OpenRaiser/Intern-Atlas huggingface dataset:
Personalised News Summarisation Using Demographic-aware BART Model
Authors: Daniel M. McFadyen, Md. Redowan Mahmud, Mahbuba Afrin, Sajib Mistry, Aneesh Krishna
Venue: WWW
Year: 2025

The exponential growth of digital media has led to a saturation of online news content, making it increasingly important to deliver information that resonates with readers' specific backgrounds and interests. While traditional summarisation methods treat all audiences homogeneously, recent advances highlight the need for demographic-aware approaches. However, existing automated news summarising systems rarely incorporate these nuances, often defaulting to one-size-fits-all summaries. Therefore, this research introduces a novel framework for personalised news summarisation that accounts for reader-specific factors such as age, geography, and cultural background. We developed a specialised dataset linking demographic attributes to narrative priorities and used this to fine-tune an open-source BART model for dynamic, audience-aligned information emphasis. Integrated into a user-friendly web application, this model enables the generation of tailored summaries that better meet individual preferences. Comparative evaluations reveal that our approach substantially surpasses traditional summarisation tools, including leading models like ChatGPT 3.5 and Gemini, by providing more contextually relevant content.
Security Considerations for Post-Quantum Signatures in DNSSEC via Request-Based Fragmentation
Authors: Cameron McGowan, James Liu, Sushmita Ruj
Venue: WWW
Year: 2025

The Domain Name System (DNS) is one of the most widely used systems in the world, invoked every time anyone visits a website or connects to a server over the internet. DNS security extensions (DNSSEC) provide users with security that the websites they access are legitimate. However, the emerging threat of quantum computers requires new quantum-resistant techniques to maintain DNSSEC's security. Our paper addresses a memory exhaustion vulnerability in a recently proposed resource record fragmentation (ARRF) technique which efficiently provides a post-quantum (PQ) DNSSEC. In fixing this vulnerability with minimal performance penalties, we hope to bring ARRF closer to deployment in real-world environments.
The Duality of Persuasion: Between Personalization and Detection
Authors: Elyas Meguellati
Venue: WWW
Year: 2025

This PhD project investigates the duality of artificial intelligence in persuasive communication, exploring both the generation of personalized content and the detection of persuasive strategies. Insights are drawn from four studies. The first demonstrates how large language models (LLMs) can generate personalized online advertisements targeting specific personality traits, such as openness and neuroticism, with effectiveness comparable to human-written ads. The second introduces a lightweight transformer model for persuasive text detection, achieving state-of-the-art performance in Subtask 3 of SemEval 2023 and applying it to Facebook political ads from the 2022 Australian Federal election campaign. The third, ongoing study extends to persuasive memes detection by employing LLMs for preprocessing and data augmentation before model analysis. Finally, a planned study will explore a dynamic personalization: Developing adaptive LLM frameworks to tailor personalized messages over time based on user feedback or interactions. These studies collectively contribute to the understanding and ethical application of AI in persuasive communication, offering insights for academia, industry, and policymakers while addressing societal implications of AI-driven persuasion.
Are Large Language Models Good Data Preprocessors?
Authors: Elyas Meguellati, Nardiena A. Pratama, Shazia Sadiq, Gianluca Demartini
Venue: WWW
Year: 2025

High-quality textual training data is essential for the success of multimodal data processing tasks, yet outputs from image captioning models like BLIP and GIT often contain errors and anomalies that are difficult to rectify using rule-based methods. While recent work addressing this issue has predominantly focused on using GPT models for data preprocessing on relatively simple public datasets, there is a need to explore a broader range of Large Language Models (LLMs) and tackle more challenging and diverse datasets. In this study, we investigate the use of multiple LLMs, including LLaMA 3.1 70B, GPT-4 Turbo, and Sonnet 3.5 v2 to refine and clean the textual outputs of BLIP and GIT. We assess the impact of LLM-assisted data cleaning by comparing downstream-task (SemEval 2024 Subtask 'Multilabel Persuasion Detection in Memes'') models trained on cleaned versus non-cleaned data. While our experimental results show improvements when using LLM-cleaned captions, statistical tests reveal that most of these improvements are not significant. This suggests that while LLMs have the potential to enhance data cleaning and repairing, their effectiveness may be limited depending on the context they are applied to and the complexity of the task and the level of noise in the text. Our findings highlight the need for further research into the capabilities and limitations of LLMs in data preprocessing pipelines, especially when dealing with challenging datasets, contributing empirical evidence to the ongoing discussion about integrating LLMs into data preprocessing pipelines.
SigScope: Detecting and Understanding Off-Chain Message Signing-related Vulnerabilities in Decentralized Applications
Authors: Sajad Meisami, Hugo Dabadie, Song Li, Yuzhe Tang, Yue Duan
Venue: WWW
Year: 2025

