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docs/awesome/amas.md

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docs/awesome/awesome-agi-cocosci.md

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* [Anomaly detection through explanations](https://dspace.mit.edu/handle/1721.1/129250) - ***Ph.D Dissertation MIT***, 2018. [[All Versions](https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Anomaly+detection+through+explanations&btnG=)]. An application of abduction in anomaly detection.
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* [Automated Biodesign Engineering by Abductive Meta-Interpretive Learning](https://arxiv.org/abs/2105.07758) - ***AAAI Spring Symposium Series 2021 on Artificial Intelligence for Synthetic Biology***, 2021. [[All Versions](https://scholar.google.com/scholar?cluster=543730388062329581&as_sdt=0,5)]. This work proposes an automated biodesign engineering framework empowered by Abductive Meta-Interpretive Learning (MetaAbd), a novel machine learning approach that combines symbolic and sub-symbolic machine learning, to further enhance the design-build-test-learn cycle by enabling the learning machine to 1) exploit domain knowledge and learn human-interpretable models that are expressed by formal languages such as first-order logic; 2) simultaneously optimise the structure and parameters of the models to make accurate numerical predictions; 3) reduce the cost of experiments and effort on data annotation by actively generating hypotheses and examples.
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* [Human Comprehensible Active Learning of Genome-Scale Metabolic Networks](https://arxiv.org/abs/2308.12740) - ***AAAI Spring Symposium Series 2023 on Computational Scientific Discovery***, 2023. [[All Versions](https://scholar.google.com/scholar?oi=bibs&cluster=10875437066608527790)]. [[Extended Abstract](http://cogsys.org/symposium/discovery-2023/abstracts/Abstract_3169.pdf)]. [[Slides](http://cogsys.org/symposium/discovery-2023/talks/Ai.pdf)]. This work introduces a novel machine learning framework ILP-iML1515 based on Inductive Logic Programming (ILP) that performs abductive logical reasoning and actively learns from training examples. The ILP-iML1515 framework 1) allows high-throughput simulations and 2) actively selects experiments that reduce the experimental cost of learning gene functions in comparison to randomly selected experiments.
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* [Foundations of intuitive power analyses in children and adults](https://eccl.mit.edu/s/Pelz_Foundations-of-intuitive-power-analyses-in-children-and-adults.pdf) - ***Nature Human Behavior***, 2022. [[All Versions](https://scholar.google.com/scholar?cluster=4370839893505978405&hl=en&as_sdt=0,5)]. Evidences support that people have some of the foundations for 'intuitive power analyses', which help people use intuitive statistical reasoning and metacognitive strategies to estimate how much information they might need to solve different discrimination problems.
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* [Cognitive Science as a Source of Forward and Inverse Models of Human Decisions for Robotics and Control](https://cocosci.princeton.edu/papers/ho2022cognitive.pdf) - ***Annual Review of Control, Robotics, and Autonomous Systems***, 2022. [[All Versions](https://scholar.google.com/scholar?oi=bibs&cluster=14055765901243029337)]. The review focuses on how cognitive science can provide forward models of human decision-making and inverse models of how humans think about others’ decision-making. The authors highlight relevant recent developments, including approaches that synthesize black box and theory-driven modeling, accounts that recast heuristics and biases as forms of bounded optimality, and models that characterize human theory of mind and communication in decision-theoretic terms.
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#### Cognitive Architecture
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docs/awesome/awesome-audit-algorithms.md

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## Papers
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### 2024
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- [FairProof: Confidential and Certifiable Fairness for Neural Networks](https://arxiv.org/pdf/2402.12572v1.pdf) - *Proposes an alternative paradigm to traditional auditing using crytographic tools like Zero-Knowledge Proofs; gives a system called FairProof for verifying fairness of small neural networks.*
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- [Under manipulations, are some AI models harder to audit?](https://grodino.github.io/projects/manipulated-audits/preprint.pdf) - (SATML) *Relates the difficulty of black-box audits
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to the capacity of the targeted models, using the Rademacher complexity.*
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- [Improved Membership Inference Attacks Against Language Classification Models](https://arxiv.org/pdf/2310.07219.pdf) - (ICLR) *Presents a framework for running membership inference attacks against classifier, in audit mode.*
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- [Auditing Fairness by Betting](https://arxiv.org/pdf/2305.17570.pdf) - (Neurips) [[Code]](https://github.com/bchugg/auditing-fairness) *Sequential methods that allows for the continuous monitoring of incoming data from a black-box classifier or regressor.*
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### 2023
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- [XAudit : A Theoretical Look at Auditing with Explanations](https://arxiv.org/pdf/2206.04740.pdf) - *Formalizes the role of explanations in auditing and investigates if and how model explanations
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can help audits.*
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- [Keeping Up with the Language Models: Robustness-Bias Interplay in NLI Data and Models](https://arxiv.org/pdf/2305.12620.pdf) - *Proposes a way to extend the shelf-life of auditing datasets by using language models themselves; also finds problems with the current bias auditing metrics and proposes alternatives -- these alternatives highlight that model brittleness superficially increased the previous bias scores.*
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- [Online Fairness Auditing through Iterative Refinement](https://dl.acm.org/doi/pdf/10.1145/3580305.3599454) - (KDD) *Provides an adaptive process that automates the inference of probabilistic guarantees associated with estimating fairness metrics.*
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- [Stealing the Decoding Algorithms of Language Models](https://people.cs.umass.edu/~amir/papers/CCS23-LM-stealing.pdf) - (CCS) *Steal the type and hyperparameters of the decoding algorithms of a LLM.*
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- [Modeling rabbit‑holes on YouTube](https://link.springer.com/epdf/10.1007/s13278-023-01105-9?sharing_token=h-O-asHI49VUWS9FxN1Gsve4RwlQNchNByi7wbcMAY6I98PKW1PqhFQJ_JqQyk3TrB05qDb3LUzMDmKOgrupccQliViDle-rwKEi2MZ8xBViaAQhyN41oZBKLLeXchoeIW2kklVHC094I5KD8pxja4-if6-iB0uAI1FnqnYoxjU%3D) - (SNAM) *Models the trapping dynamics of users in rabbit holes in YouTube, and provides a measure of this enclosure.*

