OpenRaiser/Intern-Atlas huggingface dataset:
FL@FM-TheWebConf'24: International Workshop on Federated Foundation Models for the Web
Authors: Irwin King, Guodong Long, Zenglin Xu, Han Yu
Venue: WWW
Year: 2024

In the evolving landscape of machine learning research, two significant developments have emerged to prominence: foundation models and federated learning. The FL@FM-TheWebConf'24 workshop provides an exciting forum for researchers and practitioners to discuss promising works and chart potential trajectories forward for this emerging field.
Revisiting the Behavioral Foundations of User Modeling Algorithms
Authors: Jon M. Kleinberg
Venue: WWW
Year: 2024

One of the fundamental problems that platform algorithms face is the process of inferring user preferences from observed behavior; the vast amounts of data a platform collects become much less useful if they cannot effectively inform this type of inference. Traditional approaches to this problem rely on an often unstated revealed-preference assumption: that choice reveals preference. Yet a long line of work in psychology and behavioral economics reveals the gaps that can open up between choice and preference, and experience with platform dynamics makes clear how it can arise in some of the most basic online settings; for example, we might choose content to consume in the present and then later regret the time we spent on it. More generally, behavioral biases and inconsistent preferences make it highly challenging to appropriately interpret the user data that we observe. We discuss a set of models and algorithms that address this challenge through a process of "inversion", in which an algorithm must try inferring mental states that are not directly measured in the data. The talk is based on joint work with Jens Ludwig, Sendhil Mullainathan, and Manish Raghavan.
Travel Demand Prediction with Application to Commuter Demand Estimation on Urban Railways
Authors: Yohei Kodama, Yuki Akeyama, Yusuke Miyazaki, Koh Takeuchi
Venue: WWW
Year: 2024

Travel demand forecasting is a vital problem in the development of smart cities, infrastructure planning, and transportation management. The advent of contactless smart card systems has enabled the collection of data regarding daily transit and purchasing activities, providing a rich source of insights into citizen behavior. In this paper, we introduce a new problem of predicting changes in travel demand resulting from the installation of a new facility while preserving privacy. To address this problem, we propose a simple but effective supervised learning method that can capture the relationships between residential areas and existing facility locations, and exploit spatial features to forecast future demand in response to a new facility location. As a workable example, we employ real-world data to predict the future travel demand triggered by the installation of a new station in a railway system. Through extensive experiments, we demonstrate that our method improves the prediction accuracy.
Characterizing the Solana NFT Ecosystem
Authors: Dechao Kong, Xiaoqi Li, Wenkai Li
Venue: WWW
Year: 2024

Non-Fungible Tokens (NFTs) are digital assets recorded on the blockchain, providing cryptographic proof of ownership over digital or physical items. Although Solana has only begun to gain popularity in recent years, its NFT market has seen substantial transaction volumes. In this paper, we conduct the first systematic research on the characteristics of Solana NFTs from two perspectives: longitudinal measurement and wash trading security audit. We gathered 132,736 Solana NFT from Solscan and analyzed the sales data within these collections. Investigating users'economic activity and NFT owner information reveals that the top users in Solana NFT are skewed toward a higher distribution of purchases. Subsequently, we employ the Local Outlier Factor algorithm to conduct a wash trading audit on 2,175 popular Solana NFTs. We discovered that 138 NFT pools are involved in wash trading, with 8 of these NFTs having a wash trading rate exceeding 50%. Fortunately, none of these NFTs have been entirely washed out.
Universal Knowledge Graph Embeddings
Authors: N&apos, Dah Jean Kouagou, Caglar Demir, Hamada M. Zahera, Adrian Wilke, Stefan Heindorf, Jiayi Li, Axel-Cyrille Ngonga Ngomo
Venue: WWW
Year: 2024

