Codes of Good Governance in Hungary

The purpose of the paper is to account for the short history of the soft law regulation of corporate conduct on the Budapest Stock Exchange (BSE). In theory, voluntary codes of good governance are expected to improve the deficiences of the existing mechanisms of corporate governance. In case of the Hungarian public companies the most important corporate governance problems are those related to the fragile safeguards of the interests of minority shareholders and to the lack of incentives for a much higher degree of transparency and disclosure. It is these two sets of issues on which the present analysis concentrates. The empirical core of the paper assesses the quality of information to be gained from the corporate governance reports of listed companies on the BSE. In order to discover links between the quality of information and firm characteristics we categorized the declarations based on their adequacy and applied binary regression analysis. We found inverse relationship between ownership concentration and the quality of information, while the higher liquidity of shares enhanced the adequacy of declarations.Corporate governance, company law, voluntary codes of governance

The Valuation of Different Island Destinations Using Gravity Models

Islands are one of the most important destinations for tourism and leisure. However, islands exhibit different levels of attractiveness in the course of time and comparing with other islands. The objective of this paper is to analyze this subject for the Archipelago of the Azores, using gravity models and the travel cost method. The study aims to understand different performances along time and between islands caused by changes in the supply side (e.g. number of hotel beds, animation activities, events, etc.). The regression analysis includes two moments: it starts with the calibration of attractiveness; in a second moment it is focused in answering the question “what affects attractiveness?â€. Beyond its introduction and conclusions, the study is divided into three main parts: model explanation; application of the analytical concepts for the Azores islands; and finally, the analysis of the most relevant results.

Simple SVM based whole-genome segmentation

We present a support vector machine (SVM) based framework for DNA segmentation into binary classes. Two applications are explored: transcription start site prediction and transcription factor binding prediction. Experiments demonstrate our approach has significantly better performance than other methods on both tasks

Codes of Good Governance in Hungary

The purpose of the paper is to account for the short history of the soft law regulation of corporate conduct on the Budapest Stock Exchange (BSE). In theory, voluntary codes of good governance are expected to improve the deficiences of the existing mechanisms of corporate governance. In case of the Hungarian public companies the most important corporate governance problems are those related to the fragile safeguards of the interests of minority shareholders and to the lack of incentives for a much higher degree of transparency and disclosure. It is these two sets of issues on which the present analysis concentrates. The empirical core of the paper assesses the quality of information to be gained from the corporate governance reports of listed companies on the BSE. In order to discover links between the quality of information and firm characteristics we categorized the declarations based on their adequacy and applied binary regression analysis. We found inverse relationship between ownership concentration and the quality of information, while the higher liquidity of shares enhanced the adequacy of declarations

The identification of wheat genetic resources with high dietary fiber content

The quality properties of different variety mixtures and composite cross populations were studied with the aim of identifying genotypes with high dietary fiber content and to cultivate and examine the effect of these components on the end-use quality. Based on the results of a Europe-wide trial, we could detect two populations and variety mixtures which had significantly higher total (TOTAX) and water extractable arabinoxylan (WEAX) content, than most of the studied genotypes, with positive effect on the human health. These populations/mixtures are promising dietary fiber resources and suitable not only for organic but also for conventional farming, especially in Central Europe. The seeds of the best population (Mv Elit CCP) was multiplied to supply it for interested farmers in Hungary in the frame of the European trial on organic heterogeneous materials

The Valuation of Different Island Destinations Using Gravity Models

Islands are one of the most important destinations for tourism and leisure. However, islands exhibit different levels of attractiveness in the course of time and comparing with other islands. The objective of this paper is to analyze this subject for the Archipelago of the Azores, using gravity models and the travel cost method. The study aims to understand different performances along time and between islands caused by changes in the supply side (e.g. number of hotel beds, animation activities, events, etc.). The regression analysis includes two moments: it starts with the calibration of attractiveness; in a second moment it is focused in answering the question "what affects attractiveness?". Beyond its introduction and conclusions, the study is divided into three main parts: model explanation; application of the analytical concepts for the Azores islands; and finally, the analysis of the most relevant results

Distributed Learning in Hierarchical Networks

International audienceIn this article, we propose distributed learning based approaches to study the evolution of a decentralized hierarchical system, an illustration of which is the smart grid. Smart grid management requires the control of non-renewable energy production and the inegration of renewable energies which might be highly unpredictable. Indeed, their production levels rely on uncontrolable factors such as sunshine, wind strength, etc. First, we derive optimal control strategies on the non-renewable energy productions and compare competitive learning algorithms to forecast the energy needs of the end users. Second, we introduce an online learning algorithm based on regret minimization enabling the agents to forecast the production of renewable energies. Additionally, we define organizations of the market promoting collaborative learning which generate higher performance for the whole smart grid than full competition

Techniques for effective and efficient fire detection from social media images

Social media could provide valuable information to support decision making in crisis management, such as in accidents, explosions and fires. However, much of the data from social media are images, which are uploaded in a rate that makes it impossible for human beings to analyze them. Despite the many works on image analysis, there are no fire detection studies on social media. To fill this gap, we propose the use and evaluation of a broad set of content-based image retrieval and classification techniques for fire detection. Our main contributions are: (i) the development of the Fast-Fire Detection method (FFDnR), which combines feature extractor and evaluation functions to support instance-based learning, (ii) the construction of an annotated set of images with ground-truth depicting fire occurrences -- the FlickrFire dataset, and (iii) the evaluation of 36 efficient image descriptors for fire detection. Using real data from Flickr, our results showed that FFDnR was able to achieve a precision for fire detection comparable to that of human annotators. Therefore, our work shall provide a solid basis for further developments on monitoring images from social media.Comment: 12 pages, Proceedings of the International Conference on Enterprise Information Systems. Specifically: Marcos Bedo, Gustavo Blanco, Willian Oliveira, Mirela Cazzolato, Alceu Costa, Jose Rodrigues, Agma Traina, Caetano Traina, 2015, Techniques for effective and efficient fire detection from social media images, ICEIS, 34-4

Efficiency of different marker systems for genotype fingerprinting and for genetic diversity studies in barley (Hordeum vulgare L.)

AbstractGenetic relationships between 38 barley genotypes were determined with the aid of 36 RAPD, 54 STS and 26 SSR markers. The dendrogram groups showed high coincidence with growth habit and ear type. There were significant correlations between the Jaccard coefficients obtained using the matrices of each single marker type and their combined matrix. When the varieties were grouped using markers with above-average Polymorphic Information Content (PIC) values, the same groups were obtained as when using all markers, outlining their usefulness for estimating diversity between the varieties. Three RAPD or four SSR primers were sufficient to distinguish all the barley varieties from each other. The applicability of the various types of primers differed. The STS markers could best be used for estimating relationships between the varieties and the SSR markers for distinguishing genotypes from each other, while RAPD markers could be employed both for estimating the relationships between varieties and for variety identification