OpenRaiser/Intern-Atlas huggingface dataset:
STKOpt: Automated Spatio-Temporal Knowledge Optimization for Traffic Prediction
Authors: Yayao Hong, Liyue Chen, Leye Wang, Xiuhuai Xie, Guofeng Luo, Cheng Wang, Longbiao Chen
Venue: WWW
Year: 2025

Ubiquitous sensors and mobile devices have spurred the growth of Web-of-Things (WoT) services in smart cities, making accurate spatio-temporal traffic predictions increasingly crucial. Leveraging advances in deep learning, recent Spatio-Temporal Graph Neural Networks (STGNNs) have achieved remarkable results. However, these methods address scenario-specific spatio-temporal heterogeneity by designing model architectures, often overlooking the importance of selecting optimal spatio-temporal knowledge (i.e., model inputs). In this paper, we propose an automated framework for spatio-temporal knowledge optimization to address this challenge. Our framework seamlessly integrates with downstream models, enhancing their performance across various prediction tasks. Specifically, we design a knowledge search space composed of parameters that represent scenario-specific spatio-temporal correlations within data. Additionally, we employ a bandit-based multi-fidelity algorithm for knowledge optimization to solve the constraint of limited resource. Furthermore, we adopt a meta-learner to extract transferable meta-knowledge about optimal knowledge, facilitating efficient exploration of the search space. Extensive experiments on five widely used real-world datasets demonstrate the effectiveness of our proposed framework. To the best of our knowledge, we are the first to automatically optimize spatio-temporal knowledge for spatio-temporal traffic prediction.
Dimension-Wise Gated Cross-Attention for Multimodal Sentiment Analysis
Authors: Md Shakil Hossain, Md. Mithun Hossain, Sudipto Chaki, Muhammad Firoz Mridha, Md. Saifur Rahman, Mohammad Ali Moni
Venue: WWW
Year: 2025

Multimodal sentiment analysis necessitates the seamless integration of textual and visual signals for the precise interpretation of user-generated material. In this paper, we introduce Dimension-Wise Gated Cross-Attention (DGCA). This new fusion mechanism fine-tunes the interaction between language and images more precisely than prior methods. Our method uses a bidirectional cross-attention module to iteratively enhance text and image features. We use a dimension-wise gating technique in which each latent dimension independently learns to weigh contributions from text or image signals using softmax-normalized modality gates. The approach uses selective per-dimension fusion to highlight important cues from one modality while minimizing less useful characteristics from another. On the SemEval-2020 Memotion dataset, DGCA outperformed the state-of-the-art (SOTA) baselines by 2.27%, highlighting its ability to detect subtle affective cues. In summary, DGCA improves performance and interpretability, enabling fine-grained and context-aware multimodal sentiment analysis.
Surprisingly Popular Voting with Concentric Rank-Order Models
Authors: Hadi Hosseini, Debmalya Mandal, Amrit Puhan
Venue: WWW
Year: 2025

An important problem on social information sites is the recovery of ground truth from individual reports when the experts are in the minority. The wisdom of the crowd, i.e. the collective opinion of a group of individuals fails in such a scenario. However, the surprisingly popular (SP) algorithm[15] can recover the ground truth even when the experts are in the minority, by asking the individuals to report additional prediction reports--their beliefs about the reports of others. Several recent works have extended the surprisingly popular algorithm to an equivalent voting rule (SP-voting) to recover the ground truth ranking over a set of m alternatives. However, we are yet to fully understand when SP-voting can recover the ground truth ranking, and if so, how many samples (votes and predictions) it needs. We answer this question by proposing two rank-order models and analyzing the sample complexity of SP-voting under these models. In particular, we propose concentric mixtures of Mallows and Plackett-Luce models with G (≥ 2) groups. Our models generalize previously proposed concentric mixtures of Mallows models with 2 groups, and we highlight the importance of G > 2 groups by identifying three distinct groups (expert, intermediate, and non-expert) from existing datasets. Next, we provide conditions on the parameters of the underlying models so that SP-voting can recover ground-truth rankings with high probability, and also derive sample complexities under the same. We complement the theoretical results by evaluating SP-voting on simulated and real datasets.
Towards Whole Life-Cycle Management of Prefabricated Buildings based on Knowledge-Data Dual Driven Methods
Authors: Xingchen Hu
Venue: WWW
Year: 2025

