ARCHIVED: A category for parsing your iOS app's embedded.mobileprovision at runtime. Use it to, among other things, determine at runtime whether your app is being distributed as dev, release, ad hoc, app store, or enterprise.

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swati1024 / Torrents

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Skip to content Search… All gists Back to GitHub Sign in Sign up Instantly share code, notes, and snippets. @giansalex giansalex/torrent-courses-download-list.md forked from M-Younus/torrent courses download-list Last active 2 days ago 15188 Code Revisions 15 Stars 151 Forks 88 <script src="https://gist.github.com/giansalex/4cd3631e94433bbbd71bf07aedb33a7b.js"></script> torrent-courses-download-list.md Torrent Courses List Download http://kickass.to/infiniteskills-learning-jquery-mobile-working-files-t7967156.html http://kickass.to/lynda-bootstrap-3-advanced-web-development-2013-eng-t8167587.html http://kickass.to/lynda-css-advanced-typographic-techniques-t7928210.html http://kickass.to/lynda-html5-projects-interactive-charts-2013-eng-t8167670.html http://kickass.to/vtc-html5-css3-responsive-web-design-course-t7922533.html http://kickass.to/10gen-m101js-mongodb-for-node-js-developers-2013-eng-t8165205.html http://kickass.to/cbt-nuggets-amazon-web-services-aws-foundations-t7839734.html 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http://www.seedpeer.me/details/6571637/TutsPlus---Modern-Testing-in-PHP-with-Codeception.html http://www.seedpeer.me/details/6095651/Tutsplus---Parallax-Scrolling-for-Web-Design.html http://www.seedpeer.me/details/6574591/TutsPlus---Say-Yo-to-Yeoman.html http://www.seedpeer.me/details/4811335/Tutsplus---Test-Driven-Development-in-Ruby.html http://www.seedpeer.me/details/6268980/TutsPlus-Test-Driven-Development-With-CoffeeScript-and-Jasmine.html http://www.seedpeer.me/details/6185755/TutsPlus---The-MVC-Mindser-Jeffery-Way---ICARUS.html http://www.seedpeer.me/details/5024493/TutsPlus---Venture-Into-Vim.html http://www.seedpeer.me/details/6286416/Tutsplus---Vim-for-Advanced-Users.html http://www.seedpeer.me/details/6585031/Tutsplus---WordPress-Hackers-Guide-to-the-Galaxy.html http://www.seedpeer.me/details/4848477/TutsPlus---Writing-Modular-JavaScript.html @giansalex Owner Author giansalex commented on 26 Feb 2018 • SOLID http://www.allitebooks.com/beginning-solid-principles-and-design-patterns-for-asp-net-developers/ @giansalex Owner Author giansalex commented on 7 Mar 2018 Udemy: AWS Arquitecto de Soluciones Certificado Asociado https://mega.co.nz/#!ZzhGWSAL!wuthFca0SdJBjmaP5lFX0QF6PeMsrdclKFXlZL1Rsi4 Pass: gratismas.org @giansalex Owner Author giansalex commented on 7 Mar 2018 Go lang Complete https://www.freetutorials.us/wp-content/uploads/2017/11/FreeTutorials.Us-Udemy-go-the-complete-developers-guide.torrent @GCPBigData GCPBigData commented on 15 Jul 2018 go books https://drive.google.com/open?id=1d6OsFAn8kpHCXtw0bcoYuyHqrAdGZva0 @freisrael freisrael commented on 14 Aug 2018 giansalex thanks for sharing. I am looking for learning phython with Joe Marini. It would be great if you post it. @FirstBoy1 FirstBoy1 commented on 25 May 2019 Can anyone provide this book "Getting started with Spring Framework: covers Spring 5" by " J Sharma (Author), Ashish Sarin ". Thanks in advance @okreka okreka commented on 31 May 2019 Can anyone provide "Windows Presentation Foundation Masterclass" course from Udemy. Thanks in advance @singhaltanvi singhaltanvi commented on 8 Aug 2019 can anyone provide 'sedimentology and petroleum geology' course from Udemy. Thanks in advance. @kumarsreenivas051 kumarsreenivas051 commented on 9 Sep 2019 Can anyone provide "Programming languages A,B and C" course from Coursera. Thanks in advance. @BrunoMoreno BrunoMoreno commented on 11 Sep 2019 The link for the torrents in piratebay, now is .org to the correct url. @sany2k8 sany2k8 commented on 24 Sep 2019 Can anyone add this The Complete Hands-On Course to Master Apache Airflow @pharaoh1 pharaoh1 commented on 30 Sep 2019 can you pls add this course to your list https://www.udemy.com/course/advanced-python3/ @SushantDhote936 SushantDhote936 commented on 1 Oct 2019 Can you add Plural Sight CISSP @allayGerald allayGerald commented on 1 Oct 2019 open directive for lynda courses: https://drive.google.com/drive/folders/1zQan1cq1ZnqXmueRF5IqKoOtpFxl6Y4G @ezekielskottarathil ezekielskottarathil commented on 3 Oct 2019 can anyone provide 'sedimentology and petroleum geology' course from Udemy. Thanks in advance. "wrong place boy" @pulkitd2699 pulkitd2699 commented on 8 Oct 2019 Does anyone has a link for 'Cyber security: Python and web applications' course? Thanks @mohanrajrc mohanrajrc commented on 19 Oct 2019 • Can anyone provide torrent file for Mastering React By Mosh Hamedani. Thanks https://codewithmosh.com/p/mastering-react @evilprince2009 evilprince2009 commented on 27 Oct 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 @nunusandio nunusandio commented on 30 Oct 2019 Can anyone post torrent file for ASP.NET Authentication: The Big Picture https://app.pluralsight.com/library/courses/aspdotnet-authentication-big-picture/table-of-contents @EslamElmadny EslamElmadny commented on 9 Dec 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? @Genius-K-SL Genius-K-SL commented on 14 Dec 2019 hay brother! do you have html5 game development with javascript course ? @Genius-K-SL Genius-K-SL commented on 14 Dec 2019 This link is not working brother! http://www.seedpeer.me/details/4657790/Lynda.com-Building-Facebook-Applications-with-HTML-and-JavaScript.html @smithtuka smithtuka commented on 20 Dec 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? @AbdOoSaed AbdOoSaed commented on 22 Dec 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff @EslamElmadny EslamElmadny commented on 23 Dec 2019 • Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj @jedi2610 jedi2610 commented on 27 Dec 2019 Can anyone provide me with Code with Mosh's Ultimate Java Mastery Series link? plis @InnocentZaib InnocentZaib commented on 31 Dec 2019 Please provide the link of codewithmosh The ultimate data structures and algorithms Bundle the link is given below. Please give me the torrnet file or link to download https://codewithmosh.com/p/data-structures-algorithms @edward-teixeira edward-teixeira commented on 1 Jan 2020 Please provide the link of codewithmosh The ultimate data structures and algorithms Bundle the link is given below. Please give me the torrnet file or link to download https://codewithmosh.com/p/data-structures-algorithms Yea i'm looking for it too @kaneyxx kaneyxx commented on 1 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj could you please share the part-1 & part-3? @edward-teixeira edward-teixeira commented on 2 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? @ravisharmaa ravisharmaa commented on 7 Jan Please add this . https://www.letsbuildthatapp.com/course/AppStore-JSON-APIs @WaleedAlrashed WaleedAlrashed commented on 13 Jan This one kindly. https://www.udemy.com/course/flutter-build-a-complex-android-and-ios-apps-using-firestore/ @Sopheakmorm Sopheakmorm commented on 19 Jan Anyone have this course: https://www.udemy.com/course/mcsa-web-application-practice-test70-480-70-483-70-486 @EslamElmadny EslamElmadny commented on 19 Jan Anyone have this course: https://www.udemy.com/course/mcsa-web-application-practice-test70-480-70-483-70-486 +1 @EslamElmadny EslamElmadny commented on 20 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? https://vminhsang.name.vn/category/it-courses/codewithmosh/ this link includes almost all mosh courses @mohanrajrc mohanrajrc commented on 22 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? https://vminhsang.name.vn/category/it-courses/codewithmosh/ this link includes almost all mosh courses Yes. Java mastery and Data Structures 1, 2, 3 are available in this site. free download. @shihab122 shihab122 commented on 22 Jan Please give me the torrnet file or link to download The Ultimate Design Patterns @EslamElmadny EslamElmadny commented on 22 Jan • Please give me the torrnet file or link to download The Ultimate Design Patterns Waiting for it also :D @K-wachira K-wachira commented on 23 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? https://vminhsang.name.vn/category/it-courses/codewithmosh/ this link includes almost all mosh courses Yes. Java mastery and Data Structures 1, 2, 3 are available in this site. free download. You are a saviour .. Altho i feel bad i cant really buy the course... its really good @msdyn95 msdyn95 commented 25 days ago • Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ @K-wachira K-wachira commented 23 days ago This one kindly. https://www.udemy.com/course/flutter-build-a-complex-android-and-ios-apps-using-firestore/ Hey did you find this one? @edward-teixeira edward-teixeira commented 22 days ago Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ Did you find those? @msdyn95 msdyn95 commented 21 days ago Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ Did you find those? unfortunately not. @edward-teixeira edward-teixeira commented 20 days ago Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ Did you find those? unfortunately not. Found it ! https://vminhsang.name.vn/category/it-courses/codewithmosh/ @ZainA14 ZainA14 commented 16 days ago • Can someone please link me to this mosh course for torrent or direct download link https://codewithmosh.com/p/the-ultimate-full-stack-net-developer-bundle @khushiigupta khushiigupta commented 9 days ago Can any one please provide me link for jenkins so that I can learn as al as possible to join this conversation on GitHub. Already have an account? Sign in to comment © 2020 GitHub, Inc. Terms Privacy Security Status Help Contact GitHub Pricing API Training Blog About

