OpenRaiser/Intern-Atlas huggingface dataset:
Semantic interlinking of Immigration Data using LLMs for Knowledge Graph Construction
Authors: Radhakrishnan Venkatakrishnan, Emrah Tanyildizi, M. Abdullah Canbaz
Venue: WWW
Year: 2024

The challenge of managing immigration data is exacerbated by its reliance on paper-based, evidence-driven records maintained by legal professionals, creating obstacles for efficient processing and analysis due to inherent trust issues with AI-based systems. This paper introduces a cutting-edge framework to surmount these hurdles by synergizing Large Language Models (LLMs) with Knowledge Graphs (KGs), revolutionizing traditional data handling methods. Our method transforms archaic, paper-based immigration records into a structured, interconnected knowledge network that intricately mirrors the legal and procedural nuances of immigration, ensuring a dynamic and trustworthy platform for data analysis. Utilizing LLMs, we extract vital entities and relationships from diverse legal documents to forge a comprehensive knowledge graph, encapsulating the complex legalities and procedural disparities in immigration processes and mapping the multifaceted interactions among stakeholders like applicants, sponsors, and legal experts. This graph not only facilitates a deep dive into the legal stipulations but also incorporates them, significantly boosting the system's reliability and precision. With the integration of Retrieval Augmented Generation (RAG) for exact, context-aware data retrieval and Augmented Knowledge Creation for developing a conversational interface via LLMs, our framework offers a scalable, adaptable solution to immigration data management. This innovative amalgamation of LLMs, KGs, and RAG techniques marks a paradigm shift towards more informed, efficient, and trustworthy decision-making in the sphere of global migration, setting a new benchmark for legal technology and data source management.
SocialGenPod: Privacy-Friendly Generative AI Social Web Applications with Decentralised Personal Data Stores
Authors: Vidminas Vizgirda, Rui Zhao, Naman Goel
Venue: WWW
Year: 2024

We present SocialGenPod, a decentralised and privacy-friendly way of deploying generative AI Web applications. Unlike centralised Web and data architectures that keep user data tied to application and service providers, we show how one can use Solid - a decentralised Web specification - to decouple user data from generative AI applications. We demonstrate SocialGenPod using a prototype that allows users to converse with different Large Language Models, optionally leveraging Retrieval Augmented Generation to generate answers grounded in private documents stored in any Solid Pod that the user is allowed to access, directly or indirectly. SocialGenPod makes use of Solid access control mechanisms to give users full control of determining who has access to data stored in their Pods. SocialGenPod keeps all user data (chat history, app configuration, personal documents, etc) securely in the user's personal Pod; separate from specific model or application providers. Besides better privacy controls, this approach also enables portability across different services and applications. Finally, we discuss challenges, posed by the large compute requirements of state-of-the-art models, that future research in this area should address. Our prototype is open-source and available at: https://github.com/Vidminas/socialgenpod/.
From Files to Streams: Revisiting Web History and Exploring Potentials for Future Prospects
Authors: Lucas Vogel, Thomas Springer, Matthias Wählisch
Venue: WWW
Year: 2024

Over the last 30 years, the World Wide Web has changed significantly. In this paper, we argue that common practices to prepare web pages for delivery conflict with many efforts to present content with minimal latency, one fundamental goal that pushed changes in the WWW. To bolster our arguments, we revisit reasons that led to changes of HTTP and compare them systematically with techniques to prepare web pages. We found that the structure of many web pages leverages features of HTTP/1.1 but hinders the use of recent HTTP features to present content quickly. To improve the situation in the future, we propose fine-grained content segmentation. This would allow to exploit streaming capabilities of recent HTTP versions and to render content as quickly as possible without changing underlying protocols or web browsers.
Blockchain Censorship
Authors: Anton Wahrstätter, Jens Ernstberger, Aviv Yaish, Liyi Zhou, Kaihua Qin, Taro Tsuchiya, Sebastian Steinhorst, Davor Svetinovic, Nicolas Christin, Mikolaj Barczentewicz, Arthur Gervais
Venue: WWW
Year: 2024

