OpenRaiser/Intern-Atlas huggingface dataset:
Unifying Local and Global Knowledge: Empowering Large Language Models as Political Experts with Knowledge Graphs
Authors: Xinyi Mou, Zejun Li, Hanjia Lyu, Jiebo Luo, Zhongyu Wei
Venue: WWW
Year: 2024

Large Language Models (LLMs) have revolutionized solutions for general natural language processing (NLP) tasks. However, deploying these models in specific domains still faces challenges like hallucination. While existing knowledge graph retrieval-based approaches offer partial solutions, they cannot be well adapted to the political domain. On one hand, existing generic knowledge graphs lack vital political context, hindering deductions for practical tasks. On the other hand, the nature of political questions often renders the direct facts elusive, necessitating deeper aggregation and comprehension of retrieved evidence. To address these challenges, we propose a Political Experts through Knowledge Graph Integration (PEG) framework. PEG entails the creation and utilization of a multi-view political knowledge graph (MVPKG), which integrates U.S. legislative, election, and diplomatic data, as well as conceptual knowledge from Wikidata. With MVPKG as its foundation, PEG enhances existing methods through knowledge acquisition, aggregation, and injection. This process begins with refining evidence through semantic filtering, followed by its aggregation into global knowledge via implicit or explicit methods. The integrated knowledge is then utilized by LLMs through prompts. Experiments on three real-world datasets across diverse LLMs confirm PEG's superiority in tackling political modeling tasks.
Automatic Construction of Expiration Time Expression Dataset from Retweets
Authors: Hirotaka Nagashima, Keishi Tajima
Venue: WWW
Year: 2024

Documents describing information with expiration time often include time expressions specifying the expiration time. To train a classifier determining if a time expression represents an expiration time, we need a labeled dataset. We propose a method of automatically constructing such a dataset. Our method collects tweets including time expressions, and automatically determines whether the time expressions represent expiration times based on temporal changes in the frequency of retweets. Our experimental result shows that our method produces an effective dataset.
Breaking the Time-Frequency Granularity Discrepancy in Time-Series Anomaly Detection
Authors: Youngeun Nam, Susik Yoon, Yooju Shin, Minyoung Bae, Hwanjun Song, Jae-Gil Lee, Byung Suk Lee
Venue: WWW
Year: 2024

In light of the remarkable advancements made in time-series anomaly detection(TSAD), recent emphasis has been placed on exploiting the frequency domain as well as the time domain to address the difficulties in precisely detecting pattern-wise anomalies. However, in terms of anomaly scores, the window granularity of the frequency domain is inherently distinct from the data-point granularity of the time domain. Owing to this discrepancy, the anomaly information in the frequency domain has not been utilized to its full potential for TSAD. In this paper, we propose a TSAD framework, Dual-TF, that simultaneously uses both the time and frequency domains while breaking the time-frequency granularity discrepancy. To this end, our framework employs nested-sliding windows, with the outer and inner windows responsible for the time and frequency domains, respectively, and aligns the anomaly scores of the two domains. As a result of the high resolution of the aligned scores, the boundaries of pattern-wise anomalies can be identified more precisely. In six benchmark datasets, our framework outperforms state-of-the-art methods by 12.0--147%, as demonstrated by experimental results.
Experimental Security Analysis of Sensitive Data Access by Browser Extensions
Authors: Asmit Nayak, Rishabh Khandelwal, Earlence Fernandes, Kassem Fawaz
Venue: WWW
Year: 2024