Aryia-Behroziuan / Neurons

An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so

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Poole, Mackworth & Goebel 1998, p. 1. Russell & Norvig 2003, p. 55. Definition of AI as the study of intelligent agents: Poole, Mackworth & Goebel (1998), which provides the version that is used in this article. These authors use the term "computational intelligence" as a synonym for artificial intelligence.[1] Russell & Norvig (2003) (who prefer the term "rational agent") and write "The whole-agent view is now widely accepted in the field".[2] Nilsson 1998 Legg & Hutter 2007 Russell & Norvig 2009, p. 2. McCorduck 2004, p. 204 Maloof, Mark. "Artificial Intelligence: An Introduction, p. 37" (PDF). georgetown.edu. Archived (PDF) from the original on 25 August 2018. "How AI Is Getting Groundbreaking Changes In Talent Management And HR Tech". Hackernoon. Archived from the original on 11 September 2019. Retrieved 14 February 2020. Schank, Roger C. (1991). "Where's the AI". AI magazine. Vol. 12 no. 4. p. 38. Russell & Norvig 2009. "AlphaGo – Google DeepMind". Archived from the original on 10 March 2016. Allen, Gregory (April 2020). "Department of Defense Joint AI Center - Understanding AI Technology" (PDF). AI.mil - The official site of the Department of Defense Joint Artificial Intelligence Center. Archived (PDF) from the original on 21 April 2020. Retrieved 25 April 2020. Optimism of early AI: * Herbert Simon quote: Simon 1965, p. 96 quoted in Crevier 1993, p. 109. * Marvin Minsky quote: Minsky 1967, p. 2 quoted in Crevier 1993, p. 109. Boom of the 1980s: rise of expert systems, Fifth Generation Project, Alvey, MCC, SCI: * McCorduck 2004, pp. 426–441 * Crevier 1993, pp. 161–162,197–203, 211, 240 * Russell & Norvig 2003, p. 24 * NRC 1999, pp. 210–211 * Newquist 1994, pp. 235–248 First AI Winter, Mansfield Amendment, Lighthill report * Crevier 1993, pp. 115–117 * Russell & Norvig 2003, p. 22 * NRC 1999, pp. 212–213 * Howe 1994 * Newquist 1994, pp. 189–201 Second AI winter: * McCorduck 2004, pp. 430–435 * Crevier 1993, pp. 209–210 * NRC 1999, pp. 214–216 * Newquist 1994, pp. 301–318 AI becomes hugely successful in the early 21st century * Clark 2015 Pamela McCorduck (2004, p. 424) writes of "the rough shattering of AI in subfields—vision, natural language, decision theory, genetic algorithms, robotics ... and these with own sub-subfield—that would hardly have anything to say to each other." This list of intelligent traits is based on the topics covered by the major AI textbooks, including: * Russell & Norvig 2003 * Luger & Stubblefield 2004 * Poole, Mackworth & Goebel 1998 * Nilsson 1998 Kolata 1982. Maker 2006. Biological intelligence vs. intelligence in general: Russell & Norvig 2003, pp. 2–3, who make the analogy with aeronautical engineering. McCorduck 2004, pp. 100–101, who writes that there are "two major branches of artificial intelligence: one aimed at producing intelligent behavior regardless of how it was accomplished, and the other aimed at modeling intelligent processes found in nature, particularly human ones." Kolata 1982, a paper in Science, which describes McCarthy's indifference to biological models. Kolata quotes McCarthy as writing: "This is AI, so we don't care if it's psychologically real".[19] McCarthy recently reiterated his position at the AI@50 conference where he said "Artificial intelligence is not, by definition, simulation of human intelligence".[20]. Neats vs. scruffies: * McCorduck 2004, pp. 421–424, 486–489 * Crevier 1993, p. 168 * Nilsson 1983, pp. 10–11 Symbolic vs. sub-symbolic AI: * Nilsson (1998, p. 7), who uses the term "sub-symbolic". General intelligence (strong AI) is discussed in popular introductions to AI: * Kurzweil 1999 and Kurzweil 2005 See the Dartmouth proposal, under Philosophy, below. McCorduck 2004, p. 34. McCorduck 2004, p. xviii. McCorduck 2004, p. 3. McCorduck 2004, pp. 340–400. This is a central idea of Pamela McCorduck's Machines Who Think. She writes: "I like to think of artificial intelligence as the scientific apotheosis of a venerable cultural tradition."[26] "Artificial intelligence in one form or another is an idea that has pervaded Western intellectual history, a dream in urgent need of being realized."[27] "Our history is full of attempts—nutty, eerie, comical, earnest, legendary and real—to make artificial intelligences, to reproduce what is the essential us—bypassing the ordinary means. Back and forth between myth and reality, our imaginations supplying what our workshops couldn't, we have engaged for a long time in this odd form of self-reproduction."[28] She traces the desire back to its Hellenistic roots and calls it the urge to "forge the Gods."[29] "Stephen Hawking believes AI could be mankind's last accomplishment". BetaNews. 21 October 2016. Archived from the original on 28 August 2017. Lombardo P, Boehm I, Nairz K (2020). "RadioComics – Santa Claus and the future of radiology". Eur J Radiol. 122 (1): 108771. doi:10.1016/j.ejrad.2019.108771. PMID 31835078. Ford, Martin; Colvin, Geoff (6 September 2015). "Will robots create more jobs than they destroy?". The Guardian. Archived from the original on 16 June 2018. Retrieved 13 January 2018. AI applications widely used behind the scenes: * Russell & Norvig 2003, p. 28 * Kurzweil 2005, p. 265 * NRC 1999, pp. 216–222 * Newquist 1994, pp. 189–201 AI in myth: * McCorduck 2004, pp. 4–5 * Russell & Norvig 2003, p. 939 AI in early science fiction. * McCorduck 2004, pp. 17–25 Formal reasoning: * Berlinski, David (2000). The Advent of the Algorithm. Harcourt Books. ISBN 978-0-15-601391-8. OCLC 46890682. Archived from the original on 26 July 2020. Retrieved 22 August 2020. Turing, Alan (1948), "Machine Intelligence", in Copeland, B. Jack (ed.), The Essential Turing: The ideas that gave birth to the computer age, Oxford: Oxford University Press, p. 412, ISBN 978-0-19-825080-7 Russell & Norvig 2009, p. 16. Dartmouth conference: * McCorduck 2004, pp. 111–136 * Crevier 1993, pp. 47–49, who writes "the conference is generally recognized as the official birthdate of the new science." * Russell & Norvig 2003, p. 17, who call the conference "the birth of artificial intelligence." * NRC 1999, pp. 200–201 McCarthy, John (1988). "Review of The Question of Artificial Intelligence". Annals of the History of Computing. 10 (3): 224–229., collected in McCarthy, John (1996). "10. Review of The Question of Artificial Intelligence". Defending AI Research: A Collection of Essays and Reviews. CSLI., p. 73, "[O]ne of the reasons for inventing the term "artificial intelligence" was to escape association with "cybernetics". Its concentration on analog feedback seemed misguided, and I wished to avoid having either to accept Norbert (not Robert) Wiener as a guru or having to argue with him." Hegemony of the Dartmouth conference attendees: * Russell & Norvig 2003, p. 17, who write "for the next 20 years the field would be dominated by these people and their students." * McCorduck 2004, pp. 129–130 Russell & Norvig 2003, p. 18. Schaeffer J. (2009) Didn't Samuel Solve That Game?. In: One Jump Ahead. Springer, Boston, MA Samuel, A. L. (July 1959). "Some Studies in Machine Learning Using the Game of Checkers". IBM Journal of Research and Development. 3 (3): 210–229. CiteSeerX 10.1.1.368.2254. doi:10.1147/rd.33.0210. "Golden years" of AI (successful symbolic reasoning programs 1956–1973): * McCorduck 2004, pp. 243–252 * Crevier 1993, pp. 52–107 * Moravec 1988, p. 9 * Russell & Norvig 2003, pp. 18–21 The programs described are Arthur Samuel's checkers program for the IBM 701, Daniel Bobrow's STUDENT, Newell and Simon's Logic Theorist and Terry Winograd's SHRDLU. DARPA pours money into undirected pure research into AI during the 1960s: * McCorduck 2004, p. 131 * Crevier 1993, pp. 51, 64–65 * NRC 1999, pp. 204–205 AI in England: * Howe 1994 Lighthill 1973. Expert systems: * ACM 1998, I.2.1 * Russell & Norvig 2003, pp. 22–24 * Luger & Stubblefield 2004, pp. 227–331 * Nilsson 1998, chpt. 17.4 * McCorduck 2004, pp. 327–335, 434–435 * Crevier 1993, pp. 145–62, 197–203 * Newquist 1994, pp. 155–183 Mead, Carver A.; Ismail, Mohammed (8 May 1989). Analog VLSI Implementation of Neural Systems (PDF). The Kluwer International Series in Engineering and Computer Science. 80. Norwell, MA: Kluwer Academic Publishers. doi:10.1007/978-1-4613-1639-8. ISBN 978-1-4613-1639-8. Archived from the original (PDF) on 6 November 2019. Retrieved 24 January 2020. Formal methods are now preferred ("Victory of the neats"): * Russell & Norvig 2003, pp. 25–26 * McCorduck 2004, pp. 486–487 McCorduck 2004, pp. 480–483. Markoff 2011. "Ask the AI experts: What's driving today's progress in AI?". McKinsey & Company. Archived from the original on 13 April 2018. Retrieved 13 April 2018. Administrator. "Kinect's AI breakthrough explained". i-programmer.info. Archived from the original on 1 February 2016. Rowinski, Dan (15 January 2013). "Virtual Personal Assistants & The Future Of Your Smartphone [Infographic]". ReadWrite. Archived from the original on 22 December 2015. "Artificial intelligence: Google's AlphaGo beats Go master Lee Se-dol". BBC News. 12 March 2016. Archived from the original on 26 August 2016. Retrieved 1 October 2016. Metz, Cade (27 May 2017). "After Win in China, AlphaGo's Designers Explore New AI". Wired. Archived from the original on 2 June 2017. "World's Go Player Ratings". May 2017. Archived from the original on 1 April 2017. "柯洁迎19岁生日雄踞人类世界排名第一已两年" (in Chinese). May 2017. Archived from the original on 11 August 2017. Clark, Jack (8 December 2015). "Why 2015 Was a Breakthrough Year in Artificial Intelligence". Bloomberg News. Archived from the original on 23 November 2016. Retrieved 23 November 2016. After a half-decade of quiet breakthroughs in artificial intelligence, 2015 has been a landmark year. Computers are smarter and learning faster than ever. "Reshaping Business With Artificial Intelligence". MIT Sloan Management Review. Archived from the original on 19 May 2018. Retrieved 2 May 2018. Lorica, Ben (18 December 2017). "The state of AI adoption". O'Reilly Media. Archived from the original on 2 May 2018. Retrieved 2 May 2018. Allen, Gregory (6 February 2019). "Understanding China's AI Strategy". Center for a New American Security. Archived from the original on 17 March 2019. "Review | How two AI superpowers – the U.S. and China – battle for supremacy in the field". Washington Post. 2 November 2018. Archived from the original on 4 November 2018. Retrieved 4 November 2018. at 10:11, Alistair Dabbs 22 Feb 2019. "Artificial Intelligence: You know it isn't real, yeah?". www.theregister.co.uk. Archived from the original on 21 May 2020. Retrieved 22 August 2020. "Stop Calling it Artificial Intelligence". Archived from the original on 2 December 2019. Retrieved 1 December 2019. "AI isn't taking over the world – it doesn't exist yet". GBG Global website. Archived from the original on 11 August 2020. Retrieved 22 August 2020. Kaplan, Andreas; Haenlein, Michael (1 January 2019). "Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence". Business Horizons. 62 (1): 15–25. doi:10.1016/j.bushor.2018.08.004. Domingos 2015, Chapter 5. Domingos 2015, Chapter 7. Lindenbaum, M., Markovitch, S., & Rusakov, D. (2004). Selective sampling for nearest neighbor classifiers. Machine learning, 54(2), 125–152. Domingos 2015, Chapter 1. Intractability and efficiency and the combinatorial explosion: * Russell & Norvig 2003, pp. 9, 21–22 Domingos 2015, Chapter 2, Chapter 3. Hart, P. E.; Nilsson, N. J.; Raphael, B. (1972). "Correction to "A Formal Basis for the Heuristic Determination of Minimum Cost Paths"". SIGART Newsletter (37): 28–29. doi:10.1145/1056777.1056779. S2CID 6386648. Domingos 2015, Chapter 2, Chapter 4, Chapter 6. "Can neural network computers learn from experience, and if so, could they ever become what we would call 'smart'?". Scientific American. 2018. Archived from the original on 25 March 2018. Retrieved 24 March 2018. Domingos 2015, Chapter 6, Chapter 7. Domingos 2015, p. 286. "Single pixel change fools AI programs". BBC News. 3 November 2017. Archived from the original on 22 March 2018. Retrieved 12 March 2018. "AI Has a Hallucination Problem That's Proving Tough to Fix". WIRED. 2018. Archived from the original on 12 March 2018. Retrieved 12 March 2018. Matti, D.; Ekenel, H. K.; Thiran, J. P. 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Communications of the ACM. 58 (9): 92–103. doi:10.1145/2701413. S2CID 13583137. Archived from the original on 22 August 2020. Retrieved 6 April 2020. Winograd, Terry (January 1972). "Understanding natural language". Cognitive Psychology. 3 (1): 1–191. doi:10.1016/0010-0285(72)90002-3. "Don't worry: Autonomous cars aren't coming tomorrow (or next year)". Autoweek. 2016. Archived from the original on 25 March 2018. Retrieved 24 March 2018. Knight, Will (2017). "Boston may be famous for bad drivers, but it's the testing ground for a smarter self-driving car". MIT Technology Review. Archived from the original on 22 August 2020. Retrieved 27 March 2018. Prakken, Henry (31 August 2017). "On the problem of making autonomous vehicles conform to traffic law". Artificial Intelligence and Law. 25 (3): 341–363. doi:10.1007/s10506-017-9210-0. Lieto, Antonio (May 2018). "The knowledge level in cognitive architectures: Current limitations and possible developments". Cognitive Systems Research. 48: 39–55. doi:10.1016/j.cogsys.2017.05.001. hdl:2318/1665207. S2CID 206868967. Problem solving, puzzle solving, game playing and deduction: * Russell & Norvig 2003, chpt. 3–9, * Poole, Mackworth & Goebel 1998, chpt. 2,3,7,9, * Luger & Stubblefield 2004, chpt. 3,4,6,8, * Nilsson 1998, chpt. 7–12 Uncertain reasoning: * Russell & Norvig 2003, pp. 452–644, * Poole, Mackworth & Goebel 1998, pp. 345–395, * Luger & Stubblefield 2004, pp. 333–381, * Nilsson 1998, chpt. 19 Psychological evidence of sub-symbolic reasoning: * Wason & Shapiro (1966) showed that people do poorly on completely abstract problems, but if the problem is restated to allow the use of intuitive social intelligence, performance dramatically improves. (See Wason selection task) * Kahneman, Slovic & Tversky (1982) have shown that people are terrible at elementary problems that involve uncertain reasoning. (See list of cognitive biases for several examples). * Lakoff & Núñez (2000) have controversially argued that even our skills at mathematics depend on knowledge and skills that come from "the body", i.e. sensorimotor and perceptual skills. (See Where Mathematics Comes From) Knowledge representation: * ACM 1998, I.2.4, * Russell & Norvig 2003, pp. 320–363, * Poole, Mackworth & Goebel 1998, pp. 23–46, 69–81, 169–196, 235–277, 281–298, 319–345, * Luger & Stubblefield 2004, pp. 227–243, * Nilsson 1998, chpt. 18 Knowledge engineering: * Russell & Norvig 2003, pp. 260–266, * Poole, Mackworth & Goebel 1998, pp. 199–233, * Nilsson 1998, chpt. ≈17.1–17.4 Representing categories and relations: Semantic networks, description logics, inheritance (including frames and scripts): * Russell & Norvig 2003, pp. 349–354, * Poole, Mackworth & Goebel 1998, pp. 174–177, * Luger & Stubblefield 2004, pp. 248–258, * Nilsson 1998, chpt. 18.3 Representing events and time:Situation calculus, event calculus, fluent calculus (including solving the frame problem): * Russell & Norvig 2003, pp. 328–341, * Poole, Mackworth & Goebel 1998, pp. 281–298, * Nilsson 1998, chpt. 18.2 Causal calculus: * Poole, Mackworth & Goebel 1998, pp. 335–337 Representing knowledge about knowledge: Belief calculus, modal logics: * Russell & Norvig 2003, pp. 341–344, * Poole, Mackworth & Goebel 1998, pp. 275–277 Sikos, Leslie F. (June 2017). Description Logics in Multimedia Reasoning. Cham: Springer. doi:10.1007/978-3-319-54066-5. ISBN 978-3-319-54066-5. S2CID 3180114. Archived from the original on 29 August 2017. Ontology: * Russell & Norvig 2003, pp. 320–328 Smoliar, Stephen W.; Zhang, HongJiang (1994). "Content based video indexing and retrieval". IEEE Multimedia. 1 (2): 62–72. doi:10.1109/93.311653. S2CID 32710913. Neumann, Bernd; Möller, Ralf (January 2008). "On scene interpretation with description logics". Image and Vision Computing. 26 (1): 82–101. doi:10.1016/j.imavis.2007.08.013. Kuperman, G. J.; Reichley, R. M.; Bailey, T. C. (1 July 2006). "Using Commercial Knowledge Bases for Clinical Decision Support: Opportunities, Hurdles, and Recommendations". Journal of the American Medical Informatics Association. 13 (4): 369–371. doi:10.1197/jamia.M2055. PMC 1513681. PMID 16622160. MCGARRY, KEN (1 December 2005). "A survey of interestingness measures for knowledge discovery". The Knowledge Engineering Review. 20 (1): 39–61. doi:10.1017/S0269888905000408. S2CID 14987656. Bertini, M; Del Bimbo, A; Torniai, C (2006). "Automatic annotation and semantic retrieval of video sequences using multimedia ontologies". MM '06 Proceedings of the 14th ACM international conference on Multimedia. 14th ACM international conference on Multimedia. Santa Barbara: ACM. pp. 679–682. Qualification problem: * McCarthy & Hayes 1969 * Russell & Norvig 2003[page needed] While McCarthy was primarily concerned with issues in the logical representation of actions, Russell & Norvig 2003 apply the term to the more general issue of default reasoning in the vast network of assumptions underlying all our commonsense knowledge. Default reasoning and default logic, non-monotonic logics, circumscription, closed world assumption, abduction (Poole et al. places abduction under "default reasoning". Luger et al. places this under "uncertain reasoning"): * Russell & Norvig 2003, pp. 354–360, * Poole, Mackworth & Goebel 1998, pp. 248–256, 323–335, * Luger & Stubblefield 2004, pp. 335–363, * Nilsson 1998, ~18.3.3 Breadth of commonsense knowledge: * Russell & Norvig 2003, p. 21, * Crevier 1993, pp. 113–114, * Moravec 1988, p. 13, * Lenat & Guha 1989 (Introduction) Dreyfus & Dreyfus 1986. Gladwell 2005. Expert knowledge as embodied intuition: * Dreyfus & Dreyfus 1986 (Hubert Dreyfus is a philosopher and critic of AI who was among the first to argue that most useful human knowledge was encoded sub-symbolically. See Dreyfus' critique of AI) * Gladwell 2005 (Gladwell's Blink is a popular introduction to sub-symbolic reasoning and knowledge.) * Hawkins & Blakeslee 2005 (Hawkins argues that sub-symbolic knowledge should be the primary focus of AI research.) Planning: * ACM 1998, ~I.2.8, * Russell & Norvig 2003, pp. 375–459, * Poole, Mackworth & Goebel 1998, pp. 281–316, * Luger & Stubblefield 2004, pp. 314–329, * Nilsson 1998, chpt. 10.1–2, 22 Information value theory: * Russell & Norvig 2003, pp. 600–604 Classical planning: * Russell & Norvig 2003, pp. 375–430, * Poole, Mackworth & Goebel 1998, pp. 281–315, * Luger & Stubblefield 2004, pp. 314–329, * Nilsson 1998, chpt. 10.1–2, 22 Planning and acting in non-deterministic domains: conditional planning, execution monitoring, replanning and continuous planning: * Russell & Norvig 2003, pp. 430–449 Multi-agent planning and emergent behavior: * Russell & Norvig 2003, pp. 449–455 Turing 1950. Solomonoff 1956. Alan Turing discussed the centrality of learning as early as 1950, in his classic paper "Computing Machinery and Intelligence".[120] In 1956, at the original Dartmouth AI summer conference, Ray Solomonoff wrote a report on unsupervised probabilistic machine learning: "An Inductive Inference Machine".[121] This is a form of Tom Mitchell's widely quoted definition of machine learning: "A computer program is set to learn from an experience E with respect to some task T and some performance measure P if its performance on T as measured by P improves with experience E." Learning: * ACM 1998, I.2.6, * Russell & Norvig 2003, pp. 649–788, * Poole, Mackworth & Goebel 1998, pp. 397–438, * Luger & Stubblefield 2004, pp. 385–542, * Nilsson 1998, chpt. 3.3, 10.3, 17.5, 20 Jordan, M. I.; Mitchell, T. M. (16 July 2015). "Machine learning: Trends, perspectives, and prospects". Science. 349 (6245): 255–260. Bibcode:2015Sci...349..255J. doi:10.1126/science.aaa8415. PMID 26185243. S2CID 677218. Reinforcement learning: * Russell & Norvig 2003, pp. 763–788 * Luger & Stubblefield 2004, pp. 442–449 Natural language processing: * ACM 1998, I.2.7 * Russell & Norvig 2003, pp. 790–831 * Poole, Mackworth & Goebel 1998, pp. 91–104 * Luger & Stubblefield 2004, pp. 591–632 "Versatile question answering systems: seeing in synthesis" Archived 1 February 2016 at the Wayback Machine, Mittal et al., IJIIDS, 5(2), 119–142, 2011 Applications of natural language processing, including information retrieval (i.e. text mining) and machine translation: * Russell & Norvig 2003, pp. 840–857, * Luger & Stubblefield 2004, pp. 623–630 Cambria, Erik; White, Bebo (May 2014). "Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article]". IEEE Computational Intelligence Magazine. 9 (2): 48–57. doi:10.1109/MCI.2014.2307227. S2CID 206451986. Vincent, James (7 November 2019). "OpenAI has published the text-generating AI it said was too dangerous to share". The Verge. Archived from the original on 11 June 2020. Retrieved 11 June 2020. Machine perception: * Russell & Norvig 2003, pp. 537–581, 863–898 * Nilsson 1998, ~chpt. 6 Speech recognition: * ACM 1998, ~I.2.7 * Russell & Norvig 2003, pp. 568–578 Object recognition: * Russell & Norvig 2003, pp. 885–892 Computer vision: * ACM 1998, I.2.10 * Russell & Norvig 2003, pp. 863–898 * Nilsson 1998, chpt. 6 Robotics: * ACM 1998, I.2.9, * Russell & Norvig 2003, pp. 901–942, * Poole, Mackworth & Goebel 1998, pp. 443–460 Moving and configuration space: * Russell & Norvig 2003, pp. 916–932 Tecuci 2012. Robotic mapping (localization, etc): * Russell & Norvig 2003, pp. 908–915 Cadena, Cesar; Carlone, Luca; Carrillo, Henry; Latif, Yasir; Scaramuzza, Davide; Neira, Jose; Reid, Ian; Leonard, John J. (December 2016). "Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age". IEEE Transactions on Robotics. 32 (6): 1309–1332. arXiv:1606.05830. Bibcode:2016arXiv160605830C. doi:10.1109/TRO.2016.2624754. S2CID 2596787. Moravec, Hans (1988). Mind Children. Harvard University Press. p. 15. Chan, Szu Ping (15 November 2015). "This is what will happen when robots take over the world". Archived from the original on 24 April 2018. Retrieved 23 April 2018. "IKEA furniture and the limits of AI". The Economist. 2018. Archived from the original on 24 April 2018. Retrieved 24 April 2018. Kismet. Thompson, Derek (2018). "What Jobs Will the Robots Take?". The Atlantic. Archived from the original on 24 April 2018. Retrieved 24 April 2018. Scassellati, Brian (2002). "Theory of mind for a humanoid robot". Autonomous Robots. 12 (1): 13–24. doi:10.1023/A:1013298507114. S2CID 1979315. Cao, Yongcan; Yu, Wenwu; Ren, Wei; Chen, Guanrong (February 2013). "An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination". IEEE Transactions on Industrial Informatics. 9 (1): 427–438. arXiv:1207.3231. doi:10.1109/TII.2012.2219061. 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"The superhero of artificial intelligence: can this genius keep it in check?". the Guardian. 16 February 2016. Archived from the original on 23 April 2018. Retrieved 26 April 2018. Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A.; Veness, Joel; Bellemare, Marc G.; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K.; Ostrovski, Georg; Petersen, Stig; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane; Hassabis, Demis (26 February 2015). "Human-level control through deep reinforcement learning". Nature. 518 (7540): 529–533. Bibcode:2015Natur.518..529M. doi:10.1038/nature14236. PMID 25719670. S2CID 205242740. Sample, Ian (14 March 2017). "Google's DeepMind makes AI program that can learn like a human". the Guardian. Archived from the original on 26 April 2018. Retrieved 26 April 2018. "From not working to neural networking". The Economist. 2016. Archived from the original on 31 December 2016. Retrieved 26 April 2018. Domingos 2015. Artificial brain arguments: AI requires a simulation of the operation of the human brain * Russell & Norvig 2003, p. 957 * Crevier 1993, pp. 271 and 279 A few of the people who make some form of the argument: * Moravec 1988 * Kurzweil 2005, p. 262 * Hawkins & Blakeslee 2005 The most extreme form of this argument (the brain replacement scenario) was put forward by Clark Glymour in the mid-1970s and was touched on by Zenon Pylyshyn and John Searle in 1980. Goertzel, Ben; Lian, Ruiting; Arel, Itamar; de Garis, Hugo; Chen, Shuo (December 2010). "A world survey of artificial brain projects, Part II: Biologically inspired cognitive architectures". Neurocomputing. 74 (1–3): 30–49. doi:10.1016/j.neucom.2010.08.012. Nilsson 1983, p. 10. Nils Nilsson writes: "Simply put, there is wide disagreement in the field about what AI is all about."[163] AI's immediate precursors: * McCorduck 2004, pp. 51–107 * Crevier 1993, pp. 27–32 * Russell & Norvig 2003, pp. 15, 940 * Moravec 1988, p. 3 Haugeland 1985, pp. 112–117 The most dramatic case of sub-symbolic AI being pushed into the background was the devastating critique of perceptrons by Marvin Minsky and Seymour Papert in 1969. See History of AI, AI winter, or Frank Rosenblatt. Cognitive simulation, Newell and Simon, AI at CMU (then called Carnegie Tech): * McCorduck 2004, pp. 139–179, 245–250, 322–323 (EPAM) * Crevier 1993, pp. 145–149 Soar (history): * McCorduck 2004, pp. 450–451 * Crevier 1993, pp. 258–263 McCarthy and AI research at SAIL and SRI International: * McCorduck 2004, pp. 251–259 * Crevier 1993 AI research at Edinburgh and in France, birth of Prolog: * Crevier 1993, pp. 193–196 * Howe 1994 AI at MIT under Marvin Minsky in the 1960s : * McCorduck 2004, pp. 259–305 * Crevier 1993, pp. 83–102, 163–176 * Russell & Norvig 2003, p. 19 Cyc: * McCorduck 2004, p. 489, who calls it "a determinedly scruffy enterprise" * Crevier 1993, pp. 239–243 * Russell & Norvig 2003, p. 363−365 * Lenat & Guha 1989 Knowledge revolution: * McCorduck 2004, pp. 266–276, 298–300, 314, 421 * Russell & Norvig 2003, pp. 22–23 Frederick, Hayes-Roth; William, Murray; Leonard, Adelman. "Expert systems". AccessScience. doi:10.1036/1097-8542.248550. Embodied approaches to AI: * McCorduck 2004, pp. 454–462 * Brooks 1990 * Moravec 1988 Weng et al. 2001. Lungarella et al. 2003. Asada et al. 2009. Oudeyer 2010. Revival of connectionism: * Crevier 1993, pp. 214–215 * Russell & Norvig 2003, p. 25 Computational intelligence * IEEE Computational Intelligence Society Archived 9 May 2008 at the Wayback Machine Hutson, Matthew (16 February 2018). "Artificial intelligence faces reproducibility crisis". Science. pp. 725–726. Bibcode:2018Sci...359..725H. doi:10.1126/science.359.6377.725. Archived from the original on 29 April 2018. Retrieved 28 April 2018. Norvig 2012. Langley 2011. Katz 2012. The intelligent agent paradigm: * Russell & Norvig 2003, pp. 27, 32–58, 968–972 * Poole, Mackworth & Goebel 1998, pp. 7–21 * Luger & Stubblefield 2004, pp. 235–240 * Hutter 2005, pp. 125–126 The definition used in this article, in terms of goals, actions, perception and environment, is due to Russell & Norvig (2003). Other definitions also include knowledge and learning as additional criteria. Agent architectures, hybrid intelligent systems: * Russell & Norvig (2003, pp. 27, 932, 970–972) * Nilsson (1998, chpt. 25) Hierarchical control system: * Albus 2002 Lieto, Antonio; Lebiere, Christian; Oltramari, Alessandro (May 2018). "The knowledge level in cognitive architectures: Current limitations and possibile developments". Cognitive Systems Research. 48: 39–55. doi:10.1016/j.cogsys.2017.05.001. hdl:2318/1665207. S2CID 206868967. Lieto, Antonio; Bhatt, Mehul; Oltramari, Alessandro; Vernon, David (May 2018). "The role of cognitive architectures in general artificial intelligence". Cognitive Systems Research. 48: 1–3. doi:10.1016/j.cogsys.2017.08.003. hdl:2318/1665249. S2CID 36189683. Russell & Norvig 2009, p. 1. White Paper: On Artificial Intelligence - A European approach to excellence and trust (PDF). Brussels: European Commission. 2020. p. 1. Archived (PDF) from the original on 20 February 2020. Retrieved 20 February 2020. CNN 2006. Using AI to predict flight delays Archived 20 November 2018 at the Wayback Machine, Ishti.org. N. Aletras; D. Tsarapatsanis; D. Preotiuc-Pietro; V. Lampos (2016). "Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective". PeerJ Computer Science. 2: e93. doi:10.7717/peerj-cs.93. "The Economist Explains: Why firms are piling into artificial intelligence". The Economist. 31 March 2016. Archived from the original on 8 May 2016. Retrieved 19 May 2016. Lohr, Steve (28 February 2016). "The Promise of Artificial Intelligence Unfolds in Small Steps". The New York Times. Archived from the original on 29 February 2016. Retrieved 29 February 2016. Frangoul, Anmar (14 June 2019). "A Californian business is using A.I. to change the way we think about energy storage". CNBC. Archived from the original on 25 July 2020. Retrieved 5 November 2019. Wakefield, Jane (15 June 2016). "Social media 'outstrips TV' as news source for young people". BBC News. Archived from the original on 24 June 2016. Smith, Mark (22 July 2016). "So you think you chose to read this article?". BBC News. Archived from the original on 25 July 2016. Brown, Eileen. "Half of Americans do not believe deepfake news could target them online". ZDNet. Archived from the original on 6 November 2019. Retrieved 3 December 2019. The Turing test: Turing's original publication: * Turing 1950 Historical influence and philosophical implications: * Haugeland 1985, pp. 6–9 * Crevier 1993, p. 24 * McCorduck 2004, pp. 70–71 * Russell & Norvig 2003, pp. 2–3 and 948 Dartmouth proposal: * McCarthy et al. 1955 (the original proposal) * Crevier 1993, p. 49 (historical significance) The physical symbol systems hypothesis: * Newell & Simon 1976, p. 116 * McCorduck 2004, p. 153 * Russell & Norvig 2003, p. 18 Dreyfus 1992, p. 156. Dreyfus criticized the necessary condition of the physical symbol system hypothesis, which he called the "psychological assumption": "The mind can be viewed as a device operating on bits of information according to formal rules."[206] Dreyfus' critique of artificial intelligence: * Dreyfus 1972, Dreyfus & Dreyfus 1986 * Crevier 1993, pp. 120–132 * McCorduck 2004, pp. 211–239 * Russell & Norvig 2003, pp. 950–952, Gödel 1951: in this lecture, Kurt Gödel uses the incompleteness theorem to arrive at the following disjunction: (a) the human mind is not a consistent finite machine, or (b) there exist Diophantine equations for which it cannot decide whether solutions exist. Gödel finds (b) implausible, and thus seems to have believed the human mind was not equivalent to a finite machine, i.e., its power exceeded that of any finite machine. He recognized that this was only a conjecture, since one could never disprove (b). Yet he considered the disjunctive conclusion to be a "certain fact". The Mathematical Objection: * Russell & Norvig 2003, p. 949 * McCorduck 2004, pp. 448–449 Making the Mathematical Objection: * Lucas 1961 * Penrose 1989 Refuting Mathematical Objection: * Turing 1950 under "(2) The Mathematical Objection" * Hofstadter 1979 Background: * Gödel 1931, Church 1936, Kleene 1935, Turing 1937 Graham Oppy (20 January 2015). "Gödel's Incompleteness Theorems". Stanford Encyclopedia of Philosophy. Archived from the original on 22 April 2016. Retrieved 27 April 2016. These Gödelian anti-mechanist arguments are, however, problematic, and there is wide consensus that they fail. Stuart J. Russell; Peter Norvig (2010). "26.1.2: Philosophical Foundations/Weak AI: Can Machines Act Intelligently?/The mathematical objection". Artificial Intelligence: A Modern Approach (3rd ed.). Upper Saddle River, NJ: Prentice Hall. ISBN 978-0-13-604259-4. even if we grant that computers have limitations on what they can prove, there is no evidence that humans are immune from those limitations. Mark Colyvan. An introduction to the philosophy of mathematics. Cambridge University Press, 2012. From 2.2.2, 'Philosophical significance of Gödel's incompleteness results': "The accepted wisdom (with which I concur) is that the Lucas-Penrose arguments fail." Iphofen, Ron; Kritikos, Mihalis (3 January 2019). "Regulating artificial intelligence and robotics: ethics by design in a digital society". Contemporary Social Science: 1–15. doi:10.1080/21582041.2018.1563803. ISSN 2158-2041. "Ethical AI Learns Human Rights Framework". Voice of America. Archived from the original on 11 November 2019. Retrieved 10 November 2019. Crevier 1993, pp. 132–144. In the early 1970s, Kenneth Colby presented a version of Weizenbaum's ELIZA known as DOCTOR which he promoted as a serious therapeutic tool.[216] Joseph Weizenbaum's critique of AI: * Weizenbaum 1976 * Crevier 1993, pp. 132–144 * McCorduck 2004, pp. 356–373 * Russell & Norvig 2003, p. 961 Weizenbaum (the AI researcher who developed the first chatterbot program, ELIZA) argued in 1976 that the misuse of artificial intelligence has the potential to devalue human life. Wendell Wallach (2010). Moral Machines, Oxford University Press. Wallach, pp 37–54. Wallach, pp 55–73. Wallach, Introduction chapter. Michael Anderson and Susan Leigh Anderson (2011), Machine Ethics, Cambridge University Press. "Machine Ethics". aaai.org. Archived from the original on 29 November 2014. Rubin, Charles (Spring 2003). "Artificial Intelligence and Human Nature". The New Atlantis. 1: 88–100. Archived from the original on 11 June 2012. Brooks, Rodney (10 November 2014). "artificial intelligence is a tool, not a threat". Archived from the original on 12 November 2014. "Stephen Hawking, Elon Musk, and Bill Gates Warn About Artificial Intelligence". Observer. 19 August 2015. Archived from the original on 30 October 2015. Retrieved 30 October 2015. Chalmers, David (1995). "Facing up to the problem of consciousness". Journal of Consciousness Studies. 2 (3): 200–219. Archived from the original on 8 March 2005. Retrieved 11 October 2018. See also this link Archived 8 April 2011 at the Wayback Machine Horst, Steven, (2005) "The Computational Theory of Mind" Archived 11 September 2018 at the Wayback Machine in The Stanford Encyclopedia of Philosophy Searle 1980, p. 1. This version is from Searle (1999), and is also quoted in Dennett 1991, p. 435. Searle's original formulation was "The appropriately programmed computer really is a mind, in the sense that computers given the right programs can be literally said to understand and have other cognitive states." [230] Strong AI is defined similarly by Russell & Norvig (2003, p. 947): "The assertion that machines could possibly act intelligently

