Robot Updated at:11 Nov 2024 21:10:55 GMT

docs/awesome/awesome-agi-cocosci.md

@@ -150,7 +150,7 @@ Contributions are greatly welcomed! Please refer to [Contribution Guidelines](ht
 
 * [Interpretation as abduction](https://www.sciencedirect.com/science/article/abs/pii/0004370293900154?via%3Dihub) - ***Artificial Intelligence***, 1993. [[All Versions](https://scholar.google.com/scholar?cluster=12658433318211361322)]. Abduction is inference to the best explanation. The authors have developed an approach to abductive inference, called “weighted abduction”, that has resulted in a significant simplification of how the problem of interpreting texts is conceptualized. The interpretation of a text is the minimal explanation of why the text would be true. More precisely, to interpret a text, one must prove the logical form of the text from what is already mutually known, allowing for coercions, merging redundancies where possible, and making assumptions where necessary. It is shown how such “local pragmatics” problems as reference resolution, the interpretation of compound nominals, the resolution of syntactic ambiguity and metonymy, and schema recognition can be solved in this manner. Moreover, this approach of “interpretation as abduction” can be combined with the older view of “parsing as deduction” to produce an elegant and thorough integration of syntax, semantics, and pragmatics, one that spans the range of linguistic phenomena from phonology to discourse structure.
 
-* [Probabilistic Horn abduction and Bayesian networks](https://www.sciencedirect.com/science/article/abs/pii/000437029390061F?via%3Dihub) - ***Artificial Intelligence***, 1993. [[All Versions](https://scholar.google.com/scholar?cluster=7728248035489349629&hl=en&as_sdt=0,5)]. Casual abduction in Bayesian networks.
+* [Probabilistic Horn abduction and Bayesian networks](https://www.sciencedirect.com/science/article/abs/pii/000437029390061F?via%3Dihub) - ***Artificial Intelligence***, 1993. [[All Versions](https://scholar.google.com/scholar?cluster=7728248035489349629)]. This paper presents a simple framework for Horn-clause abduction, with probabilities associated with hypotheses. The framework incorporates assumptions about the rule base and independence assumptions amongst hypotheses. It is shown how any probabilistic knowledge representable in a discrete Bayesian belief network can be represented in this framework. The main contribution is in finding a relationship between logical and probabilistic notions of evidential reasoning. This provides a useful representation language in its own right, providing a compromise between heuristic and epistemic adequacy.
 
 * [Abductive Inference in Bayesian Networks: A Review](https://link.springer.com/chapter/10.1007/978-3-540-39879-0_6) - ***Advances in Bayesian Networks***, 2004. [[All Versions](https://scholar.google.com/scholar?cluster=8502276402734843212&hl=en&as_sdt=0,5)].
 

docs/awesome/awesome-angular.md

@@ -556,6 +556,7 @@ become an Angular expert.
 * [ngx-xapi](https://github.com/BerryCloud/ngx-xapi) - Lightweight Angular wrapper for [xAPI](https://xapi.com/).
 * [angular-rsocket](https://github.com/saleweaver/angular-rsocket) - This service allows you to easily connect to an [RSocket](https://rsocket.io/) server, handle streams and messages, and manage authentication tokens flexibly via a token provider.
 * [ngx-pendo](https://github.com/yociduo/ngx-pendo) - A simple wrapper to load Pendo in Angular.
+* [ngx-surreal](https://github.com/vandaeldev/ngx-surreal) - Lightweight Angular wrapper for the [SurrealDB](https://surrealdb.com/) JavaScript SDK.
 
 #### Internationalization
 
@@ -807,6 +808,7 @@ become an Angular expert.
 * [angular-yandex-smart-captcha](https://github.com/flowXM/angular-yandex-smart-captcha) - This library adds the Yandex SmartCaptcha component to your Angular application, providing an easy way to integrate CAPTCHA protection into your forms and other user interactions.
 * [go-captcha-angular](https://github.com/wenlng/go-captcha-angular) - A simple, easy-to-use, interactive, and secure behavioral verification code that implements verification modes such as text/graphic clicking, sliding/dragging, and rotation.
 * [ng-recaptcha-2](https://github.com/LakhveerChahal/ng-recaptcha-2) - Angular 18 fork of [ng-recaptcha](https://github.com/DethAriel/ng-recaptcha). Alternatively, you create your own service that implements Google's reCAPTCHA with the help of this [article](https://ben-5.azurewebsites.net/2024/9/5/google-recaptcha-v3-with-angular/#google_vignette).
+* [ngx-slider-recaptcha](https://github.com/mrzinkowin/ngx-slider-recaptcha) - Customizable Angular library that provides a slider-based CAPTCHA component to help secure forms from spam and bot submissions.
 
