Tootfinder

Technical Report for Egocentric Mistake Detection for the HoloAssist Challenge
Constantin Patsch, Marsil Zakour, Yuankai Wu, Eckehard Steinbach
https://arxiv.org/abs/2506.06174

Technical Report for Egocentric Mistake Detection for the HoloAssist Challenge
In this report, we address the task of online mistake detection, which is vital in domains like industrial automation and education, where real-time video analysis allows human operators to correct errors as they occur. While previous work focuses on procedural errors involving action order, broader error types must be addressed for real-world use. We introduce an online mistake detection framework that handles both procedural and execution errors (e.g., motor slips or tool misuse). Upon detect…

Passive Detection in Multi-Static ISAC Systems: Performance Analysis and Joint Beamforming Optimization
Renjie He, Yiqiu Wang, Meixia Tao, Shu Sun
https://arxiv.org/abs/2506.07019

Passive Detection in Multi-Static ISAC Systems: Performance Analysis and Joint Beamforming Optimization
This paper investigates the passive detection problem in multi-static integrated sensing and communication (ISAC) systems, where multiple sensing receivers (SRs) jointly detect a target using random unknown communication signals transmitted by a collaborative base station. Unlike traditional active detection, the considered passive detection does not require complete prior knowledge of the transmitted communication signals at each SR. First, we derive a generalized likelihood ratio test detecto…

Aha, #gold. Sollte man weiter verfolgen https://home.cern/news/news/physics/alice-detects-conversion-lead-gold-lhc

TRIDENT -- A Three-Tier Privacy-Preserving Propaganda Detection Model in Mobile Networks using Transformers, Adversarial Learning, and Differential Privacy
Al Nahian Bin Emran, Dhiman Goswami, Md Hasan Ullah Sadi, Sanchari Das
https://arxiv.org/abs/2506.05421

TRIDENT -- A Three-Tier Privacy-Preserving Propaganda Detection Model in Mobile Networks using Transformers, Adversarial Learning, and Differential Privacy
The proliferation of propaganda on mobile platforms raises critical concerns around detection accuracy and user privacy. To address this, we propose TRIDENT - a three-tier propaganda detection model implementing transformers, adversarial learning, and differential privacy which integrates syntactic obfuscation and label perturbation to mitigate privacy leakage while maintaining propaganda detection accuracy. TRIDENT leverages multilingual back-translation to introduce semantic variance, charact…

Large Language Models for Multilingual Vulnerability Detection: How Far Are We?
Honglin Shu, Michael Fu, Junji Yu, Dong Wang, Chakkrit Tantithamthavorn, Junjie Chen, Yasutaka Kamei
https://arxiv.org/abs/2506.07503

Large Language Models for Multilingual Vulnerability Detection: How Far Are We?
Various deep learning-based approaches utilizing pre-trained language models (PLMs) have been proposed for automated vulnerability detection. With recent advancements in large language models (LLMs), several studies have begun exploring their application to vulnerability detection tasks. However, existing studies primarily focus on specific programming languages (e.g., C/C++) and function-level detection, leaving the strengths and weaknesses of PLMs and LLMs in multilingual and multi-granularit…

The ILD Detector: A Versatile Detector for an Electron-Positron Collider at Energies up to 1 TeV
H. Abramowicz, D. Ahmadi, J. Alcaraz, O. Alonso, L. Andricek, J. Anguiano, O. Arquero, F. Arteche, D. Attie, O. Bach, M. Basso, J. Baudot, A. Bean, T. Behnke, A. Bellerive, Y. Benhammou, M. Berggren, G. Bertolone, M. Besancon, A. Besson, O. Bezshyyko, G. Blazey, B. Bliewert, J. Bonis, R. Bosley, V. Boudry, C. Bourgeois, I. Bozovic Jelisavcic, D. Breton, J. -C. Brient, B. Brudnowski, V. Bues…

The ILD Detector: A Versatile Detector for an Electron-Positron Collider at Energies up to 1 TeV
The International Large Detector, ILD, is a detector concept for an experiment at a future high energy lepton collider. The detector has been optimised for precision physics in a range of energies from 90~GeV to about 1~TeV. ILD features a high precision, large volume combined silicon and gaseous tracking system, together with a high granularity calorimeter, all inside a central solenoidal magnetic field. The paradigm of particle flow has been the guiding principle of the design of ILD. ILD is …

On 16WW Mains Inlet Water Temperature - Domestic mains water temperature data for 16WW on tap; seasonal min/max about 10C/20C in winter/summer. #dataset #water #temperature -

On 16WW Mains Inlet Water Temperature
Domestic mains water temperature data for 16WW on tap; seasonal min/max about 10C/20C in winter/summer. #dataset #water #temperature

This https://arxiv.org/abs/2504.13416 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

STAMP Your Content: Proving Dataset Membership via Watermarked Rephrasings
Given how large parts of publicly available text are crawled to pretrain large language models (LLMs), data creators increasingly worry about the inclusion of their proprietary data for model training without attribution or licensing. Their concerns are also shared by benchmark curators whose test-sets might be compromised. In this paper, we present STAMP, a framework for detecting dataset membership-i.e., determining the inclusion of a dataset in the pretraining corpora of LLMs. Given an origi…

This https://arxiv.org/abs/2506.03988 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

RAID: A Dataset for Testing the Adversarial Robustness of AI-Generated Image Detectors
AI-generated images have reached a quality level at which humans are incapable of reliably distinguishing them from real images. To counteract the inherent risk of fraud and disinformation, the detection of AI-generated images is a pressing challenge and an active research topic. While many of the presented methods claim to achieve high detection accuracy, they are usually evaluated under idealized conditions. In particular, the adversarial robustness is often neglected, potentially due to a la…

This https://arxiv.org/abs/2506.00558 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

ViVo: A Dataset for Volumetric VideoReconstruction and Compression
As research on neural volumetric video reconstruction and compression flourishes, there is a need for diverse and realistic datasets, which can be used to develop and validate reconstruction and compression models. However, existing volumetric video datasets lack diverse content in terms of both semantic and low-level features that are commonly present in real-world production pipelines. In this context, we propose a new dataset, ViVo, for VolumetrIc VideO reconstruction and compression. The da…