Mailman 3 March 2024 - Isbg

Can You Play Papa's Games Without Flash
by adelebrown68＠gmail.com 20 Jun '24

20 Jun '24

There are currently a lot of free online Papa's games on https://papas-games.io website. Flipline Studios created the Papa's games series of restaurant management games. The games involve running a pizza shop, burger shop, drinks shop, bakery, and other businesses. The first Papa's game for the iPhone, iPad, and iPod Touch was released in 2007. The player can take on the role of either Papa Louie or his son, Bobby. So far, the series has twelve games. Among these are well-known games such as Papa's Scooperia, Burgeria, Bakeria, and others. Pizzeria is the most popular game. The player begins by opening a small pizzeria with only one oven, two counters, and two tables for customers to eat at. The player unlocks new equipment throughout the game that allows them to make more pizzas faster or cook more foods at once.

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Departing as a maintainer for ISBG :(
by Louis-Philippe Véronneau 15 Jun '24

15 Jun '24

Hi folks! I'm sad to announce I will no longer be able to contribute and maintain ISBG. I was working on ISBG for work, but I no longer work there and won't be using it anymore. I don't feel continuing to maintain ISBG while no longer using it myself it a great idea. Don't despair! Although I won't be working on ISBG myself, work hired someone to replace me and they will be taking over my regular duties and will thus continue to work on ISBG and maintain the ISBG package in Debian in my stead. I have given developer access on Gitlab to Taowa [1], a fellow Debian Developer and the person who's been hired to replace me at work. I trust Lukas Erlacher will continue to be of great help and will happily review Taowa's contributions until he's confident enough to grant him maintainer access :) ISBG is very cool and I learned a lot while working on it. I hope it continues to live on for a while :D [1] https://nm.debian.org/person/taowa/ -- ⢀⣴⠾⠻⢶⣦⠀ ⣾⠁⢠⠒⠀⣿⡁ Louis-Philippe Véronneau ⢿⡄⠘⠷⠚⠋ pollo(a)debian.org / veronneau.org ⠈⠳⣄

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What is the purpose of the EM algorithm in machine learning?
by Hardeep Singh 12 Jun '24

12 Jun '24

The Expectation-Maximization (EM) algorithm is a cornerstone in the field of machine learning, particularly in the realms of statistical estimation and clustering. It can be used as a powerful tool to determine the maximum probability estimates of the parameters that are derived from probabilistic theories, specifically where the model relies on latent variables that are not observed. This EM algorithm is known for its use across a variety of areas like natural computer vision, language processing bioinformatics, bioinformatics, and many more. Its capability to deal with incomplete data sets and its versatility in formulating models make it an essential tool for both researchers and professionals. https://www.sevenmentor.com/data-science-course-in-pune.php Understanding the EM Algorithm The EM algorithm is an iterative process to discover the most likely or maximum A posteriori (MAP) estimations of the parameters in statistical models, in which the model is based on unknown latent variables. The algorithm is based on two steps that are called the expectation step (E-step) as well as the Maximization step (M-step) which is why it has the name. Step of Expectation (E-step): In this step, it is where the algorithm calculates an expected amount of log-likelihood formula about the conditional spread of latent variables, based on the observations of data as well as the current estimate of the parameters of the model. This is filling in the data that is missing by estimating, which makes the next step of maximization computationally feasible. Maximization Step (M-step): Based on the assumptions computed in this step, the M step determines the parameters that will maximize the log-likelihood expected during step E. The M-step updates the models' parameters to improve the probability of results based on these updated parameters. The algorithm alternates between the two steps until it reaches a point of convergence, which means that the change in the log-likelihood, or parameter estimates are below a threshold that is predefined indicating that a local peak in the probability function is identified.