Browser extensions offer a variety of valuable features and functionalities. They also pose a significant security risk if not properly designed or reviewed. Prior works have shown that browser extensions can access and manipulate data fields, including sensitive data such as passwords, credit card numbers, and Social Security numbers. In this paper, we present an empirical study of the security risks posed by browser extensions. Specifically, we first build a proof-of-concept extension that can steal sensitive user information. We find that the extension passes the Chrome Webstore review process. We then perform a measurement study on the top 10K website login pages to check if the extension access to password fields via JS. We find that none of the password fields are actively protected, and can be accessed using JS. Moreover, we found that 1K websites store passwords in plaintext in their page source, including popular websites like Google.com and Cloudflare.com. We also analyzed over 160K Chrome Web Store extensions for malicious behavior, finding that 28K have permission to access sensitive fields and 190 store password fields in variables. To analyze the behavioral workflow of the potentially malicious extensions, we propose an LLM-driven framework, Extension Reviewer. Finally, we discuss two countermeasures to address these risks: a bolt-on JavaScript package for immediate adoption by website developers allowing them to protect sensitive input fields, and a browser-level solution that alerts users when an extension accesses sensitive input fields. Our research highlights the urgent need for improved security measures to protect sensitive user information online.
How Contentious Terms About People and Cultures are Used in Linked Open Data
Authors: Andrei Nesterov, Laura Hollink, Jacco van Ossenbruggen
Venue: WWW
Year: 2024

Web resources in linked open data (LOD) are comprehensible to humans through literal textual values attached to them, such as labels, notes, or comments. Word choices in literals may not always be neutral. When culturally stereotyping terminology is used in literals, they may appear as offensive to users in interfaces and propagate stereotypes to algorithms trained on them. We study how frequently and in which literals contentious terms about people and cultures occur in LOD and whether there are attempts to mark the usage of such terms. For our analysis, we reuse English and Dutch terms from a knowledge graph that provides opinions of experts from the cultural heritage domain about terms' contentiousness. We inspect occurrences of these terms in four widely used datasets: Wikidata, The Getty Art & Architecture Thesaurus, Princeton WordNet, and Open Dutch WordNet. Some terms are ambiguous and contentious only in particular senses. Applying word sense disambiguation, we generate a set of literals relevant to our analysis. We found that contentious terms frequently appear in descriptive and labelling literals, such as preferred labels that are usually displayed in interfaces and used for indexing. In some cases, LOD contributors mark contentious terms with words and phrases in literals (implicit markers) or properties linked to resources (explicit markers). However, such marking is rare and non-consistent in all datasets. Our quantitative and qualitative insights could be helpful in developing more systematic approaches to address the propagation of stereotypes via LOD.
Love-Hate Dataset: A Multi-Modal Multi-Platform Dataset Depicting Emotions in the 2023 Israel-Hamas War
Authors: Lynnette Hui Xian Ng, Adrian Xuan Wei Lim, Roy Ka-Wei Lee
Venue: WWW
Year: 2024

War brings about strong feelings of hate, and showcases the love of humanity. During war, social media is utilized for citizen journalism, supply organization and activism, but also for people to express their emotions. In this paper, we present a multi-modal multi-platform dataset that depicts the expression of love and hate towards both sides of the Israel-Hamas War. This dataset presents posts in English from Facebook and Instagram that contain the terms "love" or "hate" in the context of the war during 7 October 2023 (onset of war) to 31 December 2023. We find that over time, the number of posts on the war decreased, suggesting interest in the war has waned; posts about Love reference religion while posts about Hate references hostility; and emojis in Love posts represent hearts, peace and listening while emojis in Hate posts represent being watched, sadness and warning. Finally, we generated Instagram posts with GPT4-V using our dataset as a reference, and the model returned posts of generic love messages with art-form images. We hope our dataset is useful to researchers studying multi-modal and multi-platform information and emotions on social media during a war.
Diffusion-based Negative Sampling on Graphs for Link Prediction
Authors: Trung-Kien Nguyen, Yuan Fang
Venue: WWW
Year: 2024

Link prediction is a fundamental task for graph analysis with important applications on the Web, such as social network analysis and recommendation systems, \etc\ Modern graph link prediction methods often employ a contrastive approach to learn robust node representations, where negative sampling is pivotal. Typical negative sampling methods aim to retrieve hard examples based on either predefined heuristics or automatic adversarial approaches, which might be inflexible or difficult to control. Furthermore, in the context of link prediction, most previous methods sample negative nodes from existing substructures of the graph, missing out on potentially more optimal samples in the latent space. To address these issues, we investigate a novel strategy of multi-level negative sampling that enables negative node generation with flexible and controllable "hardness'' levels from the latent space. Our method, called Conditional Diffusion-based Multi-level Negative Sampling (DMNS), leverages the Markov chain property of diffusion models to generate negative nodes in multiple levels of variable hardness and reconcile them for effective graph link prediction. We further demonstrate that DMNS follows the sub-linear positivity principle for robust negative sampling. Extensive experiments on several benchmark datasets demonstrate the effectiveness of DMNS.
Human vs ChatGPT: Effect of Data Annotation in Interpretable Crisis-Related Microblog Classification
Authors: Thi Huyen Nguyen, Koustav Rudra
Venue: WWW
Year: 2024

