OpenRaiser/Intern-Atlas huggingface dataset:
Entity Disambiguation with Extreme Multi-label Ranking
Authors: Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhang, Cho-Jui Hsieh, Hsiang-Fu Yu
Venue: WWW
Year: 2024

Entity disambiguation is one of the most important natural language tasks to identify entities behind ambiguous surface mentions within a knowledge base. Although many recent studies apply deep learning to achieve decent results, they need exhausting pre-training and mediocre recall in the retrieval stage. In this paper, we propose a novel framework, eXtreme Multi-label Ranking for Entity Disambiguation (XMRED), to address this challenge. An efficient zero-shot entity retriever with auxiliary data is first pre-trained to recall relevant entities based on linear models. Specifically, the retrieval process can be considered as an extreme multi-label ranking (XMR) task. Entities are first clustered at different scales to form a label tree, thereby learning multi-scale entity retrievers over the label tree with high recall. Moreover, XMRED applies deep cross-encoder as a re-ranker to achieve high precision based on high-quality candidates. Extensive experimental results based on the AIDA-CoNLL benchmark and five zero-shot testing datasets demonstrate that XMRED obtains 98% and over 95% recall scores for in-domain and zero-shot datasets with top-10 retrieved entities. With a deep cross-encoder as the re-ranker, XMRED further outperforms the previous state-of-the-art by 1.74% in In-KB micro-F1 scores on average with a significant improvement on the training efficiency from days to 3.48 hours. In addition, XMRED also beats the state-of-the-art for page-level document retrieval by 2.38% in accuracy and 1.90% in recall@5.
GRACE: Generating Cause and Effect of Disaster Sub-Events from Social Media Text
Authors: Xinxi Jiang, Xiang Li, Qifeng Zhou, Qing Wang
Venue: WWW
Year: 2024

In recent years, social media has emerged as a pivotal source of emergency response for natural disasters. Causal analysis of disaster sub-events is one of crucial concerns. However, the design and implementation of its application scenario present significant challenges, due to the intricate nature of events and information overload. In this work, we introduce GRACE, a system designed for generating the cause and effect of disaster sub-events from social media text. GRACE aims to provide a rapid, comprehensive, and real-time analysis of disaster intelligence. Different from conventional information digestion systems, GRACE employs event evolution reasoning by constructing a causal knowledge graph for disaster sub-events (referred to as DSECG) and fine-tuning GPT-2 on DSECG. This system offers users a comprehensive understanding of disaster events and supports human organizations in enhancing response efforts during disaster situations. Moreover, an online demo is accessible, allowing user interaction with GRACE and providing a visual representation of the cause and effect of disaster sub-events.
Rethinking Sequential Relationships: Improving Sequential Recommenders with Inter-Sequence Data Augmentation
Authors: Yang Jiao, Fan Yang, Yetian Chen, Yan Gao, Jia Liu, Yi Sun
Venue: WWW
Year: 2024

Predicting customer preferences for each item is a prerequisite module for most recommender systems in e-commerce. However, the sparsity of behavioral data is often a challenge to learn accurate prediction models. Given millions of items, each customer may only be able to interact with a small subset of them over time. This sparse behavioral data is insufficient to represent item-customer and item-item relations for a machine learning model to digest, resulting in limited prediction accuracy that hinders recommendation performance. To mitigate this issue, this study introduces an inter-sequence data augmentation method, SDAinter, that enhances data density by leveraging cross-customer behavioral patterns to enrich item relations. Tested on three public and one proprietary e-commerce dataset, SDAinter significantly increases data density, leading to notable improvements in both evaluation and business metrics. Our findings demonstrate SDAinter's effectiveness and its potential to complement existing data augmentation strategies in recommender systems. See https://github.com/ML-apollo/SDA_inter.
Disentangled Anomaly Detection For Multivariate Time Series
Authors: Xin Jie, Xixi Zhou, Chanfei Su, Zijun Zhou, Yuqing Yuan, Jiajun Bu, Haishuai Wang
Venue: WWW
Year: 2024