In Web 3.0, an emerging paradigm of building decentralized applications or DApps is off-chain message signing, which has advantages in performance, cost efficiency, and usability compared to conventional transaction-signing schemes. However, message signing burdens DApp developers with extra coding complexity and message designing, leading to new security risks. This paper presents the first systematic study to uncover and characterize the security issues in off-chain message signing schemes and the DApps built atop them. We present a holistic static-analysis framework, SigScope, that uniquely combines the insights extracted from DApp front-end code (HTML and Javascript) off-chain and back-end smart contracts on-chain. We evaluate SigScope using the top 100 DApps to showcase its effectiveness and efficiency. Further, we leverage SigScope to study a large dataset of 4937 real-world DApps and show that 1579 DApps (including 73% of the top 100) rely on the off-chain message signing feature, and 1154 contain vulnerabilities. Finally, we use two real-world vulnerabilities in popular DApps to showcase our findings.
Beyond Single Tabs: A Transformative Few-Shot Approach to Multi-Tab Website Fingerprinting Attacks
Authors: Wenwen Meng, Chuan Ma, Ming Ding, Chunpeng Ge, Yuwen Qian, Tao Xiang
Venue: WWW
Year: 2025

Website Fingerprinting (WF) attacks allow passive eavesdroppers to deduce the websites a user visits by analyzing encrypted traffic, threatening user privacy. While current WF attacks achieve high accuracy, they typically assume single-tab browsing, which is unrealistic as users often open multiple tabs, creating mixed traffic. Existing multi-tab WF approaches require large datasets and frequent retraining due to evolving website content, limiting their practicality. In this paper, we introduce Few-shot Multi-tab Website Fingerprinting (FMWF), a novel approach designed to address the limitations of existing multi-tab WF attacks. FMWF directly tackles the challenges of mixed, overlapping traffic traces generated from multi-tab browsing, leveraging two key innovations: (1) an advanced data augmentation technique that synthesizes realistic multi-tab traffic sequences from easily collected single-tab traces, thereby dramatically reducing the need for large-scale real-world traffic data; and (2) a powerful fine-tuning algorithm based on transfer learning that adapts pre-trained models to new, multi-tab environments with minimal additional data. This two-stage framework enables FMWF to capture the complex effectively, overlapping traffic patterns inherent in multi-tab browsing while maintaining a high level of flexibility and significantly lowering computational and data collection burdens. Our experiments, conducted using real traffic traces collected from three widely-used browsers-Microsoft Edge, Google Chrome, and Tor Browser-highlight the superior performance of FMWF in both closed-world and open-world scenarios. Notably, FMWF achieves a minimum 12.3% improvement in accuracy compared to ARES (SP'23) [7], TMWF (CCS'23) [13], and BAPM (ACSAC'21) [10] in the open-world scenario. The code with related datasets is available at https://github.com/WW-Meng/FMWF.
The Collaborative Intelligence Mathematics Tutoring Service
Authors: Hao Meng
Venue: WWW
Year: 2025

This study presents an advanced AI-driven educational ecosystem tailored to revolutionize K-12 mathematics education through three seamlessly integrated platforms: a homework assessment system augmented with a cognitive path correction module to mitigate learning deficits, and a real-time adaptive learning engine for delivering personalized instructional content. Addressing critical challenges in contemporary mathematics education, including the creation of individualized learning trajectories, the reduction of teacher workload in assessment, and the facilitation of timely pedagogical interventions. We further delineate future research trajectories to empirically validate the system's educational impact, laying a robust foundation for AI-enabled innovations in K-12 settings. This comprehensive exploration underscores the pivotal role of artificial intelligence in advancing equitable, effective, and scalable solutions in education.
FediLive: A Framework for Collecting and Preprocessing Snapshots of Decentralized Online Social Networks
Authors: Shaojie Min, Shaobin Wang, Yaxiao Luo, Min Gao, Qingyuan Gong, Yu Xiao, Yang Chen
Venue: WWW
Year: 2025