A variety of knowledge graph embedding approaches have been developed. Most of them obtain embeddings by learning the structure of the knowledge graph within a link prediction setting. As a result, the embeddings reflect only the structure of a single knowledge graph, and embeddings for different knowledge graphs are not aligned, e.g., they cannot be used to find similar entities across knowledge graphs via nearest neighbor search. However, knowledge graph embedding applications such as entity disambiguation require a more global representation, i.e., a representation that is valid across multiple sources. We propose to learn universal knowledge graph embeddings from large-scale interlinked knowledge sources. To this end, we fuse large knowledge graphs based on the owl:sameAs relation such that every entity is represented by a unique identity. We instantiate our idea by computing universal embeddings based on DBpedia and Wikidata yielding embeddings for about 180 million entities, 15 thousand relations, and 1.2 billion triples. We believe our computed embeddings will support the emerging field of graph foundation models. Moreover, we develop a convenient API to provide embeddings as a service. Experiments on link prediction suggest that universal knowledge graph embeddings encode better semantics compared to embeddings computed on a single knowledge graph. For reproducibility purposes, we provide our source code and datasets open access.
Anonymity Analysis of the Umbra Stealth Address Scheme on Ethereum
Authors: Alex Márk Kovács, István András Seres
Venue: WWW
Year: 2024

Stealth addresses are a privacy-enhancing technology that provides recipient anonymity on blockchains. In this work, we investigate the recipient anonymity and unlinkability guarantees of Umbra, the most widely used implementation of the stealth address scheme on Ethereum, and its three off-chain scalability solutions, i.e., Arbitrum, Optimism, and Polygon. Specifically, we define and evaluate four heuristics to uncover the real recipients of stealth payments. We find that for the majority of Umbra payments, it is straightforward to establish the recipient, hence nullifying the benefits of using Umbra. In particular, we identify the real recipient of 48.5%, 25.8%, 65.7%, and 52.6% of all Umbra transactions on the Ethereum main net, Polygon, Arbitrum, and Optimism networks, respectively. Finally, we suggest easily implementable countermeasures to evade our deanonymization and linking attacks.
A Method for Assessing Inference Patterns Captured by Embedding Models in Knowledge Graphs
Authors: Narayanan Asuri Krishnan, Carlos R. Rivero
Venue: WWW
Year: 2024

Various methods embed knowledge graphs with the goal of predicting missing edges. Inference patterns are the logical relationships that occur in a graph. To make proper predictions, models trained by embedding methods must capture inference patterns. There are several theoretical analyses studying pattern-capturing capabilities. Unfortunately, these analyses are challenging and many embedding methods remain unstudied. Also, they do not quantify how accurately a pattern is captured in real-world datasets. Existing empirical studies have studied a small subset of simple inference patterns, and the analysis methods used have varied depending on the models evaluated. In this paper, we present a model-agnostic method to empirically quantify how patterns are captured by trained embedding models. We collect the most plausible predictions to form a new graph, and use it to globally assess pattern-capturing capabilities. For a given pattern, we study positive and negative evidence, i.e., edges that the pattern deems correct and incorrect based on the partial completeness assumption. As far as we know, it is the first time negative evidence is analyzed. Our experiments show that several models effectively capture the positive evidence of inference patterns. However, the performance is poor for negative evidence, which entails that models fail to learn the partial completeness assumption. We also identify new inference patterns not studied before. Surprisingly, models generally achieve better performance in these new patterns that we introduce.
Advancing Stance Detection of Political Fan Pages: A Multimodal Approach
Authors: Kuan-Hung Kuo, Ming-Hung Wang, Hung-Yu Kao, Yu-Chen Dai
Venue: WWW
Year: 2024

