Tootfinder

from my link log —
Creating a read-only PostgreSQL user.
https://blog.crunchydata.com/blog/creating-a-read-only-postgres-user
saved 2021-05-12

Creating a Read-Only Postgres User | Crunchy Data Blog
Learn how to create a read only user in Postgres both now and with a look ahead to Postgres 14.

qa_user: User interactions on Q&A websites (2016)
Networks of interactions among users from four online Q&A sites: Stack Overflow, Math Overflow, Super User, and Ask Ubuntu. A directed edge (i,j) indicates a user i responded to user j's post. Edges are timestamped. For each Q&A site, four differently defined networks are provided, based on the definition of an edge: (i) a user answered a question, (ii) a user commented on a question, (iii) a user commented on an answer…

US-Geheimdienstchefin: Großbritannien will doch keine Hintertür für Apple-User
Laut Angaben der amerikanischen Geheimdienstkoordinatorin Tulsi Gabbard gibt es einen "Deal" mit London. Dort wollte man eine weltweite iCloud-Backdoor.

Reactive Semantics for User Interface Description Languages
Basile Pesin (Federation ENAC ISAE-SUPAERO ONERA, Universite de Toulouse, France), Celia Picard (Federation ENAC ISAE-SUPAERO ONERA, Universite de Toulouse, France), Cyril Allignol (Federation ENAC ISAE-SUPAERO ONERA, Universite de Toulouse, France)
https://arxiv.org/abs/2508.1361…

Reactive Semantics for User Interface Description Languages
User Interface Description Languages (UIDLs) are high-level languages that facilitate the development of Human-Machine Interfaces, such as Graphical User Interface (GUI) applications. They usually provide first-class primitives to specify how the program reacts to an external event (user input, network message), and how data flows through the program. Although these domain-specific languages are now widely used to implement safety-critical GUIs, little work has been invested in their formalizat…

X: Für Dauer von Bezahlabo kann man bald ungenutzte Kontonamen übernehmen
Pläne für die Weitergabe ungenutzter Kontonamen gibt es bei X schon lange, nun wird es konkret. User mit den teuersten Bezahlabos sollen bald zum Zuge kommen.

Multi-User Contextual Cascading Bandits for Personalized Recommendation
Jiho Park, Huiwen Jia
https://arxiv.org/abs/2508.13981 https://arxiv.org/pdf/2508.1…

Multi-User Contextual Cascading Bandits for Personalized Recommendation
We introduce a Multi-User Contextual Cascading Bandit model, a new combinatorial bandit framework that captures realistic online advertising scenarios where multiple users interact with sequentially displayed items simultaneously. Unlike classical contextual bandits, MCCB integrates three key structural elements: (i) cascading feedback based on sequential arm exposure, (ii) parallel context sessions enabling selective exploration, and (iii) heterogeneous arm-level rewards. We first propose Uppe…

Heterogeneous Influence Maximization in User Recommendation
Hongru Hou, Jiachen Sun, Wenqing Lin, Wendong Bi, Xiangrong Wang, Deqing Yang
https://arxiv.org/abs/2508.13517 https:…

Heterogeneous Influence Maximization in User Recommendation
User recommendation systems enhance user engagement by encouraging users to act as inviters to interact with other users (invitees), potentially fostering information propagation. Conventional recommendation methods typically focus on modeling interaction willingness. Influence-Maximization (IM) methods focus on identifying a set of users to maximize the information propagation. However, existing methods face two significant challenges. First, recommendation methods fail to unleash the candidat…

https://fanf2.user.srcf.net/hermes/doc/qsmtp/draft-fanf-wtf8.html
Encoding a character to WTF-8 proceeds as follows:
* Determine the number of octets required from the character number and the first column of the table above. It is important to note that th…

edit_wikiquote: Wikiquote edits (2010)
A bipartite user-page network extracted from Wikiquotes. A user connects to a page if that user edited that page. Edits (edges) are timestamped. Edge weights represent counts of the number of edits.
This network has 3834 nodes and 12574 edges.
Tags: Informational, Web graph, Multigraph, Timestamps

Early blind user of The vOICe vision BCI today:
"This past weekend I went exploring some caves using The vOICe. I used the Android app since I had it already loaded on my phone, and there was no service once inside. I was able to see where the other people were even though I had someone with me. I was also able to see the differing textures of the stalactite and stalagmite formations. I hadn't used the software in a few years and was surprised how quickly everything came back…

Using AI for User Representation: An Analysis of 83 Persona Prompts
Joni Salminen, Danial Amin, Bernard Jansen
https://arxiv.org/abs/2508.13047 https://arx…

Using AI for User Representation: An Analysis of 83 Persona Prompts
We analyzed 83 persona prompts from 27 research articles that used large language models (LLMs) to generate user personas. Findings show that the prompts predominantly generate single personas. Several prompts express a desire for short or concise persona descriptions, which deviates from the tradition of creating rich, informative, and rounded persona profiles. Text is the most common format for generated persona attributes, followed by numbers. Text and numbers are often generated together, a…

SOMEONE'S GONNA GET HAAAACKED still unpatched after 6 months...
Original post: #cybersecurity

