Tootfinder

One way to spend time instead of #doomscrolling has recentlt been #codewars
Bitesized problems of different difficulties for almost any language.
Not as big as #adventofcode

I'm trying out the Dusa programming language (https://dusa.rocks/), so I made a baby 4x4 Sudoku.
It was fun. ^_^
I'd love some array/list functionality; I wouldn't want to tackle a regular 9x9 Sudoku through copy-pasta. But I appreciate how easy this was with the `forbid` directive.

"My vision of a world with computers is a world in which people have a lot more time to do what they like. Playing tennis, jogging… they’ll have plenty of time to go to the shore. I’d go to the library. I could do my work at home. I could have a computer at home and talk to my office. I could live up on top of a nice mountain in New Hampshire and smell pine trees and it would be the same as if I were here in the sub-sub-subbasement of the Pentagon."
– Grace Hopper

Grace Hopper & David Letterman
Among the many documents available at the Computer History Museum website there is an interesting artifact: a commercial brochure published in 1957 by the Remington Rand UNIVAC, "a division of Sperry Rand Corporation", titled "introducing a new language for automatic programming". In it, we learn about the advantages of the new (at the time) FLOW-MATIC programming language, the brainchild of United States Navy Rear Admiral Grace Murray Hopper.

This https://arxiv.org/abs/2412.01753 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Human-Machine Interfaces for Subsea Telerobotics: From Soda-straw to Natural Language Interactions
This review explores the evolution of human-machine interfaces (HMIs) in subsea telerobotics, charting the progression from traditional first-person "soda-straw" consoles -- characterized by narrow field-of-view camera feeds -- to contemporary interfaces leveraging gesture recognition, virtual reality, and natural language processing. We systematically analyze the state-of-the-art literature through three interrelated perspectives: operator experience (including immersive feedback, cognitive wo…

This https://arxiv.org/abs/2408.02211 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGR_…

SceneMotifCoder: Example-driven Visual Program Learning for Generating 3D Object Arrangements
Despite advances in text-to-3D generation methods, generation of multi-object arrangements remains challenging. Current methods exhibit failures in generating physically plausible arrangements that respect the provided text description. We present SceneMotifCoder (SMC), an example-driven framework for generating 3D object arrangements through visual program learning. SMC leverages large language models (LLMs) and program synthesis to overcome these challenges by learning visual programs from ex…

This https://arxiv.org/abs/2505.01136 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Descriptor: C++ Self-Admitted Technical Debt Dataset (CppSATD)
In software development, technical debt (TD) refers to suboptimal implementation choices made by the developers to meet urgent deadlines and limited resources, posing challenges for future maintenance. Self-Admitted Technical Debt (SATD) is a sub-type of TD, representing specific TD instances ``openly admitted'' by the developers and often expressed through source code comments. Previous research on SATD has focused predominantly on the Java programming language, revealing a significant gap in …

Fairness Dynamics During Training
Krishna Patel, Nivedha Sivakumar, Barry-John Theobald, Luca Zappella, Nicholas Apostoloff
https://arxiv.org/abs/2506.01709

Fairness Dynamics During Training
We investigate fairness dynamics during Large Language Model (LLM) training to enable the diagnoses of biases and mitigations through training interventions like early stopping; we find that biases can emerge suddenly and do not always follow common performance metrics. We introduce two new metrics to evaluate fairness dynamics holistically during model pre-training: Average Rank and Jensen-Shannon Divergence by Parts. These metrics provide insights into the Pythia models' progression of biases…

LEGO-Compiler: Enhancing Neural Compilation Through Translation Composability
Shuoming Zhang, Jiacheng Zhao, Chunwei Xia, Zheng Wang, Yunji Chen, Xiaobing Feng, Huimin Cui
https://arxiv.org/abs/2505.20356

LEGO-Compiler: Enhancing Neural Compilation Through Translation Composability
Large language models (LLMs) have the potential to revolutionize how we design and implement compilers and code translation tools. However, existing LLMs struggle to handle long and complex programs. We introduce LEGO-Compiler, a novel neural compilation system that leverages LLMs to translate high-level languages into assembly code. Our approach centers on three key innovations: LEGO translation, which decomposes the input program into manageable blocks; breaking down the complex compilation p…

Correlating instruction-tuning (in multimodal models) with vision-language processing (in the brain)
Subba Reddy Oota, Akshett Jindal, Ishani Mondal, Khushbu Pahwa, Satya Sai Srinath Namburi, Manish Shrivastava, Maneesh Singh, Bapi S. Raju, Manish Gupta
https://arxiv.org/abs/2505.20029

Correlating instruction-tuning (in multimodal models) with vision-language processing (in the brain)
Transformer-based language models, though not explicitly trained to mimic brain recordings, have demonstrated surprising alignment with brain activity. Progress in these models-through increased size, instruction-tuning, and multimodality-has led to better representational alignment with neural data. Recently, a new class of instruction-tuned multimodal LLMs (MLLMs) have emerged, showing remarkable zero-shot capabilities in open-ended multimodal vision tasks. However, it is unknown whether MLLM…

This https://arxiv.org/abs/2505.15004 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

EASY: Emotion-aware Speaker Anonymization via Factorized Distillation
Emotion plays a significant role in speech interaction, conveyed through tone, pitch, and rhythm, enabling the expression of feelings and intentions beyond words to create a more personalized experience. However, most existing speaker anonymization systems employ parallel disentanglement methods, which only separate speech into linguistic content and speaker identity, often neglecting the preservation of the original emotional state. In this study, we introduce EASY, an emotion-aware speaker an…