Tootfinder

Begin oktober kennelijk geen politieke advertenties meer op Facebook, interessant, ook voor de Nederlandse verkiezingen eind oktober, welke partij zou hier het meest last van hebben? (Of maakt het niets uit)
https://about.fb.com/news/2025/07/endi

Ending Political, Electoral and Social Issue Advertising in the EU in Response to Incoming European Regulation
From early October 2025, we will no longer allow political, electoral and social issue ads on our platforms in the EU. This is a difficult decision - one

DACTYL: Diverse Adversarial Corpus of Texts Yielded from Large Language Models
Shantanu Thorat, Andrew Caines
https://arxiv.org/abs/2508.00619 https://arxi…

DACTYL: Diverse Adversarial Corpus of Texts Yielded from Large Language Models
Existing AIG (AI-generated) text detectors struggle in real-world settings despite succeeding in internal testing, suggesting that they may not be robust enough. We rigorously examine the machine-learning procedure to build these detectors to address this. Most current AIG text detection datasets focus on zero-shot generations, but little work has been done on few-shot or one-shot generations, where LLMs are given human texts as an example. In response, we introduce the Diverse Adversarial Corp…

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking
Shyma Alhuwaider, Motasem Alfarra, Juan C. Perez, Merey Ramazanova, Bernard Ghanem
https://arxiv.org/abs/2509.02182

ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking
We introduce a novel tracklet-based dataset for benchmarking test-time adaptation (TTA) methods. The aim of this dataset is to mimic the intricate challenges encountered in real-world environments such as images captured by hand-held cameras, self-driving cars, etc. The current benchmarks for TTA focus on how models face distribution shifts, when deployed, and on violations to the customary independent-and-identically-distributed (i.i.d.) assumption in machine learning. Yet, these benchmarks fa…

Domain-Adversarial Transfer Learning for Fault Root Cause Identification in Cloud Computing Systems
Bruce Fang, Danyi Gao
https://arxiv.org/abs/2507.02233 …

Domain-Adversarial Transfer Learning for Fault Root Cause Identification in Cloud Computing Systems
This paper addresses the challenge of fault root cause identification in cloud computing environments. The difficulty arises from complex system structures, dense service coupling, and limited fault information. To solve this problem, an intelligent identification algorithm based on transfer learning is proposed. The method introduces a shared feature extraction module and a domain adversarial mechanism to enable effective knowledge transfer from the source domain to the target domain. This imp…

Integrated information and predictive processing theories of consciousness: An adversarial collaborative review
Andrew W. Corcoran, Andrew M. Haun, Reinder Dorman, Giulio Tononi, Karl J. Friston, Cyriel M. A. Pennartz, TWCF, :, INTREPID Consortium
https://arxiv.org/abs/2509.00555

Integrated information and predictive processing theories of consciousness: An adversarial collaborative review
As neuroscientific theories of consciousness continue to proliferate, the need to assess their similarities and differences -- as well as their predictive and explanatory power -- becomes ever more pressing. Recently, a number of structured adversarial collaborations have been devised to test the competing predictions of several candidate theories of consciousness. In this review, we compare and contrast three theories being investigated in one such adversarial collaboration: Integrated Informa…

LLMHoney: A Real-Time SSH Honeypot with Large Language Model-Driven Dynamic Response Generation
Pranjay Malhotra
https://arxiv.org/abs/2509.01463 https://a…

LLMHoney: A Real-Time SSH Honeypot with Large Language Model-Driven Dynamic Response Generation
Cybersecurity honeypots are deception tools for engaging attackers and gather intelligence, but traditional low or medium-interaction honeypots often rely on static, pre-scripted interactions that can be easily identified by skilled adversaries. This Report presents LLMHoney, an SSH honeypot that leverages Large Language Models (LLMs) to generate realistic, dynamic command outputs in real time. LLMHoney integrates a dictionary-based virtual file system to handle common commands with low latency…

„Es gibt keinen Anspruch auf die Alleinnutzung öffentlicher Straßen durch Pkw“
Gerichtspräsidentin Ludgera Selting, Berliner Verfassungsgericht
Und das Gericht stellt auch die Handlungsmöglichkeiten des Landes Berlin klar. Das ist nicht nur für Berlin interessant.
Länder können neue Straßentypen wie autoreduzierte Straßen schaffen. Es zu unterlassen ist damit rein politisch, nicht rechtlich.
Eine Ausrede weniger.

Entscheid des Verfassungsgerichtshofs: Berlin darf autofrei werden
Schlappe für den Senat: Das Volksbegehren „Berlin autofrei“ ist laut Verfassungsgericht zulässig. Nun ist das Abgeordnetenhaus am Zug.

Revisiting Adversarial Patch Defenses on Object Detectors: Unified Evaluation, Large-Scale Dataset, and New Insights
Junhao Zheng, Jiahao Sun, Chenhao Lin, Zhengyu Zhao, Chen Ma, Chong Zhang, Cong Wang, Qian Wang, Chao Shen
https://arxiv.org/abs/2508.00649

Revisiting Adversarial Patch Defenses on Object Detectors: Unified Evaluation, Large-Scale Dataset, and New Insights
Developing reliable defenses against patch attacks on object detectors has attracted increasing interest. However, we identify that existing defense evaluations lack a unified and comprehensive framework, resulting in inconsistent and incomplete assessments of current methods. To address this issue, we revisit 11 representative defenses and present the first patch defense benchmark, involving 2 attack goals, 13 patch attacks, 11 object detectors, and 4 diverse metrics. This leads to the large-s…

From Noisy Labels to Intrinsic Structure: A Geometric-Structural Dual-Guided Framework for Noise-Robust Medical Image Segmentation
Tao Wang, Zhenxuan Zhang, Yuanbo Zhou, Xinlin Zhang, Yuanbin Chen, Tao Tan, Guang Yang, Tong Tong
https://arxiv.org/abs/2509.02419

From Noisy Labels to Intrinsic Structure: A Geometric-Structural Dual-Guided Framework for Noise-Robust Medical Image Segmentation
The effectiveness of convolutional neural networks in medical image segmentation relies on large-scale, high-quality annotations, which are costly and time-consuming to obtain. Even expert-labeled datasets inevitably contain noise arising from subjectivity and coarse delineations, which disrupt feature learning and adversely impact model performance. To address these challenges, this study propose a Geometric-Structural Dual-Guided Network (GSD-Net), which integrates geometric and structural cu…