Electromyography (EMG)-based gesture recognition has emerged as a promising approach for human-computer interaction. However, its performance is often limited by the scarcity of labeled EMG data, significant cross-user variability, and poor generalization to unseen gestures. To address these challenges, we propose SeqEMG-GAN, a conditional, sequence-driven generative framework that synthesizes high-fidelity EMG signals from hand joint angle sequences. Our method introduces a context-aware architecture composed of an angle encoder, a dual-layer context encoder featuring the novel Ang2Gist unit, a deep convolutional EMG generator, and a discriminator, all jointly optimized via adversarial learning. By conditioning on joint kinematic trajectories, SeqEMG-GAN is capable of generating semantically consistent EMG sequences, even for previously unseen gestures, thereby enhancing data diversity and physiological plausibility. Experimental results show that classifiers trained solely on synthetic data experience only a slight accuracy drop (from 57.77% to 55.71%). In contrast, training with a combination of real and synthetic data significantly improves accuracy to 60.53%, outperforming real-only training by 2.76%. These findings demonstrate the effectiveness of our framework, also achieves the state-of-art performance in augmenting EMG datasets and enhancing gesture recognition performance for applications such as neural robotic hand control, AI/AR glasses, and gesture-based virtual gaming systems.

Governments sometimes release information implicitly, and citizens have to understand their illocutionary acts. The present study examines when and how illocutionary acts encourage citizens to disseminate government information. Based on the Speech Act Theory, Study 1 demonstrates that commissive illocutionary acts make citizens in a low-power distance culture more likely to disseminate information. In contrast, directive illocutionary acts make citizens in a high-power distance culture more likely to disseminate information. Study 2 confirms the underlying mechanism of perceived social exchange relationship by looking at the interaction effects between illocutionary acts and power distance perception in a single cultural setting. By crawling 10,000 government microblogs, Study 3 provides objective evidence for the interaction between illocutionary acts and power distance perception on citizens dissemination behavior of information. Finally, we discuss theoretical implications for government information dissemination, managerial implications, and future research directions.

Prompt-based learning paradigm has been shown to be vulnerable to backdoor attacks.Current clean-label attack, employing a specific prompt as trigger, can achieve success without the need for external triggers and ensuring correct labeling of poisoned samples, which are more stealthy compared to the poisonedlabel attack, but on the other hand, facing significant issues with false activations and pose greater challenges, necessitating a higher rate of poisoning.Using conventional negative data augmentation methods, we discovered that it is challenging to balance effectiveness and stealthiness in a clean-label setting.In addressing this issue, we are inspired by the notion that a backdoor acts as a shortcut, and posit that this shortcut stems from the contrast between the trigger and the data utilized for poisoning.In this study, we propose a method named Contrastive Shortcut Injection (CSI), by leveraging activation values, integrates trigger design and data selection strategies to craft stronger shortcut features.With extensive experiments on fullshot and few-shot text classification tasks, we empirically validate CSI's high effectiveness and high stealthiness at low poisoning rates.