Robot Updated at:1 Mar 2025 21:10:09 GMT

Author: yiliuyan161

docs/awesome/awesome-agi-cocosci.md

@@ -635,7 +635,7 @@ English-language description for use by biologists.
 
 * [PhotoScout: Synthesis-Powered Multi-Modal Image Search](https://dl.acm.org/doi/full/10.1145/3613904.3642319) - ***ACM SIGCHI'24***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=6522231014055730719)]. This paper explores a new multi-modal image search approach that allows users to conveniently specify and perform semantic image search tasks. With the tool, PhotoScout, the user interactively provides natural language descriptions, positive and negative examples, and object tags to specify their search tasks. Under the hood, PhotoScout is powered by a program synthesis engine that generates visual queries in a domain-specific language and executes the synthesized program to retrieve the desired images.
 
-* [Expert-level protocol translation for self-driving labs](https://proceedings.neurips.cc/paper_files/paper/2024/hash/54dd9e0cff6d9214e20d97eb2a3bae49-Abstract-Conference.html) - ***NeurIPS'24***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=13997597682274906943)]. [[Project]()]. Recent development in Artificial Intelligence (AI) models has propelled their application in scientific discovery, but the validation and exploration of these discoveries require subsequent empirical experimentation. The concept of self-driving laboratories promises to automate and thus boost the experimental process following AI-driven discoveries. However, the transition of experimental protocols, originally crafted for human comprehension, into formats interpretable by machines presents significant challenges, which, within the context of specific expert domain, encompass the necessity for structured as opposed to natural language, the imperative for explicit rather than tacit knowledge, and the preservation of causality and consistency throughout protocol steps. Presently, the task of protocol translation predominantly requires the manual and labor-intensive involvement of domain experts and information technology specialists, rendering the process time-intensive. To address these issues, this work proposes a framework that automates the protocol translation process through a three-stage workflow, which incrementally constructs Protocol Dependence Graphs (PDGs) that approach structured on the syntax level, completed on the semantics level, and linked on the execution level. Quantitative and qualitative evaluations have demonstrated its performance at par with that of human experts, underscoring its potential to significantly expedite and democratize the process of scientific discovery by elevating the automation capabilities within self-driving laboratories.
+* [Expert-level protocol translation for self-driving labs](https://proceedings.neurips.cc/paper_files/paper/2024/hash/54dd9e0cff6d9214e20d97eb2a3bae49-Abstract-Conference.html) - ***NeurIPS'24***, 2024. [[All Versions](https://scholar.google.com/scholar?cluster=13997597682274906943)]. [[Project](https://autodsl.org/procedure/papers/neurips24shi.html)]. Recent development in Artificial Intelligence (AI) models has propelled their application in scientific discovery, but the validation and exploration of these discoveries require subsequent empirical experimentation. The concept of self-driving laboratories promises to automate and thus boost the experimental process following AI-driven discoveries. However, the transition of experimental protocols, originally crafted for human comprehension, into formats interpretable by machines presents significant challenges, which, within the context of specific expert domain, encompass the necessity for structured as opposed to natural language, the imperative for explicit rather than tacit knowledge, and the preservation of causality and consistency throughout protocol steps. Presently, the task of protocol translation predominantly requires the manual and labor-intensive involvement of domain experts and information technology specialists, rendering the process time-intensive. To address these issues, this work proposes a framework that automates the protocol translation process through a three-stage workflow, which incrementally constructs Protocol Dependence Graphs (PDGs) that approach structured on the syntax level, completed on the semantics level, and linked on the execution level. Quantitative and qualitative evaluations have demonstrated its performance at par with that of human experts, underscoring its potential to significantly expedite and democratize the process of scientific discovery by elevating the automation capabilities within self-driving laboratories.
 
 
 #### Cognitive Foundations

docs/awesome/awesome-angular.md

@@ -50,6 +50,7 @@ Reports will be classified by a header that describes their primary content or e
 - [Cisco](https://blog.talosintelligence.com/cisco-talos-2023-year-in-review/) - [Talos Year In Review](https://github.com/jacobdjwilson/awesome-annual-security-reports/blob/master/Annual%20Security%20Reports/2023/Cisco-Talos-Year-In-Review-2023.pdf) (2023) - Provides a comprehensive analysis of cyber threats and attack trends observed by Cisco's threat intelligence team throughout the year.
 - [CrowdStrike](https://www.crowdstrike.com/resources/reports/overwatch-threat-hunting-report/) - [Threat Hunting Report](https://github.com/jacobdjwilson/awesome-annual-security-reports/blob/master/Annual%20Security%20Reports/2024/CrowdStrike-Threat-Hunting-Report-2024.pdf) (2024) - Provides comprehensive insights into over 245 advanced persistent threats (APTs) and adversary tactics through global threat monitoring and analysis.
 - [CrowdStrike](https://www.crowdstrike.com/resources/reports/global-threat-report/) - [Global Threat Report](https://github.com/jacobdjwilson/awesome-annual-security-reports/blob/master/Annual%20Security%20Reports/2024/Crowdstrike-Global-Threat-Report-2024.pdf) (2024) - Analyzes global cyber threats, offering insights into adversary tactics, emerging attack trends, and strategies for improving cyber defense.
+- [Darktrace](https://darktrace.com/resources/annual-threat-report-2024) - [Annual Threat Report](https://github.com/jacobdjwilson/awesome-annual-security-reports/blob/master/Annual%20Security%20Reports/2024/Darktrace-Annual-Threat-Report-2024.pdf) (2024) - Highlights key threat campaigns and focusing on the persistent and evolving nature of ransomware attacks, including emerging strains.
 - [DeepInstinct](https://www.deepinstinct.com/blog/2022-cyber-threat-landscape-report) - [Threat Landscape Report](https://github.com/jacobdjwilson/awesome-annual-security-reports/blob/master/Annual%20Security%20Reports/2023/Deep-Instinct-Cyber-Threat-Landscape-Report-2023.pdf) (2023) - Examines evolving cyber threats, offering insights into attack techniques, malware trends, and strategies for enhancing organizational cybersecurity.
 - [Deepwatch](https://www.deepwatch.com/2024-ati-threat-report/) - [Annual Threat Report](https://github.com/jacobdjwilson/awesome-annual-security-reports/blob/master/Annual%20Security%20Reports/2024/Deepwatch-Annual-Threat-Report-2024.pdf) (2024) - Analyzes cybersecurity trends, observations, and metrics to provide insights and forecasts for the upcoming year.
 - [Department of Homeland Security](https://www.dhs.gov/publication/homeland-threat-assessment) - [Threat Assessment](https://github.com/jacobdjwilson/awesome-annual-security-reports/blob/master/Annual%20Security%20Reports/2025/DHS-Threat-Assessment-2025.pdf) (2025) - Outlines key threats to the U.S. homeland, including public safety, terrorism, illegal drugs, and nation-state influence, aiming to safeguard American people, homeland, and values.
@@ -212,6 +213,7 @@ Reports will be classified by a header that describes their primary content or e
 
