OpenRaiser/Intern-Atlas huggingface dataset:
Addressing the Challenges of Mental Health Conversations with Large Language Models
Authors: Shuvam Shiwakoti, Siddhant Bikram Shah, Imran Razzak, Surendrabikram Thapa, Usman Naseem
Venue: WWW
Year: 2025

Virtual Mental Health Assistants offer a promising solution to address the growing demand for accessible and scalable mental healthcare. However, existing dialogue generation models struggle with the complexities inherent in mental health conversations. In this paper, we explore the limitations of current Medical Dialogue Generation models by conducting experiments on the large language model ChatMGL. We propose modifications to ChatMGL, including fine-tuning the model on a mental health dataset without proximal policy optimization and incorporating dialogue act labels, to enhance its ability to handle the complex nature of mental health dialogues. Our results demonstrate that these modifications outperform baseline models in terms of ROUGE and BERT scores. Our work suggests that specialized fine-tuning and incorporating domain-specific knowledge can improve the efficacy of virtual assistants for mental health support.
Reward Balancing Revisited: Enhancing Offline Reinforcement Learning for Recommender Systems
Authors: Wenzheng Shu, Yanxiang Zeng, Yongxiang Tang, Teng Sha, Ning Luo, Yanhua Cheng, Xialong Liu, Fan Zhou, Peng Jiang
Venue: WWW
Year: 2025

Offline reinforcement learning (RL) has emerged as a prevalent and effective methodology for real-world recommender systems, enabling learning policies from historical data and capturing user preferences. In offline RL, reward shaping encounters significant challenges, with past efforts to incorporate prior strategies for uncertainty to improve world models or penalize underexplored state-action pairs. Despite these efforts, a critical gap remains: the simultaneous balancing of intrinsic biases in world models and the diversity of policy recommendations. To address this limitation, we present an innovative offline RL framework termed Reallocated Reward for Recommender Systems (R3S). By integrating inherent model uncertainty to tackle the intrinsic fluctuations in reward predictions, we boost diversity for decision-making to align with a more interactive paradigm, incorporating extra penalizers with decay that deter actions leading to diminished state variety at both local and global scales. The experimental results demonstrate that R3S improves the accuracy of world models and efficiently harmonizes the heterogeneous preferences of the users.
DMSNet: A Lightweight and Efficient Facial Expression Recognition Model for IoT and WoT Applications
Authors: Hemraj Singh, Prashanth Baithi, Zarka Bashir
Venue: WWW
Year: 2025

Facial expression recognition (FER) plays a crucial role in computer vision, driving advancements in gesture recognition, patient monitoring, and human-robot interaction. Despite its potential, traditional FER methods struggle with geometric variations in facial expression features within static images, often leading to an imbalance between model performance and network complexity. This results in increased computational demands, hindering their deployment in resource-constrained environments such as the Internet of Things (IoT) or Web of Things (WoT) and mobile. To address these challenges, we propose the Deformable Multi-Scale Net work (DMSNet), a lightweight and efficient model specifically designed to capture multi-scale geometric variations in spatial expression features dynamically using depthwise separable convolution, deformable convolution and Receptive Field Blocks (RFBs) while minimizing parameters. It is highly suitable for real-time applications with just 5.6 million parameters, 100.8 million floating point operations, and 30 frame-per-second (FPS) inference speeds. Extensive experiments on five benchmark datasets demonstrate superior performance compared to state-of-the-art (SOTA) models for FER.
Bias-Aware Agent: Enhancing Fairness in AI-Driven Knowledge Retrieval
Authors: Karanbir Singh, William Ngu
Venue: WWW
Year: 2025

