Атрибуция картины Дж. А. Пеллегрини из Тульского музея изобразительных искусств.
The article is devoted to centrist political thought in the period of Bourbon Restoration in France (1814–1830) represented by three major movements. Traditionalist School developed a legitimate method in philosophy and affirmed that only the monarch wields unrestricted political power. Republicans and socialists claimed that revolution achievements should be secured on the level of state institutions. And between the upper and nether millstone were liberal centrists united in the group of Doctrinaires.
In this article we are talking about the possibilities of authorship identification and linguistic analysis techniques in determining the originality of the text. Methodology proposed in the article is based on semantic analysis of definitions "novelty", "originality", "creative nature" in conjunction with authorship identification analysis, that is analysis of linguistic characteristics of individual author's style. The article also touches upon the issue of the limits of the competence of the linguist expert in the context of originality determination.
The article is devoted to pattern recognition task with the database containing small number of samples per class. By mapping of local continuous feature vectors to a discrete range, this problem is reduced to statistical classification of a set of discrete finite patterns. It is demonstrated that Bayesian decision under the assumption that probability distributions can be estimated using the Parzen kernel and the Gaussian window with a fixed variance for all the classes, implemented in the PNN, is not optimal in the classification of a set of patterns. We presented here the novel modification of the PNN with homogeneity testing which gives an optimal solution of the latter task under the same assumption about probability densities. By exploiting the discrete nature of patterns our modification prevents the well-known drawbacks of the memory-based approach implemented in both the PNN and the PNN with homogeneity testing, namely, low classification speed and high requirements to the memory usage. Our modification only requires the storage and processing of the histograms of input and training samples. We present the results of an experimental study in two practically important tasks: 1) the problem of Russian text authorship attribution with character n-grams features; and 2) face recognition with well-known datasets (AT&T, FERET and JAFFE) and comparison of color- and gradient-orientation histograms. Our results support the statement that the proposed network provides better accuracy (1-7%) and is much more resistant to change of the smoothing parameter of Gaussian kernel function in comparison with the original PNN.
The beginning of the 19th century was a period of formation of restoration school in Russia. F.K.Labensky, Curator of the Hermitage Picture Gallery from 1797 onwards till 1850, arranged a restoration studio with a permanent staff working on Imperial painting collection. Labensky’s assistant, a restorer A.F. Mitrokhin learned all known techniques of mechanical restoration – relining, cradling and even transfer of paintings, - and developed them on his own. A special school was established by the Hermitage studio in 1819, supervised by Mitrokhin, were young graduates of Imperial Academy of Art were taught both mechanical and painting restoration. The apprentices of Mitrokhin school passed his techniques to next generation of Hermitage restorers.
Authorship attribution is an important field in online security. Recently there have been numerous successful works in authorship attribution in various European languages. Character n-grams are reported to be the best choice in authorship attribution, as they encode both style and content information. We evaluate different types of character n-gram features in an authorship attribution task in a real-world noisy dataset of Russian forum posts. We also supplement them with a number of new simple n-gram features capturing syntactic and discourse patterns. We perform authorship attribution in a single-topic and a cross-topic setting, as the research question is whether character n-grams capture both style and content information. Our results show that character n-grams are indeed very successful in Russian forum post authorship attribution. However, there is no clear distinction of style and content n-grams, as the same types of n-grams work well for both single-topic and cross-topic settings. In our experiments the generalized simple n-gram features which reveals syntactic and discourse patterns were proved to be also very important in authorship attribution of short informal Russian texts. They represent a different kind of authorship information and are a successful addition to the character n-grams in authorship attribution of forum texts in the Russian language.