Modeling User Redemption Behavior in Complex Incentive Digital Environment: An Empirical Study Using Large-Scale Transactional Data
Akira Matsui, Takashi Teramoto, Eiji Motohashi, Hiroyuki Tsurumi
https://arxiv.org/abs/2509.14508

Modeling User Redemption Behavior in Complex Incentive Digital Environment: An Empirical Study Using Large-Scale Transactional Data
The digital economy implements complex incentive systems to retain users through point redemption. Understanding user behavior in such complex incentive structures presents a fundamental challenge, especially in estimating the value of these digital assets against traditional money. This study tackles this question by analyzing large-scale, real-world transaction data from a popular personal finance application that captures both monetary spending and point-based transactions across Japan's dee…

Uh, Cait - we probably need to talk about your bodycount. . .
#Fallout4

From Sea to System: Exploring User-Centered Explainable AI for Maritime Decision Support
Doreen Jirak, Pieter Maes, Armeen Saroukanoff, Dirk van Rooy
https://arxiv.org/abs/2509.15084

From Sea to System: Exploring User-Centered Explainable AI for Maritime Decision Support
As autonomous technologies increasingly shape maritime operations, understanding why an AI system makes a decision becomes as crucial as what it decides. In complex and dynamic maritime environments, trust in AI depends not only on performance but also on transparency and interpretability. This paper highlights the importance of Explainable AI (XAI) as a foundation for effective human-machine teaming in the maritime domain, where informed oversight and shared understanding are essential. To sup…

Shut them down. Easy question
https://infosec.exchange/@steelefortress/115062158543467536

I’ve basically been waiting for the inevitable addition of this user-adjustable “suck” knob before I upgrade anything. https://mastodon.social/@macrumors/115407799375913248

AIP: Subverting Retrieval-Augmented Generation via Adversarial Instructional Prompt
Saket S. Chaturvedi, Gaurav Bagwe, Lan Zhang, Xiaoyong Yuan
https://arxiv.org/abs/2509.15159 …

AIP: Subverting Retrieval-Augmented Generation via Adversarial Instructional Prompt
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by retrieving relevant documents from external sources to improve factual accuracy and verifiability. However, this reliance introduces new attack surfaces within the retrieval pipeline, beyond the LLM itself. While prior RAG attacks have exposed such vulnerabilities, they largely rely on manipulating user queries, which is often infeasible in practice due to fixed or protected user inputs. This narrow focus overlooks a …

github: GitHub contest (2009)
The bipartite project-user membership network of the software development hosting site GitHub. A user connects to a project if that user is a member of that project.
This network has 177386 nodes and 440237 edges.
Tags: Economic, Production, Unweighted
https://networks.skewed.de/net/github

We are NOT AMUSED.
If you're hosted by Aceville, you're dead to me.
#Sysadminnery #SpamAssassin #AIBots

Sycophancy under Pressure: Evaluating and Mitigating Sycophantic Bias via Adversarial Dialogues in Scientific QA
Kaiwei Zhang, Qi Jia, Zijian Chen, Wei Sun, Xiangyang Zhu, Chunyi Li, Dandan Zhu, Guangtao Zhai
https://arxiv.org/abs/2508.13743

Sycophancy under Pressure: Evaluating and Mitigating Sycophantic Bias via Adversarial Dialogues in Scientific QA
Large language models (LLMs), while increasingly used in domains requiring factual rigor, often display a troubling behavior: sycophancy, the tendency to align with user beliefs regardless of correctness. This tendency is reinforced by preference-based alignment techniques that optimize for user satisfaction but can undermine truthfulness. While relatively benign in casual dialogue, sycophancy poses serious risks in high-stakes settings such as scientific question answering (QA), where model ou…

Mechanical Automation with Vision: A Design for Rubik's Cube Solver
Abhinav Chalise, Nimesh Gopal Pradhan, Nishan Khanal, Prashant Raj Bista, Dinesh Baniya Kshatri
https://arxiv.org/abs/2508.12469 …

Mechanical Automation with Vision: A Design for Rubik's Cube Solver
The core mechanical system is built around three stepper motors for physical manipulation, a microcontroller for hardware control, a camera and YOLO detection model for real-time cube state detection. A significant software component is the development of a user-friendly graphical user interface (GUI) designed in Unity. The initial state after detection from real-time YOLOv8 model (Precision 0.98443, Recall 0.98419, Box Loss 0.42051, Class Loss 0.2611) is virtualized on GUI. To get the solution…

#UX #Design claims to be "solving user problems" and "creating value" and "building with empathy" — and yet this feature still doesn't exist.

To teraz #Gentoo jakoby zawiera "ekstremistyczne" oprogramowanie.
https://bugs.gentoo.org/964663

AUTOVR: Automated UI Exploration for Detecting Sensitive Data Flow Exposures in Virtual Reality Apps
John Y. Kim, Chaoshun Zuo, Yanjie Zhao, Zhiqiang Lin
https://arxiv.org/abs/2508.12187

AUTOVR: Automated UI Exploration for Detecting Sensitive Data Flow Exposures in Virtual Reality Apps
The rise of Virtual Reality (VR) has provided developers with an unprecedented platform for creating games and applications (apps) that require distinct inputs, different from those of conventional devices like smartphones. The Meta Quest VR platform, driven by Meta, has democratized VR app publishing and attracted millions of users worldwide. However, as the number of published apps grows, there is a notable lack of robust headless tools for user interface (UI) exploration and user event testi…

Highly transparent elements within a layered user interface is a stupid idea. Change my mind.
#UI #ios26

BREAKING: Influential fediverse user "The Pervert Genius", famed for his ironic and egy humor, found dead in the cleaning room of a Miami Buc-ee's, all his organs stripped from his body and sewn into dolls of the four Teletubbies as if they were canopic jars. The smell of Mary Jane and broken promises filled the room. Police believe the murder was motivated by a recent YouTube video exposing Mr. Genius's DL (down low) homosexual relationship with Ron DeSantis.