Recent studies have exploited the vital role of microblogging platforms, such as Twitter, in crisis situations. Various machine-learning approaches have been proposed to identify and prioritize crucial information from different humanitarian categories for preparation and rescue purposes. In crisis domain, the explanation of models' output decisions is gaining significant research momentum. Some previous works focused on human annotations of rationales to train and extract supporting evidence for model interpretability. However, such annotations are usually expensive, require much effort, and are not always available in real-time situations of a new crisis event. In this paper, we investigate the recent advances in large language models (LLMs) as data annotators on informal tweet text. We perform a detailed qualitative and quantitative evaluation of ChatGPT rationale annotations over a few-shot setup. ChatGPT annotations are quite close to humans but less precise in nature. Further, we propose an active learning-based interpretable classification model from a small set of annotated data. Our experiments show that (a). ChatGPT has the potential to extract rationales for the crisis tweet classification tasks, but the performance is slightly less than the model trained on human-annotated rationale data (\sim3-6%), (b). active learning setup can help reduce the burden of manual annotations and maintain a trade-off between performance and data size.
Graph Contrastive Learning with Kernel Dependence Maximization for Social Recommendation
Authors: Xuelian Ni, Fei Xiong, Yu Zheng, Liang Wang
Venue: WWW
Year: 2024

Contrastive learning (CL) has recently catalyzed a productive avenue of research for recommendation. The efficacy of most CL methods for recommendation may hinge on their capacity to learn representation uniformity by mapping the data onto a hypersphere. Nonetheless, applying contrastive learning to downstream recommendation tasks remains challenging, as existing CL methods encounter difficulties in capturing the nonlinear dependence of representations in high-dimensional space and struggle to learn hierarchical social dependency among users-essential points for modeling user preferences. Moreover, the subtle distinctions between the augmented representations render CL methods sensitive to noise perturbations. Inspired by the Hilbert-Schmidt independence criterion (HSIC), we propose a graph Contrastive Learning model with Kernel Dependence Maximization CL-KDM for social recommendation to address these challenges. Specifically, to explicitly learn the kernel dependence of representations and improve the robustness and generalization of recommendation, we maximize the kernel dependence of augmented representations in kernel Hilbert space by introducing HSIC into the graph contrastive learning. Additionally, to simultaneously extract the hierarchical social dependency across users while preserving underlying structures, we design a hierarchical mutual information maximization module for generating augmented user representations, which are injected into the message passing of a graph neural network to enhance recommendation. Extensive experiments are conducted on three social recommendation datasets, and the results indicate that CL-KDM outperforms various baseline recommendation methods.
Detecting Financial Bots on the Ethereum Blockchain
Authors: Thomas Niedermayer, Pietro Saggese, Bernhard Haslhofer
Venue: WWW
Year: 2024

The integration of bots in Distributed Ledger Technologies (DLTs) fosters efficiency and automation. However, their use is also associated with predatory trading and market manipulation, and can pose threats to system integrity. It is therefore essential to understand the extent of bot deployment in DLTs; despite this, current detection systems are predominantly rule-based and lack flexibility. In this study, we present a novel approach that utilizes machine learning for the detection of financial bots on the Ethereum platform. First, we systematize existing scientific literature and collect anecdotal evidence to establish a taxonomy for financial bots, comprising 7 categories and 24 subcategories. Next, we create a ground-truth dataset consisting of 133 human and 137 bot addresses. Third, we employ both unsupervised and supervised machine learning algorithms to detect bots deployed on Ethereum. The highest-performing clustering algorithm is a Gaussian Mixture Model with an average cluster purity of 82.6%, while the highest-performing model for binary classification is a Random Forest with an accuracy of 83%. Our machine learning-based detection mechanism contributes to understanding the Ethereum ecosystem dynamics by providing additional insights into the current bot landscape.
Unveiling Wash Trading in Popular NFT Markets
Authors: Yuanzheng Niu, Xiaoqi Li, Hongli Peng, Wenkai Li
Venue: WWW
Year: 2024

