OpenRaiser/Intern-Atlas huggingface dataset:
A Large-scale Dataset with Behavior, Attributes, and Content of Mobile Short-video Platform
Authors: Yu Shang, Chen Gao, Nian Li, Yong Li
Venue: WWW
Year: 2025

Short-video platforms show an increasing impact on people's daily lives nowadays, with billions of active users spending plenty of time each day. The interactions between users and online platforms give rise to many scientific problems across computational social science and artificial intelligence. However, despite the rapid development of short-video platforms, currently there are serious shortcomings in existing relevant datasets on three aspects: inadequate user-video feedback, limited user attributes and lack of video content. To address these problems, we provide a large-scale dataset with rich user behavior, attributes and video content from a real mobile short-video platform. This dataset covers 10,000 voluntary users and 153,561 videos, and we conduct four-fold technical validations of the dataset. First, we verify the richness of the behavior and attribute data. Second, we confirm the representing ability of the content features. Third, we provide benchmarking results on recommendation algorithms with our dataset. Finally, we explore the filter bubble phenomenon on the platform using the dataset. We believe the dataset could support the broad research community, including but not limited to user modeling, social science, human behavior understanding, etc. The dataset and code is available at https://github.com/tsinghua-fib-lab/ShortVideo_dataset.
Knowledge Distillation for Enhancing Walmart E-commerce Search Relevance Using Large Language Models
Authors: Hongwei Shang, Nguyen Vo, Nitin Yadav, Tian Zhang, Ajit Puthenputhussery, Xunfan Cai, Shuyi Chen, Prijith Chandran, Changsung Kang
Venue: WWW
Year: 2025

Ensuring the products displayed in e-commerce search results are relevant to users' queries is crucial for improving the user experience. With their advanced semantic understanding, deep learning models have been widely used for relevance matching in search tasks. While large language models (LLMs) offer superior ranking capabilities, it is challenging to deploy LLMs in real-time systems due to the high-latency requirements. To leverage the ranking power of LLMs while meeting the low-latency demands of production systems, we propose a novel framework that distills a high-performing LLM into a more efficient, low-latency student model. To help the student model learn more effectively from the teacher model, we first train the teacher LLM as a classification model with soft targets. Then, we train the student model to capture the relevance margin between pairs of products for a given query using mean squared error loss. Instead of using the same training data as the teacher model, we significantly expand the student model's dataset by generating unlabeled data and labeling it with the teacher model's predictions. Experimental results show that the student model's performance continues to improve as the size of the augmented training data increases. In fact, with enough augmented data, the student model can outperform the teacher model. The student model has been successfully deployed in production at Walmart.com with significantly positive metrics.
Combining A/A Data and LLMs for Improved Content Evaluation
Authors: Shiv Shankar, Ritwik Sinha, Madalina Fiterau
Venue: WWW
Year: 2025

A/B testing to evaluate user preferences and engagement is a cornerstone of the modern digital landscape. However, in the current era, the feedback cycle is considerably shortened while the experimentation space expands significantly, necessitating novel and efficient ways to assess user engagement. A/A testing, which compares identical content variants, offers a complementary approach by establishing baselines for engagement metrics and identifying natural variability in user behavior. However, A/A tests inherently lack paired samples, limiting their direct applicability to standard preference alignment methods, which require positive and negative samples for the same context. To address this gap, we propose a novel utility theory framework that enables the integration of unpaired A/A data into content evaluation systems. By translating Large Language Model (LLM) rewards into a utility framework, our approach allows for the incorporation of A/A test results into LLMs.
Propasafe: A BERT-based Offline Tool for Propaganda Detection
Authors: Vivek Sharma, Mohammad Mahdi Shokri, Shweta Jain, Sarah Ita Levitan, Elena Filatova
Venue: WWW
Year: 2025

