OpenRaiser/Intern-Atlas huggingface dataset:
HeatSnap: A Hot Page-Aware Continuous Snapshots System for Virtual Machines in Web Infrastructure
Authors: Kangyue Gao, Chuangyu Ouyang, Xinkui Zhao, Miao Ye, Chen Zhi, Guanjie Cheng, Yueshen Xu, Shuiguang Deng, Jianwei Yin
Venue: WWW
Year: 2025

Snapshot technology is crucial for data protection and system recovery in virtualized environments, particularly with the growing need for continuous snapshots to maintain the integrity of long-running web-based and distributed applications. However, traditional snapshot methods often suffer from performance bottlenecks, and inefficient storage usage. These challenges are closely tied to the way memory pages are accessed during VM execution, where memory access patterns show significant disparities between frequently accessed "hot" pages and less-used "cold" pages. In this paper, we introduce HeatSnap, a continuous snapshot system designed to address these issues by leveraging the uneven access frequencies of memory pages. HeatSnap distinguishes between intensive hot pages and dirty pages, applying specialized snapshotting and storage strategies to optimize the handling of both hot and cold memory regions. This approach aims to optimize snapshot efficiency, minimize performance impact on the VM, and decrease storage costs. Our implementation of HeatSnap on QEMU/KVM demonstrates significant improvements in VM performance loss, snapshot duration, and storage efficiency compared to existing methods, as evidenced by evaluations on common web and cloud-based workloads.
EcomMIR: Towards Intelligent Multimodal Intent Recognition in E-Commerce Dialogue Systems
Authors: Tianhong Gao, Genhang Shen, Yuxuan Wu, Zunlei Feng, Jinshan Zhang, Sheng Zhou
Venue: WWW
Year: 2025

Image scene classification and dialogue intent recognition are fundamental tasks in intelligent e-commerce. The former classifies user-uploaded images, while the latter integrates multi-turn dialogue and visual information to extract user intent. However, existing multimodal models struggle with effectively utilizing e-commerce data and generalizing to vertical domains, limiting their practical applicability. To address this, we propose EcomMIR, an E-COMmerce Multimodal Intent Recognition framework based on CN-CLIP and MiniCPM-V. EcomMIR improves model generalization and robustness through multi-level intent data denoising, high-confidence data selection, and hierarchical labeling. Specifically, CN-CLIP employs contrastive learning to align image and text embeddings for efficient scene classification, while MiniCPM-V, a multimodal large language model, deeply integrates textual and visual information to model dialogue context and accurately recognize user intent. Experimental results show that EcomMIR achieves superior performance in both tasks, ranking Top 2 in the WWW2025 Multimodal Intent Recognition for Dialogue Systems challenge, offering an effective solution for multimodal tasks in intelligent e-commerce.
Fine-Grained Data Inference via Incomplete Multi-Granularity Data
Authors: Hepeng Gao, Yijun Su, Funing Yang, Yongjian Yang
Venue: WWW
Year: 2025

Urban fine-grained data map inference, leveraging information from coarse-grained maps, has emerged as a significant area of research due to the growing complexity and data heterogeneity in urban environments. Existing methods have a priori assumption that a coarse-grained data map, one fixed-size granularity, transforms into a fine-grained data map, also one fixed-size granularity. However, in actual scenarios, the collected coarse-grained data maps are often incomplete and have significantly distinct granularities in various urban areas, which results in incomplete heterogeneous data, i.e., multi-granularity data maps in terms of spatial information. Meanwhile, different granularity data maps are needed for various urban downstream tasks, which is a multi-task problem. To that end, this paper proposes a novel framework, a multi-granularity super-resolution data map inference framework (MGSR), designed to harness spatio-temporal information to transform incomplete coarse-grained multi-granularity data maps into fine-grained multi-granularity data maps. Specifically, we design a granularity alignment network to align multi-granularity information and address missing data on each granularity data map by leveraging the other granularity data maps with a well-designed self-supervised task. Then, we introduce a feature extraction network to capture spatio-temporal dependencies and extract features. Finally, we devise a recurrent super-resolution network with shared parameters to infer multi-granularity data maps. We conduct extensive experiments on three real-world benchmark datasets and demonstrate that MGSR significantly outperforms the state-of-the-art methods for multi-granularity urban data map inference and reduces RMSE and MAE by up to 40.1% and 50.3%, respectively. The source code has been released at https://github.com/wn13/MGSR\_code.
Semi-Supervised Anomaly Detection through Denoising-Aware Contrastive Distance Learning
Authors: Jianling Gao, Chongyang Tao, Zhenchao Sun, Xiya Jiang, Shuai Ma
Venue: WWW
Year: 2025