Decentralized online social networks such as Mastodon have emerged quickly during the past years, and offer unique opportunities to investigate user behavior, moderation strategies, and community evolution. However, their decentralized nature imposes challenges for data collection and preprocessing, particularly for obtaining a real-time snapshot in a timely manner. This paper introduces FediLive, a framework designed to rapidly collect and preprocess the live feeds from Mastodon, generate a comprehensive snapshot in real-time, including user-generated contents, interaction networks, and users' demographic attributes. Such a snapshot could further be leveraged for data analysis from different angles, leading to a deeper understanding of user activities on Mastodon. Using FediLive, we collected a 13-day snapshot of Mastodon, covering the publicly-visible activities of all Mastodon users. Our study demonstrates the usefulness of FediLive, and reveals its potential in facilitating data-driven analysis for decentralized online social networks.
Stat-n-Ball: Enhancing Probabilistic Knowledge Graph Embeddings with Geometric and Confidence-Aware Models
Authors: Aniket Mitra, Vinu E. Venugopal
Venue: WWW
Year: 2025

Region-based Knowledge Graph Embedding (R-KGE) models, which represent entities as convex shapes (e.g., balls) and relations as geometric transformations in vector space, offer a promising approach for explainable and accurate reasoning over ontologies. However, existing R-KGE models assume perfect reliability of Knowledge Graphs (KGs), which is often unrealistic as real-world KGs are noisy and incomplete. To address this, Probabilistic Knowledge Graphs (P-KGs) associate axioms with confidence scores, capturing the uncertainty of their truthfulness. We propose Stat-n-Ball, a novel R-KGE framework that incorporates confidence scores by representing axioms' certainty as overlapping volumes between entities in vector space. Our approach enhances the geometric representation of KGs, enabling accurate link prediction and confidence estimation in probabilistic settings. Experimental evaluations on standard P-KG datasets demonstrate that Stat-n-Ball achieves atleast a 2× improvement in entity association detection and a minimum 5% reduction in confidence prediction error compared to state-of-the-art models. These results underscore its effectiveness in handling noisy and uncertain KGs while preserving logical and semantic integrity.
WavePulse: Real-time Content Analytics of Radio Livestreams
Authors: Govind Mittal, Sarthak Gupta, Shruti Wagle, Chirag Chopra, Anthony J. DeMattee, Nasir D. Memon, Mustaque Ahamad, Chinmay Hegde
Venue: WWW
Year: 2025

Radio remains a pervasive medium for mass information dissemination, with AM/FM stations reaching more Americans than either smartphone-based social networking or live television. Increasingly, radio broadcasts are also streamed online and accessed over the Internet. We present WavePulse, a framework that records, documents, and analyzes radio content in real-time. While our framework is generally applicable, we showcase the efficacy of WavePulse in a collaborative project with a team of political scientists focusing on the 2024 Presidential Election. We use WavePulse to monitor livestreams of 396 news radio stations over a period of three months, processing close to 500,000 hours of audio streams. These streams were converted into time-stamped, diarized transcripts and analyzed to answer key political science questions at both the national and state levels. Our analysis revealed how local issues interacted with national trends, providing insights into information flow. Our results demonstrate WavePulse's efficacy in capturing and analyzing content from radio livestreams sourced from the Web. Code and dataset can be accessed at https://wave-pulse.io
Graph Regularized Encoder Training for Extreme Classification
Authors: Anshul Mittal, Shikhar Mohan, Deepak Saini, Siddarth Asokan, Suchith Chidananda Prabhu, Lakshya Kumar, Pankaj Malhotra, Jian Jiao, Amit Singh, Sumeet Agarwal, Soumen Chakrabarti, Purushottam Kar, Manik Varma
Venue: WWW
Year: 2025

