Strong Designated Verifier Signature Schemes with Undeniable Property and Their Applications

Most of the strong designated verifier signature (SDVS) schemes cannot tell the real signature generator when the signer and the designated verifier dispute on a signature. In other words, most of the SDVS schemes do not have the undeniability property. In this paper, we propose two SDVS schemes which hold the undeniability property, namely, strong designated verifier signature with undeniability property (SDVSUP). Our two schemes are called SDVSUP-1 and SDVSUP-2. In our two SDVSUP schemes, the signer not only can designate a verifier but also can designate an arbiter who can judge the signature when the signer and the designated verifier dispute on the signature. What is more, the judgment procedure can be performed by the arbiter alone without help from the signer or the designated verifier, which increases the judgment efficiency and reduces the complexity of signature confirmation. We also demonstrate a real instance of applying our SDVSUP scheme to electronic bidding system

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

An Extreme Learning Machine Based on the Mixed Kernel Function of Triangular Kernel and Generalized Hermite Dirichlet Kernel

According to the characteristics that the kernel function of extreme learning machine (ELM) and its performance have a strong correlation, a novel extreme learning machine based on a generalized triangle Hermitian kernel function was proposed in this paper. First, the generalized triangle Hermitian kernel function was constructed by using the product of triangular kernel and generalized Hermite Dirichlet kernel, and the proposed kernel function was proved as a valid kernel function of extreme learning machine. Then, the learning methodology of the extreme learning machine based on the proposed kernel function was presented. The biggest advantage of the proposed kernel is its kernel parameter values only chosen in the natural numbers, which thus can greatly shorten the computational time of parameter optimization and retain more of its sample data structure information. Experiments were performed on a number of binary classification, multiclassification, and regression datasets from the UCI benchmark repository. The experiment results demonstrated that the robustness and generalization performance of the proposed method are outperformed compared to other extreme learning machines with different kernels. Furthermore, the learning speed of proposed method is faster than support vector machine (SVM) methods

SysML-based compositional verification and safety analysis for safety-critical cyber-physical systems

Safety-critical cyber-physical systems (SC-CPS) have the characteristics of distributed, heterogeneous, strong coupling of computing resources and physical resources. With the increased acceptance of Model-Driven Development (MDD) in the safety-critical domain, the SysML language has been broadly used. Increasing complexity results in the formal verification of the SysML models of SC-CPS often faces the so-called state-explosion problem. Moreover, safety analysis is also an important step to ensure the quality of SC-CPS. Thus, this article proposes an integrated SysML modelling and verification approach to cover specification of nominal behaviour and safety. First, an extension of SysML is presented, in which the contract information (i.e. Assume and Guarantee) is extended for SysML block diagrams and a Safety Profile is proposed to describe safety-related concepts. Second, the transformation from SysML to the compositional verification tool OCRA is given. Third, the safety analysis is achieved by translating the Safety Profile model into FTA (Fault Tree Analysis). Finally, the prototype tools including SysML2OCRA and SafetyProfile2FTA are represented, and the effectiveness of the method proposed in this paper is verified through actual industrial cases

Strong Designated Verifier Signature Schemes with Undeniable Property and Their Applications

Most of the strong designated verifier signature (SDVS) schemes cannot tell the real signature generator when the signer and the designated verifier dispute on a signature. In other words, most of the SDVS schemes do not have the undeniability property. In this paper, we propose two SDVS schemes which hold the undeniability property, namely, strong designated verifier signature with undeniability property (SDVSUP). Our two schemes are called SDVSUP-1 and SDVSUP-2. In our two SDVSUP schemes, the signer not only can designate a verifier but also can designate an arbiter who can judge the signature when the signer and the designated verifier dispute on the signature. What is more, the judgment procedure can be performed by the arbiter alone without help from the signer or the designated verifier, which increases the judgment efficiency and reduces the complexity of signature confirmation. We also demonstrate a real instance of applying our SDVSUP scheme to electronic bidding system