Semi-supervised anomaly detection (AD) has garnered growing attention due to its ability to effectively leverage limited labeled data to identify anomalies. However, current methods often impose artificial constraints on the proportion of unlabeled anomalies in the training set, thereby impeding the effective training of models for anomaly detection in real-world scenarios where several anomalies may be present in the unlabeled dataset. Additionally, existing methods often struggle to effectively exploit and model the complex relationships between data instances, which is critical for learning more discriminative features and accurate distance measures. Distance-based methods, in particular, typically rely on Euclidean distance metric, which lacks the flexibility to capture complex correlations across different data dimensions. To address the above challenges, we propose CAD, a denoising-aware Contrastive distance learning framework for semi-supervised AD. It introduces a contrastive training objective to facilitate the learning of distinctive representations by contrasting the average distance between anomalies and unlabeled samples. To fully exploit the information from the unlabeled data meanwhile mitigate the effects of noise, we incorporate a two-stage anomaly denoising and expansion strategy to refine the dataset by identifying high-confidence samples from the unlabeled set. Furthermore, we employ a parameterized bilinear tensor distance layer to learn a customized distance metric, enabling the model to capture intricate relationships among data points. Extensive experiments on 10 real-world datasets demonstrate that CAD significantly outperforms existing semi-supervised AD models. Code available at https://github.com/CADrepo/CAD.
Mitigating Forgetting in Adapting Pre-trained Language Models to Text Processing Tasks via Consistency Alignment
Authors: Jianqi Gao, Hao Wu, Yiu-ming Cheung, Jian Cao, Hang Yu, Yonggang Zhang
Venue: WWW
Year: 2025

There are a large number of text processing tasks in web applications, such as sentiment classification, summary extraction, and question answering. Recently, fine-tuning pre-trained language models (PLMs) to adapt to downstream text-processing tasks has attracted much attention. However, due to the differences in data, model, and tasks between the pre-training and fine-tuning processes, the fine-tuning process may suffer from catastrophic forgetting of pre-training knowledge, which may implicitly limit the model's performance and generalization ability. To address these challenges, we propose a novel dual-model framework, termed as consistency alignment (CoAi). The insight of CoAi lies in building an auxiliary model that simulates the distribution of pre-training knowledge in real-time according to the current task, and co-training the task-specific model and the auxiliary model to balance the pre-training knowledge and task-specific knowledge during fine-tuning. Specifically, the auxiliary model is constructed on-the-fly to maintain the pre-training knowledge. Subsequently, CoAi simulates the pre-training process by performing distributional exploration in the parameter space, which is built upon our novel insight into the transformation between data and model parameter space. However, the objectives leveraged to construct the auxiliary model lead to the misalignment between the pre-training and task-specific knowledge. To alleviate the inconsistency, we employ an auxiliary variable to align the prediction distribution of the task-specific and the auxiliary models, inspired by constrastive clustering. We validate the effectiveness of CoAi on nine classic classification tasks and three generation tasks, showing consistent and significant improvements compared with state-of-the-art methods.
Navigating Semantic Relations: Challenges for Language Models in Abstract Common-Sense Reasoning
Authors: Cole Gawin, Yidan Sun, Mayank Kejriwal
Venue: WWW
Year: 2025