Anomaly detection in time series that aims to identify unusual patterns has attracted a lot of attention recently. However, the representation of abnormal and normal data is difffcult to be distinguished because they are usually entangled. Recently, disentanglement theory based on variational auto-encoder (VAE) has shown great potential in machine learning and achieved great success in computer vision and natural language processing. In this paper, we propose a novel disentangled anomaly detection approach that adopts VAE-based disentanglement networks for anomaly detection in multivariate time series. The proposed method learns highquality disentangled latent factors in a continuous representation space to facilitate the identiffcation of anomalies from normal data. Extensive experiments demonstrate that our proposed lightweight model DA-VAE achieves state-of-the-art performance.
Understanding and Counteracting Feature-Level Bias in Click-Through Rate Prediction
Authors: Jinqiu Jin, Sihao Ding, Wenjie Wang, Fuli Feng
Venue: WWW
Year: 2024

Common click-through rate (CTR) prediction recommender models tend to exhibit feature-level bias, which leads to unfair recommendations among item groups and inaccurate recommendations for users. While existing methods address this issue by adjusting the learning of CTR models, such as through additional optimization objectives, they fail to consider how the bias is caused within these models. In this paper, we discover a generation path of feature-level bias: biased positive sample ratios → biased linear weights in CTR model → biased prediction scores → biased recommendations. Based on this understanding, we propose a minimally invasive yet effective strategy to counteract feature-level bias in CTR models by removing the biased linear weights from well-trained models. Additionally, we present a linear weight adjusting strategy that requires fewer random exposure records than relevant debiasing methods. The superiority of our proposed strategies are validated through extensive experiments on three real-world datasets. The code is available at https://github.com/mitao-cat/feature-level_bias
Unity is Strength? Benchmarking the Robustness of Fusion-based 3D Object Detection against Physical Sensor Attack
Authors: Zizhi Jin, Xuancun Lu, Bo Yang, Yushi Cheng, Chen Yan, Xiaoyu Ji, Wenyuan Xu
Venue: WWW
Year: 2024

As a safety-critical application, Autonomous Driving (AD) has received growing attention from security researchers. AD heavily relies on sensors for perception. However, sensors themselves are susceptible to various threats since they are exposed to the environments and vulnerable to malicious or interfering signals. To cope with situations where a sensor might malfunction, Multi Sensor Fusion (MSF) was proposed as a general strategy to enhance the robustness of perception models. In this paper, we focus on investigating MSF security under various sensor attacks and wish to answer the following research questions: (1)Does fusion enhance robustness or not? (2)How does the architecture of the fusion model influence robustness? To this end, we establish a rigorous benchmark for fusion-based 3D object detection robustness. Our new benchmark features 5 types of LiDAR attacks and 6 types of camera attacks. Different from traditional benchmarks, we take the physical sensor attacks into consideration during the corruption construction. Then, we systematically investigate 7 MSF-based and 5 single-modality 3D object detection models with different fusion architectures. We release the benchmarks and codes to facilitate future studies: \textcolorblue \hrefhttps://github.com/Jinzizhisir/PSA-Fusion https://github.com/Jinzizhisir/PSA-Fusion .
DCAI: Data-centric Artificial Intelligence
Authors: Wei Jin, Haohan Wang, Daochen Zha, Qiaoyu Tan, Yao Ma, Sharon Li, Su-In Lee
Venue: WWW
Year: 2024

The emergence of Data-centric AI (DCAI) represents a pivotal shift in AI development, redirecting focus from model refinement to prioritizing data quality. This paradigmatic transition emphasizes the critical role of data in AI. While past approaches centered on refining models, they often overlooked potential data imperfections, raising questions about the true potential of enhanced model performance. DCAI advocates the systematic engineering of data, complementing existing efforts and playing a vital role in driving AI success. This transition has spurred innovation in various machine learning and data mining algorithms and their applications on the Web. Therefore, we propose the DCAI Workshop at WWW'24, which offers a platform for academic researchers and industry practitioners to showcase the latest advancements in DCAI research and their practical applications in the real world.
Linked Open Literature Review using the Neuro-symbolic Open Research Knowledge Graph
Authors: Azanzi Jiomekong, Sören Auer, Allard Oelen
Venue: WWW
Year: 2024