In an era marked by the rapid consumption of digital news, the proliferation of propaganda poses significant threats to democratic values and informed decision-making. We propose a novel framework that prioritizes user privacy while assisting in the automatic detection of propaganda in news articles, allowing users to sift through the rhetoric to read the relevant objective news. As a demonstration of this framework, we present Propasafe, a BERT-based browser extension designed to alert users to propagandist tones in news articles. By highlighting instances of propaganda-like language in real-time, Propasafe empowers users to critically engage with news content, promoting objective understanding and informed readership. This tool aims to mitigate the influence of manipulative content while safeguarding user privacy.
Embedding Spatial and Semantic Contexts for Geo-Entity Typing in Smart City Applications
Authors: Basel Shbita, Binh Vu, Fandel Lin, Craig A. Knoblock
Venue: WWW
Year: 2025

Geospatial data are critical for urban planning and smart city applications, yet understanding and classifying geo-entities in diverse datasets remains challenging. Accurate representation and classification of geo-entities are essential for tasks such as geo-entity typing and linking, enabling better map understanding and informed decision-making. This paper presents a self-supervised learning approach to classify geo-entities by embedding their geometric, spatial, and semantic neighborhood contexts, creating robust representations for geo-entity typing. Using OpenStreetMap (OSM) data, our method links geo-referenced entities to Wikidata classes and OSM tags with high performance, achieving an F1 score of approximately 0.85. Beyond the technical contribution, our method addresses Responsible AI challenges, including transparency, and data standardization on the Web, aligning with sustainable smart city development.
50 Shades of Deceptive Patterns: A Unified Taxonomy, Multimodal Detection, and Security Implications
Authors: Zewei Shi, Ruoxi Sun, Jieshan Chen, Jiamou Sun, Minhui Xue, Yansong Gao, Feng Liu, Xingliang Yuan
Venue: WWW
Year: 2025

Deceptive patterns (DPs) are user interface designs deliberately crafted to manipulate users into unintended decisions, often by exploiting cognitive biases for the benefit of companies or services. While numerous studies have explored ways to identify these deceptive patterns, many existing solutions require significant human intervention and struggle to keep pace with the evolving nature of deceptive designs. To address these challenges, we expanded the deceptive pattern taxonomy from security and privacy perspectives, refining its categories and scope. We created a comprehensive dataset of deceptive patterns by integrating existing small-scale datasets with new samples, resulting in 6,725 images and 10,421 DP instances from mobile apps and websites. We then developed DPGuard, a novel automatic tool leveraging commercial multimodal large language models (MLLMs) for deceptive pattern detection. Experimental results show that DPGuard outperforms state-of-the-art methods. An extensive empirical evaluation on 2,000 popular mobile apps and websites reveals that 25.7% of mobile apps and 49.0% websites feature at least one deceptive pattern instance. Through 4 unexplored case studies that inform security implications, we highlight the critical importance of the unified taxonomy in addressing the growing challenges of Internet deception.
Helios: Learning and Adaptation of Matching Rules for Continual In-Network Malicious Traffic Detection
Authors: Zhenning Shi, Dan Zhao, Yijia Zhu, Guorui Xie, Qing Li, Yong Jiang
Venue: WWW
Year: 2025

Network Intrusion Detection Systems (NIDS) are critical for web security by identifying and blocking malicious traffic. In-network NIDS leverage programmable switches for high-speed traffic processing. However, they are unable to reconcile the fine-grained classification of known classes and the identification of unseen attacks. Moreover, they lack support for incremental updates. In this paper, we propose Helios, an in-network malicious traffic detection system, for continual adaptation in attack-incremental scenarios. First, we design a novel Supervised Mixture Prototypical Learning (SMPL) method combined with clustering initialization to learn prototypes that encapsulate the knowledge, based on the weighted infinity norm distance. SMPL enables known class classification and unseen attack identification through similarity comparison between prototypes and samples. Then, we design boundary calibration and overlap refinement to transform learned prototypes into priority-guided matching rules, ensuring precise and efficient in-network deployment. Additionally, Helios supports incremental prototype learning and rule updates, achieving low-cost hardware reconfiguration. We implement Helios on a Tofino switch and evaluation on three datasets shows that Helios achieves superior performance in classifying known classes (92%+ in ACC and F1) as well as identifying unseen attacks (62% - 98% in TPR). Helios has also reduced resource consumption and reconfiguration time, demonstrating its scalability and efficiency for real-world deployment.
Miresga: Accelerating Layer-7 Load Balancing with Programmable Switches
Authors: Xiaoyi Shi, Lin He, Jiasheng Zhou, Yifan Yang, Ying Liu
Venue: WWW
Year: 2025