How the outrage over Jimmy Kimmel's remarks on Charlie Kirk ballooned, starting with one X user, who monitors late night shows for liberal bias, posting a clip (Stuart A. Thompson/New York Times)
https://www.nytimes.com/2025/09/19/technology/kimmel-carr-outr…

How Outrage at Kimmel Grew to a Shout From a Whisper
Right-wing users on social media on Tuesday were frustrated but not yet apoplectic about Jimmy Kimmel’s monologue. Things changed.

Diagnostic-Guided Dynamic Profile Optimization for LLM-based User Simulators in Sequential Recommendation
Hongyang Liu, Zhu Sun, Tianjun Wei, Yan Wang, Jiajie Zhu, Xinghua Qu
https://arxiv.org/abs/2508.12645

Diagnostic-Guided Dynamic Profile Optimization for LLM-based User Simulators in Sequential Recommendation
Recent advances in large language models (LLMs) have enabled realistic user simulators for developing and evaluating recommender systems (RSs). However, existing LLM-based simulators for RSs face two major limitations: (1) static and single-step prompt-based inference that leads to inaccurate and incomplete user profile construction; (2) unrealistic and single-round recommendation-feedback interaction pattern that fails to capture real-world scenarios. To address these limitations, we propose D…

I've expanded a bit on my reflections about #inaturalist's push for using LLMs to regurgitate user-generated insights - and why replacing human peer-to-peer learning is likely harmful to people's motivations.
Read it here: #citizenscience

edit_wiktionary: Wiktionary edits (2010)
Three bipartite user-page networks extracted from Wiktionary, for French, German, and English. A user connects to a page if that user edited that page. Edits (edges) are timestamped. Edge weights represent counts of the number of edits.
This network has 530893 nodes and 2071885 edges.
Tags: Informational, Web graph, Multigraph, Timestamps

Query Logs Analytics: A Aystematic Literature Review
Dihia Lanasri
https://arxiv.org/abs/2508.13949 https://arxiv.org/pdf/2508.13949

Query Logs Analytics: A Aystematic Literature Review
In the digital era, user interactions with various resources such as databases, data warehouses, websites, and knowledge graphs (KGs) are increasingly mediated through digital platforms. These interactions leave behind digital traces, systematically captured in the form of logs. Logs, when effectively exploited, provide high value across industry and academia, supporting critical services (e.g., recovery and security), user-centric applications (e.g., recommender systems), and quality-of-servic…

My talk about #Thunderbird was accepted at @…
it's a talk from a community perspective and I will share my personal story (not so much) and some aspects you wont find anywhere else:

Thunderbird (a community angle) OpenAlt 2025
I am a [community](https://pontoon.mozilla.org/contributors/bogomil/) [contributor](https://bugzilla.mozilla.org/user_profile?user_id=359734) and a long-time user of [Thunderbird](https://www.thunderbird.net/). I want to share with you my vision of what Thunderbird was, is, and will be in the future. Many people are familiar with the interface and the fact that it allows you to manage your email, but this is not why I decided to spend time improving it. I'll cover topics like: - What can any …

What do you think?
https://drupal.community/@ifrik/115060791646810935

There's a Ghidra pull request to add hd6303/6301 - this is looking much better for doing Epson HX-20 stuff;
Copy the Processors/MC6800/data/languages/*6303* into a standard Ghidra world and run 'ant' in the data directory, restart - and it works!
https://github.com/NationalSecurityA…

Add support for HD6301 and HD6303 microcontrollers by depili · Pull Request #6314 · NationalSecurityAgency/ghidra
HD6303 is a Hitachi clone of 6803. This implementation has been done based on the Hitachi HD6301V1/HD6303R User's Manual. The ISA differs from 6805 and 6809. The flags are implemented as pseudo...

"We want to cut municipality ICT spending on software in half before the start of 2027. Imagine reducing the cost from 36 euros per resident per year in 2022 to just 18.50 euros in 2027. We are committed to achieving this by replacing lock-in software and non-user-friendly applications with transparent, accessible, and innovative solutions. Fair contracting is at the heart of our strategy."
Conduction · GitHub
https://github.com/ConductionNL/

Yeah, I have the issue of being the only user in my small town of 10,000. I can see nodes to the South of me, but they can’t hear me. Still hoping . . . #meshtastic
https://hackaday.com/2025/…

Meshtastic: A Tale Of Two Cities
If I’m honest with myself, I don’t really need access to an off-grid, fault-tolerant, mesh network like Meshtastic. The weather here in New Jersey isn’t quite so dynamic that ther…

Me and my new #yubikey5 part 2:
Now we get to the nitty-gritty parts. I'm using #mbsync to sync multiple #imap accounts to local

