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The COVID-19 pandemic changed the lives of millions of citizens worldwide in the manner they live and work to the so-called new norm in social standards. In addition to the extraordinary effects on society, the pandemic created a range of unique circumstances associated with cybercrime that also affected society and business. The anxiety due to the pandemic increased the probability of successful cyberattacks and as well as their number and range. For public health officials and communities, location tracking is an essential component in the efforts to combat the disease. The governments provide a lot of mobile apps to help health officials to trace the infected persons and contact them to aid and follow up on the health status, which requires an exchange of data in different forms. This paper presents the one-time stamp model as a new cryptography technique to secure different contact forms and protect the privacy of the infected person. The one-time stamp hybrid model consists of a combination of symmetric, asymmetric, and hashing cryptography in an entirely new way that is different from conventional and similar existing algorithms. Several experiments have been carried out to analyze and examine the proposed technique. Also, a comparison study has been made between our proposed technique and other state-of-the-art alternatives. Results show that the proposed one-time stamp model provides a high level of security for the encryption of sensitive data relative to other similar techniques with no extra computational cost besides faster processing time. [ABSTRACT FROM AUTHOR]

Facial recognition is one of the most challenging and interesting problems within the field of computer vision and pattern recognition. During the last few years, it has gained special attention due to its importance in relation to current issues such as security, surveillance systems and forensics analysis. Despite this high level of attention to facial recognition, the success is still limited by certain conditions; there is no method which gives reliable results in all situations. In this paper, we propose an efficient similarity index that resolves the shortcomings of the existing measures of feature and structural similarity. This measure, called the Feature-Based Structural Measure (FSM), combines the best features of the well-known SSIM (structural similarity index measure) and FSIM (feature similarity index measure) approaches, striking a balance between performance for similar and dissimilar images of human faces. In addition to the statistical structural properties provided by SSIM, edge detection is incorporated in FSM as a distinctive structural feature. Its performance is tested for a wide range of PSNR (peak signal-to-noise ratio), using ORL (Olivetti Research Laboratory, now AT&T Laboratory Cambridge) and FEI (Faculty of Industrial Engineering, São Bernardo do Campo, São Paulo, Brazil) databases. The proposed measure is tested under conditions of Gaussian noise; simulation results show that the proposed FSM outperforms the well-known SSIM and FSIM approaches in its efficiency of similarity detection and recognition of human faces. [ABSTRACT FROM AUTHOR]

We present an alternate adiabatic evolution for the Deutsch—Jozsa problem. The biggest difference of our adiabatic evolution constructed here with those appearing before is that an alternate initial Hamiltonian is used for the adiabatic evolution, with which the evolution task can be finished in O(1) time complexity. Our construction mostly resembles the one discussed by Das et al. [Phys. Rev. A 65 (2002) 062310], except for the initial system Hamiltonian of the adiabatic algorithm. [ABSTRACT FROM AUTHOR]