As online cloud services expand rapidly, layer-7 load balancing has become indispensable for maintaining service availability and performance. The emergence of programmable switches with both high performance and a certain degree of flexibility has made it possible to apply programmable switches to load balancing. Nevertheless, the limited memory capacity and the relatively sluggish speed of table entry insertion and deletion of programmable switches have severely constrained their performance. To this end, we introduce Miresga, a hybrid and high-performance layer-7 load balancing system by co-designing hardware and software. The core idea of Miresga is to maximize the utilization of hardware and software resources by rationally partitioning the layer-7 load balancing task, thereby improving performance. To achieve this, Miresga offloads the elephant flows, which account for the majority of traffic, to programmable switches that excel at packet processing, and Miresga utilizes general-purpose servers with stronger computational capabilities to parse application layer protocols and apply load balancing rules. To alleviate memory pressure on the programmable switch, Miresga employs a back-end agent to handle memory-intensive tasks, working in conjunction with the programmable switch to complete the offloaded tasks. This design leverages the performance advantages of the programmable switch while avoiding bottlenecks caused by its limited memory and table insertion speed. We implement the Miresga prototype with a 3.2 Tbps Intel Tofino switch and general-purpose servers. The evaluation results show that Miresga achieves 3.9× throughput and 0.4× latency compared to software load balancing solutions. Compared to the state-of-the-art design employing programmable switches, Miresga achieves almost the same throughput and latency for delivering large objects and 5.0× throughput and 0.2× latency when transmitting small objects.
Centralization in the Decentralized Web: Challenges and Opportunities in IPFS Data Management
Authors: Ruizhe Shi, Ruizhi Cheng, Yuqi Fu, Bo Han, Yue Cheng, Songqing Chen
Venue: WWW
Year: 2025

The InterPlanetary File System (IPFS) is a pioneering effort for Web 3.0, well-known for its decentralized infrastructure. However, some recent studies have shown that IPFS exhibits a high degree of centralization and has integrated centralized components for improved performance. While this change contradicts the core decentralized ethos of IPFS and introduces risks of hurting the data replication level and thus availability, it also opens some opportunities for better data management and cost savings through deduplication. To explore these challenges and opportunities, we start by collecting an extensive dataset of IPFS internal traffic spanning the last three years with 20+ billion messages. By analyzing this long-term trace, we obtain a more complete and accurate view of how the status of centralization evolves over an extended period. In particular, our study reveals that (1) IPFS shows a low replication level, with only 2.71% of data files replicated more than 5 times. While increasing replication enhances lookup performance and data availability, it adversely affects downloading throughput due to the overhead involved in managing peer connections, (2) there is a clear growing trend in centralization within IPFS in the last 3 years, with just 5% of peers now hosting over 80% of the content, significantly decreasing from 21.38% 3 years ago, which is largely driven by the increase of cloud nodes, (3) the default deduplication strategy of IPFS using Fixed-Size Chunking (FSC) is largely inefficient, especially with the default 256KB chunk size, showing near-zero duplication being detected. Although Content-Defined Chunking (CDC) with smaller chunks could save ~1.8 petabytes (PB) storage space, it could impact user performance negatively. We thus design and evaluate a new metadata format that optimizes deduplication without compromising performance.
Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agents
Authors: Zhengliang Shi, Shen Gao, Lingyong Yan, Yue Feng, Xiuyi Chen, Zhumin Chen, Dawei Yin, Suzan Verberne, Zhaochun Ren
Venue: WWW
Year: 2025