Advancements in retrieving accessible information have evolved faster in the last few years compared to the decades since the internet's creation. Search engines, like Google, have been the #1 way to find relevant data. They have always relied on the user's abilities to find the best information in its billions of links and sources at everybody's fingertips. The advent of large language models (LLMs) has completely transformed the field of information retrieval. LLMs excel not only at retrieving relevant knowledge, but also in summarizing it effectively, making information more accessible and consumable for users. On top of it, the rise of AI Agents has introduced another aspect to information retrieval, i.e. dynamic information retrieval which enables the integration of real-time data such as weather forecasts and financial data with the knowledge base to curate context-aware knowledge. However, despite these advancements, agents remain susceptible to issues of bias and fairness deeply rooted in the knowledge base and training of LLMs. This study introduces a novel approach to bias-aware knowledge retrieval by leveraging agentic framework and the innovative use of bias detectors as tools to identify and highlight inherent biases in the retrieved content. By empowering users with transparency and awareness, this approach aims to foster more equitable information systems and promote the development of responsible AI.
Domain-Specific Aspect Extraction for Product Design
Authors: Divyam Sobti, Mahima Agumbe Suresh, Cristina Tortora, Vimal K. Viswanathan
Venue: WWW
Year: 2025

As technology advances, computers become increasingly proficient at interpreting and translating human language into machine-understandable text. With the help of algorithms in natural language processing (NLP), machines can now translate textual data. These algorithms help identify and extract specific text components known as aspects. The aspects represent specific attributes or topics within textual data. For instance, if a product review states,'' This phone has good battery life but poor camera quality,'' and attributes like 'battery life' and 'camera quality' represent aspects in the text. Aspect extraction is a pivotal process involving identifying and isolating key features or topics within text. This research aims to compare and discuss the existing aspect extraction techniques. By effectively extracting the aspects, we will help designers gain the capability to understand and analyze sentiments, thereby enhancing their ability to derive meaningful insights from diverse textual data. Aspect-based sentiment analysis (ABSA) enables the extraction of sentiments towards specific aspects of a product the user provides. For example, when a person writes a review about a restaurant, sentiment analysis can determine whether the review is positive or negative. ABSA can separately determine the review's sentiment towards different aspects of a restaurant, such as, food quality, ambiance, etc. An important input to ABSA are the aspect keywords to find in the reviews. We propose an approach to extract features/aspects from customer-based product reviews. We experiment with different word embedding and clustering techniques to identify the best set of parameters for product design. Our experiments indicate that Global Vectors (GloVe) used on Gaussian Mixture Models (GMMs) yield the most insightful results for the specific problem domain.
MatriXSSed: A New Taxonomy for XSS in the Modern Web
Authors: Dolière Francis Somé
Venue: WWW
Year: 2025

Cross-site scripting (XSS) has constantly remained one of the most prevalent attacks on the Web. In this work, we question its current taxonomy, i.e., the client- or server-side reflected (non-persistent) or stored (persistent) matrix. The Web has extensively changed. Consequently, considering XSS with the lenses of this famous matrix has become at least imprecise, at most impossible for many code injection scenarios where (i) a service worker or an edge worker generates HTTP responses and can reflect or persist XSS payloads infecting not only JavaScript in web pages but also Web assembly, web workers and affecting one or many users automatically; (ii) an attacker sends a web push message directly to a browser push service to trigger code execution in a dormant service worker; or (iii) a cross-origin adversary tampers with code stored by a vulnerable website on the user's physical/permanent file system, etc. Our proposal --to get out of the matrix and not enter another rigid one-- expresses the essence of XSS as code infection and affection attack and allows for clearly specifying the different actors and components involved, their environments, contexts, and storages, as well as their recurrence and persistence seen as a continuum rather than a binary marker. From a defensive perspective, we showcase the challenges and limitations of current mechanisms for mitigating XSS, which targets the entire attack surface of modern websites. Finally, we demonstrate an abuse of the Service-Worker-Allowed header to control entire domains with malicious service workers.
Optimizing Discriminative Vision-Language Models for Efficient Multimodal Intent Recognition
Authors: Jinwang Song, Zhongtian Hua, Hongying Zan, Yingjie Han, Min Peng
Venue: WWW
Year: 2025