Permissionless blockchains promise resilience against censorship by a single entity. This suggests that deterministic rules, not third-party actors, decide whether a transaction is appended to the blockchain. In 2022, the U.S. ØFAC sanctioned a Bitcoin mixer and an Ethereum application, challenging the neutrality of permissionless blockchains. In this paper, we formalize, quantify, and analyze the security impact of blockchain censorship. We start by defining censorship, followed by a quantitative assessment of current censorship practices. We find that 46% of Ethereum blocks were made by censoring actors complying with OFAC sanctions, indicating the significant impact of OFAC sanctions on the neutrality of public blockchains. We discover that censorship affects not only neutrality but also security. After Ethereum's transition to ¶oS, censored transactions faced an average delay of 85%, compromising their security and strengthening sandwich adversaries.
Analysis of the Effect between the Information Type on SNSs and User Attributes during Disaster
Authors: Kosuke Wakasugi, Yu Suzuki, Akiyo Nadamoto
Venue: WWW
Year: 2024

In the event of a disaster, many people post a lot of information on SNS that promotes or inhibits action we designate this information "behavioral facilitation information". Such information is likely to have various effects on user behavior. A wide variety of people browse SNSs, and different readers perceive the same information in different ways. Therefore, in this study, we focus on users' attributes which are personality traits, age, and gender, and analyze how different users perceive information that promotes behavior. Specifically, we extract behavioral facilitation information from SNSs at the time of a disaster using deep learning, and classify the information into four user's personality traits: "suggestion," "inhibition," "encouragement," and "wish." Then, we conduct an experiment in which subjects classified by user's attributes which are personality traits, age, and gender read and judge how they feel about behavioral facilitation information. We then analyze the results, to determine the relationship between the behavioral facilitation information and the reader's attributes.
Don't Bite Off More than You Can Chew: Investigating Excessive Permission Requests in Trigger-Action Integrations
Authors: Liuhuo Wan, Kailong Wang, Kulani Mahadewa, Haoyu Wang, Guangdong Bai
Venue: WWW
Year: 2024

Web-based trigger-action platforms (TAP) allow users to integrate Internet of Things (IoT) systems and online services into trigger-action integrations (TAIs), facilitating rich automation tasks known as applets. Despite their benefits, these integrations~(typically involving the TAP, trigger, and action service providers) pose significant security and privacy challenges, such as mis-triggering and data leakage. This work investigates cross-entity permission management within TAIs to address the underlying causes of these security and privacy issues, emphasizing permission-functionality consistency to ensure fairness in permission requests. We introduce PFCon, a system that leverages GPT-based language models for analyzing required and requested permissions, revealing excessive permission requests in a large-scale study of IFTTT TAP. Our findings highlight the need for service providers to enforce permission-functionality consistency, raising awareness of the importance of security and privacy in TAI.
Is It Safe to Share Your Files? An Empirical Security Analysis of Google Workspace
Authors: Liuhuo Wan, Kailong Wang, Haoyu Wang, Guangdong Bai
Venue: WWW
Year: 2024

The increasing demand for remote work and virtual interactions has heightened the usage of business collaboration platforms~(BCPs), with Google Workspace as a prominent example. These platforms enhance team collaboration by integrating Google Docs, Slides, Calendar, and feature-rich third-party applications (add-ons). However, such integration of multiple users and entities has inadvertently introduced new and complex attack surfaces, elevating security and privacy risks in resource management to unprecedented levels. In this study, we conduct a systematic study on the effectiveness of the cross-entity resource management in Google Workspace, the most popular BCP. Our study unveils the access control enforcement in real-world BCPs for the first time. Based on this, we formulate the attack surfaces inherent in BCPs and conduct a comprehensive assessment, pinpointing three vulnerability types leading to distinct attacks. An analysis of 4,732 marketplace add-ons reveals that approximately 70% are potentially vulnerable to these attacks. We propose robust countermeasures to improve BCP security, urging immediate action and setting a foundation for future research.
A Fast Hop-Biased Approximation Algorithm for the Quadratic Group Steiner Tree Problem
Authors: Xiaoqing Wang, Gong Cheng
Venue: WWW
Year: 2024

