Tootfinder

Cybersec information overload got you down? Then don't miss today's Metacurity for a concise run-down of the most critical infosec developments you should know, including
--1,000 CISA employees have decamped from the agency since January
--Victoria's Secret postpones earnings release after cyberattack,
--New security fixes for Qualcomm chips,
--AFP nabs nearly two dozen in sextortion sting,
--Marriott can't be sued for breach,
--Trump dumps …

1,000 CISA employees have left the agency since January
Victoria's Secret postpones earnings release after cyberattack, New security fixes for Qualcomm chips, AFP nabs nearly two dozen in sextortion sting, Marriott can't be sued for breach, Trump dumps AI Safety Institute, Indian grocery delivery startup wiped out by hack, so much more

This https://arxiv.org/abs/2411.17931 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Combining Threat Intelligence with IoT Scanning to Predict Cyber Attack
While the Web has become a global platform for communication, malicious actors, including hackers and hacktivist groups, often disseminate ideological content and coordinate activities through the "Dark Web", an obscure counterpart of the conventional web. Presently, challenges such as information overload and the fragmented nature of cyber threat data impede comprehensive profiling of these actors, thereby limiting the efficacy of predictive analyses of their online activities. Concurrently, t…

Requirements Elicitation Follow-Up Question Generation
Yuchen Shen, Anmol Singhal, Travis Breaux
https://arxiv.org/abs/2507.02858 https://

Requirements Elicitation Follow-Up Question Generation
Interviews are a widely used technique in eliciting requirements to gather stakeholder needs, preferences, and expectations for a software system. Effective interviewing requires skilled interviewers to formulate appropriate interview questions in real time while facing multiple challenges, including lack of familiarity with the domain, excessive cognitive load, and information overload that hinders how humans process stakeholders' speech. Recently, large language models (LLMs) have exhibited s…

TreeReader: A Hierarchical Academic Paper Reader Powered by Language Models
Zijian Zhang, Pan Chen, Fangshi Du, Runlong Ye, Oliver Huang, Michael Liut, Al\'an Aspuru-Guzik
https://arxiv.org/abs/2507.18945

TreeReader: A Hierarchical Academic Paper Reader Powered by Language Models
Efficiently navigating and understanding academic papers is crucial for scientific progress. Traditional linear formats like PDF and HTML can cause cognitive overload and obscure a paper's hierarchical structure, making it difficult to locate key information. While LLM-based chatbots offer summarization, they often lack nuanced understanding of specific sections, may produce unreliable information, and typically discard the document's navigational structure. Drawing insights from a formative st…

Designing for Self-Regulation in Informal Programming Learning: Insights from a Storytelling-Centric Approach
Sami Saeed Alghamdi (Open Lab, School of Computing, Newcastle University), Christopher Bull (Open Lab, School of Computing, Newcastle University), Ahmed Kharrufa (Open Lab, School of Computing, Newcastle University)
https://arxiv.o…

Designing for Self-Regulation in Informal Programming Learning: Insights from a Storytelling-Centric Approach
Many people learn programming independently from online resources and often report struggles in achieving their personal learning goals. Learners frequently describe their experiences as isolating and frustrating, challenged by abundant uncertainties, information overload, and distraction, compounded by limited guidance. At the same time, social media serves as a personal space where many engage in diverse self-regulation practices, including help-seeking, using external memory aids (e.g., self…

PARK: Personalized academic retrieval with knowledge-graphs
Pranav Kasela, Gabriella Pasi, Raffaele Perego
https://arxiv.org/abs/2507.13910 https://…

PARK: Personalized academic retrieval with knowledge-graphs
Academic Search is a search task aimed to manage and retrieve scientific documents like journal articles and conference papers. Personalization in this context meets individual researchers' needs by leveraging, through user profiles, the user related information (e.g. documents authored by a researcher), to improve search effectiveness and to reduce the information overload. While citation graphs are a valuable means to support the outcome of recommender systems, their use in personalized acade…

How online misinformation works: a costly signalling perspective
Neri Marsili
https://arxiv.org/abs/2506.17158 https://arxiv.org/pdf/…

How online misinformation works: a costly signalling perspective
This chapter explores how online communication, particularly on social media, reshapes the reputational incentives that motivate speakers to communicate truthfully. Drawing on costly signalling theory (CST), it examines how online contexts alter the social mechanisms that sustain honest communication. Key characteristics of online spaces are identified and discussed, namely (i) the presence of novel speech acts like reposting, (ii) the gamification of communication, (iii) information overload, …