As an efficient and environmentally friendly construction method, prefabricated buildings have garnered increasing attention compared to traditional buildings in the context of global urbanization and sustainable development. This research addresses the digital transformation challenges in prefabricated buildings by proposing a whole life cycle management framework based on knowledge-data dual-driven methods. First, country-specific, e.g., Australian, prefabricated building standards and construction cases will be collected to build a knowledge foundation model that integrates a knowledge graph with large language models. By aligning formats of building information modelling and digital twins, automated rule checking will be realized during the design phase, alongside safety, schedule, and quality management in construction. Finally, in the operation and maintenance phase, the knowledge base will support conventional deep learning methods in fault detection and diagnosis of photovoltaic systems to enhance the interpretability of the models and provide the repair or maintenance solutions for photovoltaic systems. This PhD research aims to develop a prefabricated building Web-based knowledge base and management framework, establish a standard dataset for knowledge extraction, and ultimately advance the digital transformation and sustainability of prefabricated buildings.
Training an LLM-as-a-Judge Model: Pipeline, Insights, and Practical Lessons
Authors: Renjun Hu, Yi Cheng, Libin Meng, Jiaxin Xia, Yi Zong, Xing Shi, Wei Lin
Venue: WWW
Year: 2025

The rapid advancement of large language models (LLMs) has opened new possibilities for their adoption as evaluative judges. This paper introduces Themis, a fine-tuned LLM judge that delivers sophisticated context-aware evaluations. We provide a comprehensive overview of the development pipeline for Themis, highlighting its scenario-dependent evaluation prompts and two novel methods for controlled instruction generation. These designs enable Themis to effectively distill evaluative skills from teacher models, while retaining flexibility for continuous development. We introduce two human-labeled benchmarks for meta-evaluation, demonstrating that Themis can achieve high alignment with human preferences in an economical manner. Additionally, we explore insights into the LLM-as-a-judge paradigm, revealing nuances in performance and the varied effects of reference answers. Notably, we observe that pure knowledge distillation from strong LLMs, though common, does not guarantee performance improvement through scaling. We propose a mitigation strategy based on instruction-following difficulty. Furthermore, we provide practical guidelines covering data balancing, prompt customization, multi-objective training, and metric aggregation. We aim for our method and findings, along with the fine-tuning data, benchmarks, and model checkpoints, to support future research and development in this area.
Hypergraph-based Zero-shot Multi-modal Product Attribute Value Extraction
Authors: Jiazhen Hu, Jiaying Gong, Hongda Shen, Hoda Eldardiry
Venue: WWW
Year: 2025

It is essential for e-commerce platforms to provide accurate, complete, and timely product attribute values, in order to improve the search and recommendation experience for both customers and sellers. In the real-world scenario, it is difficult for these platforms to identify attribute values for the newly introduced products given no similar product history records for training or retrieval. Besides, how to jointly learn the product representation given various product information in multiple modalities, such as textual modality (e.g., product titles and descriptions) and visual modality (e.g., product images), is also a challenging task. To address these limitations, we propose a novel method for extracting multi-label product attribute-value pairs from multiple modalities in the zero-shot scenario, where labeled data is absent during training. Specifically, our method constructs heterogeneous hypergraphs, where product information from different modalities is represented by different types of nodes, and the text and image nodes are embedded and learned through CLIP encoders to effectively capture and integrate multi-modal product information. Then, the complex interrelations among these nodes are modeled through the hyperedges. By learning informative node representations, our method can accurately predict links between unseen product nodes and attribute-value nodes, enabling zero-shot attribute value extraction. We conduct extensive experiments and ablation studies on several categories of the public MAVE dataset and the results demonstrate that our proposed method significantly outperforms several state-of-the-art generative model baselines in multi-label, multi-modal product attribute value extraction in the zero-shot setting.
Modeling the Intuitiveness of Mobile GUI Navigation by Understanding and Simulating Users' Attention Distribution
Authors: Xiaozhu Hu, Xiaoyu Mo, Xiaofu Jin, Yongquan &apos, Owen&apos, Hu, Mingming Fan, Tristan Braud
Venue: WWW
Year: 2025