As emerging digital assets, NFTs are susceptible to anomalous trading behaviors due to the lack of stringent regulatory mechanisms, potentially causing economic losses. In this paper, we conduct the first systematic analysis of four non-fungible tokens (NFT) markets. Specifically, we analyze more than 25 million transactions within these markets, to explore the evolution of wash trade activities. Furthermore, we propose a heuristic algorithm that integrates the network characteristics of transactions with behavioral analysis, to detect wash trading activities in NFT markets. Our findings indicate that NFT markets with incentivized structures exhibit higher proportions of wash trading volume compared to those without incentives. Notably, the LooksRare and X2Y2 markets are detected with wash trading volume proportions as high as 94.5% and 84.2%, respectively.
Diffusion Recommendation with Implicit Sequence Influence
Authors: Yong Niu, Xing Xing, Zhichun Jia, Ruidi Liu, Mindong Xin, Jianfu Cui
Venue: WWW
Year: 2024

Sequence recommendation tasks often have performance bottlenecks, mainly reflected in the following two aspects: previous research relied on a single item embedding distribution, resulting in a decrease in overall modeling ability. In addition, the implicit dynamic preferences reflected in user interaction sequences are not distinguished, and the feature representation ability is insufficient. To address these issues, we propose a novel model called Diffusion Recommendation with Implicit Sequence Influence (DiffRIS). Specifically, we establish an implicit feature extraction module, which includes multi-scale CNN and residual LSTM networks that learn local and global features of sequence information, respectively, to explore the length dependence of data features. Subsequently, we use the output of the module as a conditional input for the diffusion model, guiding the denoising process based on historical interactions. Through experiments on two open-source datasets, we find that implicit features of sequences have a positive impact on the diffusion process. The proposed DiffRIS framework performs well compared to multiple baseline models, effectively improving the accuracy of sequential recommendation models. We believe that the proposed DiffRIS can provide some research ideas for diffusion sequence recommendation.
Group-wise K-anonymity meets (ε, δ) Differentially Privacy Scheme
Authors: Kenneth Odoh
Venue: WWW
Year: 2024

We studied the link between K-anonymity and differential privacy as the basis for deriving a novel method for noise estimation. Hence, we provide threefold contributions: First, we use the birthday-bound paradox for uniqueness to estimate the noise level, ε in (ε, δ) differentially privacy scheme. Second, our group-aware formulation provides resilience to a series of inference attacks by using the group privacy property in our unique group-centric formulation. Third, draw a connection between the attacker advantage, δ, and ε for univariate and multivariate cases. Finally, we demonstrate applicability in Laplacian, Gaussian, and Exponential mechanisms.
Cornac-AB: An Open-Source Recommendation Framework with Native A/B Testing Integration
Authors: Darryl Ong, Quoc-Tuan Truong, Hady W. Lauw
Venue: WWW
Year: 2024

Recommender systems significantly impact user experience across diverse domains, yet existing frameworks often prioritize offline evaluation metrics, neglecting the crucial integration of A/B testing for forward-looking assessments. In response, this paper introduces a new framework seamlessly incorporating A/B testing into the Cornac recommendation library. Leveraging a diverse collection of model implementations in Cornac, our framework enables effortless A/B testing experiment setup from offline trained models. We introduce a carefully designed dashboard and a robust backend for efficient logging and analysis of user feedback. This not only streamlines the A/B testing process but also enhances the evaluation of recommendation models in an online environment. Demonstrating the simplicity of on-demand online model evaluations, our work contributes to advancing recommender system evaluation methodologies, underscoring the significance of A/B testing and providing a practical framework for implementation. The framework is open-sourced at https://github.com/PreferredAI/cornac-ab.
Follow the Path: Hierarchy-Aware Extreme Multi-Label Completion for Semantic Text Tagging
Authors: Natalia Ostapuk, Julien Audiffren, Ljiljana Dolamic, Alain Mermoud, Philippe Cudré-Mauroux
Venue: WWW
Year: 2024