The way scholarly knowledge and in particular literature reviews are communicated today rather resembles static, unstructured, pseudo-digitized articles, which are hardly processable by machines and AI. This demo showcases a novel way to create and publish scholarly literature reviews, also called semantic reviews. The neuro-symbolic approach consists of extracting key insights from scientific papers leveraging neural models and organizing them using a symbolic scholarly knowledge graph. The food information engineering review case study will allow participants to see how this approach is implemented using the Open Research Knowledge Graph (ORKG). The real-time demo will allow participants to play with the ORKG and create their own living, semantic review.
LFDe: A Lighter, Faster and More Data-Efficient Pre-training Framework for Event Extraction
Authors: Zhigang Kan, Liwen Peng, Yifu Gao, Ning Liu, Linbo Qiao, Dongsheng Li
Venue: WWW
Year: 2024

Pre-training Event Extraction (EE) models on unlabeled data is an effective strategy that frees researchers from costly and labor-intensive data annotation. However, existing pre-training methods necessitate substantial computational resources, requiring high-performance hardware infrastructure and extensive training duration. In response to these challenges, this paper proposes a Lighter, Faster, and more Data-efficient pre-training framework for EE, named LFDe. Distinct from existing methods that strive to establish a comprehensive representation space during pre-training, our framework focuses on quickly familiarizing with the task format from a small amount of automatically constructed pseudo-events. It comprises three stages: weak-label data construction, pre-training, and fine-tuning. Specifically, during the first stage, LFDe first automatically designates pseudo-triggers and arguments based on the characteristics of real events to form pre-training samples. In the processes of pre-training and fine-tuning, the framework reframes EE as the identification of tokens semantically closest to the prompt within the given sentence. This paper also introduces a novel prompt-based sequence labeling model for EE to accommodate this reframing. Experiments on real-world datasets show that compared to similar models, our framework requires fewer pre-training data (only about 0.04%), a shorter pre-training period (about 0.03%), and lower memory requirements (about 57.6%). Simultaneously, our framework significantly improves performance in various data-scarce scenarios.
SceneDAPR: A Scene-Level Free-Hand Drawing Dataset for Web-based Psychological Drawing Assessment
Authors: Jiwon Kang, Jiwon Kim, Migyeong Yang, Chaehee Park, Taeeun Kim, Hayeon Song, Jinyoung Han
Venue: WWW
Year: 2024

Sketch-based drawing assessments are useful in understanding individuals' cognitive and psychological states, such as cognitive impairment or mental disorders. Hence, these assessments have been developed and applied on a large scale, such as in schools and workplaces, to screen individuals who may require further clinical examination. However, the interpretation of a large number of drawing assessments solely relies on human experts, requiring much time and cost. To address this issue, we introduce a novel scene-level sketch dataset, SceneDAPR, which can be used to automatically analyze the drawing assessment, Draw-A-Person-in-the-Rain (DAPR), a popular psychological drawing assessment used for identifying stressful experiences and coping behavior. The proposed dataset consists of 6,420 objects depicted in 1,399 scene sketches drawn by humans, along with detailed supplementary information about the participants. SceneDAPR includes free-hand drawings from different age groups: children & adolescents, adults, and seniors. Leveraging the proposed SceneDAPR, we develop a web-based drawing assessment system. The extensive experiments demonstrate that our system shows a robust performance across the different age groups in the object detection task as well as a considerable performance compared to human experts. We believe that the proposed new sketch dataset can be used to develop an automatic system for psychological drawing assessments, which can support human experts by reducing the time and cost of analyzing the drawing assessments for a large population. SceneDAPR and experimental code are available at https://github.com/DSAIL-SKKU/SceneDAPR.
Low Mileage, High Fidelity: Evaluating Hypergraph Expansion Methods by Quantifying the Information Loss
Authors: David Y. Kang, Qiaozhu Mei, Sang-Wook Kim
Venue: WWW
Year: 2024

