Tootfinder

Quantifying Mix Network Privacy Erosion with Generative Models
Vasilios Mavroudis, Tariq Elahi
https://arxiv.org/abs/2506.08918 https://

Quantifying Mix Network Privacy Erosion with Generative Models
Modern mix networks improve over Tor and provide stronger privacy guarantees by robustly obfuscating metadata. As long as a message is routed through at least one honest mixnode, the privacy of the users involved is safeguarded. However, the complexity of the mixing mechanisms makes it difficult to estimate the cumulative privacy erosion occurring over time. This work uses a generative model trained on mixnet traffic to estimate the loss of privacy when users communicate persistently over a per…

An Efficient Candidate-Free R-S Set Similarity Join Algorithm with the Filter-and-Verification Tree and MapReduce
Yuhong Feng, Fangcao Jian, Yixuan Cao, Xiaobin Jian, Jia Wang, Haiyue Feng, Chunyan Miao
https://arxiv.org/abs/2506.03893

An Efficient Candidate-Free R-S Set Similarity Join Algorithm with the Filter-and-Verification Tree and MapReduce
Given two different collections of sets, the exact set similarity R-S Join finds all set pairs with similarity no less than a given threshold, which has widespread applications. While existing algorithms accelerate large-scale R-S Joins using a two-stage filter-and-verification framework along with the parallel and distributed MapReduce framework, they suffer from excessive candidate set pairs, leading to significant I/O, data transfer, and verification overhead, and ultimately degrading the pe…

Ah, ook binnen het CDA voldoende (te veel) Christofascisten. Maar laf, want anoniem.
Bah.
https://www.rtl.nl/nieuws/politiek/artikel/5516729/cda-asielwetten-kritiek-tegenstem-prominenten-fout-tweede-kamer

Gemor binnen CDA over tegenstem Tweede Kamerfractie over asielwetten
In het CDA klinkt gemor over het besluit van de CDA-Tweede Kamerfractie om tegen de wetten te stemmen die het asielbeleid strenger maken. "Een domme fout", moppert een voormalig CDA-bewindspersoon.

On Efficient Bayesian Exploration in Model-Based Reinforcement Learning
Alberto Caron, Chris Hicks, Vasilios Mavroudis
https://arxiv.org/abs/2507.02639 htt…

On Efficient Bayesian Exploration in Model-Based Reinforcement Learning
In this work, we address the challenge of data-efficient exploration in reinforcement learning by examining existing principled, information-theoretic approaches to intrinsic motivation. Specifically, we focus on a class of exploration bonuses that targets epistemic uncertainty rather than the aleatoric noise inherent in the environment. We prove that these bonuses naturally signal epistemic information gains and converge to zero once the agent becomes sufficiently certain about the environment…

grrrrr. Ich wollte nicht Zahlen ausprobieren und habe mich drumrum gestümpert.
#pastpuzzle 326
🟩🟩🟩🟥 ( 2)
🟩🟩🟩🟥 (-1)
🟩🟩🟩🟥 (-2)
🟩🟩🟩🟥 ( 1)
x/4 🟥
https://www.pastpuzzle.de

Adapting Language Models to Indonesian Local Languages: An Empirical Study of Language Transferability on Zero-Shot Settings
Rifki Afina Putri
https://arxiv.org/abs/2507.01645

Adapting Language Models to Indonesian Local Languages: An Empirical Study of Language Transferability on Zero-Shot Settings
In this paper, we investigate the transferability of pre-trained language models to low-resource Indonesian local languages through the task of sentiment analysis. We evaluate both zero-shot performance and adapter-based transfer on ten local languages using models of different types: a monolingual Indonesian BERT, multilingual models such as mBERT and XLM-R, and a modular adapter-based approach called MAD-X. To better understand model behavior, we group the target languages into three categori…

**Deadline Extended** (CFP: PAMLA 2025) Haunted Belonging: Memory, Erasure, and Identity in Diasporic Literatures
https://ift.tt/mVurDfg
updated: Tuesday, June 17, 2025 - 9:41amfull name / name of organization: Wenyuan Wang / Pacific…
via Input 4 RELCFP

From Promise to Peril: Rethinking Cybersecurity Red and Blue Teaming in the Age of LLMs
Alsharif Abuadbba, Chris Hicks, Kristen Moore, Vasilios Mavroudis, Burak Hasircioglu, Diksha Goel, Piers Jennings
https://arxiv.org/abs/2506.13434

From Promise to Peril: Rethinking Cybersecurity Red and Blue Teaming in the Age of LLMs
Large Language Models (LLMs) are set to reshape cybersecurity by augmenting red and blue team operations. Red teams can exploit LLMs to plan attacks, craft phishing content, simulate adversaries, and generate exploit code. Conversely, blue teams may deploy them for threat intelligence synthesis, root cause analysis, and streamlined documentation. This dual capability introduces both transformative potential and serious risks. This position paper maps LLM applications across cybersecurity fram…

From Promise to Peril: Rethinking Cybersecurity Red and Blue Teaming in the Age of LLMs
Alsharif Abuadbba, Chris Hicks, Kristen Moore, Vasilios Mavroudis, Burak Hasircioglu, Diksha Goel, Piers Jennings
https://arxiv.org/abs/2506.13434

From Promise to Peril: Rethinking Cybersecurity Red and Blue Teaming in the Age of LLMs
Large Language Models (LLMs) are set to reshape cybersecurity by augmenting red and blue team operations. Red teams can exploit LLMs to plan attacks, craft phishing content, simulate adversaries, and generate exploit code. Conversely, blue teams may deploy them for threat intelligence synthesis, root cause analysis, and streamlined documentation. This dual capability introduces both transformative potential and serious risks. This position paper maps LLM applications across cybersecurity fram…