The WWW2025 Multimodal Dialogue System Intent Recognition Challenge aims to enhance the understanding capabilities of e-commerce intelligent customer service systems, requiring participants to develop intent recognition models that jointly analyze textual and visual information for more precise user intent interpretation. This paper presents the solution proposed by Team Adventure, which proposes an optimized framework based on Vision-Language Models (VLMs), incorporating data augmentation, generative and discriminative fine-tuning, and inference optimization. Experimental results demonstrate that discriminative fine-tuning of VLMs offers significant advantages in training efficiency and inference speed. Furthermore, we introduce the Label-Token Initialization (LTI) method, which effectively mitigates the mismatch between the pre-training and discriminative fine-tuning stages.
FedEDM: Federated Equivariant Diffusion Model for 3D Molecule Generation with Enhanced Communication Efficiency
Authors: Zixing Song, Irwin King, José Miguel Hernández-Lobato
Venue: WWW
Year: 2025

The generation of 3D molecule structures plays a crucial role in drug discovery and materials design. However, sharing molecular data across organizations raises privacy concerns due to intellectual property and competitive advantages. Federated learning offers a promising solution by enabling collaborative model training without sharing raw data, but faces challenges in communication overhead when training popular diffusion models for 3D molecule generation. In this paper, we present FedEDM, a federated learning framework for training E(3) Equivariant Diffusion Models (EDM) for 3D molecule generation with significantly reduced communication overhead. FedEDM introduces two key innovations: (1) a parameter splitting strategy where clients are randomly assigned to update specific subsets of the model parameters of the EDM model in each round, reducing the model uploading communication, and (2) a quantized parameter update mechanism that employs post-training quantization techniques specifically designed for the EDM model. Our proposed FedEDM model enables enhanced communication efficiency for the training of 3D molecule generators in a federated learning context without compromising model performance or geometric symmetric constraints.
A Self-Supervised Adaptive Tuning Approach for Out-of-Distribution Social Media Bot Detection
Authors: Yuxuan Song, Qiudan Li, Shu Wu, Zheng Xie, Daniel Dajun Zeng
Venue: WWW
Year: 2025

With the rapid advancement of generative AI and deepfake technologies, social media bots have become increasingly sophisticated, engaging in spreading misinformation. Detecting these bots early and effectively is critical for enhancing users' online experience. Existing approaches primarily rely on fixed model parameters obtained during training, which often struggle with the Out-of-Distribution (OOD) problem caused by discrepancies between training and test data. To overcome the above challenges, this paper introduces a novel adaptive feature tuning approach that incorporates a test-time fine-tuning mechanism. By leveraging self-supervised contrastive learning, this approach dynamically adjusts model parameters, enabling the extraction of more robust feature representations and better adaptation to unseen data distributions. Extensive experiments on commonly used baseline models demonstrate the effectiveness of the proposed method.
AERO: Enhancing Sharding Blockchain via Deep Reinforcement Learning for Account Migration
Authors: Mingxuan Song, Pengze Li, Bohan Zhou, Shenglin Yin, Zhen Xiao, Jieyi Long
Venue: WWW
Year: 2025

Sharding blockchain networks face significant scalability challenges due to high frequencies of cross-shard transactions and uneven workload distributions among shards. To address these scalability issues, account migration offers a promising solution. However, existing migration solutions struggle with the high computational overhead and insufficient capture of complex transaction patterns. We propose AERO, a deep reinforcement learning framework to facilitate efficient account migration in sharding blockchains. AERO employs a prefix-based grouping strategy to enable group-level migration decisions and capture complex transaction patterns and relationships between accounts. We also implement a sharding blockchain system called AEROChain, which integrates AERO and aligns with the blockchain decentralization principle. Extensive evaluation with real Ethereum transaction data demonstrates that AERO improves the system throughput by 31.77% compared to existing solutions, effectively reducing cross-shard transactions and balancing shard workloads.
Assessing and Post-Processing Black Box Large Language Models for Knowledge Editing
Authors: Xiaoshuai Song, Zhengyang Wang, Keqing He, Guanting Dong, Yutao Mou, Jinxu Zhao, Weiran Xu
Venue: WWW
Year: 2025