In this paper, we first define information loss that occurs in the hypergraph expansion and then propose a novel framework, named MILEAGE, to evaluate hypergraph expansion methods by measuring their degree of information loss. MILEAGE employs the following four steps: (1) expanding a hypergraph; (2) performing the unsupervised representation learning on the expanded graph; (3) reconstructing a hypergraph based on vector representations obtained; and (4) measuring MILEAGE-score (i.e., mileage) by comparing the reconstructed and the original hypergraphs. To demonstrate the usefulness of MILEAGE, we conduct experiments via downstream tasks on three levels (i.e., node, hyperedge, and hypergraph): node classification, hyperedge prediction, and hypergraph classification on eight real-world hypergraph datasets. Through the extensive experiments, we observe that information loss through hypergraph expansion has a negative impact on downstream tasks and MILEAGE can effectively evaluate hypergraph expansion methods through the information loss and recommend a new method that resolves the problems of existing ones.
PanoptiChrome: A Modern In-browser Taint Analysis Framework
Authors: Rahul Kanyal, Smruti R. Sarangi
Venue: WWW
Year: 2024

Taint tracking in web browsers is a problem of profound interest because it allows developers to accurately understand the flow of sensitive data across JavaScript (JS) functions. Modern websites load JS functions from either the web server or other third-party sites, hence this problem has acquired a much more complex and pernicious dimension. Sadly, for the latest version of the Chromium browser (used by 75% of users), there is no dynamic taint propagation engine primarily because it is incredibly complex to build one. The nearest contending work in this space was published in 2018 for version 57; at the time of writing, we are at Chromium version 117, and the current version is very different from the 2018 version. We outline the details of a multi-year effort in this paper that led to PanoptiChrome, which accurately tracks information flow across an arbitrary number of sources and sinks and is, to a large extent, portable across platforms. As an example use case of the platform, we experimentally show that we can discover fingerprinting APIs that can uniquely identify the browser and sometimes the user, which are missed by state-of-the-art tools, owing to our comprehensive dynamic analysis methodology. For the top 20,000 most popular websites, we discovered a total of 362 APIs that have the potential to be used for fingerprinting -- out of these, 208 APIs were previously not reported by state-of-the-art tools.
How We Refute Claims: Automatic Fact-Checking through Flaw Identification and Explanation
Authors: Wei-Yu Kao, An-Zi Yen
Venue: WWW
Year: 2024

Automated fact-checking is a crucial task in the governance of internet content. Although various studies utilize advanced models to tackle this issue, a significant gap persists in addressing complex real-world rumors and deceptive claims. To address this challenge, this paper explores the novel task of flaw-oriented fact-checking, including aspect generation and flaw identification. We also introduce RefuteClaim, a new framework designed specifically for this task. Given the absence of an existing dataset, we present FlawCheck, a dataset created by extracting and transforming insights from expert reviews into relevant aspects and identified flaws. The experimental results underscore the efficacy of RefuteClaim, particularly in classifying and elucidating false claims.
Comprehensively Auditing the TikTok Mobile App
Authors: Levi Kaplan, Piotr Sapiezynski
Venue: WWW
Year: 2024

TikTok has become a dominant force in the social media landscape of the United States, and has spawned other social media sites emulating their algorithmically-driven short form content recommendation platform (e.g. Youtube Shorts and Instagram Reels). The short-form vertical content is designed to be consumed on mobile phones, but existing audits have predominantly, and to a limited degree, investigated TikTok using the web application. Additionally, there are no advertisements on the web version of TikTok, and as such the advertising ecosystem of the platform has thusfar largely gone unstudied. In this work we propose a technique for auditing TikTok's recommendation algorithm through interfacing with emulators and intercepting network traffic. In this way we are able to measure the personalization that comes from user-specified demographics such as gender and age and better understand how ads are delivered to these groups. Future work will investigate personalization from user interaction such as liking posts and following creators based on their interest, and will study the role that algorithmic personalization plays in ad targeting.
Analyzing Ad Exposure and Content in Child-Oriented Videos on YouTube
Authors: Emaan Bilal Khan, Nida Tanveer, Aima Shahid, Mohammad Jaffer Iqbal, Haashim Ali Mirza, Armish Javed, Ihsan Ayyub Qazi, Zafar Ayyub Qazi
Venue: WWW
Year: 2024