Knowledge Graph (KG) exploration helps Web users understand the contents of a large and unfamiliar KG and extract relevant insights. The task has recently been formulated as a Quadratic Group Steiner Tree Problem (QGSTP) to search for a semantically cohesive subgraph connecting entities that match query keywords. However, on large graphs, existing algorithms for this NP-hard problem cannot meet the performance need. In this paper, we propose a novel approximation algorithm for QGSTP called HB. It finds and merges an optimal set of paths according to a Hop-Biased objective function, which not only leads to a guaranteed approximation ratio but is also decomposable by paths to enable efficient dynamic programming based search. Accompanied by a set of pruning heuristics, HB outperformed the state of the art by 1-2 orders of magnitude, empirically reducing the average time for answering a query on a million-scale graph from about one minute to one second.
Towards Robustness Analysis of E-Commerce Ranking System
Authors: Ningfei Wang, Yupin Huang, Han Cheng, Jiri Gesi, Xiaojie Wang, Vivek Mittal
Venue: WWW
Year: 2024

Information retrieval (IR) is a pivotal component in various applications. Recent advances in machine learning (ML) have enabled the integration of ML algorithms into IR, particularly in ranking systems. While there is a plethora of research on the robustness of ML-based ranking systems, these studies largely neglect commercial e-commerce systems and fail to establish a connection between real-world and manipulated query relevance. In this paper, we present the first systematic measurement study on the robustness of e-commerce ranking systems. We define robustness as the consistency of ranking outcomes for semantically identical queries. To quantitatively analyze robustness, we propose a novel metric that considers both ranking position and item-specific information that are absent in existing metrics. Our large-scale measurement study with real-world data from e-commerce retailers reveals an open opportunity to measure and improve robustness since semantically identical queries often yield inconsistent ranking results. Based on our observations, we propose several solution directions to enhance robustness, such as the use of Large Language Models. Note that the issue of robustness discussed herein does not constitute an error or oversight. Rather, in scenarios where there exists a vast array of choices, it is feasible to present a multitude of products in various permutations, all of which could be equally appealing. However, this extensive selection may lead to customer confusion. As e-commerce retailers use various techniques to improve the quality of search results, we hope that this research offers valuable guidance for measuring the robustness of the ranking systems.
A Graph-based Framework for Reducing False Positives in Authentication Alerts in Security Systems
Authors: Yanbang Wang, Karl Hallgren, Jonathan Larson
Venue: WWW
Year: 2024

The high false positive (FP) rate of authentication alerts remains to be a prominent challenge in cybersecurity nowadays. We identify two problems that cause this issue, which are unaddressed in existing learning-based anomaly detection methods. First, in industrial applications, ground-truth labels for malicious authentication events are extremely scarce. Therefore, learning-based methods must optimize their procedures for auto-generating high-quality training instances, an aspect that existing works have overlooked. Second, every existing model is based on a single form of data representation, either stream or graph snapshot, which may not be expressive enough to identify heterogeneity in behaviors of networked entities. This results in misclassifying a legitimate but differently-behaved authentication event into an anomalous one. We address these problems by proposing a new framework based on self-supervised link prediction on dynamic authentication networks, with two highlighted features: (1) our framework is based on the unification of two most popular views of dynamic interconnected systems: graph snapshots and link stream, ensuring the best coverage of behavioral heterogeneity; (2) to generate high-quality training samples, we propose a carefully designed negative sampling procedure called filtered rewiring, to ensure that the negative samples used for training are both truly negative and instructive. We validate our framework on 4 months of authentication data of 125 randomly selected, real organizations that subscribe to Microsoft's defense services.
Knowledge Graph-based Session Recommendation with Session-Adaptive Propagation
Authors: Yu Wang, Amin Javari, Janani Balaji, Walid Shalaby, Tyler Derr, Xiquan Cui
Venue: WWW
Year: 2024