Near-Real-Time Resource Slicing for QoS Optimization in 5G O-RAN using Deep Reinforcement Learning
Peihao Yan, Jie Lu, Huacheng Zeng, Y. Thomas Hou
https://arxiv.org/abs/2509.14343

Near-Real-Time Resource Slicing for QoS Optimization in 5G O-RAN using Deep Reinforcement Learning
Open-Radio Access Network (O-RAN) has become an important paradigm for 5G and beyond radio access networks. This paper presents an xApp called xSlice for the Near-Real-Time (Near-RT) RAN Intelligent Controller (RIC) of 5G O-RANs. xSlice is an online learning algorithm that adaptively adjusts MAC-layer resource allocation in response to dynamic network states, including time-varying wireless channel conditions, user mobility, traffic fluctuations, and changes in user demand. To address these net…

X plans to show new info about accounts, including their creation date and which country they are based in, "starting next week" on select X employees' profiles (Sarah Perez/TechCrunch)
https://techcrunch.com/2025/10/15/x-pl

X plans to show more information about user profiles to help improve trust | TechCrunch
X is working on new transparency features to help users identify who they’re interacting with, such as account creation date, location, username change history, and how each account uses the service.

🔄 Available in Fast and Auto modes with continuous upgrades based on user feedback
🎯 Sets new record on Pareto Intelligence frontier according to Artificial Analysis
🛠️ Native tool use capabilities with real-time search integration
📊 Democratizing advanced #AI technology for broader accessibility and innovation
🌐

»Evolution of Privacy Pass«
Does anyone of you know this and did this use this practically? Is this really a modern user login "alternative" solution to OAuth 2.0? How is your experience as a WebDev or/and user with it?
🔑 https://www.youtube.com/watch?v=n5yr-3WjSCM
📄

Jš vi IA mentindo e omitindo... Só o que me surpreende é usarmos isso para qualquer coisa, quer dizer, sermos usados por isso para qualquer coisa.
@… https://mas.to/@carnage4life/115228191

digg_reply: Digg reply network (2008)
Network of replies among users of digg.com. Each node in the network is a digg user, and each directed edge indicates that user i replied to user j.
This network has 30398 nodes and 87627 edges.
Tags: Social, Online, Unweighted, Multigraph
https://networks.skewed.de/net/di…

Overcoming Knowledge Discrepancies: Structuring Reasoning Threads through Knowledge Balancing in Interactive Scenarios
Daniel Burkhardt, Xiangwei Cheng
https://arxiv.org/abs/2508.12100

Overcoming Knowledge Discrepancies: Structuring Reasoning Threads through Knowledge Balancing in Interactive Scenarios
Reasoning in interactive problem solving scenarios requires models to construct reasoning threads that reflect user understanding and align with structured domain knowledge. However, current reasoning models often lack explicit semantic hierarchies, user-domain knowledge alignment, and principled mechanisms to prune reasoning threads for effectiveness. These limitations result in lengthy generic output that does not guide users through goal-oriented reasoning steps. To address this, we propose …

Can I Trust This Chatbot? Assessing User Privacy in AI-Healthcare Chatbot Applications
Ramazan Yener, Guan-Hung Chen, Ece Gumusel, Masooda Bashir
https://arxiv.org/abs/2509.14581

Can I Trust This Chatbot? Assessing User Privacy in AI-Healthcare Chatbot Applications
As Conversational Artificial Intelligence (AI) becomes more integrated into everyday life, AI-powered chatbot mobile applications are increasingly adopted across industries, particularly in the healthcare domain. These chatbots offer accessible and 24/7 support, yet their collection and processing of sensitive health data present critical privacy concerns. While prior research has examined chatbot security, privacy issues specific to AI healthcare chatbots have received limited attention. Our s…

MUFFIN: Mixture of User-Adaptive Frequency Filtering for Sequential Recommendation
Ilwoong Baek, Mincheol Yoon, Seongmin Park, Jongwuk Lee
https://arxiv.org/abs/2508.13670 https…

MUFFIN: Mixture of User-Adaptive Frequency Filtering for Sequential Recommendation
Sequential recommendation (SR) aims to predict users' subsequent interactions by modeling their sequential behaviors. Recent studies have explored frequency domain analysis, which effectively models periodic patterns in user sequences. However, existing frequency-domain SR models still face two major drawbacks: (i) limited frequency band coverage, often missing critical behavioral patterns in a specific frequency range, and (ii) lack of personalized frequency filtering, as they apply an identic…

CLMTracing: Black-box User-level Watermarking for Code Language Model Tracing
Boyu Zhang, Ping He, Tianyu Du, Xuhong Zhang, Lei Yun, Kingsum Chow, Jianwei Yin
https://arxiv.org/abs/2509.13982

CLMTracing: Black-box User-level Watermarking for Code Language Model Tracing
With the widespread adoption of open-source code language models (code LMs), intellectual property (IP) protection has become an increasingly critical concern. While current watermarking techniques have the potential to identify the code LM to protect its IP, they have limitations when facing the more practical and complex demand, i.e., offering the individual user-level tracing in the black-box setting. This work presents CLMTracing, a black-box code LM watermarking framework employing the rul…