 #### Carousels
 
@@ -1131,6 +1133,7 @@ to simplify usage and allow quick customization.
 * [ng-circle-progress](https://github.com/bootsoon/ng-circle-progress) - A simple circle progress component created for Angular based on SVG Graphics.
 * [ngx-loading-buttons](https://github.com/dkreider/ngx-loading-buttons) - A lightweight Angular library to add a loading spinner to your Angular Material buttons.
 * [ngx-fastboot](https://github.com/KernelPanic92/ngx-fastboot) - A dynamic configuration loader for Angular applications. It optimizes the startup performance by loading configurations in a separate chunk during compilation.
+* [nx-svg-loaders](https://github.com/ngeenx/nx-svg-loaders) - Angular, React, Svelte, Vue SVG loader/spinner collection.
 
 #### Loggers
 
@@ -1211,6 +1214,7 @@ to simplify usage and allow quick customization.
 * [dialog](https://github.com/ngneat/dialog) - A simple to use, highly customizable, and powerful modal.
 * [ngx-modal-ease](https://github.com/GreenFlag31/modal-library) - `ngx-modal-ease` is a versatile Angular library providing a lightweight, simple, and performant modal.
 * [ngx-smart-modal](https://github.com/maximelafarie/ngx-smart-modal) - Modal/Dialog component crafted for Angular (Ivy-compatible).
+* [up-window-angular](https://github.com/criar-art/up-window-angular) - An Angular library designed to create dynamic, customizable modals and window-based components for web applications.
 
 #### Notifications
 
@@ -1445,6 +1449,7 @@ for the creation of web applications developed with Angular.
 * [ngx-lamp](https://github.com/omnedia/ngx-lamp) - A simple component library to create a lamp.
 * [ngx-globe](https://github.com/omnedia/ngx-globe) - A simple component library to create a container with an animated globe.
 * [ngx-copypaste](https://github.com/JsDaddy/ngx-copypaste) - A pure and awesome copy paste directive for Angular.
+* [ngx-morse](https://github.com/monkeyscript/ngx-morse) - A simple morse code encoder and decoder for Angular.
 
 ---
 

docs/awesome/awesome-directus.md

@@ -78,6 +78,7 @@
 - [Classified Group](https://github.com/formfcw/directus-extension-classified-group) - A group to which a class can be assigned for custom styling.
 - [Tokenized Preview](https://github.com/formfcw/directus-extension-tokenized-preview) - An endpoint that adds an active auth token to your preview URL.
 - [Umami Analytics](https://github.com/egidiusmengelberg/directus-extension-umami) - Add Umami analytics to Directus.
+- [Auto generate file transformations](https://github.com/utomic-media/directus-extension-auto-generate-file-transformations) - Automatically generate selected file transformations on upload 
 
 ### Extension Scripts
 

docs/awesome/awesome-machine-learning.md

@@ -1140,6 +1140,7 @@ be
 * [Frouros](https://github.com/IFCA/frouros): Frouros is an open source Python library for drift detection in machine learning systems.
 * [CometML](https://github.com/comet-ml/comet-examples): The best-in-class MLOps platform with experiment tracking, model production monitoring, a model registry, and data lineage from training straight through to production.
 * [Okrolearn](https://github.com/Okerew/okrolearn): A python machine learning library created to combine powefull data analasys feautures with tensors and machine learning components, while mantaining support for other libraries.
+* [Opik](https://github.com/comet-ml/opik): Evaluate, trace, test, and ship LLM applications across your dev and production lifecycles.
 
 <a name="python-data-analysis--data-visualization"></a>
 #### Data Analysis / Data Visualization
@@ -1337,6 +1338,10 @@ be
 * [RLlib](https://github.com/ray-project/ray) - RLlib is an industry level, highly scalable RL library for tf and torch, based on Ray. It's used by companies like Amazon and Microsoft to solve real-world decision making problems at scale.
 * [DI-engine](https://github.com/opendilab/DI-engine) - DI-engine is a generalized Decision Intelligence engine. It supports most basic deep reinforcement learning (DRL) algorithms, such as DQN, PPO, SAC, and domain-specific algorithms like QMIX in multi-agent RL, GAIL in inverse RL, and RND in exploration problems.
 
+<a name="python-speech-recognition"></a>
+#### Speech Recognition
+* [EspNet](https://github.com/espnet/espnet) - ESPnet is an end-to-end speech processing toolkit for tasks like speech recognition, translation, and enhancement, using PyTorch and Kaldi-style data processing.
+
 <a name="ruby"></a>
 ## Ruby