Informatica

In this paper robust image authentication integrated with semi-fragile pixel-wise tamper localization is analyzed. A new pixel-wise transformation robust to blurring/sharpening while fragile to all other image processing operations is proposed. A new method featuring binary and percentage measures with novel ability to integrate human opinion for image authenticity evaluation is presented. Protection for all bits in the pixel is advantage as well as small size of the signature – less than 10% of initial image.

In a supervised learning, the relationship between the available data and the performance (what is learnt) is not well understood. How much data to use, or when to stop the learning process, are the key questions.

Hwang et al. proposed an ElGamal-like scheme for encrypting large messages, which is more efficient than its predecessor in terms of computational complexity and the amount of data transformation. They declared that the resulting scheme is semantically secure against chosen-plaintext attacks under the assumptions that the decision Diffie–Hellman problem is intractable. Later, Wang et al. pointed out that the security level of Hwang et al.'s ElGamal-like scheme is not equivalent to the original ElGamal scheme and brings about the disadvantage of possible unsuccessful decryption. At the same time, they proposed an improvement on Hwang et al.'s ElGamal-like scheme to repair the weakness and reduce the probability of unsuccessful decryption. However, in this paper, we show that their improved scheme is still insecure against chosen-plaintext attacks whether the system is operated in the quadratic residue modulus or not. Furthermore, we propose a new ElGamal-like scheme to withstand the adaptive chosen-ciphertext attacks. The security of the proposed scheme is based solely on the decision Diffie–Hellman problem in the random oracle model.

The paper proposes a novel predictive-reactive planning and scheduling framework in which both approaches are combined to complement each other in a reasonably balanced way. Neither original scheduling algorithms nor original techniques can be find in this paper. It also does not intend to invent new mechanisms or to propose some cardinally new ideas. The aim is to choose, adapt and test ideas, mechanisms and algorithms already proposed by other researchers. The focus of this research is set on make-to-order production environments. The proposed approach aims not only to absorb disruptions in shop floor level schedules but also to mitigate the impacts of potential exceptions, which disrupt mid-term level production plans. It is based on application of risk mitigation techniques and combines various simulation techniques extended by optimization procedures. The proposed approach is indented to be implemented in Advanced Planning and Scheduling system, which is an add-on for Enterprise Resources Planning system. To make it easier to understand the focus of the paper, at the beginning the position from which we start is clarified.

Conventional Blind Source Separation (BSS) algorithms separate the sources assuming the number of sources equals to that of observations. BSS algorithms have been developed based on an assumption that all sources have non-Gaussian distributions. Most of the instances, these algorithms separate speech signals with super-Gaussian distributions. However, in real world examples there exist speech signals which are sub-Gaussian. In this paper, a novel method is proposed to measure the separation qualities of both super-Gaussian and sub-Gaussian distributions. This study measures the impact of the Probability Distribution Function (PDF) of the signals on the outcomes of both sub and super-Gaussian distributions. This paper also reports the study of impact of mixing environment on the source separation. Simulation improves the results of the separated sources by 7 dB to 8 dB, and also confirms that the separated sources always have super-Gaussian characteristics irrespective of PDF of the signa ls or mixtures.

Multidimensional scaling with city-block distances is considered in this paper. The technique requires optimization of an objective function which has many local minima and can be non-differentiable at minimum points. This study is aimed at developing a fast and effective global optimization algorithm spanning the whole search domain and providing good solutions. A multimodal evolutionary algorithm is used for global optimization to prevent stagnation at bad local optima. Piecewise quadratic structure of the least squares objective function with city-block distances has been exploited for local improvement. The proposed algorithm has been compared with other algorithms described in literature. Through a comprehensive computational study, it is shown that the proposed algorithm provides the best results. The algorithm with fine-tuned parameters finds the global minimum with a high probability.

Nowadays most required products and services of companies are provided through other organisations. Outsourcing as a new approach has a significant role in management literature. Supplier should be selected by executives, when the organization decides to acquire a product or service from other organizations. Concerning supplier selection, the managers should consider more than one factor or criterion, which may be inconsistent and contradictory. Therefore, supplier selection is a multi-criteria decision-making issue. Analytic network process (ANP) is a technique to solve multi-criteria decision-making problems in which the criteria affect each other and have nonlinear correlation. In this study, the goal is to use ANP to select the supplier in a group decision-making.

This paper builds on a novel, fast algorithm for generating the convex layers on grid points with linear time complexity. Convex layers are extracted from the binary image. The obtained convex hulls are characterized by the number of their vertices and used as representative image features. A computational geometric approach to near-duplicate image detection stems from these features. Similarity of feature vectors of given images is assessed by correlation coefficient. This way, all images with closely related structure and contents can be retrieved from large databases of images quickly and efficiently. The algorithm can be used in various applications such as video surveillance, image and video duplication search, or image alignment. Our approach is rather robust up to moderate signal-to-noise ratios, tolerates lossy image compression, and copes with translated, rotated and scaled image contents.

In this paper we develop a new method for 2-tuple linguistic multiple attribute decision making, namely the 2-tuple linguistic generalized ordered weighted averaging distance (2LGOWAD) operator. This operator is an extension of the OWA operator that utilizes generalized means, distance measures and uncertain information represented as 2-tuple linguistic variables. By using 2LGOWAD, it is possible to obtain a wide range of 2-tuple linguistic aggregation distance operators such as the 2-tuple linguistic maximum distance, the 2-tuple linguistic minimum distance, the 2-tuple linguistic normalized Hamming distance (2LNHD), the 2-tuple linguistic weighted Hamming distance (2LWHD), the 2-tuple linguistic normalized Euclidean distance (2LNED), the 2-tuple linguistic weighted Euclidean distance (2LWED), the 2-tuple linguistic ordered weighted averaging distance (2LOWAD) operator and the 2-tuple linguistic Euclidean ordered weighted averaging distance (2LEOWAD) operator. We study some of its main properties, and we further generalize the 2LGOWAD operator using quasi-arithmetic means. The result is the Quasi-2LOWAD operator. Finally we present an application of the developed operators to decision-making regarding the selection of investment strategies.