Tootfinder

flixster: Flixster friendship network (2009)
A network of friendships among users of Flixster, an online social movie website that allows users to share movie ratings and to connect each other. Nodes represent users and an edge exists if two users are friends.
This network has 2523386 nodes and 7918801 edges.
Tags: Social, Online, Unweighted

So halte ich im #Browser meine #MultiColumn-Ansicht clean und sauber:
Wenn eine Spalte gelesen, dann einfach auf den Tropfen drücken und die Spalte ist leer.

Dear Apple SwiftUI friends, this bug is killing the vibe of our users: FB16257334
Repro for those in the community:
https://github.com/feedback-assistant/reports/issues/594

FB16257334: Inspector view bug · Issue #594 · feedback-assistant/reports
Date: 2024-01-07 Resolution: Open Area: SwiftUI OS: iPadOS 18.1 Type: Incorrect/Unexpected Behavior Keywords: SwiftUI, split view, iPadOS, NavigationSplitView Description Simple NavigationSplitView...

Source: the US House's CAO informed congressional staffers that WhatsApp is now banned on their government devices as the app is deemed "a high-risk to users" (Andrew Solender/Axios)
https://www.axios.com/2025/06/23/whatsapp-house-cong…

Scoop: WhatsApp banned on House staffers' devices
The House's cybersecurity office deemed the messaging app "high risk" due to its treatment of user data.

Tracker Installations Are Not Created Equal: Understanding Tracker Configuration of Form Data Collection
Julia B. Kieserman, Athanasios Andreou, Chris Geeng, Tobias Lauinger, Damon McCoy
https://arxiv.org/abs/2506.16891

Tracker Installations Are Not Created Equal: Understanding Tracker Configuration of Form Data Collection
Targeted advertising is fueled by the comprehensive tracking of users' online activity. As a result, advertising companies, such as Google and Meta, encourage website administrators to not only install tracking scripts on their websites but configure them to automatically collect users' Personally Identifying Information (PII). In this study, we aim to characterize how Google and Meta's trackers can be configured to collect PII data from web forms. We first perform a qualitative analysis of how…

I made a little Drupal module last week that does nothing more than draw the attention of admin users to that fact that a particular admin form will alter the running configuration…
https://www.drupal.org/project/config_warning

To ChatGPT: How many users of The vOICe visual-to-auditory sensory substitution for the blind are there in the world? https://chatgpt.com/share/6857c14d-074c-8004-b4d2-f6e6fc925e02

ChatGPT - E-bike Injury Odds in NYC
Shared via ChatGPT

Data Compression with Relative Entropy Coding
Gergely Flamich
https://arxiv.org/abs/2506.16309 https://arxiv.org/pdf/2506.16309

Data Compression with Relative Entropy Coding
Over the last few years, machine learning unlocked previously infeasible features for compression, such as providing guarantees for users' privacy or tailoring compression to specific data statistics (e.g., satellite images or audio recordings of animals) or users' audiovisual perception. This, in turn, has led to an explosion of theoretical investigations and insights that aim to develop new fundamental theories, methods and algorithms better suited for machine learning-based compressors. In…

Reinforcing User Interest Evolution in Multi-Scenario Learning for recommender systems
Zhijian Feng, Wenhao Zheng, Xuanji Xiao
https://arxiv.org/abs/2506.17682

Reinforcing User Interest Evolution in Multi-Scenario Learning for recommender systems
In real-world recommendation systems, users would engage in variety scenarios, such as homepages, search pages, and related recommendation pages. Each of these scenarios would reflect different aspects users focus on. However, the user interests may be inconsistent in different scenarios, due to differences in decision-making processes and preference expression. This variability complicates unified modeling, making multi-scenario learning a significant challenge. To address this, we propose a n…

revolution: American Revolutionary groups (1765-1783)
A bipartite network of the memberships of notable people and organizations, from the American Revolution (1765-1783) between users and groups on YouTube, extracted from a larger YouTube network in 2007.
This network has 141 nodes and 160 edges.
Tags: Social, Affiliation, Unweighted
http…