User eXperience Perception Insights Dataset (UXPID): Synthetic User Feedback from Public Industrial Forums
Mikhail Kulyabin, Jan Joosten, Choro Ulan uulu, Nuno Miguel Martins Pacheco, Fabian Ries, Filippos Petridis, Jan Bosch, Helena Holmstr\"om Olsson
https://arxiv.org/abs/2509.11777

User eXperience Perception Insights Dataset (UXPID): Synthetic User Feedback from Public Industrial Forums
Customer feedback in industrial forums reflect a rich but underexplored source of insight into real-world product experience. These publicly shared discussions offer an organic view of user expectations, frustrations, and success stories shaped by the specific contexts of use. Yet, harnessing this information for systematic analysis remains challenging due to the unstructured and domain-specific nature of the content. The lack of structure and specialized vocabulary makes it difficult for tradi…

RMSL: Weakly-Supervised Insider Threat Detection with Robust Multi-sphere Learning
Yang Wang, Yaxin Zhao, Xinyu Jiao, Sihan Xu, Xiangrui Cai, Ying Zhang, Xiaojie Yuan
https://arxiv.org/abs/2508.11472

RMSL: Weakly-Supervised Insider Threat Detection with Robust Multi-sphere Learning
Insider threat detection aims to identify malicious user behavior by analyzing logs that record user interactions. Due to the lack of fine-grained behavior-level annotations, detecting specific behavior-level anomalies within user behavior sequences is challenging. Unsupervised methods face high false positive rates and miss rates due to the inherent ambiguity between normal and anomalous behaviors. In this work, we instead introduce weak labels of behavior sequences, which have lower annotatio…

visualizeus: vi.sualize.us picture tagging network
Three bipartite networks of tag-picture, user-picture, and user-tag linkages that represent the folksonomy of the picture tagging network of vi.sualize.us. The date of this snapshot is uncertain.
This network has 577437 nodes and 2298816 edges.
Tags: Informational, Folksonomy, Unweighted, Multigraph

Why Johnny Can't Use Agents: Industry Aspirations vs. User Realities with AI Agent Software
Pradyumna Shome, Sashreek Krishnan, Sauvik Das
https://arxiv.org/abs/2509.14528 h…

Why Johnny Can't Use Agents: Industry Aspirations vs. User Realities with AI Agent Software
There is growing imprecision about what "AI agents" are, what they can do, and how effectively they can be used by their intended users. We pose two key research questions: (i) How does the tech industry conceive of and market "AI agents"? (ii) What challenges do end-users face when attempting to use commercial AI agents for their advertised uses? We first performed a systematic review of marketed use cases for 102 commercial AI agents, finding that they fall into three umbrella categories: orc…

Behind India's ChatGPT Conversations: A Retrospective Analysis of 238 Unedited User Prompts
Kalyani Khona
https://arxiv.org/abs/2509.13337 https://arxi…

Behind India's ChatGPT Conversations: A Retrospective Analysis of 238 Unedited User Prompts
Understanding how users authentically interact with Large Language Models (LLMs) remains a significant challenge in human-computer interaction research. Most existing studies rely on self-reported usage patterns or controlled experimental conditions, potentially missing genuine behavioral adaptations. This study presents a behavioral analysis of the use of English-speaking urban professional ChatGPT in India based on 238 authentic, unedited user prompts from 40 participants in 15+ Indian cities…

ENCODE: Breaking the Trade-Off Between Performance and Efficiency in Long-Term User Behavior Modeling
Wenji Zhou, Yuhang Zheng, Yinfu Feng, Yunan Ye, Rong Xiao, Long Chen, Xiaosong Yang, Jun Xiao
https://arxiv.org/abs/2508.13567

ENCODE: Breaking the Trade-Off Between Performance and Efficiency in Long-Term User Behavior Modeling
Long-term user behavior sequences are a goldmine for businesses to explore users' interests to improve Click-Through Rate. However, it is very challenging to accurately capture users' long-term interests from their long-term behavior sequences and give quick responses from the online serving systems. To meet such requirements, existing methods "inadvertently" destroy two basic requirements in long-term sequence modeling: R1) make full use of the entire sequence to keep the information as much a…

Einige der zuletzt hier besonders häufig geteilten #News:
Österreichs Bundesheer stellt auf LibreOffice um
https://www.

bibsonomy: BibSonomy
Three bipartite networks that make up the BibSonomy folksonomy, representing the tag-publication, user-publication, and user-tag networks. The date of this snapshot is uncertain.
This network has 972120 nodes and 2555080 edges.
Tags: Informational, Folksonomy, Unweighted, Multigraph, Timestamps
https://

from my link log —
OSM Perfect Intersection Editor official launch announcement.
https://www.openstreetmap.org/user/Mikhail Kuzin/diary/407577
saved 2025-10-15

OSM Perfect Intersection Editor official launch announcement
We’re excited to announce the official launch of our OSM editor.

google_plus: Google (2013)
Snapshot of connections among users of Google , collected in 2012. Nodes are users and a directed edge (i,j) represents user i added user j to i's circle.
This network has 211187 nodes and 1506896 edges.
Tags: Social, Online, Unweighted
https://networks.skewed.de/net/google_