bermufine / Dcmp

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Channel","thumb":"https://od.lk/s/M18yNDg1NzczODRf/mychannel.jpg","title":"RESURRECTION TV / GHANA"},{"description":"CDIRECT TV est la chaîne une chaîne généraliste présente une vitrine positive du Congo, conçoit des programmes inédits et innovants qui s'adressent aux congolais résidents, la diaspora congolaise, ainsi qu'à l'ensemble des africains francophones à travers le monde entier. Sa ligne éditoriale est axée sur les deux Congo décomplexé, un Congo qui va de l'avant et gagne !.","sources":["http://cms-streamaniak.top/Cdirect/CDIRECT/index.m3u8"],"subtitle":"By Channel","thumb":"https://cdirect.tv/assets/img/logo-cdirect.ico","title":"CDIRECT TV / Kinshasa-Brazzaville"},{"description":"DBM TV ou digital black Music est une Chaîne TV à thématique musicale, DBM a pour vocation de révéler et promouvoir la musique Afro Urbaine, qu’elle soit d’Afrique ou d’ailleurs info@dbm-tv.com. .","sources":["https://dbmtv.vedge.infomaniak.com/livecast/smil:dbmtv.smil/manifest.m3u8"],"subtitle":"By Channel","thumb":"https://www.dbm-tv.fr/wp-content/uploads/2017/12/logo-dbm.png","title":"DBM TV / Music "},{"description":"La LUMIÈRE, ministère Chrétien pour annoncer l’évangile de Jésus Christ partout dans le monde, toucher changer et sauver des vies par la puissance de la parole de DIEU avec des enseignements prédications adorations louanges partages de prières, d’exhortations et de témoignages","sources":["https://video1.getstreamhosting.com:1936/8248/8248/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://cdn.shortpixel.ai/client/q_glossy,ret_img,w_124,h_124/https://telepack.net/wp-content/uploads/2020/05/lumiere-tv.png","title":"La Lumiere TV / Gospel"},{"description":"La Télévision Togolaise (TVT) est le nom de l'unique chaîne de télévision publique togolaise, Crée depuis 1979.","sources":["http://54.38.92.12/tvt.m3u8"],"subtitle":"By Google","thumb":"https://amp.live-tv-channels.org/pt-data/uploads/logo/tg-tv.jpg","title":"Télévision Togolais"},{"description":"CRTV est un service de radio et de télévision contrôlé par le gouvernement au Cameroun. Cela a commencé sous le nom de Cameroon Television (CTV) et a ensuite fusionné avec le service de radio pour devenir CRTV. Il couvre l'ensemble des dix régions du Cameroun, ce qui en fait le diffuseur indomptable parmi plusieurs chaînes de télévision privées du pays. Sa couverture des événements est généralement considérée comme pro-gouvernementale. Les programmes de la CRTV comprennent des documentaires, des magazines, des analyses d'actualités et des séries importées d'Asie et du Brésil..","sources":["http://178.33.237.146/crtv.m3u8"],"subtitle":"By Channel","thumb":"http://www.cameroonconcordnews.com/wp-content/uploads/2018/03/CRTV-new.jpg","title":"Cameroune Radio Télévision"},{"description":"Impact TV c'est une premiere Chaine televisee chretienne diffusant au Burkina-Fasso sur satelite innauguree le 07/03/2008 par Marie Sophie.","sources":["https://edge10.vedge.infomaniak.com/livecast/impacttele/chunklist_w973675047.m3u8"],"subtitle":"By Channel","thumb":"https://i1.wp.com/www.livetvliveradio.com/wp-content/uploads/2017/07/impact-tv.jpg?fit=259%2C194","title":"Impact TV / Burkina Fasso"},{"description":"Kigali Channel 2 ( Là pour vous) est une chaine televisee Rwandaise emmetant a Kigali. KC2 se diversite par sa diffusion des emitions exceptionnelle ainsi que des films nouveautes et plein d'autres.","sources":["https://5c46fa289c89f.streamlock.net/kc2/kc2/chunklist.m3u8"],"subtitle":"By Channel","thumb":"https://encrypted-tbn0.gstatic.com/images?q=tbn%3AANd9GcQfQjI8jUhMPReWg0MOdw1xpAAXMP7YAuZKBg&usqp=CAU","title":"KC2 TV / Rwanda"},{"description":"Equinox est une chaîne de télévision basée au Cameroun. Peu de temps après son lancement, il est devenu l'un des critiques les plus virulents du régime de Paul Biya. La station était connue pour avoir diffusé des images en direct d'une manifestation politique contre le changement constitutionnel au Cameroun qui favorisait le maintien au pouvoir du président Biya après 2011, alors qu'il lui était interdit par la Constitution de se présenter à nouveau. La télévision appartient au magnat des affaires de la région ouest du Cameroun, Severin Tchounke, qui possède également un quotidien critique, La Nouvelle Expression.","sources":["http://178.33.237.146/equinoxetv.m3u8"],"subtitle":"By Channel","thumb":"https://camer-press.com/wp-content/uploads/2020/04/Equinoxe-Tv.jpg","title":"Equinoxetv"},{"description":"Rwanda Télévision (RTV) est la premiere chaîne public du Rwanda qui fournit des informations et des divertissements quotidiens au public rwandais en trois langues: anglais, français et kinyarwanda géré par l'industrie de la télévision rwandaise , mais ce derniere est composée de 12 chaînes de télévision dont 84% télévisions sont détenues par des privés (10 sur 12) tandis que 8% appartiennent respectivement à des organisations publiques et religieuses. L'Agence nationale de radiodiffusion rwandaise.","sources":["https://5c46fa289c89f.streamlock.net/rtv/rtv/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://maps.prodafrica.com/wp-content/uploads/2020/03/10191_RBA_002.png","title":"RTV"},{"description":"IBN TV est un radiodiffuseur islamique de télévision et de radio qui transmet IBN TV et Radio Maarifa de Dar es Salaam et Tanga respectivement. Il a été crée sous la direction de la Fondation Al Itrah et a été diffusé officiellement depuis Mars 2003. IBN TV est un média privé qui a commencé après la libéralisation de l’industrie des médias en Tanzanie. IBN TV est la première chaîne islamique en Tanzanie. Il couvre presque toute la région de Dar es Salaam, Tanga, Arusha et Mwanza. IBN TV diffuse en quatre langues différentes, à savoir l’anglais, le swahili, le gujarati et l’ourdou.","sources":["http://138.68.138.119:8080/low/5a8993709ea19/index.m3u8"],"subtitle":"By Channel","thumb":"http://www.alitrah.co.tz/wp-content/uploads/sites/3/2015/10/ibntvafrica.png","title":"IBN TV"},{"description":"RTB est une chaîne de télévision publique générale dirigée par l’Établissement public d’État. Son siège social est situé dans la capitale du Burkina Faso, à Ouagadougou. Il est diffusé en direct à la télévision terrestre et sur Internet. Cette chaîne africaine diffuse des nouvelles télévisées en Français. Mais en général, les flashs de nouvelles sont dans la langue nationale comme Lobiri, Bwamu, Gulmancéma ainsi que Bissa. RTB offre un programme avec de nombreux magazines sur le sport, l’économie, la culture, la santé et la jeunesse.","sources":["https://edge8.vedge.infomaniak.com/livecast/ik:rtbtvlive1/manifest.m3u8"],"subtitle":"By Channel","thumb":"https://upload.wikimedia.org/wikipedia/en/c/c0/RTB_Sukmaindera.png","title":"Radio Television Burkina Fasso"},{"description":"Eri-TV est une chaîne de télévision érythréenne appartenant à l'État. Basée dans la capitale du pays, Asmara, elle diffuse 24 heures sur 24. La station propose des bulletins d'information 24 heures sur 24, des émissions-débats et des programmes culturels et éducatifs. Eri-TV a une large base d'audience en dehors de l'Érythrée, que la chaîne publique reconnaît et utilise pour communiquer avec les Érythréens vivant à l'étranger. Le réseau compte environ 1 à 2 millions de téléspectateurs par semaine. Eri-TV reconnaît la culture minoritaire érythréenne et a largement adopté un partage de temps égal entre chacune des langues parlées du pays.","sources":["http://217.182.137.206/eri.m3u8"],"subtitle":"By Channel","thumb":"https://eri.tv/images/eri-tv-live.png","title":"ERITRIE TV"},{"description":"Créée au Sénégal par le GROUPE D-MEDIA, SENTV, 1ère Chaîne Urbaine au Sénégal, consacre sa programmation au traitement de l'actualité nationale et internationale et à la culture urbaine sénégalaise et africaine en générale. Elle émet sur hertzien depuis 2009 et est désormais disponible sur satellite via le bouquet Canal + Afrique et les bouquets IPTV à l'international. Une chaîne généraliste et orientée urbaine, constituant ainsi une offre originale et unique au Sénégal. Une part importante de ses programmes est constituée par des rendez-vous d’actualité sur une rythmique quotidienne et des émissions phares orientées Société et Divertissement.","sources":["http://46.105.114.82/tfm_senegal.m3u8"],"subtitle":"By Channel","thumb":"https://www.xalat.info/wp-content/uploads/2019/02/maxresdefault-2.jpg","title":"SENEGAL TV"},{"description":"RTB diffuse des emissions ainsi que les Sports, Musique, Culture et Films d'Action.","sources":["http://46.105.114.82/rtb1.m3u8"],"subtitle":"By Channel","thumb":"https://live-tv-channels.org/pt-data/uploads/logo/bf-rtb-tv-8682.jpg","title":"RTB"},{"description":"LEEEKO est un ensemble de médias web radio et tv, créé le 1er Decembre 2016 par Serges OLUBI, passionné de musiques. LEEEKO diffuse une diversité des musique telsque: Rhumba, Zouk, Ndombolo, Rnb, Classic, Jazz et autres à travers l'Afrique.","sources":["http://livetvsteam.com:1935/leeeko/leeeko/chunklist.m3u8"],"subtitle":"By Channel","thumb":"https://is2-ssl.mzstatic.com/image/thumb/Purple113/v4/c9/76/95/c9769524-8604-49ac-108e-efca1d025a99/source/512x512bb.jpg","title":"LEEKO MUSIQUE TV"},{"description":"La Radio Télévision Guinéenne (RTG), l’un des premiers organes de presse public du pays, est absente dans plusieurs villes de l’intérieur du pays. Et ce après 42 ans depuis sa création. Par endroits, les signaux de la RTG sont totalement absents depuis plusieurs années. Par contre, dans certaines préfectures, malgré la réception des signaux, faute d’énergie, les populations sont privées des émissions de la RTG, a-t-on constaté.","sources":["http://178.33.237.146/rtg.m3u8"],"subtitle":"By Blender Channel","thumb":"http://maliactu.info/wp-content/uploads/2019/08/rtg-radio-television-guineenne.png","title":"Radio Television Guinéenne "},{"description":"MTA Africa 1 (anciennement MTA Africa) est la quatrième chaîne de télévision par satellite du réseau MTA International. Il a été lancé début août 2016, diffusant spécifiquement pour les téléspectateurs africains, à travers l'Afrique et l'Europe. La chaîne a été créée sous les auspices de Mirza Masroor Ahmad, le chef spirituel de la communauté musulmane Ahmadiyya. MTA Africa est géré et financé volontairement par les Ahmadis.","sources":["https://ooyalahd2-f.akamaihd.net/i/mtaengaudio_delivery@138280/index_3000_av-p.m3u8"],"subtitle":"By Blender Channel","thumb":"https://pbs.twimg.com/profile_images/950498775893774338/XKhzDO2.jpg","title":"MTA AFRICA"},{"description":"L’Office de Radiodiffusion et Télévision du Bénin (ORTB) est le service public de l’audiovisuel du Bénin. C’est un établissement public à caractères social, culturel et scientifique doté de la personnalité morale et de l’autonomie financière. ORTB, pas sans vous !/ Tél: +229 21 30 00 48/ Whatsapp: +229 69 70 55 55/ Email: contact@ortb.bj","sources":["http://51.77.223.83/ortb.m3u8"],"subtitle":"By Channel","thumb":"https://www.lavoixduconsommateur.org/images/services/1533219563.jpg","title":"ORTB / Bénin"},{"description":"Dream Channel est une chaine télévisée ematant au cameroune qui diffuse de la musique de toutes tendances.","sources":["http://connectiktv.ddns.net:5000/dreamchannel/dreamchannel/chunklist.m3u8"],"subtitle":"By Channel","thumb":"https://connectik.tv/wp-content/uploads/2019/06/c1b45634-2f8c-47e7-8849-e6d7ea620465-300x169.jpg","title":"DREAM CHANNEL TV / Cameroune"},{"description":"Canal Algérie est la deuxième chaîne de télévision nationale grand public algérienne. La chaîne fait partie du groupe EPTV qui comprend également TV1, TV3, TV4, TV5, TV6 et TV7. C'est une chaîne francophone. La chaîne diffuse ses programmes 24h / 24 et 7j / 7 via différentes plateformes et partout dans le monde.","sources":["http://46.105.114.82/canal_algerie.m3u8"],"subtitle":"By Channel","thumb":"https://upload.wikimedia.org/wikipedia/commons/thumb/2/22/Logo_Canal_Algerie.svg/800px-Logo_Canal_Algerie.svg.png","title":"Canal Algerie"},{"description":"Radio Télévision Sénegalaise est une station de radio diffusée sur le réseau de Radiodiffusion Télévision Sénégalaise (RTS1 HD) de Dakar, au Sénégal, fournissant des informations, des sports, des débats, des émissions en direct et des informations sur la culture ainsi que la musique.","sources":["http://46.105.114.82/rts1.m3u8"],"subtitle":"By Channel","thumb":"https://lh3.googleusercontent.com/VZyPxURRRo-C0lEWHggT8C-dDJvFNFTVxKrn1yKUNROoT85XnOl9VcmM5HFzyRDwvgs","title":"Radio Télévision Sénegalaise 1 HD"},{"description":"Kalsan est une chaîne de télévision Somalienne dont le siège est à Londres. Elle a commencé à diffuser en 2013. La chaîne est axée sur les Somaliens. La programmation est principalement axée sur les actualités et les divertissements.","sources":["http://cdn.mediavisionuae.com:1935/live/kalsantv.stream/chunklist.m3u8"],"subtitle":"By Channel","thumb":"https://xogdoonnews.net/wp-content/uploads/2017/11/kalsan-tv.jpg","title":"KALSAN TV / Somalie"},{"description":"One Africa Television est une chaine de television namibien crée en 2003 et couvrant à l'origine uniquement Windhoek, Rehoboth et Okahandja, One Africa Television a connu une croissance significative, avec son signal diffusé via 29 émetteurs analogiques à travers la Namibie. En 2013, One Africa Television a rejoint l'ère numérique, et la chaîne est depuis disponible sur le réseau de télévision numérique terrestre de la Namibian Broadcasting Corporation (Channel 301) ainsi que sur la plateforme DStv Namibia de MultiChoice (Channel 284) ainsi que sur le réseau numérique terrestre GoTV de MultiChoice. Président du groupe d'Africa Television, Paul van Schalkwyk, a été tué dans un accident d'avion le 10 mars 2014.","sources":["https://za-tv2a-wowza-origin02.akamaized.net/oneafrica/smil:oneafrica/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://neweralive.na/uploads/2016/11/Untitled-1.jpg","title":"ONE AFRICA TV / Namibia"},{"description":"VISION4 TV est une chaine television panafricanisme Camerounais crée en 2008. qui diffuse des Émissions hauts de gamme telsque : Afro Café, Matinale infos, le journal d'afrique, tour d'horizon, journal de 12, women's story, The 6h00 pm news, Let's talk, Meeting point le grand live, le grand journal de 20h, santé spirituelle, sport time, Arrêt majeur, Au cœur du mystère, parole d'artistes, Femme attitude, Panafritude, Rendez-vous santé, afro zik, Club d'élites, Plateau du Jaguar, Dimanche bonheur, face aux dinosaures. Vision 4 Le Groupe Anecdote Vision 4 TV, Satelite FM, Africa Express Siège social : Yaoundé - Cameroun (Nsam) Secrétariat PDG : Tel : +237 242 71 88 13 / Fax : +237 222 31 67 81 Service de l'information : Tel : +237 242 71 87 68 Yaoundé Centre B.P 25070 Cameroun","sources":["http://cdnamd-hls-globecast.akamaized.net/live/ramdisk/vision4/hls_video/index.m3u8"],"subtitle":"By Channel","thumb":"https://upload.wikimedia.org/wikipedia/commons/thumb/9/94/Vision_4.jpg/600px-Vision_4.jpg","title":"VISION 4"},{"description":"Nago TV is a Haitian television channel 100% devoted to music videos(Compass, Rap Creole, Racine).","sources":["http://haititivi.com:8088/haititv/tele6NY/index.m3u8"],"subtitle":"By Channel","thumb":"https://lh3.googleusercontent.com/GdAVtX7AU8834RaKoUC4c3itv2A_R1k8XATBf26G_IgQKnvxEtAew0cJOr_kWOpWkpY","title":"NAGO TV / Haiti"},{"description":"Lagos Television has been a trail blazer right from inception. Apart from being the first TV station outside the NTA family, the station took the Nigerian TV industry by storm in the early 80s with the introduction of a 60-hour non stop weekend from 7pm on Fridays till 7am on Mondays. The then Lagos weekend Television was the first marathon TV station in Africa. It’s unprecedented public approval transformed TV viewership especially within the Lagos precinct and brought a change in the call sign LTV/LWT.","sources":["http://185.105.4.193:1935/ltv/myStream/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://www.lagostelevision.com/wp-content/uploads/2015/10/logo.png","title":"Lagos Television"},{"description":"Emmanuel TV is the television station of The Synagogue, Church Of All Nations, broadcasting 24/7 around the globe via satellite and on the internet. The purpose of Emmanuel TV is to preach the Good News to all mankind. That is what we are born for, living for and what we shall die for. Emmanuel TV is committed to changing lives, changing nations and changing the whole world through the Gospel of our Lord Jesus Christ. Jesus Christ is the inspiration behind Emmanuel TV; as such, God’s purpose is our purpose.","sources":["https://api.new.livestream.com/accounts/23202872/events/7200883/live.m3u8"],"subtitle":"By Channel","thumb":"https://scoan-website-emmanueltv.netdna-ssl.com/wp-content/blogs.dir/12/files/2016/09/emmanuel_tv_icon.png","title":"Emmanuel TV"},{"description":"Addis TV is a City Channel based in Addis Ababa, Ethiopia, which broadcasts News and Programs 24/7.","sources":["https://rrsatrtmp.tulix.tv/addis1/addis1multi.smil/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://et.heytv.org/wp-content/uploads/2019/04/Addis-webtvonlive-com.jpg","title":"Addis TV / Ethiopia"},{"description":"Resurrection TV is a Christian based station aimed at uplifting your soul with an unadulterated word of God. it ensures a distinction between sin and righteousness.","sources":["http://rtmp.ottdemo.rrsat.com/rrsatrtv1/rrsatrtvmulti.smil/chunklist.m3u8"],"subtitle":"By Channel","thumb":"https://i.pinimg.com/564x/9e/f5/ae/9ef5aeb5c1ddd05a20d27faaf5d9b931.jpg","title":"Résurrection TV/ Ghana"},{"description":"CTV frique est une television camerounaise basee a yaounde.","sources":["http://connectiktv.ddns.me:8080/ctv-africa/index.m3u8"],"subtitle":"By Channel","thumb":"http://connectik.tv/wp-content/uploads/2020/01/CTV-1-300x212.jpeg","title":"CTV AFRICA"},{"description":"BEL TV est une station de télévision haïtienne qui diffuse sur le web via diverses plateformes et par câble. Notre vision est de créer une télévision standard dont la qualité du programme est aussi instructive que divertissante. À cote de cette vision, BEL TV s’est fixé pour mission de promouvoir la Culture haïtienne, à savoir le Cinéma, la musique, la littérature et bien plus encore, ce à travers la Caraïbe et le monde entier. BEL TV c’est une toute autre façon de faire la télé.","sources":["http://connectiktv.ddns.me:8080/afriqueplustv/index.m3u8"],"subtitle":"By Channel","thumb":"http://connectik.tv/wp-content/uploads/2019/07/NEW-LOGO0047_00000_00000-300x169.png","title":"AFRICA PLUS TV "},{"description":"1 ok.","sources":["http://connectiktv.ddns.me:8080/mygospel/index.m3u8"],"subtitle":"By Channel","thumb":"http://connectik.tv/wp-content/uploads/2020/05/mygospel-300x140.png","title":"MY GOSPEL TV"},{"description":"2 ok.","sources":["http://connectiktv.ddns.me:8080/media-prime/index.m3u8"],"subtitle":"By Channel","thumb":"http://connectik.tv/wp-content/uploads/2020/05/myprime15-300x140.png","title":"MEDIA PRIME TV"},{"description":"3 ok.","sources":["http://connectiktv.ddns.me:8080/mymusic/index.m3u8"],"subtitle":"By Channel","thumb":"http://connectik.tv/wp-content/uploads/2020/05/mymusic1.png","title":"MY MUSIC TV "},{"description":"4 ok.","sources":["http://connectiktv.ddns.me:8080/mymovie-en/index.m3u8"],"subtitle":"By Channel","thumb":"http://connectik.tv/wp-content/uploads/2020/05/myenglish-300x140.png","title":"MY MOVIE TV / English"},{"description":"5 ok.","sources":["http://connectiktv.ddns.me:8080/mymovie-fr/index.m3u8"],"subtitle":"By Channel","thumb":"http://connectik.tv/wp-content/uploads/2020/05/myfresh-300x140.png","title":"MY MOVIE TV / Francais"},{"description":"6 ok","sources":["http://connectiktv.ddns.me:8080/bikutsitv/index.m3u8"],"subtitle":"By Channel","thumb":"http://connectik.tv/wp-content/uploads/2019/09/bikutsi-300x63.jpeg","title":"BIKUTSI TV"},{"description":"7 ok.","sources":["http://connectiktv.ddns.me:8080/cam10tv/index.m3u8"],"subtitle":"By