Tootfinder

#SeriouslyIfYoureStillUsingInternetExplorerYouShouldStop

fresh_webs: Freshwater stream webs
A set of 26 networks of trophic-level species interactions in streams in New Zealand, Maine and North Carolina. Networks include the identities of aquatic insect, algae and fish species and their trophic interactions. Excel and plain text adjacency matrices available; webs can be downloaded individually or as a group.
This network has 83 nodes and 335 edges.
Tags: Biological, Food web, Unweighted, Metadata

KGMark: A Diffusion Watermark for Knowledge Graphs
Hongrui Peng, Haolang Lu, Yuanlong Yu, Weiye Fu, Kun Wang, Guoshun Nan
https://arxiv.org/abs/2505.23873 …

KGMark: A Diffusion Watermark for Knowledge Graphs
Knowledge graphs (KGs) are ubiquitous in numerous real-world applications, and watermarking facilitates protecting intellectual property and preventing potential harm from AI-generated content. Existing watermarking methods mainly focus on static plain text or image data, while they can hardly be applied to dynamic graphs due to spatial and temporal variations of structured data. This motivates us to propose KGMARK, the first graph watermarking framework that aims to generate robust, detectable…

Urban Diaspora IV 🎈
城市放逐 IV 🎈
📷 Nikon FE
🎞️Ilford FP4 Plus, expired 1994
buy me ☕️ ?/请我喝杯☕️？
#filmphotography

Hate in Plain Sight: On the Risks of Moderating AI-Generated Hateful Illusions
Yiting Qu, Ziqing Yang, Yihan Ma, Michael Backes, Savvas Zannettou, Yang Zhang
https://arxiv.org/abs/2507.22617

Hate in Plain Sight: On the Risks of Moderating AI-Generated Hateful Illusions
Recent advances in text-to-image diffusion models have enabled the creation of a new form of digital art: optical illusions--visual tricks that create different perceptions of reality. However, adversaries may misuse such techniques to generate hateful illusions, which embed specific hate messages into harmless scenes and disseminate them across web communities. In this work, we take the first step toward investigating the risks of scalable hateful illusion generation and the potential for bypa…

Page image classification for content-specific data processing
Kateryna Lutsai, Pavel Stra\v{n}\'ak
https://arxiv.org/abs/2507.21114 https://arxiv.org/…

Page image classification for content-specific data processing
Digitization projects in humanities often generate vast quantities of page images from historical documents, presenting significant challenges for manual sorting and analysis. These archives contain diverse content, including various text types (handwritten, typed, printed), graphical elements (drawings, maps, photos), and layouts (plain text, tables, forms). Efficiently processing this heterogeneous data requires automated methods to categorize pages based on their content, enabling tailored d…

The story of Rogue #rogue #roguelikes

HathiTrust was founded in 2008 as a not-for-profit collaborative of academic and research libraries now preserving 18 million digitized items in the HathiTrust Digital Library. We offer reading access to the fullest extent allowable by U.S. and international copyright law, text and data mining tools for the entire corpus, and other emerging services based on the combined collection.

fresh_webs: Freshwater stream webs
A set of 26 networks of trophic-level species interactions in streams in New Zealand, Maine and North Carolina. Networks include the identities of aquatic insect, algae and fish species and their trophic interactions. Excel and plain text adjacency matrices available; webs can be downloaded individually or as a group.
This network has 69 nodes and 187 edges.
Tags: Biological, Food web, Unweighted, Metadata

Zathras is having Win11 issues. https://mastodon.art/@pentup/115050095384100543

CardiffNLP at CLEARS-2025: Prompting Large Language Models for Plain Language and Easy-to-Read Text Rewriting
Mutaz Ayesh, Nicol\'as Guti\'errez-Rol\'on, Fernando Alva-Manchego
https://arxiv.org/abs/2508.03240

CardiffNLP at CLEARS-2025: Prompting Large Language Models for Plain Language and Easy-to-Read Text Rewriting
This paper details the CardiffNLP team's contribution to the CLEARS shared task on Spanish text adaptation, hosted by IberLEF 2025. The shared task contained two subtasks and the team submitted to both. Our team took an LLM-prompting approach with different prompt variations. While we initially experimented with LLaMA-3.2, we adopted Gemma-3 for our final submission, and landed third place in Subtask 1 and second place in Subtask 2. We detail our numerous prompt variations, examples, and experi…

Today i wrote a parser & import to convert some semi-structured plain text into 40 different newsletter issues for an upcoming newsletter relaunch. And also fixed the internal structure of some of the mail widgets we are using, and their HTML representation.

