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In recent decades, spectral clustering has found widespread applications in various real-world scenarios, showcasing its effectiveness. Traditional spectral clustering typically follows a two-step procedure to address the optimization problem. However, this approach may result in substantial information loss and performance decline. Furthermore, the eigenvalue decomposition, a key step in spectral clustering, entails cubic computational complexity. This paper incorporates linear embedding into the objective function of spectral clustering and proposes a direct method to solve the indicator matrix. Moreover, our method achieves a linear time complexity with respect to the input data size. Our method, referred to as Spectral Clustering with Linear Embedding (SCLE), achieves a direct and efficient solution and naturally handles out-of-sample data. SCLE initiates the process with balanced and hierarchical K-means, effectively partitioning the input data into balanced clusters. After generating anchors, we compute a similarity matrix based on the distances between the input data points and the generated anchors. In contrast to the conventional two-step spectral clustering approach, we directly solve the cluster indicator matrix at a linear time complexity. Extensive experiments across multiple datasets underscore the efficiency and effectiveness of our proposed SCLE method. • Addressing spectral clustering time issues with a faster graph construction method. • Eliminating eigendecomposition, our approach saves computational time. • Avoiding intermediate variables, our method prevents information loss in updates. • Our algorithm achieves direct clustering matrix solution in linear time. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet of Things technology and the promotion of embedded devices' computation performance, smart devices are probably open to security threats and attacks while connecting with rich and novel Internet. Attracting lots of attention in embedded system security community recently, Trusted Execution Environment (TEE), allows for the execution of arbitrary code within environments completely isolated from the rest of a system. However, existing memory protection methods in a TEE are inadequate. In general, the software-based formal methods are not practical and the hardware-based implementation approaches lack of theoretical proof. To address the memory isolation and protection problems in TEE, in this paper, we propose a practical memory integrity protection method on an ARM-based platform, called MIPE, to defend against security threats including kernel data attacks and direct memory access attacks. MIPE utilizes TrustZone technique to create a isolated execution environment, which can protect the sensitive code and data against attacks. To present the integrity protection strategies, we provide the design of MIPE using B method, which is a practical formal method. We also implement MIPE on the Xilinx Zynq ZC702 evaluation board. The evaluation results show that the automatic proof rate of machines using B method is about 78.32%, and the proposed method is effective and feasible in terms of both load time and overhead. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc network (VANET) is an increasing important paradigm, which not only provides safety enhancement but also improves roadway system efficiency. However, the security issues of data confidentiality, and access control over transmitted messages in VANET have remained to be solved. In this paper, we propose a secure and efficient message dissemination scheme (SEMD) with policy enforcement in VANET, and construct an outsourcing decryption of ciphertext-policy attribute-based encryption (CP-ABE) to provide differentiated access control services, which makes the vehicles delegate most of the decryption computation to nearest roadside unit (RSU). Performance evaluation demonstrates its efficiency in terms of computational complexity, space complexity, and decryption time. Security proof shows that it is secure against replayable choosen-ciphertext attacks (RCCA) in the standard model. [ABSTRACT FROM AUTHOR]

With the growing availability of various optical and laser scanners, it is easy to capture different kinds of mesh models which are inevitably corrupted with noise. Although many mesh denoising methods proposed in recent years can produce encouraging results, most of them still suffer from their computational efficiencies. In this paper, we propose a highly efficient approach for mesh denoising while preserving geometric features. Specifically, our method consists of three steps: initial vertex filtering, normal estimation, and vertex update. At the initial vertex filtering step, we introduce a fast iterative vertex filter to substantially reduce noise interference. With the initially filtered mesh from the above step, we then estimate face and vertex normals: an unstandardized bilateral filter to efficiently smooth face normals, and an efficient scheme to estimate vertex normals with the filtered face normals. Finally, at the vertex update step, by utilizing both the filtered face normals and estimated vertex normals obtained from the previous step, we propose a novel iterative vertex update algorithm to efficiently update vertex positions. The qualitative and quantitative comparisons show that our method can outperform the selected state of the art methods, in particular, its computational efficiency (up to about 32 times faster). [ABSTRACT FROM AUTHOR]