800-519-6268Follow Dr. Jerome Corsi on X: @corsijerome1Our link to where to get the Marco Polo 650-Page Book on the Hunter Biden laptop & Biden family crimes free online:https://www.thetruthcentral.com/marco-polo-publishes-650-page-book-on-hunter-biden-laptop-biden-family-crimes-available-free-online/Become a supporter of this podcast: https://www.spreaker.com/podcast/the-truth-central-with-dr-jerome-corsi--5810661/support.

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Mech Interp Challenge: January - Deciphering the Caesar Cipher Model, published by CallumMcDougall on January 1, 2024 on The AI Alignment Forum. I'm writing this post to discuss solutions to the November challenge, and present the challenge for this January. If you've not read the first post in this sequence, I'd recommend starting there - it outlines the purpose behind these challenges, and recommended prerequisite material. January Problem The problem for this month is interpreting a model which has been trained to classify a sequence according to the Caeser cipher shift value which was used to encode it. The sequences have been generated by taking English sentences containing only lowercase letters & punctuation, and choosing a random value X between 0 and 25 to rotate the letters (e.g. if the value was 3, then a becomes d, b becomes e, and so on, finishing with z becoming c). The model was trained using cross entropy loss to predict the shift value X for the text it's been fed, at every sequence position (so for a single sequence, the correct value will be the same at every sequence position, but since the model has bidirectional attention, it will find it easier to predict the value of X at later sequence positions). There are 3 different modes to the problem, to give you some more options! Each mode corresponds to a different dataset, but the same task & same model architecture. Easy mode In easy mode, the data was generated by: Choosing the 100 most frequent 3-letter words in the English Language (as approximated from a text file containing the book "Hitchhiker's Guide To The Galaxy") Choosing words from this len-100 list, with probabilities proportional to their frequency in the book Separating these words with spaces The model uses single-character tokenization. The vocabulary size is 27: each lowercase letter, plus whitespace. Medium mode This is identical to easy, the only difference is that the words are drawn from this len-100 list uniformly, rather than according to their true frequencies. Hard mode In hard mode, the data was generated from random slices of OpenWebText (i.e. natural language text from the internet). It was processed by converting all uppercase characters to lowercase, then removing all characters except for the 26 lowercase letters plus the ten characters "n .,:;?!'" (i.e. newline, space, and 8 common punctuation characters). In all 3 modes, the model's architecture is the same, and it was trained the same way. The model is attention only. It has 2 attention layers, with 2 heads per layer. It was trained with weight decay, and an Adam optimizer with linearly decaying learning rate. I don't expect this problem to be as difficult as some of the others in this sequence, however the presence of MLPs does provide a different kind of challenge. You can find more details on the Streamlit page, or this Colab notebook. Feel free to reach out if you have any questions! November Problem - Solutions The single attention head implements uniform attention to all previous tokens in the sequence. The OV matrix is essentially one-dimensional: it projects each token with value s onto su, where u is some vector in the residual stream learned by the model. The component of the residual stream in this direction then represents the cumulative mean (note, the cumulative mean rather than the cumulative sum, because attention is finite - for example, we expect the component to be the same after the sequences (1, 1, 2) and (1, 1, 2, 1, 1, 2) because net attention to each different token value will be the same). The model's "positive cumsum prediction direction" aligns closely with u, and vice-versa for the "negative cumsum prediction direction" - this allows the model to already get >50% accuracy before the MLP even comes into play. But without the MLP, the mod...

This episode is also available as a blog post: https://karatecoder.tech/python-cryptography-using-simple-caesar-cipher/

Dan, with the help of Alice, delves into the history of one of the most commonly known cipher - the caesar cipher. We discuss what type of code it is, the origins, and how it's been broken. We also talk a bit about the vigenère cipher and how it works. Find our sources on twitter @nerdhourspod.

Rapid Unscheduled Dissassembly - https://en.wiktionary.org/wiki/rapid_unplanned_disassembly ・ 80s Editor Template: https://www.schrankmonster.de/2019/05/23/80s-code-editor-theme/ ・ Erich Gamma - Design Patterns Buch - https://en.wikipedia.org/wiki/Design_Patterns ・ Erich Gamma wechselt zu Microsoft - https://www.heise.de/developer/artikel/Design-Patterns-Tools-und-Co-Im-Gespraech-mit-Erich-Gamma-2750465.html ・ HAL9000: https://en.wikipedia.org/wiki/HAL_9000 ・ Caesar Cipher: http://practicalcryptography.com/ciphers/caesar-cipher/ ・ Symbolic Links on Windows: https://www.howtogeek.com/howto/16226/complete-guide-to-symbolic-links-symlinks-on-windows-or-linux/ ・ Symbolic Links allgemein - https://en.wikipedia.org/wiki/Symbolic_link ・ Typora Markdown Editor - https://typora.io/ ・ MindForger Markdown and IDE - https://www.mindforger.com/ ・ Magisches Denken - https://de.wikipedia.org/wiki/Magisches_Denken ・ Kognitive Dissonanz - https://de.wikipedia.org/wiki/Kognitive_Dissonanz ・ Nicht ganz preiswerter aber effizientester Felgendynamo: Dynamo SPECIAL - http://www.velogical-engineering.com/velogical-felgendynamo---standard-fahrrad-dynamo---leichtlauf-gewicht-effizienz ・ USB Werk busch+müller: https://www.bumm.de/de/produkte/stromversorgung/parent/3610/produkt/361bw.html ・ Minimallader selbst bauen - https://fahrradzukunft.de/12/minimal-lader/ ・ Microsoft Project XCloud: https://twitter.com/Xbox/status/1137833126959280128 ・ Shodan.io - https://Shodan.io ・ Rossmann Group Videos: https://www.youtube.com/user/rossmanngroup ・ Espressif: https://www.espressif.com/ ・ Geschirrspüler würfelt wer ihn leer räumt: https://www.schrankmonster.de/2019/02/16/how-do-you-determine-who-needs-to-clear-out-the-dishwasher/ ・ Verbraucherschutz Garantie - Recht auf Geräte in akzeptabler Qualität: https://www.heise.de/newsticker/meldung/Drastische-Strafe-fuer-LG-nach-unzureichendem-Kundensupport-4516429.html