Channel","thumb":"http://connectik.tv/wp-content/uploads/2020/07/CAM10-REVUE.jpg","title":"CAM 10 TV / Cameroune"},{"description":"8 ok.","sources":["http://connectiktv.ddns.me:8080/leadergospel/index.m3u8"],"subtitle":"By Channel","thumb":"http://connectik.tv/wp-content/uploads/2020/05/leader-cable.jpg","title":"LEADER GOSPEL TV / Religion"},{"description":"9 ok.","sources":["http://connectiktv.ddns.me:8080/vstv/index.m3u8"],"subtitle":"By Channel","thumb":"http://connectik.tv/wp-content/uploads/2020/05/VS-TV-300x168.jpg","title":"VS tv"},{"description":"10 0k.","sources":["http://connectiktv.ddns.me:8080/mytv/index.m3u8"],"subtitle":"By Channel","thumb":"http://connectik.tv/wp-content/uploads/2019/04/mytv-channel-hd-300x169.jpg","title":"MY TV CHANNEL"},{"description":"Radio Tele Puissance est une chaine chrétienne qui diffuse en direct des programmes chrétien avec des vidéos et des films Gospel de premier ordre, des documentaires. radio Tele Puissance est une station très divertissante..","sources":["https://video1.getstreamhosting.com:1936/8560/8560/chunklist.m3u8"],"subtitle":"By Channel","thumb":"https://radioendirect.net/assets/images/radio/item/119251.jpg","title":"Radio Tele Puissance"},{"description":"Christ live est une chaine télévision de divertissement chrétienne disponible sur le satellite.","sources":["https://raw.githubusercontent.com/exodiver/IPTV/master/M3U8/Token/Cliv.m3u8"],"subtitle":"By Channel","thumb":"http://www.centraltv.fr/wp-content/uploads/christ-tv_logo.jpg","title":"CHRIST TV / Religion"},{"description":"QTV Gambia is the First Private Television Station","sources":["https://player.qtv.gm/hls/live.stream.m3u8"],"subtitle":"By Channel","thumb":"https://standard.gm/wp-content/uploads/2020/08/QTV-696x495.jpg","title":"QTV / Gambia"},{"description":"TVM International, or TVM Internacional, is the international channel of Mozambique's national TV broadcaster, Televisão de Moçambique (TVM), broadcasting for 24 hours per day. The channel will showcase local programming featuring Mozambican culture, tourism and sports.","sources":["http://196.28.226.121:1935/live/smil:Channel2.smil/chunklist_b714000_slpor.m3u8"],"subtitle":"By Channel","thumb":"https://clubofmozambique.com/wp-content/uploads/2020/03/tvmint.rm.jpg","title":"TVM Internacional"},{"description":"K24 TV est une chaine de télévision généraliste Kényane fondée en 2007 basé à Longonot Place, P. O. Box 49640 Kijabe St Tél : +254 20 2124801. K24 TV diffuse sur la télévision terrestre et en streaming sur Dailymotion et sur son site internet..","sources":["https://raw.githubusercontent.com/exodiver/IPTV/master/M3U8/Token/K24.m3u8"],"subtitle":"By Channel","thumb":"http://www.centraltv.fr/wp-content/uploads/k24-tv_logo.jpg","title":"K24 TV / Kenya"},{"description":"Afrobeat tv is a division of kaycee records .Kaycee Records is an independent record label established in the United Kingdom, and Nigeria Owned by Kennedy Kesidi Richard from Oguta in Imo State Nigeria .Afro beat tv is the new musical innovation to promote African art and and as a platform to promote and create awareness for up coming African artist all around the globe","sources":["http://connectiktv.ddns.net:8080/afrobit/index.m3u8"],"subtitle":"By Channel","thumb":"http://connectik.tv/wp-content/uploads/2019/05/AFROBIT-png-300x168.png","title":"AFROBITS / Music"},{"description":"Dunamis International Gospel Centre (DIGC) Jos Central is a powerfully anointed church, where God's Presence and power are saving, healing and restoring human destinies and dignities! Located in Alheri, Jos, Plateau State with HQT in Abuja Nigeria. Dunamis (Doo'na-mis) is the Greek word that means POWER.","sources":["https://christianworld.ashttp9.visionip.tv/live/visiontvuk-religion-dunamistv-SD/chunklist.m3u8"],"subtitle":"By Channel","thumb":"https://live-tv-channels.org/pt-data/uploads/logo/ng-dunamis-tv-2163-300x225.jpg","title":"DUNAMIS TV / Religion"},{"description":"France tv sport, c’est d’abord l’actualité de TOUS les sports. De l’analyse en temps réel, du live ou encore des replays vidéo sont disponibles à tout moment. Enrichissez votre expérience et plongez au cœur de l'actualité du sport.","sources":["https://streamserv.mytvchain.com/sportenfrance/SP1564435593_720p/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://liberador.net/media/images/FranceTv_Sport.max-640x640.jpg","title":"SPORTS FRANCE TV"},{"description":"Darut Tarbiyah La télévision en direct du Réseau islamique de Trinité-et-Tobago Chaîne de télévision religieuse / Darut Tarbiyah Le Réseau islamique (T.I.N.) est une chaîne de télévision câblée locale de Trinité-et-Tobago diffusant des programmes islamiques. La station est transportée sur le canal 96 ou 116 sur le système de câble Flow Trinidad. DARUT TARBIYAH - LE RÉSEAU ISLAMIQUE. Darut Tarbiyah Drive, Ramgoolie Trace North, Cunupia, Trinidad Antilles. Tél: (868) 693-1722, 693-1393","sources":["http://162.244.81.145:2215/live/livestream/playlist.m3u8"],"subtitle":"By Channel","thumb":"http://theislamicnetwork.org/wp-content/uploads/musicpro/bd-uploads/logo_logo_TIN-Logo-White-Text.png","title":"THE ISLAMIC NETWORK"},{"description":"D Sports HD est une chaine qui se fcalise sur les Sports en General : WWE, BOX, Football: Ligue brésilienne, Super League chinoise, Ligue portugaise, Major League Soccer (USA) Courses hippiques: courses quotidiennes diffusées en direct du Royaume-Uni et d'Irlande Golf: British Open (The Open Championship), US Open, PGA Championship, LPGA Motorsports: NASCAR, Championnat du Monde de Rallycross FIA Rugby: 6 Nations Rugby Cyclisme: Tour de France (propriété d'Eurosport).","sources":["http://jiocgehub.jio.ril.com/Dsports_HD/Dsports_HD.m3u8?fluxustv.m3u8"],"subtitle":"By Channel","thumb":"https://kccl.tv/sites/default/files/dsportjpg.jpg","title":"D Sports TV"},{"description":"Africa Sports TV est la première chaîne francophone d’information en continue de sport en Afrique. C’est un média fédérateur des sports africains. On parle de compétition locales, des ligues nationales sur toutes les disciplines du continent, dont le basketball, le football, la lutte… Il y aura beaucoup de lutte, qui prend un essor important sur le continent. Il y a tout un lobby autour de la lutte. Africa Sports TV est disponible Sur Le Canal 56 de la BbOX – Sur Le Canal 614 du Bouquet Africain Max de TV ORANGE.","sources":["https://strhls.streamakaci.tv/str_africasportstv_africasportstv/str_africasportstv_multi/str_africasportstv_africasportstv/str_africasportstv_player_1080p/chunks.m3u8"],"subtitle":"By Channel","thumb":"https://pbs.twimg.com/profile_images/1215646342812401668/SOnvVloX_400x400.jpg","title":"Africa Sports TV"},{"description":"Real Madrid TV est une chaîne de télévision numérique gratuite, exploitée par le Real Madrid, spécialisée dans le club de football espagnol. La chaîne est disponible en espagnol et en anglais. Il est situé à Ciudad Real Madrid à Valdebebas (Madrid), le centre de formation du Real Madrid.","sources":["http://rmtv24hweblive-lh.akamaihd.net/i/rmtv24hwebes_1@300661/index_3_av-b.m3u8"],"subtitle":"By Channel","thumb":"https://files.cults3d.com/uploaders/13539675/illustration-file/9c08780f-eb52-427b-aad7-b0a8c0fb83a1/real_madrid_ref1_large.JPG","title":"Real Madrid TV"},{"description":"Real Madrid Club de Fútbol, ce qui signifie Royal Madrid Football Club), communément appelé Real Madrid, est un club de football professionnel espagnol basé à Madrid. Fondé le 6 mars 1902 sous le nom de Madrid Football Club, le club porte traditionnellement un maillot blanc à domicile depuis sa création. Le mot réel est espagnol pour royal et a été accordé au club par le roi Alfonso XIII en 1920 avec la couronne royale dans l'emblème. L'équipe a disputé ses matchs à domicile dans le stade Santiago Bernabéu d'une capacité de 81 044 places au centre-ville de Madrid depuis 1947.","sources":["http://rmtv24hweblive-lh.akamaihd.net/i/rmtv24hweben_1@300662/master.m3u8"],"subtitle":"By Channel","thumb":"https://i.pinimg.com/564x/e4/de/18/e4de1869c0eba3beab9ffc9d01660e65.jpg","title":"Real Madrid TV"},{"description":" EPT SPORTS HD est la nouvelle chaîne exclusivement sportive de l’audiovisuel public, ERT Sports HD, sa première officielle à 06h00 le matin du samedi 9 février 2019.","sources":["https://ert-live.siliconweb.com/media/ert_sports/ert_sportshigh.m3u8"],"subtitle":"By Channel","thumb":"https://png.pngitem.com/pimgs/s/681-6814150_ert-sports-hd-logo-ert-sports-hd-hd.png","title":"EPT Sports HD"},{"description":"Sports Tonight Live, branded simply as Sports Tonight, was a British television show and channel, owned by VISION247 based in Central London. It was launched online on 29 August 2011.","sources":["http://sports.ashttp9.visionip.tv/live/visiontvuk-sports-sportstonightlive-hsslive-25f-4x3-SD/chunklist.m3u8"],"subtitle":"By Channel","thumb":"https://embeddedplayer.visionip.tv/portals/Sports_Tonight_Live/Sports_Tonight_Live/overlay_logos/Sports%20Tonight%20Live-plBackground-1308.png","title":"Sports Tonight"},{"description":"Arryadia HD TV est une chaîne sportive de télévision publique marocaine. Il fait partie du groupe public SNRT avec Al Aoula, Athaqafia, Al Maghribia, Assadissa, Aflam TV, Tamazight TV et Laayoune TV. La chaîne a été lancée le 16 septembre 2006. Arryadia est le diffuseur officiel de la ligue marocaine Botola.","sources":["http://cdn-hls.globecast.tv/live/ramdisk/arriadia/hls_snrt/index.m3u8"],"subtitle":"By Channel","thumb":"https://4.bp.blogspot.com/-lnh_8LWuXaw/WZ09LDkMsZI/AAAAAAAAEeI/9FKtxdQjbl4UVqmZjqN4R-fE9uOLG2ccQCLcBGAs/s1600/FB_IMG_1503465850383.jpg","title":"Arryadia TV / Maroc"},{"description":"Assadissa TV est une chaîne de télévision publique marocaine dédiée aux affaires religieuses. Il fait partie du groupe public SNRT avec Al Aoula, Arryadia, Athaqafia, Al Maghribia, Aflam TV, Tamazight TV et Laayoune TV. La chaîne a été lancée le 3 novembre 2005. Outre les lectures du Coran, il existe également des programmes de services religieux, de débats et de documentaires. Il est diffusé tous les jours de 2h00 à 23h00. Le samedi, il est de 6h00 à 21h00.","sources":["http://cdn-hls.globecast.tv/live/ramdisk/assadissa/hls_snrt/index.m3u8"],"subtitle":"By Channel","thumb":"https://upload.wikimedia.org/wikipedia/commons/7/7e/Assadissa.png","title":"Assadissa TV/ Maroc"},{"description":"Al Aoula, anciennement appelée TVM (Télévision marocaine, arabe: ??????? ????????), est la première chaîne de télévision publique marocaine. Il fait partie du groupe public SNRT avec Arryadia, Athaqafia, Al Maghribia, Assadissa, Aflam TV, Tamazight TV et Laayoune TV. Le réseau diffuse des programmes en arabe, berbère, français et espagnol. Son siège est situé à Rabat. Lancé en 1962, Al Aoula a été le premier réseau de télévision à produire et à diffuser ses propres programmes dans le pays. En 1962, il a commencé des émissions en couleur.","sources":["http://cdn-hls.globecast.tv/live/ramdisk/al_aoula_inter/hls_snrt/index.m3u8"],"subtitle":"By Channel","thumb":"https://live.staticflickr.com/1853/44065447112_7a93bb434f.jpg","title":"Al Aoula TV/ Maroc"},{"description":"2M TV est une chaîne de télévision marocaine gratuite. Il a été créé par le conglomérat royal, ONA, avant d'être en partie vendu à l'État marocain. 20,7% de 2M appartiennent à la société holding de Mohammed VI SNI. Alors qu'environ 60% sont contrôlés par l'État marocain. Il est basé à Casablanca. Il est disponible gratuitement localement sur signal numérique avec une couverture sur tout le Maroc et sur la télévision par satellite via Globecast, Nilesat et Arabsat. 2M propose des services en arabe, français et berbère.","sources":["https://cdnamd-hls-globecast.akamaized.net/live/ramdisk/2m_monde/hls_video_ts/2m_monde.m3u8"],"subtitle":"By Channel","thumb":"https://caidal.ma/wp-content/uploads/2019/04/ob_febd69_2-m-maroc-en-ligne.jpg","title":"2M TV / Maroc"},{"description":"La chaîne Al Magharibia diffuse des programmes politiques, sociaux et économiques depuis sa base privée de Londres. La chaîne est diffusée en arabe et s'adresse aux pays du Mahgreb, l'Algérie en particulier. Le ton d'Al Magharibia est fermement basé sur un discours politique et idéologique. Le ton d'Al Magharibia est fermement basé sur un discours politique et idéologique.","sources":["https://cdnamd-hls-globecast.akamaized.net/live/ramdisk/al_maghribia_snrt/hls_snrt/index.m3u8"],"subtitle":"By Channel","thumb":"https://cdn.sat.tv/wp-content/uploads/2016/05/SNRT-AlMaghribia.png","title":"Al maghribia TV / Maroc"},{"description":"Athaqafia TV est une chaîne gratuite disponible sur le satellite Hotbird et propose une gamme de programmes allant des documentaires et des programmes éducatifs ainsi que de la musique, des dessins animés et des divertissements familiaux. La chaîne s'adresse principalement aux familles et est diffusée principalement en arabe mais parfois en langue française et berbère. La chaîne a été créée par la société de production marocaine appartenant à l'État, SNRT.","sources":["http://cdn-hls.globecast.tv/live/ramdisk/arrabiaa/hls_snrt/index.m3u8"],"subtitle":"By Channel","thumb":"https://cdn.sat.tv/wp-content/uploads/2016/05/SNRTAThaqafia.png","title":"Athaqafia TV / Maroc"},{"description":"Tele Maroc est la nouvelle chaîne satellitaire généraliste marocaine créée par rachid Niny. Siège à Madrid. « C’est donc une chaéne de télévision légalement espagnole avec un contenu marocain.","sources":["https://api.new.livestream.com/accounts/27130247/events/8196478/live.m3u8"],"subtitle":"By Channel","thumb":"https://i.pinimg.com/564x/9e/1d/b5/9e1db51201d4debce634f6e8b44a2424.jpg","title":"Tele Maroc"},{"description":"Tamazinght TV est une chaîne de télévision publique marocaine créée le 6 janvier 2010, propriété de la Société nationale de radiodiffusion et de télévision. La chaîne a pour objectif la promotion et la préservation de la culture amazighe au Maroc et dans la région de l'Afrique du Nord. en langue berbère. 70% en tashelhit, tarifit et tamazight (les 3 variantes du berbère du Maroc), le reste en arabe.","sources":["https://cdnamd-hls-globecast.akamaized.net/live/ramdisk/tamazight_tv8_snrt/hls_snrt/index.m3u8"],"subtitle":"By Channel","thumb":"http://hub.tv-ark.org.uk/images/International/international_images/morocco_images/tamazight/Tamazight_TV_ident4_060912a.jpg","title":"Tamazight TV / Maroc"},{"description":"EMCI TV est une chaîne de télévision chrétienne évangélique francophone. Les studios de la chaîne se trouvent dans la ville de Québec, Canada. Le contenu de la programmation est assez varié et provient de divers pays francophones d’Afrique, d’Europe et d’Amérique. Des clips musicaux, des enseignements bibliques, des prédications, la Bible en vidéo, des temps de prière, des reportages, des documentaires, des films ainsi que des séries y sont présentés.","sources":["https://emci-fr-hls.akamaized.net/hls/live/2007265/emcifrhls/index.m3u8"],"subtitle":"By Channel","thumb":"https://www.enseignemoi-files.com/site/view/images/dyn-cache/pages/image/img/23/62/1522940482_236277_1200x630x1.f.jpg?v=2018021301","title":"EMCI TV / Religion"},{"description":"CIS TV est une chaine tv guinéen consacré au sport et à la culture. basée à Conakry, fondé en 2016 par Mamadou Antonio Souaré. CIS TV est diffuse via le satellite Fréquence Tv 3689: Symbole 1083: Satelite eutelsat 10a ZONES DE DIFFUSION : tiers d'Afrique.","sources":["http://51.81.109.113:1935/CDNLIVE/CISTV/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://www.senegal7.com/wp-content/uploads/2019/03/f9180cb9286c49c4f3a6c793798b9ddf.png","title":"CIS TV / Guinee"},{"description":"Faso TV est une initiative de Magic Communication et Médias, une société à responsabilité limitée basée à Ouagadougou, capitale du Burkina Faso. C’est une chaîne de télévision en ligne destinée à l’événementiel. Nous entendons par événementiel toutes manifestations ou activités à caractère culturel, économique, éducatif ou sportif dont l’objectif est de susciter la mobilisation, l’adhésion, l’engouement de la population ou d’un public cible au plan local, national ou international. Autrement dit, notre stratégie éditoriale consiste à faire la promotion de toutes activités qui contribuent au développement socio-économique et culturel, à l’éducation, au divertissement et au bien être de la population burkinabé et de sa diaspora.","sources":["https://playtv4kpro.com:5443/LiveApp/streams/163893638025530331068059.m3u8"],"subtitle":"By Channel","thumb":"https://fasotv.net/wp-content/uploads/2019/10/logo-final-sans-slogan.png","title":"FASO TV / Burkina Fasso "},{"description":"Plex tv une chaîne généraliste spécialisé dans la retransmissions des événement. émission et qui diffuse aussi des films, musiques, divertissement, sport, magasine etc et une multitude de programme en haute définition.","sources":["http://connectiktv.ddns.net:5000/plextv/@plextv/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://www.lifetimepremiumaccounts.com/wp-content/uploads/2019/03/plex-logo.jpg","title":"PLEX TV / "},{"description":"PLAY TV est une chaine de télévision musicale Camerounaise basée à Yaoundé, elle diffuse un programme 100% musicale la musique d’ici et d’ailleurs en haute définition..","sources":["http://connectiktv.ddns.net:5000/playtv/@playtv/playlist.m3u8"],"subtitle":"By Channel","thumb":"http://connectik.tv/wp-content/uploads/2019/04/logo-play-tv-300x113.jpg","title":"PLAT TV / Cameroune "},{"description":"TVR Rennes 35 Bretagne est une chaîne de télévision locale née en mars 1987 sous le nom de TV Rennes. TVR Rennes 35 Bretagne fut inaugurée à son lancement par le président de la République, elle fut la première télévision locale créée en France elle est diffusée Canal 35 sur la TNT / Canal 30 sur Orange, Freebox et BBox / Canal 95 sur Numéricable et en direct streaming sur son site Internet.","sources":["https://streamtv.cdn.dvmr.fr/TVR/ngrp:tvr.stream_all/master.m3u8"],"subtitle":"By Channel","thumb":"https://w0.pngwave.com/png/890/19/tvr-tv-rennes-35-logo-television-channel-tvr-t350-png-clip-art.png","title":"Rennes TV / France Sports "},{"description":"Chaîne franco-marocaine basée à Tanger et destinée au Maghreb. Programmation culturelle avec information, reportages et documentaires. En arabe et en Français. Fin 2010, elle a également commencé à diffuser à la télévision analogique terrestre au Maroc, en plus de la télévision numérique par satellite. Il a été rebaptisé Medi 1 TV.","sources":["http://streaming.medi1tv.com/live/Medi1tvmaghreb.sdp/chunklist.m3u8"],"subtitle":"By Channel","thumb":"http://www.logotypes101.com/logos/807/C85CC3231EAD10CEC61C182C7DED072D/medi1tvlogo.png","title":"Medi 1 TV / Maroc"},{"description":"M24 Television est la chaîne d’info en continu de l’agence marocaine de presse (MAP). Une chaîne qui couvre l’actualité marocaine et internationale. Une chaîne fidèle aux valeurs de la MAP qui est le premier producteur d'information au Maroc. Le fil de la MAP se décline en cinq langues : Arabe, Amazighe, Français, Anglais et Espagnol. la MAP présente dans toutes les régions du Royaume et dans les cinq continents, elle fournit tous les médias en informations, reportages, analyses et portraits.","sources":["https://www.m24tv.ma/live/smil:OutStream1.smil/chunklist.m3u8"],"subtitle":"By Channel","thumb":"https://is5-ssl.mzstatic.com/image/thumb/Purple114/v4/e4/c6/3e/e4c63e4e-b8ff-a14e-cacd-3593f09c1f78/source/512x512bb.jpg","title":"M24 TV / Maroc"},{"description":"La chaîne Al Magharibia diffuse des programmes politiques, sociaux et économiques depuis sa base privée de Londres. La chaîne est diffusée en arabe et s'adresse aux pays du Mahgreb, l'Algérie en particulier. Le ton d'Al Magharibia est fermement basé sur un discours politique et idéologique. Le ton d'Al Magharibia est fermement basé sur un discours politique et idéologique.","sources":["https://cdnamd-hls-globecast.akamaized.net/live/ramdisk/al_maghribia_snrt/hls_snrt/index.m3u8"],"subtitle":"By Channel","thumb":"https://cdn.sat.tv/wp-content/uploads/2016/05/SNRT-AlMaghribia.png","title":"Al