Analyzing users' attention distribution is an effective way to evaluate whether the primary UI content captures users' engagement. This has led to the development of eye trackers that gather data on users' attention, as well as computational models that simulate this distribution on individual UI screens. However, there is limited research on how users' attention distribution correlates with the intuitiveness of goal-oriented UI navigation. Additionally, simulating user attention as they reason and identify the correct UI element to navigate to the target screen remains unexplored. To address this gap, we introduce an AI-driven model, the UI Link Transformer (UILT), which predicts users' attention distribution as they navigate from the current UI screen to the target UI screen. This model helps designers to evaluate the intuitiveness of UI navigation. Our initial study aimed to understand how users typically identify the UI element that links two consecutive UI screens and how this identification relates to the intuitiveness of the UI navigation design. The insights gained from this study offer designers actionable recommendations to improve the intuitiveness of mobile GUI navigation, with a focus on users' attention distribution. Moreover, the dataset collected during this study supports the development of the UILT. Building on the insights and data from the initial study, we designed, trained, and evaluated the UILT.
Interactive Visualization Recommendation with Hier-SUCB
Authors: Songwen Hu, Ryan A. Rossi, Tong Yu, Junda Wu, Handong Zhao, Sungchul Kim, Shuai Li
Venue: WWW
Year: 2025

Visualization recommendation aims to enable rapid visual analysis of massive datasets. In real-world scenarios, it is essential to quickly gather and comprehend user preferences to cover users from diverse backgrounds, including varying skill levels and analytical tasks. Previous approaches to personalized visualization recommendations are non-interactive and rely on initial user data for new users. As a result, these models cannot effectively explore options or adapt to real-time feedback. To address this limitation, we propose an interactive personalized visualization recommendation (PVisRec) system that learns on user feedback from previous interactions. For more interactive and accurate recommendations, we propose Hier-SUCB, a contextual combinatorial semi-bandit in the PVisRec setting. Theoretically, we show an improved overall regret bound with the same rank of time but an improved rank of action space. We further demonstrate the effectiveness of Hier-SUCB through extensive experiments where it is comparable to offline methods and outperforms other bandit algorithms in the setting of visualization recommendation.
The Solution to the WWW25 Text-based Person Anomaly Search Challenge
Authors: Xiaoxing Hu, Tianlu Zheng, Kaicheng Yang, Ziyong Feng
Venue: WWW
Year: 2025

This paper outlines our solution to the WWW25 Text-based Person Anomaly Search Challenge, structured into two primary strategies: 1) Continuous pre-training of existing models, and 2) Model ensemble techniques. Initially, we adapt open-source pre-trained models such as EVA-CLIP [4] and OPENCLIP [3] through continuous training on the specific competition datasets. We then implement two model ensemble strategies to refine prediction accuracy. Our approach culminates in achieving Recall@1, Recall@5, and Recall@10 values of 89.28%, 99.34%, and 99.75% on the challenge's online leaderboard.
Beyond Visual Confusion: Understanding How Inconsistencies in ENS Normalization Facilitate Homoglyph Attacks
Authors: Jianwei Huang, Sridatta Raghavendra Chintapalli, Mengxiao Wang, Guofei Gu
Venue: WWW
Year: 2025