Episode 2 Show Notes (http://pythonoutloud.com/episode2): pythonoutloud.com/episode2 (http://pythonoutloud.com/episode2) In this episode, Kevin’s mental math skills are put to the test as we explore options for calculating exponentials in Python. The discussion begins with two popular sites for practicing programming, projecteuler.net (https://projecteuler.net/) and pythonchallenge.com (http://pythonchallenge.com/), the latter being the inspiration for this episode. Both sites have fun problems to tackle, but we find Python Challenge more interesting because it requires more creative thinking. After some “quality” jokes our statistician followers will enjoy, as well as bad comic book references, we also highlight Python’s flexibility for solving mathematical problems in many, many, many, many ways. In the process, we find out why the caret (^) isn’t used in Python for exponentiation, per the following Stack Exchange discussion: https://softwareengineering.stackexchange.com/a/331392 That’s not to say you can’t just inherit from the built-in int class to create other uses for carets in Python because.... again.… many, many, many, many ways to do the same thing in Python. For more background, we recommend the following blog post: Episode 2 Teaser Companion Blog Post (http://pythonoutloud.com/articles/the-magic-behind-integer-arithmetic-in-python): pythonoutloud.com/articles/the-magic-behind-integer-arithmetic-in-python (http://pythonoutloud.com/articles/the-magic-behind-integer-arithmetic-in-python) This episode features the song "Happy Ukulele" by Scott Holmes (http://freemusicarchive.org/music/Scott_Holmes/) and the songs "And So Then", "Curiousity", and "Puzzle Pieces" by Lee Rosevere (http://freemusicarchive.org/music/Lee_Rosevere/). Contest: No, you’re not hearing a strange, alien language at the end of the episode. We’re telling a joke that’s been encoded with a Caesar Cipher as a preview of our next episode! Tweet the properly deciphered version of the dialog below to @PythonoutLoud with hashtags #WhenInRome and #PythonOutLoud, and we’ll randomly select a winner for a special prize! Mpqem, Mpqem. Yjq’u vjgtg? Ecguct! Ecguct yjq? Ecguct swkemna dghqtg ujg igvu cyca!

Here is Podcast number 4 which covers a program for which I scripted several of its items in order for it to encrypt a string of words using a Caesar Cipher and similarly to decode a string that was encrypted with a Caesar Cipher.  Part of the program to encrypt and decode Caesar Cipher messages on the right and the same program in action on the left. Subscribe to the podcast for free in iTunes!

Melvyn Bragg and guests discuss the origins and history of codes. In October 1586, in the forbidding hall of Fotheringhay Castle, Mary Queen of Scots was on trial for her life. Accused of treason and denied legal representation, she sat alone in the shadow of a vast and empty throne belonging to her absent cousin and arch rival Elizabeth I of England. Walsingham, Elizabeth's Principal Secretary, had already arrested and executed Mary's fellow conspirators, her only hope lay in the code she had used in all her letters concerning the plot. If her cipher remained unbroken she might yet be saved. Not for the first time the life of an individual and the course of history depended on the arcane art of Cryptography.What are the origins of this secretive science? And what links the ‘Caesar Cipher' with the complex algorithms which underpin so much of our modern age?With Simon Singh, science writer and author of The Code Book: The Secret History of Codes and Code-Breaking; Professor Fred Piper, Director of the Information Security Group at Royal Holloway, University of London and co-author of Cryptography: A Very Short Introduction; Lisa Jardine, Professor of Renaissance Studies at Queen Mary, University of London and author of Ingenious Pursuits.

Melvyn Bragg and guests discuss the origins and history of codes. In October 1586, in the forbidding hall of Fotheringhay Castle, Mary Queen of Scots was on trial for her life. Accused of treason and denied legal representation, she sat alone in the shadow of a vast and empty throne belonging to her absent cousin and arch rival Elizabeth I of England. Walsingham, Elizabeth’s Principal Secretary, had already arrested and executed Mary’s fellow conspirators, her only hope lay in the code she had used in all her letters concerning the plot. If her cipher remained unbroken she might yet be saved. Not for the first time the life of an individual and the course of history depended on the arcane art of Cryptography.What are the origins of this secretive science? And what links the ‘Caesar Cipher’ with the complex algorithms which underpin so much of our modern age?With Simon Singh, science writer and author of The Code Book: The Secret History of Codes and Code-Breaking; Professor Fred Piper, Director of the Information Security Group at Royal Holloway, University of London and co-author of Cryptography: A Very Short Introduction; Lisa Jardine, Professor of Renaissance Studies at Queen Mary, University of London and author of Ingenious Pursuits.