maghribia TV / Maroc"},{"description":"Athaqafia TV est une chaîne gratuite disponible sur le satellite Hotbird et propose une gamme de programmes allant des documentaires et des programmes éducatifs ainsi que de la musique, des dessins animés et des divertissements familiaux. La chaîne s'adresse principalement aux familles et est diffusée principalement en arabe mais parfois en langue française et berbère. La chaîne a été créée par la société de production marocaine appartenant à l'État, SNRT.","sources":["http://cdn-hls.globecast.tv/live/ramdisk/arrabiaa/hls_snrt/index.m3u8"],"subtitle":"By Channel","thumb":"https://cdn.sat.tv/wp-content/uploads/2016/05/SNRTAThaqafia.png","title":"Athaqafia TV / Maroc"},{"description":"Al-Fath channel is the property of Sheikh Ahmed Awad Abdo, and is considered the satellite channel of the Islamic religious channels that follow the Sunnah, and offers a series of programs interpretation for the Quran Al-Kareem, and many true prophetic and the CEO is Prof. Ahmed Abdou Awad, the Islamic Scholar.","sources":["https://svs.itworkscdn.net/alfatehlive/fatehtv/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://live-tv-channels.org/pt-data/uploads/logo/eg-alfath-tv.jpg","title":"Al Fath TV / Egypte"},{"description":"Al Hayah started broadcasting in 2008 during the last years of Mubarak's rule, which saw a revival in the ownership of the media. It was founded by businessman El Sayed El Badawi as part of Al Hayah Channels Network. El Badawi assumed the presidency of the Wafd Party from May 2010 until March 2018. El Badawi is one of the businessmen who played political roles in addition to owning media outlets, such as Al Dostor (link to profile). ","sources":["http://media.islamexplained.com:1935/live/_definst_mp4:ahme.stream_360p/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://i.pinimg.com/474x/76/1a/a4/761aa46eb54c24d21ca5866f21442426.jpg","title":"Al hayat TV / Maroc"},{"description":"The El Sharq channel broadcasts Various programs, from Egypt country in the Arabic language, last updated time on March 25, 2016. El Sharq which considered to view as a Free to air satellite TV channel.","sources":["https://mn-nl.mncdn.com/elsharq_live/live/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://egytvs.com/wp-content/uploads/2014/06/al-sharq-200x75.jpg","title":"Al sharq TV / Maroc"},{"description":"Guinée TV1 est une chaine de télévision généraliste Guinéenne basée à Conakry. Elle diffuse de la musique des informations des documentaires. des programmes religieux et autre.","sources":["https://playtv4kpro.com:5443/LiveApp/streams/664825404798849938149128.m3u8"],"subtitle":"By Channel","thumb":"https://gtv1love.com/wp-content/uploads/2019/10/logo4.png","title":"GUINEE TV / Guinee "},{"description":"Inooro TV chaînes de télévision généraliste Kényane en langue Kikuyu lancé le 26 octobre 2015. Elle diffuse 24 heures sur 24. Inooro TV est une chaine du groupe Royal Media Services (RMS).","sources":["https://vidcdn.vidgyor.com/inoorotv-origin/liveabr/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://royalmedia.s3.amazonaws.com/wp-content/uploads/2019/10/inoorotv.jpg","title":"INOORO TV / Kenya "},{"description":"Citizen TV Kenya est une station nationale Kényane détenue par Royal Media Services Ltd.Elle diffuse principalement en anglais et en swahili. Elle a été lancé en 1999 et relancé en Juin 2006 c’est la station de télévision avec la plus forte croissance au Kenya avec un fort accent sur ??la programmation locale Basé au Communication Centre,Maalim Juma Road,Off Dennis Pritt Road, Nairobi, 7498-00300.","sources":["https://vidcdn.vidgyor.com/citizentv-origin/liveabr/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://www.innocirc.com/wp-content/uploads/2017/07/citizen.jpg","title":"CITIZEN TV / Kenya "},{"description":"RTJ TV ( Radio Télévision Jeune ) est une chaine de télévision culturel Sénégalaise. Elle diffuse des programme de divertissement( WatZapp le Zapping), Musique (playlist Mix Afro Mix Zouk Mix Hip Hop Musique sénégalaise), bien être, documentaire, Émission éducatif qui consiste à joindre l’utile à l’agréable à travers l'éducation des enfants, interviews ect.","sources":["http://public.acangroup.org:1935/output/rtjtv.stream/playlist.m3u8"],"subtitle":"By Channel","thumb":"http://rtjtv.com/images/rtjtv.png","title":"RTJ TV / Senegal"},{"description":"Mouride tv est une chaine de télévision généraliste sénégalaise basé à touba, Senegal. Mouride tv c’est la télévision base au coeur des événement mourides magal, thiante, wakhtane, khassaide, kourel en direct..","sources":["http://51.81.109.113:1935/Livemouridetv/mouridetv/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://live-tv-channels.org/pt-data/uploads/logo/sn-mouride-tv.jpg","title":"MOURIDE TV / Senegal"},{"description":"ANN TV est une chaîne d’Informations générales et de Divertissement. Elle est produite par JUUF COMMUNICATION et diffusée sur le site d’informations générales multimédia ANN. La plateforme ANN comporte un journal en ligne (ANN), une WebRadio (ANN FM) et une WebTV (ANN TV).","sources":["http://vod.acangroup.org:1935/output/anntv.stream/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://an-news.com/wp-content/uploads/2019/05/aan.png","title":"ANN TV / Senegal"},{"description":"Louga TV est une chaine culturelle et religieuse Senegalaise qui se veut attractive et objective. En temps réel, elle produit des vidéos de qualité qui tiennent compte de la spécificité de l’information et de la crédibilité de ses sources. Également, l’équipe technique et rédactionnelle est constituée de techniciens chevronnés aux compétences avérées. Dans son approche des enjeux de l’information capitale, la chaine louga tv offre des vidéos qui informent, forment et transforment le citoyen dans l’approche de son monde en devenir..","sources":["http://ira.dyndns.tv:8080/live/louga/CAnhiMtR6C/1708.m3u8"],"subtitle":"By Channel","thumb":"https://i.ytimg.com/vi/3Gnt2_SndXw/maxresdefault.jpg","title":"LOUGA TV / Senegal"},{"description":"Dieu TV est une chaine de télévision généraliste chrétienne pour la Francophonie.Elle proclame la Bonne Nouvelle du Salut en Jésus-Christ pour atteindre les 400 millions de Francophones dans le monde. Fondée en 2007. Dieu TV diffuse sur le Satellite Eutelsat 5WA (Europe et Afrique du Nord), et le Satellite Amos 5 et en streaming sur son site interne","sources":["https://katapy.hs.llnwd.net/dieutvwza1/DIEUTVLIVE/smil:dieutv.smil/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://live-tv-channels.org/pt-data/uploads/logo/fr-dieu-tv.jpg","title":"DIEU TV / Religion "},{"description":"Radio Télévision Hirondelle : La nouvelle couleur du Sud. Elle diffuse des émissions pour divers catégories nouvelles locales, nationales et internationales le sport du monde Entier la musique et videos clips Promotions des artistes Locaux.","sources":["http://play.acwstream.com:2000/live/acw_01/index.m3u8"],"subtitle":"By Channel","thumb":"https://radiotelehirondelle.com/wp-content/uploads/2020/08/logo.png","title":"HIRONDELLE TV"},{"description":"BEL TV est une station de télévision haïtienne qui diffuse sur le web via diverses plateformes et par câble. Notre vision est de créer une télévision standard dont la qualité du programme est aussi instructive que divertissante. À cote de cette vision, BEL TV s’est fixé pour mission de promouvoir la Culture haïtienne, à savoir le Cinéma, la musique, la littérature et bien plus encore, ce à travers la Caraïbe et le monde entier. BEL TV c’est une toute autre façon de faire la télé.","sources":["https://hbiptv.live/player/sakchotv/index.m3u8"],"subtitle":"By Channel","thumb":"https://image.roku.com/developer_channels/prod/1de97a21d9bd773a115a5467974be0b859d1157256316bd1e72ed48965c0191a.png","title":"BEL TV / Haiti "},{"description":"The Middle East Broadcasting Center (MBC) Group is the first private free-to-air satellite broadcasting company in the Arab World. It was launched in London in 1991 and later moved to its headquarters in Dubai in 2002. MBC Group provides multiple channels of information, interaction and entertainment. MBC Group includes 10 television channels: MBC1 (general family entertainment via terrestrial), MBC2 and MBC MAX (24-hour movies), MBC3 (children’s entertainment), MBC4 (entertainment for new Arab women via terrestrial).","sources":["https://shls-masr-prod.shahid.net/masr-prod.m3u8"],"subtitle":"By Channel","thumb":"https://upload.wikimedia.org/wikipedia/commons/7/7c/MBC_Masr_Logo.png","title":"MBC MSR 1 / Egypte"},{"description":"The Middle East Broadcasting Center (MBC) Group is the first private free-to-air satellite broadcasting company in the Arab World. It was launched in London in 1991 and later moved to its headquarters in Dubai in 2002. MBC Group provides multiple channels of information, interaction and entertainment. MBC Group includes 10 television channels: MBC1 (general family entertainment via terrestrial), MBC2 and MBC MAX (24-hour movies), MBC3 (children’s entertainment), MBC4 (entertainment for new Arab women via terrestrial).","sources":["https://shls-masr2-prod.shahid.net/masr2-prod.m3u8"],"subtitle":"By Channel","thumb":"https://i.pinimg.com/564x/01/3c/21/013c218c3ce9b3cfc883bdcdb121e5e6.jpg","title":"MBC MSR 2 / Egypte"},{"description":"Mekameleen TV is an Egyptian opposition TV Channel. It is based in Istanbul. It's known to be supportive of the Muslim Brotherhood","sources":["https://mn-nl.mncdn.com/mekameleen/smil:mekameleentv.smil/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://i.pinimg.com/564x/12/5b/1f/125b1febb8ada3a4f83475c2643adeb7.jpg","title":"Mekameleen TV / Egypte"},{"description":"The Kingdome Sat is television from Egypte founded in 2009 by Dr. Michael Yousef, the KingdomSat channel aims to introduce written teachings from the East and West to complement the vision given by God to the loss of the faraway and to encourage believers in the Middle East and North Africa region.","sources":["https://bcovlive-a.akamaihd.net/87f7c114719b4646b7c4263c26515cf3/eu-central-1/6008340466001/profile_0/chunklist.m3u8"],"subtitle":"By Channel","thumb":"https://live-tv-channels.org/pt-data/uploads/logo/eg-kingdom-sat-channe.jpg","title":"The Kingdom Sat TV / Egypt"},{"description":"D5 TV Music est une nouvelle chaîne de télévision musicale internationale, elle est dédiée aux musiques et aux cultures urbaines du monde entier (Rap, R&B, Hip-Hop, Pop, Rai, Naija, Olschool etc.) ciblant un public très large. D5Music entend devenir la chaîne référence musicale des 5 continents","sources":["https://www.rti.ci/direct_rti2.html"],"subtitle":"By Channel","thumb":"https://d5music.tv/wp-content/uploads/2020/07/cropped-LOGO-D5-MUSIC-BLANCROUGE_carre-192x192.png","title":"RTI 2 TV"},{"description":"A2iTV la chaine 100% immigration Senegalais, qui est née de la synergie de personnes qui ont décidé d’ unir leur force, leur compétence et leur ressources matérielles et financiéres pour participer avec l’aide des nouvelles technologies à informer sur l’ immigration .","sources":["http://51.158.31.93:1935/a2itv/myStream/playlist.m3u8"],"subtitle":"By Channel","thumb":"http://www.centraltv.fr/wp-content/uploads/A2itv_logo.jpg","title":"A2i TV / Senegal"},{"description":"A2i music est une chaine culturelle destinée à la Diaspora avec des programmes musicales et des dramatiques. A2i music couvre aussi les autres parties du monde, notament les Etats Unis, le Canada, l’Asie, etc. à travers les boitiers Roku fournis par AfricaAstv, Acantv, My African pack de Invevo et Sénégal.","sources":["http://51.158.31.93:1935/a2itvtwo/myStream/playlist.m3u8"],"subtitle":"By Channel","thumb":"http://www.centraltv.fr/wp-content/uploads/a2i-music_logo.jpg","title":"A2i TV / Music "},{"description":"A2i tv Relegion est une chaine culturelle destinée à la Diasporat senegalais avec des programme chretiens.","sources":["http://51.158.31.93:1935/a2itvthree/myStream/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://lh3.googleusercontent.com/wwumXcAbY83D0q-NgUv2veS-p54FJTq6LAvsPRwYwWo-70ggeDkCM1VdhqhibRQNk4o=s180-rw","title":"A2i TV / Religion "},{"description":"Love World Plus TV is your Christian faith and lifestyle channel destined to bring a new level of dynamism into Christian television programming through satellite and the internet. The reach of LoveWorld Plus is limitless.","sources":["http://hls.live.metacdn.com/2450C7/bedkcjfty/lwplus_628.m3u8"],"subtitle":"By Channel","thumb":"https://d3c5pcohbexzc4.cloudfront.net/videos/thumbs/be214-loveworldplus.jpg","title":"Love World Plus TV"},{"description":"A2i naija est une nouvelle chaîne de télévision musicale internationale, elle est dédiée aux musiques et aux cultures urbaines du monde entier (Rap, R&B, Hip-Hop, Pop, Rai, Naija, Olschool etc.) ciblant un public très large. D5Music entend devenir la chaîne référence musicale des 5 continents","sources":["http://51.158.31.93:1935/devtv/myStream/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://image.winudf.com/v2/image/YTVjZW50cy5hMmlfc2NyZWVuXzVfMTUxNTk5NTEyNl8wNTQ/screen-5.jpg?fakeurl=1&type=.jpg","title":"A2i / naija Music"},{"description":"BOK TV is an online and public access variety show and the show's log line what would happen if In Living Color and The Daily Show had a bastard child! BOKTV is what would happen and he show is split into segments: MONOLOGUE, SKETCH, ROUND TABLE, COMMERCIAL, BLACK TWITTER. create a platform of discourse that encourages exchange as opposed to polarity, and to showcase the talents of the host and other cast members.","sources":["http://boktv.interworks.in:1935/live/boktv/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://bokradio.co.za/wp-content/uploads/2017/07/button_cameratv.jpg","title":"BOK TV"},{"description":"Salt TV is a Christian television channel station from Uganda. Salt TV is based in Kampala. Matthew 5:13-16 (NKJV) Believers Are Salt and Light 13 You are the salt of the earth, but if the salt loses its flavor, how shall it be seasoned? It is then good for nothing but to be thrown out and trampled underfoot by men.","sources":["http://dcunilive38-lh.akamaihd.net/i/dclive_1@692676/index_150_av-p.m3u8"],"subtitle":"By Channel","thumb":"https://www.saltmedia.ug/images/NOV/SALT-TV.jpg","title":"Salt TV/ Uganda"},{"description":"TFM is Senegal’s privately-owned television channel.Owned by Senegalese musician Youssou N Dour, who owns a major media group in Dakar.","sources":["http://46.105.114.82/tfm_senegal.m3u8"],"subtitle":"By Channel","thumb":"https://3.bp.blogspot.com/-eyo4UyKqjlI/WWTobvXxLqI/AAAAAAAAB_g/BFn1KiR6vcYQMilgX4nWhGJHbHMEP_l0ACLcBGAs/s1600/tfm%2Bsenegal.png","title":"TFM TV/ Senegal"},{"description":"Africa tv1 est une télévision africaine qui travaille pour aider les peuples a se communiquer avec DIEU et surtout sensibiliser les Africains musulmans de partout.","sources":["http://africatv.live.net.sa:1935/live/africatv/playlist.m3u8"],"subtitle":"By Channel","thumb":"http://www.africagroup.tv/img/bgTV1.png","title":"Africa TV 1"},{"description":"Africa tv2 est une télévision africaine qui travaille pour aider les peuples a se communiquer avec DIEU et surtout sensibiliser les Africains musulmans de partout.","sources":["http://africatv.live.net.sa:1935/live/africatv2/playlist.m3u8"],"subtitle":"By Channel","thumb":"http://www.africagroup.tv/img/bgTV2.png","title":"Africa TV 2"},{"description":"Africa tv3 est une télévision africaine qui travaille pour aider les peuples a se communiquer avec DIEU et surtout sensibiliser les Africains de langue haoussa.","sources":["http://africatv.live.net.sa:1935/live/africatv3/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://www.africagroup.tv/img/bgTV3.png","title":"Africa TV 3"},{"description":"La télévision nationale tunisienne 1 est la chaîne publique nationale tunisienne. Il a été officiellement lancé le 31 mai 1966, mais diffuse des programmes pilotes de manière irrégulière depuis octobre 1965, puis régulièrement depuis janvier 1966 et s’appelle la radio et la télévision tunisienne (ATT). Elle est devenue Channel 7 en 1992 et Tunisia 7 en 1997, mais elle est restée une filiale de la Société tunisienne de radio et de télévision jusqu’en 2008, a conservé le siège qu’elle partageait et la Société tunisienne de télévision avec ses chaînes de télévision nationales tunisiennes et La Tunisie 21 plus tard connue sous le nom de Télévision nationale tunisienne 2 est devenue son nouveau siège. Après le déclenchement de la révolution populaire tunisienne et la défection de zine El Abidine Ben Ali du pays, il est devenu Télévision nationale tunisienne.","sources":["http://54.36.122.126/tunisie1.m3u8"],"subtitle":"By Channel.","thumb":"https://www.histoiredesfax.com/wp-content/uploads/2015/11/Television-nationale-watania.jpg","title":"TUNISIA 1"},{"description":"Sahel TV est la plateforme unique, ouverte à la société civile, aux citoyens et à l'autorité locale de la ville et de sa région pour leur permettre de s’exprimer librement, proposer leurs idées et accéder à toutes les informations économiques, environnementale, culturelle, sportive. Vos idées et vos propositions sont les bienvenues.","sources":["http://142.44.214.231:1935/saheltv/myStream/playlist.m3u8"],"subtitle":"By Channel","thumb":"https://mobiletv.mobibase.com/html/logo/hd/channel_ld_747.png","title":"SAHEL TV / Tunisie"},{"description":"NIGERIA TELEVISION AUTORTEAutorité a commencé sous le nom de Western Nigerian Television Services (WNTV), qui a transmis ses premiers signaux au peuple nigérian et à toute l'Afrique le 31 octobre 1959. Au début de 1962, les trois gouvernements régionaux qui existaient au Nigéria avaient mis en place le Service de télévision nigérian (NTS). Télévision ont été créés et en 1976, l'Autorité de la télévision nigériane est née en tant que seule entité responsable de Diffusion télévisée au Nigéria.","sources":["http://54.38.93.93/nta.m3u8"],"subtitle":"By Channel","thumb":"https://static.squarespace.com/static/53d2a092e4b0125510bfe57d/53d2a2c6e4b018cd23e33d7b/53d2a2c6e4b018cd23e33f6f/1362042333867/1000w/nta.jpg","title":"NTA TV / Nigeria"},{"description":"ESPACE TV est une télévision basée à Conakry dans la commune de Matoto Kondeboungny au bord de l'autoroute Fidèle Castro (République de Guinée). La télé diffuse des informations du pays et du monde en temps réel. Des magazines axés sur les réalités des terroirs et des séries de divertissement. Détenue par le groupe Hadafo Médias, cette chaîne est la première du pays en terme d'audience; selon le rapport de Stat view international en 2019.","sources":["http://46.105.114.82/espacetv.m3u8"],"subtitle":"By Channel","thumb":"https://lh3.googleusercontent.com/ric-bS2gzvt-UyrhBIEdWENN9U-fL9Bnlhv12GEYSzSkZFWEIr7hc74k83kfLPqZDk0","title":"Espace TV / Guinée"},{"description":" Movies Now is an Indian high-definition television channel featuring Hollywood films. It was launched on 19 December 2010 with a picture quality of 1080i and 5.1 surround sound. The channel is owned by The Times Group. It has exclusive content licensing from films produced or distributed by MGM and has content licensing from Universal Studios, Walt Disney Studios, Marvel Studios, 20th Century Studios, Warner Bros and Paramount Pictures.","sources":["https://timesnow.airtel.tv/live/MN_pull/master.m3u8"],"subtitle":"By Channel","thumb":"https://upload.wikimedia.org/wikipedia/en/4/49/Movies_Now_logo.png","title":"Movies Now HD"},{"description":"Tunisie Immobilier TV, la première chaîne de l’immobilier en Tunisie Vous présente toutes les semaines, les actualités immobilières et économiques en Tunisie et dans le monde à travers des reportages.contact; E-mail:tunisieimmob@planet.tn/ Tel:(+216) 71 894500.","sources":["https://5ac31d8a4c9af.streamlock.net/tunimmob/myStream/chunklist.m3u8"],"subtitle":"By Channel","thumb":"https://i2.wp.com/www.tunisieimmobiliertv.net/wp-content/uploads/2016/10/fb.jpg?fit=1024%2C500&ssl=1","title":"Tunisie Immobilier TV"}]}]}