The rapid evolution of the Web as a key platform for information dissemination has led to the growing integration of large language models (LLMs) in Web-based applications. However, the swift changes in web content present challenges in maintaining these models' relevance and accuracy. The task of Knowledge Editing (KE) is aimed at efficiently and precisely adjusting the behavior of large language models (LLMs) to update specific knowledge while minimizing any adverse effects on other knowledge. Current research predominantly concentrates on editing white-box LLMs, neglecting a significant scenario: editing black-box LLMs, where access is limited to interfaces and only textual output is provided. In this paper, we initially officially introduce KE on black-box LLMs, followed by presenting a thorough evaluation framework. This framework operates without requiring logits and considers pre- and post-edit consistency, addressing the limitations of current evaluations that are inadequate for black-box LLMs editing and lack comprehensiveness. To address privacy leaks of editing data and style over-editing in existing approaches, we propose a new postEdit framework. postEdit incorporates a retrieval mechanism for editing knowledge and a purpose-trained editing plugin called post-editor, ensuring privacy through downstream processing and maintaining textual style consistency via fine-grained editing. Experiments and analysis conducted on two benchmarks show that postEdit surpasses all baselines and exhibits robust generalization, notably enhancing style retention by an average of +20.82%. Our code is available on github https://github.com/songxiaoshuai/postEdit.
MemHateCaptioning: Enhancing Hate Speech Detection in Memes with Context-Aware Captioning and Chain-of-Thought
Authors: Rishik Sood, Ali Anaissi, Weidong Huang, Ali Braytee
Venue: WWW
Year: 2025

Hate speech has become increasingly prevalent on social media, with memes presenting a unique challenge due to their multimodal nature, combining text, images, and often subtle cultural cues that spread hate online. Several hate speech detection models have recently been proposed to identify hate in multimodal memes. However, these methods may suffer from issues like context ambiguity, subtle visual cues, and multimodal complexity. Generating accurate captions from memes while considering the context, images, symbols, and text can help capture the intended meaning and, consequently, improve the accuracy of hate speech detection. This study introduces MemHateCaptioning, a framework designed to generate clear, human-like explanations to contextualize why a meme is flagged as hateful. MemHateCaptioning leverages recent advancements in vision-language models (VLMs) and large language models (LLMs), integrating ClipCap for image captioning, BLIP for language-image pretraining, and T5 for explanation generation. The framework incorporates Chain-of-Thought (CoT) prompting to enhance interpretability, enabling the model to break down complex reasoning step by step, which helps in comprehending the subtle interplay between text and images in hateful content. MemHateCaptioning is evaluated on the HatReD dataset and demonstrates strong performance, achieving higher BLEU and ROUGE-L scores compared to existing models. It also effectively reduces issues such as hallucinations and context misinterpretation by providing detailed, context-aware explanations.
Legal and Ethical Challenges of Femtech: Protecting Women's Data & Rights in the Age of Surveillance Capitalism
Authors: Luana Mathias Souto, Jörg Müller
Venue: WWW
Year: 2025

This paper explores the potential legal and ethical challenges of safeguarding women in the European Union under the context of a rising Femtech industry, focusing on emerging rights violations related to data collection on menstruation and period track applications. Historically, data concerning women's fertility and reproductive decisions was stored individually and locally, with legal protections against disclosure. However, with the advent of Femtech, women worldwide now freely provide this information to tech companies operating globally, potentially yielding unprecedented power to companies, third parties or governments over the health and reproductive behavior of women. As such, Femtech represents a paradigmatic relationship between data/algorithm based applications and citizen rights, health care, and social inequalities, marking a new era of biopolitical management and surveillance. Despite this, social and political sciences, as well as ethical and legal frameworks, do not sufficiently address the complexity of these issues. Based upon a scoping literature review, our contribution will summarize the main trends and challenges from a legal and ethical point of view. We content that effective policy in this area will have to bringing up a more gender sensitive impact on women's data protection regulation under the Femtech industry context.
Federated Fine-Tuning of Large Language Models: Kahneman-Tversky vs. Direct Preference Optimization
Authors: Fernando Spadea, Oshani Seneviratne
Venue: WWW
Year: 2025