Extreme Classification (XC) offers a scalable and efficient solution for retrieving highly relevant ads in Sponsored Search settings, significantly enhancing user engagement and ad performance. Most tasks in sponsored search involve highly skewed distributions over the data point (query) and label (ads) space with limited or no labeled training data. One approach to tackle this long-tail classification problem is to use additional data, often in the form of a graph such as similar queries, same session queries etc. that are associated with user queries/ads, called graph metadata. Graph-based approaches, particularly Graph Convolutional Networks (GCNs), have been successfully proposed to leverage this graph metadata and improve classification performance. However, for tail inputs/labels, GCNs induce graph connections that can be noisy, leading to down- stream inaccuracies while also incurring significant computation and memory overheads. To address these limitations, we introduce a novel approach, RAMEN, that harnesses graph metadata as a regularizer while training a lightweight encoder rather than a compute- and memory- intensive GCN-based method. This avoids the inaccuracies incurred by noisy graph induction and sidesteps the computational costs of GCNs via an easy-to-train and deploy encoder. The proposed approach is a scalable and efficient solution that significantly outperforms GCN-based methods. Extensive A/B tests conducted on live multi-lingual Bing Ads search engine traffic revealed that RAMEN increases revenue by 1.25-1.5% and click-through rates by 0.5-0.6% while improving quality of predictions across different markets. Additionally, evaluations on public benchmarks show that RAMEN achieves up to 5% higher accuracy compared to state-of-the-art methods while being 50% faster to infer, and having 70% fewer parameters.
Leveraging Prompt Engineering with Lightweight Large Language Models to Label and Extract Clinical Information from Radiology Reports
Authors: Chayan Mondal, Duc-Son Pham, Ashu Gupta, Tele Tan, Tom Gedeon
Venue: WWW
Year: 2025

Chest X-ray imaging plays a critical role in diagnosing chest diseases, making it a cornerstone in clinical and research domains. Automating disease diagnosis and extracting relevant clinical information from chest X-ray reports have become essential for developing AI-driven healthcare systems. While effective, deep learning models require extensive labelled datasets, making the labelling of diseases from radiology reports crucial. Traditionally, rule-based labelling approaches have been employed, but the emergence of large language models (LLMs) has introduced new possibilities through instruction-based prompt engineering. In this study, we explore various prompt engineering techniques, including in-context learning and prompt chaining, to label multilabel disease reports and extract key clinical findings from radiology reports. We conducted ablation studies on both proprietary LLMs (e.g., GPT-4 Turbo, GPT-3.5 Turbo) and publicly available LLMs (e.g., Llama2-7B, Llama2-13B, Llama3-8B, Llama2-70B), comparing their performance in terms of clinical accuracy, privacy, and computational cost. Our findings demonstrate that well-crafted prompts on publicly available and lightweight LLMs can achieve competitive results compared to larger and/or proprietary models, offering a cost-effective and privacy-preserving solution for clinical applications. These results highlight the potential of leveraging advanced prompt engineering to streamline disease labelling and enhance the quality of automated report generation in radiology.
Towards Efficient Hypergraph Representation Learning for Multi-Behaviour Recommender Systems
Authors: Tendai Mukande, Esraa Ali, Annalina Caputo, Ruihai Dong, Noel E. O&apos, Connor
Venue: WWW
Year: 2025

Recommender systems (RS) are essential for the modern web, providing personalized suggestions that alleviate information overload and enhance the user experience across various platforms. Graph neural networks (GNN) have been proposed for RS and demonstrate significant potential. However, GNN-based methods are prone to oversmoothing in practical settings, limiting their expressive power and ability to capture complex data patterns effectively. Recent research has also explored graph-transformer-based RS methods that, while improving performance, tend to increase computational costs, particularly in large-scale scenarios. To address these challenges, we introduce FAHMRec, an efficient hypergraph-based model for multi-behaviour recommendation. Experimental results demonstrate that our method outperforms state-of-the-art baselines in recommendation quality while also reducing memory and time costs.
A Fairer Client Selection Framework for Federated Internet of Things: Equality, Equity, and Trade-off Perspectives
Authors: Noorain Mukhtiar
Venue: WWW
Year: 2025