The evolution of political campaigns is evident with the ascent of social media. Ideological beliefs are increasingly disseminated through political-affiliated fan pages. The interaction between politicians and the general public on these platforms plays a pivotal role in election outcomes. In this study, we utilize a multimodal approach to explore and quantify similarities of ideologies among political fan pages. we employed visualization techniques to demonstrate the political stance of each fan page. To validate our proposal, we concentrated on an analysis of the 2021 national referendums in Taiwan, encompassing a collection of fan pages and their corresponding posts that were related to these referendums. Through a qualitative analysis of the content of these fan pages, the efficacy of our multimodal framework in clustering fan pages according to their respective political ideologies was evaluated. The findings of this study underscore the significant enhancement in the accuracy of stance detection when integrating multiple modalities of data, namely textual content, visual imagery, and user interactions.
Shock! Quantifying the Impact of Core Developers' Dropout on the Productivity of OSS Projects
Authors: Giuseppe Russo Latona, Christoph Gote, Christian Zingg, Giona Casiraghi, Luca Verginer, Frank Schweitzer
Venue: WWW
Year: 2024

Open Source Software (OSS) projects play a critical role in the digital infrastructure of companies and services provided to millions of people. Given their importance, understanding the resilience of OSS projects is paramount. A primary reason for OSS project failure is the shock caused by the dropout of a core developer, which can jeopardize productivity and project survival. Using a difference-in-differences (DiD) analysis, this study investigates the repercussions of this shock on the productivity of 8,234 developers identified among 9,573 OSS GitHub projects. Our findings reveal the indirect impact of the core developer's dropout. The remaining developers experienced a 20% productivity drop. This observation is troubling because it suggests that the shock might push other developers to drop out, putting the collaboration structure of the project at risk. Also, projects with higher productivity before the shock experienced a larger drop-down after the shock. This points to a tradeoff between productivity and resilience, i.e., the ability of OSS projects to recover from the dropout of a core developer. Our findings underscore the importance of a balanced approach in OSS project management, harmonizing productivity goals with resilience considerations.
A Study of Vulnerability Repair in JavaScript Programs with Large Language Models
Authors: Tan Khang Le, Saba Alimadadi, Steven Y. Ko
Venue: WWW
Year: 2024

In recent years, JavaScript has become the most widely used programming language, especially in web development. However, writing secure JavaScript code is not trivial, and programmers often make mistakes that lead to security vulnerabilities in web applications. Large Language Models (LLMs) have demonstrated substantial advancements across multiple domains, and their evolving capabilities indicate their potential for automatic code generation based on a required specification, including automatic bug fixing. In this study, we explore the accuracy of LLMs, namely ChatGPT and Bard, in finding and fixing security vulnerabilities in JavaScript programs. We also investigate the impact of context in a prompt on directing LLMs to produce a correct patch of vulnerable JavaScript code. Our experiments on real-world software vulnerabilities show that while LLMs are promising in automatic program repair of JavaScript code, achieving a correct bug fix often requires an appropriate amount of context in the prompt.
AdFlush: A Real-World Deployable Machine Learning Solution for Effective Advertisement and Web Tracker Prevention
Authors: Kiho Lee, Chaejin Lim, Beomjin Jin, Taeyoung Kim, Hyoungshick Kim
Venue: WWW
Year: 2024

Conventional ad blocking and tracking prevention tools often fall short in addressing web content manipulation. Machine learning approaches have been proposed to enhance detection accuracy, yet aspects of practical deployment have frequently been overlooked. This paper introduces AdFlush, a novel machine learning model for real-world browsers. To develop AdFlush, we evaluated the effectiveness of 883 features, ultimately selecting 27 key features for optimal performance. We tested AdFlush on a dataset of 10,000 real-world websites, achieving an F1 score of 0.98, thereby outperforming AdGraph (F1 score: 0.93), WebGraph (F1 score: 0.90), and WTAgraph (F1 score: 0.84). Additionally, AdFlush significantly reduces computational overhead, requiring 56% less CPU and 80% less memory than AdGraph. We also assessed AdFlush's robustness against adversarial manipulations, demonstrating superior resilience with F1 scores ranging from 0.89 to 0.98, surpassing the performance of AdGraph and WebGraph, which recorded F1 scores between 0.81 and 0.87. A six-month longitudinal study confirmed that AdFlush maintains a high F1 score above 0.97 without the need for retraining, underscoring its effectiveness.
Deep Learning for Hate Speech Detection: A Personality-based Approach
Authors: Kyuhan Lee, Sudha Ram
Venue: WWW
Year: 2024

