Informatica

Image synthesis techniques are present in a wide range of applications as they leverage the amount of information required for creating realistic visualizations. For fast hardware rendering they usually employ a triangle-based representation describing the geometry of the scene. In this paper, we introduce a new and simple framework for performing on-the-fly refinement and simplification of meshes completely on the GPU. As we aim at making easy the integration of level-of-detail management into the creation workflow of artists, the presented method is easy to be implemented. We only need a coarse mesh, its displacement map and a geometry shader. At rendering time, we employ a geometry shader to parallelize the tessellation and displacement steps. The tessellation step performs uniform refinement or simplification operations by applying a fixed subdivision criterion. Our method also exploits coherence by taking advantage of the last computed mesh. We provide a method which offers a flexible integration with standard 3D tools, easy to be implemented, coherence exploitation and wholly processed by the GPU.

In this paper we focus on a specific class of XML schema inference approaches – so-called heuristic approaches. Contrary to grammar-inferring approaches, their result does not belong to any specific class of grammars and, hence, we cannot say anything about their features from the point of view of theory of languages. However, the heuristic approaches still form a wider and more popular set of approaches due to natural and user-friendly strategies. We describe a general framework of the inference algorithms and we show how its particular phases can be further enhanced and optimized to get more reasonable and realistic output. The aim of the paper is (1) to provide a general overview of the heuristic inference process and existing approaches, (2) to sum up the improvements and optimizations we have proposed so far in our research group, and (3) to discuss possible extensions and open problems which need to be solved. Hence, it enables the reader to get acquainted with the field fast.

An Unequal Error Protection (UEP) scheme for image steganography is proposed in this paper. When stego images are transmitted through a noisy channel, the embedded message may be more sensitive to noise than the cover message. Therefore, we propose a Turbo UEP coding scheme for steganographic communication in a noisy channel, which provides higher error protection for the embedded message and lower error protection for the cover message. Simulations show that this coding scheme provides different error protection levels in one coding process and maintains the coding rate constant. In addition, an application scheme of the proposed Turbo UEP code for steganographic communication is presented, and the experimental results show that the extracted secret images have better quality than the cover images after decoding.

The symbiosis between an enterprise architecture and service-oriented architecture results in so-called service-oriented enterprise architecture and brings up new problems for service-oriented enterprise systems engineering. One of the most important is a business service quality definition, specification and evaluation. The paper proposes a formal model of enterprise business service quality evaluation framework to encompass and balance all the viewpoints and perspectives on an enterprise business service quality.

In this article, we propose a practical algorithm for capacitated facility location problems (CFLP). There are some approaches which can obtain primal solutions while simultaneously exploiting the primal structure and the dual structure. One of these approaches is the mean value cross decomposition (MVCD) method that ensures convergence without solving master problems. However, MVCD has been previously applied only to uncapacitated facility location problems (UFLP), due to the fact that the performance is highly dependent on the structure of the problem. The proposed algorithm, named the dynamic mean value cross decomposition algorithm (DMVCD), is effectively integrated with MVCD and cutting plane methods in order to tighten the bounds by reducing the duality gap. Computational results of various instances are also reported to verify the effectiveness and efficiency of DMVCD.

Two mathematical models of an amperometric bienzyme biosensor are analysed digitally. The models hold different boundary conditions describing the singularity of the electrode (transducer). The influence of the initial and boundary conditions on the biosensors action at different sets of parameters is analysed.

The aim of this paper is to investigate intuitionistic fuzzy multiple attribute group decision making problems where the attribute values provided by experts are expressed in intuitionistic fuzzy numbers, and the weight information about the experts is to be determined. We present a new method to derive the weights of experts and rank the preference order of alternatives based on projection models. We first derive the weights of the decision makers according to the projection of the individual decision on the ideal decision. The expert has a large weight if his evaluation value is close to the ideal decision, and has a small weight if his evaluation value is far from the ideal decision. Then, based on the weighted projection of the alternatives on the intuitionistic fuzzy ideal solution (IFIS), we develop a straightforward and practical algorithm to rank alternatives. Furthermore, we extend the developed model and algorithm to the multiple attribute group decision making problems with interval-valued intuitionistic fuzzy information. Finally, an illustrative example is given to verify the developed approach and to demonstrate its practicality and effectiveness.

In the paper we describe the bus routing problem (BRP), which the goal is to find a route from the start stop to the final stop minimizing the time and the cost of travel and the length of the route. Additionally the time of starting travel at the start stop is given. Analysis of the problem is presented and in particular we point at properties of routes. The BRP is an example of multicriteria optimization problem (MOP), which the solution is the set of non-dominated solutions. This paper proposes a label correcting algorithm with storing partial solutions for solving the BRP. The algorithm makes possible to find all routes which belong to the set of non-dominated solutions. Apart from that the results of experimental tests are presented. Additionally the results are compared with results for the BRP where the goal is to minimize only the time and the cost of travel and the length of the route is not taken into consideration.

The paper is devoted to goodness of fit tests based on probability density estimates generated by kernel functions. The test statistic is considered in the form of maximum of the normalized deviation of the estimate from its expected value or a hypothesized distribution density function. A comparative Monte Carlo power study of the investigated criterion is provided. Simulation results show that the proposed test is a powerful competitor to the existing classical criteria testing goodness of fit against a specific type of alternative hypothesis. An analytical way for establishing the asymptotic distribution of the test statistic is proposed, using the theory of high excursions of close to Gaussian random processes and fields introduced by Rudzkis (1992, 2012).

The performance of an automatic speech recognition system heavily depends on the used feature set. Quality of speech recognition features is estimated by classification error, but then the recognition experiments must be performed, including both front-end and back-end implementations. We propose a method for features quality estimation that does not require recognition experiments and accelerate automatic speech recognition system development. The key component of our method is usage of metrics right after front-end features computation. The experimental results show that our method is suitable for recognition systems with back-end Euclidean space classifiers.