Viha27 / Python Devops

A Devops pipeline is set of automated processes and tools that the development (Dev) and operations (Ops) teams implement to build, test, and deploy software faster and easier. In this course you will complete DevOps pipeline generally consists of a set of tools which are normally broken down into the following categories: Plan Code Integrate Test Release Deploy Operate This learning path will cover: Git is an open-source and distributed version control system. Github is git repository hosting service used for code sharing, bug tracking, feature request and much more. PyCharm is an integrated development environment (IDE) for python programing language. Flask is a python web framework. HTML is the standard markup language for Web pages. CSS is a style sheet language use to style a HTML document. SQLAlchemy is an open-source SQL toolkit and object-relational mapper which gives full power and flexibility of SQL. Selenium is used to automate web browser interaction. Pytest is unit testing framework that allows users to write test codes. Ngrok allows to expose a web server running on your local machine to the internet. Github Action enables you to include Continues Integration (CI) and continuous deployment (CD) capabilities and many other features directly in your repository. Docker is an open source containerization platform enables developers to package applications into containers. Docker Hub is a cloud-based repository for finding and sharing container images with your team. Kubernetes is an open-source container orchestration for automating deployment, scaling, and management of containerized applications. This course is one stop shop where you will learn web development, continuous integration, continuous deployment, containerization, writing neat and quality code, devops concepts and much more with python programing language. What you’ll learn Learn to build Continuous Integration Continuous Deployment pipeline Build CI CD tool to update docker image after any update Learn to create dockerfile Learn the fundamental concepts of Docker Learn the fundamental concepts of Kubernetes Learn to create Kubernetes YAML files Learn to deploy high availability, fault tolerance, scalable application Learn all the basic and advanced git commands Learn different types of branches like master, developer, feature, release and hotfix branch Learn fundamental concepts of Version Control System Learn to use Github actions for CI CD pipeline Learn to build python flask web application Learn to use SQL Alchemy Lean to create HTML pages using HTML, CSS and bootstrap Are there any course requirements or prerequisites? Git installed Docker installed Kubernetes installed Any IDE Github account Docker hub account Who this course is for: Anyone who wants to Enhance their skills in DevOps domain Developers and IT Pros Instructor User photo Pranjal Srivastava Docker | Kubernetes | AWS | Azure | ML | Linux | Python I am an Instructor, Devops engineer, machine learning enthusiast, cloud expert and passionate developer. I have authored 60+ courses with over 50,000+ students worldwide across 175+ countries on wide array of technologies like containerization, machine learning, Linux, programming languages and cloud computing platforms like Microsoft Azure, Amazon Web Service and IBM Cloud.

