Informatica

In this paper, a reliable information hiding scheme based on support vector machine and error correcting codes is proposed. To extract the hidden information bits from a possibly tampered watermarked image with a lower error probability, information hiding is modeled as a digital communication problem, and both the good generalization ability of support vector machine and the error correction code BCH are applied. Due to the good learning ability of support vector machine, it can learn the relationship between the hidden information and corresponding watermarked image; when the watermarked image is attacked by some intentional or unintentional attacks, the trained support vector machine can recover the right hidden information bits. The reliability of the proposed scheme has been tested under different attacks. The experimental results show that the embedded information bits are perceptually transparent and can successfully resist common image processing, jitter attack, and geometrical distortions. When the host image is heavily distorted, the hidden information can also be extracted recognizably, while most of existing methods are defeated. We expect this approach provide an alternative way for reliable information hiding by applying machine learning technologies.

The quadratic assignment problem (QAP) is one of the well‐known combinatorial optimization problems and is known for its various applications. In this paper, we propose a modified simulated annealing algorithm for the QAP – M‐SA‐QAP. The novelty of the proposed algorithm is an advanced formula of calculation of the initial and final temperatures, as well as an original cooling schedule with oscillation, i.e., periodical decreasing and increasing of the temperature. In addition, in order to improve the results obtained, the simulated annealing algorithm is combined with a tabu search approach based algorithm. We tested our algorithm on a number of instances from the library of the QAP instances – QAPLIB. The results obtained from the experiments show that the proposed algorithm appears to be superior to earlier versions of the simulated annealing for the QAP. The power of M‐SA‐QAP is also corroborated by the fact that the new best known solution was found for the one of the largest QAP instances – THO150.

The problem of parameter clustering on the basis of their correlation matrix is considered. The convergence in probability of parameter clustering based on the simulated annealing is investigated theoretically.

In this paper we are concerned with global optimization, which can be defined as the problem of finding points on a bounded subset of R^m, in which some real-valued function f(x) assumes its optimal value. We consider here a global optimization algorithm. We present a stochastic approach, which is based on the simulated annealing algorithm. The optimization function f(x) here is discrete and with noise.

In this paper we deal with the problem of extremal parameter grouping. The problem formulation, the algorithms of parameter grouping and the fields of implementation are presented. The deterministic algorithms of extremal parameter grouping often find the local maximum of the functional, characterizing the quality of a partition. The problem has been formulated as a problem of combinatorial optimization and attempted to be solved using the simulated annealing strategy. The algorithms, realizing such a strategy and devoted to the solving of the problem concerned, are proposed and investigated.