As a popular choice for video and entertainment streaming, YouTube hosts a large audience, including children, who form a growing proportion of its users. Despite separate "made for kids" labelling and stricter moderation of these videos, inappropriate advertising remains a concern as it threatens the safety of YouTube for young viewers. This paper is the first comparative measurement study that explores how advertisement exposure and content vary across child-oriented videos on YouTube. We do this by conducting a cross-regional advertisement analysis on highly viewed "made for kids" labelled content across a total of ten countries with varying regulation. A second front of comparison is carried out between ad patterns on unlabelled and labelled child-oriented videos. Our analysis reveals that the safety of a child's YouTube experience is shaped significantly by their external environment. There also appears to be lax enforcement of YouTube ad and child protection policies, indicated by the presence of unlabelled child-oriented content with weak ad regulation. We discuss the implications of inappropriate exposure on children and suggest policy and implementation measures to mitigate this threat.
From Bollywood Son Preference to Moral Policing on Women in Iran - A 360° View of Gender Bias
Authors: Ashiqur R. KhudaBukhsh
Venue: WWW
Year: 2024

Do longitudinal studies reveal a skewed gender distribution among newborn babies depicted in Bollywood movies? Who dominates the speaking time in political conversations on 24x7 news networks in the United States-men or women? How does Twitter discourse on gender equality evolve when a woman dies in police custody in Iran after being arrested (reportedly) due to improper headscarf-wearing? What is the representation of women in divorce court proceedings in India? This broad talk, where cutting-edge AI intersects with social science research questions, encompasses a diverse array of studies that unveil gender bias in various forms. In this presentation, I will describe the substantive findings, social impact, methodological challenges, scope for multimodal investigations, and the novelties entailed in this research. I will conclude the talk with our findings on worrisome gender bias in several large language models.
Hierarchical Position Embedding of Graphs with Landmarks and Clustering for Link Prediction
Authors: Minsang Kim, Seung Baek
Venue: WWW
Year: 2024

Learning positional information of nodes in a graph is important for link prediction tasks. We propose a representation of positional information using representative nodes called landmarks. A small number of nodes with high degree centrality are selected as landmarks, which serve as reference points for the nodes' positions. We justify this selection strategy for well-known random graph models and derive closed-form bounds on the average path lengths involving landmarks. In a model for power-law graphs, we prove that landmarks provide asymptotically exact information on inter-node distances. We apply theoretical insights to practical networks and propose Hierarchical Position embedding with Landmarks and Clustering (HPLC). HPLC combines landmark selection and graph clustering, where the graph is partitioned into densely connected clusters in which nodes with the highest degree are selected as landmarks. HPLC leverages the positional information of nodes based on landmarks at various levels of hierarchy such as nodes' distances to landmarks, inter-landmark distances, and hierarchical grouping of clusters. Experiments show that HPLC achieves state-of-the-art performance of link prediction on various datasets in terms of HIT@K, MRR, and AUC. The code is available at https://github.com/kmswin1/HPLC.
Making Cloud Spot Instance Interruption Events Visible
Authors: Kyunghwan Kim, Kyungyong Lee
Venue: WWW
Year: 2024

Public cloud computing providers offer a surplus of computing resources at a lower price with a service of a spot instance. Despite the possible great cost savings from using spot instances, sudden resource interruption can occur as resource demand changes. To help users estimate cost savings and the possibility of interruption when using spot instances, vendors provide diverse datasets. However, the effectiveness of using the datasets has not yet been quantitatively evaluated, and many users still rely on the guess when choosing spot instances. To help users lower the chance of interruption of the spot instance for reliable usage, in this paper, we thoroughly analyze various datasets of the spot instance and present the feasibility for value prediction. Then, to measure how the public datasets reflect real-world spot instance interruption events, we conduct real-world experiments for spot instances of AWS, Azure, and Google Cloud. Combining the dataset analysis, modeling, and the real-world spot instance interruption experiment, we present a significant improvement in reducing the possibility of interruption events.