Existence-dependent sentence type classification

This chapter examines expressions of commands (imperatives) in Wolaitta and the ways in which the imperative is distinguished from statements and questions. Although each sentence type is formally distinct, imperatives and questions share a number of morpho-syntactic properties. Similar to declarative and interrogative sentences, imperatives in Wolaitta involve verbal grammatical categories such as the distinction of person, number, and gender of the subject as well as negative and positive polarity. In contrast to previous studies, the present contribution establishes the function of a set of morphemes based on -árk and -érk to be the expression of plea or appeal to an addressee rather than politeness when issuing a command. Instead, politeness in commands is expressed by using plural (pro)nominal and verbal elements. The imperative in Wolaitta is a robust construction which is also used in formulaic speeches such as leave-taking as well as in blessing, curses, and advice.

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Moroccan Video Intelligent Transport System: Vehicle Type Classification Based on Three-Dimensional and Two-Dimensional Features

IEEE Access ◽

10.1109/access.2019.2920740 ◽

2019 ◽

Vol 7 ◽

pp. 72528-72537 ◽

Cited By ~ 2

Author(s):

Hatim Derrouz ◽

Abderrahim Elbouziady ◽

Hamd Ait Abdelali ◽

Rachid Oulad Haj Thami ◽

Sanaa El Fkihi ◽

...

Keyword(s):

Transport System ◽

Three Dimensional ◽

Intelligent Transport System ◽

Two Dimensional ◽

Intelligent Transport ◽

Vehicle Type ◽

Vehicle Type Classification ◽

Type Classification

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Two-Pass Technique for Clone Detection and Type Classification Using Tree-Based Convolution Neural Network

Applied Sciences ◽

10.3390/app11146613 ◽

2021 ◽

Vol 11 (14) ◽

pp. 6613

Author(s):

Young-Bin Jo ◽

Jihyun Lee ◽

Cheol-Jung Yoo

Keyword(s):

Neural Network ◽

Average Rate ◽

Convolution Neural Network ◽

Clone Detection ◽

Code Clones ◽

Classification Technique ◽

Development Costs ◽

Code Quality ◽

Type Information ◽

Type Classification

Appropriate reliance on code clones significantly reduces development costs and hastens the development process. Reckless cloning, in contrast, reduces code quality and ultimately adds costs and time. To avoid this scenario, many researchers have proposed methods for clone detection and refactoring. The developed techniques, however, are only reliably capable of detecting clones that are either entirely identical or that only use modified identifiers, and do not provide clone-type information. This paper proposes a two-pass clone classification technique that uses a tree-based convolution neural network (TBCNN) to detect multiple clone types, including clones that are not wholly identical or to which only small changes have been made, and automatically classify them by type. Our method was validated with BigCloneBench, a well-known and wildly used dataset of cloned code. Our experimental results validate that our technique detected clones with an average rate of 96% recall and precision, and classified clones with an average rate of 78% recall and precision.

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Identification of peat type and humification by laboratory VNIR/SWIR hyperspectral imaging of peat profiles with focus on fen-bog transition in aapa mires

Plant and Soil ◽

10.1007/s11104-020-04775-y ◽

2020 ◽

Author(s):

L. Granlund ◽

M. Keinänen ◽

T. Tahvanainen

Keyword(s):

Hyperspectral Imaging ◽

Near Infrared ◽

Support Vector ◽

High Resolution Imaging ◽

Factors Affecting ◽

Vector Machines ◽

Resolution Imaging ◽

Shortwave Infrared ◽

Type Classification ◽

Wide Potential

Abstract Aims Hyperspectral imaging (HSI) has high potential for analysing peat cores, but methodologies are deficient. We aimed for robust peat type classification and humification estimation. We also explored other factors affecting peat spectral properties. Methods We used two laboratory setups: VNIR (visible to near-infrared) and SWIR (shortwave infrared) for high resolution imaging of intact peat profiles with fen-bog transitions. Peat types were classified with support vector machines, indices were developed for von Post estimation, and K-means clustering was used to analyse stratigraphic patterns in peat quality. With separate experiments, we studied spectral effects of drying and oxidation. Results Despite major effects, oxidation and water content did not impede robust HSI classification. The accuracy between Carex peat and Sphagnum peat in validation was 80% with VNIR and 81% with SWIR data. The spectral humification indices had accuracies of 82% with VNIR and 56%. Stratigraphic HSI patterns revealed that 36% of peat layer shifts were inclined by over 20 degrees. Spectral indices were used to extrapolate visualisations of element concentrations. Conclusions HSI provided reliable information of basic peat quality and was useful in visual mapping, that can guide sampling for other analyses. HSI can manage large amounts of samples to widen the scope of detailed analysis beyond single profiles and it has wide potential in peat research beyond the exploratory scope of this paper. We were able to confirm the capacity of HSI to reveal shifts of peat quality, connected to ecosystem-scale change.

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Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification

Journal of Imaging ◽

10.3390/jimaging7030051 ◽

2021 ◽

Vol 7 (3) ◽

pp. 51

Author(s):

Emanuela Paladini ◽

Edoardo Vantaggiato ◽

Fares Bougourzi ◽

Cosimo Distante ◽

Abdenour Hadid ◽

...

Keyword(s):

Colorectal Cancer ◽

Deep Learning ◽

Digital Pathology ◽

Texture Features ◽

Tissue Type ◽

Cancer Tissue ◽

Learning Methods ◽

Feature Based ◽

Type Classification ◽

Whole Slide Images

In recent years, automatic tissue phenotyping has attracted increasing interest in the Digital Pathology (DP) field. For Colorectal Cancer (CRC), tissue phenotyping can diagnose the cancer and differentiate between different cancer grades. The development of Whole Slide Images (WSIs) has provided the required data for creating automatic tissue phenotyping systems. In this paper, we study different hand-crafted feature-based and deep learning methods using two popular multi-classes CRC-tissue-type databases: Kather-CRC-2016 and CRC-TP. For the hand-crafted features, we use two texture descriptors (LPQ and BSIF) and their combination. In addition, two classifiers are used (SVM and NN) to classify the texture features into distinct CRC tissue types. For the deep learning methods, we evaluate four Convolutional Neural Network (CNN) architectures (ResNet-101, ResNeXt-50, Inception-v3, and DenseNet-161). Moreover, we propose two Ensemble CNN approaches: Mean-Ensemble-CNN and NN-Ensemble-CNN. The experimental results show that the proposed approaches outperformed the hand-crafted feature-based methods, CNN architectures and the state-of-the-art methods in both databases.

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A Novel Computer-Aided Cloud Type Classification Method Based on Convolutional Neural Network with Squeeze-And- Excitation

Journal of Physics Conference Series ◽

10.1088/1742-6596/1802/3/032051 ◽

2021 ◽

Vol 1802 (3) ◽

pp. 032051

Author(s):

Guanshen Li

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Classification Method ◽

Cloud Type ◽

Computer Aided ◽

Type Classification

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