A crucial element in the combat against hate speech is the development of efficient algorithms for automatically detecting hate speech. Previous research, however, has primarily neglected important insights from the field of psychology literature, particularly the relationship between personality and hate, resulting in suboptimal performance in hate speech detection. To this end, we propose a novel framework for detecting hate speech focusing on people's personality factors reflected in their writing. Our framework has two components: (i) a knowledge distillation model for fully automating the process of personality inference from text and (ii) a personality-based deep learning model for hate speech detection. Our approach is unique in that it incorporates low-level personality factors, which have been largely neglected in prior literature, into automated hate speech detection and proposes novel deep learning components for fully exploiting the intricate relationship between personality and hate (i.e., intermediate personality factors). The evaluation shows that our model significantly outperforms state-of-the-art baselines. Our study paves the way for future research by incorporating personality aspects into the design of automated hate speech detection. In addition, it offers substantial assistance to online social platforms and governmental authorities facing challenges in effectively moderating hate speech.
Aligning Language Models for Versatile Text-based Item Retrieval
Authors: Yuxuan Lei, Jianxun Lian, Jing Yao, Mingqi Wu, Defu Lian, Xing Xie
Venue: WWW
Year: 2024

This paper addresses the gap between general-purpose text embeddings and the specific demands of item retrieval tasks. We demonstrate the shortcomings of existing models in capturing the nuances necessary for zero-shot performance on item retrieval tasks. To overcome these limitations, we propose generate in-domain dataset from ten tasks tailored to unlocking models' representation ability for item retrieval. Our empirical studies demonstrate that fine-tuning embedding models on the dataset leads to remarkable improvements in a variety of retrieval tasks. We also illustrate the practical application of our refined model in a conversational setting, where it enhances the capabilities of LLM-based Recommender Agents like Chat-Rec. Our code is available at https://github.com/microsoft/RecAI.
DeFiTail: DeFi Protocol Inspection through Cross-Contract Execution Analysis
Authors: Wenkai Li, Xiaoqi Li, Yuqing Zhang, Zongwei Li
Venue: WWW
Year: 2024

Decentralized finance (DeFi) protocols are crypto projects developed on the blockchain to manage digital assets. Attacks on DeFi have been frequent and have resulted in losses exceeding 77 billion. However, detection methods for malicious DeFi events are still lacking. In this paper, we propose DeFiTail, the first framework that utilizes deep learning to detect access control and flash loan exploits that may occur on DeFi. Since the DeFi protocol events involve invocations with multi-account transactions, which requires execution path unification with different contracts. Moreover, to mitigate the impact of mistakes in Control Flow Graph (CFG) connections, we validate the data path by employing the symbolic execution stack. Furthermore, we feed the data paths through our model to achieve the inspection of DeFi protocols. Experimental results indicate that DeFiTail achieves the highest accuracy, with 98.39% in access control and 97.43% in flash loan exploits. DeFiTail also demonstrates an enhanced capability to detect malicious contracts, identifying 86.67% accuracy from the CVE dataset.
Dynamic Search Results Re-ranking Method by Advertisement Relevance Feedback based on Users' Unconscious Expectations for Listing Advertisement
Authors: Da Li, Shigenaga Hamaguchi, Ruman Suyama, Shinsuke Nakajima, Yukiko Kawai
Venue: WWW
Year: 2024

Search results hold paramount importance for both users and advertisers. However, re-ranking results based on the timing of user clicks on listing advertisements poses a considerable challenge. In this study, we introduce a dynamic re-ranking method to re-rank search results triggered by the viewing of listing advertisements containing search terms after the initial presentation of search results. The proposed method leverages relevance feedback to enhance search results, providing valuable support to users in their searches. We conducted a comparative verification of accuracy rates between methods with and without the added weight. The results suggest that considering the timings of clicking on listing ads for re-ranking results can more effectively reflect user interest compared to approaches that don't take this timing into account. This study contributes to the advancement of search result presentation strategies by incorporating dynamic user behavior considerations.
The 12th International Workshop on Natural Language Processing for Social Media (SocialNLP 2024)
Authors: Cheng-Te Li, Lun-Wei Ku, Yu-Che Tsai
Venue: WWW
Year: 2024

