On affine classification of permutations on the space GF(2)3

2019 ◽  
Vol 29 (6) ◽  
pp. 363-371
Author(s):  
Fedor M. Malyshev
Keyword(s):  
Elementary Proof ◽  
Affine Equivalence ◽  
Left And Right ◽  
Affine Permutations ◽  
Coordinate Functions ◽  
Number Of Classes

Abstract We give an elementary proof that by multiplication on left and right by affine permutations A, B ∈ AGL(3, 2) each permutation π : GF(2)3 → GF(2)3 may be reduced to one of the 4 permutations for which the 3 × 3-matrices consisting of the coefficients of quadratic terms of coordinate functions have as an invariant the rank, which is either 3, or 2, or 1, or 0, respectively. For comparison, we evaluate the number of classes of affine equivalence by the Pólya enumerative theory.

scholarly journals Vegetation of Croatia: Phytosociological classification of the high-rank syntaxa

2017 ◽  
Vol 76 (2) ◽  
pp. 200-224 ◽  
Author(s):  
Željko Škvorc ◽  
Nenad Jasprica ◽  
Antun Alegro ◽  
Sanja Kovačić ◽  
Jozo Franjić ◽  
...  
Keyword(s):  
Vascular Plants ◽  
High Rank ◽  
Vegetation Diversity ◽  
Central European ◽  
Floristic Richness ◽  
Level Of Knowledge ◽  
European Method ◽  
Number Of Classes ◽  
Rich Countries

AbstractCroatia is among the most ecologically diverse and floristically rich countries in Europe, with a great variety of communities. The vegetation elaboration according to the standard central European method was initiated in Croatia at the beginning of the 20thcentury. In previous overviews of Croatian vegetation, the number of classes and alliances was underrepresented in relation to the country’s floristic richness. Furthermore, the level of knowledge and the amount of available data varied greatly among the various types of vegetation. The aims of this paper are mainly to compile a stabile syntaxonomic list of classes, orders and alliances dominated by vascular plants in Croatia and to adjust Croatian vegetation to the new European syntaxonomic system (EuroVegChecklist). It introduces a consistent description of high-rank syntaxa in Croatian. In conclusion, the vegetation of Croatia comprises 66 classes, 121 orders and 201 alliances. The number of syntaxa shows vegetation diversity that is rather high compared to most other European countries; this is related to the high floristic richness and endemism. The list points out the obvious problems and gaps in our knowledge of vegetation in Croatia and can serve as a baseline for the future vegetation studies.

Hypermetrical Irregularities in Compound Phrases

2021 ◽  
pp. 124-172
Author(s):  
Danuta Mirka
Keyword(s):  
Final Part ◽  
Left And Right

The chapter is devoted to hypermetrical irregularities caused by techniques of phrase linkage, including overlap and elision. It traces the historical pedigree of these concepts and compares their use by Fred Lerdahl and Ray Jackendoff (1983) to that by William Rothstein (1989). It supplements the concepts of left and right elision, distinguished by the former authors, with the concepts of left and right deletion. It then concentrates on phrase linkages that occur at formal junctures marked by cadences and it presents a classification of all hypermetric scenarios at such junctures. In the final part it evaluates the concept of “shadow hypermeter.”

scholarly journals Classification of Hyperspectral Reflectance Images With Physical and Statistical Criteria

2020 ◽  
Vol 12 (14) ◽  
pp. 2335 ◽  
Author(s):  
Alexandre Alakian ◽  
Véronique Achard
Keyword(s):  
Classification Algorithms ◽  
Type Ii ◽  
Spectral Indices ◽  
Shape Features ◽  
Hyperspectral Reflectance ◽  
Roof Tile ◽  
Dark Green ◽  
Number Of Classes ◽  
Statistical Criteria