Extreme Multi Label (XML) problems, and in particular XML completion -- the task of prediction the missing labels of an entity -- have attracted significant attention in the past few years. Most XML completion problems can organically leverage a label hierarchy, which can be represented as a tree that encodes the relations between the different labels. In this paper, we propose a new algorithm, HECTOR - Hierarchical Extreme Completion for Text based on TransfORmer, to solve XML Completion problems more effectively. HECTOR operates by directly predicting paths in the label tree rather than individual labels, thus taking advantage of information encoded in the hierarchy. Due to the sequential aspect of these paths, HECTOR can leverage the effectiveness and performance of the Transformer architecture to outperform state-of-the-art of XML completion methods. Extensive evaluations on three real-world datasets demonstrate the effectiveness of our approach for XML completion. We compare HECTOR with several state-of-the-art XML completion methods for various completion problems, and in particular for label refinement, i.e., the scenario where only the coarse labels (i.e. the first few top levels in a taxonomy) are observed. Empirical results on three different datasets show that our method significantly outperforms the state of the art, with HECTOR frequently outperforming previous techniques by more than 10% according to multiple metrics.
Breaking the Bot Barrier: Evaluating Adversarial AI Techniques Against Multi-Modal Defense Models
Authors: Behzad Ousat, Dongsheng Luo, Amin Kharraz
Venue: WWW
Year: 2024

Websites utilize several approaches to detect automated agents. The agents are deployed either for beneficial purposes such as search engine crawlers, or to perform tasks on behalf of the adversary such as scanning for vulnerabilities. Recent methods in detecting such agents include the analysis of the behavior that the agents show when visiting the website. In this paper, I) we describe a deep learning framework that analyzes the triggered browser events to classify the visitor. II) We develop two adversarial attacks in order to bypass the defense by generating adversarial vectors that are misclassified by the model. III) We discuss how applicable the attacks are by reviewing the limitations of the popular tools (i.e., Selenium and Puppeteer) used for the development of automated agents based on full-fledged browsers.
The Matter of Captchas: An Analysis of a Brittle Security Feature on the Modern Web
Authors: Behzad Ousat, Esteban Schafir, Duc C. Hoang, Mohammad Ali Tofighi, Cuong V. Nguyen, Sajjad Arshad, A. Selcuk Uluagac, Amin Kharraz
Venue: WWW
Year: 2024

The web ecosystem is a fast-paced environment. In this dynamic landscape, new security features are offered one after another to enhance the security and robustness of web applications and the operations they handle. This paper focuses on a fragile but still in-use security feature, text-based CAPTCHAs, that had been wildly used by web applications in the past to protect against automated attacks such as credential stuffing and account hijacking. The paper first investigates what it takes to develop automated scanners that can solve previously unseen text-based CAPTCHAs. We evaluated the possibility of developing and integrating a pre-trained CAPTCHA solver in the automated web scanning process without using a significantly large training dataset. We also perform an analysis of the impact of such autonomous scanners on CAPTCHA-enabled websites. Our analysis shows that solvable text-based CAPTCHAs on login, contact, and comment pages of websites are not uncommon. In particular, we identified over 3,100 text-based CAPTCHA websites in critical sectors such as finance, government, and health with hundreds of thousands of users. We showed that a web scanner with a pre-trained solver could solve more than 20% of previously unseen CAPTCHAs in just one single attempt. This result is worrisome considering the substantial potential to autonomously run the operation across thousands of websites on a daily basis with minimal training. The findings suggest that the integration of autonomous scanning with pre-training and local optimization of models can significantly increase adversaries' asymmetric power to launch their attacks cheaper and faster.