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Found 15 papers, 5 papers with code
From Images to Signals: Are Large Vision Models Useful for Time Series Analysis?
Transformer-based models have gained increasing attention in time series research, driving interest in Large Language Models (LLMs) and foundation models for time series analysis.
Multi-Modal View Enhanced Large Vision Models for Long-Term Time Series Forecasting
Time series, typically represented as numerical sequences, can also be transformed into images and texts, offering multi-modal views (MMVs) of the same underlying signal.
Harnessing Vision Models for Time Series Analysis: A Survey
Time series analysis has witnessed the inspiring development from traditional autoregressive models, deep learning models, to recent Transformers and Large Language Models (LLMs).
Rethinking Membership Inference Attacks Against Transfer Learning
The interaction between teacher and student models in transfer learning has not been thoroughly explored in MIAs, potentially resulting in an under-examined aspect of privacy vulnerabilities within transfer learning.
HyperSMOTE: A Hypergraph-based Oversampling Approach for Imbalanced Node Classifications
We synthesize new nodes based on samples from minority classes and their neighbors.
CHASE: A Causal Heterogeneous Graph based Framework for Root Cause Analysis in Multimodal Microservice Systems
In recent years, the widespread adoption of distributed microservice architectures within the industry has significantly increased the demand for enhanced system availability and robustness.
Moderating Illicit Online Image Promotion for Unsafe User-Generated Content Games Using Large Vision-Language Models
Online user generated content games (UGCGs) are increasingly popular among children and adolescents for social interaction and more creative online entertainment.
An Investigation of Large Language Models for Real-World Hate Speech Detection
Our study reveals that a meticulously crafted reasoning prompt can effectively capture the context of hate speech by fully utilizing the knowledge base in LLMs, significantly outperforming existing techniques.
Moderating New Waves of Online Hate with Chain-of-Thought Reasoning in Large Language Models
HATEGUARD further achieves prompt-based zero-shot detection by automatically generating and updating detection prompts with new derogatory terms and targets in new wave samples to effectively address new waves of online hate.
Exploiting Spatial-temporal Data for Sleep Stage Classification via Hypergraph Learning
Sleep stage classification is crucial for detecting patients' health conditions.
Purifier: Defending Data Inference Attacks via Transforming Confidence Scores
Besides, our further experiments show that PURIFIER is also effective in defending adversarial model inversion attacks and attribute inference attacks.
Wavelet Regularization Benefits Adversarial Training
Since adversarial vulnerability can be regarded as a high-frequency phenomenon, it is essential to regulate the adversarially-trained neural network models in the frequency domain.
SoK: On the Semantic AI Security in Autonomous Driving
In this paper, we perform the first systematization of knowledge of such growing semantic AD AI security research space.
Understanding and Measuring Robustness of Multimodal Learning
We then introduce a new type of multimodal adversarial attacks called decoupling attack in MUROAN that aims to compromise multimodal models by decoupling their fused modalities.
Moving Target Defense for Web Applications using Bayesian Stackelberg Games
We also address the issue of prioritizing vulnerabilities that when fixed, improves the security of the MTD system.
