The widespread deployment of public computing devices (e.g., shared kiosks, medical IoT terminals) in semiopen environments raises critical security challenges for data access control. While Ciphertext-Policy Attribute-Based Encryption (CP-ABE) enables fine-grained policy enforcement, existing schemes face two key limitations: high computational overhead on resource-constrained client devices and the lack of efficient verification mechanisms for outsourced decryption results from semi-trusted cloud servers. In this paper, we propose VO-PABE (Verifiable and Optimized Public-device ABE), an efficient outsourced CP-ABE scheme with authority-auditable verification. Our approach leverages an optimized bilinear group structure combined with enhanced hash and pairing computation techniques to concentrate most operations within smaller groups, significantly improving encryption/decryption efficiency. Under the public equipment environment framework, VO-PABE achieves secure outsourced computation via novel conversion keys. Furthermore, we design an authoritative verification mechanism using ciphertext-embedded verifiable parameters to ensure the correctness of outsourced decryption results. Extensive experiments demonstrate that VO-PABE reduces user-side computational overhead by 30% compared to state-of-the-art schemes while successfully enabling verifiable outsourcing. Importantly, VO-PABE achieves adaptive security under the Generic Group Model (GGM).

Accurate and robust object detection for surveillance videos hold immense potential applications in the field of public security. However, because of the artifacts in the surveillance video, such as motion blur, noise, low illumination, etc., relying solely on single-frame object detection algorithms may not guarantee the trustiness of surveillance video data analysis. Nevertheless, surveillance videos also exhibit characteristics of stable background and high inter-frame correlation. Therefore, in this paper, we introduce a novel model for surveillance object detection based on vision transformer with inter-frame multiscale probabilistic cross-attention. This model leverages Inter-Frame Semantic Cross-Attention (IFSC) to capture dynamic spatio-temporal features, thereby improving detection performance in low-quality frames. Additionally, it employs Inter-Frame Probabilistic Sparse Cross-Attention (IFPSC) to highlight salient features and suppress background features, enhancing the robustness of surveillance object detection. The experimental results on the UA-DETRAC dataset have demonstrated that the proposed surveillance object detector outperforms other SOTA models and achieves an optimal balance between speed and accuracy.

We propose a biharmonic model for cross-object volumetric mapping. This new computational model aims to facilitate the mapping of solid models with complicated geometry or heterogeneous inner structures. In order to solve cross-shape mapping between such models through divide and conquer, solid models can be decomposed into subparts upon which mappings is computed individually. The biharmonic volumetric mapping can be performed in each subregion separately. Unlike the widely used harmonic mapping which only allows C0 continuity along the segmentation boundary interfaces, this biharmonic model can provide C1 smoothness. We demonstrate the efficacy of our mapping framework on various geometric models with complex geometry (which are decomposed into subparts with simpler and solvable geometry) or heterogeneous interior structures (whose different material layers can be segmented and processed separately).

This paper presents a volumetric modeling framework to construct a novel spline scheme called restricted trivariate polycube splines (RTP-splines). The RTP-spline aims to generalize both trivariate T-splines and tensor-product B-splines; it uses solid polycube structure as underlying parametric domains and strictly bounds blending functions within such domains. We construct volumetric RTP-splines in a top-down fashion in four steps: 1) Extending the polycube domain to its bounding volume via space filling; 2) building the B-spline volume over the extended domain with restricted boundaries; 3) inserting duplicate knots by adding anchor points and performing local refinement; and 4) removing exterior cells and anchors. Besides local refinement inherited from general T-splines, the RTP-splines have a few attractive properties as follows: 1) They naturally model solid objects with complicated topologies/bifurcations using a one-piece continuous representation without domain trimming/patching/merging. 2) They have guaranteed semistandardness so that the functions and derivatives evaluation is very efficient. 3) Their restricted support regions of blending functions prevent control points from influencing other nearby domain regions that stay opposite to the immediate boundaries. These features are highly desirable for certain applications such as isogeometric analysis. We conduct extensive experiments on converting complicated solid models into RTP-splines, and demonstrate the proposed spline to be a powerful and promising tool for volumetric modeling and other scientific/engineering applications where data sets with multiattributes are prevalent.