Large language models (LLMs) have achieved remarkable performance in generating human-like text and solving reasoning tasks of moderate complexity, such as question-answering and mathematical problem-solving. However, their capabilities in tasks requiring deeper cognitive skills, such as common-sense understanding and abstract reasoning, remain under-explored. In this paper, we systematically evaluate abstract common-sense reasoning in LLMs using the ConceptNet knowledge graph. We propose two prompting approaches: instruct prompting, where models predict plausible semantic relationships based on provided definitions, and few-shot prompting, where models identify relations using examples as guidance. Our experiments with the gpt-4o-mini model show that in instruct prompting, consistent performance is obtained when ranking multiple relations but with substantial decline when the model is restricted to predicting only one relation. In few-shot prompting, the model's accuracy improves significantly when selecting from five relations rather than the full set, although with notable bias toward certain relations. These results suggest significant gaps still, even in commercially used LLMs' abstract common-sense reasoning abilities, compared to human-level understanding. However, the findings also highlight the promise of careful prompt engineering, based on selective retrieval, for obtaining better performance.
The DenseCap-Guided Attention Network For Image-Text Matching
Authors: Xuri Ge, Linqing Li, Songpei Xu, Kaiwen Zheng, Yaoqin He, Junchen Fu, Joemon M. Jose
Venue: WWW
Year: 2025

Image-text matching is a typical cross-modal task, which has recently attracted great interest in multimedia and computer vision. Previous image-text matching methods mostly rely on coarse appearance features to guide the learning of image and text monotonous representations whereby attention-aware mechanism is introduced for image-text matching. Such coarse feature guided representations lack fine-grained and diverse semantic information for linking images and sentences which are widely regarded as the important cues in aligning the image-text pairs, leading to mismatches between images and texts at a fine-grained level. In this paper, we propose a novel Densecap-guided Attention Network, termed DAN, as the bridge that allows the integration of fine-grained and diverse dense caption representations as mediation to link images and texts. In particular, the dense captions are first extracted from the given image by a densecap-parser. Then, a densecap-guided attention module is designed to mine the fine-grained and discriminate correspondence of image and sentence. Finally, a graph-structured matching network is utilized to learn the associations and alignments between visual and textual attention-aware features. Quantitative results show that the proposed DAN can outperform the state-of-the-art and alternative approaches under various standard evaluation metrics on two public benchmarks, Microsoft COCO and Flickr30K.
CATALOG: Exploiting Joint Temporal Dependencies for Enhanced Phishing Detection on Ethereum
Authors: Medhasree Ghosh, Swapnil Srivastava, Apoorva Upadhyaya, Raju Halder, Joydeep Chandra
Venue: WWW
Year: 2025

Phishing scams on Ethereum have expanded with the surge of the platform, posing substantial challenges due to the sheer similarity in user behaviours and sparse temporal instances. Current methods often fail to tackle these concerns and overlook the temporal sequence of transactions, resulting in suboptimal performance. In this paper, we aim to address these gaps by focusing on the alignment of two aspects: (1) User-specific local temporal behavior, and (2) Divergences from global activity patterns of the network. Hence, we introduce CATALOG (CApturing joint TemporAl dependencies from LOcal and Global user behaviour), a novel representation learning model that jointly captures the local and global user behviours and their correlations by leveraging a dual cross-attention mechanism paired with a bi-directional Masked Language Modelling (MLM) transformer. Our proposed model simultaneously learns from local behavioral shifts, global market trends, and contextually enriched embeddings, effectively distinguishing phishing from non-phishing users while addressing existing research gaps. Extensive experiments on real-world Ethereum transaction data show that our framework improves phishing detection by 7-8% in the F1-Score along with demonstrating the generalization to Ethereum versions 1.0 and 2.0.
Fair Network Communities through Group Modularity
Authors: Christos Gkartzios, Evaggelia Pitoura, Panayiotis Tsaparas
Venue: WWW
Year: 2025