michealbalogun / Horizon Dashboard

Copyright 2012 United States Government as represented by the # Administrator of the National Aeronautics and Space Administration. # All Rights Reserved. # # Copyright 2012 Nebula, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import glob import logging import os import sys import warnings from django.utils.translation import pgettext_lazy from django.utils.translation import ugettext_lazy as _ from horizon.utils.escape import monkeypatch_escape from openstack_dashboard import enabled from openstack_dashboard import exceptions from openstack_dashboard.local import enabled as local_enabled from openstack_dashboard import theme_settings from openstack_dashboard.utils import config from openstack_dashboard.utils import settings as settings_utils monkeypatch_escape() _LOG = logging.getLogger(__name__) warnings.formatwarning = lambda message, category, *args, **kwargs: \ '%s: %s' % (category.__name__, message) ROOT_PATH = os.path.dirname(os.path.abspath(__file__)) if ROOT_PATH not in sys.path: sys.path.append(ROOT_PATH) DEBUG = False SITE_BRANDING = 'OpenStack Dashboard' WEBROOT = '/' LOGIN_URL = None LOGOUT_URL = None LOGIN_ERROR = None LOGIN_REDIRECT_URL = None MEDIA_ROOT = None MEDIA_URL = None STATIC_ROOT = None STATIC_URL = None SELECTABLE_THEMES = None INTEGRATION_TESTS_SUPPORT = False NG_TEMPLATE_CACHE_AGE = 2592000 ROOT_URLCONF = 'openstack_dashboard.urls' HORIZON_CONFIG = { 'user_home': 'openstack_dashboard.views.get_user_home', 'ajax_queue_limit': 10, 'auto_fade_alerts': { 'delay': 3000, 'fade_duration': 1500, 'types': ['alert-success', 'alert-info'] }, 'bug_url': None, 'help_url': "https://docs.openstack.org/", 'exceptions': {'recoverable': exceptions.RECOVERABLE, 'not_found': exceptions.NOT_FOUND, 'unauthorized': exceptions.UNAUTHORIZED}, 'modal_backdrop': 'static', 'angular_modules': [], 'js_files': [], 'js_spec_files': [], 'external_templates': [], 'plugins': [], 'integration_tests_support': INTEGRATION_TESTS_SUPPORT } # The OPENSTACK_IMAGE_BACKEND settings can be used to customize features # in the OpenStack Dashboard related to the Image service, such as the list # of supported image formats. OPENSTACK_IMAGE_BACKEND = { 'image_formats': [ ('', _('Select format')), ('aki', _('AKI - Amazon Kernel Image')), ('ami', _('AMI - Amazon Machine Image')), ('ari', _('ARI - Amazon Ramdisk Image')), ('docker', _('Docker')), ('iso', _('ISO - Optical Disk Image')), ('ova', _('OVA - Open Virtual Appliance')), ('ploop', _('PLOOP - Virtuozzo/Parallels Loopback Disk')), ('qcow2', _('QCOW2 - QEMU Emulator')), ('raw', _('Raw')), ('vdi', _('VDI - Virtual Disk Image')), ('vhd', _('VHD - Virtual Hard Disk')), ('vhdx', _('VHDX - Large Virtual Hard Disk')), ('vmdk', _('VMDK - Virtual Machine Disk')), ] } MIDDLEWARE = ( 'openstack_auth.middleware.OpenstackAuthMonkeyPatchMiddleware', 'debreach.middleware.RandomCommentMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'horizon.middleware.OperationLogMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'horizon.middleware.HorizonMiddleware', 'horizon.themes.ThemeMiddleware', 'django.middleware.locale.LocaleMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', 'openstack_dashboard.contrib.developer.profiler.middleware.' 'ProfilerClientMiddleware', 'openstack_dashboard.contrib.developer.profiler.middleware.' 'ProfilerMiddleware', ) CACHED_TEMPLATE_LOADERS = [ 'django.template.loaders.filesystem.Loader', 'django.template.loaders.app_directories.Loader', 'horizon.loaders.TemplateLoader' ] ADD_TEMPLATE_LOADERS = [] ADD_TEMPLATE_DIRS = [] TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [os.path.join(ROOT_PATH, 'templates')], 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.i18n', 'django.template.context_processors.request', 'django.template.context_processors.media', 'django.template.context_processors.static', 'django.contrib.messages.context_processors.messages', 'horizon.context_processors.horizon', 'openstack_dashboard.context_processors.openstack', ], 'loaders': [ 'horizon.themes.ThemeTemplateLoader' ], }, }, ] STATICFILES_FINDERS = ( 'django.contrib.staticfiles.finders.FileSystemFinder', 'horizon.contrib.staticfiles.finders.HorizonStaticFinder', 'compressor.finders.CompressorFinder', ) COMPRESS_PRECOMPILERS = ( ('text/scss', 'horizon.utils.scss_filter.HorizonScssFilter'), ) COMPRESS_CSS_FILTERS = ( 'compressor.filters.css_default.CssAbsoluteFilter', ) COMPRESS_ENABLED = True COMPRESS_OUTPUT_DIR = 'dashboard' COMPRESS_CSS_HASHING_METHOD = 'hash' COMPRESS_PARSER = 'compressor.parser.HtmlParser' INSTALLED_APPS = [ 'openstack_dashboard', 'django.contrib.contenttypes', 'django.contrib.auth', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'django.contrib.humanize', 'django_pyscss', 'debreach', 'openstack_dashboard.django_pyscss_fix', 'compressor', 'horizon', 'openstack_auth', ] AUTHENTICATION_BACKENDS = ('openstack_auth.backend.KeystoneBackend',) AUTHENTICATION_URLS = ['openstack_auth.urls'] AUTH_USER_MODEL = 'openstack_auth.User' MESSAGE_STORAGE = 'django.contrib.messages.storage.fallback.FallbackStorage' SESSION_ENGINE = 'django.contrib.sessions.backends.cache' CACHES = { 'default': { 'BACKEND': 'django.core.cache.backends.memcached.MemcachedCache', 'LOCATION': '127.0.0.1:11211', }, } SESSION_COOKIE_HTTPONLY = True SESSION_EXPIRE_AT_BROWSER_CLOSE = True SESSION_COOKIE_SECURE = False # Control whether the SESSION_TIMEOUT period is refreshed due to activity. If # False, SESSION_TIMEOUT acts as a hard limit. SESSION_REFRESH = True # This SESSION_TIMEOUT is a method to supercede the token timeout with a # shorter horizon session timeout (in seconds). If SESSION_REFRESH is True (the # default) SESSION_TIMEOUT acts like an idle timeout rather than being a hard # limit, but will never exceed the token expiry. If your token expires in 60 # minutes, a value of 1800 will log users out after 30 minutes of inactivity, # or 60 minutes with activity. Setting SESSION_REFRESH to False will make # SESSION_TIMEOUT act like a hard limit on session times. SESSION_TIMEOUT = 3600 # When using cookie-based sessions, log error when the session cookie exceeds # the following size (common browsers drop cookies above a certain size): SESSION_COOKIE_MAX_SIZE = 4093 # when doing upgrades, it may be wise to stick to PickleSerializer # NOTE(berendt): Check during the K-cycle if this variable can be removed. # https://bugs.launchpad.net/horizon/+bug/1349463 SESSION_SERIALIZER = 'django.contrib.sessions.serializers.PickleSerializer' # MEMOIZED_MAX_SIZE_DEFAULT allows setting a global default to help control # memory usage when caching. It should at least be 2 x the number of threads # with a little bit of extra buffer. MEMOIZED_MAX_SIZE_DEFAULT = 25 CSRF_FAILURE_VIEW = 'openstack_dashboard.views.csrf_failure' LANGUAGES = ( ('cs', 'Czech'), ('de', 'German'), ('en', 'English'), ('en-au', 'Australian English'), ('en-gb', 'British English'), ('eo', 'Esperanto'), ('es', 'Spanish'), ('fr', 'French'), ('id', 'Indonesian'), ('it', 'Italian'), ('ja', 'Japanese'), ('ko', 'Korean (Korea)'), ('pl', 'Polish'), ('pt-br', 'Portuguese (Brazil)'), ('ru', 'Russian'), ('tr', 'Turkish'), ('zh-cn', 'Simplified Chinese'), ('zh-tw', 'Chinese (Taiwan)'), ) LANGUAGE_CODE = 'en' LANGUAGE_COOKIE_NAME = 'horizon_language' USE_I18N = True USE_L10N = True USE_TZ = True # Set OPENSTACK_CLOUDS_YAML_NAME to provide a nicer name for this cloud for # the clouds.yaml file than "openstack". OPENSTACK_CLOUDS_YAML_NAME = 'openstack' # If this cloud has a vendor profile in os-client-config, put it's name here. OPENSTACK_CLOUDS_YAML_PROFILE = '' OPENSTACK_KEYSTONE_DEFAULT_ROLE = '_member_' DEFAULT_EXCEPTION_REPORTER_FILTER = 'horizon.exceptions.HorizonReporterFilter' POLICY_FILES_PATH = os.path.join(ROOT_PATH, "conf") # Map of local copy of service policy files POLICY_FILES = { 'identity': 'keystone_policy.json', 'compute': 'nova_policy.json', 'volume': 'cinder_policy.json', 'image': 'glance_policy.json', 'network': 'neutron_policy.json', } # Services for which horizon has extra policies are defined # in POLICY_DIRS by default. POLICY_DIRS = { 'compute': ['nova_policy.d'], 'volume': ['cinder_policy.d'], } SECRET_KEY = None LOCAL_PATH = None SECURITY_GROUP_RULES = { 'all_tcp': { 'name': _('All TCP'), 'ip_protocol': 'tcp', 'from_port': '1', 'to_port': '65535', }, 'all_udp': { 'name': _('All UDP'), 'ip_protocol': 'udp', 'from_port': '1', 'to_port': '65535', }, 'all_icmp': { 'name': _('All ICMP'), 'ip_protocol': 'icmp', 'from_port': '-1', 'to_port': '-1', }, } ADD_INSTALLED_APPS = [] # NOTE: The default value of USER_MENU_LINKS will be set after loading # local_settings if it is not configured. USER_MENU_LINKS = None # 'key', 'label', 'path' AVAILABLE_THEMES = [ ( 'default', pgettext_lazy('Default style theme', 'Default'), 'themes/default' ), ( 'material', pgettext_lazy("Google's Material Design style theme", "Material"), 'themes/material' ), ] # The default theme if no cookie is present DEFAULT_THEME = 'default' # Theme Static Directory THEME_COLLECTION_DIR = 'themes' # Theme Cookie Name THEME_COOKIE_NAME = 'theme' POLICY_CHECK_FUNCTION = 'openstack_auth.policy.check' CSRF_COOKIE_AGE = None COMPRESS_OFFLINE_CONTEXT = 'horizon.themes.offline_context' SHOW_KEYSTONE_V2_RC = False SHOW_OPENRC_FILE = True SHOW_OPENSTACK_CLOUDS_YAML = True # Dictionary of currently available angular features ANGULAR_FEATURES = { 'images_panel': True, 'key_pairs_panel': True, 'flavors_panel': False, 'domains_panel': False, 'users_panel': False, 'groups_panel': False, 'roles_panel': True } # Notice all customizable configurations should be above this line XSTATIC_MODULES = settings_utils.BASE_XSTATIC_MODULES OPENSTACK_PROFILER = { 'enabled': False } if not LOCAL_PATH: LOCAL_PATH = os.path.join(ROOT_PATH, 'local') LOCAL_SETTINGS_DIR_PATH = os.path.join(LOCAL_PATH, "local_settings.d") _files = glob.glob(os.path.join(LOCAL_PATH, 'local_settings.conf')) _files.extend( sorted(glob.glob(os.path.join(LOCAL_SETTINGS_DIR_PATH, '*.conf')))) _config = config.load_config(_files, ROOT_PATH, LOCAL_PATH) # Apply the general configuration. config.apply_config(_config, globals()) try: from local.local_settings import * # noqa: F403,H303 except ImportError: _LOG.warning("No local_settings file found.") # configure templates if not TEMPLATES[0]['DIRS']: TEMPLATES[0]['DIRS'] = [os.path.join(ROOT_PATH, 'templates')] TEMPLATES[0]['DIRS'] += ADD_TEMPLATE_DIRS # configure template debugging TEMPLATES[0]['OPTIONS']['debug'] = DEBUG # Template loaders if DEBUG: TEMPLATES[0]['OPTIONS']['loaders'].extend( CACHED_TEMPLATE_LOADERS + ADD_TEMPLATE_LOADERS ) else: TEMPLATES[0]['OPTIONS']['loaders'].extend( [('django.template.loaders.cached.Loader', CACHED_TEMPLATE_LOADERS)] + ADD_TEMPLATE_LOADERS ) # allow to drop settings snippets into a local_settings_dir LOCAL_SETTINGS_DIR_PATH = os.path.join(ROOT_PATH, "local", "local_settings.d") if os.path.exists(LOCAL_SETTINGS_DIR_PATH): for (dirpath, dirnames, filenames) in os.walk(LOCAL_SETTINGS_DIR_PATH): for filename in sorted(filenames): if filename.endswith(".py"): try: with open(os.path.join(dirpath, filename)) as f: # pylint: disable=exec-used exec(f.read()) except Exception as e: _LOG.exception( "Can not exec settings snippet %s", filename) # The purpose of OPENSTACK_IMAGE_FORMATS is to provide a simple object # that does not contain the lazy-loaded translations, so the list can # be sent as JSON to the client-side (Angular). OPENSTACK_IMAGE_FORMATS = [fmt for (fmt, name) in OPENSTACK_IMAGE_BACKEND['image_formats']] if USER_MENU_LINKS is None: USER_MENU_LINKS = [] if SHOW_KEYSTONE_V2_RC: USER_MENU_LINKS.append({ 'name': _('OpenStack RC File v2'), 'icon_classes': ['fa-download', ], 'url': 'horizon:project:api_access:openrcv2', }) if SHOW_OPENRC_FILE: USER_MENU_LINKS.append({ 'name': (_('OpenStack RC File v3') if SHOW_KEYSTONE_V2_RC else _('OpenStack RC File')), 'icon_classes': ['fa-download', ], 'url': 'horizon:project:api_access:openrc', }) if not WEBROOT.endswith('/'): WEBROOT += '/' if LOGIN_URL is None: LOGIN_URL = WEBROOT + 'auth/login/' if LOGOUT_URL is None: LOGOUT_URL = WEBROOT + 'auth/logout/' if LOGIN_ERROR is None: LOGIN_ERROR = WEBROOT + 'auth/error/' if LOGIN_REDIRECT_URL is None: LOGIN_REDIRECT_URL = WEBROOT if MEDIA_ROOT is None: MEDIA_ROOT = os.path.abspath(os.path.join(ROOT_PATH, '..', 'media')) if MEDIA_URL is None: MEDIA_URL = WEBROOT + 'media/' if STATIC_ROOT is None: STATIC_ROOT = os.path.abspath(os.path.join(ROOT_PATH, '..', 'static')) if STATIC_URL is None: STATIC_URL = WEBROOT + 'static/' AVAILABLE_THEMES, SELECTABLE_THEMES, DEFAULT_THEME = ( theme_settings.get_available_themes( AVAILABLE_THEMES, DEFAULT_THEME, SELECTABLE_THEMES ) ) # Discover all the directories that contain static files STATICFILES_DIRS = theme_settings.get_theme_static_dirs( AVAILABLE_THEMES, THEME_COLLECTION_DIR, ROOT_PATH) # Ensure that we always have a SECRET_KEY set, even when no local_settings.py # file is present. See local_settings.py.example for full documentation on the # horizon.utils.secret_key module and its use. if not SECRET_KEY: if not LOCAL_PATH: LOCAL_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'local') # pylint: disable=ungrouped-imports from horizon.utils import secret_key SECRET_KEY = secret_key.generate_or_read_from_file(os.path.join(LOCAL_PATH, '.secret_key_store')) # populate HORIZON_CONFIG with auto-discovered JavaScript sources, mock files, # specs files and external templates. settings_utils.find_static_files(HORIZON_CONFIG, AVAILABLE_THEMES, THEME_COLLECTION_DIR, ROOT_PATH) INSTALLED_APPS = list(INSTALLED_APPS) # Make sure it's mutable settings_utils.update_dashboards( [ enabled, local_enabled, ], HORIZON_CONFIG, INSTALLED_APPS, ) INSTALLED_APPS[0:0] = ADD_INSTALLED_APPS NG_TEMPLATE_CACHE_AGE = NG_TEMPLATE_CACHE_AGE if not DEBUG else 0 # Include xstatic_modules specified in plugin XSTATIC_MODULES += HORIZON_CONFIG['xstatic_modules'] # Discover all the xstatic module entry points to embed in our HTML STATICFILES_DIRS += settings_utils.get_xstatic_dirs( XSTATIC_MODULES, HORIZON_CONFIG) # This base context objects gets added to the offline context generator # for each theme configured. HORIZON_COMPRESS_OFFLINE_CONTEXT_BASE = { 'WEBROOT': WEBROOT, 'STATIC_URL': STATIC_URL, 'HORIZON_CONFIG': HORIZON_CONFIG, 'NG_TEMPLATE_CACHE_AGE': NG_TEMPLATE_CACHE_AGE, } if DEBUG: logging.basicConfig(level=logging.DEBUG) # Here comes the Django settings deprecation section. Being at the very end # of settings.py allows it to catch the settings defined in local_settings.py # or inside one of local_settings.d/ snippets.

