OpenRaiser/Intern-Atlas huggingface dataset:
A Simple Feature Extraction based Approach to Solving the Brick by Brick 2024 Challenge
Authors: Kaushik Gopalan, Prithvi Dhyani, Arunima Srikant
Venue: WWW
Year: 2025

We present a machine-learning-based approach to efficiently classify IoT device data in the AIcrowd | Brick by Brick 2024 competition, enabling automation in smart building management. The increasing prevalence of IoT devices in modern buildings has led to an increase in time-series data from diverse sources. Efficiently classifying and organizing this data according to an ontology is crucial for applications such as energy optimization, fault detection, and predictive maintenance. However, manual classification is costly and time-consuming. To address this, we propose a Quartile-Based Shape Segmentation (QBaSS) method, which extracts lower-dimensional time-series representations for stratified classification using gradient boosting. Our results demonstrate reliable classification for most device types, achieving a Macro F1 score of 0.509 with the test data.
Mental Health and Relations: Detection of Mental Health Disorders Related to Relationship Issues Through Reddit Posts
Authors: Leela Prasad Gorrepati, Raj Sonani, Venkatesh Velugubantla, Ravi Teja Potla, MSVPJ Sathvik
Venue: WWW
Year: 2025

Relationship challenges, such as breakups and marital conflicts, often lead to mental health issues like depression and suicidal thoughts. To address this, we present a new binary classification dataset sourced from Reddit posts, specifically aimed at detecting mental health concerns triggered by relationship-related stress. The dataset categorizes text as either indicating mental health distress or not, offering valuable insights into the emotional effects of relationship problems. By applying advanced natural language processing techniques, this dataset supports early detection and intervention efforts. Furthermore, it can serve as a useful resource for government and non-government organizations (GOs and NGOs) to provide guidance and relationship counseling, offering timely support to individuals in need. The classifiers achieved an accuracy score of 78.43%.
ChartCitor: Answer Citations for ChartQA via Multi-Agent LLM Retrieval
Authors: Kanika Goswami, Puneet Mathur, Ryan A. Rossi, Franck Dernoncourt
Venue: WWW
Year: 2025

Large Language Models (LLMs) can perform chart question answering tasks but often generate unverified hallucinated responses. Existing answer attribution methods struggle to ground responses in source charts due to limited visual-semantic context, complex visual-text alignment requirements, and difficulties in bounding box prediction across complex layouts. We present ChartCitor, a multi-agent framework that provides fine-grained bounding box citations by identifying supporting evidence within chart images. The system orchestrates LLM agents to perform chart-to-table extraction, answer reformulation, table augmentation, evidence retrieval through pre-filtering and re-ranking, and table-to-chart mapping. ChartCitor outperforms existing baselines across different chart types. Qualitative user studies show that ChartCitor helps increase user trust in Generative AI by providing enhanced explainability for LLM-assisted chart QA and enables professionals to be more productive.
PlotGen: Multi-Agent LLM-based Scientific Data Visualization via Multimodal Retrieval Feedback
Authors: Kanika Goswami, Puneet Mathur, Ryan A. Rossi, Franck Dernoncourt
Venue: WWW
Year: 2025

Scientific data visualization is pivotal for transforming raw data into comprehensible visual representations, enabling pattern recognition, forecasting, and the presentation of data-driven insights. However, novice users often face difficulties due to the complexity of selecting appropriate tools and mastering visualization techniques. Large Language Models (LLMs) have recently demonstrated potential in assisting code generation, though they struggle with accuracy and require iterative debugging. In this paper, we propose PlotGen, a novel multi-agent framework aimed at automating the creation of precise scientific visualizations. PlotGen orchestrates multiple LLM-based agents, including: (1) a Query Planning Agent that breaks down complex user requests into executable steps; (2) a Code Generation Agent that converts pseudocode into executable Python code; and three retrieval feedback agents-(3) a Numeric Feedback Agent, (4) a Lexical Feedback Agent, and (5) a Visual Feedback Agent-that leverage multimodal LLMs to iteratively refine the data accuracy, textual labels, and visual correctness of generated plots via self-reflection. Extensive experiments show that PlotGen outperforms strong baselines on the MatPlotBench dataset, leading to enhanced user trust in LLM-generated visualizations and improved novice productivity due to reduced debugging time needed for plot errors.
[PromptEng] Second International Workshop on Prompt Engineering for Pre-Trained Language Models
Authors: Damien Graux, Sébastien Montella, Hajira Jabeen, Claire Gardent, Jeff Z. Pan
Venue: WWW
Year: 2025