Augmenting large language models (LLMs) with external tools has emerged as a promising approach to extend their utility, enabling them to solve practical tasks. Previous methods manually parse tool documentation and create in-context demonstrations, transforming tools into structured formats for LLMs to use in their step-by-step reasoning. However, this manual process requires domain expertise and struggles to scale to large toolsets. Additionally, these methods rely heavily on ad-hoc inference techniques or special tokens to integrate free-form LLM generation with tool-calling actions, limiting the LLM's flexibility in handling diverse tool specifications and integrating multiple tools. In this work, we propose AutoTools, a framework that enables LLMs to automate the tool-use workflow. Specifically, the LLM automatically transforms tool documentation into callable functions, verifying syntax and runtime correctness. Then, the LLM integrates these functions into executable programs to solve practical tasks, flexibly grounding tool-use actions into its reasoning processes. Extensive experiments on existing and newly collected, more challenging benchmarks illustrate the superiority of our framework. Inspired by these promising results, we further investigate how to improve the expertise of LLMs, especially open-source LLMs with fewer parameters, within AutoTools. Thus, we propose the AutoTools-Learning approach, training the LLMs with three learning tasks on 34k instances of high-quality synthetic data, including documentation understanding, relevance learning, and function programming. Fine-grained results validate the effectiveness of our overall training approach and each individual task. Our methods are an important step towards the use of LLMs for solving real-world tasks with external tools.
PETravel: Personalized E-bike Travel Recommendation System with Unlimited Endurance
Authors: Yuduo Shi, Zhao Li, Xuming Pan, Yaoling Chen, Bo Zhou, Haitao Xu, Wenrui Ma, Yuchen Li, Shichang Hu
Venue: WWW
Year: 2025

E-bike tourism has become an increasingly popular and sustainable mode of travel. A significant concern for riders is managing battery range and mitigating range anxiety during extended journeys. Battery swapping has emerged as an innovative solution to these challenges. However, existing e-bike route planning systems fail to account for the need for battery swapping. To bridge this gap, we present PETravel, a personalized e-bike travel recommendation system that integrates battery-swapping strategies directly into route planning. In addition, it enhances the tourism experience by accommodating diverse personalized requirements across various scenarios. Evaluations demonstrate PETravel's effectiveness in providing tailored solutions for personalized and sustainable tourism while supporting a variety of e-bike travel scenarios. A video demonstration of PETravel is available here: https://youtu.be/bu5UO8ArYZg.
Scalable Overload-Aware Graph-Based Index Construction for 10-Billion-Scale Vector Similarity Search
Authors: Yang Shi, Yiping Sun, Jiaolong Du, Xiaocheng Zhong, Zhiyong Wang, Yao Hu
Venue: WWW
Year: 2025

Approximate Nearest Neighbor Search (ANNS) is essential for modern data-driven applications that require efficient retrieval of top-k results from massive vector databases. Although existing graph-based ANNS algorithms achieve a high recall rate on billion-scale datasets, their slow construction speed and limited scalability hinder their applicability to large-scale industrial scenarios. In this paper, we introduce SOGAIC, the first Scalable Overload-Aware Graph-Based ANNS Index Construction system tailored for ultra-large-scale vector databases: 1) We propose a dynamic data partitioning algorithm with overload constraints that adaptively introduces overlaps among subsets; 2) To enable efficient distributed subgraph construction, we employ an a load-balancing task scheduling framework combined with an agglomerative merging strategy; 3) Extensive experiments on various datasets demonstrate a reduction of 47.3% in average construction time compared to existing methods. The proposed method has also been successfully deployed in a real-world industrial search engine, managing over 10 billion daily updated vectors and serving hundreds of millions of users.
A Spatial-temporal Deep Probabilistic Diffusion Model for Reliable Hail Nowcasting with Radar Echo Extrapolation
Authors: Haonan Shi, Long Tian, Jie Tao, Yufei Li, Liming Wang, Xiyang Liu
Venue: WWW
Year: 2025

