Awesome List Updates on Feb 16, 2022
18 awesome lists updated today.
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1. Awesome Playcanvas
Product Configurators / YouTube Playables
- WFI - Workstation configurator.
2. Awesome Newsletters
Miscellaneous / Svelte
- Figures. Receive high-paying tech jobs based on your minimum salary requirement.
3. Awesome Transit
GTFS Analysis Tools / Rust
- R5: Rapid Realistic Routing on Real-world and Reimagined networks (⭐302) - A Java-based routing engine developed by Conveyal for multimodal (transit/bike/walk/car) networks. It currently plans many trips over a time window for scenario planning and analytics purposes. A related R wrapper package (r5r (⭐184)) is developed independently by IPEA. See also the performance comparison from Higgins et al. (2022), linked below.
Academic papers / General GIS Applications for making transit visualizations
- Higgins et al. - "Calculating place-based transit accessibility: Methods, tools and algorithmic dependence" (2022) - Compares software tools for calculating accessibility by walking and public transit including ArcGIS Pro, Emme, R5R, and OpenTripPlanner.
4. Awesome Terraform
Tutorials and Blog Posts / How-To
- Google Cloud Platform for 10$ a month using terraform (⭐47) - Shows how to use terraform to create a secure Google Kubernetes Cluster, Google Cloud Run Services and other infrastructure elements for less than 10$ a month.
Self-Hosted Registries / Miscellaneous
- terustry (⭐66) - Open Source terraform provider registry acting as a proxy for gitlab or github releases.
Providers / Vendor supported providers
- terraform-provider-openstack (⭐402) - Plugin for OpenStack.
Boilerplates / IDE
- Terraform GitOps Framework - Everything you need to build reliable automation for AKS, EKS, and GKE Kubernetes clusters in one free and open-source framework.
5. Awesome Bash
Games
- wordle - Wordle in less than 50 lines of Bash.
6. Awesome Zsh Plugins
Plugins / superconsole - Windows-only
- ddev (⭐1) - A ZSH plugin for the ddev (⭐3k) tool for setting up PHP development environments.
7. Awesome Jmeter
Related / Awesome Lists
- Awesome Locust (⭐95) - Open-source scalable load testing framework written in Python.
8. Awesome Kotlin
Libraries/Frameworks / Misc
- Kotlin/kotlinx.atomicfu (⭐931) - The idiomatic way to use atomic operations in Kotlin.
9. Awesome Malware Persistence
Techniques / Firmware
- MoonBounce: the dark side of UEFI firmware - An in-depth write up about one particular UEFI bootkit.
10. Awesome Integration
Resources / Standard APIs
- OData - An open protocol that enables the creation and consumption of queryable and interoperable REST APIs. OData simplifies the development of REST APIs and offers a standardized way to access data.
11. Awesome Audit Algorithms
Papers / 2021
- Setting the Record Straighter on Shadow Banning - (INFOCOM) (Code) Considers the possibility of shadow banning in Twitter (ie, the moderation black-box algorithm), and measures the probability of several hypothesis.
- FairLens: Auditing black-box clinical decision support systems - (Information Processing & Management) Presents a pipeline to detect and explain potential fairness issues in Clinical DSS, by comparing different multi-label classification disparity measures.
- Auditing Algorithmic Bias on Twitter - (WebSci).
Papers / 2020
- Adversarial Model Extraction on Graph Neural Networks - (AAAI Workshop on Deep Learning on Graphs: Methodologies and Applications) Introduces GNN model extraction and presents a preliminary approach for this.
- Remote Explainability faces the bouncer problem - (Nature Machine Intelligence volume 2, pages529–539) (Code) (⭐4) Shows the impossibility (with one request) or the difficulty to spot lies on the explanations of a remote AI decision.
- GeoDA: a geometric framework for black-box adversarial attacks - (CVPR) (Code) (⭐32) Crafts adversarial examples to fool models, in a pure blackbox setup (no gradients, inferred class only).
- The Imitation Game: Algorithm Selectionby Exploiting Black-Box Recommender (⭐2) - (Netys) (Code) (⭐1) Parametrize a local recommendation algorithm by imitating the decision of a remote and better trained one.