The recent achievements and availability of Large Language Models have paved the road to a new range of applications and use-cases. Pre-trained language models are now being involved at-scale in many fields where they were until now absent from. More specifically, the progress made by causal generative models has open the door to using them through textual instructions aka. prompts. Unfortunately, the performances of these prompts are highly dependent on the exact phrasing used and therefore practitioners need to adopt fail-retry strategies. Based on the success of the past edition, this second international workshop on prompt engineering gathers practitioners (both from Academia and Industry) to exchange about good practices, optimizations, results and novel paradigms about the design of efficient and safe prompts.
PoliMeme: Exploring Offensive Meme Propagation in the Israel-Palestine Conflict
Authors: Pratham Grover, Vijayrajsinh Gohil, Bhavya Goel, Hariram Veeramani, Siddhant Bikram Shah, Sandesh Rajendra Jain, Surendrabikram Thapa, Imran Razzak, Usman Naseem
Venue: WWW
Year: 2025

Social media platforms have become powerful tools for communication, often harnessing memes to convey ideas, opinions, and at times misinformation. In the midst of global polarized issues, such as the Israel-Palestine conflict, memes can rapidly spread offensive content and speech, influencing public perception. We introduce PoliMeme, a novel multimodal dataset encompassing text-embedded memes relevant to the Israel-Palestine conflict, meticulously annotated for sentiment analysis (positive, negative, neutral) and offensive content detection, further subdivided into direction of offense (directed vs. undirected) and targeted entities (individuals, organizations, communities). We benchmark several state-of-the-art unimodal and multimodal models to evaluate their performance in classifying these memes. Our findings underscore the necessity of multimodal methods to fully capture the subtle interplay between textual cues and visual context. We further highlight challenges unique to politically charged and culturally sensitive content, emphasizing the importance of precise annotation and robust processing strategies. With the PoliMeme dataset and accompanying analysis, we present a timely contribution to advance the understanding of how offensive speech and sentiment are propagated through text-embedded memes, while also contributing to safer digital spaces by informing content moderation practices. The dataset is available at https://github.com/PrathamLearnsToCode/PoliMeme
ORFA: Exploring WebAssembly as a Turing Complete Query Language for Web APIs
Authors: Yuhao Gu, Chunyu Chen, Jiangsu Du, Xiaoxi Zhang, Xianwei Zhang
Venue: WWW
Year: 2025

Web APIs are the primary communication form for Web services, with RESTful design being the predominant paradigm. However, RESTful APIs are typically fixed once defined, causing data under- or over-fetching as they can't meet clients' varying Web service needs. While semantic enriched API query languages like GraphQL mitigates this problem, they still face expressiveness limitations for logical operations such as indirect queries and loop traversals. To address this, we propose ORFA (One Request For All), the first in literature that employs WebAssembly (Wasm) as a Web API query language to achieve complete expressiveness of client requests. ORFA's key advantage lies in its use of Wasm's Turing completeness to allow clients to compose arbitrary operations within a single request, thus significantly eliminating redundant data transmission and boosting communication efficiency. Technically, ORFA provides a runtime for executing Wasm query programs and incorporates new module splitting strategies and a caching mechanism customized for integrating Wasm into Web API services, which can enable lightweight code transfer and fast request responses. Experimental results on a realistic testbed and popular Web applications show that ORFA effectively reduces latency by 18.4% and network traffic by 24.5% on average, compared to the state-of-the-art GraphQL.
DecETT: Accurate App Fingerprinting Under Encrypted Tunnels via Dual Decouple-based Semantic Enhancement
Authors: Zheyuan Gu, Chang Liu, Xiyuan Zhang, Chen Yang, Gaopeng Gou, Gang Xiong, Zhen Li, Sijia Li
Venue: WWW
Year: 2025

