Vol. 35 · No. 6

Natural and man-made catastrophic events appear be steadily increasing in intensity and frequency. Proper preparation, response and recovery are essential if mankind and its vital systems are to cope with and survive large-scale disasters. The organisations responsible for delivering emergency response services often under-perform due to a lack of proper interoperation and collaboration. This paper discuss the interoperability issues for data interchange among first responders agency participating in emergency situation and provides exhaustive overview of the recent studies and attempts to solve the problem. The approach taken by the EU funded REDIRNET project for development of a core ontology enabling exchange of data among first responder agencies is presented. The novelty included in the ontology tree is described and the implementation in the meta-data gateway is introduced. The paper provides insight in the implementation of the REDIRNET platform and the designed semantic interoperable services. The paper ends with the discussion of the presented work and the concluding remarks.

The paper presents a source-to-source compiler, TRACO, for automatic extraction of both coarse- and fine-grained parallelism available in C/C++ loops. Parallelization techniques implemented in TRACO are based on the transitive closure of a relation describing all the dependences in a loop. Coarse- and fine-grained parallelism is represented with synchronization-free slices (space partitions) and a legal loop statement instance schedule (time partitions), respectively. TRACO enables also applying scalar and array variable privatization as well as parallel reduction. On its output, TRACO produces compilable parallel OpenMP C/C++ and/or OpenACC C/C++ code. The effectiveness of TRACO, efficiency of parallel code produced by TRACO, and the time of parallel code production are evaluated by means of the NAS Parallel Benchmark and Polyhedral Benchmark suites. These features of TRACO are compared with closely related compilers such as ICC, Pluto, Par4All, and Cetus. Feature work is outlined.

Fourier-Motzkin elimination is a computationally expensive but powerful method to solve a system of linear inequalities. These systems arise e.g. in execution order analysis for loop nests or in integer linear programming. This paper focuses on the analysis, design and implementation of a parallel solver for distributed memory for large systems of linear inequalities using the Fourier-Motzkin elimination algorithm. We also measure the speedup of parallel solver and prove that this implementation results in good scalability.

This paper addresses the problem of recovering 3D human pose from single 2D images using Sparse Representation. While recent Sparse Representation (SR) based 3D human pose estimation methods have attained promising results estimating human poses from single images, their performance depends on the availability of large labeled datasets. However, in many real world applications, accessing to sufficient labeled data may be expensive and/or time consuming, but it is relatively easy to acquire a large amount of unlabeled data. Moreover, all SR based 3D pose estimation methods only consider the information of the input feature space and they cannot utilize the information of the pose space. In this paper, we propose a new framework based on sparse representation for 3D human pose estimation which uses both the labeled and unlabeled data. Furthermore, the proposed method can exploit the information of the pose space to improve the pose estimation accuracy. Experimental results show that the performance of the proposed method is significantly better than the state of the art 3D human pose estimation methods.

Through the development and accessibility of the Internet, nowadays the cloud computing has become a very popular. This concept has the potential to change the use of information technologies. Cloud computing is the technology that provides infrastructure, platform or software as a service via the network to a huge number of remote users. The main benefit of cloud computing is the utilization of elastic resources and virtualization. Two main properties are required from clouds by users: interoperability and privacy. This article focuses on interoperability. Nowadays it is difficult to migrate an application between clouds offered by different providers. The article deals with that problem in multicloud environment. Specifically, it focuses on the application performance optimization in a multicloud environment. A new method is suggested based on the state of the art. The method is divided into three parts: multicloud architecture, method of a horizontal scalability, and taxonomy for multicriteria optimization. The principles of the method were applied in a design of multicriteria optimization architecture, which we verified experimentally. The aim of our experiment is carried on a portal offering a platform according to the users' requirements.