The proliferation of the Internet of Things (IoT) devices has led to the generation of vast amounts of data containing users' private information. Federated Learning (FL) has emerged as a transformative approach to harness large volumes of data generated by IoT devices for intelligent insights while maintaining individual privacy. In FL-based IoT environments, where devices are highly diverse and operate under varying conditions, performance can fluctuate due to multiple factors, i.e., mobility, network congestion, and energy constraints. This variability contributes to the inherent heterogeneity, thereby posing significant challenges in maintaining performance consistency and fairness in resource distribution among participants in FL training. Through an extensive review of the state-of-the-art literature, we identify gaps in fairness-aware client selection strategies, particularly with dual dimensions of fairness: equality and equity. This paper explores a conceptual framework for fairness-aware client selection, formulates three research questions based on existing research gaps, discusses preliminary insights, and outlines key directions for future research, including algorithm design and performance evaluation under fairness constraints.
Imitating Human Reasoning to Extract 5W1H in News
Authors: Carlos Muñoz, Marcelo Mendoza, Hans Lobel, Brian Keith
Venue: WWW
Year: 2025

Extracting key information from news articles is crucial for advancing search systems. Historically, the 5W1H framework, which organises information based on 'Who', 'What', 'When', 'Where', 'Why', and 'How', has been a predominant method in digital journalism empowering search tools. The rise of Large Language Models (LLMs) has sparked new research into their potential for performing such information extraction tasks effectively. Our study examines a novel approach to employing LLMs in the 5W1H extraction process, particularly focusing on their capacity to mimic human reasoning. We introduce two innovative Chain-of-Thought (COT) prompting techniques to extract 5W1H in news: extractive reasoning and question-level reasoning. The former directs the LLM to pinpoint and highlight essential details from texts, while the latter encourages the model to emulate human-like reasoning at the question-response level. Our research methodology includes experiments with leading LLMs using prompting strategies to ascertain the most effective approach. The results indicate that COT prompting significantly outperforms other methods. In addition, we show that the effectiveness of LLMs in such tasks depends greatly on the nature of the questions posed.
EFF BI: Effortless Business Intelligence -Text-to-Vis from LLM Agents
Authors: Vaishnav Muralidharan, Ho Cheng En Bryan, Tan Wee Kian Justin, Muhammad Reyaaz, Suzanna Sia
Venue: WWW
Year: 2025

Traditional Business Intelligence (BI) tools rely on complex user interfaces and modeling languages, creating significant accessibility barriers for non-technical users. This often forces domain experts to rely on BI developers to translate and refine their requirements, resulting in time-intensive workflows. In this paper, we introduce Eff BI (Effortless Business Intelligence), an open-source, LLM-powered Text-to-Visualization (Text-to-Vis) tool that enables users to retrieve data from enterprise databases, and generate graphs and charts instantly through natural language. Eff BI's open-source architecture supports plug-and-play functionality with proprietary LLMs, ensuring data remains secure within company servers. Our application is fully open-source and accompanied by documentation for both users and future developers. An example of the visualization and corresponding SQL output is shown in Figure 1.
The Poorest Man in Babylon: A Longitudinal Study of Cryptocurrency Investment Scams
Authors: Muhammad Muzammil, Abisheka Pitumpe, Xigao Li, Amir Rahmati, Nick Nikiforakis
Venue: WWW
Year: 2025

Governments and regulatory bodies have recognized investment scams as a prevalent form of cryptocurrency fraud. These scams typically use professional-looking websites to lure unsuspecting victims with promises of unrealistically high returns. In this paper, we introduce Crimson, a distributed system designed to continuously detect cryptocurrency investment scam websites as they are created in the wild. During the first 8 months of 2024, Crimson processed approximately 6 billion domain names and classified 43,572 unique cryptocurrency investment scam websites in real-time. Beyond detection, we provide insights into the design and infrastructure of these websites that can help users recognize scam patterns and assist hosting providers in detecting and blocking such sites. Furthermore, we investigate the inclusion of our detected scam websites in block-lists used by popular web browsers and applications, finding that the vast majority of these websites were absent. On the financial side, by analyzing the transactions incoming to scammer wallets on 6.7% of the sites detected by Crimson, we observe an estimated lower bound of 2.04M USD in losses due to cryptocurrency investment scams.