A classification method of hyperspectral reflectance images named CHRIPS (Classification of Hyperspectral Reflectance Images with Physical and Statistical criteria) is presented. This method aims at classifying each pixel from a given set of thirteen classes: unidentified dark surface, water, plastic matter, carbonate, clay, vegetation (dark green, dense green, sparse green, stressed), house roof/tile, asphalt, vehicle/paint/metal surface and non-carbonated gravel. Each class is characterized by physical criteria (detection of specific absorptions or shape features) or statistical criteria (use of dedicated spectral indices) over spectral reflectance. CHRIPS input is a hyperspectral reflectance image covering the spectral range [400–2500 nm]. The presented method has four advantages, namely: (i) is robust in transfer, class identification is based on criteria that are not very sensitive to sensor type; (ii) does not require training, criteria are pre-defined; (iii) includes a reject class, this class reduces misclassifications; (iv) high precision and recall, F 1 score is generally above 0.9 in our test. As the number of classes is limited, CHRIPS could be used in combination with other classification algorithms able to process the reject class in order to decrease the number of unclassified pixels.

EEG-based classification of imaginary left and right foot movements using beta rebound

2013 ◽  
Vol 124 (11) ◽  
pp. 2153-2160 ◽  
Author(s):  
Yasunari Hashimoto ◽  
Junichi Ushiba
Keyword(s):  
Left And Right

scholarly journals Illiteracy Classification Using K Means-Naïve Bayes Algorithm

2018 ◽  
Vol 2 (3) ◽  
pp. 153 ◽  
Author(s):  
Muhammad Firman Aji Saputra ◽  
Triyanna Widiyaningtyas ◽  
Aji Prasetya Wibawa
Keyword(s):  
Error Rate ◽  
Naive Bayes ◽  
Research Result ◽  
Naïve Bayes ◽  
Optimal Number ◽  
Testing Method ◽  
The World ◽  
Bayes Algorithm ◽  
Number Of Classes

Illiteracy is an inability to recognize characters, both in order to read and write. It is a significant problem for countries all around the world including Indonesia. In Indonesia, illiteracy rate is generally set as an indicator to see whether or not education in Indonesia is successful. If this problem is not going to be overcome, it will affect people’s prosperity. One system that has been used to overcome this problem is prioritizing the treatment from areas with the highest illiteracy rate and followed by areas with lower illiteracy rate. The method is going to be a way easier to be applied if it is supported by classification process. Since the classification process needs a class, and there has not been any fine classification of illiteracy rate, there is needed a clustering process before classification process. This research is aimed to get optimal number of classes through clustering process and know the result of illiteracy classification process. The clustering process is conducted by using k means algorithm, and for the classification process is conducted by using Naïve Bayes algorithm. The testing method used to assess the success of classification process is 10-fold method. Based on the research result, it can be concluded that the optimal illiteracy classes are three classes with the classification accuracy value of 96.4912% and error rate value of 3.5088%. Whereas the classification with two classes get the accuracy value of 93.8596% and error rate value of 6.1404%. And for the classification with five classes get the accuracy value of 90.3509% and error rate value of 9.6491%.

scholarly journals Potential and Use of the Googlenet Ann for the Purposes of Inland Water Ships Classification

2020 ◽  
Vol 27 (4) ◽  
pp. 170-178
Author(s):  
Katarzyna Bobkowska ◽  
Izabela Bodus-Olkowska
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Video Recording ◽  
Inland Water ◽  
Video Cameras ◽  
Image Repository ◽  
Ship Recognition ◽  
Number Of Classes ◽  
Classification Quality

AbstractThis article presents an analysis of the possibilities of using the pre-degraded GoogLeNet artificial neural network to classify inland vessels. Inland water authorities monitor the intensity of the vessels via CCTV. Such classification seems to be an improvement in their statutory tasks. The automatic classification of the inland vessels from video recording is a one of the main objectives of the Automatic Ship Recognition and Identification (SHREC) project. The image repository for the training purposes consists about 6,000 images of different categories of the vessels. Some images were gathered from internet websites, and some were collected by the project’s video cameras. The GoogLeNet network was trained and tested using 11 variants. These variants assumed modifications of image sets representing (e.g., change in the number of classes, change of class types, initial reconstruction of images, removal of images of insufficient quality). The final result of the classification quality was 83.6%. The newly obtained neural network can be an extension and a component of a comprehensive geoinformatics system for vessel recognition.

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