Chaotic Image Cryptography Systems: A Review

In recent decades, image encryption has been a popular and important field of research. The image encryption techniques have been studied thoroughly to ensure the safety of digital images on transmission through the networks. A large range of algorithms for chaotic-based cryptographic systems has been suggested and submitted to enhance the efficiency of the encryption methods. The chaotic map is one technique to guarantee security. The benefits of chaotic image encryption include the fact that it is simple to implement; it has a faster encryption speed, and it is powerful against attacks. Due to their extreme sensitivity to initial conditions, unpredictability, and random-like behaviours, many image encryption systems using chaotic maps have been proposed. This study paper presents a scientific review of many types of researches during the (2014-2020) years that used chaotic with its various types (one-dimensional, multi-dimensional, or hyper-chaotic) to process the digital images in the encryption stage or the scrambling phase. Furthermore, it presents a future reading of researches that has a wider role in developing the cryptography field by improving the efficiency of Algorithms where using a chaotic map with other methods gives better results than using chaotic alone in scrambling and encryption methods.

On the Efficiency of Algorithms with Multi-level Parallelism

The paper investigates the efficiency of algorithms for solving computational mathematics problems that use a multilevel model of parallel computing on heterogeneous computer systems. A methodology for estimating the acceleration of algorithms for computers using a multilevel model of parallel computing is proposed. As an example, the parallel algorithm of the iteration method on a subspace for solving the generalized algebraic problem of eigenvalues of symmetric positive definite matrices of sparse structure is considered. For the presented algorithms, estimates of acceleration coefficients and efficiency were obtained on computers of hybrid architecture using graphics accelerators, on multi-core computers with shared memory and multi-node computers of MIMD-architecture.

Efficient Pre-Solve Algorithms for the Schwerin and Falkenauer_U Bin Packing Benchmark Problems for Getting Optimal Solutions with High Probability

Bin Packing is one of the research areas of Operations Research with many industrial applications, as well as rich theoretical impact. In this article, the authors deal with Bin Packing on the practical side: they consider two Bin Packing Benchmark classes. These benchmark problems are often used to check the “usefulness”, efficiency of algorithms. The problem is well-known to be NP-hard. Instead of introducing some exact, heuristic, or approximation method (as usual), the problem is attacked here with some kind of greedy algorithm. These algorithms are very fast; on the other hand, they are not always able to provide optimal solutions. Nevertheless, they can be considered as pre-processing algorithms for solving the problem. It is shown that almost all problems in the considered two benchmark classes are, in fact, easy to solve. In case of the Schwerin class, where there are 200 instances, it is obtained that all instances are solved by the greedy algorithm, optimally, in a very short time. The Falkenauer U class is a little bit harder, but, here, still more than 91% of the instances are solved optimally very fast, with the help of another greedy algorithm. Based on the above facts, the main contribution of the paper is to show that pre-processing is very useful for solving such kinds of problems.

A unified schedule policy of distributed machine learning framework for CPU-GPU cluster

With the widespread using of GPU hardware facilities, more and more distributed machine learning applications have begun to use CPU-GPU hybrid cluster resources to improve the efficiency of algorithms. However, the existing distributed machine learning scheduling framework either only considers task scheduling on CPU resources or only considers task scheduling on GPU resources. Even considering the difference between CPU and GPU resources, it is difficult to improve the resource usage of the entire system. In other words, the key challenge in using CPU-GPU clusters for distributed machine learning jobs is how to efficiently schedule tasks in the job. In the full paper, we propose a CPU-GPU hybrid cluster schedule framework in detail. First, according to the different characteristics of the computing power of the CPU and the computing power of the GPU, the data is divided into data fragments of different sizes to adapt to CPU and GPU computing resources. Second, the paper introduces the task scheduling method under the CPU-GPU hybrid. Finally, the proposed method is verified at the end of the paper. After our verification for K-Means, using the CPU-GPU hybrid computing framework can increase the performance of K-Means by about 1.5 times. As the number of GPUs increases, the performance of K-Means can be significantly improved.

