Sarcasm often arises from subtle contrasts between literal meaning and speaker intention. As online communication increasingly includes voice-based content, detecting sarcasm across speech and text becomes more important—and more complex. The existing methods usually focus on generic multimodal fusion but often miss how sarcasm manifests differently in each modality. We propose a model that explicitly encodes audio signals into the textual representation space, allowing prosodic cues to inform language understanding. To extract relevant features at different levels, we use a multi-scale convolutional architecture. The experiments show consistent gains over prior models on both text and speech sarcasm detection tasks.

This paper explores the physical layer security performance of collaborative drone fleets enabled by visible light communication (VLC) in a multi-eavesdropper scenario, where multiple drones leverage VLC to serve terrestrial users. To strengthen system security, we formulate a sum worst-case secrecy rate maximization problem. To address the non-convex optimization challenge of this problem, we develop two innovative Q-learning-based position decision algorithms (Q-PDA and Q-PDA-lite) with a dynamic reward mechanism, allowing drones to adaptively optimize their positions. Additionally, we propose an enhanced Tabu Search-based grouping algorithm (TS-GA) to establish the suboptimal user equipment (UE)–drone association by balancing candidate solution exploration and tabu constraint exploitation. Simulation results demonstrate that the proposed Q-PDA and Q-PDA-lite achieve worst-case secrecy rates significantly exceeding those of Random-PDA and K-means-PDA. While Q-PDA-lite exhibits 2% lower performance than Q-PDA, it offers reduced complexity. Additionally, the proposed TS-GA achieves a worst-case secrecy rate that substantially outperforms random grouping, UE-channel-gain-based grouping, and channel-gain-based grouping. Collectively, the hybrid approach integrating Q-PDA and TS-GA achieves 10% near-global optimality with guaranteed convergence, while preserving computational efficiency. Furthermore, this hybrid approach outperforms other combinations in terms of security metrics.