System-driven Interactive Design Support for Cloud Architecture: A Qualitative User Experience Study with Novice Engineers
Ryosuke Kohita, Akira Kasuga
https://arxiv.org/abs/2508.12385

System-driven Interactive Design Support for Cloud Architecture: A Qualitative User Experience Study with Novice Engineers
Cloud architecture design presents significant challenges due to the necessity of clarifying ambiguous requirements and systematically addressing complex trade-offs, especially for novice engineers with limited cloud experience. While recent advances in the use of AI tools have broadened available options, system-driven approaches that offer explicit guidance and step-by-step information management may be especially effective in supporting novices during the design process. This study qualitati…

Understanding Distribution Structure on Calibrated Recommendation Systems
Diego Correa da Silva, Denis Robson Dantas Boaventura, Mayki dos Santos Oliveira, Eduardo Ferreira da Silva, Joel Machado Pires, Frederico Ara\'ujo Dur\~ao
https://arxiv.org/abs/2508.13568

Understanding Distribution Structure on Calibrated Recommendation Systems
Traditional recommender systems aim to generate a recommendation list comprising the most relevant or similar items to the user's profile. These approaches can create recommendation lists that omit item genres from the less prominent areas of a user's profile, thereby undermining the user's experience. To solve this problem, the calibrated recommendation system provides a guarantee of including less representative areas in the recommended list. The calibrated context works with three distributi…

qa_user: User interactions on Q&A websites (2016)
Networks of interactions among users from four online Q&A sites: Stack Overflow, Math Overflow, Super User, and Ask Ubuntu. A directed edge (i,j) indicates a user i responded to user j's post. Edges are timestamped. For each Q&A site, four differently defined networks are provided, based on the definition of an edge: (i) a user answered a question, (ii) a user commented on a question, (iii) a user commented on an answer…

Zum Abend noch einige der heute besonders häufig geteilten #News:
Österreichs Bundesheer stellt auf LibreOffice um
https://www.…

libimseti: Libimseti.cz social network (2012)
A network of ratings given between users at Libimseti.cz, a Czech online dating website. A directed edge (i,j) means that user i rate user j, and the corresponding edge weight is the given rating, on a scale of 1-10.
This network has 220970 nodes and 17359346 edges.
Tags: Social, Online, Weighted

libimseti: Libimseti.cz social network (2012)
A network of ratings given between users at Libimseti.cz, a Czech online dating website. A directed edge (i,j) means that user i rate user j, and the corresponding edge weight is the given rating, on a scale of 1-10.
This network has 220970 nodes and 17359346 edges.
Tags: Social, Online, Weighted

At the Speed of the Heart: Evaluating Physiologically-Adaptive Visualizations for Supporting Engagement in Biking Exergaming in Virtual Reality
Oliver Hein, Sandra Wackerl, Changkun Ou, Florian Alt, Francesco Chiossi
https://arxiv.org/abs/2508.13095

At the Speed of the Heart: Evaluating Physiologically-Adaptive Visualizations for Supporting Engagement in Biking Exergaming in Virtual Reality
Many exergames face challenges in keeping users within safe and effective intensity levels during exercise. Meanwhile, although wearable devices continuously collect physiological data, this information is seldom leveraged for real-time adaptation or to encourage user reflection. We designed and evaluated a VR cycling simulator that dynamically adapts based on users' heart rate zones. First, we conducted a user study (N=50) comparing eight visualization designs to enhance engagement and exertio…

myspace_aminer: MySpace social graph
This network contains the social graph of MySpace, a social networking website which also has a strong music emphasis. A directed edge (i,j) means that user i follows user j.
This network has 854498 nodes and 6489736 edges.
Tags: Social, Online, Unweighted
https://netwo…

bitcoin_alpha: Bitcoin Alpha trust network (2017)
A network of who-trusts-whom relationships among users of the Bitcoin Alpha platform. Each directed edge (i,j,w) represents the rating of user j by user i, in which i assigns j a weight w on a scale of -10 (total distrust) to 10 (total trust) in steps of 1.
This network has 3783 nodes and 24186 edges.
Tags: Social, Online, Weighted, Signed, Timestamps

anybeat: Anybeat social network (2013)
A snapshot of the Anybeat online social network from 2013, before it was shut down. Nodes are users and links represent friendships. The edge direction (i, j) denotes the user i follows user j.
This network has 12645 nodes and 67053 edges.
Tags: Social, Online, Unweighted
https://ne…

Mind the Gap: Aligning Knowledge Bases with User Needs to Enhance Mental Health Retrieval
Amanda Chan, James Jiayu Liu, He Kai, Onno P. Kampman
https://arxiv.org/abs/2509.13626 …

Mind the Gap: Aligning Knowledge Bases with User Needs to Enhance Mental Health Retrieval
Access to reliable mental health information is vital for early help-seeking, yet expanding knowledge bases is resource-intensive and often misaligned with user needs. This results in poor performance of retrieval systems when presented concerns are not covered or expressed in informal or contextualized language. We present an AI-based gap-informed framework for corpus augmentation that authentically identifies underrepresented topics (gaps) by overlaying naturalistic user data such as forum po…

livejournal_aminer: Livejournal social graph
This network contains the social graph of Livejournal, a free on-line social network where users can keep a blog, journal or diary, where a directed edge (i,j) means that user i follows user j.
This network has 3017286 nodes and 87037567 edges.
Tags: Social, Online, Unweighted
https://…

foursquare: Foursquare NYC restaurants (2012)
Two bipartite networks of users and restaurant locations in New York City on Foursquare, from 24 October 2011 to 20 February 2012. In one network, an edge denotes a check-in event of a user at a restaurant. In the other, an edge exists if a user left a tip/comment on a restaurant. Metadata include comments.
This network has 6410 nodes and 10377 edges.
Tags: Social, Online, Unweighted, Metadata