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by Aidan Roy 12 Jun '24

12 Jun '24

Wetware Computers: In the ever-expanding realm of computing technology, a fascinating concept has emerged that blurs the lines between biology and technology: wetware computers. These cutting-edge systems harness the power of biological components to perform computational tasks, opening up new possibilities in fields such as neuroscience, biotechnology, and artificial intelligence. In this comprehensive article, we’ll explore the definition, types, challenges, and more surrounding wetware computers, diving into the intricate world where biology meets computing. Defining Wetware Computers Wetware computers, also known as biocomputers or organic computers, are computing systems that utilize biological components, such as living cells or biochemical molecules, to perform computational tasks. Unlike traditional silicon-based computers, which rely on electronic circuits and digital logic gates, wetware computers leverage the inherent computational capabilities of biological systems, including their ability to process information, sense stimuli, and adapt to changing environments. The term “wetware” stems from the biological nature of the components used in these systems, which are often characterized by their fluid, aqueous environments. Wetware computers draw inspiration from biological processes found in living organisms, such as neural networks in the brain, genetic regulatory networks in cells, and metabolic pathways in biochemical reactions. Types of Wetware ComputersWetware computers encompass a diverse range of architectures and implementations, each tailored to specific applications and objectives. Some common types of wetware computers include: Neural Networks: Neural networks are computational models inspired by the construction and function of the human brain. Wetware neural networks use biological neurons, either cultured in vitro or integrated into living organisms, to perform tasks such as pattern credit, data analysis, and decision-making. These networks exhibit emergent behavior and adaptive learning capabilities, making them well-suited for tasks that require complex information processing and self-organization. DNA Computing: DNA computing exploits the information storage and processing capabilities of DNA molecules to perform computational tasks. Wetware DNA computers use DNA strands as information carriers and molecular reactions as computational operations. By encoding data in DNA sequences and manipulating them using biochemical techniques, researchers can solve optimization problems, simulate biological processes, and execute algorithms in parallel, leveraging the massive parallelism and information density of DNA molecules. Synthetic Biology: Synthetic biology combines principles from biology, chemistry, and engineering to design and construct artificial biological systems with novel functions. Wetware synthetic biology platforms employ genetically engineered cells, organisms, or biochemical pathways to perform specific tasks, such as biosensing, biomanufacturing, and environmental remediation. By programming genetic circuits and cellular behavior, researchers can create living organisms that exhibit programmable behaviors and respond to external stimuli in predetermined ways. Molecular Computing: Molecular computing utilizes molecules, such as proteins, enzymes, and small molecules, as computational substrates to perform logic and arithmetic operations. Wetware molecular computers exploit the biochemical properties of molecules to implement logic gates, circuits, and algorithms, enabling molecular-scale computation and information processing. These systems hold promise for applications in drug discovery, molecular diagnostics, and nanotechnology, where precise control and manipulation of molecular interactions are crucial. Brain-Computer Interfaces (BCIs): Brain-computer interfaces establish direct message pathways between the brain and external devices, enabling users to control computers, prosthetics, or other devices using neural signals. Wetware BCIs interface with the brain’s neural circuits, either non-invasively through electrodes placed on the scalp or invasively through implanted electrodes, to decode neural activity and translate it into commands or feedback signals. These interfaces hold potential for applications in assistive technology, neuroprosthetics, and cognitive enhancement, empowering individuals with disabilities to interact with the world around them using their thoughts alone. https://www.technowclub.in/wetware-computers/

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11 Jun '24

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by elizabethaddision2＠gmail.com 10 Jun '24

10 Jun '24

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04 Jun '24

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Version 2.1.0 is out!
by Louis-Philippe Véronneau 03 Jun '24

03 Jun '24

We just released version 2.1.0. Thanks a lot to all the people who contributed to the project since the last release! A large part of ISBG has be rewritten since the last release. This should fix a lot of the bugs people had. Here the changelog for this version: * Don't raise a error when learning a too big email spam (spamc return code 98). * Password is now stored as json data file. * Using standard cache xdg_cache_home to store information, usually $HOME/.cache/isbg/. If you have stored your password with and old releases you should re-store it and delete the old stored files, you should find the old ones as $HOME/.isbg. * We use cchardet and chardet if installed for better detect the mails encoding. * A more robust package, it can be used as a module and a script. * Using isbg/__main__ or python -m isbg to run as script. * python3 support. * Modularized. * Added a default --partialrun of 50. Use --partialrun=0 to retain the old behaviour. * Renamed sa_unwrap script to isbg_sa_unwrap, added command line options. Now it also depends of docopt. * Documentation: * Use sphinx and support to upload it to Read the docs. * Added development documentation. * Added manpages, built with sphinx. * Examples: added a crontab example in the bash_scripts directory. Note that you will need to save your passwords again since they are now being store as plaintext in a JSON file. You can find the signed release for 2.1.0 here: https://github.com/isbg/isbg/releases/tag/2.1.0 -- ,''`. : :' : Louis-Philippe Véronneau `. `'` pollo(a)debian.org / veronneau.org `-

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by bholaonlinebook23 03 Jun '24

03 Jun '24

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01 Jun '24

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