No abstract

No abstract

Topic of quantum chaos has begun to draw increasing attention in recent years. So, to ensure the security of digital image, an image encryption algorithm based on combining a hyperchaotic system and quantum 3D logistic map is proposed. This algorithm is applied in four stages. Initially, the key generator builds upon the foundation of mean for any row or column of the edges of the plain image. Its output value is used to yield initial conditions and parameters of the proposed image encryption scheme. Next, it diffuses the plain image by the random sequences generated by 3D hyperchaotic system, and the diffusion process is realized by implementing XOR operation. Then, the diffused image and chaotic sequences are produced by the 3D quantum chaotic logistic map, expressed as a quantum superposition state using density matrix which is a representation of the state of a quantum system, and finally the resulting quantum image is then confused and diffused simultaneously by a unitary matrix generated by logistic chaos using XNOR operation to obtain the final cipher image. Because of the dependence on the plain image, the algorithm can frustrate the chosen-plaintext and known-plaintext attacks. Simulation results and theoretical analysis verify that the presented scheme has high safety performance, a good encryption effect, and a large key space. The method can effectively resist exhaustive, statistical, and differential attacks. Moreover, the encryption time of the proposed method is satisfactory, and the method can be efficiently used in practice for the secure transmission of image information.

Nowadays, students face many difficulties to practice for exams. Professors and teachers spend a lot of time and effort to make exams. Automatic Question Generation Model proposes a solution to save time, effort, and student's learning process which helps in educational purposes. AQGM is user-friendly which is implemented as a GUI-based system that generates Wh-questions which mean" WH" (" What"," Who", and" Where") and formatted into two types of templates, Question Bank template, and Exam template. Exams have different difficulty levels (Easy, Medium, and Hard). Therefore, students can measure their level and teachers will know to what extent the students understand the course. Researches have shown that this method is helpful and successful for educational purposes. AQGM generates questions automatically by using its model that generated by using sequence-to-sequence approach specially encoder-decoder technique with copy mechanism and attention decoder. AQGM model uses SQuAD as a training dataset which helps to get more accurate results. This model gets a BLEU-4 score 11.3 which is good according to it generated automatically using deep learning approaches.

No abstract