Tootfinder

from my link log —
ucs-detect: automatically test the Unicode version and support level of a terminal emulator.
https://ucs-detect.readthedocs.io/
saved 2025-11-15 https:/…

When Personalization Tricks Detectors: The Feature-Inversion Trap in Machine-Generated Text Detection
Lang Gao, Xuhui Li, Chenxi Wang, Mingzhe Li, Wei Liu, Zirui Song, Jinghui Zhang, Rui Yan, Preslav Nakov, Xiuying Chen
https://arxiv.org/abs/2510.12476

When Personalization Tricks Detectors: The Feature-Inversion Trap in Machine-Generated Text Detection
Large language models (LLMs) have grown more powerful in language generation, producing fluent text and even imitating personal style. Yet, this ability also heightens the risk of identity impersonation. To the best of our knowledge, no prior work has examined personalized machine-generated text (MGT) detection. In this paper, we introduce \dataset, the first benchmark for evaluating detector robustness in personalized settings, built from literary and blog texts paired with their LLM-generated…

movielens_100k: MovieLens 100K (1998)
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies. These data capture the tag-movie, user-movie, and user-tag networks. (Also available from MovieLens are 1M, 10M and 20M folksonomy datasets.).
This network has 24129 nodes and 95580 edges.
Tags: Informational, Folksonomy, Unweighted, Multigraph, Timestamps

Attack-Specialized Deep Learning with Ensemble Fusion for Network Anomaly Detection
Nisith Dissanayake (University of Moratuwa), Uthayasanker Thayasivam (University of Moratuwa)
https://arxiv.org/abs/2510.12455

Attack-Specialized Deep Learning with Ensemble Fusion for Network Anomaly Detection
The growing scale and sophistication of cyberattacks pose critical challenges to network security, particularly in detecting diverse intrusion types within imbalanced datasets. Traditional intrusion detection systems (IDS) often struggle to maintain high accuracy across both frequent and rare attacks, leading to increased false negatives for minority classes. To address this, we propose a hybrid anomaly detection framework that integrates specialized deep learning models with an ensemble meta-c…

KoALA: KL-L0 Adversarial Detector via Label Agreement
Siqi Li, Yasser Shoukry
https://arxiv.org/abs/2510.12752 https://arxiv.org/pdf/2510.12752

KoALA: KL-L0 Adversarial Detector via Label Agreement
Deep neural networks are highly susceptible to adversarial attacks, which pose significant risks to security- and safety-critical applications. We present KoALA (KL-L0 Adversarial detection via Label Agreement), a novel, semantics-free adversarial detector that requires no architectural changes or adversarial retraining. KoALA operates on a simple principle: it detects an adversarial attack when class predictions from two complementary similarity metrics disagree. These metrics-KL divergence an…

BeSTAD: Behavior-Aware Spatio-Temporal Anomaly Detection for Human Mobility Data
Junyi Xie, Jina Kim, Yao-Yi Chiang, Lingyi Zhao, Khurram Shafique
https://arxiv.org/abs/2510.12076

BeSTAD: Behavior-Aware Spatio-Temporal Anomaly Detection for Human Mobility Data
Traditional anomaly detection in human mobility has primarily focused on trajectory-level analysis, identifying statistical outliers or spatiotemporal inconsistencies across aggregated movement traces. However, detecting individual-level anomalies, i.e., unusual deviations in a person's mobility behavior relative to their own historical patterns, within datasets encompassing large populations remains a significant challenge. In this paper, we present BeSTAD (Behavior-aware Spatio-Temporal Anoma…

Detection of quantum information masking via machine learning
Sheng-Ao Mao, Lin Zhang, Bo Li
https://arxiv.org/abs/2510.12507 https://arxiv.org/pdf/2510.12…

Detection of quantum information masking via machine learning
Recently, machine learning has been widely applied in the field of quantum information, notably in tasks such as entanglement detection, steering characterization, and nonlocality verification. However, few studies have focused on utilizing machine learning to detect quantum information masking. In this work, we investigate supervised machine learning for detecting quantum information masking in both pure and mixed qubit states. For pure qubit states, we randomly generate the corresponding dens…

Pristine ices in a planet-forming disk revealed by heavy water: #PlanetForming disk: https://public.nrao.edu/news/first-ever-detection-of-heavy-water-in-a-planet-forming-disk/ - new ALMA data traces water found in comets, and planet formation, back to the dawn of the cosmos / How old is our water? https://www.eso.org/public/blog/water-origin/

PSA about food labeling in the US
We have a gluten detection service dog because many things that should be gluten free/say they’re gluten free are not actually gluten free.
Stuff gets contaminated when growing (e.g. next to wheat field), by shared equipment, in factories, from packaging, during transport and in-store.
Every US consumer should know:
1. The list of ingredients on food isn't exhaustive
2. Allergen labeling:
a) limited to just some allergens
b) manufacturers don't actually have to test
c) "certified" foods are tested—but not continuously
d) testing only works with enough contamination
Some certifications may require batch-testing, but usually they don't.
A "certified gluten free" product may e.g. contain oats which sometimes are contaminated with gluten—but as not every batch is tested it's impossible to know unless you test yourself (hence the service dog).
Even if the product is properly batch-tested, you might get a part of the product that has the allergen in it, whereas the tested part didn't.
Or the threshold was too low (our dog can detect gluten better than any available lab testing equipment; yes, dogs are amazing).
Food products also contain ingredients that do not have to be included on the label when they're "incidental" (included in an another ingredient) or if they're considered part of the manufacturing process but not of the final product (e.g. various coatings on factory equipment).
Don't need to list flavors or specific spices either. ¯\_(ツ)_/¯
As for allergens, only those responsible for ~90% of food allergies* have to be specifically declared, and they're not tested for as it's simply based on the ingredients list.
Good luck if you have other allergies.
*milk, egg, egg, fish, Crustacean shellfish, tree nuts, wheat, peanuts, soybeans

First time I've seen a MCU datasheet list the actual TSMC cell name of their PLL IP core.
I guess now we know STM32MP2 is 16FF lol.