Communities in networks are groups of nodes that are more densely connected to each other than to the rest of the network, forming clusters with strong internal relationships. When nodes have sensitive attributes, such as demographic groups in social networks, a key question is whether nodes in each group are equally well-connected within each community. We model connectivity fairness using group modularity, an adaptation of modularity that accounts for group structures. We introduce two versions of group modularity, each grounded on a different null model, and propose fairness-aware community detection algorithms. Finally, we provide experimental results on real and synthetic networks, evaluating both the connectivity fairness of community structures in networks and the performance of our fairness-aware algorithms.
Procurement Auctions with Best and Final Offers
Authors: Vasilis Gkatzelis, Randolph Preston McAfee, Renato Paes Leme
Venue: WWW
Year: 2025

We study sequential procurement auctions where the sellers are provided with a ''best and final offer'' (BAFO) strategy. This strategy allows each seller i to effectively ''freeze'' their price while remaining active in the auction, and it signals to the buyer, as well as all other sellers, that seller i would reject any price lower than that. This is in contrast to prior work, e.g., on descending auctions, where the options provided to each seller are to either accept a price reduction or reject it and drop out. As a result, the auctions that we consider induce different extensive form games and our goal is to study the subgame perfect equilibria of these games. We focus on settings involving multiple sellers who have full information regarding each other's cost (i.e., the minimum price that they can accept) and a single buyer (the auctioneer) who has no information regarding these costs. Our main result shows that the auctions enhanced with the BAFO strategy can guarantee efficiency in every subgame perfect equilibrium, even if the buyer's valuation function is an arbitrary monotone function. This is in contrast to prior work which required that the buyer's valuation satisfies restrictive properties, like gross substitutes, to achieve efficiency. We then also briefly analyze the seller's cost in the subgame perfect equilibria of these auctions and we show that even if the auctions all return the same outcome, the cost that they induce for the buyer can vary significantly.
Few-shot Learning over Graphs Using Topological Prompts
Authors: Jaidev Goel, Yuzhou Chen, Yulia R. Gel
Venue: WWW
Year: 2025

Prompt-based fine-tuning of pre-trained models has recently emerged as a promising trend for few-shot learning over graphs. Despite its significant potential, high variability and sensitivity to noise and perturbations remain the major challenges on the way of a wider adoption of prompt-based fine-tuning. We propose a new solution to these open problems by introducing the machinery of persistent homology to graph prompts. In particular, to better guide the fine-tuning process on downstream tasks, we extract intrinsic topological descriptors of the activation graphs of the pre-trained models in a form of Fréchet Means and incorporate this inherent topological information into the prompt-tuning process. Addition- ally, we implement bootstrapping over the topological summaries to mitigate the high variability, typically observed in prompt-based methods. Our extensive validation shows that the new Topo-Prompt tool results not only in relative gains in node classification accuracy up to 11% but also in up to 4 times reduction of variability with respect to the state-of-the-art prompt tuning methods. Further- more, Topo-Prompt delivers superior robustness to perturbations, outperforming its competitors up to 25% under noisy conditions.
Adaptive Confidence-aware Preference-based Reinforcement Learning with Noisy Feedback
Authors: Yuhao Gong, Zhenbo Lu, Wanxuan Lu, Wengang Zhou, Houqiang Li
Venue: WWW
Year: 2025

As AI systems become more integrated into society, ensuring they align with human values and exhibit responsible behavior is crucial. Preference-based Reinforcement Learning (PbRL), which learns directly from human feedback, offers a promising approach to achieving this alignment. However, PbRL faces significant challenges related to data quality, like inconsistent feedback, especially when crowdsourcing is used to collect feedback. To address these issues, we propose a method to enhance the robustness of PbRL algorithms with noisy preference data. Our approach provides a standardized framework for handling noisy feedback data by incorporating confidence analysis, temporal smoothing, and an adaptive penalty mechanism to reduce the impact of noisy data and improve learning stability. These techniques work synergistically to ensure the algorithm's ability to mitigate the negative effects of data imperfections and maintain a stable learning trajectory. We demonstrate the effectiveness of our approach, CA-PbRL, in the Meta-World environment, showing that it can recover the performance of existing PbRL algorithms under various noise conditions, thereby facilitating their application in real-world environments.
Unseen Fake News Detection Through Casual Debiasing
Authors: Shuzhi Gong, Richard O. Sinnott, Jianzhong Qi, Cécile Paris
Venue: WWW
Year: 2025

