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 ...

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 ...

In 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 ...

Current 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 ...

This 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 ...

Online 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 ...

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 ...

In 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 ...

Real-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, ...

Harmful 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 ...

In 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 ...

Multimodal 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 ...

Children 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 ...

As 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 (...

Predicting 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". ...

The 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 ...

While 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 ...

Tabular 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, ...

Anomaly 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 ...

Anonymous 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 ...