Due to the growing demand for privacy protection, encrypted tunnels have become increasingly popular among mobile app users, which brings new challenges to app fingerprinting (AF)-based network management. Existing methods primarily transfer traditional AF methods to encrypted tunnels directly, ignoring the core obfuscation and re-encapsulation mechanism of encrypted tunnels, thus resulting in unsatisfactory performance. In this paper, we propose DecETT, a dual decouple-based semantic enhancement method for accurate AF under encrypted tunnels. Specifically, DecETT improves AF under encrypted tunnels from two perspectives: app-specific feature enhancement and irrelevant tunnel feature decoupling. Considering the obfuscated app-specific information in encrypted tunnel traffic, DecETT introduces TLS traffic with stronger app-specific information as a semantic anchor to guide and enhance the fingerprint generation for tunnel traffic. Furthermore, to address the app-irrelevant tunnel feature introduced by the re-encapsulation mechanism, DecETT is designed with a dual decouple-based fingerprint enhancement module, which decouples the tunnel feature and app semantic feature from tunnel traffic separately, thereby minimizing the impact of tunnel features on accurate app fingerprint extraction. Evaluation under five prevalent encrypted tunnels indicates that DecETT outperforms state-of-the-art methods in accurate AF under encrypted tunnels, and further demonstrates its superiority under tunnels with more complicated obfuscation. Project page: https://github.com/DecETT/DecETT
A Masked AutoEncoder with Strong-Weak Mutual Information for Anomaly Detection in Dynamic Incomplete Graphs
Authors: Junquan Gu, Hang Yu, Xiangfeng Luo
Venue: WWW
Year: 2025

Most graph anomaly detection methods leverage GNNs to learn from relatively high-quality graph data. Unfortunately, such ideal scenarios are rare in real-world applications, where most data suffer from issues such as missing labels, dynamic changes, and incompleteness, collectively referred to as Dynamic Incomplete Graphs (DIGs). To address the challenges of GNNs failing under extreme conditions, we propose the Graph Masked AutoEncoder with Strong-Weak Mutual Information (GMAE-SWMI). This framework simulates real-world DIG scenarios by masking graph structures (nodes/edges) and node features. Additionally, through the SWMI loss, it captures the relationship between structure and features while maintaining structural integrity, reducing overfitting, and improving generalization. We evaluated our approach on eight real-world graph datasets, demonstrating its superiority over state-of-the-art methods across different levels of DIG scenarios across various downstream tasks and representation evaluations, both with and without labels.
Wearing Privacy at Risk: Privacy Leakages and Defense Strategies in Encrypted Traffic for Wearable Devices
Authors: Ransika Gunasekara
Venue: WWW
Year: 2025

Wearable IoT devices, such as fitness trackers and smartwatches, transmit sensitive user data over Bluetooth protocols, making them vulnerable to traffic analysis attacks even when encryption is employed. However, existing research in encrypted traffic analysis has predominantly focused on Ethernet-based traffic, leaving a significant gap in understanding privacy leakages from wearable IoT devices. To address this gap, we first investigate the privacy threats posed by encrypted Bluetooth traffic analysis in wearable IoT devices, highlighting how adversaries can infer important data from encrypted communications. For this, we propose leveraging advanced time-series analysis techniques and few-shot learning models to tackle challenges such as limited training data and the heterogeneous nature of wearable IoT traffic. Next, we explore privacy-preserving defenses against encrypted traffic analysis designed specifically for wearable devices, considering their limited computational resources. We examine lightweight traffic-shaping mechanisms that enhance privacy while maintaining acceptable performance levels.
GAIN: Gradual Adaptation for Continual Learning in Low-resource Environments
Authors: Shih-Wei Guo, Yao-Chung Fan
Venue: WWW
Year: 2025