Multi-Granularity Distribution Modeling for Video Watch Time Prediction via Exponential-Gaussian Mixture Network
Xu Zhao, Ruibo Ma, Jiaqi Chen, Weiqi Zhao, Ping Yang, Yao Hu
https://arxiv.org/abs/2508.12665

Multi-Granularity Distribution Modeling for Video Watch Time Prediction via Exponential-Gaussian Mixture Network
Accurate watch time prediction is crucial for enhancing user engagement in streaming short-video platforms, although it is challenged by complex distribution characteristics across multi-granularity levels. Through systematic analysis of real-world industrial data, we uncover two critical challenges in watch time prediction from a distribution aspect: (1) coarse-grained skewness induced by a significant concentration of quick-skips1, (2) fine-grained diversity arising from various user-video in…

A Large-Scale Web Search Dataset for Federated Online Learning to Rank
Marcel Gregoriadis, Jingwei Kang, Johan Pouwelse
https://arxiv.org/abs/2508.12353 https://

A Large-Scale Web Search Dataset for Federated Online Learning to Rank
The centralized collection of search interaction logs for training ranking models raises significant privacy concerns. Federated Online Learning to Rank (FOLTR) offers a privacy-preserving alternative by enabling collaborative model training without sharing raw user data. However, benchmarks in FOLTR are largely based on random partitioning of classical learning-to-rank datasets, simulated user clicks, and the assumption of synchronous client participation. This oversimplifies real-world dynami…

baseball: Baseball steroid use (2008)
Two networks representing steroid use among baseball players. First, a bipartite network of players and their steroid providers (of illegal performance-enhancing substances). Second, a one-mode projection of players, which are linked if they have a common supplier.
This network has 84 nodes and 84 edges.
Tags: Social, Offline, Weighted, Projection

baseball: Baseball steroid use (2008)
Two networks representing steroid use among baseball players. First, a bipartite network of players and their steroid providers (of illegal performance-enhancing substances). Second, a one-mode projection of players, which are linked if they have a common supplier.
This network has 84 nodes and 84 edges.
Tags: Social, Offline, Weighted, Projection

foursquare_friendships: Foursquare global friendships (2013)
A network of user friendships on Foursquare, from April 2012 to September 2013. The 'old' and 'new' dataset correspond to two snapshots taken before and after the check-in data collection period.
This network has 114324 nodes and 607333 edges.
Tags: Social, Online, Unweighted

foursquare_friendships: Foursquare global friendships (2013)
A network of user friendships on Foursquare, from April 2012 to September 2013. The 'old' and 'new' dataset correspond to two snapshots taken before and after the check-in data collection period.
This network has 114324 nodes and 363704 edges.
Tags: Social, Online, Unweighted

bibsonomy: BibSonomy
Three bipartite networks that make up the BibSonomy folksonomy, representing the tag-publication, user-publication, and user-tag networks. The date of this snapshot is uncertain.
This network has 972120 nodes and 2555080 edges.
Tags: Informational, Folksonomy, Unweighted, Multigraph, Timestamps
https://

edit_wiktionary: Wiktionary edits (2010)
Three bipartite user-page networks extracted from Wiktionary, for French, German, and English. A user connects to a page if that user edited that page. Edits (edges) are timestamped. Edge weights represent counts of the number of edits.
This network has 195 nodes and 204 edges.
Tags: Informational, Web graph, Multigraph, Timestamps

digg_votes: Digg story votes (2009)
A bipartite network between users and stories on digg.com from 2009. Each user node connects to all story nodes on which that user voted. Edges are timestamped.
This network has 142962 nodes and 3018197 edges.
Tags: Economic, Preferences, Multigraph, Timestamps
https://netwo…

jester: Jester joke ratings (2001)
Two bipartite networks of users and jokes, extracted from the online joke recommender system Jester. A user connects to all jokes for which that user entered a rating. Edge weights give the rating score, scaled from -10 to 10. The two files differ by how many joke nodes are included, 100 or 150.
This network has 73521 nodes and 4136360 edges.
Tags: Economic, Preferences, Weighted

jester: Jester joke ratings (2001)
Two bipartite networks of users and jokes, extracted from the online joke recommender system Jester. A user connects to all jokes for which that user entered a rating. Edge weights give the rating score, scaled from -10 to 10. The two files differ by how many joke nodes are included, 100 or 150.
This network has 73521 nodes and 4136360 edges.
Tags: Economic, Preferences, Weighted

myspace_aminer: MySpace social graph
This network contains the social graph of MySpace, a social networking website which also has a strong music emphasis. A directed edge (i,j) means that user i follows user j.
This network has 854498 nodes and 6489736 edges.
Tags: Social, Online, Unweighted
https://netwo…

