Domain Generalization (DG) aims to train models that generalize to unseen target domains but often overfit to domain-specific features, known as undesired correlations. Gradient-based DG methods typically guide gradients in a dominant direction but often inadvertently reinforce spurious correlations. Recent work has employed dropout to regularize overconfident parameters, but has not explicitly adjusted gradient alignment or ensured balanced parameter updates. We propose GENIE (Generalization-ENhancing Iterative Equalizer), a novel optimizer that leverages the One-Step Generalization Ratio (OSGR) to quantify each parameter's contribution to loss reduction and assess gradient alignment. By dynamically equalizing OSGR via a preconditioning factor, GENIE prevents a small subset of parameters from dominating optimization, thereby promoting domain-invariant feature learning. Theoretically, GENIE balances convergence contribution and gradient alignment among parameters, achieving higher OSGR while retaining SGD's convergence rate. Empirically, it outperforms existing optimizers and enhances performance when integrated with various DG and single-DG methods.

This paper presents a pioneering investigation into a novel form of scam advertising method on YouTube, termed "social scam bots'' (SSBs). These bots have evolved to emulate benign user behavior by posting comments and engaging with other users, oftentimes appearing prominently among the top rated comments. We analyzed the YouTube video comments and proposed a method to identify SSBs and extract the underlying scam domains. Our study revealed 1,134 SSBs promoting 72 scam campaigns responsible for infecting 31.73% of crawled videos. Further investigation revealed that SSBs exhibit advances that surpass traditional bots. Notably, they targeted specific audience by aligning scam campaigns with related video content, effectively leveraging the YouTube recommendation algorithm. We monitored these SSBs over a period of six months, enabling us to evaluate the effectiveness of YouTube's mitigation efforts. We also uncovered various strategies they use to evade mitigation attempts, including a novel strategy called "self-engagement," aimed at boosting their comment ranking. By shedding light on the phenomenon of SSBs and their evolving tactics, our study aims to raise awareness and contribute to the prevention of these malicious actors, ultimately fostering a safer online platform.

This paper presents a new approach to the incremental online learning of behaviors by a robot from multiple kinesthetic teaching trials. The approach enables a robot to refine and reproduce a specific behavior every time a new teaching trial is provided and to decide autonomously whether to accept or reject each trial. The robot neglects bad teaching trials and learns a behavior based on adequate teaching trials. The framework of this approach consists of the projection of motion data to a latent space and the description of motion data in a Gaussian mixture model (GMM). To realize the incremental online learning, the latent space and the GMM are refined incrementally after each proper teaching trial. The trial data are discarded after being used. The number of Gaussian components in the GMM is not initially fixed but is autonomously selected by the robot over the trials. The proposed method is more suitable for practical human-robot interaction. The experiments with a humanoid robot show the feasibility of the approach. We demonstrate that the robot can incrementally refine and reproduce learned behaviors that accurately represent the essential characteristics of the teaching trials through our learning algorithm and that it can reject erroneous teaching trials to improve learning performance.