Continual Learning (CL) faces challenges such as catastrophic forgetting and data scarcity in low-resource tasks, which limit the model's ability to adapt to gradually changing tasks. We propose a general CL method for low-resource tasks to address these challenges called GAIN. This method addresses cross-domain continual learning through two approaches: (i) the Gradual Adaptation Module, which incrementally stacks lightweight adapters to effectively retain knowledge from previous tasks and adapt to new ones, thereby mitigating catastrophic forgetting, and (ii) a bottleneck layer size tuning strategy to improve learning efficiency in low-resource scenarios. Finally, we have extensive experiments on four datasets to validate the effectiveness and robustness of GAIN, showing that it alleviates catastrophic forgetting and enhances learning efficiency across various task types.
Robust Aggregation with Adversarial Experts
Authors: Yongkang Guo, Yuqing Kong
Venue: WWW
Year: 2025

We consider a robust aggregation problem in the presence of both truthful and adversarial experts. The truthful experts will report their private signals truthfully, while the adversarial experts can report arbitrarily. We assume experts are marginally symmetric in the sense that they share the same common prior and marginal posteriors. The rule maker needs to design an aggregator to predict the true world state from these experts' reports, without knowledge of the underlying information structures or adversarial strategies. We aim to find the optimal aggregator that outputs a forecast minimizing regret under the worst information structure and adversarial strategies. The regret is defined by the difference in expected loss between the aggregator and a benchmark who aggregates optimally given the information structure and reports of truthful experts. We focus on binary states and reports. Under L1 loss, we show that the truncated mean aggregator is optimal. When there are at most k adversaries, this aggregator discards the k lowest and highest reported values and averages the remaining ones. For L2 loss, the optimal aggregators are piecewise linear functions. All the optimalities hold when the ratio of adversaries is bounded above by a value determined by the experts' priors and posteriors. The regret only depends on the ratio of adversaries, not on their total number. For hard aggregators that output a decision, we prove that a random version of the truncated mean is optimal for both L1 and L2. This aggregator randomly follows a remaining value after discarding the k lowest and highest reported values. We extend the hard aggregator to multi-state setting. We evaluate our aggregators numerically in an ensemble learning task. We also obtain negative results for general adversarial aggregation problems under broader information structures and report spaces.
Mitigating the Participation Bias by Balancing Extreme Ratings
Authors: Yongkang Guo, Yuqing Kong, Jialiang Liu
Venue: WWW
Year: 2025

Rating aggregation plays a crucial role in various fields, such as product recommendations, hotel rankings, and teaching evaluations. However, traditional averaging methods can be affected by participation bias, where some raters do not participate in the rating process, leading to potential distortions. In this paper, we consider a robust rating aggregation task under the participation bias. We assume that raters may not reveal their ratings with a certain probability depending on their individual ratings, resulting in partially observed samples. Our goal is to minimize the expected squared loss between the aggregated ratings and the average of all underlying ratings (possibly unobserved) in the worst-case scenario. We focus on two settings based on whether the sample size (i.e. the number of raters) is known. In the first setting, where the sample size is known, we propose an aggregator, named as the Balanced Extremes Aggregator. It estimates unrevealed ratings with a balanced combination of extreme ratings. When the sample size is unknown, we derive another aggregator, the Polarizing-Averaging Aggregator, which becomes optimal as the sample size grows to infinity. Numerical results demonstrate the superiority of our proposed aggregators in mitigating participation bias, compared to simple averaging and the spectral method. Furthermore, we validate the effectiveness of our aggregators on a real-world dataset.
FQora: Towards Fair and Quality-based Online Data Marketplace
Authors: Songyue Guo, Maocheng Li, Zhao Chen, Caleb Chen Cao, Lei Chen
Venue: WWW
Year: 2025