The work described in this paper aims at effective computation resilience for complex simulations in high performance and distributed environments. Computation resilience is a complicated and delicate area; it deals with many types of simulation cores, many types of data on various input levels and also with many types of end-users, which have different requirements and expectations. Predictions about system and computation behaviors must be done based on deep knowledge about underlying infrastructures, and simulations' mathematical and realization backgrounds. Our conceptual framework is intended to allow independent collaborations between domain experts as end-users and providers of the computational power by taking on all of the deployment troubles arising within a given computing environment. The goal of our work is to provide a generalized approach for effective scalable usage of the computing power and to help domain-experts, so that they could concentrate more intensive on their domain solutions without the need of investing efforts in learning and adapting to the new IT backbone technologies.

In this paper the use of Fire Dynamics Simulator (FDS) for parallel computer simulation of the smoke movement during a fire of two passenger cars in an underground car park is investigated. The simulations were executed on a high-performance computer cluster. A specific problem of FDS parallel computation using Message-Passing Interface (MPI) is a separate solution of governing equations on computational subdomains causing a loss of accuracy. Therefore, the impact of parallelisation on simulation accuracy in the case of using a greater number of computational cores of the computer cluster is studied with the aim to increase the computational performance and enable practical application of such simulations for fire safety measures. The geometrical model and material properties of the cars used in the simulation have been verified by a full-scale fire experiment in open air. We describe the results of a series of simulations of several fire scenarios with different numbers of parked cars and ventilation configurations and determine times and locations at which conditions in the car park become untenable for human life. The simulation indicates that proper ventilation prolongs tenable conditions by several minutes.

New hardware FPGA implementations for the efficient computations of division, natural logarithm and exponential function are proposed. The proposed implementations use generic floating-point adder and multiplier with small additional resources that are shared to compute more frequently used multiply and accumulate operations. Hardware sharing improved the resource utilization. The time of the computation has been reduced to only 6 clock cycles when the natural logarithm and exponential function are calculated. The division is calculated in 5 clock cycles. They are designed as technology independent high throughput computing cores with minimized memory requirements which can be used in higher numbers to significantly increased calculation speed in spectral processing. A new universal arithmetic floating-point unit is also proposed.

This paper describes the development of a Slovak text-to-speech system which applies a technique wherein speech is directly synthesized from hidden Markov models. Statistical models for Slovak speech units are trained by using the newly created female and male phonetically balanced speech corpora. In addition, contextual informations about phonemes, syllables, words, phrases, and utterances were determined, as well as questions for decision tree-based context clustering algorithms. In this paper, recent statistical parametric speech synthesis methods including the conventional, STRAIGHT and AHOcoder speech synthesis systems are implemented and evaluated. Objective evaluation methods (mel-cepstral distortion and fundamental frequency comparison) and subjective ones (mean opinion score and semantically unpredictable sentences test) are carried out to compare these systems with each other and evaluation of their overall quality. The result of this work is a set of text to speech systems for Slovak language which are characterized by very good intelligibility and quite good naturalness of utterances at the output of these systems. In the subjective tests of intelligibility the STRAIGHT based female voice and AHOcoder based male voice reached the highest scores.

This study deals with the problems of aggregating the opinions of a group of people in such a way that the quality of the group decision surpasses the quality of the decision of the most experienced individual within the group. The methods we have studied fall in the research domain of the so called collective intelligence. We provide an overview of the state-of-the-art in the collective intelligence. We describe the method based on adaptive boosting we have proposed aggregatig the opinions of a group of people. We have implemented a web application to gather opinions of people and used the application to collect data for the experimental analysis. The model problem was to identify whether there is or there is not a tumor present in the series of X-ray images of human lungs. We have compared our proposed method to conventional methods such as majority voting. We have concluded that our proposed method can be successfully used to aggregate opinions of a group of people to increase their collective intelligence above the level of the most successful individual within the group in many cases. We have observed that the highest increase in the collective intelligence may be achieved for intelligence wise homogeneous groups what confirms the results of previous studies.