Tootfinder

Automatic Speech Recognition (ASR) for African Low-Resource Languages: A Systematic Literature Review
Sukairaj Hafiz Imam, Tadesse Destaw Belay, Kedir Yassin Husse, Ibrahim Said Ahmad, Idris Abdulmumin, Hadiza Ali Umar, Muhammad Yahuza Bello, Joyce Nakatumba-Nabende, Seid Muhie Yimam, Shamsuddeen Hassan Muhammad
https://arxiv.org/abs/2510.…

Automatic Speech Recognition (ASR) for African Low-Resource Languages: A Systematic Literature Review
ASR has achieved remarkable global progress, yet African low-resource languages remain rigorously underrepresented, producing barriers to digital inclusion across the continent with more than +2000 languages. This systematic literature review (SLR) explores research on ASR for African languages with a focus on datasets, models and training methods, evaluation techniques, challenges, and recommends future directions. We employ the PRISMA 2020 procedures and search DBLP, ACM Digital Library, Goog…

Dynamic Function Configuration and its Management in Serverless Computing: A Taxonomy and Future Directions
Siddharth Agarwal, Maria A. Rodriguez, Rajkumar Buyya
https://arxiv.org/abs/2510.02404

Dynamic Function Configuration and its Management in Serverless Computing: A Taxonomy and Future Directions
The serverless cloud computing model offers a framework where the service provider abstracts the underlying infrastructure management from developers. In this serverless model, FaaS provides an event-driven, function-oriented computing service characterised by fine-grained, usage-based pricing that eliminates cost for idle resources. Platforms like AWS Lambda, Azure Functions, and Cloud Run Functions require developers to configure their function(s) with minimum operational resources for its su…

WenetSpeech-Yue: A Large-scale Cantonese Speech Corpus with Multi-dimensional Annotation
Longhao Li, Zhao Guo, Hongjie Chen, Yuhang Dai, Ziyu Zhang, Hongfei Xue, Tianlun Zuo, Chengyou Wang, Shuiyuan Wang, Jie Li, Xin Xu, Hui Bu, Binbin Zhang, Ruibin Yuan, Ziya Zhou, Wei Xue, Lei Xie
https://arxiv.org/abs/2509.03959

WenetSpeech-Yue: A Large-scale Cantonese Speech Corpus with Multi-dimensional Annotation
The development of speech understanding and generation has been significantly accelerated by the availability of large-scale, high-quality speech datasets. Among these, ASR and TTS are regarded as the most established and fundamental tasks. However, for Cantonese (Yue Chinese), spoken by approximately 84.9 million native speakers worldwide, limited annotated resources have hindered progress and resulted in suboptimal ASR and TTS performance. To address this challenge, we propose WenetSpeech-Pip…

Day 8 (a bit late): Timnit Gebru
Academic authors are authors too, and there are a bunch of people I deeply respect both in my fields and adjacent.
Gebru is someone I have huge respect for because she stood up for her (mild, completely reasonable) principles to the point of losing her job on Google's AI ethics team (since disbanded entirely), and then went ahead and founded an independent research institute to continue doing AI ethics research.
Why was she fired? Because she insisted on publishing her "Stochastic Parrots" paper after it passed Google internal review only to have extra nonstandard scrutiny applied at the last minute. Why did Google want to suppress her paper (which included an academic co-author)? Because it expressed valid criticisms of the large language models fad, and Google was planning to make money off that fad. Personally, I don't think I'd hire an "AI ethics" team only to then try to suppress their publications, and Google seems to now agree, having scrapped the team (during the initial furor, Timnit's boss also effectively quit to support her).
That "Stochastic Parrots" paper? Indeed, it predicts the core underlying problems with large language models that lead to so many of their user-side harms today. You can read it here: #20AuthorsNoMen

What happens when stakeholders along the #supply chain join forces to eliminate #genderbased violence? Find out how investigations by an NGO led to a unique labor impact story in my latest feature for Triple Pundit.

Benchmarking Machine Learning Models for Fault Classification and Localization in Power System Protection
Julian Oelhaf, Georg Kordowich, Changhun Kim, Paula Andrea P\'erez-Toro, Christian Bergler, Andreas Maier, Johann J\"ager, Siming Bayer
https://arxiv.org/abs/2510.00831

Benchmarking Machine Learning Models for Fault Classification and Localization in Power System Protection
The increasing integration of distributed energy resources (DERs), particularly renewables, poses significant challenges for power system protection, with fault classification (FC) and fault localization (FL) being among the most critical tasks. Conventional protection schemes, based on fixed thresholds, cannot reliably identify and localize short circuits with the increasing complexity of the grid under dynamic conditions. Machine learning (ML) offers a promising alternative; however, systemat…

A Unified Denoising and Adaptation Framework for Self-Supervised Bengali Dialectal ASR
Swadhin Biswas, Imran, Tuhin Sheikh
https://arxiv.org/abs/2509.00988 https://

A Unified Denoising and Adaptation Framework for Self-Supervised Bengali Dialectal ASR
Automatic Speech Recognition (ASR) for Bengali, the world's fifth most spoken language, remains a significant challenge, critically hindering technological accessibility for its over 270 million speakers. This challenge is compounded by two persistent and intertwined factors: the language's vast dialectal diversity and the prevalence of acoustic noise in real-world environments. While state-of-the-art self-supervised learning (SSL) models have advanced ASR for low-resource languages, they often…

Automatic Building Code Review: A Case Study
Hanlong Wan, Weili Xu, Michael Rosenberg, Jian Zhang, Aysha Siddika
https://arxiv.org/abs/2510.02634 https://a…

Automatic Building Code Review: A Case Study
Building officials, particularly those in resource-constrained or rural jurisdictions, face labor-intensive, error-prone, and costly manual reviews of design documents as projects increase in size and complexity. The growing adoption of Building Information Modeling (BIM) and Large Language Models (LLMs) presents opportunities for automated code review (ACR) solutions. This study introduces a novel agent-driven framework that integrates BIM-based data extraction with automated verification usin…

Regression Language Models for Code
Yash Akhauri, Xingyou Song, Arissa Wongpanich, Bryan Lewandowski, Mohamed S. Abdelfattah
https://arxiv.org/abs/2509.26476 https://

Regression Language Models for Code
We study code-to-metric regression: predicting numeric outcomes of code executions, a challenging task due to the open-ended nature of programming languages. While prior methods have resorted to heavy and domain-specific feature engineering, we show that a single unified Regression Language Model (RLM) can simultaneously predict directly from text, (i) the memory footprint of code across multiple high-level languages such as Python and C++, (ii) the latency of Triton GPU kernels, and (iii) the …

Model Merging to Maintain Language-Only Performance in Developmentally Plausible Multimodal Models
Ece Takmaz, Lisa Bylinina, Jakub Dotlacil
https://arxiv.org/abs/2510.01845 htt…

Model Merging to Maintain Language-Only Performance in Developmentally Plausible Multimodal Models
State-of-the-art vision-and-language models consist of many parameters and learn from enormous datasets, surpassing the amounts of linguistic data that children are exposed to as they acquire a language. This paper presents our approach to the multimodal track of the BabyLM challenge addressing this discrepancy. We develop language-only and multimodal models in low-resource settings using developmentally plausible datasets, with our multimodal models outperforming previous BabyLM baselines. One…