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The expansion of Internet of Things (IoT) networks has enabled real-time data collection and automation across smart cities, healthcare, and agriculture, delivering greater convenience and efficiency; however, exposure to diverse threats has also increased. Machine learning-based Intrusion Detection Systems (IDSs) provide an effective means of defense, yet they require large volumes of data, and the use of raw IoT network data containing sensitive information introduces new privacy risks. This study proposes a novel privacy-preserving synthetic data generation model based on a tabular diffusion framework that incorporates Differential Privacy (DP). Among the three diffusion models (TabDDPM, TabSyn, and TabDiff), TabDiff with Utility-Preserving DP (UP-DP) achieved the best Synthetic Data Vault (SDV) Fidelity (0.98) and higher values on multiple statistical metrics, indicating improved utility. Furthermore, by employing the DisclosureProtection and attribute inference to infer and compare sensitive attributes on both real and synthetic datasets, we show that the proposed approach reduces privacy risk of the synthetic data. Additionally, a Membership Inference Attack (MIA) was also used for demonstration on models trained with both real and synthetic data. This approach decreases the risk of leaking patterns related to sensitive information, thereby enabling secure dataset sharing and analysis.

Recent advances in 3D foundation models have led to growing interest in reconstructing humans and their surrounding environments. However, most existing approaches focus on monocular inputs, and extending them to multi-view settings requires additional overhead modules or preprocessed data. To this end, we present CHROMM, a unified framework that jointly estimates cameras, scene point clouds, and human meshes from multi-person multi-view videos without relying on external modules or preprocessing. We integrate strong geometric and human priors from Pi3X and Multi-HMR into a single trainable neural network architecture, and introduce a scale adjustment module to solve the scale discrepancy between humans and the scene. We also introduce a multi-view fusion strategy to aggregate per-view estimates into a single representation at test-time. Finally, we propose a geometry-based multi-person association method, which is more robust than appearance-based approaches. Experiments on EMDB, RICH, EgoHumans, and EgoExo4D show that CHROMM achieves competitive performance in global human motion and multi-view pose estimation while running over 8x faster than prior optimization-based multi-view approaches. Project page: https://nstar1125.github.io/chromm.

Hybrid heterostructures composed of graphene and perovskite oxides provide a promising platform for exploiting synergetic interfacial functionalities. Conventional fabrication methods of the hybrid heterostructures rely on transferring graphene grown on metallic substrates-- a process that is time-consuming, labor-intensive, and prone to introducing numerous defects. In this study, we present a universal, catalyst-free method for the direct growth of graphene on insulating substrates by employing three different perovskite oxide substrates (SrTiO\\(_3\\), LaAlO\\(_3\\), and (La\\(_0.18\\)Sr\\(_0.82\\))(Al\\(_0.59\\)Ta\\(_0.41\\))O\\(_3\\)) using atmospheric chemical vapor deposition. Comprehensive characterization via Raman spectroscopy, X-ray spectroscopy, scanning probe microscopy, and electron microscopy confirmed the formation of a uniform, continuous monolayer graphene on all substrates. We identified that growth temperature critically governs graphene quality, as excessive active species may lead to secondary nucleation and the formation of multilayer graphene. Notably, all substrates shared the same optimal growth conditions. Low-temperature Raman spectroscopy and scanning tunneling microscopy of the graphene/SrTiO\\(_3\\) hybrid heterostructure revealed cooperative phenomena, including substrate-induced lattice-phonon and electron-phonon coupling. Our work establishes a reproducible, transfer-free fabrication route for graphene/perovskite oxide hybrid heterostructures and provides empirical support for the universal growth of graphene on insulating substrates.