Session-based recommender systems (SBRSs) predict users' next interacted items based on their historical activities. While most SBRSs capture purchasing intentions locally within each session, capturing items' global information across different sessions is crucial in characterizing their general properties. Previous works capture this cross-session information by constructing graphs and incorporating neighbor information. However, this incorporation cannot vary adaptively according to the unique intention of each session, and the constructed graphs consist of only one type of user-item interaction. To address these limitations, we propose knowledge graph-based session recommendation with session-adaptive propagation. Specifically, we build a knowledge graph by connecting items with multi-typed edges to characterize various user-item interactions. Then, we adaptively aggregate items' neighbor information considering user intention within the learned session. Experimental results demonstrate that equipping our constructed knowledge graph and session-adaptive propagation enhances session recommendation backbones by 10%-20%. Moreover, we provide an industrial case study showing our proposed framework achieves 2% performance boost over an existing well-deployed model at The Home Depot e-platform.
Object-level Copy-Move Forgery Image Detection based on Inconsistency Mining
Authors: Jingyu Wang, Niantai Jing, Ziyao Liu, Jie Nie, Yuxin Qi, Chi-Hung Chi, Kwok-Yan Lam
Venue: WWW
Year: 2024

In copy-move tampering operations, perpetrators often employ techniques, such as blurring, to conceal tampering traces, posing significant challenges to the detection of object-level targets with intact structures. Focus on these challenges, this paper proposes an Object-level Copy-Move Forgery Image Detection based on Inconsistency Mining (IMNet). To obtain complete object-level targets, we customize prototypes for both the source and tampered regions and dynamically update them. Additionally, we extract inconsistent regions between coarse similar regions obtained through self-correlation calculations and regions composed of prototypes. The detected inconsistent regions are used as supplements to coarse similar regions to refine pixel-level detection. We operate experiments on three public datasets which validate the effectiveness and the robustness of the proposed IMNet.
Fair Surveillance Assignment Problem
Authors: Fangxiao Wang, Bo Li
Venue: WWW
Year: 2024

Monitoring a specific set of locations serves multiple purposes, such as infrastructure inspection and safety surveillance. We study a generalization of the surveillance problem, where the monitoring area, represented by a graph, is divided and assigned to a set of agents with personalized cost functions. In this paper, each agent's patrolling cost towards receiving a subgraph is measured by the weight of the minimum vertex cover therein, and our objective is to design algorithms to compute fair assignments of the surveillance tasks. The fairness is assessed using maximin share (MMS) fairness proposed by Budish [J. Political Econ., 2011]. Our main result is an algorithm which ensures a 4.562-approximate MMS allocation for any number of agents with arbitrary vertex weights. We then prove that no algorithm can be better than 2-approximate MMS. For scenarios involving no more than four agents, we improve the approximation ratio to 2, which is thus the optimal achievable ratio.
Large Language Models as Data Augmenters for Cold-Start Item Recommendation
Authors: Jianling Wang, Haokai Lu, James Caverlee, Ed H. Chi, Minmin Chen
Venue: WWW
Year: 2024

The reasoning and generalization capabilities of LLMs can help us better understand user preferences and item characteristics, offering exciting prospects to enhance recommendation systems. Though effective while user-item interactions are abundant, conventional recommendation systems struggle to recommend cold-start items without historical interactions. To address this, we propose utilizing LLMs as data augmenters to bridge the knowledge gap on cold-start items during training. We employ LLMs to infer user preferences for cold-start items based on textual description of user historical behaviors and new item descriptions. The augmented training signals are then incorporated into learning the downstream recommendation models through an auxiliary pairwise loss. Through experiments on public Amazon datasets, we demonstrate that LLMs can effectively augment the training signals for cold-start items, leading to significant improvements in cold-start item recommendation for various recommendation models.
Unified Uncertainty Estimation for Cognitive Diagnosis Models
Authors: Fei Wang, Qi Liu, Enhong Chen, Chuanren Liu, Zhenya Huang, Jinze Wu, Shijin Wang
Venue: WWW
Year: 2024