In recent years, the Ethereum Name Service (ENS) has garnered significant attention within the community for enabling the use of Unicode in domain names, thereby facilitating the inclusion of a wide array of character sets such as Greek, Cyrillic, Arabic, and Chinese. While this feature enhances the versatility and global accessibility of domain names, it concurrently introduces a substantial security vulnerability due to the presence of homoglyphs-characters that are visually similar to others across Unicode and ASCII sets. These similarities can be exploited in homoglyph attacks, posing a distinct threat to domain name integrity. Despite community efforts to counteract this issue through a normalization process prior to domain resolution, our analysis uncovers significant discrepancies in how the normalization processes are applied across various applications. This inconsistency could result in the same domain name being resolved to different addresses in different applications, underscoring a critical vulnerability. We also discovered the new attack scenario in ENS which may cause legitimate domains resolved into malicious addresses even when they are verified by authorities. To systematically evaluate this inconsistency, we designed a tool for detecting application-level discrepancies in domain normalization process without requiring access to the application's source code. Our evaluation on hundreds of real-world Web3 applications identifies widespread deviations from established homoglyph mitigation practices, with more than 60% digital wallets and 80% dApps (decentralized applications) not able to produce consistent ENS resolving results, potentially impacting millions of users. This analysis underscores the urgent need for a standardized implementation of normalization processes to safeguard the integrity and security of ENS domains.
HyperGate: Hierarchical Perceptive Gating Network for Multi-domain Multi-task Recommendation
Authors: Xu Huang, Xiaolong Chen, Yichao Wang, Weiwen Liu, Yang Yang, Xingmei Wang, Defu Lian, Ruiming Tang
Venue: WWW
Year: 2025

The growing prevalence of interactive pages in online services presents novel challenges for industrial recommendation systems, particularly in the realms of multi-domain and multi-task recommendations. These systems must not only model commonality across various domains but also account for distinctions between domains and tasks. However, the lack of explicit semantic information regarding domains and tasks complicates the process. In this paper, we propose a novel parameter-sharing model for multi-domain multi-task recommendations, termed the Hierarchical Perceptive Gating Network (pronounced as HyperGate). Drawing inspiration from Collective Behavior Theory, our approach first augments semantic information for domains and tasks using contrastive learning, which then serves as perceptive control for the subsequent hierarchical gating network. The hierarchical gating network consists of three key components, including a domain-perceptive embedding gate, a domain-perceptive biasing gate, and a dual-perceptive fusion gate, thereby constructing a domain- and task-perceptive parameter-sharing network from the bottom up. Experiments conducted on two real-world datasets validate the effectiveness of HyperGate, demonstrating substantial improvements in recommendation accuracy. Additionally, online A/B tests on the industrial platform further validate the efficacy of HyperGate.
Enhancing Cross-domain Link Prediction via Evolution Process Modeling
Authors: Xuanwen Huang, Wei Chow, Yize Zhu, Yang Wang, Ziwei Chai, Chunping Wang, Lei Chen, Yang Yang
Venue: WWW
Year: 2025

This paper proposes CrossLink, a novel framework for cross-domain link prediction. CrossLink learns the evolution pattern of a specific downstream graph and subsequently makes pattern-specific link predictions. It employs a technique called conditioned link generation, which integrates both evolution and structure modeling to perform evolution-specific link prediction. This conditioned link generation is carried out by a transformer-decoder architecture, enabling efficient parallel training and inference. CrossLink is trained on extensive dynamic graphs across diverse domains, encompassing 6 million dynamic edges. Extensive experiments on eight untrained graphs demonstrate that CrossLink achieves state-of-the-art performance in cross-domain link prediction. Compared to advanced baselines under the same settings, CrossLink shows an average improvement of 11.40% in Average Precision across eight graphs. Impressively, it surpasses the fully supervised performance of 8 advanced baselines on 6 untrained graphs. Project Page is https://zjunet.github.io/CrossLink/
WaSCR: A WebAssembly Instruction-Timing Side Channel Repairer
Authors: Liyan Huang, Junzhou He, Chao Wang, Weihang Wang
Venue: WWW
Year: 2025

