And Quiet Flows the Don: the Sholokhov-Kryukov authorship debate

‘And Quiet Flows the Don’ is an epic novel, considered one of the most significant works of Russian and world literature. The debate on the authorship of ‘And Quiet Flows the Don’ had been surrounding the novel since its first release in 1928 by Mikhail Sholokhov, who was repeatedly accused of plagiarism. The supporters of the plagiarism theory often indicate that the real author of the novel is the Cossack writer, Fyodor Kryukov, who died before ‘And Quiet Flows the Don’ was published. In the present study we applied the information-based similarity analysis (Yang et al., 2003a, Linguistic analysis of human heartbeats using frequency and rank order statistics. Physical Review Letters, 90: 108103; Yang et al., 2003b, Information categorization approach to literary authorship disputes. Physica A, 329, 473) and Burrows's Delta (Burrows, 2002, ‘Delta’: a measure of stylistic difference and a guide to likely authorship. Literary and Linguistic Computing, 17(3):267–87) to a corpus of Russian literature of XIX and XX centuries. We next used these two methods to compare ‘And Quiet Flows the Don’ to Sholokhov’s and Kryukov’s writings. It was found that Fyodor Kryukov writings are distinct from ‘And Quiet Flows the Don’, whilst Sholokhov’s writings being close to the Don novel. The results also highlight how both information similarity analysis and Delta analysis can be used Russian language.

Speech enhancement using robust estimators and rank-order statistics

Purpose The purpose of the present work is to design robust estimators for speech enhancement by incorporation of calculation rank-order statistics and locally-adaptive neighborhoods. The proposed estimators are able to increase the speech quality of a noisy signal, to preserve better speech intelligibility, and to introduce less artifacts comparing with known speech enhancement estimators. Design/methodology/approach We design a novel speech enhancement algorithm based on rank-order statistics and local adaptive signal processing to improve the accuracy of existing speech enhancement estimators, in terms of speech quality, intelligibility, and introduction of artificial artifacts. Findings We found that by using the proposed estimators for speech enhancement we obtain a better adaptation to nonstationary characteristics of speech and noise processes comparing with that of known speech enhancement estimators. The proposed algorithm increases speech quality, preserves better speech intelligibility, and introduces less artifacts comparing with known speech enhancement estimators. Research limitations/implications The proposed approach for speech enhancement is a locally-adaptive signal processing performed for each element of a noisy speech signal. Thus, the main limitation of the proposed approach is an increase of computational complexity compared with that of nonadaptive conventional techniques. Practical implications In order to perform real-time speech enhancement with the proposed approach, it is recommended to use a digital system with a fast processor. Another option is by using a parallel architecture such as a FPGA. Originality/value We propose a novel local-adaptive algorithm for robust speech enhancement by incorporation of calculation of rank-order statistics and local-adaptive neighborhoods. The proposed algorithm is able to adjust itself in response to changes in the statistical properties of ambience noise.

3D SLAM in texture-less environments using rank order statistics

SUMMARYWe present a real time 3D SLAM system for texture-less scenes using only depth information provided by a low cost RGB-D sensor. The proposed method is based on a novel informative sampling scheme that extracts points carrying the most useful 3D information for registration. The aim of the proposed sampling technique is to informatively sample a point cloud into a subset of points based on their 3D information. The flatness of a point is measured by applying a rank order statistics based robust segmentation method to surface normals in its local vicinity. The extracted keypoints from sequential frames are then matched and a rank order statistics based robust estimator is employed to refine the matches and estimate a rigid-body transformation between the frames. Experimental evaluations show that the proposed keypoint extraction method is highly repeatable and outperforms the state of the art methods in terms of accuracy and repeatability. We show that the performance of the registration algorithm is also comparable to other well-known methods in texture-less environments.