Das ist einer der unterhaltsamsten Vorträge die ich kenne zu Schei? encoding und den Problemen bei Localization und Internationalization: Dylan Beattie – There's no such thing as plain text. Enjoy: https://www.youtube.com/watch?v=ajfb5LSbQVM

"Please use HTML enabled email client to view this message."
Ehm... No... You need to send #plain text #email. Do not put the onus on me to take care of secure communications.

$\Delta$-AttnMask: Attention-Guided Masked Hidden States for Efficient Data Selection and Augmentation
Jucheng Hu, Suorong Yang, Dongzhan Zhou
https://arxiv.org/abs/2508.09199 h…

$Δ$-AttnMask: Attention-Guided Masked Hidden States for Efficient Data Selection and Augmentation
Visual Instruction Finetuning (VIF) is pivotal for post-training Vision-Language Models (VLMs). Unlike unimodal instruction finetuning in plain-text large language models, which mainly requires instruction datasets to enable model instruction-following ability, VIF also requires multimodal data to enable joint visual and textual understanding; therefore, it typically requires more data. Consequently, VIF imposes stricter data selection challenges: the method must scale efficiently to handle lar…

Urban Plantations ? 🟩
都市植被？🟩
📷 Pentax MX
🎞️Ilford FP4 Plus, expired 1994
buy me ☕️ ?/请我喝杯☕️？
#filmphotography

fresh_webs: Freshwater stream webs
A set of 26 networks of trophic-level species interactions in streams in New Zealand, Maine and North Carolina. Networks include the identities of aquatic insect, algae and fish species and their trophic interactions. Excel and plain text adjacency matrices available; webs can be downloaded individually or as a group.
This network has 71 nodes and 148 edges.
Tags: Biological, Food web, Unweighted, Metadata

In his keynote, Raphael Troncy is asking whether we should keep building knowledge graphs....taking into account 20 years of experience in building knowledge graphs
https://2025.eswc-conferences.org/keynotes-announcement-raphael-troncy/

Luca Faloni Baseball Cap LINEN $ 220
Made in It;ay.
No logos on them
#Faloni
#noMAGA

Been playing this game I dig on the Android tablet called Dungeon Raid. It's a card flipping dungeon crawler (the dev calls it an 'adventure card roguelike') that's fairly easy to figure out-- lots of strategy and replayability. At first, you can start out playing as either a knight or a vampire, delving down, trying not to die of hunger.
If a relic in a game you can choose is a fried egg, you can probably assume it's a game for me.
There's also Linux, M…

In Mastodon, is it possible to search for posts that do *not* contain a specific hashtag? I don't mean full-text search, but plain old hashtag search only for absence of hashtag instead of presence. 🤔
#question #askMastodon #search #Mastodon #MastodonSearch

#ShamelesslyStolenFromSomewhereElseOnTheInternetHonestlyICantKeepTrackOfThisStuffAnymore

The Hidden Threat in Plain Text: Attacking RAG Data Loaders
Alberto Castagnaro, Umberto Salviati, Mauro Conti, Luca Pajola, Simeone Pizzi
https://arxiv.org/abs/2507.05093

The Hidden Threat in Plain Text: Attacking RAG Data Loaders
Large Language Models (LLMs) have transformed human-machine interaction since ChatGPT's 2022 debut, with Retrieval-Augmented Generation (RAG) emerging as a key framework that enhances LLM outputs by integrating external knowledge. However, RAG's reliance on ingesting external documents introduces new vulnerabilities. This paper exposes a critical security gap at the data loading stage, where malicious actors can stealthily corrupt RAG pipelines by exploiting document ingestion. We propose a t…

fresh_webs: Freshwater stream webs
A set of 26 networks of trophic-level species interactions in streams in New Zealand, Maine and North Carolina. Networks include the identities of aquatic insect, algae and fish species and their trophic interactions. Excel and plain text adjacency matrices available; webs can be downloaded individually or as a group.
This network has 110 nodes and 718 edges.
Tags: Biological, Food web, Unweighted, Metadata