Floquet-enhanced spin swaps

The transfer of information between quantum systems is essential for quantum communication and computation. In quantum computers, high connectivity between qubits can improve the efficiency of algorithms, assist in error correction, and enable high-fidelity readout. However, as with all quantum gates, operations to transfer information between qubits can suffer from errors associated with spurious interactions and disorder between qubits, among other things. Here, we harness interactions and disorder between qubits to improve a swap operation for spin eigenstates in semiconductor gate-defined quantum-dot spins. We use a system of four electron spins, which we configure as two exchange-coupled singlet–triplet qubits. Our approach, which relies on the physics underlying discrete time crystals, enhances the quality factor of spin-eigenstate swaps by up to an order of magnitude. Our results show how interactions and disorder in multi-qubit systems can stabilize non-trivial quantum operations and suggest potential uses for non-equilibrium quantum phenomena, like time crystals, in quantum information processing applications. Our results also confirm the long-predicted emergence of effective Ising interactions between exchange-coupled singlet–triplet qubits.

Revisiting Modified Greedy Algorithm for Monotone Submodular Maximization with a Knapsack Constraint

Monotone submodular maximization with a knapsack constraint is NP-hard. Various approximation algorithms have been devised to address this optimization problem. In this paper, we revisit the widely known modified greedy algorithm. First, we show that this algorithm can achieve an approximation factor of 0.405, which significantly improves the known factors of 0.357 given by Wolsey and (1-1/e)/2\approx 0.316 given by Khuller et al. More importantly, our analysis closes a gap in Khuller et al.'s proof for the extensively mentioned approximation factor of (1-1/\sqrte )\approx 0.393 in the literature to clarify a long-standing misconception on this issue. Second, we enhance the modified greedy algorithm to derive a data-dependent upper bound on the optimum. We empirically demonstrate the tightness of our upper bound with a real-world application. The bound enables us to obtain a data-dependent ratio typically much higher than 0.405 between the solution value of the modified greedy algorithm and the optimum. It can also be used to significantly improve the efficiency of algorithms such as branch and bound.

THE ALGORITHM FOR MARKING VERTEXES. COMPARISON OF ALGORITHMS FOR SEARCHING FOR CONNECTIVITY COMPONENTS IN A GRAPH USING THE EXAMPLE OF COMBINING WALLETS IN A BITCOIN DATABASE

The article is devoted to comparing the efficiency of algorithms for processing Bitcoin blockchain transaction database. The article describes the algorithm of vertex marking developed by the group. Based on the comparison of this and other algorithms, it is expected to identify the most effective algorithm for clustering addresses based on belonging to a single user. The Bitcoin database contains information about millions of financial transactions. Even though information about transactions is anonymous, there are methods for combining user addresses into wallets. In this article, we study algorithms of searching connectivity components, which are based on one of the methods of combining wallets based on the heuristic feature of the «total waste» of one user. The emphasis is placed on the practical aspects of implementation – hardware limitations in processing big data sets, as well as the choice of a solution for many graph connectivity components – the maximum connected set of graph vertices, in other words, a set of nonempty vertex sets and a set of vertex pairs.

Pyramid HBAO — a Scalable Horizon-based Ambient Occlusion Method

Screen-space Ambient Occlusion (SSAO) methods have become an integral part of the process of calculating global illumination effects in real-time applications. The use of ambient occlusion improves the perception of the geometry of the scene, and also makes a significant contribution to the realism of the rendered image. However, the problems of accuracy and efficiency of algorithms of calculating ambient occlusion remain relevant. Most of the existing methods have similar algorithmic complexity, what makes their use in real-time applications very limited. The performance issues of methods working in the screen space are particularly acute in the current growing spreadness of 4K (3840 x 2160 pixels) resolution of the rendered image. In this paper we provide our own algorithm Pyramid HBAO, which enhances the classic HBAO method by changing its calculation complexity for high resolution.

Preliminaries

Blockchains are heavily based on mathematical concepts, in particular on algebraic structures. This chapter starts with an introduction to the main aspects in number theory, such as the divisibility of integers, prime numbers and Euler’s totient function. Based on these basics, it follows a very detailed introduction to modern algebra, including group theory, ring theory and field theory. The algebraic main results are then applied to describe the structure of cyclic groups and finite fields, which are needed to construct cryptographic primitives. The chapter closes with an introduction to complexity theory, examining the efficiency of algorithms.

Experimental investigations of text graph representation model efficiency for borrowings detection

The paper deals with investigation of time and functional efficiency of the developed software system for text borrowings detection. Base of this system is constructive-synthesizing text graph representation model. The experiment revealed a linear relationship between the time of the text borrowing check operation and the size of the text base for comparison. The conducted experiments showed that there is a difference in the results of the checking text documents by the developed system and analogue. For its estimation the degree of functional efficiency advantage is calculated by similarly to S-estimation of efficiency of algorithms. The reasons for the difference in results are identified. Attention is drawn to obstacles in analogues use for experimental efficacy investigations.