Acquiring high-quality training data is a fundamental challenge in building accurate and robust machine learning models, particularly with the rise of foundational models. Existing online data marketplaces face limitations, including a lack of quality guarantees for purchased data and an overemphasis on sellers' benefits, resulting in unfair pricing and reduced buyer activity. To address these issues, we present FQora, a demonstration system for a fair and quality-based data marketplace. FQora introduces quality-constrained queries using quality-based pricing functions and effective quality assessment mechanisms. To ensure a balanced and sustainable market, FQora implements a mean-variance constraint and a novel Balanced Pareto Optimization to maximize utilities for both buyers and sellers. This system demonstrates advantages in usability, fairness, and market stability. The code of our prototype is open-sourced and available at https://github.com/Songyue-Guo/FQora_web_demo.
Enhancing Knowledge Tracing through Decoupling Cognitive Pattern from Error-Prone Data
Authors: Teng Guo, Yu Qin, Yubin Xia, Mingliang Hou, Zitao Liu, Feng Xia, Weiqi Luo
Venue: WWW
Year: 2025

Knowledge tracing (KT) aims to predict students' future performance based on their past learning activities. However, no one is perfect. Factors such as carelessness, fatigue, and stress often cause students to make mistakes on problems they have already mastered, leading to anomalies in their historical learning data. These anomalies disrupt inherent patterns in the data, misleading the KT model. Extracting cognitive patterns that accurately reflect students' knowledge mastery from such error-prone data remains a significant challenge. Against this background, this paper proposes a novel KT method named RoubstKT, inspired by educational measurement theory and frequency-based decomposition. A cognitive decoupling analyzer is proposed to decouple the student's cognitive pattern and random factors from the data through smoothing and subtraction operations, then recombine them using a gating mechanism or adaptive parameter fusion strategy. To more effectively diagnose students' knowledge mastery, we employ a decay-based attention mechanism that focuses on random behaviors at adjacent time steps. We conducted comprehensive experiments based on real-world datasets and targeted datasets with added random noise. The experimental results demonstrated the effectiveness of the proposed method.
Unified and Generalizable Reinforcement Learning for Facility Location Problems on Graphs
Authors: Wenxuan Guo, Runzhong Wang, Yanyan Xu, Yaohui Jin
Venue: WWW
Year: 2025

Facility location problems on graphs are ubiquitous in the real world and hold significant importance, yet their resolution is often impeded by NP-hardness. MIP solvers can find the optimal solutions but fail to handle large instances, while algorithm efficiency has a higher priority in cases of emergency. Recently, machine learning methods have been proposed to tackle such classical problems with fast inference, but they are limited to the myopic constructive pattern and only consider simple cases in Euclidean space. This paper introduces a unified and generalizable approach to tackle facility location problems on weighted graphs with deep reinforcement learning, demonstrating a keen awareness of complex graph structures. Striking a harmonious balance between solution quality and running time, our method stands out with superior efficiency and steady performance. Our model trained on small graphs is highly scalable and consistently generates high-quality solutions, achieving a speedup of more than 2000 times to Gurobi on instances with 1000 nodes. The experiments on Shanghai road networks further demonstrate its practical value in solving real-world problems. The source codes are available at https://github.com/AryaGuo/PPO-swap.
MH-Diff: Muti-scale Hyperbolic Model for Information Diffusion Prediction
Authors: Fuxia Guo, Xiaowen Wang, Lanjun Wang
Venue: WWW
Year: 2025

Information diffusion prediction aims to predict which users are more likely to engage in the diffusion path on the next timestamp. Previous methods focus on either capturing dynamic dependencies in Euclidean space or static implicit dependencies in hyperbolic space, lacking the double consideration of both the implicit hierarchical relationships that are hard captured in Euclidean space and dynamic user preferences over time. In this study, we propose a novel Multi-scale Hyperbolic Diffusion model (MH-Diff), which leverages the unique properties of hyperbolic space to capture the complex hierarchical relationships in both social graphs and diffusion cascades. Specifically, we utilize the hyperbolic space to represent user dependencies and implicit tree-like structures, which are difficult to model in traditional Euclidean space. Next, we introduce a dynamic hyperbolic learning module with multi-scale sequential hypergraph attention network to captures users' dynamic preferences at different time scales. Moreover, we leverage a contextual prediction module to enhance the interaction among user hyperbolic representations in the current cascade. Finally, we evaluate MH-Diff on three real-world datasets and demonstrate that it significantly outperforms state-of-the-art models, validating the superior capability of hyperbolic space in capturing both global statics and temporal dynamics.