Ruixing Ren

Ruixing Ren, Junhui Zhao, Fangfang Wang

Cross-platform deployment of offensive comment detection for Chinese social media suffers performance degradation. The paper proposes a dual-threshold hard mining method to address this. First, the clean-Chinese-base RoBERTa is finetuned on COLD to establish a binary baseline for fair comparison. Second, a three-class fine-labeled test set covering Weibo, Xiaohongshu, Tieba, and Zhihu is constructed, domain distances from the source are quantified using Jaccard and Proxy-A Distance, as well as the degradation bottleneck of the baseline under domain shift is systematically revealed. Herein, a dual threshold hard example mining strategy is proposed. High- and low-confidence error-prone samples are filtered from unlabeled corpora by prediction confidence. The model is secondarily finetuned under implicit contexts with merely a small set of manually labeled hard examples, realizing low-cost cross-platform domain adaptation. Experiments reveal significant performance gains of the optimized model across four platforms.

Ruixing Ren, Junhui Zhao, He Fang

Leveraging their flexible and efficient deployment capabilities, unmanned aerial vehicle (UAV) swarms have been widely applied in various mission scenarios. However, the open communication environment also exposes them to the threat of Byzantine attacks. Most existing studies assume independent decision-making by each UAV, neglecting that local conformity amplifies false information propagation. This paper constructs an evolutionary game model for UAV swarm under malicious attacks based on graph evolutionary game theory, revealing how local conformity rules govern the spread of deceptive strategies. Using death-birth updating rules, we derive the macroscopic dynamic equation for the fraction of deceptive strategies and the analytical solutions to its evolutionary stable states. Sim ulations reveal observation errors weaken malicious induction, while higher proportions of malicious nodes and greater attack intensity drastically amplify attack impacts. Moreover, the model exhibits strong topological robustness across regular, scale-free and random networks.