WebAssembly (Wasm) is a platform-independent, low-level binary language that enables near-native performance in web applications. Given its growing importance in the web ecosystem, securing WebAssembly programs becomes increasingly important. A key security concern with WebAssembly is the threat of instruction-timing side-channel attacks, which exploit timing variations in branch instructions dependent on sensitive data, allowing attackers to infer sensitive information through timing measurement. In this paper, we introduce WaSCR, an automated WebAssembly instruction-timing Side-Channel Repairer. WaSCR uses control and data dependencies to trace the flow of sensitive data and prevent its leakage. It employs rule-based code transformations to linearize the program, eliminating branches dependent on sensitive data and substituting them with constant-time selectors. Our evaluation demonstrates that WaSCR effectively eliminates instruction-timing side channels while maintaining program correctness, with efficient repairs and moderate performance overhead.
Towards Agentic AI for Science: Hypothesis Generation, Comprehension, Quantification, and Validation
Authors: Lifu Huang, Danai Koutra, Adithya Kulkarni, Temiloluwa Prioleau, Qingyun Wu, Yujun Yan, Yaoqing Yang, James Zou, Dawei Zhou
Venue: WWW
Year: 2025

AI models for science [14] have the potential to harness large datasets, accelerate scientific discoveries and transform numerous fields. For example, AlphaFold [13] has accurately predicted protein structures, significantly reducing research time in biology and medicine. Similarly, AI has been used to identify compounds for diseases like COVID-19 [10] and Cancer [21], dramatically shortening the timelines for breakthroughs in healthcare. In mathematics, AlphaTensor [7] has discovered faster algorithms for matrix-matrix multiplication than Strassen's algorithm. AI systems have been applied in fields such as healthcare [10, 21], material science [15], physics [19], mathematics [7], education [16], psychology [18], and software development [20]. However, these models are often developed ad-hoc. In contrast, agentic AI [14] has recently emerged as a Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). WWW Companion '25, April 28-May 2, 2025, Sydney, NSW, Australia © 2025 Copyright held by the owner/author(s). ACM ISBN 979-8-4007-1331-6/2025/04 https://doi.org/10.1145/3701716.3717754 systematic approach to advancing scientific discovery, offering the ability to autonomously perform complex tasks and interact with the researchers.
Intelligent Welding System for Aluminum Coils by Adaptive Path Planning with Real-world Dataset
Authors: Xiaowei Huang, Huanghuang Liang, Zhili He, Sihan Feng, Jiawei Jiang, Kanye Ye Wang, Chuang Hu, Dazhao Cheng
Venue: WWW
Year: 2025

Intelligent welding systems (IWS) are gradually replacing manual welding in industrial production due to their ability to respond in real time to changes in the welding process interface and adjust welding parameters accordingly. However, IWS has primarily been used for welding flat materials, and effective solutions for welding coils are still lacking. This is mainly because welding coils involves complex three-dimensional positioning and planning, which places higher demands on the feedback capabilities of IWS. To address this gap, we propose an smart Al coil welding system(SAWCS). SAWCS combines 2D imaging, 3D point cloud data, and deep learning-based segmentation to identify weldable areas with high accuracy. A dedicated dataset, HRALCoil, was developed to train models for robust feature recognition and adaptive path planning. Through real-world tests, SAWCS demonstrated improved precision and reliability in weld path planning, meeting industrial demands for efficient and stable welds. This approach sets a foundation for expanding intelligent welding applications in manufacturing. Future work will focus on enhancing system adaptability under varying production conditions.