Tootfinder

Packers acquire Cowboys star Micah Parsons: Breaking down the trade using PFF data https://www.pff.com/news/packers-acquire-cowboys-star-micah-parsons-breaking-down-the-trade-using-pff-data

Enhancing Speech Emotion Recognition Leveraging Aligning Timestamps of ASR Transcripts and Speaker Diarization
Hsuan-Yu Wang, Pei-Ying Lee, Berlin Chen
https://arxiv.org/abs/2507.19356

Enhancing Speech Emotion Recognition Leveraging Aligning Timestamps of ASR Transcripts and Speaker Diarization
In this paper, we investigate the impact of incorporating timestamp-based alignment between Automatic Speech Recognition (ASR) transcripts and Speaker Diarization (SD) outputs on Speech Emotion Recognition (SER) accuracy. Misalignment between these two modalities often reduces the reliability of multimodal emotion recognition systems, particularly in conversational contexts. To address this issue, we introduce an alignment pipeline utilizing pre-trained ASR and speaker diarization models, syste…

Canceled the Google account for my small business... Less money going to AI bullshit and Google's evil practices.
And I let them know.
(I had to click through a lot of screens where they tried to get me to stay, offered discounts, or offered to archive data for a few dollars a month. No thanks.)
#noAI

Em Portugal, provavelmente, também vamos ter de começar pela administração local.
Talvez seja algo que valha a pena pôr na agenda agora em algumas das corridas autšrquicas.
https://mastodon.cloud/@OpenForumEurope/114936538200485234

OpenForum Europe (@OpenForumEurope@mastodon.cloud)
Attached: 1 image The City of Lyon is transitioning from proprietary to open source software, supporting digital independence, sustainable IT, and aligning with EU public sector trends. Read the full article: https://interoperable-europe.ec.europa.eu/collection/open-source-observatory-osor/news/municipality-lyon-moves-towards-open-source

1. Order two bags of cat litter.
2. The seller doesn't send it on time. No matter, you're not in a hurry.
3. Recall that they still didn't send it. Decide to resign.
4. Turns out they send it just before you resign.
5. The parcels come. Open the box.
6. Discover that the bag is damaged. Take a photo just in case.
7. Carefully take it out and check how damaged it is. Well, it's only ripped at the top, no point in returning it over that.
8. Notice they've sent the wrong litter (you've ordered non-scented) 🤦.
9. Call them asking whether there's a point in opening the other box. They say it's going to be the wrong litter too, just make a return request and attach photos.
10. You do that, requesting money return. They request you to make damage protocol from delivery company.
11. You do that. Maybe you'd agree to a 20 PLN discount for next shopping instead of returning two bags of the wrong cat litter?
12. They finally accept the return. And they prepare the return… for one bag 🤦.
So I was supposed to work less today. Instead, I've wasted a lot of time on dealing with damaged parcel and the vendor. And a lot of tape, so it wouldn't fall apart on the return journey.

Exploring the Use of LLMs for Requirements Specification in an IT Consulting Company
Liliana Pasquale, Azzurra Ragone, Emanuele Piemontese, Armin Amiri Darban
https://arxiv.org/abs/2507.19113

Exploring the Use of LLMs for Requirements Specification in an IT Consulting Company
In practice, requirements specification remains a critical challenge. The knowledge necessary to generate a specification can often be fragmented across diverse sources (e.g., meeting minutes, emails, and high-level product descriptions), making the process cumbersome and time-consuming. In this paper, we report our experience using large language models (LLMs) in an IT consulting company to automate the requirements specification process. In this company, requirements are specified using a Fun…

Does Calibration Affect Human Actions?
Meir Nizri, Amos Azaria, Chirag Gupta, Noam Hazon
https://arxiv.org/abs/2508.18317 https://arxiv.org/pdf/2508.18317

Does Calibration Affect Human Actions?
Calibration has been proposed as a way to enhance the reliability and adoption of machine learning classifiers. We study a particular aspect of this proposal: how does calibrating a classification model affect the decisions made by non-expert humans consuming the model's predictions? We perform a Human-Computer-Interaction (HCI) experiment to ascertain the effect of calibration on (i) trust in the model, and (ii) the correlation between decisions and predictions. We also propose further correct…

Roux schemes which carry association schemes locally
Alexander L. Gavrilyuk, Jesse Lansdown, Akihiro Munemasa, Sho Suda
https://arxiv.org/abs/2507.18960 https://

Roux schemes which carry association schemes locally
A roux scheme is an association scheme formed from a special "roux" matrix and the regular permutation representation of an associated group. They were introduced by Iverson and Mixon for their connection to equiangular tight frames and doubly transitive lines. We show how roux matrices can be produced from association schemes and characterise roux schemes for which the neighbourhood of a vertex induces an association scheme possessing the same number of relations as the thin radical. An import…

FMimic: Foundation Models are Fine-grained Action Learners from Human Videos
Guangyan Chen, Meiling Wang, Te Cui, Yao Mu, Haoyang Lu, Zicai Peng, Mengxiao Hu, Tianxing Zhou, Mengyin Fu, Yi Yang, Yufeng Yue
https://arxiv.org/abs/2507.20622

FMimic: Foundation Models are Fine-grained Action Learners from Human Videos
Visual imitation learning (VIL) provides an efficient and intuitive strategy for robotic systems to acquire novel skills. Recent advancements in foundation models, particularly Vision Language Models (VLMs), have demonstrated remarkable capabilities in visual and linguistic reasoning for VIL tasks. Despite this progress, existing approaches primarily utilize these models for learning high-level plans from human demonstrations, relying on pre-defined motion primitives for executing physical inte…

Benchmarking Training Paradigms, Dataset Composition, and Model Scaling for Child ASR in ESPnet
Anyu Ying, Natarajan Balaji Shankar, Chyi-Jiunn Lin, Mohan Shi, Pu Wang, Hye-jin Shim, Siddhant Arora, Hugo Van hamme, Abeer Alwan, Shinji Watanabe
https://arxiv.org/abs/2508.16576

Benchmarking Training Paradigms, Dataset Composition, and Model Scaling for Child ASR in ESPnet
Despite advancements in ASR, child speech recognition remains challenging due to acoustic variability and limited annotated data. While fine-tuning adult ASR models on child speech is common, comparisons with flat-start training remain underexplored. We compare flat-start training across multiple datasets, SSL representations (WavLM, XEUS), and decoder architectures. Our results show that SSL representations are biased toward adult speech, with flat-start training on child speech mitigating the…