- Auditing News Curation Systems:A Case Study Examining Algorithmic and Editorial Logic in Apple News - (ICWSM) Audit study of Apple News as a sociotechnical news curation system (trending stories section).
- Auditing Algorithms: On Lessons Learned and the Risks of DataMinimization - (AIES) A practical audit for a well-being recommendation app developed by Telefónica (mostly on bias).
- Extracting Training Data from Large Language Models - (arxiv) Performs a training data extraction attack to recover individual training examples by querying the language model.
Papers / 2019
- Adversarial Frontier Stitching for Remote Neural Network Watermarking - (Neural Computing and Applications) (Alternative implementation) (⭐23) Check if a remote machine learning model is a "leaked" one: through standard API requests to a remote model, extract (or not) a zero-bit watermark, that was inserted to watermark valuable models (eg, large deep neural networks).
- Knockoff Nets: Stealing Functionality of Black-Box Models - (CVPR) Ask to what extent can an adversary steal functionality of such "victim" models based solely on blackbox interactions: image in, predictions out.
- Opening Up the Black Box:Auditing Google's Top Stories Algorithm - (Flairs-32) Audit of the Google's Top stories panel that pro-vides insights into its algorithmic choices for selectingand ranking news publisher
- Making targeted black-box evasion attacks effective andefficient - (arXiv) Investigates how an adversary can optimally use its query budget for targeted evasion attacks against deep neural networks.
- Online Learning for Measuring Incentive Compatibility in Ad Auctions - (WWW) Measures the incentive compatible- (IC) mechanisms (regret) of black-box auction platforms.
- TamperNN: Efficient Tampering Detection of Deployed Neural Nets - (ISSRE) Algorithms to craft inputs that can detect the tampering with a remotely executed classifier model.
- Neural Network Model Extraction Attacks in Edge Devicesby Hearing Architectural Hints - (arxiv) Through the acquisition of memory access events from bus snooping, layer sequence identification bythe LSTM-CTC model, layer topology connection according to the memory access pattern, and layer dimension estimation under data volume constraints, it demonstrates one can accurately recover the a similar network architecture as the attack starting point
- Stealing Knowledge from Protected Deep Neural Networks Using Composite Unlabeled Data - (ICNN) Composite method which can be used to attack and extract the knowledge ofa black box model even if it completely conceals its softmaxoutput.
- Neural Network Inversion in Adversarial Setting via Background Knowledge Alignment - (CCS) Model inversion approach in the adversary setting based on training an inversion model that acts as aninverse of the original model. With no fullknowledge about the original training data, an accurate inversion is still possible by training the inversion model on auxiliary samplesdrawn from a more generic data distribution.
Papers / 2018
- Towards Reverse-Engineering Black-Box Neural Networks - (ICLR) (Code) (⭐54) Infer inner hyperparameters (eg number of layers, non-linear activation type) of a remote neural network model by analysing its response patterns to certain inputs.
- Data driven exploratory attacks on black box classifiers in adversarial domains - (Neurocomputing) Reverse engineers remote classifier models (e.g., for evading a CAPTCHA test).
- xGEMs: Generating Examplars to Explain Black-Box Models - (arXiv) Searches bias in the black box model by training an unsupervised implicit generative model. Thensummarizes the black-box model behavior quantitatively by perturbing data samples along the data manifold.
- Learning Networks from Random Walk-Based Node Similarities - (NIPS) Reversing graphs by observing some random walk commute times.
- Identifying the Machine Learning Family from Black-Box Models - (CAEPIA) Determines which kind of machine learning model is behind the returned predictions.
- Stealing Neural Networks via Timing Side Channels - (arXiv) Stealing/approximating a model through timing attacks usin queries.
- Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data - (IJCNN) (Code) (⭐27) Stealing black-box models (CNNs) knowledge by querying them with random natural images (ImageNet and Microsoft-COCO).
Papers / 2017
- Uncovering Influence Cookbooks : Reverse Engineering the Topological Impact in Peer Ranking Services - (CSCW) Aims at identifying which centrality metrics are in use in a peer ranking service.
- The topological face of recommendation: models and application to bias detection - (Complex Networks) Proposes a bias detection framework for items recommended to users.
