Tootfinder

"Bits & Böses": Wenn der beste Freund zum digitalen Stalker wird
In der zweiten Folge des heise-Podcasts erzählt eine junge Frau davon, wie aus einer Freundschaft ein Albtraum für sie und ihre Familie wurde.

Alleviating Attack Data Scarcity: SCANIA's Experience Towards Enhancing In-Vehicle Cyber Security Measures
Frida Sundfeldt, Bianca Widstam, Mahshid Helali Moghadam, Kuo-Yun Liang, Anders Vesterberg
https://arxiv.org/abs/2507.02607

Alleviating Attack Data Scarcity: SCANIA's Experience Towards Enhancing In-Vehicle Cyber Security Measures
The digital evolution of connected vehicles and the subsequent security risks emphasize the critical need for implementing in-vehicle cyber security measures such as intrusion detection and response systems. The continuous advancement of attack scenarios further highlights the need for adaptive detection mechanisms that can detect evolving, unknown, and complex threats. The effective use of ML-driven techniques can help address this challenge. However, constraints on implementing diverse attack…

My greatest act of anti-consumerism to date: My decade-old printer is still working (albeit with some hiccups) and I refuse to buy a new one.
Though I guess using Linux instead of Windows also counts here...

Oh great, now we’ll be reliant on the Chinese for sodium and iron. \s https://fed.brid.gy/r/https://bsky.app/profile/did:plc:twjze4qqf6fgxz33ct52wlnl/post/3lsxhwuota22g

Finished "Far from the Tree" by Robin Benway last night. For me, it was a gripping and absolutely delightful read. I can recognize that I'm absolutely unequipped to judge whether it authentically reflects the experiences of many or few of the people whose situations are similar to the protagonists', but I find myself fervently hopeful for the former, even as I recognize such hopes in the part of people like me can be part of ultimately harmful publishing selection feedback loops. (If anyone reading this happens to have read the book and found it distasteful, I'd love to hear that.) Notwithstanding my probably overcautious uncertainty, I'll definitely be seeking out more books by Benway. At the very least, I really enjoyed her little joining and framing metaphors, and the pacing and plot construction were good, with lots of nicely interwoven implications arising through the buildup, and an ending that left me satisfied (albeit, I'm an inveterate optimist, YMMV).
#AmReading

This https://arxiv.org/abs/2505.11396 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Finding Counterfactual Evidences for Node Classification
Counterfactual learning is emerging as an important paradigm, rooted in causality, which promises to alleviate common issues of graph neural networks (GNNs), such as fairness and interpretability. However, as in many real-world application domains where conducting randomized controlled trials is impractical, one has to rely on available observational (factual) data to detect counterfactuals. In this paper, we introduce and tackle the problem of searching for counterfactual evidences for the GNN…

No time to train! Training-Free Reference-Based Instance Segmentation
Miguel Espinosa, Chenhongyi Yang, Linus Ericsson, Steven McDonagh, Elliot J. Crowley
https://arxiv.org/abs/2507.02798

No time to train! Training-Free Reference-Based Instance Segmentation
The performance of image segmentation models has historically been constrained by the high cost of collecting large-scale annotated data. The Segment Anything Model (SAM) alleviates this original problem through a promptable, semantics-agnostic, segmentation paradigm and yet still requires manual visual-prompts or complex domain-dependent prompt-generation rules to process a new image. Towards reducing this new burden, our work investigates the task of object segmentation when provided with, al…

This https://arxiv.org/abs/2504.00420 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Think Small, Act Big: Primitive Prompt Learning for Lifelong Robot Manipulation
Building a lifelong robot that can effectively leverage prior knowledge for continuous skill acquisition remains significantly challenging. Despite the success of experience replay and parameter-efficient methods in alleviating catastrophic forgetting problem, naively applying these methods causes a failure to leverage the shared primitives between skills. To tackle these issues, we propose Primitive Prompt Learning (PPL), to achieve lifelong robot manipulation via reusable and extensible primi…

Computational Thinking Reasoning in Large Language Models
Kechi Zhang, Ge Li, Jia Li, Huangzhao Zhang, Jingjing Xu, Hao Zhu, Lecheng Wang, Jia Li, Yihong Dong, Jing Mai, Bin Gu, Zhi Jin
https://arxiv.org/abs/2506.02658

Computational Thinking Reasoning in Large Language Models
While large language models (LLMs) have demonstrated remarkable reasoning capabilities, they often struggle with complex tasks that require specific thinking paradigms, such as divide-and-conquer and procedural deduction, \etc Previous researches integrate external, reliable tools to alleviate logical inconsistencies and hallucinations in LLMs' problem-solving processes. However, we argue that the root challenge is more profound: LLMs lack the complex thinking paradigms (\ie, computational thin…

MLorc: Momentum Low-rank Compression for Large Language Model Adaptation
Wei Shen, Yaxiang Zhang, Minhui Huang, Mengfan Xu, Jiawei Zhang, Cong Shen
https://arxiv.org/abs/2506.01897

MLorc: Momentum Low-rank Compression for Large Language Model Adaptation
With increasing size of large language models (LLMs), full-parameter fine-tuning imposes substantial memory demands. To alleviate this, we propose a novel memory-efficient training paradigm called Momentum Low-rank compression (MLorc). By directly compressing and reconstructing momentum rather than gradients, MLorc avoids imposing a fixed-rank constraint on weight update matrices and better preserves the training dynamics of full-parameter fine-tuning, in contrast to existing low-rank approache…