Lightning graph matching

Graph matching aims to find correspondences between two graphs. It is a fundamental task in pattern recognition. The classical spectral matching algorithm has time complexity $\mathcal{O}(n^4)$ and space complexity $\mathcal{O}(n^4)$, where $n$ is the number of nodes. Such a complexity limits the applicability to large-scale graph matching tasks. This paper proposes an efficient redesign of spectral matching by transforming the graph matching problem into a 1D linear assignment problem, which can be solved efficiently by sorting two $n \times 1$ vectors. The resulting algorithm is named the lightning spectral assignment method (LiSA), which enjoys a complexity of $\mathcal{O}(n^2)$. Numerical experiments demonstrate the efficiency and the theoretical analysis of the strategy

Adaptive Softassign via Hadamard-Equipped Sinkhorn

Softassign is a crucial step in several popular algorithms for graph matching or other learning targets. Such softassign-based algorithms perform very well for small graph matching tasks. However, the performance of such algorithms is sensitive to a parameter in the softassign in large-scale problems, especially when handling noised data. Turning the parameter is difficult and almost done empirically. This paper constructs an adaptive softassign method by delicately taking advantage of Hadamard operations in Sinkhorn. Compared with the previous state-of-the-art algorithms such as the scalable Gromov-Wasserstein Learning (S-GWL), the resulting algorithm enjoys both a higher accuracy and a significant improvement in efficiency for large graph matching problems. In particular, on the protein network matching benchmark problems (1004 nodes), our algorithm can improve the accuracy from $56.3\%$ by the S-GWL to $75.1\%$, at the same time, it can achieve 3X+ speedup in efficiency

Fuzzy matching: multi-authority attribute searchable encryption without central authority

Attribute-based keyword search (ABKS) supports the access control on the search result based upon fuzzy identity over encrypted data, when the search operation is performed over outsourced encrypted data in cloud. However, almost ABKS schemes trust a single authority to monitor the attribute key for users. In practice, we usually have different entities responsible for monitoring different attribute keys to a user. Thus, it is not realistic to trust a single authority to monitor all attributes keys for ABKS scheme in practical situation. Although a large body of ABKS schemes have been proposed, few works have been done on multi-authority attribute searchable encryption. We propose a multi-authority attribute searchable encryption without central authority in this paper. Comparing previous ABKS schemes, we extend the single-authority ABKS scheme to multi-authority ABKS scheme and remove the central authority in multi-authority ABKS scheme. We analyze our scheme in terms of security and efficiency

Confidentiality-preserving publicly verifiable computation schemes for polynomial evaluation and matrix-vector multiplication

With the development of cloud services, outsourcing computation tasks to a commercial cloud server has drawn attention of various communities, especially in the Big Data era. Public verifiability offers a flexible functionality in real circumstance where the cloud service provider (CSP) may be untrusted or some malicious users may slander the CSP on purpose. However, sometimes the computational result is sensitive and is supposed to remain undisclosed in the public verification phase, while existing works on publicly verifiable computation (PVC) fail to achieve this requirement. In this paper, we highlight the property of result confidentiality in publicly verifiable computation and present confidentiality-preserving public verifiable computation (CPPVC) schemes for multivariate polynomial evaluation and matrix-vector multiplication, respectively. The proposed schemes work efficiently under the amortized model and, compared with previous PVC schemes for these computations, achieve confidentiality of computational results, while maintaining the property of public verifiability. The proposed schemes proved to be secure, efficient, and result-confidential. In addition, we provide the algorithms and experimental simulation to show the performance of the proposed schemes, which indicates that our proposal is also acceptable in practice

A secure and efficient data sharing and searching scheme in wireless sensor networks

Wireless sensor networks (WSN) generally utilize cloud computing to store and process sensing data in real time, namely, cloud-assisted WSN. However, the cloud-assisted WSN faces new security challenges, particularly outsourced data confidentiality. Data Encryption is a fundamental approach but it limits target data retrieval in massive encrypted data. Public key encryption with keyword search (PEKS) enables a data receiver to retrieve encrypted data containing some specific problem, namely, the keyword guessing attack (KGA). KGA includes off-line KGA and on-line KGA. To date, the existing literature on PEKS cannot simultaneously resist both off-line KGA and on-line KGA performed by an external adversary and an internal adversary. In this work, we propose a secure and efficient data sharing and searching scheme to address the aforementioned problem such that our scheme is secure against both off-line KGA and on-line KGA performed by external and internal adversaries. We would like to stress that our scheme simultaneously achieves document encryption/decryption and keyword search functions. We also prove our scheme achieves keyword security and document security. Furthermore, our scheme is more efficient than previous schemes by eliminating the pairing computation

Motor Dynamic Loading and Comprehensive Test System Based on FPGA and MCU

In view of the problem that the traditional motor test system cannot directly test the transient parameters of the motor and the dynamic arbitrary load loading requirements during motor loading, as well as the high cost of implementation, this research uses STM32+FPGA as the core to form the main control of the motor test system unit, combining the superior control performance of the ARM processor and the high-speed data processing advantages of FPGA. FPGA and STM32 are controlled by the FSMC bus communication and data ping-pong algorithm. Using this method, a small-size control core board in the motor test system is manufactured. It can be embedded in the existing traditional dynamometer system to improve the dynamometer transient parameter test and the dynamic motor loading performance. The experimental results show that the system can basically meet the requirements of the motor transient test and dynamic loading, and can achieve the fastest data refresh rate of 1 ms when measuring the motor’s speed and torque, as well as arbitrary waveform loading within a 100 M sampling frequency, with a loading error of 0.8%. It satisfies the motor transient test and dynamic loading requirements

Ultrasensitive Rapid Detection of Human Serum Antibody Biomarkers by Biomarker-Capturing Viral Nanofibers

Candida albicans (C. albicans) infection causes high mortality rates within cancer patients. Due to the low sensitivity of the current diagnosis systems, a new sensitive detection method is needed for its diagnosis. Toward this end, here we exploited the capability of genetically displaying two functional peptides, one responsible for recognizing the biomarker for the infection (antisecreted aspartyl proteinase 2 IgG antibody) in the sera of cancer patients and another for binding magnetic nanoparticles (MNPs), on a single filamentous fd phage, a human-safe bacteria-specific virus. The resultant phage is first decorated with MNPs and then captures the biomarker from the sera. The phage-bound biomarker is then magnetically enriched and biochemically detected. This method greatly increases the sensitivity and specificity of the biomarker detection. The average detection time for each serum sample is only about 6 h, much shorter than the clinically used gold standard method, which takes about 1 week. The detection limit of our nanobiotechnological method is approximately 1.1 pg/mL, about 2 orders of magnitude lower than that of the traditional antigen-based method, opening up a new avenue to virus-based disease diagnosis