Hail nowcasting is a considerable contributor to meteorological disasters and there is a great need to mitigate its socioeconomic effects through precise forecast that has high resolution, long lead times and local details with large landscapes. Existing medium-range weather forecasting methods primarily rely on changes in upper air currents and cloud layers to predict precipitation events, such as heavy rainfall, which are unsuitable for hail nowcasting since it is mainly caused by low-altitude local strong convection associated with terrains. Additionally, radar captures the status of low cloud layers, such as water vapor, droplets, and ice crystals, providing rich signals suitable for hail nowcasting. To this end, we introduce a Spatial-Temporal gEnerAtive Model called SteamCast for hail nowcasting with radar echo extrapolation, it is a deep probabilistic diffusion model based on spatial-temporal representations including radar echoes as well as their position/time embeddings, which we trained on historical reanalysis archive from Yan'an Meteorological Bureau in China, where the crop yield like apple suffers greatly from hail damage. Considering the short-term nature of hail, SteamCast provides 30-minute nowcasts at 6-minute intervals for a single radar reflectivity variable, across 9 different vertical angles, on a latitude-longitude grid with approximately 1 km × 1 km resolution per pixel in Yan'an City, China. By successfully fusing the spatial-temporal features of radar echoes, SteamCast delivers competitive, and in some cases superior, results compared to other deep learning-based models such as PredRNN and VMRNN.
Dr. Docker: A Large-Scale Security Measurement of Docker Image Ecosystem
Authors: Hequan Shi, Lingyun Ying, Libo Chen, Haixin Duan, Ming Liu, Zhi Xue
Venue: WWW
Year: 2025

Docker has transformed modern software development, enabling the widespread reuse of containerized applications. Currently, Docker images are primarily distributed through centralized registries, among which Docker Hub is the largest, allowing developers to share and reuse images easily. The threats within these images also spread through the supply chain via dependency relationships, posing risks to anyone using the image and all images built based on it. However, it is unclear to what extent the threats within Docker images are distributed and propagated. In this paper, we investigate five potential security risks in Docker images and propose a security analysis framework DITector based on these security issues. We then utilize DITector to conduct a large-scale security measurement of the Docker image ecosystem. We collect descriptions of over 12 million image repositories from Docker Hub, construct an image dependency graph based on the layer information of the images, and select two sets of influential images based on their pull counts and dependency weight, totaling 33,952 images. Our findings are alarming: 93.7% of analyzed images contain known vulnerabilities, 4,437 images have secret leaks, 50 images contain misconfigurations and 24 malicious images. Furthermore, we identify 334 downstream images affected by malicious images and uncover patterns of attack propagation within the supply chain. We have discussed the measures to mitigate these issues, reported our findings to the relevant parties, and received positive responses.
BEAST: Leveraging Contrastive Learning and Unsupervised Sentence Embeddings for Improved Drug Abuse Detection
Authors: Shuvam Shiwakoti, Siddhant Bikram Shah, Wei Wang, Surendrabikram Thapa, Imran Razzak, Usman Naseem
Venue: WWW
Year: 2025

Prescription drug abuse is a growing public health crisis worldwide. In the digital age, social media platforms offer a unique opportunity to monitor drug abuse trends in real-time. However, traditional machine learning models struggle with the informal language, sarcasm, and figurative speech used on social media. This paper proposes BEAST, a novel approach that leverages contrastive learning to improve the detection of drug abuse references hidden within figurative language. Additionally, the integration of SimCSE and Target-Based Generating Strategy further enhances the model's performance by generating superior representations from both labeled and unlabeled data. We test our model on three datasets, and the experimental results demonstrate the superiority of BEAST over the baseline in accurately identifying drug-related references hidden within figurative language on social media. Our work paves the way for more effective public health interventions in this increasingly digital era.