 ## AI and Emerging Technologies
 - [HiddenLayer](https://hiddenlayer.com/threatreport2024/) - [AI Threat Report](https://github.com/jacobdjwilson/awesome-annual-security-reports/blob/master/Annual%20Security%20Reports/2024/HiddenLayer-AI-Threat-Landscape-Report-2024.pdf) (2024) - Provides insights into the AI threat landscape across various industries.
+- [Salt](https://salt.security/api-security-trends) - [State of API Security](https://github.com/jacobdjwilson/awesome-annual-security-reports/blob/master/Annual%20Security%20Reports/2024/Salt-State-of-API-Security-2024.pdf) (2024) - Analyzes the state of API security covering adoption drivers, development trends, security challenges, and customer data analysis. 
 - [Snyk](https://snyk.io/reports/ai-code-security/) - [AI Generated Code Security Report](https://github.com/jacobdjwilson/awesome-annual-security-reports/blob/master/Annual%20Security%20Reports/2023/Snyk-AI-Generated-Code-Security-Report-2023.pdf) (2023) - Examines the security implications of AI-generated code across different sectors.
 
 ## Resources

docs/awesome/awesome-annual-security-reports.md

@@ -1130,7 +1130,11 @@ Monitoring
 
 * [prometheus.cl](https://github.com/deadtrickster/prometheus.cl) - Prometheus.io client. Grafana dashboard for SBCL and Hunchentoot metrics (memory, threads, requests per second,…). [MIT][200].
   * [prometheus-g](https://github.com/40ants/prometheus-gc) - Extension for prometheus.cl which collects metrics about garbage collector state.
+* [lisp-sentry](https://gitlab.com/lockie/lisp-sentry) - A full-featured Common Lisp client library for Sentry application monitoring software. MIT.
+  * light in dependencies, provides Sentry with source code in stack traces, supports  file attachments, event breadcrumbs, automatically populated execution contexts, threads and user reports, GPU information.
+  * supports only SBCL
 * [cl-sentry-client](https://github.com/mmontone/cl-sentry-client) - a Sentry client for Common Lisp, the cloud-based error monitoring system. [MIT][200].
+  * based on dexador for HTTP communication and swank for stack traces. It also features an async HTTP client via the simple-tasks library.
 * [rollbar.lisp](https://github.com/adventuring/rollbar.lisp) - interface to [Rollbar.com](https://rollbar.com/), an error tracking software.
 
 

docs/awesome/awesome-cl.md

@@ -953,6 +953,10 @@ There're also some great commercial libraries, like [amchart](https://www.amchar
 * [Knex](https://github.com/knex/knex) A query builder for PostgreSQL, MySQL, CockroachDB, SQL Server, SQLite3 and Oracle, designed to be flexible, portable, and fun to use.
 * [MikroORM](https://github.com/mikro-orm/mikro-orm) TypeScript ORM for Node.js based on Data Mapper, Unit of Work and Identity Map patterns.
 
+## WebSockets
+
+* [Socket.io](https://github.com/socketio/socket.io) The most widely used WebSocket library for real-time applications. Supports auto-reconnection, rooms, and fallbacks (e.g., polling).
+
 ## Misc
 
 * [echo](https://github.com/toddmotto/echo) - Lazy-loading images with data-* attributes.

docs/awesome/awesome-javascript.md

@@ -1808,6 +1808,7 @@ See also [Are we web yet?](https://www.arewewebyet.org) and [Rust web framework
   * [branca](https://crates.io/crates/branca) - Implementation of Branca for Authenticated and Encrypted API tokens.
   * [c410-f3r/wtx](https://github.com/c410-f3r/wtx) - Low and high level HTTP/2 server
   * [carllerche/tower-web](https://github.com/carllerche/tower-web) [[tower-web](https://crates.io/crates/tower-web)] - A fast, boilerplate free, web framework
+  * [Cot](https://github.com/cot-rs/cot) - The Rust web framework for lazy developers.
   * [GildedHonour/frank_jwt](https://github.com/GildedHonour/frank_jwt) - JSON Web Token implementation.
   * [Gotham](https://github.com/gotham-rs/gotham) - A flexible web framework that does not sacrifice safety, security or speed.
   * [Graphul](https://github.com/graphul-rs/graphul) - An Express-inspired web framework. [![crate](https://img.shields.io/crates/v/create-rust-app.svg)](https://crates.io/crates/graphul)