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- keynoteMay 2024
AI for Materials Innovation: Self-Improving Photosensitizer Discovery System via Bayesian Search with First-Principles Simulation
Artificial intelligence (AI) based self-learning or self-improving material discovery systems will enable next-generation material discovery. Herein, we demonstrate how to combine accurate prediction of material performance via first-principles ...
- keynoteMay 2024
Challenges Toward AGI and Its Impact to the Web
Large language models have substantially advanced the state of the art in various AI tasks, such as natural language understanding and text generation, and image processing, and multimodal modeling. In this talk, we will first introduce the development ...
- research-articleMay 2024
GraphLeak: Patient Record Leakage through Gradients with Knowledge Graph
WWW '24: Proceedings of the ACM Web Conference 2024Pages 4706–4716https://doi.org/10.1145/3589334.3648157In real clinics, the medical data are scattered over multiple hospitals. Due to security and privacy concerns, it is almost impossible to gather all the data together and train a unified model. Therefore, multi-node machine learning systems are currently ...
- research-articleMay 2024
Infrastructure Ombudsman: Mining Future Failure Concerns from Structural Disaster Response
WWW '24: Proceedings of the ACM Web Conference 2024Pages 4664–4673https://doi.org/10.1145/3589334.3648153Current research concentrates on studying discussions on social media related to structural failures to improve disaster response strategies. However, detecting social web posts discussing concerns about anticipatory failures is under-explored. If such ...
- research-articleMay 2024
MMAdapt: A Knowledge-guided Multi-source Multi-class Domain Adaptive Framework for Early Health Misinformation Detection
WWW '24: Proceedings of the ACM Web Conference 2024Pages 4653–4663https://doi.org/10.1145/3589334.3648152This paper studies a critical problem of emergent health misinformation detection, aiming to mitigate the spread of misinformation in emergent health domains to support well-informed healthcare decisions towards a Web for good health. Our work is ...
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MemeCraft: Contextual and Stance-Driven Multimodal Meme Generation
WWW '24: Proceedings of the ACM Web Conference 2024Pages 4642–4652https://doi.org/10.1145/3589334.3648151Online memes have emerged as powerful digital cultural artifacts in the age of social media, offering not only humor but also platforms for political discourse, social critique, and information dissemination. Their extensive reach and influence in ...
SceneDAPR: A Scene-Level Free-Hand Drawing Dataset for Web-based Psychological Drawing Assessment
WWW '24: Proceedings of the ACM Web Conference 2024Pages 4630–4641https://doi.org/10.1145/3589334.3648150Sketch-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 ...
- research-articleMay 2024
Bayesian Iterative Prediction and Lexical-based Interpretation for Disturbed Chinese Sentence Pair Matching
- Muzhe Guo,
- Muhao Guo,
- Juntao Su,
- Junyu Chen,
- Jiaqian Yu,
- Jiaqi Wang,
- Hongfei Du,
- Parmanand Sahu,
- Ashwin Assysh Sharma,
- Fang Jin
WWW '24: Proceedings of the ACM Web Conference 2024Pages 4618–4629https://doi.org/10.1145/3589334.3648149In an era dominated by web-based intelligent customer services, the applications of Sentence Pair Matching are profoundly broad. Web agents, for example, automatically respond to customer queries by finding similar past questions, significantly reducing ...
- research-articleMay 2024
Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systems
- Zijie Huang,
- Jeehyun Hwang,
- Junkai Zhang,
- Jinwoo Baik,
- Weitong Zhang,
- Dominik Wodarz,
- Yizhou Sun,
- Quanquan Gu,
- Wei Wang
WWW '24: Proceedings of the ACM Web Conference 2024Pages 4607–4617https://doi.org/10.1145/3589334.3648148Real-world multi-agent systems are often dynamic and continuous, where the agents co-evolve and undergo changes in their trajectories and interactions over time. For example, the COVID-19 transmission in the U.S. can be viewed as a multi-agent system, ...
- research-articleMay 2024
CapAlign: Improving Cross Modal Alignment via Informative Captioning for Harmful Meme Detection
WWW '24: Proceedings of the ACM Web Conference 2024Pages 4585–4594https://doi.org/10.1145/3589334.3648146Harmful memes detection is challenging due to the semantic gap between different modalities. Previous studies mainly focus on feature extraction and fusion to learn discriminative information from memes. However, they ignore the misalignment of the ...