This https://arxiv.org/abs/2504.09200 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNI_…

A Datagram Extension to DNS over QUIC: Proven Resource Conservation in the Internet of Things
In this paper, we investigate the Domain Name System (DNS) over QUIC (DoQ) and propose a non-disruptive extension, which can greatly reduce DoQ's resource consumption. This extension can benefit all DNS clients - especially Internet of Things (IoT) devices. This is important because even resource-constrained IoT devices can generate dozens of DNS requests every hour. DNS is a crucial service that correlates IP addresses and domain names. It is traditionally sent as plain-text, favoring low-late…

fresh_webs: Freshwater stream webs
A set of 26 networks of trophic-level species interactions in streams in New Zealand, Maine and North Carolina. Networks include the identities of aquatic insect, algae and fish species and their trophic interactions. Excel and plain text adjacency matrices available; webs can be downloaded individually or as a group.
This network has 105 nodes and 343 edges.
Tags: Biological, Food web, Unweighted, Metadata

This https://arxiv.org/abs/2412.04338 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Numerical Aspects of Large Deviations
An introduction to numerical large-deviation sampling is provided. First, direct biasing with a known distribution is explained. As simple example, the Bernoulli experiment is used throughout the text. Next, Markov chain Monte Carlo (MCMC) simulations are introduced. In particular, the Metropolis-Hastings algorithm is explained. As first implementation of MCMC, sampling of the plain Bernoulli model is shown. Next, an exponential bias is used for the same model, which allows one to obtain the ta…

Building Open-Retrieval Conversational Question Answering Systems by Generating Synthetic Data and Decontextualizing User Questions
Christos Vlachos, Nikolaos Stylianou, Alexandra Fiotaki, Spiros Methenitis, Elisavet Palogiannidi, Themos Stafylakis, Ion Androutsopoulos
https://arxiv.org/abs/2507.04884

Building Open-Retrieval Conversational Question Answering Systems by Generating Synthetic Data and Decontextualizing User Questions
We consider open-retrieval conversational question answering (OR-CONVQA), an extension of question answering where system responses need to be (i) aware of dialog history and (ii) grounded in documents (or document fragments) retrieved per question. Domain-specific OR-CONVQA training datasets are crucial for real-world applications, but hard to obtain. We propose a pipeline that capitalizes on the abundance of plain text documents in organizations (e.g., product documentation) to automatically …

fresh_webs: Freshwater stream webs
A set of 26 networks of trophic-level species interactions in streams in New Zealand, Maine and North Carolina. Networks include the identities of aquatic insect, algae and fish species and their trophic interactions. Excel and plain text adjacency matrices available; webs can be downloaded individually or as a group.
This network has 71 nodes and 155 edges.
Tags: Biological, Food web, Unweighted, Metadata

This https://arxiv.org/abs/2506.00261 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

GPR: Empowering Generation with Graph-Pretrained Retriever
Graph retrieval-augmented generation (GRAG) places high demands on graph-specific retrievers. However, existing retrievers often rely on language models pretrained on plain text, limiting their effectiveness due to domain misalignment and structure ignorance. To address these challenges, we propose GPR, a graph-based retriever pretrained directly on knowledge graphs. GPR aligns natural language questions with relevant subgraphs through LLM-guided graph augmentation and employs a structure-aware…

GPR: Empowering Generation with Graph-Pretrained Retriever
Xiaochen Wang, Zongyu Wu, Yuan Zhong, Xiang Zhang, Suhang Wang, Fenglong Ma
https://arxiv.org/abs/2506.00261

GPR: Empowering Generation with Graph-Pretrained Retriever
Graph retrieval-augmented generation (GRAG) places high demands on graph-specific retrievers. However, existing retrievers often rely on language models pretrained on plain text, limiting their effectiveness due to domain misalignment and structure ignorance. To address these challenges, we propose GPR, a graph-based retriever pretrained directly on knowledge graphs. GPR aligns natural language questions with relevant subgraphs through LLM-guided graph augmentation and employs a structure-aware…