InfoFlood: Jailbreaking Large Language Models with Information Overload
Advait Yadav, Haibo Jin, Man Luo, Jun Zhuang, Haohan Wang
https://arxiv.org/abs/2506.12274

InfoFlood: Jailbreaking Large Language Models with Information Overload
Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains. However, their potential to generate harmful responses has raised significant societal and regulatory concerns, especially when manipulated by adversarial techniques known as "jailbreak" attacks. Existing jailbreak methods typically involve appending carefully crafted prefixes or suffixes to malicious prompts in order to bypass the built-in safety mechanisms of these models. In this work, we identif…

Privacy-Preserving Multimodal News Recommendation through Federated Learning
Mehdi Khalaj, Shahrzad Golestani Najafabadi, Julita Vassileva
https://arxiv.org/abs/2507.15460

Privacy-Preserving Multimodal News Recommendation through Federated Learning
Personalized News Recommendation systems (PNR) have emerged as a solution to information overload by predicting and suggesting news items tailored to individual user interests. However, traditional PNR systems face several challenges, including an overreliance on textual content, common neglect of short-term user interests, and significant privacy concerns due to centralized data storage. This paper addresses these issues by introducing a novel multimodal federated learning-based approach for n…

TLB-VFI: Temporal-Aware Latent Brownian Bridge Diffusion for Video Frame Interpolation
Zonglin Lyu, Chen Chen
https://arxiv.org/abs/2507.04984 https://

TLB-VFI: Temporal-Aware Latent Brownian Bridge Diffusion for Video Frame Interpolation
Video Frame Interpolation (VFI) aims to predict the intermediate frame $I_n$ (we use n to denote time in videos to avoid notation overload with the timestep $t$ in diffusion models) based on two consecutive neighboring frames $I_0$ and $I_1$. Recent approaches apply diffusion models (both image-based and video-based) in this task and achieve strong performance. However, image-based diffusion models are unable to extract temporal information and are relatively inefficient compared to non-diffusi…

The Evolving Role of Large Language Models in Scientific Innovation: Evaluator, Collaborator, and Scientist
Haoxuan Zhang, Ruochi Li, Yang Zhang, Ting Xiao, Jiangping Chen, Junhua Ding, Haihua Chen
https://arxiv.org/abs/2507.11810

The Evolving Role of Large Language Models in Scientific Innovation: Evaluator, Collaborator, and Scientist
Scientific innovation is undergoing a paradigm shift driven by the rapid advancement of Large Language Models (LLMs). As science faces mounting challenges including information overload, disciplinary silos, and diminishing returns on conventional research methods, LLMs are emerging as powerful agents capable not only of enhancing scientific workflows but also of participating in and potentially leading the innovation process. Existing surveys mainly focus on different perspectives, phrases, and…

SEP-GCN: Leveraging Similar Edge Pairs with Temporal and Spatial Contexts for Location-Based Recommender Systems
Tan Loc Nguyen, Tin T. Tran
https://arxiv.org/abs/2506.16003

SEP-GCN: Leveraging Similar Edge Pairs with Temporal and Spatial Contexts for Location-Based Recommender Systems
Recommender systems play a crucial role in enabling personalized content delivery amidst the challenges of information overload and human mobility. Although conventional methods often rely on interaction matrices or graph-based retrieval, recent approaches have sought to exploit contextual signals such as time and location. However, most existing models focus on node-level representation or isolated edge attributes, underutilizing the relational structure between interactions. We propose SEP-GC…

Mobile Application Review Summarization using Chain of Density Prompting
Shristi Shrestha, Anas Mahmoud
https://arxiv.org/abs/2506.14192 https://

Mobile Application Review Summarization using Chain of Density Prompting
Mobile app users commonly rely on app store ratings and reviews to find apps that suit their needs. However, the sheer volume of reviews available on app stores can lead to information overload, thus impeding users' ability to make informed app selection decisions. To address this challenge, we leverage Large Language Models (LLMs) to summarize mobile app reviews. In particular, we use the Chain of Density (CoD) prompt to guide OpenAI GPT-4 to generate abstractive, semantically dense, and easil…