Tootfinder

Conducting Mission-Critical Voice Experiments with Automated Speech Recognition and Crowdsourcing
Jan Janak, Kahlil Dozier, Lauren Berny, Liang Hu, Dan Rubenstein, Charles Jennings, Henning Schulzrinne
https://arxiv.org/abs/2509.13724

Conducting Mission-Critical Voice Experiments with Automated Speech Recognition and Crowdsourcing
Mission-critical voice (MCV) communications systems have been a critical tool for the public safety community for over eight decades. Public safety users expect MCV systems to operate reliably and consistently, particularly in challenging conditions. Because of these expectations, the Public Safety Communications Research (PSCR) Division of the National Institute of Standards and Technology (NIST) has been interested in correlating impairments in MCV communication systems and public safety user…

✅ 𝗗𝗢𝗡𝗘 #EveryNameCounts 135.000 von 135.000 erfasst - DP 3 Karten* (Nachnamen mit "𝐒" und "𝗭") ✊🏋️
Weiter geht`s mit "𝗦" und "𝗭" (Teil 2, noch mal 135.000). 📑🔛
📌👀 𝗙𝗿𝗶𝗲𝗻𝗱𝗹𝘆 𝗥𝗲𝗺𝗶𝗻𝗱𝗲𝗿: Macht ihr schon|noch|wieder|mal mit? 💚 Datenerfassung für ein digitales Denkmal der Opfer Überlebenden des NS im

Machine-learning competition to grade EEG background patterns in newborns with hypoxic-ischaemic encephalopathy
Fabio Magarelli, Geraldine B. Boylan, Saeed Montazeri, Feargal O'Sullivan, Dominic Lightbody, Minoo Ashoori, Tamara Skoric Ceranic, John M. O'Toole
https://arxiv.org/abs/2509.09695

Machine-learning competition to grade EEG background patterns in newborns with hypoxic-ischaemic encephalopathy
Machine learning (ML) has the potential to support and improve expert performance in monitoring the brain function of at-risk newborns. Developing accurate and reliable ML models depends on access to high-quality, annotated data, a resource in short supply. ML competitions address this need by providing researchers access to expertly annotated datasets, fostering shared learning through direct model comparisons, and leveraging the benefits of crowdsourcing diverse expertise. We compiled a retro…

One for the speech-to-text / Automatic Speech Recognition #AI4LAM folk: 'Building an Accurate Open-Source Hebrew ASR System through Crowdsourcing' https://www.isca-archive.org/interspeech_2

What Makes a Visualization Complex?
Mengdi Chu, Zefeng Qiu, Meng Ling, Shuning Jiang, Robert S. Laramee, Michael Sedlmair, Jian Chen
https://arxiv.org/abs/2510.08332 https://

What Makes a Visualization Complex?
We investigate the perceived visual complexity (VC) in data visualizations using objective image-based metrics. We collected VC scores through a large-scale crowdsourcing experiment involving 349 participants and 1,800 visualization images. We then examined how these scores align with 12 image-based metrics spanning information-theoretic, clutter, color, and our two object-based metrics. Our results show that both low-level image properties and the high-level elements affect perceived VC in vis…

I've blogged about how I'm using #FreshRSS to get full-text #RSS feeds – and about crowdsourcing configs that will allow folks to subscribe to more things thanks to the web scraping feature!
https://tzovar.as/fulltext-freshrss/
(Responses to this toot will also become blog comments)

PolyLink: A Blockchain Based Decentralized Edge AI Platform for LLM Inference
Hongbo Liu, Jiannong Cao, Bo Yang, Dongbin Bai, Yinfeng Cao, Xiaoming Shen, Yinan Zhang, Jinwen Liang, Shan Jiang, Mingjin Zhang
https://arxiv.org/abs/2510.02395

PolyLink: A Blockchain Based Decentralized Edge AI Platform for LLM Inference
The rapid advancement of large language models (LLMs) in recent years has revolutionized the AI landscape. However, the deployment model and usage of LLM services remain highly centralized, creating significant trust issues and costs for end users and developers. To address these issues, we propose PolyLink, a blockchain-based decentralized AI platform that decentralizes LLM development and inference. Specifically, PolyLink introduces a decentralized crowdsourcing architecture that supports sin…

Crowdsourcing Star-Formation Research and the Power of Participatory Science
Grace Wolf-Chase (Planetary Science Institute), Charles Kerton (Iowa State University), Kathryn Devine (The College of Idaho), Nicholas Larose (Iowa State University), Maya Coleman (The College of Idaho)
https://arxiv.org/abs/2508.14188

Crowdsourcing Star-Formation Research and the Power of Participatory Science
We review participatory science programs that have contributed to the understanding of star formation. The Milky Way Project (MWP), one of the earliest participatory science projects launched on the Zooniverse platform, produced the largest catalog of ``bubbles'' associated with feedback from hot young stars to date, and enabled the identification of a new class of compact star-forming regions (SFRs) known as ``yellowballs'' (YBs). The analysis of YBs through their infrared colors and catalog c…

Comparative Study of Subjective Video Quality Assessment Test Methods in Crowdsourcing for Varied Use Cases
Babak Naderi, Ross Cutler
https://arxiv.org/abs/2509.20118 https://…

Comparative Study of Subjective Video Quality Assessment Test Methods in Crowdsourcing for Varied Use Cases
In crowdsourced subjective video quality assessment, practitioners often face a choice between Absolute Category Rating (ACR), ACR with Hidden Reference (ACR-HR), and Comparison Category Rating (CCR). We conducted a P.910-compliant, side-by-side comparison across six studies using 15 talking-head sources of good and fair quality, processed with realistic degradations (blur, scaling, compression, freezing, and their combinations), as well as a practical bitrate-ladder task at 720p and 1080p reso…

Bubble, Bubble, AI's Rumble: Why Global Financial Regulatory Incident Reporting is Our Shield Against Systemic Stumbles
Anchal Gupta, Gleb Pappyshev, James T Kwok
https://arxiv.org/abs/2509.26150

Bubble, Bubble, AI's Rumble: Why Global Financial Regulatory Incident Reporting is Our Shield Against Systemic Stumbles
"Double, double toil and trouble; Fire burn and cauldron bubble." As Shakespeare's witches foretold chaos through cryptic prophecies, modern capital markets grapple with systemic risks concealed by opaque AI systems. According to IMF, the August 5, 2024, plunge in Japanese and U.S. equities can be linked to algorithmic trading yet ab-sent from existing AI incidents database exemplifies this transparency crisis. Current AI incident databases, reliant on crowdsourcing or news scraping, systematic…

Crosslisted article(s) found for stat.ME. https://arxiv.org/list/stat.ME/new
[1/1]:
- Crowdsourcing Without People: Modelling Clustering Algorithms as Experts
Jordyn E. A. Lorentz, Katharine M. Clark

Ensuring Reliable Participation in Subjective Video Quality Tests Across Platforms
Babak Naderi, Ross Cutler
https://arxiv.org/abs/2509.20001 https://arxiv…

Ensuring Reliable Participation in Subjective Video Quality Tests Across Platforms
Subjective video quality assessment (VQA) is the gold standard for measuring end-user experience across communication, streaming, and UGC pipelines. Beyond high-validity lab studies, crowdsourcing offers accurate, reliable, faster, and cheaper evaluation-but suffers from unreliable submissions by workers who ignore instructions or game rewards. Recent tests reveal sophisticated exploits of video metadata and rising use of remote-desktop (RD) connections, both of which bias results. We propose o…