- Membership Inference Attacks Against Machine Learning Models - (Symposium on Security and Privacy) Given a machine learning model and a record, determine whether this record was used as part of the model's training dataset or not.
- Practical Black-Box Attacks against Machine Learning - (Asia CCS) Understand how vulnerable is a remote service to adversarial classification attacks.
Papers / 2016
- Bias in Online Freelance Marketplaces: Evidence from TaskRabbit - (dat workshop) Measures the TaskRabbit's search algorithm rank.
- Stealing Machine Learning Models via Prediction APIs - (Usenix Security) (Code) (⭐343) Aims at extracting machine learning models in use by remote services.
- “Why Should I Trust You?”Explaining the Predictions of Any Classifier - (arXiv) (Code) (⭐314) Explains a blackbox classifier model by sampling around data instances.
- Back in Black: Towards Formal, Black Box Analysis of Sanitizers and Filters - (Security and Privacy) Black-box analysis of sanitizers and filters.
- Algorithmic Transparency via Quantitative Input Influence: Theory and Experiments with Learning Systems - (Security and Privacy) Introduces measures that capture the degree of influence of inputs on outputs of the observed system.
Papers / 2015
- Peeking Beneath the Hood of Uber - (IMC) Infer implementation details of Uber's surge price algorithm.
Papers / 2014
- XRay: Enhancing the Web's Transparency with Differential Correlation - (USENIX Security) Audits which user profile data were used for targeting a particular ad, recommendation, or price.
Papers / 2013
- Measuring Personalization of Web Search - (WWW) Develops a methodology for measuring personalization in Web search result.
Papers / 2012
- Query Strategies for Evading Convex-Inducing Classifiers - (JMLR) Evasion methods for convex classifiers. Considers evasion complexity.
Papers / 2008
- Privacy Oracle: a System for Finding Application Leakswith Black Box Differential Testing - (CCS) Privacy Oracle: a system that uncovers applications' leaks of personal information in transmissions to remoteservers.
Papers / 2005
- Adversarial Learning - (KDD) Reverse engineering of remote linear classifiers, using membership queries.
12. Awesome Vue
Components & Libraries / Utilities
- pinia-orm (⭐480) - The Pinia plugin to enable Object-Relational Mapping access to the Pinia Store. 🍍
13. Awesome Neovim
Marks / Diagnostics
- ThePrimeagen/harpoon (⭐7.7k) - A per project, auto updating and editable marks utility for fast file navigation.
14. Awesome Osint
Username Check / Steam
- User Searcher - User-Searcher is a powerful and free tool to help you search username in 2000+ websites.
15. Awesome Go
Trees
- treemap (⭐61) - Generic key-sorted map using a red-black tree under the hood.
- mailx (⭐19) - Mailx is a library that makes it easier to send email via SMTP. It is an enhancement of the golang standard library
net/smtp.
16. Awesome Agi Cocosci
Domain Specific Language / Logic DSL Applications
- Qualitative and quantitative simulation: bridging the gap - Artificial Intelligence, 1997. [All Versions]. Shortcomings of qualitative simulation and of quantitative simulation motivate combining them to do simulations exhibiting strengths of both. The resulting class of techniques is called semiquantitative simulation. One approach to semi-quantitative simulation is to use numeric intervals to represent incomplete quantitative information. This research demonstrates semi-quantitative simulation using intervals in an implemented semi-quantitative simulator called Q3. Q3 progressively refines a qualitative simulation, providing increasingly specific quantitative predictions which can converge to a numerical simulation in the limit while retaining important correctness guarantees from qualitative and interval simulation techniques.
17. Awesome Datascience
Bloggers / Book Deals (Affiliated)
- Yet Another Data Blog Yet Another Data Blog
- Tevfik Kosar - Magnus Notitia
- Springboard - A blog with resources for data science learners
- Sebastian's Blog - Blog for NLP and transfer learning!
Facebook Accounts / Book Deals (Affiliated)
Datasets / Book Deals (Affiliated)
Other Awesome Lists / Book Deals (Affiliated)
18. Public Apis
Animals
API: xeno-canto
Description: Bird recordings
Auth: No
HTTPS: Yes
CORS: Unknown
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