Image compression methods are usually optimized isolatedly for human perception or machine analysis tasks. We reveal fundamental commonalities between these objectives: preserving accurate semantic information is paramount, as it directly dictates the integrity of critical information for intelligent tasks and aids human understanding. Concurrently, enhanced perceptual quality not only improves visual appeal but also, by ensuring realistic image distributions, benefits semantic feature extraction for machine tasks. Based on this insight, we propose Diff-ICMH, a generative image compression framework aiming for harmonizing machine and human vision in image compression. It ensures perceptual realism by leveraging generative priors and simultaneously guarantees semantic fidelity through the incorporation of Semantic Consistency loss (SC loss) during training. Additionally, we introduce the Tag Guidance Module (TGM) that leverages highly semantic image-level tags to stimulate the pre-trained diffusion model's generative capabilities, requiring minimal additional bit rates. Consequently, Diff-ICMH supports multiple intelligent tasks through a single codec and bitstream without any task-specific adaptation, while preserving high-quality visual experience for human perception. Extensive experimental results demonstrate Diff-ICMH's superiority and generalizability across diverse tasks, while maintaining visual appeal for human perception.

Hand-crafted image quality metrics, such as PSNR and SSIM, are commonly used to evaluate model privacy risk under reconstruction attacks. Under these metrics, reconstructed images that are determined to resemble the original one generally indicate more privacy leakage. Images determined as overall dissimilar, on the other hand, indicate higher robustness against attack. However, there is no guarantee that these metrics well reflect human opinions, which offers trustworthy judgement for model privacy leakage. In this paper, we comprehensively study the faithfulness of these hand-crafted metrics to human perception of privacy information from the reconstructed images.

Sycophantic response patterns in Large Language Models (LLMs) have been increasingly claimed in the literature. We review methodological challenges in measuring LLM sycophancy and identify five core operationalizations. Despite sycophancy being inherently human-centric, current research does not evaluate human perception. Our analysis highlights the difficulties in distinguishing sycophantic responses from related concepts in AI alignment and offers actionable recommendations for future research. Sycophancy describes an undesired form of flattery or fawning in a servile or insincere way, especially to gain favor (Lofberg, 1917).

Abstract--As robots become increasingly integrated into everyday environments, understanding how they communicate with humans is critical. Sound offers a powerful channel for interaction, encompassing both operational noises and intentionally designed auditory cues. In this study, we examined the effects of consequential and functional sounds on human perception and behavior, including a novel exploration of spatial sound through localization and handover tasks. Results show that consequential sounds of the Kinova Gen3 manipulator did not negatively affect perceptions, spatial localization is highly accurate for lateral cues but declines for frontal cues, and spatial sounds can simultaneously convey task-relevant information while promoting warmth and reducing discomfort. These findings highlight the potential of functional and transformative auditory design to enhance human-robot collaboration and inform future sound-based interaction strategies. UDIO Augmented Reality remains a comparatively un-derexplored domain within the broader field of Augmented Reality (AR) research [1]. While recent advancements in AR technologies have spurred extensive investigation into visual augmentation--where virtual objects are seamlessly integrated into the physical environment--research on auditory augmentation has lagged behind.

Specifically, Szegedy et al. [2013] constructed image distortions, imperceptible to humans, that