The widespread dissemination of fake news on social media poses significant risks, necessitating timely and accurate detection. However, existing methods struggle with unseen news due to their reliance on training data from past events and domains, leaving the challenge of detecting novel fake news largely unresolved. To address this, we identify biases in training data tied to specific domains and propose a debiasing solution FNDCD. Originating from causal analysis, FNDCD employs a reweighting strategy based on classification confidence and propagation structure regularization to reduce the influence of domain-specific biases, enhancing the detection of unseen fake news. Experiments on real-world datasets with non-overlapping news domains demonstrate FNDCD's effectiveness in improving generalization across domains.
Exploring Hypergraph Condensation via Variational Hyperedge Generation and Multi-Aspectual Amelioration
Authors: Zheng Gong, Shuheng Shen, Changhua Meng, Ying Sun
Venue: WWW
Year: 2025

Hypergraph neural networks (HyperGNNs) show promise in modeling online networks with high-order correlations. Despite notable progress, training these models on large-scale raw hypergraphs entails substantial computational and storage costs, thereby increasing the need of hypergraph size reduction. However, existing size reduction methods primarily capture pairwise association pattern within conventional graphs, making them challenging to adapt to hypergraphs with high-order correlations. To fill this gap, we introduce a novel hypergraph condensation framework, HG-Cond, designed to distill large-scale hypergraphs into compact, synthetic versions while maintaining comparable HyperGNN performance. Within this framework, we develop a Neural Hyperedge Linker to capture the high-order connectivity pattern through variational inference, achieving linear complexity with respect to the number of nodes. Moreover, We propose a multi-aspectual amelioration strategy including a Gradient-Parameter Synergistic Matching objective to holistically refine synthetic hypergraphs by coordinating improvements in node attributes, high-order connectivity, and label distributions. Extensive experiments demonstrate the efficacy of HG-Cond in hypergraph condensation, notably outperforming the original test accuracy on the 20News dataset while concurrently reducing the hypergraph size to a mere 5% of its initial scale. Furthermore, the condensed hypergraphs demonstrate robust cross-architectural generalizability and potential for expediting neural architecture search.
Latent Customer Segmentation and Value-Based Recommendation Leveraging a Two-Stage Model with Missing Labels
Authors: Keerthi Gopalakrishnan, Tianning Dong, Chia-Yen Ho, Yokila Arora, Topojoy Biswas, Jason Cho, Sushant Kumar, Kannan Achan
Venue: WWW
Year: 2025

The success of businesses relies heavily on their ability to convert consumers into loyal customers. The value proposition of a customer is considered the primary determinant in this conversion process, and establishing intrinsic product worth at a specified value point is essential. While affordability remains a significant factor for online shoppers, brands are transitioning away from broad-scale promotions and campaigns that reduce the perceived brand worth. Implementing such widespread marketing campaigns can inadvertently erode brand equity and diminish the probability of achieving the desired return on marketing investment. Existing solutions have fallen short in addressing the twin challenges of finding the optimal balance between value proposition and long-term brand value. At the same time, existing dynamic economic algorithms often misidentify highly engaging customers as ideal campaign targets, leading to sub-optimal engagement and conversion rates, thereby diminishing customer loyalty. This article introduces a two-stage multi-model architecture that employs Self-Paced Loss to enhance customer categorization. The first layer is a Multi-Class Neural Network that differentiates high engaging customers stimulated by a campaign, high engaging customers not influenced by campaign nudges, and low engaging customers. The Binary Label Correction Model forms the second layer, further refining the classification of highly engaged customers by differentiating between those responding to a campaign and those engaging organically. The actual customer's intent can be determined using a missing label framework. This level gathers data from customers displaying variable value proposition behavior, rectifying their true state labels during training for more efficient customer segmentation. By distinguishing customer engagement intent (prompted vs organic), the suggested solution allows businesses to enhance their marketing campaign strategies and target prompted engaged segment, reducing exposure rates while boosting conversion rates. When testing this solution using an A/B test framework, we noticed an increase of over 100 basis points in the success metric.