arXiv subjects

Jialin Huang

Publications and source records attributed to Jialin Huang.

3 recordsLinked to original sources

arXiv2026

HandMade: Spatial Prompting for Generative 3D Creation with Part-Labeled VR Sketches

Text-to-3D generation lowers the barrier to 3D content creation, but text alone is a weak interface for specifying spatial intent: where parts should be placed, how they relate, and how an object should be organized in 3D. We present HandMade, a workflow that combines VR 3D sketching and language for open-domain 3D asset generation. HandMade treats coarse, part-labeled 3D sketches not as incomplete geometry to reconstruct directly, but as spatial prompts for existing generative models. It converts segmented VR strokes into multi-view part guidance and structured prompts, allowing users to specify object layout and part relationships through 3D sketching while using language for identity, material, style, and local details. A technical evaluation shows that HandMade better preserves user-authored spatial scaffolds than text-only and sketch-based baselines on 20 varied examples. A user study with eight participants characterizes how users make use of 3D sketching for spatial layout and language for identity, materials, and details across initial authoring and subsequent revision. HandMade contributes an interaction paradigm and interface-to-generation pipeline for spatially guided 3D creation.

cs.HC↗

arXiv2026

Exploring Robust Intrusion Detection: A Benchmark Study of Feature Transferability in IoT Botnet Attack Detection

Cross-domain intrusion detection remains a critical challenge due to significant variability in network traffic characteristics and feature distributions across environments. This study evaluates the transferability of three widely used flow-based feature sets (Argus, Zeek and CICFlowMeter) across four widely used datasets representing heterogeneous IoT and Industrial IoT network conditions. Through extensive experiments, we evaluate in- and cross-domain performance across multiple classification models and analyze feature importance using SHapley Additive exPlanations (SHAP). Our results show that models trained on one domain suffer significant performance degradation when applied to a different target domain, reflecting the sensitivity of IoT intrusion detection systems to distribution shifts. Furthermore, the results evidence that the choice of classification algorithm and feature representations significantly impact transferability. Beyond reporting performance differences and thorough analysis of the transferability of features and feature spaces, we provide practical guidelines for feature engineering to improve robustness under domain variability. Our findings suggest that effective intrusion detection requires both high in-domain performance and resilience to cross-domain variability, achievable through careful feature space design, appropriate algorithm selection and adaptive strategies.

cs.CR↗

arXiv2026

Stability analysis of nontrivial stationary solution and constant equilibrium point of reaction-diffusion neural networks with time delays under Dirichlet zero boundary value

In this paper, Lyapunov-Razumikhin technique, design of state-dependent switching laws, a fixed point theorem and variational methods are employed to derive the existence and the unique existence results of globally exponentially stable (positive) stationary solution of delayed reaction-diffusion cell neural networks under Dirichlet zero boundary value, including the global stability criteria in the classical meaning. Next, sufficient conditions are proposed to guarantee the global stability invariance of ordinary differential systems under the influence of diffusions. New theorems show that the diffusion is a double-edged sword in judging the stability of diffusion systems. Besides, an example is constructed to illuminate that any non-zero constant equilibrium point must be not in the phase plane of dynamic system under Dirichlet zero boundary value, or it must lead to a contradiction. Next, under Lipschitz assumptions on active function, another example is designed to prove that the small diffusion effect will cause the essential change of the phase plane structure of the dynamic behavior of the delayed neural networks via a Saddle point theorem. Finally, a numerical example illustrates the feasibility of the proposed methods. It is worth mentioning that some interesting mathematical problems are originally put forward in the last chapter.

math.DS↗