docs/awesome/awesome-browser-extensions-for-github.md

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Next level code navigation for GitHub.
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Visualize GitHub repos as d3 force-directed graphs. The extension gives you a quick overview of the size and composition of any repo.
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Displays size of each file, download link and an option of copying file contents directly to clipboard
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Installs: 657 | Stars: 1975 | Last update: 22 Apr 2021
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Installs: 659 | Stars: 1978 | Last update: 22 Apr 2021
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Easily search GIPHY to add a GIF into any GitHub comment box.
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Quickly browse the history of a file from any git repository.
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Installs: 217 | Stars: 13470 | Last update: 13 Oct 2023
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GitHub provides a page that only shows diffs with a .diff at the end of the URL of the pull request. This browser extension makes it easier to view csv diffs by using daff on that page.
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Code folding - the ability to selectively hide and display sections of a code - is an invaluable feature in many text editors and IDEs. Now, developers can utilize that same style code-folding while poring over source code on the web in GitHub. Works for any type of indentation- spaces or tabs.
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Installs: 123 | Stars: 288 | Last update: 12 Jan 2017
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Installs: 124 | Stars: 288 | Last update: 12 Jan 2017
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Set custom tab size for code view on GitHub.com
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Neat hovercards for GitHub.
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Installs: 17812 | Stars: 1800 | Last update: n/a
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Colorize issue and PR links to see their status (open, closed, merged).
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Installs: 103 | Stars: 296 | Last update: 9 Dec 2023
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Displays npm package stats on GitHub
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This extension adds the simplicity of WYSIWYG editing to issues, comments, pull requests, wikis and discussions in GitHub. It accepts Markdown input and offers productive writing, starting with tables.
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Installs: 126 | Stars: 364 | Last update: 14 Nov 2023
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View and create Linear tickets from any GitHub PR or issue.
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Show [vscode-icons](https://github.com/vscode-icons/vscode-icons) in the repository browser.
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Installs: 2000 | Stars: 291 | Last update: n/a
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It can make the sub-directories and files of github repository as zip and download it
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Installs: 3578 | Stars: 35 | Last update: 21 Aug 2023
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Create, save, edit, pin, search and delete filters that you commonly use on the Github Issues and Pull Requests pages. You are able to scope filters to be shown globally (on each repo) or only have them show up on the repo you create them on. Pinning filters is also a feature that this extension allows you to do. So if you have several filters you use daily - you have a way to quickly access them, at the top of your list.
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Installs: 35 | Stars: 227 | Last update: 30 Jun 2021
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Replace browser new tab screen with GitHub trending projects.
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Hide dotfiles from the GitHub file browser.
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Installs: 40 | Stars: 318 | Last update: 16 Sep 2021
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Allows you to toggle between the normal GitHub contribution chart and an isometric pixel art version.
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Installs: 439 | Stars: 2629 | Last update: 1 Jan 2024
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Chrome extension that adds a button in browser and links this button to a GitHub repository that you will configure, then on any webpage just click this extension button and it will add the given link with the title of the page in that repository.
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Show Material icons for files/folders in repository file viewer. Display the same icons from vscode-material-icon-theme VSCode extension.
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Installs: 785 | Stars: 430 | Last update: 20 Feb 2024
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An extension that creates direct links to imported modules, external or internal, on source code on GitHub. Supports multiple languages, including common ones like Rust, Go, Python and Ruby, but also odd ones like Nim, Haskell, Julia and Elm.
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Installs: 40 | Stars: 249 | Last update: 27 Nov 2019
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Installs: 73 | Stars: 140 | Last update: 6 Apr 2023
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OctoLinker is the easiest and best way to navigate between files and projects on GitHub. It supports languages such as JavaScript, Ruby, Go, PHP, JAVA and more. It works with package.json as well as with Gemfiles.
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OctoPermalinker is a browser extension that searches GitHub comments/files for links to files on branches, and adds a link to where the branch pointed when the comment/file was made/updated. This helps you avoid following a link that was broken after being posted. For context, here's some discussion about broken GitHub links: [Don't link to line numbers in GitHub](https://news.ycombinator.com/item?id=8046710).
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gitpod streamlines developer workflows by providing ready-to-code development environments in your browser - powered by vs code.
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docs/awesome/awesome-certificates.md