bitcoin_alpha: Bitcoin Alpha trust network (2017)
A network of who-trusts-whom relationships among users of the Bitcoin Alpha platform. Each directed edge (i,j,w) represents the rating of user j by user i, in which i assigns j a weight w on a scale of -10 (total distrust) to 10 (total trust) in steps of 1.
This network has 3783 nodes and 24186 edges.
Tags: Social, Online, Weighted, Signed, Timestamps

slashdot_threads: Slashdot thread replies (2007)
A network of replies among users of the website Slashdot on the various discussion threads on the site. Nodes are user accounts and each directed edge represents a reply from user i to user j. Replies are timestamped.
This network has 51083 nodes and 140778 edges.
Tags: Social, Communication, Unweighted, Multigraph, Timestamps

lkml_thread: Linux kernel mailing list
A bipartite network of contributions by users to threads on the Linux kernel mailing list. A left node is a person, and a right node is a thread, and each timestamped edge (i,j,t) denotes that user i contributed to thread j at time t. The date of the snapshot is not given.
This network has 379554 nodes and 1565683 edges.
Tags: Social, Communication, Unweighted, Timestamps

lastfm_aminer: Last.fm social graph
This network contains the social graph of last.fm, a site that provides a streaming radio service, where users can search music and get personalized recommendation. A directed edge (i,j) means that user i follows user j.
This network has 136409 nodes and 1685524 edges.
Tags: Social, Online, Unweighted

epinions_trust: Epinions trust network (2003)
A who-trusts-whom online social network of the general consumer review site Epinions.com. Members can decide whether to "trust" each other. These trust relationships are combined with review ratings to determine which reviews are shown to the user.
This network has 75888 nodes and 508837 edges.
Tags: Social, Online, Unweighted

prosper: Prosper user loans (2016)
A network of loans between users on prosper.com, a pear-to-pear lending marketplace. Edge direction (i, j) denotes person i loaned money to person j.
This network has 89269 nodes and 3394979 edges.
Tags: Social, Online, Unweighted
https://networks.skewed.de/net/prosper

edit_wikinews: Wikipedia news edits (2010)
Two bipartite user-page networks extracted from Wikipedia, about news events. A user connects to a page if that user edited that page. Edits (edges) are timestamped. Edge weights represent counts of the number of edits.
This network has 61822 nodes and 686017 edges.
Tags: Informational, Web graph, Multigraph, Timestamps

lastfm: last.fm listening habits (2007)
Two bipartite networks from the music website last.fm, giving the user-song and user-band interactions. Nodes are users and songs/bands, and an edge (i,j) denotes that a user i listens to the song/band j.
This network has 1085612 nodes and 19150868 edges.
Tags: Social, Online, Unweighted
https://

lastfm: last.fm listening habits (2007)
Two bipartite networks from the music website last.fm, giving the user-song and user-band interactions. Nodes are users and songs/bands, and an edge (i,j) denotes that a user i listens to the song/band j.
This network has 1085612 nodes and 19150868 edges.
Tags: Social, Online, Unweighted
https://

bibsonomy: BibSonomy
Three bipartite networks that make up the BibSonomy folksonomy, representing the tag-publication, user-publication, and user-tag networks. The date of this snapshot is uncertain.
This network has 972120 nodes and 2555080 edges.
Tags: Informational, Folksonomy, Unweighted, Multigraph, Timestamps
https://

bibsonomy: BibSonomy
Three bipartite networks that make up the BibSonomy folksonomy, representing the tag-publication, user-publication, and user-tag networks. The date of this snapshot is uncertain.
This network has 972120 nodes and 2555080 edges.
Tags: Informational, Folksonomy, Unweighted, Multigraph, Timestamps
https://

jester: Jester joke ratings (2001)
Two bipartite networks of users and jokes, extracted from the online joke recommender system Jester. A user connects to all jokes for which that user entered a rating. Edge weights give the rating score, scaled from -10 to 10. The two files differ by how many joke nodes are included, 100 or 150.
This network has 73521 nodes and 4136360 edges.
Tags: Economic, Preferences, Weighted

twitter: Twitter followers (2010)
A directed network of following relationships from Twitter, from a snowball sample crawl across "quality" users in 2009. A directed edge (i, j) indicates that user i follows user j.
This network has 465017 nodes and 834797 edges.
Tags: Social, Online, Unweighted
https://network…

anybeat: Anybeat social network (2013)
A snapshot of the Anybeat online social network from 2013, before it was shut down. Nodes are users and links represent friendships. The edge direction (i, j) denotes the user i follows user j.
This network has 12645 nodes and 67053 edges.
Tags: Social, Online, Unweighted
https://ne…

movielens_100k: MovieLens 100K (1998)
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies. These data capture the tag-movie, user-movie, and user-tag networks. (Also available from MovieLens are 1M, 10M and 20M folksonomy datasets.).
This network has 24129 nodes and 95580 edges.
Tags: Informational, Folksonomy, Unweighted, Multigraph, Timestamps

digg_reply: Digg reply network (2008)
Network of replies among users of digg.com. Each node in the network is a digg user, and each directed edge indicates that user i replied to user j.
This network has 30398 nodes and 87627 edges.
Tags: Social, Online, Unweighted, Multigraph
https://networks.skewed.de/net/di…