Cognitive diagnosis models have been widely used in different areas, especially intelligent education, to measure users' proficiency levels on knowledge concepts, based on which users can get personalized instructions. As the measurement is not always reliable due to the weak links of the models and data, the uncertainty of measurement also offers important information for decisions. However, the research on the uncertainty estimation lags behind that on advanced model structures for cognitive diagnosis. Existing approaches have limited efficiency and leave an academic blank for sophisticated models which have interaction function parameters (e.g., deep learning-based models). To address these problems, we propose a unified uncertainty estimation approach for a wide range of cognitive diagnosis models. Specifically, based on the idea of estimating the posterior distributions of cognitive diagnosis model parameters, we first provide a unified objective function for mini-batch based optimization that can be more efficiently applied to a wide range of models and large datasets. Then, we modify the reparameterization approach in order to adapt to parameters defined on different domains. Furthermore, we decompose the uncertainty of diagnostic parameters into data aspect and model aspect, which better explains the source of uncertainty. Extensive experiments demonstrate that our method is effective and can provide useful insights into the uncertainty of cognitive diagnosis.
Exploring Neural Scaling Law and Data Pruning Methods For Node Classification on Large-scale Graphs
Authors: Zhen Wang, Yaliang Li, Bolin Ding, Yule Li, Zhewei Wei
Venue: WWW
Year: 2024

Recently, how the model performance scales with the training sample size has been extensively studied for large models on vision and language related domains. Nevertheless, the ubiquitous node classification tasks on web-scale graphs were ignored, where the traits of these tasks, such as non-IIDness and transductive setting, are likely to cause different scaling laws and motivate novel techniques to beat the law. Therefore, we first explore the neural scaling law for node classification tasks on three large-scale graphs. Then, we benchmark several state-of-the-art data pruning methods on these tasks, not only validating the possibility of improving the original unsatisfactory power law but also gaining insights into a hard-and-representative principle on picking an effective subset of training nodes. Moreover, we leverage the transductive setting to propose a novel data pruning method, which instantiates our principle in a test set-targeted manner. Our method consistently outperforms related methods on all three datasets. Meanwhile, we utilize a PAC-Bayesian framework to analyze our method, extending prior results to account for both hardness and representativeness. In addition to a promising way to ease GNN training on web-scale graphs, our study offers knowledge of the relationship between training nodes and GNN generalization.
Causally Debiased Time-aware Recommendation
Authors: Lei Wang, Chen Ma, Xian Wu, Zhaopeng Qiu, Yefeng Zheng, Xu Chen
Venue: WWW
Year: 2024

Time-aware recommendation has been widely studied for modeling the user dynamic preference and a lot of models have been proposed. However, these models often overlook the fact that users may not behave evenly on the timeline, and observed datasets can be biased by user intrinsic preferences or previous recommender systems, leading to degraded model performance. We propose a causally debiased time-aware recommender framework to accurately learn user preference. We formulate the task of time-aware recommendation by a causal graph, identifying two types of biases on the item and time levels. To optimize the ideal unbiased learning objective, we propose a debiased framework based on the inverse propensity score (IPS) and extend it to the doubly robust method. Considering that the user preference can be diverse and complex, which may result in unmeasured confounders, we develop a sensitivity analysis method to obtain more accurate IPS. We theoretically draw a connection between the proposed method and the ideal learning objective, which to the best of our knowledge, is the first time in the research community. We conduct extensive experiments on three real-world datasets to demonstrate the effectiveness of our model. To promote this research direction, we have released our project at https://paitesanshi.github.io/CDTR/.