- research-articleMay 2024
Modularized Networks for Few-shot Hateful Meme Detection
WWW '24: Proceedings of the ACM Web Conference 2024Pages 4575–4584https://doi.org/10.1145/3589334.3648145In this paper, we address the challenge of detecting hateful memes in the low-resource setting where only a few labeled examples are available. Our approach leverages the compositionality of Low-rank adaptation (LoRA), a widely used parameter-efficient ...
- research-articleMay 2024
Contrastive Learning for Multimodal Classification of Crisis related Tweets
WWW '24: Proceedings of the ACM Web Conference 2024Pages 4555–4564https://doi.org/10.1145/3589334.3648143Multimodal tasks require learning a joint representation of the constituent modalities of data. Contrastive learning learns a joint representation by using a contrastive loss. For example, CLIP takes as input image-caption pairs and is trained to ...
- research-articleMay 2024
Triage of Messages and Conversations in a Large-Scale Child Victimization Corpus
WWW '24: Proceedings of the ACM Web Conference 2024Pages 4544–4554https://doi.org/10.1145/3589334.3648142Children are among the most vulnerable online populations. Reports of child sexual exploitation on social media and apps have grown annually at an alarming rate and are overwhelming investigators. Even a single case can require examining millions of ...
- research-articleMay 2024
MentaLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Models
WWW '24: Proceedings of the ACM Web Conference 2024Pages 4489–4500https://doi.org/10.1145/3589334.3648137As an integral part of people's daily lives, social media is becoming a rich source for automatic mental health analysis. As traditional discriminative methods bear poor generalization ability and low interpretability, the recent large language models (...
- research-articleMay 2024
NETEVOLVE: Social Network Forecasting using Multi-Agent Reinforcement Learning with Interpretable Features
WWW '24: Proceedings of the ACM Web Conference 2024Pages 2542–2551https://doi.org/10.1145/3589334.3647982Predicting how social networks change in the future is important in many applications. Results in social network research have shown that the change in the network can be explained by a small number of concepts, such as "homophily" and "transitivity". ...
Toward Practical Entity Alignment Method Design: Insights from New Highly Heterogeneous Knowledge Graph Datasets
- Xuhui Jiang,
- Chengjin Xu,
- Yinghan Shen,
- Yuanzhuo Wang,
- Fenglong Su,
- Zhichao Shi,
- Fei Sun,
- Zixuan Li,
- Jian Guo,
- Huawei Shen
WWW '24: Proceedings of the ACM Web Conference 2024Pages 2325–2336https://doi.org/10.1145/3589334.3645720The flourishing of knowledge graph (KG) applications has driven the need for entity alignment (EA) across KGs. However, the heterogeneity of practical KGs, characterized by differing scales, structures, and limited overlapping entities, greatly surpasses ...
- research-articleMay 2024
Understanding Human Preferences: Towards More Personalized Video to Text Generation
WWW '24: Proceedings of the ACM Web Conference 2024Pages 3952–3963https://doi.org/10.1145/3589334.3645711While previous video to text models have achieved remarkable successes, they mostly focus on how to understand the video contents in a general sense, but fail to capture the human personalized preferences, which is highly demanded for an engaging ...
- research-articleMay 2024
Towards Cross-Table Masked Pretraining for Web Data Mining
WWW '24: Proceedings of the ACM Web Conference 2024Pages 4449–4459https://doi.org/10.1145/3589334.3645707Tabular data pervades the landscape of the World Wide Web, playing a foundational role in the digital architecture that underpins online information. Given the recent influence of large-scale pretrained models like ChatGPT and SAM across various domains, ...
- research-articleMay 2024
Friend or Foe? Mining Suspicious Behavior via Graph Capsule Infomax Detector against Fraudsters
WWW '24: Proceedings of the ACM Web Conference 2024Pages 2684–2693https://doi.org/10.1145/3589334.3645706Anomaly detection on graphs has recently attracted considerable attention due to its broad range of high-impact applications, including cybersecurity, financial transactions, and recommendation systems. Although many efforts have thus far been made, how ...
- research-articleMay 2024
AN-Net: an Anti-Noise Network for Anonymous Traffic Classification
WWW '24: Proceedings of the ACM Web Conference 2024Pages 4417–4428https://doi.org/10.1145/3589334.3645691Anonymous networks employ a triple proxy to transmit packets to enhance user privacy, causing traffic packets from all applications and web services to form a unified flow. The traditional approach of applying flow-level encrypted traffic classification ...