We evaluate Kahneman-Tversky Optimization (KTO) as a fine-tuning method for large language models (LLMs) in federated learning (FL) settings, comparing it against Direct Preference Optimization (DPO). Using Alpaca-7B as the base model, we fine-tune on a realistic dataset under both methods and evaluate performance using MT-Bench-1, Vicuna, and AdvBench benchmarks. Additionally, we introduce a redistributed dataset setup, where only KTO is applicable due to its ability to handle single-response feedback, unlike DPO's reliance on paired responses. Our results demonstrate that KTO, in both its original (KTOO) and redistributed (KTOR) configurations, consistently outperforms DPO across all benchmarks. In the redistributed setup, KTO further validates its flexibility and resilience by maintaining superior performance in scenarios where DPO cannot be applied. These findings establish KTO as a robust and scalable fine-tuning method for FL, motivating its adoption for privacy-preserving, decentralized, and heterogeneous environments.
15th Temporal Web Analytics Workshop (TempWeb) Overview
Authors: Marc Spaniol, Omar Alonso, Ricardo Baeza-Yates
Venue: WWW
Year: 2025

In times of change and uncertainty people strive for ''safe harbor''. While there are potentially many of them, TempWeb has become such a venue for the web community investigating the many temporal facets of the continuously emerging Web. Celebrating its 15th anniversary, the TempWeb workshop series has become a well-established co-located event at The Web Conference. In its nature, TempWeb is a melting pot for researchers and practitioners across various domains that study a wide range of core computer science aspects as well as application scenarios, e.g., in the social sciences. To this end, contributions to TempWeb cover temporal IE/IR, web mining, web archiving and large scale data analysis to just name a few. Further, studies on infrastructures, scalable methods, and innovative software for aggregating, querying, and analyzing heterogeneous data at web scale are bridging the gap between academia and industry. As a result, TempWeb brings forward innovations intertwining application domains with computer science research and industry. Given the many frequent and returning contributors, we consider TempWeb as a ''safe harbor'' for temporal web researchers. In its 15th edition, topics include, e.g., event and social network analysis, topic modeling, and urban planning.
Another Brick in the Wall: Leveraging Feature Extraction and Ensemble Learning for Building Data Classification
Authors: Bram Steenwinckel, Sofie Van Hoecke, Femke Ongenae
Venue: WWW
Year: 2025

Accurately classifying building time series data enables the identification of patterns, detecting anomalies, and the understanding of how people behave within a building and eventually leads to the automated control, predictive maintenance, and efficient energy management of smart buildings. The 2024 Brick by Brick competition aimed to create machine learning models that classify devices, which output building time series data according to the standardized Brick schema. Since manually labeling all these devices can require significant effort, the competition's goal was to automate this process. In this work, the competition's multi-label classification problem is reframed as a multiclass task in order to apply a classical multiclass classification approach. Our solution involves a structured pipeline where we extract statistical, temporal and frequency-domain features from various time intervals and apply feature selection techniques to enhance the model's efficiency. An extra-trees classifier, optimized using stratified k-fold cross-validation, forms the core of our model. Our method achieves a macro F1-score of 0.5767 on the Brick by Brick's public leaderboard test set, resulting in the 4th place and demonstrating its effectiveness in automating building device classification. By reducing manual effort and improving classification accuracy, our approach contributes to more scalable and intelligent building management systems.