pingyourid / MasonryHelper

a category help masoonry to complete distribute on one direction.

universal

Updated 2y ago

JiaxiSun / Intelligent Distributed Wireless Radio Map Construction

The 7th Place Solution of the First 6G Intelligent Wireless Communication System Competition - Intelligent Distributed Wireless Radio Map Construction Category

universal

Updated 4mo ago

PagerDuty / Go Pdagent

A new PagerDuty agent.

universal

agentcategory-distributeddevops+3

Updated 5d ago

SibaMishra / Clustering Glossary Terms Extracted From Large Sized Software Requirements Using FastText

This repository contains the results of automatic glossary terms extraction and their clustering considering two important qualitative attributes, i.e. feature and benefit of the original CrowdRE requirement specifications dataset. In the original CrowdRE dataset, each entry has 6 attributes, i.e., role, feature, benefit, domain, tags and date-time of creation. Since, we are interested in extracting domain-specific terms from this dataset, we only focus on feature and benefit attributes of this dataset. The dataset used in our experiments containing only the feature and benefit attributes of the original CrowdRE dataset can be viewed in the file named "CrowdRE Requirements Dataset.csv". However, the original CrowdRE dataset is devloped by P. K. Murukannaiah et al. and can be accessed from "The smarthome crowd requirements dataset", https://crowdre.github.io/murukannaiah-smarthome-requirements-dataset/, April, 2017. We have computed and reported the ground truth set for a random subset of 100 requirement specifications of the used CrowdRE dataset. In total, we have manually identified a total of 120 ground truth glossary terms with 30 overlapping clusters. The ground truth glossary terms have been calculated from the best intuition of the people (s) involved in this project in an unbiased manner, as there exists no benchmark or gold standard related to the ground truth extraction and clustering for the CrowdRE dataset. The file named "Ground Truth Clusters.docx" shows the ground truth glossary terms along with the manually formulated semantically similar clusters. Note: the clusters are separated with (######) symbol in the file. Further, the manually identified 120 glossary terms in the ground truth set are shown in the third column of the file named as "Extracted Glossary Terms (With and Without WordNet Removal) and Ground Truth Glossary Terms.csv". We have extracted a total of 143 and 292 glossary terms from the CrowdRE dataset with or without removing some words specified in the WordNet lexical database (https://wordnet.princeton.edu/) using a mature text chunking approach. The results are shown in the first and second column of the file named "Extracted Glossary Terms (With and Without WordNet Removal) and Ground Truth Glossary Terms.csv". The extracted glossary terms are trained with the help of a domain specific corpora that is most related to used CrowdRE dataset, i.e. (Wikipedia Home Automation Category for a maximum depth of two, "https://en.wikipedia.org/wiki/Category:Home_automation") and with a pre-trained word vectors UMBC webbase corpus and statmt.org news dataset trained with subwords information in wikipedia 2017 (T. Mikolov, E. Grave, P. Bojanowski, C. Puhrsch, A. Joulin. Advances in Pre-Training Distributed Word Representations) using FastText word embedding vectors (https://fasttext.cc/docs/en/english-vectors.html). The main purpose of the training is to deduce the clusters by forming a the similarity matrix for the extracted glossary terms. For this, we have used two clustering algorithms, viz. K-Means and EM clustering algorithms. The similarity matrix have been developed using the computed semantic similarity scores (cosine similarity) between the word vectors using the word embedding based FastText model. The results in terms of automated formulated clusters for the random subset of 100 requirement specifications of the CrowdRE dataset for which the ground truth glossary terms are calculated are shown in the files named "Automated Ideal (Ground Truth) Clusters.docx" and "Automated Extraction and Clustering.docx" respectively. Note: there exists a maximum of n/2 clusters for n glossary terms. For evaluating the efficacy of the clustering algorithms, we used some commonly used performance evaluation metrics like (precision, recall, f-scores). The evaluation graphs utilizing the area under curve plots (AUC) and evaluating the normalized AUC scores for all the used clustering algorithms are trained on two different datasets and the evaluation results are shown in the two separate files namely, "Cluster Plots.docx" and "Extraction +Clustering Plots.docx" respectively.

pdwytr / Forecasting Sales Of Walmart Retail Goods

Note: This is one of the two complementary competitions that together comprise the M5 forecasting challenge. Can you estimate, as precisely as possible, the point forecasts of the unit sales of various products sold in the USA by Walmart? If you are interested in estimating the uncertainty distribution of the realized values of the same series, be sure to check out its companion competition How much camping gear will one store sell each month in a year? To the uninitiated, calculating sales at this level may seem as difficult as predicting the weather. Both types of forecasting rely on science and historical data. While a wrong weather forecast may result in you carrying around an umbrella on a sunny day, inaccurate business forecasts could result in actual or opportunity losses. In this competition, in addition to traditional forecasting methods you’re also challenged to use machine learning to improve forecast accuracy. The Makridakis Open Forecasting Center (MOFC) at the University of Nicosia conducts cutting-edge forecasting research and provides business forecast training. It helps companies achieve accurate predictions, estimate the levels of uncertainty, avoiding costly mistakes, and apply best forecasting practices. The MOFC is well known for its Makridakis Competitions, the first of which ran in the 1980s. In this competition, the fifth iteration, you will use hierarchical sales data from Walmart, the world’s largest company by revenue, to forecast daily sales for the next 28 days. The data, covers stores in three US States (California, Texas, and Wisconsin) and includes item level, department, product categories, and store details. In addition, it has explanatory variables such as price, promotions, day of the week, and special events. Together, this robust dataset can be used to improve forecasting accuracy. If successful, your work will continue to advance the theory and practice of forecasting. The methods used can be applied in various business areas, such as setting up appropriate inventory or service levels. Through its business support and training, the MOFC will help distribute the tools and knowledge so others can achieve more accurate and better calibrated forecasts, reduce waste and be able to appreciate uncertainty and its risk implications. Acknowledgements Additional thanks go to other partner organizations and prize sponsors, National Technical University of Athens (NTUA), INSEAD, Google, Uber and IIF.

rundeck / Ts Rundeck

Rundeck TypeScript SDK

universal

category-distributed

Updated 8mo ago

sun3y21 / Visual Cryptography

Visual Cryptography is a category of secret sharing where a secret image is divided into n images called as shares. Each share is a random binary pattern which is printed onto transparencies and distributed among n participants. When any k of the n transparencies of the shares are superimposed then the secret image can be perfectly reconstructed, while even complete knowledge of (k–1) shares reveals absolutely no information about the secret image.

universal

image-processingmedical-image-processingpixel-expansion-solved+1

Updated 6mo ago

zvdas / App Rating Prediction

Objective: Make a model to predict the app rating, with other information about the app provided. Problem Statement: Google Play Store team is about to launch a new feature wherein, certain apps that are promising, are boosted in visibility. The boost will manifest in multiple ways including higher priority in recommendations sections (“Similar apps”, “You might also like”, “New and updated games”). These will also get a boost in search results visibility. This feature will help bring more attention to newer apps that have the potential. Domain: General Analysis to be done: The problem is to identify the apps that are going to be good for Google to promote. App ratings, which are provided by the customers, is always a great indicator of the goodness of the app. The problem reduces to: predict which apps will have high ratings. Content: Dataset: Google Play Store data (“googleplaystore.csv”) Fields in the data – • App: Application name • Category: Category to which the app belongs • Rating: Overall user rating of the app • Reviews: Number of user reviews for the app • Size: Size of the app • Installs: Number of user downloads/installs for the app • Type: Paid or Free • Price: Price of the app • Content Rating: Age group the app is targeted at - Children / Mature 21+ / Adult • Genres: An app can belong to multiple genres (apart from its main category). For example, a musical family game will belong to Music, Game, Family genres. • Last Updated: Date when the app was last updated on Play Store • Current Ver: Current version of the app available on Play Store • Android Ver: Minimum required Android version Steps to perform: 1. Load the data file using pandas. 2. Check for null values in the data. Get the number of null values for each column. 3. Drop records with nulls in any of the columns. 4. Variables seem to have incorrect type and inconsistent formatting. You need to fix them: 1. Size column has sizes in Kb as well as Mb. To analyze, you’ll need to convert these to numeric. 1. Extract the numeric value from the column 2. Multiply the value by 1,000, if size is mentioned in Mb 2. Reviews is a numeric field that is loaded as a string field. Convert it to numeric (int/float). 3. Installs field is currently stored as string and has values like 1,000,000+. 1. Treat 1,000,000+ as 1,000,000 2. remove ‘+’, ‘,’ from the field, convert it to integer 4. Price field is a string and has $ symbol. Remove ‘$’ sign, and convert it to numeric. 5. Sanity checks: 1. Average rating should be between 1 and 5 as only these values are allowed on the play store. Drop the rows that have a value outside this range. 2. Reviews should not be more than installs as only those who installed can review the app. If there are any such records, drop them. 3. For free apps (type = “Free”), the price should not be >0. Drop any such rows. 5. Performing univariate analysis: • Boxplot for Price • Are there any outliers? Think about the price of usual apps on Play Store. • Boxplot for Reviews • Are there any apps with very high number of reviews? Do the values seem right? • Histogram for Rating • How are the ratings distributed? Is it more toward higher ratings? • Histogram for Size Note down your observations for the plots made above. Which of these seem to have outliers? 6. Outlier treatment: 1. Price: From the box plot, it seems like there are some apps with very high price. A price of $200 for an application on the Play Store is very high and suspicious! 1. Check out the records with very high price 1. Is 200 indeed a high price? 2. Drop these as most seem to be junk apps 2. Reviews: Very few apps have very high number of reviews. These are all star apps that don’t help with the analysis and, in fact, will skew it. Drop records having more than 2 million reviews. 3. Installs: There seems to be some outliers in this field too. Apps having very high number of installs should be dropped from the analysis. 1. Find out the different percentiles – 10, 25, 50, 70, 90, 95, 99 2. Decide a threshold as cutoff for outlier and drop records having values more than that 7. Bivariate analysis: Let’s look at how the available predictors relate to the variable of interest, i.e., our target variable rating. Make scatter plots (for numeric features) and box plots (for character features) to assess the relations between rating and the other features. 1. Make scatter plot/joinplot for Rating vs. Price 1. What pattern do you observe? Does rating increase with price? 2. Make scatter plot/joinplot for Rating vs. Size 1. Are heavier apps rated better? 3. Make scatter plot/joinplot for Rating vs. Reviews 1. Does more review mean a better rating always? 4. Make boxplot for Rating vs. Content Rating 1. Is there any difference in the ratings? Are some types liked better? 5. Make boxplot for Ratings vs. Category 1. Which genre has the best ratings? For each of the plots above, note down your observation. 8. Data preprocessing For the steps below, create a copy of the dataframe to make all the edits. Name it inp1. 1. Reviews and Install have some values that are still relatively very high. Before building a linear regression model, you need to reduce the skew. Apply log transformation (np.log1p) to Reviews and Installs. 2. Drop columns App, Last Updated, Current Ver, and Android Ver. These variables are not useful for our task. 3. Get dummy columns for Category, Genres, and Content Rating. This needs to be done as the models do not understand categorical data, and all data should be numeric. Dummy encoding is one way to convert character fields to numeric. Name of dataframe should be inp2. 9. Train test split and apply 70-30 split. Name the new dataframes df_train and df_test. 10. Separate the dataframes into X_train, y_train, X_test, and y_test. 11 . Model building • Use linear regression as the technique • Report the R2 on the train set 12. Make predictions on test set and report R2.

aiok03 / Final Mining

Descriptive statistics and Explanatory data analysis In order to have an idea of the received data, we look through our table transactions and train. The shape of the train is 6000 rows and 2 columns (client_id and target – gender). Also we considered the info of transactions and noticed that there are no empty values, all of them are equal to 130039. After that we merged two tables and called it as data. To display unique codes and types we used ‘unique’ function and noticed that unique codes 173 and unique types 61. Using ‘describe’ function we can see minimal code, type, sum and the same parameters but maximum. The first hypothesis was to find what gender makes lots of requests. For conveniency we used for loop to make values in percentile view. And according to the barplot the biggest number of processes are made by females. The second hypothesis was to find the code with the biggest sum. For that we grouped by code and counted the mean of all sums. This list we converted from series to frame for further working process. The problem was that the code interpreted the code as the index, that’s why we have to fix it with ‘reset_index’ function. After that we plotted the graph and noticed that the most high sum is with 4722 code and proved it with another code under the graph. The third hypothesis is to find the distribution of sums relatively to the gender. But the first graph didn’t replaced this information because the scatter of the data is too high. The sign is not normally distributed and it is not symmetrical. It is hard to asses, that’s why we grouped information by gender and counted mean of the sum. According to this information we noticed that males spend more money than women. The same process we made with median and got the same conclusion. And since the mean and median values are not equal, our assumption about unnormalized data was proved. The last hypothesis was to find number of clients for each type and code – to find the most popular request within clients. For that we applied ‘str’ to each parameter for correct visualization on the graph. Counted the number of each request for type and code and reflected it in the graphs. According to them the most popular is 1010 type and 6011 code. Lastly, for further working process we returned type and code to the int type. Feature engineering Client’s balance condition We took every sum from dataframe data, grouped for every client and found the sum for each of them. We calculated the income and expenses for each client. Some clients with minus value made more expenses, some of them not, that means that he got more income. In minus is 0, in plus is 1. RFM In RFM section we started from Recency. For each client we grouped the information about them and found the maximum date where the transaction was done. The datetime column consisted from two values – date and time, for further working process in future engineering section we divided them for different columns. The most recent day we equaled to 457 and according to this value started to count the recency of last transactions for each client by subtraction. The next step is Frequency. We used ‘group by’ function and counted appearance of each client in our database. The last step is Monetary (to count expenses). Using group by function and condition, where the sum is less than 0 (expenses are negative values), we counted the total expenses of each client and noticed one point. That some clients didn’t spend any money at all. Segmentation based on RFM We merged all the tables into one and made a rank according to the best values in each segment using percentage. Using the formula we divided clients by 5 score scale, by this database and elbow method, plotted the graph, where 3 clusters were optimal solution. With KMeans library we plotted the k-mean illustration of clients according to the distance from randomly chosen centroids, showed distribution of clients in clusters. After the work done we gathered basic table with clusters using prefixes to each of them. Clustering for codes Now we'll work with codes to create clustering codes, and we'll utilize TF IDF and k-means to do it. We will also employ limitization, tokenization, and stop word elimination. We import the pymorphy2 library for limiting, and limiting is when words take their original form. Tokenization by sentences is the process of dividing a written language into component sentences. We also need to delete stop words, a stop word is a commonly used word (such as “the”, “a”, “an”, “in”) that a search engine has been programmed to ignore, both when indexing entries for searching and when retrieving them as the result of a search query. We would not want these words to take up space in our database, or taking up valuable processing time. We also make use of the re – Regular expression operations library, which is a library for regular expression operations. In this section we also use MorphAnalyzer() - Morphological analysis is the identification of a word's features based on how it is spelt. Morphological analysis does not make use of information about nearby words. For morphological analysis of words, there is a MorphAnalyzer class in pymorphy2. If we apply directly the clustering on those matrix, we will have issues as our matrices are very sparse and the computation of distances will be a mess. What we can do, is to perform IS to reduce data to a dense matrix of dimension 156 by applying SVD. Singular Value Decomposition (SVD) is one of the widely used methods for dimensionality reduction. We defined that 156 is the right number in our case. We used the Silhouette score to evaluate the quality of clusters created using K-Means. By Silhouette score we chose number of clusters and performed k means clustering on our tf-idf matrix. Then we tried to do a visualization of our clusters and we applied t-sne . t-SNE is a tool to visualize high-dimensional data. And then we added clusters to data and df dataframe. Finally we created word cloud by our clusters Clustering for types Data cleaning for types Firstly, we noticed that there were 155 types. However in data, there are 61 types. When we merge the data and that types, the total number of types become 58. This means that 3 types have no any description and that’s why we replace them with the mode value. Also we found that some types have type description ‘н.д’ which means no data and their total number in data is 26. Also we noticed that type description repeats for several types and we dropped duplicates and replaced them with first accurancy type in data. Creating clusters for types We manually divided them into the 5 categories according to dome key words in description. And merged them with our dataframe. Then we noticed outliers in recency and frequency. We found 0.999 and 0.001 quantile, where the first one is considered as the high, and the second is the low boundary. Everything above 0,999 and below 0.001 is considered as an outlier. We removed them for both recency and frequency. After that we checked dataframe by describe and concluded that everything become normal. Supervised learning The time for prediction came. We divided our dataframe into train and test and used KNN, Decision Tree Classifier and Random Forest, Logistic Regression for further predictions. We decided to investigate the accuracy from 1 to 20 with step 2 for each neighbor in train and test. And built the plot. The best result is accuracy 58 for 19 neighbors. Decision Tree gave us 54 for test set and Random Forest’s accuracy was 64. We investigated feature importance for both of them and noticed that monetary had the most influence on predicting the data. For Grid Search we manually set the hyper parameters and for cross validation equals to four folds. Best estimater for random forest classifier for grid search was found. After that good estimaters were chosen for random forest, and the same accuracy occurred. Best accuracy for random forest with default hyper parameters. We built confusion matrix and calculated recall, precision and f-1 score. Also we decided to build lofistic regression but the accuracy was too small, that’s why we build roc-auc and precision-recall curve. Conclusion All the models showed that taken data was not enough and actually not the best for gender prediction. Actions for increase the accuracy were done, such as adding more features, removing outliers. According to this investigation the best choice was random forest.

Ananiaslitz / ItemBalancer

ItemBalancer is a flexible library that allows distributing items among pre-defined categories based on specified proportions. With a cache-backed mechanism, it ensures efficient real-time calculations for balanced item assignments.

universal

Updated 2y ago