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| [Deep Learning with TensorFlow](https://cognitiveclass.ai/courses/deep-learning-tensorflow) | IBM / Cognitive Class |Beginner|3 | [🏅](https://www.youracclaim.com/org/ibm/badge/deep-learning-using-tensorflow)|
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| [Machine Learning with Python](https://cognitiveclass.ai/courses/machine-learning-with-python/) | IBM / Cognitive Class |Beginner|3 | [🏅](https://www.youracclaim.com/org/ibm/badge/machine-learning-with-python-level-1)|
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| [Machine Learning with Python Certification](https://www.freecodecamp.org/learn/machine-learning-with-python/) | FreeCodeCamp |Professional|300 | 🏆|
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| [Elements of AI](https://www.elementsofai.com/) | University of Helsinki |Beginner|30 | 🏆|
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| [Intro to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning) | Kaggle | Beginner | 3 | 🏆 |
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| [Intermediate Machine Learning](https://www.kaggle.com/learn/intermediate-machine-learning) | Kaggle | Intermediate | 4 | 🏆 |
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| [Machine Learning Explainability](https://www.kaggle.com/learn/machine-learning-explainability) | Kaggle | Intermediate | 4 | 🏆 |
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| [Intro to Deep Learning](https://www.kaggle.com/learn/intro-to-deep-learning) | Kaggle | Intermediate | 4 | 🏆 |
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| [Time Series](https://www.kaggle.com/learn/time-series) | Kaggle | Intermediate | 5 | 🏆 |
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| [Intro to AI Ethics](https://www.kaggle.com/learn/intro-to-ai-ethics) | Kaggle | Intermediate | 4 | 🏆 |
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| [Intro to Game AI and Reinforcement Learning](https://www.kaggle.com/learn/intro-to-game-ai-and-reinforcement-learning) | Kaggle | Intermediate | 4 | 🏆 |
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| [Feature Engineering](https://www.kaggle.com/learn/feature-engineering) | Kaggle | Intermediate | 5 | 🏆 |
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| [Computer Vision](https://www.kaggle.com/learn/computer-vision) | Kaggle | Intermediate | 4 | 🏆 |
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| [Introduction to Artificial Intelligence](https://www.mygreatlearning.com/academy/learn-for-free/courses/introduction-to-artificial-intelligence-1) | Great Learning |Beginner| 1.5 | 🏆 |
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## Artificial Intelligence
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| Course Name | Course Provider | Level | Hour(s) | Reward |
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| :------------- |:-------------|:-------------|:-------------:|:-----:|
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| [Elements of AI](https://www.elementsofai.com/) | University of Helsinki | Beginner |30 | 🏆|
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| [Intro to AI Ethics](https://www.kaggle.com/learn/intro-to-ai-ethics) | Kaggle | Intermediate | 4 | 🏆 |
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| [Intro to Game AI and Reinforcement Learning](https://www.kaggle.com/learn/intro-to-game-ai-and-reinforcement-learning) | Kaggle | Intermediate | 4 | 🏆 |
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| [Introduction to Generative AI](https://www.cloudskillsboost.google/course_templates/536) | Google Cloud Skills Boost | Intermediate | 0.75 | 🏆 |
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| [Introduction to Artificial Intelligence](https://www.mygreatlearning.com/academy/learn-for-free/courses/introduction-to-artificial-intelligence-1) | Great Learning | Beginner| 1.5 | 🏆 |
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| [Foundations of Prompt Engineering](https://explore.skillbuilder.aws/learn/course/external/view/elearning/17763/foundations-of-prompt-engineering) | Amazon | Intermediate | 4 | 🏆 |
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| [Generative AI for Decision Makers](https://explore.skillbuilder.aws/learn/public/learning_plan/view/1909/generative-ai-learning-plan-for-decision-makers) | Amazon | Beginner| 3 | 🏆 |
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| [Generative AI Fundamentals](https://www.databricks.com/resources/learn/training/generative-ai-fundamentals) | Data Bricks |Beginner|0.5 | 🏅 |
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| [Career Essentials in Generative AI](https://www.linkedin.com/learning/paths/career-essentials-in-generative-ai-by-microsoft-and-linkedin) | LinkedIn & Microsoft | Beginner| 4.5 | 🏆 |
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## Networking

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