The field of SocialNLP stands at the confluence of two pivotal domains: natural language processing (NLP) and social computing, offering a multidisciplinary framework for exploring the intersections between these areas. This domain is characterized by its tri-directional focus. First, it endeavors to tackle prevalent challenges within the realm of social computing by leveraging advanced NLP methodologies. Second, it aims to address traditional NLP problems by harnessing the vast and dynamic data available from social networks and social media platforms. Third, SocialNLP is dedicated to identifying and solving emerging issues that lie at the nexus of social computing and NLP. The 12th iteration of the SocialNLP workshop, a hallmark event in this vibrant field, is scheduled to convene at TheWebConf (WWW) 2024. This edition has witnessed the acceptance of nine rigorously peer-reviewed papers, chosen from a competitive pool, reflecting an acceptance ratio of 53.8%. This achievement underscores the high caliber of research and the innovative approaches that the submitted works propose to advance the field of SocialNLP. The organizing committee extends its heartfelt gratitude to all contributing authors for their submissions, to the members of the program committee for their indispensable role in the rigorous review process, and to the workshop chairs for their visionary leadership and dedication. Their collective efforts have been instrumental in fostering an environment of academic excellence and collaboration that continues to drive forward the frontiers of knowledge in the interdisciplinary area of SocialNLP.
StateGuard: Detecting State Derailment Defects in Decentralized Exchange Smart Contract
Authors: Zongwei Li, Wenkai Li, Xiaoqi Li, Yuqing Zhang
Venue: WWW
Year: 2024

Decentralized Exchanges (DEXs), leveraging blockchain technology and smart contracts, have emerged in decentralized finance. However, the DEX project with multi-contract interaction is accompanied by complex state logic, which makes it challenging to solve state defects. In this paper, we conduct the first systematic study on state derailment defects of DEXs. These defects could lead to incorrect, incomplete, or unauthorized changes to the system state during contract execution, potentially causing security threats. We propose StateGuard, a deep learning-based framework to detect state derailment defects in DEX smart contracts. StateGuard constructs an Abstract Syntax Tree (AST) of the smart contract, extracting key features to generate a graph representation. Then, it leverages a Graph Convolutional Network (GCN) to discover defects. Evaluating StateGuard on 46 DEX projects with 5,671 smart contracts reveals its effectiveness, with a precision of 92.24%. To further verify its practicality, we used StateGuard to audit real-world smart contracts and successfully authenticated multiple novel CVEs.
Full-Attention Driven Graph Contrastive Learning: with Effective Mutual Information Insight
Authors: Long Li, Zemin Liu, Chenghao Liu, Jianling Sun
Venue: WWW
Year: 2024

Graph contrastive learning often faces challenges when data augmentations compromise the graph's critical attributes, introducing the risk of generating noise-positive pairs. Although recent methods have attempted to address these issues, they either fall short of ensuring effective data augmentation or suffer from excessive computational demands. The advent of full-attention graph Transformers, with their enhanced capacity for graph representation learning, has sparked significant interest. Despite their potential, employing full-attention graph Transformers for contrastive learning can introduce issues such as noisy redundancies. In this work, we propose the Graph Attention Contrastive Learning (GACL) model, which innovatively combines a full-attention transformer with a message-passing graph neural network as its encoder. To mitigate the noise associated with full-attention mechanisms, we apply a denoising modification. Our GACL model effectively tackles the challenges associated with full-attention mechanisms and introduces a novel approach for data augmentation. Moreover, we propose the concept of effective mutual information to theoretically underpin our methodology. Utilizing this framework, we explore the impact of the denoising matrix within GACL's contrastive learning process and delve into comprehensive discussions on its implications. Empirical assessments underscore GACL's exceptional performance, establishing it as a state-of-the-art solution in graph contrastive learning.